Agent trace

Production two-tower improvement sol-0.153.4-max-03

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

Thinking text is unavailable for this run. The archive contains 196 empty thinking entries; tool calls are shown separately.

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Agent: gpt-5.6-sol · Codex · max effort
Recorded conversation: 47:13 · 202 steps
Private reward: 0.017481 · Pass
Attempt cost: $7.72

Thinking
Model text
Tool call
Tool response

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System instructions

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System instructions

You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.

Personality

As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend.

You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique.

Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them.

Writing style

Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable.

If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering.

Technical communication

Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice.

You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details.

Working with the user

You have two channels for staying in conversation with the user:

You share updates in the commentary channel.
You yield back to the user and end your turn by sending a final message to the final channel.

The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task.

When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events.

Intermediate commentary

As you work, you send messages to the commentary channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify.

If the user's request requires calling tools, start with a message in the commentary channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work.

Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users.

Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do <this good thing> rather than <this obviously bad thing>", "I will do <X>, not <Y>".

Final answer

In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary.

Formatting rules

Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:

You may format with GitHub-flavored Markdown.
When referencing a real local file, prefer a clickable markdown link.
Clickable file links should look like app.py: plain label, absolute target, with optional line number inside the target.
If a file path has spaces, wrap the target in angle brackets: [My Report.md](</abs/path/My Project/My Report.md:3>).
Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.
Do not use URIs like file://, vscode://, or https:// for file links.
Do not provide ranges of lines.
Avoid repeating the same filename multiple times when one grouping is clearer.

Visualizations

Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps.

Good candidates include:

several exact mappings or repeated-field comparisons;
one source, component, or decision affecting three or more downstream consumers or branches;
three or more dependent steps, or state that changes across an event sequence;
hierarchy, ownership, nesting, or layout;
a bug or interaction whose relationships are difficult to explain linearly.

Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout.

Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations.

Rules for getting work done

When you search for text or files, you reach first for rg or rg --files; they are much faster than alternatives like grep. If rg is unavailable, you use the next best tool without fuss.
When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.
Do not chain shell commands with separators like echo "===="; or printf '---'; the output becomes noisy in a way that makes the user's side of the conversation worse.
Exercise caution when escaping text for exec_command calls - backticks and $() passed to the cmd argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.
Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.
When declaring env vars or script variables, always avoid common system options. Never repurpose $HOME, $home, or $CODEX_HOME. Instead, use a task-specific variable name.

File editing constraints

Use apply_patch for local file edits. Do not create or edit files with cat or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need apply_patch. Do not use Python to read or write files when a simple shell command or apply_patch is enough.

You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user.

Never use destructive commands like git reset --hard or git checkout -- unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands.

Autonomy and persistence

Adapt accordingly based on the user’s request type. When asked to:

Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant.
Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation.
Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains.
Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker.

You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications).

A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives.

You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user.

When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront.

If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission.

Destructive actions

Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.

Before taking a destructive action:

Make sure the action is clearly within the user's request.
Resolve the exact targets with read-only checks when necessary.
Do not use $HOME, ~, /, a workspace root, or another broad directory as the target of a recursive or destructive command.
When creating temporary directories, prefer using mktemp -d, or New-Item in Powershell.
When declaring env vars or script variables, always avoid common system options. Never repurpose $HOME, $home, or $CODEX_HOME. Instead, use a task-specific variable name.
When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.
Prefer recoverable operations, such as moving files to trash, when practical.
If the target or scope is unclear, stop and ask the user.

Never run commands such as rm -rf $HOME or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.

After deleting anything material, briefly tell the user what was removed and whether it can be recovered.

Using skills

A skill is a set of instructions provided through a SKILL.md source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.

How to use skills

Discovery: When a ## Skills section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its SKILL.md. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as r0 to their filesystem roots. Expand the alias before accessing the skill.
Trigger rules: If the user names an available skill (with $SkillName or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
Missing/blocked: If a named skill is not available or its SKILL.md cannot be read, say so briefly and continue with the best fallback.
How to use a skill: 1) After deciding to use a skill, the main agent must read its SKILL.md completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from ### Skill roots, then open and read its SKILL.md completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call skills.list with {"authority":{"kind":"orchestrator"}}, select the matching package, and pass its main_resource to skills.read. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When SKILL.md references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed SKILL.md. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to skills.read; do not treat skill:// identifiers as filesystem paths. 3) If SKILL.md points to extra folders such as references/, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if scripts/ exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use skills.read and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if assets/ or templates exist).
Coordination and sequencing:
If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
Announce which skills you're using and why. If you skip an obvious skill, say why.
Context hygiene:
Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.
Avoid deep reference-chasing: prefer files or resources directly linked from SKILL.md unless blocked.
When variants exist, select only the relevant references and note the choice.
Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.

When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill.

Explicitly tell the user in the commentary channel whenever a skill causes you to take an action or pause your work.

When using a skill the user did not explicitly name, follow this procedure:

First, tell the user in the commentary channel why you are using the skill.
Then, use the skill as long as it stays within the scope of the task.
Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response).

If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected.

Developer instructions

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Developer instructions

<skills_instructions>

Skills

A skill is a set of local instructions to follow that is stored in a SKILL.md file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.

Skill roots

r0 = /tmp/codex-home/skills/.system

Available skills

imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)
openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)
plugin-creator: Create and scaffold plugin directories for Codex with a required .codex-plugin/plugin.json, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)
skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)
skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) </skills_instructions>

<permissions instructions> Filesystem sandboxing defines which files can be read or written. sandbox_mode is danger-full-access: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the sandbox_permissions for any reason, commands will be rejected. </permissions instructions>

<apps_instructions>

Apps (Connectors)

Apps (Connectors) can be explicitly triggered in user messages in the format [$app-name](app://{connector_id}). Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the codex_apps MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the tool_search tool. If tool_search is available, the apps that are searchable by tools_search will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. </apps_instructions>

Developer instructions

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Developer instructions

You are /root, the primary agent in a team of agents collaborating to fulfill the user's goals.

At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools.

You can use spawn_agent to create a new agent, followup_task to give an existing agent a new task and trigger a turn, and send_message to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the fork_turns parameter.

You will receive messages in the analysis channel in the form:

Message Type: MESSAGE | FINAL_ANSWER
Task name: <recipient>
Sender: <author>
Payload:
<payload text>

They may be addressed as to=/root

Note that collaboration tools cannot be called from inside functions.exec. Call spawn_agent, send_message, followup_task, wait_agent, interrupt_agent, and list_agents only as direct tool calls using the recipient shown in their tool definitions, such as to=functions.collaboration.spawn_agent, since they are intentionally absent from the functions.exec tools.* namespace. Available tools in functions.exec are explicitly described with a tools namespace in the developer message.

All agents share the same directory. In detail:

All agents have access to the same container and filesystem as you.
All agents use the same current working directory.
As a result, edits made by one agent are immediately visible to all other agents.

When calling wait_agent, prefer longer waits (minutes) to avoid busy polling.

There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.

Full-history forks (fork_turns omitted or "all") inherit the parent model and reasoning effort and do not accept overrides. Only set model or reasoning_effort when explicitly requested by the user, applicable AGENTS.md instructions, or skill instructions; when doing so, set fork_turns to "none" or a positive integer string.

Developer instructions

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Developer instructions

<multi_agent_mode>Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work.</multi_agent_mode>

User context

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User context

<recommended_plugins> Here is a list of plugins that are available but not installed.

Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)
Box (box@openai-curated-remote)
Codex Security (codex-security@openai-curated-remote)
Figma (figma@openai-curated-remote)
GitHub (github@openai-curated-remote)
Gmail (gmail@openai-curated-remote)
Google Calendar (google-calendar@openai-curated-remote)
Google Drive (google-drive@openai-curated-remote)
Linear (linear@openai-curated-remote)
Notion (notion@openai-curated-remote)
OpenAI Developers (openai-developers@openai-curated-remote)
Outlook Calendar (outlook-calendar@openai-curated-remote)
Outlook Email (outlook-email@openai-curated-remote)
SharePoint (sharepoint@openai-curated-remote)
Slack (slack@openai-curated-remote)
Teams (teams@openai-curated-remote) </recommended_plugins>

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-25</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>

Harness context world state

{
  "type": "world_state",
  "full": true,
  "state": {
    "agents_md": {},
    "apps_instructions": true,
    "collaboration_mode": {
      "mode": "default",
      "model": "gpt-5.6-sol",
      "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d"
    },
    "context_window_guidance": "",
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-25",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "managed_developer_instructions": {},
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "persistent_mode": {},
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}

Harness context turn context

{
  "type": "turn_context",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-25",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "max",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "max",
  "summary": "auto"
}

Task instructions

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Task instructions

Improve a production music-discovery two-tower

You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team's next launch gate.

Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis.

The source used to reproduce the production model is included under /task/current_model/. You may copy it into /app/solution/, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact.

Production Context

The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table.

The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above.

What You Have

All public inputs are under /task/data/. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is 24786800, when train_* ends. A 30-minute gap precedes the seven-day val_* window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window.

The package includes:

/task/current_model/train_current_model.py: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting.
/task/current_model/two_tower_model.py: model primitives used by the production script.
/task/evaluate_public.py: the public validation evaluator for development.
/task/data/validation_eligible_candidates.npz and /task/data/test_eligible_candidates.npz: platform-owned candidate sets for the validation and hidden-test cutoffs.
/task/data/validation_preference_state.parquet and /task/data/test_preference_state.parquet: active explicit preference state at the corresponding cutoffs.
/task/requirements.txt: the exact offline Python distribution inventory.

You can reproduce the production public backtest with:

python /task/current_model/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet

Data

For each event family there is a train_*.parquet and val_*.parquet file:

Family	Meaning	Columns
`listens`	Playback events	`uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds`
`likes`	Explicit positive actions	`uid`, `timestamp`, `item_id`, `is_organic`
`dislikes`	Explicit negative actions	`uid`, `timestamp`, `item_id`, `is_organic`
`unlikes`	Reversal of a prior like	`uid`, `timestamp`, `item_id`, `is_organic`
`undislikes`	Reversal of a prior dislike	`uid`, `timestamp`, `item_id`, `is_organic`

is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.

The two public windows have different roles:

For model development and selection, train only from train_* and score the subsequent val_* outcomes.
After selecting an improvement, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.

Additional files under /task/data/:

candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").

Validation candidates exclude all train_* encounters. Test candidates exclude all train_* and val_* encounters. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery Objective

Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:

grade 3: the user organically likes the track;
grade 1: the user organically listens to at least 80% of the track;
grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.

The public evaluator reports this same objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities; treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Deliverable

Submit one final ranking as /app/solution/submission.parquet or /app/solution/submission.csv. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates submission.parquet and ignores submission.csv.

The ranking must:

include the columns uid, item_id, and rank; only these columns are evaluated;
use finite, integer-valued entries in all three required columns;
cover exactly the users in target_users.parquet, with no missing or extra users;
contain exactly 100 rows and 100 distinct items for every target user;
contain every integer rank from 1 through 100 exactly once per user; and
use only items present in that user's test_eligible_candidates.npz candidate set.

Also leave the improved implementation used to build the artifact at /app/solution/train_current_model.py, along with any local modules it imports. A concise /app/solution/README.md with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate.

Constraints

Write deliverables and intermediate artifacts only under /app/solution/.
Treat /task/ and the installed Python environment as read-only.
Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
Do not install packages or use network access except for the model-provider connection managed by Harbor.
Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels.
Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint.
The environment includes CPU PyTorch 2.8.0. No accelerator is available.
You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search.
Keep the best valid submission saved throughout the attempt so it survives the deadline.

Agent message

+00:10

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

I’ll first reproduce the public baseline and inspect the sampler/objective, then run a small set of targeted negative-sampling experiments. I’ll keep the best valid artifact under /app/solution/ as I go, refit the winner on train_* + val_*, and validate the final submission schema and eligibility.

exec command

+00:13

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +00:16

const r = await tools.exec_command({"cmd":"find /task/current_model -maxdepth 2 -type f -print && find /task/data -maxdepth 1 -type f -printf '%f\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py && sed -n '1,260p' /task/evaluate_public.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":40000});
text(r.output);

Tool response +00:16

Script completed
Wall time 0.1 seconds
Output:

/task/current_model/train_current_model.py
/task/current_model/two_tower_model.py
candidate_catalog.parquet
target_users.parquet
test_eligible_candidates.npz
test_preference_state.parquet
train_dislikes.parquet
train_likes.parquet
train_listens.parquet
train_undislikes.parquet
train_unlikes.parquet
val_dislikes.parquet
val_likes.parquet
val_listens.parquet
val_undislikes.parquet
val_unlikes.parquet
validation_eligible_candidates.npz
validation_preference_state.parquet
#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations

import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)
class CandidateSet:
    uids: np.ndarray
    item_ids: np.ndarray
    packed: np.ndarray

    @classmethod
    def read(cls, path: Path) -> "CandidateSet":
        with np.load(path, allow_pickle=False) as artifact:
            result = cls(
                uids=artifact["uids"].astype(np.int64, copy=True),
                item_ids=artifact["item_ids"].astype(np.int64, copy=True),
                packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
            )
        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
        if result.packed.shape != expected:
            raise ValueError(
                f"invalid candidate artifact shape {result.packed.shape}; "
                f"expected {expected}"
            )
        return result


@dataclass(frozen=True)
class SequenceData:
    item_ids: np.ndarray
    item_features: np.ndarray
    user_ids: np.ndarray
    offsets: np.ndarray
    sequence_timestamps: np.ndarray
    sequence_items: np.ndarray
    event_features: np.ndarray
    activity_features: np.ndarray
    state_ids: np.ndarray
    liked_states: np.ndarray
    disliked_states: np.ndarray
    state_counts: np.ndarray
    inference_user_rows: np.ndarray
    inference_liked_items: np.ndarray
    inference_disliked_items: np.ndarray
    inference_user_features: np.ndarray

    @property
    def lengths(self) -> np.ndarray:
        return np.diff(self.offsets)

    @property
    def target_count(self) -> int:
        return int(np.maximum(self.lengths - 1, 0).sum())


@dataclass(frozen=True)
class TargetPartition:
    """A contiguous recent target window and its chronological BCE split."""

    window_starts: np.ndarray
    training_counts: np.ndarray
    validation_counts: np.ndarray
    history_training_counts: np.ndarray

    @property
    def selected_count(self) -> int:
        return int(self.training_counts.sum() + self.validation_counts.sum())


def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def seed_everything(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


def model_config(config: Config) -> base.TowerConfig:
    """Map the dense trainer configuration onto the unchanged tower."""
    return base.TowerConfig(
        sequence_length=config.bptt_steps,
        embedding_dim=config.embedding_dim,
        hidden_dim=config.hidden_dim,
        user_hidden_1=config.user_hidden_1,
        user_hidden_2=config.user_hidden_2,
        item_hidden_1=config.item_hidden_1,
        item_hidden_2=config.item_hidden_2,
        dropout=config.dropout,
        epochs=config.epochs,
        learning_rate=config.learning_rate,
        weight_decay=config.weight_decay,
        gradient_clip=config.gradient_clip,
        uniform_negatives=config.uniform_negatives,
        inbatch_negatives=config.inbatch_negatives,
        inbatch_correction_power=config.inbatch_correction_power,
        inbatch_correction_min=config.inbatch_correction_min,
        inbatch_correction_max=config.inbatch_correction_max,
        seed=config.seed,
    )


def append_state_snapshot(
    active_likes: dict[int, int],
    active_dislikes: dict[int, int],
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> int:
    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
    disliked_states.append(
        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
    )
    state_counts.append((len(active_likes), len(active_dislikes)))
    return len(liked_states) - 1


def causal_activity_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    organic: np.ndarray,
    completion: np.ndarray,
) -> np.ndarray:
    """Compute the first four reviewed user features at every event cutoff."""
    length = len(items)
    result = np.zeros((length, 4), dtype=np.float16)
    if not length:
        return result
    left_rows = np.searchsorted(
        timestamps,
        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
        side="left",
    )
    rows = np.arange(length, dtype=np.int64)
    counts = rows - left_rows + 1
    organic_sum = np.cumsum(organic.astype(np.float64))
    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
    before_organic = np.where(
        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
    )
    before_completion = np.where(
        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
    )

    unique_counts = np.empty(length, dtype=np.int32)
    frequencies: dict[int, int] = {}
    left = 0
    for right, raw_item in enumerate(items):
        item = int(raw_item)
        frequencies[item] = frequencies.get(item, 0) + 1
        required_left = int(left_rows[right])
        while left < required_left:
            old_item = int(items[left])
            remaining = frequencies[old_item] - 1
            if remaining:
                frequencies[old_item] = remaining
            else:
                del frequencies[old_item]
            left += 1
        unique_counts[right] = len(frequencies)

    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
    result[:, 2] = (
        (organic_sum - before_organic) / np.maximum(counts, 1)
    ).astype(np.float16)
    result[:, 3] = (
        (completion_sum - before_completion)
        / np.maximum(counts, 1)
        / 100.0
    ).astype(np.float16)
    return result


def build_sequence_parts(
    listens: base.ListenEvents,
    explicit: base.ExplicitEvents,
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
    items = listens.items[qualifying].astype(np.uint16, copy=False)
    organic = listens.organic[qualifying].astype(np.float32, copy=False)
    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
    if not len(items):
"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations

import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator

import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn


DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100


@dataclass(frozen=True)
class TowerConfig:
    sequence_length: int = 100
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    seed: int = 29


@dataclass(frozen=True)
class ListenEvents:
    timestamps: np.ndarray
    items: np.ndarray
    organic: np.ndarray
    completion_pct: np.ndarray


@dataclass(frozen=True)
class ExplicitEvents:
    timestamps: np.ndarray
    items: np.ndarray
    channel: np.ndarray
    active: np.ndarray
    organic_like: np.ndarray


def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
    """Return four standardized features built only from the public catalog."""
    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
    features = np.column_stack(
        [
            np.log1p(positives),
            np.log1p(organic),
            organic / np.maximum(positives, 1.0),
            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
        ]
    )
    means = features.mean(axis=0, keepdims=True)
    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
    standardized = ((features - means) / scales).astype(np.float32)
    if not np.isfinite(standardized).all():
        raise RuntimeError("catalog features contain non-finite values")
    return standardized


def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
    if len(parts) == 1:
        return parts[0]
    timestamps = np.concatenate([part.timestamps for part in parts])
    items = np.concatenate([part.items for part in parts])
    organic = np.concatenate([part.organic for part in parts])
    completion = np.concatenate([part.completion_pct for part in parts])
    order = np.argsort(timestamps, kind="stable")
    return ListenEvents(
        timestamps=timestamps[order],
        items=items[order],
        organic=organic[order],
        completion_pct=completion[order],
    )


def iter_listen_users(
    path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
    """Stream one uid-sorted public listen file one user at a time."""
    parquet = pq.ParquetFile(path)
    pending_uid: int | None = None
    pending: list[ListenEvents] = []
    previous_uid = -1
    for batch in parquet.iter_batches(
        batch_size=750_000,
        columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
    ):
        frame = batch.to_pandas()
        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
        keep = mapped >= 0
        if not np.any(keep):
            continue
        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
        items = mapped[keep].astype(np.int32, copy=False)
        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
        if np.any(uids[1:] < uids[:-1]):
            raise RuntimeError(f"{path.name} is not sorted by uid")
        boundaries = np.r_[
            0,
            np.flatnonzero(uids[1:] != uids[:-1]) + 1,
            len(uids),
        ]
        for start, end in zip(boundaries[:-1], boundaries[1:]):
            uid = int(uids[start])
            part = ListenEvents(
                timestamps[start:end].copy(),
                items[start:end].copy(),
                organic[start:end].copy(),
                completion[start:end].copy(),
            )
            if pending_uid is None:
                pending_uid = uid
            elif uid != pending_uid:
                if pending_uid < previous_uid:
                    raise RuntimeError(f"{path.name} user order regressed")
                yield pending_uid, _combine_listen_chunks(pending)
                previous_uid = pending_uid
                pending_uid = uid
                pending = []
            pending.append(part)
    if pending_uid is not None:
        yield pending_uid, _combine_listen_chunks(pending)


def merge_listen_streams(
    paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
    """Merge uid-sorted public listen files without materializing them."""
    streams = [iter(iter_listen_users(path, item_index)) for path in paths]
    heads: list[tuple[int, ListenEvents] | None] = [
        next(stream, None) for stream in streams
    ]
    while any(head is not None for head in heads):
        uid = min(head[0] for head in heads if head is not None)
        parts: list[ListenEvents] = []
        for position, head in enumerate(heads):
            if head is not None and head[0] == uid:
                parts.append(head[1])
                heads[position] = next(streams[position], None)
        yield uid, _combine_listen_chunks(parts)


def load_explicit_events(
    data: Path,
    prefixes: tuple[str, ...],
    item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
    """Load public explicit actions as chronological state changes."""
    specifications = (
        ("likes", 0, 1, 0),
        ("unlikes", 0, 0, 1),
        ("dislikes", 1, 1, 0),
        ("undislikes", 1, 0, 1),
    )
    parts: list[pd.DataFrame] = []
    for family, channel, active, tie_priority in specifications:
        for prefix in prefixes:
            frame = pd.read_parquet(
                data / f"{prefix}_{family}.parquet",
                columns=["uid", "timestamp", "item_id", "is_organic"],
            )
            mapped = item_index.get_indexer(
                frame.item_id.to_numpy(dtype=np.int64, copy=False)
            )
            keep = mapped >= 0
            if not np.any(keep):
                continue
            retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
            retained["item"] = mapped[keep].astype(np.int32, copy=False)
            retained["channel"] = np.int8(channel)
            retained["active"] = np.int8(active)
            retained["tie_priority"] = np.int8(tie_priority)
            retained["organic_like"] = np.int8(
                family == "likes"
            ) * retained.is_organic.astype(np.int8)
            parts.append(retained.drop(columns="is_organic"))
    if not parts:
        return {}
    events = pd.concat(parts, ignore_index=True)
    events = events.sort_values(
        ["uid", "timestamp", "tie_priority"], kind="mergesort"
    ).reset_index(drop=True)
    result: dict[int, ExplicitEvents] = {}
    for uid, rows in events.groupby("uid", sort=False):
        result[int(uid)] = ExplicitEvents(
            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
            items=rows.item.to_numpy(dtype=np.int32, copy=True),
            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
            active=rows.active.to_numpy(dtype=np.int8, copy=True),
            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
        )
    log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
    return result


def empty_explicit_events() -> ExplicitEvents:
    return ExplicitEvents(
        timestamps=np.empty(0, dtype=np.int64),
        items=np.empty(0, dtype=np.int32),
        channel=np.empty(0, dtype=np.int8),
        active=np.empty(0, dtype=np.int8),
        organic_like=np.empty(0, dtype=np.int8),
    )


def pack_active_items(
    active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
    packed = np.full(width, padding_item, dtype=np.uint16)
    if not active:
        return packed
    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
    packed[: len(ordered)] = np.fromiter(
        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
    )
    return packed


def load_inference_state(
    path: Path,
    item_index: pd.Index,
    uids: np.ndarray,
    n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Pack the task-provided active explicit state."""
    state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
    keep = mapped >= 0
    state = state.loc[keep, ["uid", "state"]].copy()
    state["item"] = mapped[keep].astype(np.int32, copy=False)
    user_rows = pd.Index(uids)
    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
    like_counts = np.zeros(len(uids), dtype=np.int32)
    dislike_counts = np.zeros(len(uids), dtype=np.int32)
    for value, width, destination, counts in (
        ("liked", LIKE_ITEMS, liked, like_counts),
        ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
    ):
        selected = state[state.state == value]
        for uid, rows in selected.groupby("uid", sort=False):
            row = int(user_rows.get_indexer([int(uid)])[0])
            if row < 0:
                continue
            items = np.sort(rows.item.unique())
            counts[row] = len(items)
            retained = items[:width]
            destination[row, : len(retained)] = retained.astype(np.uint16)
    return liked, disliked, like_counts, dislike_counts


class SequentialTwoTower(nn.Module):
    """Order-sensitive user tower and independent catalog-aware item tower."""

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
            input_size=config.hidden_dim,
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
        )
    return ranked_items(frame)


def score(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with public relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5ba44534c87d29bedad1fc32d95e3",
  "usage": {
    "input_tokens": 14900,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 393,
    "reasoning_output_tokens": 184,
    "total_tokens": 15293
  },
  "turn_token_usage": {
    "input_tokens": 14900,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 393,
    "reasoning_output_tokens": 184,
    "total_tokens": 15293
  },
  "thread_token_usage": {
    "input_tokens": 14900,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 393,
    "reasoning_output_tokens": 184,
    "total_tokens": 15293
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0bdff781-0dfb-49f1-aae5-f010fee1e120",
    "process_id": "60423",
    "command": [
      "/bin/sh",
      "-lc",
      "find /task/current_model -maxdepth 2 -type f -print && find /task/data -maxdepth 1 -type f -printf '%f\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py && sed -n '1,260p' /task/evaluate_public.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "find /task/current_model -maxdepth 2 -type f -print",
        "path": "current_model"
      },
      {
        "type": "list_files",
        "cmd": "find /task/data -maxdepth 1 -type f -printf '%f\n'",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,260p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,300p' /task/current_model/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/task/current_model/two_tower_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,260p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "stderr": "",
    "aggregated_output": "/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3090
    },
    "formatted_output": "/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n"
  }
}

exec command

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Tool call · Input +00:26

const r = await tools.exec_command({"cmd":"rg -n \"^(def|class) |negative|sample|loss|validation|rank|argparse|Config\\(\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

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Script completed
Wall time 0.0 seconds
Output:

/task/current_model/two_tower_model.py:27:class TowerConfig:
/task/current_model/two_tower_model.py:40:    uniform_negatives: int = 24
/task/current_model/two_tower_model.py:41:    inbatch_negatives: int = 8
/task/current_model/two_tower_model.py:49:class ListenEvents:
/task/current_model/two_tower_model.py:57:class ExplicitEvents:
/task/current_model/two_tower_model.py:65:def log(message: str) -> None:
/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
/task/current_model/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
/task/current_model/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
/task/current_model/two_tower_model.py:106:def iter_listen_users(
/task/current_model/two_tower_model.py:157:def merge_listen_streams(
/task/current_model/two_tower_model.py:175:def load_explicit_events(
/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:
/task/current_model/two_tower_model.py:238:def pack_active_items(
/task/current_model/two_tower_model.py:251:def load_inference_state(
/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):
/task/current_model/two_tower_model.py:346:def invalid_negative_mask(
/task/current_model/two_tower_model.py:347:    negatives: torch.Tensor,
/task/current_model/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])
/task/current_model/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
/task/current_model/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
/task/current_model/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):
/task/current_model/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(
/task/current_model/two_tower_model.py:358:            negatives[:, :column]
/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(
/task/current_model/two_tower_model.py:374:    """Draw uniform and unique-target in-batch negatives."""
/task/current_model/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
/task/current_model/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
/task/current_model/two_tower_model.py:412:    negatives = negatives_cpu.to(device)
/task/current_model/two_tower_model.py:415:        invalid = invalid_negative_mask(
/task/current_model/two_tower_model.py:416:            negatives, targets, context_items, liked_items
/task/current_model/two_tower_model.py:419:            return negatives, correction
/task/current_model/two_tower_model.py:426:        negatives[invalid] = replacements
/task/current_model/two_tower_model.py:428:    invalid = invalid_negative_mask(
/task/current_model/two_tower_model.py:429:        negatives, targets, context_items, liked_items
/task/current_model/two_tower_model.py:432:        raise RuntimeError("mixed negative resampling failed false-negative filter")
/task/current_model/two_tower_model.py:433:    return negatives, correction
/task/current_model/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and
/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the
/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state
/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
/task/current_model/train_current_model.py:20:import argparse
/task/current_model/train_current_model.py:41:class Config:
/task/current_model/train_current_model.py:55:    loss_microbatch_size: int = 65_536
/task/current_model/train_current_model.py:56:    uniform_negatives: int = 24
/task/current_model/train_current_model.py:57:    inbatch_negatives: int = 8
/task/current_model/train_current_model.py:61:    negative_filter_scope: str = "full-training-history"
/task/current_model/train_current_model.py:67:    validation_fraction: float = 0.10
/task/current_model/train_current_model.py:69:    max_validation_targets_per_user: int = 16
/task/current_model/train_current_model.py:76:class CandidateSet:
/task/current_model/train_current_model.py:99:class SequenceData:
/task/current_model/train_current_model.py:127:class TargetPartition:
/task/current_model/train_current_model.py:132:    validation_counts: np.ndarray
/task/current_model/train_current_model.py:137:        return int(self.training_counts.sum() + self.validation_counts.sum())
/task/current_model/train_current_model.py:140:def log(message: str) -> None:
/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:
/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:
/task/current_model/train_current_model.py:152:    return base.TowerConfig(
/task/current_model/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,
/task/current_model/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,
/task/current_model/train_current_model.py:174:def append_state_snapshot(
/task/current_model/train_current_model.py:190:def causal_activity_features(
/task/current_model/train_current_model.py:247:def build_sequence_parts(
/task/current_model/train_current_model.py:314:def final_user_features(
/task/current_model/train_current_model.py:341:def prepare_data(
/task/current_model/train_current_model.py:346:        "popularity_rank"
/task/current_model/train_current_model.py:354:    if stage == "validation":
/task/current_model/train_current_model.py:357:        candidate_name = "validation_eligible_candidates.npz"
/task/current_model/train_current_model.py:358:        state_name = "validation_preference_state.parquet"
/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
/task/current_model/train_current_model.py:477:        "event_time_feature": "exp-negative-gap-over-60-days",
/task/current_model/train_current_model.py:481:def save_cache(
/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
/task/current_model/train_current_model.py:511:def load_or_prepare(
/task/current_model/train_current_model.py:518:        "validation_eligible_candidates.npz"
/task/current_model/train_current_model.py:519:        if stage == "validation"
/task/current_model/train_current_model.py:534:def build_item_correction(
/task/current_model/train_current_model.py:578:def build_known_positive_filter(
/task/current_model/train_current_model.py:603:        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(
/task/current_model/train_current_model.py:610:    negatives: np.ndarray,
/task/current_model/train_current_model.py:616:    """Return row-level invalid negatives using the complete training prefix."""
/task/current_model/train_current_model.py:617:    byte_columns = negatives >> 3
/task/current_model/train_current_model.py:618:    bit_columns = negatives & 7
/task/current_model/train_current_model.py:621:    invalid |= negatives == targets[:, None]
/task/current_model/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
/task/current_model/train_current_model.py:623:    for column in range(1, negatives.shape[1]):
/task/current_model/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]
/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(
/task/current_model/train_current_model.py:641:    """Draw mixed negatives and reject all known public-training positives."""
/task/current_model/train_current_model.py:647:        (batch_size, config.uniform_negatives),
/task/current_model/train_current_model.py:653:    if config.inbatch_negatives:
/task/current_model/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
/task/current_model/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:
/task/current_model/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),
/task/current_model/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
/task/current_model/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)
/task/current_model/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count
/task/current_model/train_current_model.py:688:        invalid = invalid_full_history_negatives(
/task/current_model/train_current_model.py:689:            negatives,
/task/current_model/train_current_model.py:697:                torch.from_numpy(negatives).to(device),
/task/current_model/train_current_model.py:706:        negatives[invalid] = replacements
/task/current_model/train_current_model.py:709:    invalid = invalid_full_history_negatives(
/task/current_model/train_current_model.py:710:        negatives,
/task/current_model/train_current_model.py:717:        raise RuntimeError("full-history negative resampling failed")
/task/current_model/train_current_model.py:719:        torch.from_numpy(negatives).to(device),
/task/current_model/train_current_model.py:724:def draw_training_negatives(
/task/current_model/train_current_model.py:736:    if config.negative_filter_scope == "full-training-history":
/task/current_model/train_current_model.py:738:            raise RuntimeError("full-history negative filter was not built")
/task/current_model/train_current_model.py:739:        return draw_full_history_filtered_negatives(
/task/current_model/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(
/task/current_model/train_current_model.py:755:        config.uniform_negatives,
/task/current_model/train_current_model.py:756:        config.inbatch_negatives,
/task/current_model/train_current_model.py:763:def event_sequence(
/task/current_model/train_current_model.py:774:def pool_explicit_states(
/task/current_model/train_current_model.py:798:def user_queries(
/task/current_model/train_current_model.py:813:def candidate_vectors(
/task/current_model/train_current_model.py:816:    negatives: torch.Tensor,
/task/current_model/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])
/task/current_model/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(
/task/current_model/train_current_model.py:823:        len(positives), negatives.shape[1], -1
/task/current_model/train_current_model.py:825:    return positive_vectors, negative_vectors
/task/current_model/train_current_model.py:828:def history_windows(
/task/current_model/train_current_model.py:845:def corrected_negative_loss(
/task/current_model/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives
/task/current_model/train_current_model.py:853:    if config.uniform_negatives:
/task/current_model/train_current_model.py:854:        result += config.uniform_negatives * raw[
/task/current_model/train_current_model.py:855:            :, : config.uniform_negatives
/task/current_model/train_current_model.py:857:    if config.inbatch_negatives:
/task/current_model/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)
/task/current_model/train_current_model.py:860:        result += config.inbatch_negatives * (
/task/current_model/train_current_model.py:867:def retained_target_window_starts(
/task/current_model/train_current_model.py:869:    validation_counts: np.ndarray,
/task/current_model/train_current_model.py:873:    training_region_ends = total_targets - validation_counts
/task/current_model/train_current_model.py:878:        or np.any(validation_counts < 0)
/task/current_model/train_current_model.py:880:        raise RuntimeError("chronological target partition produced negative counts")
/task/current_model/train_current_model.py:882:        window_starts + training_counts + validation_counts, total_targets
/task/current_model/train_current_model.py:888:def chronological_target_partition(
/task/current_model/train_current_model.py:890:    validation_fraction: float,
/task/current_model/train_current_model.py:892:    max_validation_targets_per_user: int,
/task/current_model/train_current_model.py:896:    validation = np.floor(totals * validation_fraction).astype(np.int64)
/task/current_model/train_current_model.py:897:    validation[(totals >= 2) & (validation == 0)] = 1
/task/current_model/train_current_model.py:898:    if max_validation_targets_per_user:
/task/current_model/train_current_model.py:899:        validation = np.minimum(validation, max_validation_targets_per_user)
/task/current_model/train_current_model.py:901:    history_training = totals - validation
/task/current_model/train_current_model.py:905:    window_starts = retained_target_window_starts(totals, validation, training)
/task/current_model/train_current_model.py:909:        validation_counts=validation,
/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(
/task/current_model/train_current_model.py:918:    prefixes = ("train",) if stage == "validation" else ("train", "val")
/task/current_model/train_current_model.py:955:def build_target_weights(
/task/current_model/train_current_model.py:981:                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
/task/current_model/train_current_model.py:991:                first_validation_target = last_training_target + 1
/task/current_model/train_current_model.py:993:                    int(prepared.sequence_timestamps[first_validation_target])
/task/current_model/train_current_model.py:994:                    if first_validation_target < end
/task/current_model/train_current_model.py:1035:def build_dense_block(
/task/current_model/train_current_model.py:1111:def score_target_microbatches(
/task/current_model/train_current_model.py:1119:    negatives: torch.Tensor,
/task/current_model/train_current_model.py:1120:    negative_weights: torch.Tensor,
/task/current_model/train_current_model.py:1121:    sample_weights: np.ndarray,
/task/current_model/train_current_model.py:1127:    total_weight = float(sample_weights.sum())
/task/current_model/train_current_model.py:1129:        raise RuntimeError("target block has no positive sample weight")
/task/current_model/train_current_model.py:1132:        "loss_sum": 0.0,
/task/current_model/train_current_model.py:1134:        "negative_sum": 0.0,
/task/current_model/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):
/task/current_model/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)
/task/current_model/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(
/task/current_model/train_current_model.py:1165:            negatives[begin:end],
/task/current_model/train_current_model.py:1168:        negative_logits = torch.einsum(
/task/current_model/train_current_model.py:1169:            "bd,bkd->bk", query_vectors, negative_vectors
/task/current_model/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)
/task/current_model/train_current_model.py:1172:        negative_loss = corrected_negative_loss(
/task/current_model/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config
/task/current_model/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)
/task/current_model/train_current_model.py:1178:        loss_sum = (
/task/current_model/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0
/task/current_model/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):
/task/current_model/train_current_model.py:1182:            raise RuntimeError("dense next-item training produced non-finite loss")
/task/current_model/train_current_model.py:1184:            (loss_sum / total_weight).backward()
/task/current_model/train_current_model.py:1188:        statistics["loss_sum"] += float(loss_sum.detach().cpu())
/task/current_model/train_current_model.py:1190:        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
/task/current_model/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:
/task/current_model/train_current_model.py:1203:def run_training_epoch(
/task/current_model/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives
/task/current_model/train_current_model.py:1228:    total_loss = 0.0
/task/current_model/train_current_model.py:1232:    total_negative = 0.0
/task/current_model/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(
/task/current_model/train_current_model.py:1314:                negatives,
/task/current_model/train_current_model.py:1315:                negative_weights,
/task/current_model/train_current_model.py:1327:            total_loss += statistics["loss_sum"]
/task/current_model/train_current_model.py:1331:            total_negative += statistics["negative_sum"]
/task/current_model/train_current_model.py:1337:                batch_bce = statistics["loss_sum"] / max(
/task/current_model/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)
/task/current_model/train_current_model.py:1351:        "loss": total_loss / max(total_weight, 1.0e-12),
/task/current_model/train_current_model.py:1353:        "negative_logit": total_negative / max(total_targets * negative_count, 1),
/task/current_model/train_current_model.py:1355:        / max(total_targets * negative_count, 1),
/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(
/task/current_model/train_current_model.py:1371:    validation_counts: np.ndarray,
/task/current_model/train_current_model.py:1378:    totals = training_counts + validation_counts
/task/current_model/train_current_model.py:1379:    users_with_validation = np.flatnonzero(validation_counts > 0)
/task/current_model/train_current_model.py:1380:    ordered = users_with_validation[
/task/current_model/train_current_model.py:1381:        np.argsort(totals[users_with_validation], kind="stable")
/task/current_model/train_current_model.py:1388:    total_loss = 0.0
/task/current_model/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(
/task/current_model/train_current_model.py:1469:                negatives,
/task/current_model/train_current_model.py:1470:                negative_weights,
/task/current_model/train_current_model.py:1475:            total_loss += statistics["loss_sum"]
/task/current_model/train_current_model.py:1481:        "validation_loss": total_loss / max(total_weight, 1.0e-12),
/task/current_model/train_current_model.py:1482:        "validation_targets": float(total_targets),
/task/current_model/train_current_model.py:1483:        "validation_target_weight_sum": total_weight,
/task/current_model/train_current_model.py:1484:        "validation_seconds": time.monotonic() - started,
/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(
/task/current_model/train_current_model.py:1521:def train_model(
/task/current_model/train_current_model.py:1532:        config.validation_fraction,
/task/current_model/train_current_model.py:1534:        config.max_validation_targets_per_user,
/task/current_model/train_current_model.py:1537:    validation_counts = partition.validation_counts
/task/current_model/train_current_model.py:1540:        f"validation={int(validation_counts.sum()):,} "
/task/current_model/train_current_model.py:1567:        if config.negative_filter_scope == "full-training-history"
/task/current_model/train_current_model.py:1571:    best_loss = float("inf")
/task/current_model/train_current_model.py:1601:            "negative_filter_scope", "last-100"
/task/current_model/train_current_model.py:1619:            stored_config.get("validation_fraction", 0.10),
/task/current_model/train_current_model.py:1621:            stored_config.get("max_validation_targets_per_user", 0),
/task/current_model/train_current_model.py:1624:            config.validation_fraction,
/task/current_model/train_current_model.py:1626:            config.max_validation_targets_per_user,
/task/current_model/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope
/task/current_model/train_current_model.py:1657:            prior.pop("validation_loss", None)
/task/current_model/train_current_model.py:1660:                f"{stored_filter_scope}->{config.negative_filter_scope}, "
/task/current_model/train_current_model.py:1664:                "recomputing the resume checkpoint's validation BCE"
/task/current_model/train_current_model.py:1666:        if "validation_loss" not in prior:
/task/current_model/train_current_model.py:1668:                evaluate_validation_bce(
/task/current_model/train_current_model.py:1675:                    validation_counts,
/task/current_model/train_current_model.py:1682:        best_loss = float(prior["validation_loss"])
/task/current_model/train_current_model.py:1686:            f"resumed validation-BCE training: epoch={start_epoch} "
/task/current_model/train_current_model.py:1687:            f"loss={best_loss:.6f}"
/task/current_model/train_current_model.py:1705:        validation = evaluate_validation_bce(
/task/current_model/train_current_model.py:1712:            validation_counts,
/task/current_model/train_current_model.py:1717:        diagnostics.update(validation)
/task/current_model/train_current_model.py:1731:            diagnostics["validation_loss"]
/task/current_model/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta
/task/current_model/train_current_model.py:1735:            best_loss = diagnostics["validation_loss"]
/task/current_model/train_current_model.py:1754:            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
/task/current_model/train_current_model.py:1755:            f"validation_loss={diagnostics['validation_loss']:.5f} "
/task/current_model/train_current_model.py:1758:            f"validation_targets={int(diagnostics['validation_targets']):,} "
/task/current_model/train_current_model.py:1760:            f"validation_seconds={diagnostics['validation_seconds']:.1f}"
/task/current_model/train_current_model.py:1766:                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
/task/current_model/train_current_model.py:1775:        f"restored best validation-BCE checkpoint: epoch={best_epoch} "
/task/current_model/train_current_model.py:1776:        f"loss={best_loss:.6f}"
/task/current_model/train_current_model.py:1782:def inference_queries(
/task/current_model/train_current_model.py:1861:def rank_candidates(
/task/current_model/train_current_model.py:1867:    ranking_batch_size: int,
/task/current_model/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):
/task/current_model/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))
/task/current_model/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)
/task/current_model/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)
/task/current_model/train_current_model.py:1900:            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
/task/current_model/train_current_model.py:1909:    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
/task/current_model/train_current_model.py:1922:def main() -> int:
/task/current_model/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)
/task/current_model/train_current_model.py:1925:        "--stage", choices=("validation", "submission"), default="validation"
/task/current_model/train_current_model.py:1939:        "--loss-microbatch-size",
/task/current_model/train_current_model.py:1941:        default=Config.loss_microbatch_size,
/task/current_model/train_current_model.py:1944:        "--negative-filter-scope",
/task/current_model/train_current_model.py:1946:        default=Config.negative_filter_scope,
/task/current_model/train_current_model.py:1974:        "--validation-fraction",
/task/current_model/train_current_model.py:1976:        default=Config.validation_fraction,
/task/current_model/train_current_model.py:1982:        help="retain this many recent pre-validation targets per user; 0 is uncapped",
/task/current_model/train_current_model.py:1985:        "--max-validation-targets-per-user",
/task/current_model/train_current_model.py:1987:        default=Config.max_validation_targets_per_user,
/task/current_model/train_current_model.py:1988:        help="retain this many final validation targets per user; 0 is uncapped",
/task/current_model/train_current_model.py:2003:    parser.add_argument("--ranking-batch-size", type=int, default=64)
/task/current_model/train_current_model.py:2012:        or args.loss_microbatch_size < 1
/task/current_model/train_current_model.py:2020:        or args.max_validation_targets_per_user < 0
/task/current_model/train_current_model.py:2023:    if not 0.0 < args.validation_fraction < 1.0:
/task/current_model/train_current_model.py:2024:        parser.error("validation fraction must be strictly between zero and one")
/task/current_model/train_current_model.py:2028:    config = Config(
/task/current_model/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,
/task/current_model/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,
/task/current_model/train_current_model.py:2040:        validation_fraction=args.validation_fraction,
/task/current_model/train_current_model.py:2042:        max_validation_targets_per_user=args.max_validation_targets_per_user,
/task/current_model/train_current_model.py:2063:        config.validation_fraction,
/task/current_model/train_current_model.py:2065:        config.max_validation_targets_per_user,
/task/current_model/train_current_model.py:2078:                    "selected_validation_targets": int(
/task/current_model/train_current_model.py:2079:                        target_partition.validation_counts.sum()
/task/current_model/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(
/task/current_model/train_current_model.py:2108:        args.ranking_batch_size,
/task/current_model/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())
/task/current_model/train_current_model.py:2117:                "rows": len(ranking),
/task/current_model/train_current_model.py:2118:                "users": int(ranking.uid.nunique()),
/task/current_model/train_current_model.py:2126:                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
/task/current_model/train_current_model.py:2128:                    "model_selection": "chronological-validation-BCE-early-stopping",
/task/current_model/train_current_model.py:2129:                    "loss_execution": "gradient-accumulated-microbatches",
/task/current_model/train_current_model.py:2135:                    "objective": "sample-weighted-BCE-at-every-timestep",
/task/current_model/train_current_model.py:2136:                    "negative_sampling": (
/task/current_model/train_current_model.py:2139:                    "false_negative_filter": config.negative_filter_scope,
/task/current_model/train_current_model.py:2148:                "selected_validation_targets": int(
/task/current_model/train_current_model.py:2149:                    target_partition.validation_counts.sum()

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d2e6c9bf-c2ad-44f7-8b53-1a03828e69be",
    "process_id": "95658",
    "command": [
      "/bin/sh",
      "-lc",
      "rg -n \"^(def|class) |negative|sample|loss|validation|rank|argparse|Config\\(\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "search",
        "cmd": "rg -n '^(def|class) |negative|sample|loss|validation|rank|argparse|Config'\"\\\\(\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py",
        "query": "^(def|class) |negative|sample|loss|validation|rank|argparse|Config\\(",
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    "source": "unified_exec_startup",
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    "stdout": "/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40:    uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41:    inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347:    negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358:            negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419:            return negatives, correction\n/task/current_model/two_tower_model.py:426:        negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433:    return negatives, correction\n/task/current_model/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56:    uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57:    inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67:    validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69:    max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132:    validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137:        return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152:    return base.TowerConfig(\n/task/current_model/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346:        \"popularity_rank\"\n/task/current_model/train_current_model.py:354:    if stage == \"validation\":\n/task/current_model/train_current_model.py:357:        candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358:        state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518:        \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519:        if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610:    negatives: np.ndarray,\n/task/current_model/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617:    byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618:    bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653:    if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689:            negatives,\n/task/current_model/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706:        negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710:        negatives,\n/task/current_model/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755:        config.uniform_negatives,\n/task/current_model/train_current_model.py:756:        config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816:    negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825:    return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853:    if config.uniform_negatives:\n/task/current_model/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855:            :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857:    if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860:        result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869:    validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873:    training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878:        or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882:        window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890:    validation_fraction: float,\n/task/current_model/train_current_model.py:892:    max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896:    validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897:    validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898:    if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899:        validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901:    history_training = totals - validation\n/task/current_model/train_current_model.py:905:    window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909:        validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918:    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981:                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991:                first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993:                    int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994:                    if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119:    negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121:    sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127:    total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129:        raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165:            negatives[begin:end],\n/task/current_model/train_current_model.py:1168:        negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178:        loss_sum = (\n/task/current_model/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228:    total_loss = 0.0\n/task/current_model/train_current_model.py:1232:    total_negative = 0.0\n/task/current_model/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314:                negatives,\n/task/current_model/train_current_model.py:1315:                negative_weights,\n/task/current_model/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371:    validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378:    totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379:    users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380:    ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381:        np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388:    total_loss = 0.0\n/task/current_model/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469:                negatives,\n/task/current_model/train_current_model.py:1470:                negative_weights,\n/task/current_model/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482:        \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483:        \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484:        \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532:        config.validation_fraction,\n/task/current_model/train_current_model.py:1534:        config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537:    validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540:        f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571:    best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619:            stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621:            stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624:            config.validation_fraction,\n/task/current_model/train_current_model.py:1626:            config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664:                \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668:                evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675:                    validation_counts,\n/task/current_model/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686:            f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705:        validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712:            validation_counts,\n/task/current_model/train_current_model.py:1717:        diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758:            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760:            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775:        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867:    ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925:        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944:        \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974:        \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976:        default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982:        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985:        \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987:        default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988:        help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020:        or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023:    if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024:        parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028:    config = Config(\n/task/current_model/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040:        validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042:        max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063:        config.validation_fraction,\n/task/current_model/train_current_model.py:2065:        config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078:                    \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079:                        target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108:        args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117:                \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118:                \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128:                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135:                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136:                    \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148:                \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149:                    target_partition.validation_counts.sum()\n",
    "stderr": "",
    "aggregated_output": "/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40:    uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41:    inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347:    negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358:            negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419:            return negatives, correction\n/task/current_model/two_tower_model.py:426:        negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433:    return negatives, correction\n/task/current_model/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56:    uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57:    inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67:    validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69:    max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132:    validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137:        return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152:    return base.TowerConfig(\n/task/current_model/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346:        \"popularity_rank\"\n/task/current_model/train_current_model.py:354:    if stage == \"validation\":\n/task/current_model/train_current_model.py:357:        candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358:        state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518:        \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519:        if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610:    negatives: np.ndarray,\n/task/current_model/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617:    byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618:    bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653:    if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689:            negatives,\n/task/current_model/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706:        negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710:        negatives,\n/task/current_model/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755:        config.uniform_negatives,\n/task/current_model/train_current_model.py:756:        config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816:    negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825:    return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853:    if config.uniform_negatives:\n/task/current_model/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855:            :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857:    if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860:        result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869:    validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873:    training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878:        or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882:        window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890:    validation_fraction: float,\n/task/current_model/train_current_model.py:892:    max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896:    validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897:    validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898:    if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899:        validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901:    history_training = totals - validation\n/task/current_model/train_current_model.py:905:    window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909:        validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918:    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981:                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991:                first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993:                    int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994:                    if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119:    negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121:    sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127:    total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129:        raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165:            negatives[begin:end],\n/task/current_model/train_current_model.py:1168:        negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178:        loss_sum = (\n/task/current_model/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228:    total_loss = 0.0\n/task/current_model/train_current_model.py:1232:    total_negative = 0.0\n/task/current_model/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314:                negatives,\n/task/current_model/train_current_model.py:1315:                negative_weights,\n/task/current_model/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371:    validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378:    totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379:    users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380:    ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381:        np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388:    total_loss = 0.0\n/task/current_model/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469:                negatives,\n/task/current_model/train_current_model.py:1470:                negative_weights,\n/task/current_model/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482:        \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483:        \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484:        \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532:        config.validation_fraction,\n/task/current_model/train_current_model.py:1534:        config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537:    validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540:        f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571:    best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619:            stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621:            stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624:            config.validation_fraction,\n/task/current_model/train_current_model.py:1626:            config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664:                \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668:                evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675:                    validation_counts,\n/task/current_model/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686:            f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705:        validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712:            validation_counts,\n/task/current_model/train_current_model.py:1717:        diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758:            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760:            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775:        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867:    ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925:        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944:        \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974:        \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976:        default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982:        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985:        \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987:        default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988:        help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020:        or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023:    if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024:        parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028:    config = Config(\n/task/current_model/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040:        validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042:        max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063:        config.validation_fraction,\n/task/current_model/train_current_model.py:2065:        config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078:                    \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079:                        target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108:        args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117:                \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118:                \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128:                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135:                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136:                    \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148:                \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149:                    target_partition.validation_counts.sum()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4051
    },
    "formatted_output": "/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40:    uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41:    inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347:    negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358:            negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419:            return negatives, correction\n/task/current_model/two_tower_model.py:426:        negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433:    return negatives, correction\n/task/current_model/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56:    uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57:    inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67:    validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69:    max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132:    validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137:        return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152:    return base.TowerConfig(\n/task/current_model/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346:        \"popularity_rank\"\n/task/current_model/train_current_model.py:354:    if stage == \"validation\":\n/task/current_model/train_current_model.py:357:        candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358:        state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518:        \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519:        if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610:    negatives: np.ndarray,\n/task/current_model/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617:    byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618:    bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653:    if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689:            negatives,\n/task/current_model/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706:        negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710:        negatives,\n/task/current_model/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755:        config.uniform_negatives,\n/task/current_model/train_current_model.py:756:        config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816:    negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825:    return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853:    if config.uniform_negatives:\n/task/current_model/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855:            :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857:    if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860:        result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869:    validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873:    training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878:        or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882:        window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890:    validation_fraction: float,\n/task/current_model/train_current_model.py:892:    max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896:    validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897:    validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898:    if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899:        validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901:    history_training = totals - validation\n/task/current_model/train_current_model.py:905:    window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909:        validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918:    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981:                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991:                first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993:                    int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994:                    if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119:    negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121:    sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127:    total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129:        raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165:            negatives[begin:end],\n/task/current_model/train_current_model.py:1168:        negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178:        loss_sum = (\n/task/current_model/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228:    total_loss = 0.0\n/task/current_model/train_current_model.py:1232:    total_negative = 0.0\n/task/current_model/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314:                negatives,\n/task/current_model/train_current_model.py:1315:                negative_weights,\n/task/current_model/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371:    validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378:    totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379:    users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380:    ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381:        np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388:    total_loss = 0.0\n/task/current_model/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469:                negatives,\n/task/current_model/train_current_model.py:1470:                negative_weights,\n/task/current_model/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482:        \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483:        \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484:        \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532:        config.validation_fraction,\n/task/current_model/train_current_model.py:1534:        config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537:    validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540:        f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571:    best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619:            stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621:            stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624:            config.validation_fraction,\n/task/current_model/train_current_model.py:1626:            config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664:                \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668:                evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675:                    validation_counts,\n/task/current_model/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686:            f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705:        validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712:            validation_counts,\n/task/current_model/train_current_model.py:1717:        diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758:            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760:            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775:        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867:    ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925:        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944:        \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974:        \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976:        default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982:        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985:        \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987:        default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988:        help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020:        or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023:    if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024:        parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028:    config = Config(\n/task/current_model/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040:        validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042:        max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063:        config.validation_fraction,\n/task/current_model/train_current_model.py:2065:        config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078:                    \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079:                        target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108:        args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117:                \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118:                \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128:                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135:                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136:                    \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148:                \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149:                    target_partition.validation_counts.sum()\n"
  }
}

exec command

+00:34

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +00:36

const r = await tools.exec_command({"cmd":"sed -n '247,760p' /task/current_model/train_current_model.py && sed -n '760,1210p' /task/current_model/train_current_model.py && sed -n '1200,1785p' /task/current_model/train_current_model.py && sed -n '1780,2165p' /task/current_model/train_current_model.py && sed -n '284,455p' /task/current_model/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":60000});
text(r.output);

Tool response +00:36

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 19542)
Total output lines: 2086

def build_sequence_parts(
    listens: base.ListenEvents,
    explicit: base.ExplicitEvents,
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
    items = listens.items[qualifying].astype(np.uint16, copy=False)
    organic = listens.organic[qualifying].astype(np.float32, copy=False)
    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
    if not len(items):
        return None

    event_features = np.zeros((len(items), 3), dtype=np.float16)
    event_features[:, 0] = organic.astype(np.float16)
    event_features[:, 1] = (
        np.minimum(completion, 100.0) / 100.0
    ).astype(np.float16)
    if len(items) > 1:
        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
        event_features[1:, 2] = np.exp(
            -gaps.astype(np.float64) / (60.0 * base.DAY)
        ).astype(np.float16)

    activity = causal_activity_features(
        timestamps, items, organic, completion
    )
    states = np.zeros(len(items), dtype=np.uint32)
    active_likes: dict[int, int] = {}
    active_dislikes: dict[int, int] = {}
    explicit_position = 0
    current_state = 0
    for row, timestamp in enumerate(timestamps):
        changed = False
        while (
            explicit_position < len(explicit.timestamps)
            and int(explicit.timestamps[explicit_position]) < int(timestamp)
        ):
            item = int(explicit.items[explicit_position])
            event_timestamp = int(explicit.timestamps[explicit_position])
            destination = (
                active_likes
                if int(explicit.channel[explicit_position]) == 0
                else active_dislikes
            )
            if int(explicit.active[explicit_position]) == 1:
                destination[item] = event_timestamp
            else:
                destination.pop(item, None)
            explicit_position += 1
            changed = True
        if changed:
            current_state = append_state_snapshot(
                active_likes,
                active_dislikes,
                n_items,
                liked_states,
                disliked_states,
                state_counts,
            )
        states[row] = np.uint32(current_state)
    return timestamps, items.copy(), event_features, activity, states


def final_user_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    event_features: np.ndarray,
    history_end: int,
    like_count: int,
    dislike_count: int,
) -> np.ndarray:
    start = int(
        np.searchsorted(
            timestamps,
            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
            side="left",
        )
    )
    result = np.zeros(6, dtype=np.float32)
    recent_items = items[start:]
    if len(recent_items):
        result[0] = np.log1p(len(recent_items)) / 8.0
        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
        result[2] = float(np.mean(event_features[start:, 0]))
        result[3] = float(np.mean(event_features[start:, 1]))
    result[4] = np.log1p(like_count) / 6.0
    result[5] = np.log1p(dislike_count) / 4.0
    return result


def prepare_data(
    public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
    data = public / "data"
    catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
        "popularity_rank"
    )
    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
    item_index = pd.Index(item_ids)
    n_items = len(item_ids)
    if n_items >= np.iinfo(np.uint16).max:
        raise RuntimeError("uint16 item storage is too small for this catalog")

    if stage == "validation":
        prefixes = ("train",)
        history_end = base.TRAIN_END
        candidate_name = "validation_eligible_candidates.npz"
        state_name = "validation_preference_state.parquet"
    else:
        prefixes = ("train", "val")
        history_end = base.VALIDATION_END
        candidate_name = "test_eligible_candidates.npz"
        state_name = "test_preference_state.parquet"
    eligibility = CandidateSet.read(data / candidate_name)
    if not np.array_equal(eligibility.item_ids, item_ids):
        raise RuntimeError("catalog and candidate artifact item order differ")

    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
    inference_likes, inference_dislikes, like_counts, dislike_counts = (
        base.load_inference_state(
            data / state_name,
            item_index,
            eligibility.uids,
            n_items,
        )
    )
    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)

    # Snapshot zero is the shared empty explicit state.
    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
    disliked_states = [
        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
    ]
    state_counts: list[tuple[int, int]] = [(0, 0)]
    user_ids: list[int] = []
    offsets = [0]
    timestamp_parts: list[np.ndarray] = []
    item_parts: list[np.ndarray] = []
    event_parts: list[np.ndarray] = []
    activity_parts: list[np.ndarray] = []
    state_parts: list[np.ndarray] = []

    listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
    started = time.monotonic()
    for seen_users, (uid, listens) in enumerate(
        base.merge_listen_streams(listen_paths, item_index), start=1
    ):
        parts = build_sequence_parts(
            listens,
            explicit_by_user.get(uid, base.empty_explicit_events()),
            n_items,
            liked_states,
            disliked_states,
            state_counts,
        )
        if parts is None:
            continue
        sequence_row = len(user_ids)
        user_ids.append(uid)
        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
        item_parts.append(parts[1])
        event_parts.append(parts[2])
        activity_parts.append(parts[3])
        state_parts.append(parts[4])
        offsets.append(offsets[-1] + len(parts[1]))
        inference_row = inference_rows.get(uid)
        if inference_row is not None:
            inference_sequence_rows[inference_row] = sequence_row
            inference_user_features[inference_row] = final_user_features(
                parts[0],
                parts[1],
                parts[2],
                history_end,
                int(like_counts[inference_row]),
                int(dislike_counts[inference_row]),
            )
        if seen_users % 1_000 == 0:
            log(
                f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
                f"states={len(liked_states):,} "
                f"elapsed={time.monotonic() - started:.1f}s"
            )

    missing = np.flatnonzero(inference_sequence_rows < 0)
    if len(missing):
        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
        inference_user_features[missing, 5] = (
            np.log1p(dislike_counts[missing]) / 4.0
        )
        log(f"target users without qualifying catalog sequence={len(missing):,}")

    prepared = SequenceData(
        item_ids=item_ids,
        item_features=base.build_item_features(catalog),
        user_ids=np.asarray(user_ids, dtype=np.int64),
        offsets=np.asarray(offsets, dtype=np.int64),
        sequence_timestamps=np.concatenate(timestamp_parts),
        sequence_items=np.concatenate(item_parts),
        event_features=np.concatenate(event_parts),
        activity_features=np.concatenate(activity_parts),
        state_ids=np.concatenate(state_parts),
        liked_states=np.stack(liked_states),
        disliked_states=np.stack(disliked_states),
        state_counts=np.asarray(state_counts, dtype=np.int32),
        inference_user_rows=inference_sequence_rows,
        inference_liked_items=inference_likes,
        inference_disliked_items=inference_dislikes,
        inference_user_features=inference_user_features,
    )
    log(
        f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
        f"tokens={len(prepared.sequence_items):,} "
        f"next_item_targets={prepared.target_count:,} "
        f"states={len(prepared.liked_states):,}"
    )
    return prepared, eligibility


def cache_metadata(stage: str, config: Config) -> dict[str, object]:
    return {
        "cache_version": CACHE_VERSION,
        "stage": stage,
        "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
        "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
        "event_time_feature": "exp-negative-gap-over-60-days",
    }


def save_cache(
    path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    np.savez(
        path,
        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
        **prepared.__dict__,
    )
    log(f"saved public-derived full-sequence cache: {path}")


def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
    with np.load(path, allow_pickle=False) as artifact:
        actual = json.loads(str(artifact["metadata"].item()))
        expected = cache_metadata(stage, config)
        if actual != expected:
            raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
        values = {
            field: artifact[field].copy()
            for field in SequenceData.__dataclass_fields__
        }
    prepared = SequenceData(**values)
    log(
        f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
        f"targets={prepared.target_count:,}"
    )
    return prepared


def load_or_prepare(
    public: Path,
    stage: str,
    config: Config,
    cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
    candidate_name = (
        "validation_eligible_candidates.npz"
        if stage == "validation"
        else "test_eligible_candidates.npz"
    )
    eligibility = CandidateSet.read(public / "data" / candidate_name)
    if cache is not None and cache.is_file():
        prepared = load_cache(cache, stage, config)
        if not np.array_equal(prepared.item_ids, eligibility.item_ids):
            raise RuntimeError("cached catalog differs from candidate artifact")
        return prepared, eligibility
    prepared, built_eligibility = prepare_data(public, stage, config)
    if cache is not None:
        save_cache(cache, prepared, stage, config)
    return prepared, built_eligibility


def build_item_correction(
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
    raw = np.power(
        (1.0 / len(counts)) / probabilities,
        config.inbatch_correction_power,
    )
    normalization = float(np.sum(counts * raw) / max(total, 1.0))
    correction = np.clip(
        raw / max(normalization, 1.0e-12),
        config.inbatch_correction_min,
        config.inbatch_correction_max,
    ).astype(np.float32)
    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
    log(
        f"inbatch correction: min={correction.min():.3f} "
        f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
    )
    return torch.from_numpy(correction)


def build_known_positive_filter(
    prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
    """Pack each user's complete public-training positive-item set."""
    width = (len(prepared.item_ids) + 7) // 8
    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
    retained = 0
    for user_row, target_count in enumerate(training_counts):
        if not target_count:
            continue
        offset = int(prepared.offsets[user_row])
        # T adjacent targets consume sequence positions 0 through T.  The
        # chronological BCE suffix starts at T + 1 and is intentionally absent.
        items = np.unique(
            prepared.sequence_items[
                offset : offset + int(target_count) + 1
            ].astype(np.int64)
        )
        byte_columns = items >> 3
        bit_values = np.left_shift(
            np.uint8(1), (items & 7).astype(np.uint8)
        )
        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
        retained += len(items)
    log(
        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
        f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
    )
    return packed


def invalid_full_history_negatives(
    negatives: np.ndarray,
    targets: np.ndarray,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray,
    liked_items: np.ndarray,
) -> np.ndarray:
    """Return row-level invalid negatives using the complete training prefix."""
    byte_columns = negatives >> 3
    bit_columns = negatives & 7
    known = known_positive_packed[user_rows[:, None], byte_columns]
    invalid = ((known >> bit_columns) & 1).astype(bool)
    invalid |= negatives == targets[:, None]
    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= (
            negatives[:, column, None] == negatives[:, :column]
        ).any(axis=1)
    return invalid


def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:
        correction[
            :, config.uniform_negatives : config.uniform_negatives + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]].numpy()
    for _ in range(32):
        invalid = invalid_full_history_negatives(
            negatives,
            targets_cpu,
            user_rows,
            known_positive_packed,
            liked_cpu,
        )
        if not np.any(invalid):
            return (
                torch.from_numpy(negatives).to(device),
                torch.from_numpy(correction).to(device),
            )
        replacements = torch.randint(
            n_items,
            (int(invalid.sum()),),
            generator=generator,
            device="cpu",
        ).numpy()
        negatives[invalid] = replacements
        # Rejected in-batch items become ordinary uniform draws.
        correction[invalid] = 1.0
    invalid = invalid_full_history_negatives(
        negatives,
        targets_cpu,
        user_rows,
        known_positive_packed,
        liked_cpu,
    )
    if np.any(invalid):
        raise RuntimeError("full-history negative resampling failed")
    return (
        torch.from_numpy(negatives).to(device),
        torch.from_numpy(correction).to(device),
    )


def draw_training_negatives(
    positive_items: torch.Tensor,
    known_history: torch.Tensor,
    liked_items: torch.Tensor,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
    prepared: SequenceData,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    if config.negative_filter_scope == "full-training-history":
        if known_positive_packed is None:
            raise RuntimeError("full-history negative filter was not built")
        return draw_full_history_filtered_negatives(
            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
    return base.draw_filtered_mixed_negatives(
        positive_items,
        known_history,
        liked_items,
        len(prepared.item_ids),
        config.uniform_negatives,
        config.inbatch_negatives,
        item_correction,
        generator,
        device,
    )
    )


def event_sequence(
    model: base.SequentialTwoTower,
    items: torch.Tensor,
    features: torch.Tensor,
    hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    inputs = torch.cat([model.context_embedding(items), features], dim=-1)
    projected = model.event_projection(inputs)
    return model.sequence_encoder(projected, hidden)


def pool_explicit_states(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    state_ids: np.ndarray,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    liked_items = torch.from_numpy(
        prepared.liked_states[unique_states].astype(np.int64)
    ).to(device)
    disliked_items = torch.from_numpy(
        prepared.disliked_states[unique_states].astype(np.int64)
    ).to(device)
    liked_vectors = model._mean_context_channel(liked_items)
    disliked_vectors = model._mean_context_channel(disliked_items)
    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
    counts = prepared.state_counts[state_ids]
    return (
        liked_vectors[inverse_rows],
        disliked_vectors[inverse_r…9542 tokens truncated…  (len(output_rows), steps),
                len(prepared.item_ids),
                dtype=np.uint16,
            )
            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
            counts = np.zeros(len(output_rows), dtype=np.int32)
            for local_row, sequence_row in enumerate(rows):
                if sequence_row < 0:
                    continue
                count = min(steps, max(int(group_lengths[local_row]) - start, 0))
                if not count:
                    continue
                offset = int(prepared.offsets[sequence_row])
                selected = slice(offset + start, offset + start + count)
                items[local_row, :count] = prepared.sequence_items[selected]
                features[local_row, :count] = prepared.event_features[selected]
                counts[local_row] = count
            outputs, _ = event_sequence(
                model,
                torch.from_numpy(items.astype(np.int64)).to(device),
                torch.from_numpy(features.astype(np.float32)).to(device),
                hidden,
            )
            next_rows: list[torch.Tensor] = []
            for local_row, count in enumerate(counts):
                next_rows.append(
                    outputs[local_row, count - 1]
                    if count
                    else hidden[0, local_row]
                )
            hidden = torch.stack(next_rows).unsqueeze(0)

        liked_items = torch.from_numpy(
            prepared.inference_liked_items[output_rows].astype(np.int64)
        ).to(device)
        disliked_items = torch.from_numpy(
            prepared.inference_disliked_items[output_rows].astype(np.int64)
        ).to(device)
        queries = user_queries(
            model,
            hidden.squeeze(0),
            model._mean_context_channel(liked_items),
            model._mean_context_channel(disliked_items),
            torch.from_numpy(
                prepared.inference_user_features[output_rows].astype(np.float32)
            ).to(device),
        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


@torch.inference_mode()
def rank_candidates(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
    model.eval()
    queries = inference_queries(model, prepared, config, device)
    item_parts: list[torch.Tensor] = []
    for start in range(0, len(prepared.item_ids), 2_048):
        rows = torch.arange(
            start,
            min(start + 2_048, len(prepared.item_ids)),
            dtype=torch.long,
            device=device,
        )
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
    rows: list[tuple[int, int, int]] = []
    for start in range(0, len(eligibility.uids), ranking_batch_size):
        end = min(start + ranking_batch_size, len(eligibility.uids))
        scores = queries[start:end] @ items.T
        eligible = np.unpackbits(
            eligibility.packed[start:end],
            axis=1,
            count=len(prepared.item_ids),
            bitorder="little",
        ).astype(bool, copy=False)
        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
        for local_row, columns in enumerate(selected):
            uid = int(eligibility.uids[start + local_row])
            rows.extend(
                (uid, int(prepared.item_ids[column]), rank)
                for rank, column in enumerate(columns, start=1)
            )
        if end == len(eligibility.uids) or end % 1_024 == 0:
            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
    diagnostics = {
        "query_norm_mean": float(query_norm.mean()),
        "query_norm_std": float(query_norm.std()),
        "item_norm_mean": float(item_norm.mean()),
        "item_norm_std": float(item_norm.std()),
    }
    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics


def write_ranking(frame: pd.DataFrame, output: Path) -> None:
    output.parent.mkdir(parents=True, exist_ok=True)
    if output.suffix == ".parquet":
        frame.to_parquet(output, index=False, compression="zstd")
    elif output.suffix == ".csv":
        frame.to_csv(output, index=False)
    else:
        raise ValueError("--output must end in .parquet or .csv")


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
    )
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--public-dir", type=Path, required=True)
    parser.add_argument("--prepared-cache", type=Path)
    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
    parser.add_argument("--threads", type=int, default=8)
    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
    parser.add_argument(
        "--user-batch-size", type=int, default=Config.user_batch_size
    )
    parser.add_argument("--epochs", type=int, default=Config.epochs)
    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
    parser.add_argument(
        "--loss-microbatch-size",
        type=int,
        default=Config.loss_microbatch_size,
    )
    parser.add_argument(
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--target-weighting",
        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
        "--organic-strong-target-weight",
        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
    parser.add_argument(
        "--discovery-horizon-days",
        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
    parser.add_argument(
        "--max-train-targets-per-user",
        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
        help="retain this many final validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )
    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1
        or args.early_stopping_patience < 1
        or args.early_stopping_min_delta < 0
        or args.ordinary_target_weight <= 0
        or args.organic_strong_target_weight <= 0
        or args.organic_like_target_weight <= 0
        or args.discovery_horizon_days < 1
        or args.max_train_targets_per_user < 0
        or args.max_validation_targets_per_user < 0
    ):
        parser.error("training counts, learning rate, and patience are invalid")
    if not 0.0 < args.validation_fraction < 1.0:
        parser.error("validation fraction must be strictly between zero and one")
    public = args.public_dir.resolve()
    if not (public / "data" / "candidate_catalog.parquet").is_file():
        parser.error(f"public task data not found under {public}")
    config = Config(
        bptt_steps=args.bptt_steps,
        user_batch_size=args.user_batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate,
        loss_microbatch_size=args.loss_microbatch_size,
        negative_filter_scope=args.negative_filter_scope,
        target_weighting=args.target_weighting,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
        organic_like_target_weight=args.organic_like_target_weight,
        discovery_horizon_days=args.discovery_horizon_days,
        validation_fraction=args.validation_fraction,
        max_train_targets_per_user=args.max_train_targets_per_user,
        max_validation_targets_per_user=args.max_validation_targets_per_user,
        early_stopping_patience=args.early_stopping_patience,
        early_stopping_min_delta=args.early_stopping_min_delta,
        seed=args.seed,
    )
    torch.set_num_threads(args.threads)
    torch.set_num_interop_threads(1)
    seed_everything(config.seed)
    device = torch.device(args.device)
    if device.type == "mps" and not torch.backends.mps.is_available():
        parser.error("MPS was requested but is unavailable")

    started = time.monotonic()
    prepared, eligibility = load_or_prepare(
        public,
        args.stage,
        config,
        args.prepared_cache.resolve() if args.prepared_cache else None,
    )
    target_partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    if args.prepare_only:
        print(
            json.dumps(
                {
                    "stage": args.stage,
                    "sequence_users": len(prepared.user_ids),
                    "sequence_tokens": len(prepared.sequence_items),
                    "next_item_targets": prepared.target_count,
                    "selected_training_targets": int(
                        target_partition.training_counts.sum()
                    ),
                    "selected_validation_targets": int(
                        target_partition.validation_counts.sum()
                    ),
                    "discarded_old_targets": int(
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
                indent=2,
            )
        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
        device,
        args.ranking_batch_size,
    )
    write_ranking(ranking, args.output.resolve())
    print(
        json.dumps(
            {
                "baseline": "yambda-windowed-sequence-two-tower-base",
                "stage": args.stage,
                "output": str(args.output.resolve()),
                "rows": len(ranking),
                "users": int(ranking.uid.nunique()),
                "runtime_seconds": round(time.monotonic() - started, 3),
                "public_inputs_only": True,
                "architecture": {
                    "family": "pure-windowed-sequence-two-tower",
                    "sequence_training": (
                        "every-adjacent-target-within-recent-chronological-window"
                    ),
                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
                    "bptt_hidden_state": "carried-and-detached",
                    "model_selection": "chronological-validation-BCE-early-stopping",
                    "loss_execution": "gradient-accumulated-microbatches",
                    "user_id_embedding": False,
                    "user_mlp": "582-512-256-192",
                    "item_mlp": "196-512-256-192",
                    "normalization": "none",
                    "score": "raw-dot-product",
                    "objective": "sample-weighted-BCE-at-every-timestep",
                    "negative_sampling": (
                        "24-uniform-plus-8-propensity-corrected-inbatch"
                    ),
                    "false_negative_filter": config.negative_filter_scope,
                    "target_weighting": config.target_weighting,
                },
                "config": asdict(config),
                "sequence_tokens": len(prepared.sequence_items),
                "all_next_item_targets": prepared.target_count,
                "selected_training_targets": int(
                    target_partition.training_counts.sum()
                ),
                "selected_validation_targets": int(
                    target_partition.validation_counts.sum()
                ),
                "discarded_old_targets": int(target_partition.window_starts.sum()),
                "training_history": training_history,
                "embedding_diagnostics": embedding_diagnostics,
                "torch_version": torch.__version__,
                "device": str(device),
            },
            indent=2,
        )
    )
    return 0


if __name__ == "__main__":
    raise SystemExit(main())
class SequentialTwoTower(nn.Module):
    """Order-sensitive user tower and independent catalog-aware item tower."""

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
            input_size=config.hidden_dim,
            hidden_size=dim,
            num_layers=1,
            batch_first=True,
        )
        self.user_tower = nn.Sequential(
            nn.Linear(dim * 3 + 6, config.user_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_1, config.user_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_2, dim),
        )
        self.candidate_embedding = nn.Embedding(n_items, dim)
        self.register_buffer(
            "item_features", torch.from_numpy(item_features), persistent=True
        )
        self.item_tower = nn.Sequential(
            nn.Linear(dim + n_item_features, config.item_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_1, config.item_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_2, dim),
        )
        nn.init.normal_(self.context_embedding.weight, std=0.02)
        nn.init.normal_(self.candidate_embedding.weight, std=0.02)
        with torch.no_grad():
            self.context_embedding.weight[n_items].zero_()

    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
        mask = items.ne(self.n_items)
        vectors = self.context_embedding(items)
        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
            dim=1, keepdim=True
        ).clamp_min(1)

    def encode_items(self, items: torch.Tensor) -> torch.Tensor:
        inputs = torch.cat(
            [self.candidate_embedding(items), self.item_features[items]], dim=1
        )
        return self.item_tower(inputs)


def invalid_negative_mask(
    negatives: torch.Tensor,
    targets: torch.Tensor,
    context_items: torch.Tensor,
    liked_items: torch.Tensor,
) -> torch.Tensor:
    """Identify targets, known positives, active likes, and row duplicates."""
    invalid = negatives.eq(targets[:, None])
    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= negatives[:, column, None].eq(
            negatives[:, :column]
        ).any(dim=1)
    return invalid


def draw_filtered_mixed_negatives(
    targets: torch.Tensor,
    context_items: torch.Tensor,
    liked_items: torch.Tensor,
    n_items: int,
    uniform_count: int,
    inbatch_count: int,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw uniform and unique-target in-batch negatives."""
    batch_size = len(targets)
    uniform = torch.randint(
        n_items,
        (batch_size, uniform_count),
        generator=generator,
        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
    corrected_count = 0
    if inbatch_count:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
                -1.0,
            )
            columns = torch.topk(priorities, k=corrected_count, dim=1).indices
            inbatch = unique_targets[columns]
        if corrected_count < inbatch_count:
            filler = torch.randint(
                n_items,
                (batch_size, inbatch_count - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
    if corrected_count:
        correction_cpu[
            :, uniform_count : uniform_count + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]]
    negatives = negatives_cpu.to(device)
    correction = correction_cpu.to(device)
    for _ in range(32):
        invalid = invalid_negative_mask(
            negatives, targets, context_items, liked_items
        )
        if not bool(invalid.any()):
            return negatives, correction
        replacements = torch.randint(
            n_items,
            (int(invalid.sum().detach().cpu()),),
            generator=generator,
            device="cpu",
        ).to(device)
        negatives[invalid] = replacements
        correction[invalid] = 1.0
    invalid = invalid_negative_mask(
        negatives, targets, context_items, liked_items
    )
    if bool(invalid.any()):
        raise RuntimeError("mixed negative resampling failed false-negative filter")
    return negatives, correction

Harness context codex item completed

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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "def build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n        return None\n\n    event_features = np.zeros((len(items), 3), dtype=np.float16)\n    event_features[:, 0] = organic.astype(np.float16)\n    event_features[:, 1] = (\n        np.minimum(completion, 100.0) / 100.0\n    ).astype(np.float16)\n    if len(items) > 1:\n        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n        event_features[1:, 2] = np.exp(\n            -gaps.astype(np.float64) / (60.0 * base.DAY)\n        ).astype(np.float16)\n\n    activity = causal_activity_features(\n        timestamps, items, organic, completion\n    )\n    states = np.zeros(len(items), dtype=np.uint32)\n    active_likes: dict[int, int] = {}\n    active_dislikes: dict[int, int] = {}\n    explicit_position = 0\n    current_state = 0\n    for row, timestamp in enumerate(timestamps):\n        changed = False\n        while (\n            explicit_position < len(explicit.timestamps)\n            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n        ):\n            item = int(explicit.items[explicit_position])\n            event_timestamp = int(explicit.timestamps[explicit_position])\n            destination = (\n                active_likes\n                if int(explicit.channel[explicit_position]) == 0\n                else active_dislikes\n            )\n            if int(explicit.active[explicit_position]) == 1:\n                destination[item] = event_timestamp\n            else:\n                destination.pop(item, None)\n            explicit_position += 1\n            changed = True\n        if changed:\n            current_state = append_state_snapshot(\n                active_likes,\n                active_dislikes,\n                n_items,\n                liked_states,\n                disliked_states,\n                state_counts,\n            )\n        states[row] = np.uint32(current_state)\n    return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    event_features: np.ndarray,\n    history_end: int,\n    like_count: int,\n    dislike_count: int,\n) -> np.ndarray:\n    start = int(\n        np.searchsorted(\n            timestamps,\n            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n            side=\"left\",\n        )\n    )\n    result = np.zeros(6, dtype=np.float32)\n    recent_items = items[start:]\n    if len(recent_items):\n        result[0] = np.log1p(len(recent_items)) / 8.0\n        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n        result[2] = float(np.mean(event_features[start:, 0]))\n        result[3] = float(np.mean(event_features[start:, 1]))\n    result[4] = np.log1p(like_count) / 6.0\n    result[5] = np.log1p(dislike_count) / 4.0\n    return result\n\n\ndef prepare_data(\n    public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n    data = public / \"data\"\n    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n        \"popularity_rank\"\n    )\n    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n    item_index = pd.Index(item_ids)\n    n_items = len(item_ids)\n    if n_items >= np.iinfo(np.uint16).max:\n        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n    if stage == \"validation\":\n        prefixes = (\"train\",)\n        history_end = base.TRAIN_END\n        candidate_name = \"validation_eligible_candidates.npz\"\n        state_name = \"validation_preference_state.parquet\"\n    else:\n        prefixes = (\"train\", \"val\")\n        history_end = base.VALIDATION_END\n        candidate_name = \"test_eligible_candidates.npz\"\n        state_name = \"test_preference_state.parquet\"\n    eligibility = CandidateSet.read(data / candidate_name)\n    if not np.array_equal(eligibility.item_ids, item_ids):\n        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n        base.load_inference_state(\n            data / state_name,\n            item_index,\n            eligibility.uids,\n            n_items,\n        )\n    )\n    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n    # Snapshot zero is the shared empty explicit state.\n    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n    disliked_states = [\n        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n    ]\n    state_counts: list[tuple[int, int]] = [(0, 0)]\n    user_ids: list[int] = []\n    offsets = [0]\n    timestamp_parts: list[np.ndarray] = []\n    item_parts: list[np.ndarray] = []\n    event_parts: list[np.ndarray] = []\n    activity_parts: list[np.ndarray] = []\n    state_parts: list[np.ndarray] = []\n\n    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n    started = time.monotonic()\n    for seen_users, (uid, listens) in enumerate(\n        base.merge_listen_streams(listen_paths, item_index), start=1\n    ):\n        parts = build_sequence_parts(\n            listens,\n            explicit_by_user.get(uid, base.empty_explicit_events()),\n            n_items,\n            liked_states,\n            disliked_states,\n            state_counts,\n        )\n        if parts is None:\n            continue\n        sequence_row = len(user_ids)\n        user_ids.append(uid)\n        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n        item_parts.append(parts[1])\n        event_parts.append(parts[2])\n        activity_parts.append(parts[3])\n        state_parts.append(parts[4])\n        offsets.append(offsets[-1] + len(parts[1]))\n        inference_row = inference_rows.get(uid)\n        if inference_row is not None:\n            inference_sequence_rows[inference_row] = sequence_row\n            inference_user_features[inference_row] = final_user_features(\n                parts[0],\n                parts[1],\n                parts[2],\n                history_end,\n                int(like_counts[inference_row]),\n                int(dislike_counts[inference_row]),\n            )\n        if seen_users % 1_000 == 0:\n            log(\n                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n                f\"states={len(liked_states):,} \"\n                f\"elapsed={time.monotonic() - started:.1f}s\"\n            )\n\n    missing = np.flatnonzero(inference_sequence_rows < 0)\n    if len(missing):\n        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n        inference_user_features[missing, 5] = (\n            np.log1p(dislike_counts[missing]) / 4.0\n        )\n        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n    prepared = SequenceData(\n        item_ids=item_ids,\n        item_features=base.build_item_features(catalog),\n        user_ids=np.asarray(user_ids, dtype=np.int64),\n        offsets=np.asarray(offsets, dtype=np.int64),\n        sequence_timestamps=np.concatenate(timestamp_parts),\n        sequence_items=np.concatenate(item_parts),\n        event_features=np.concatenate(event_parts),\n        activity_features=np.concatenate(activity_parts),\n        state_ids=np.concatenate(state_parts),\n        liked_states=np.stack(liked_states),\n        disliked_states=np.stack(disliked_states),\n        state_counts=np.asarray(state_counts, dtype=np.int32),\n        inference_user_rows=inference_sequence_rows,\n        inference_liked_items=inference_likes,\n        inference_disliked_items=inference_dislikes,\n        inference_user_features=inference_user_features,\n    )\n    log(\n        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n        f\"tokens={len(prepared.sequence_items):,} \"\n        f\"next_item_targets={prepared.target_count:,} \"\n        f\"states={len(prepared.liked_states):,}\"\n    )\n    return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n    return {\n        \"cache_version\": CACHE_VERSION,\n        \"stage\": stage,\n        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n    }\n\n\ndef save_cache(\n    path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n    path.parent.mkdir(parents=True, exist_ok=True)\n    np.savez(\n        path,\n        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n        **prepared.__dict__,\n    )\n    log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n    with np.load(path, allow_pickle=False) as artifact:\n        actual = json.loads(str(artifact[\"metadata\"].item()))\n        expected = cache_metadata(stage, config)\n        if actual != expected:\n            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n        values = {\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n    )\n    ordered = np.argsort(lengths, kind=\"stable\")\n    result = torch.zeros(\n        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n    )\n    for group_start in range(0, len(ordered), config.user_batch_size):\n        output_rows = ordered[group_start : group_start + config.user_batch_size]\n        rows = sequence_rows[output_rows]\n        group_lengths = lengths[output_rows]\n        hidden = torch.zeros(\n            1, len(output_rows), config.embedding_dim, device=device\n        )\n        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n            steps = min(\n                config.bptt_steps,\n                int(group_lengths.max(initial=0)) - start,\n            )\n            items = np.full(\n                (len(output_rows), steps),\n                len(prepared.item_ids),\n                dtype=np.uint16,\n            )\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n",
    "stderr": "",
    "aggregated_output": "def build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n        return None\n\n    event_features = np.zeros((len(items), 3), dtype=np.float16)\n    event_features[:, 0] = organic.astype(np.float16)\n    event_features[:, 1] = (\n        np.minimum(completion, 100.0) / 100.0\n    ).astype(np.float16)\n    if len(items) > 1:\n        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n        event_features[1:, 2] = np.exp(\n            -gaps.astype(np.float64) / (60.0 * base.DAY)\n        ).astype(np.float16)\n\n    activity = causal_activity_features(\n        timestamps, items, organic, completion\n    )\n    states = np.zeros(len(items), dtype=np.uint32)\n    active_likes: dict[int, int] = {}\n    active_dislikes: dict[int, int] = {}\n    explicit_position = 0\n    current_state = 0\n    for row, timestamp in enumerate(timestamps):\n        changed = False\n        while (\n            explicit_position < len(explicit.timestamps)\n            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n        ):\n            item = int(explicit.items[explicit_position])\n            event_timestamp = int(explicit.timestamps[explicit_position])\n            destination = (\n                active_likes\n                if int(explicit.channel[explicit_position]) == 0\n                else active_dislikes\n            )\n            if int(explicit.active[explicit_position]) == 1:\n                destination[item] = event_timestamp\n            else:\n                destination.pop(item, None)\n            explicit_position += 1\n            changed = True\n        if changed:\n            current_state = append_state_snapshot(\n                active_likes,\n                active_dislikes,\n                n_items,\n                liked_states,\n                disliked_states,\n                state_counts,\n            )\n        states[row] = np.uint32(current_state)\n    return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    event_features: np.ndarray,\n    history_end: int,\n    like_count: int,\n    dislike_count: int,\n) -> np.ndarray:\n    start = int(\n        np.searchsorted(\n            timestamps,\n            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n            side=\"left\",\n        )\n    )\n    result = np.zeros(6, dtype=np.float32)\n    recent_items = items[start:]\n    if len(recent_items):\n        result[0] = np.log1p(len(recent_items)) / 8.0\n        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n        result[2] = float(np.mean(event_features[start:, 0]))\n        result[3] = float(np.mean(event_features[start:, 1]))\n    result[4] = np.log1p(like_count) / 6.0\n    result[5] = np.log1p(dislike_count) / 4.0\n    return result\n\n\ndef prepare_data(\n    public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n    data = public / \"data\"\n    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n        \"popularity_rank\"\n    )\n    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n    item_index = pd.Index(item_ids)\n    n_items = len(item_ids)\n    if n_items >= np.iinfo(np.uint16).max:\n        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n    if stage == \"validation\":\n        prefixes = (\"train\",)\n        history_end = base.TRAIN_END\n        candidate_name = \"validation_eligible_candidates.npz\"\n        state_name = \"validation_preference_state.parquet\"\n    else:\n        prefixes = (\"train\", \"val\")\n        history_end = base.VALIDATION_END\n        candidate_name = \"test_eligible_candidates.npz\"\n        state_name = \"test_preference_state.parquet\"\n    eligibility = CandidateSet.read(data / candidate_name)\n    if not np.array_equal(eligibility.item_ids, item_ids):\n        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n        base.load_inference_state(\n            data / state_name,\n            item_index,\n            eligibility.uids,\n            n_items,\n        )\n    )\n    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n    # Snapshot zero is the shared empty explicit state.\n    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n    disliked_states = [\n        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n    ]\n    state_counts: list[tuple[int, int]] = [(0, 0)]\n    user_ids: list[int] = []\n    offsets = [0]\n    timestamp_parts: list[np.ndarray] = []\n    item_parts: list[np.ndarray] = []\n    event_parts: list[np.ndarray] = []\n    activity_parts: list[np.ndarray] = []\n    state_parts: list[np.ndarray] = []\n\n    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n    started = time.monotonic()\n    for seen_users, (uid, listens) in enumerate(\n        base.merge_listen_streams(listen_paths, item_index), start=1\n    ):\n        parts = build_sequence_parts(\n            listens,\n            explicit_by_user.get(uid, base.empty_explicit_events()),\n            n_items,\n            liked_states,\n            disliked_states,\n            state_counts,\n        )\n        if parts is None:\n            continue\n        sequence_row = len(user_ids)\n        user_ids.append(uid)\n        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n        item_parts.append(parts[1])\n        event_parts.append(parts[2])\n        activity_parts.append(parts[3])\n        state_parts.append(parts[4])\n        offsets.append(offsets[-1] + len(parts[1]))\n        inference_row = inference_rows.get(uid)\n        if inference_row is not None:\n            inference_sequence_rows[inference_row] = sequence_row\n            inference_user_features[inference_row] = final_user_features(\n                parts[0],\n                parts[1],\n                parts[2],\n                history_end,\n                int(like_counts[inference_row]),\n                int(dislike_counts[inference_row]),\n            )\n        if seen_users % 1_000 == 0:\n            log(\n                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n                f\"states={len(liked_states):,} \"\n                f\"elapsed={time.monotonic() - started:.1f}s\"\n            )\n\n    missing = np.flatnonzero(inference_sequence_rows < 0)\n    if len(missing):\n        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n        inference_user_features[missing, 5] = (\n            np.log1p(dislike_counts[missing]) / 4.0\n        )\n        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n    prepared = SequenceData(\n        item_ids=item_ids,\n        item_features=base.build_item_features(catalog),\n        user_ids=np.asarray(user_ids, dtype=np.int64),\n        offsets=np.asarray(offsets, dtype=np.int64),\n        sequence_timestamps=np.concatenate(timestamp_parts),\n        sequence_items=np.concatenate(item_parts),\n        event_features=np.concatenate(event_parts),\n        activity_features=np.concatenate(activity_parts),\n        state_ids=np.concatenate(state_parts),\n        liked_states=np.stack(liked_states),\n        disliked_states=np.stack(disliked_states),\n        state_counts=np.asarray(state_counts, dtype=np.int32),\n        inference_user_rows=inference_sequence_rows,\n        inference_liked_items=inference_likes,\n        inference_disliked_items=inference_dislikes,\n        inference_user_features=inference_user_features,\n    )\n    log(\n        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n        f\"tokens={len(prepared.sequence_items):,} \"\n        f\"next_item_targets={prepared.target_count:,} \"\n        f\"states={len(prepared.liked_states):,}\"\n    )\n    return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n    return {\n        \"cache_version\": CACHE_VERSION,\n        \"stage\": stage,\n        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n    }\n\n\ndef save_cache(\n    path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n    path.parent.mkdir(parents=True, exist_ok=True)\n    np.savez(\n        path,\n        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n        **prepared.__dict__,\n    )\n    log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n    with np.load(path, allow_pickle=False) as artifact:\n        actual = json.loads(str(artifact[\"metadata\"].item()))\n        expected = cache_metadata(stage, config)\n        if actual != expected:\n            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n        values = {\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n    )\n    ordered = np.argsort(lengths, kind=\"stable\")\n    result = torch.zeros(\n        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n    )\n    for group_start in range(0, len(ordered), config.user_batch_size):\n        output_rows = ordered[group_start : group_start + config.user_batch_size]\n        rows = sequence_rows[output_rows]\n        group_lengths = lengths[output_rows]\n        hidden = torch.zeros(\n            1, len(output_rows), config.embedding_dim, device=device\n        )\n        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n            steps = min(\n                config.bptt_steps,\n                int(group_lengths.max(initial=0)) - start,\n            )\n            items = np.full(\n                (len(output_rows), steps),\n                len(prepared.item_ids),\n                dtype=np.uint16,\n            )\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 1970
    },
    "formatted_output": "Warning: truncated output (original token count: 19542)\nTotal output lines: 2086\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n        return None\n\n    event_features = np.zeros((len(items), 3), dtype=np.float16)\n    event_features[:, 0] = organic.astype(np.float16)\n    event_features[:, 1] = (\n        np.minimum(completion, 100.0) / 100.0\n    ).astype(np.float16)\n    if len(items) > 1:\n        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n        event_features[1:, 2] = np.exp(\n            -gaps.astype(np.float64) / (60.0 * base.DAY)\n        ).astype(np.float16)\n\n    activity = causal_activity_features(\n        timestamps, items, organic, completion\n    )\n    states = np.zeros(len(items), dtype=np.uint32)\n    active_likes: dict[int, int] = {}\n    active_dislikes: dict[int, int] = {}\n    explicit_position = 0\n    current_state = 0\n    for row, timestamp in enumerate(timestamps):\n        changed = False\n        while (\n            explicit_position < len(explicit.timestamps)\n            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n        ):\n            item = int(explicit.items[explicit_position])\n            event_timestamp = int(explicit.timestamps[explicit_position])\n            destination = (\n                active_likes\n                if int(explicit.channel[explicit_position]) == 0\n                else active_dislikes\n            )\n            if int(explicit.active[explicit_position]) == 1:\n                destination[item] = event_timestamp\n            else:\n                destination.pop(item, None)\n            explicit_position += 1\n            changed = True\n        if changed:\n            current_state = append_state_snapshot(\n                active_likes,\n                active_dislikes,\n                n_items,\n                liked_states,\n                disliked_states,\n                state_counts,\n            )\n        states[row] = np.uint32(current_state)\n    return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    event_features: np.ndarray,\n    history_end: int,\n    like_count: int,\n    dislike_count: int,\n) -> np.ndarray:\n    start = int(\n        np.searchsorted(\n            timestamps,\n            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n            side=\"left\",\n        )\n    )\n    result = np.zeros(6, dtype=np.float32)\n    recent_items = items[start:]\n    if len(recent_items):\n        result[0] = np.log1p(len(recent_items)) / 8.0\n        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n        result[2] = float(np.mean(event_features[start:, 0]))\n        result[3] = float(np.mean(event_features[start:, 1]))\n    result[4] = np.log1p(like_count) / 6.0\n    result[5] = np.log1p(dislike_count) / 4.0\n    return result\n\n\ndef prepare_data(\n    public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n    data = public / \"data\"\n    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n        \"popularity_rank\"\n    )\n    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n    item_index = pd.Index(item_ids)\n    n_items = len(item_ids)\n    if n_items >= np.iinfo(np.uint16).max:\n        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n    if stage == \"validation\":\n        prefixes = (\"train\",)\n        history_end = base.TRAIN_END\n        candidate_name = \"validation_eligible_candidates.npz\"\n        state_name = \"validation_preference_state.parquet\"\n    else:\n        prefixes = (\"train\", \"val\")\n        history_end = base.VALIDATION_END\n        candidate_name = \"test_eligible_candidates.npz\"\n        state_name = \"test_preference_state.parquet\"\n    eligibility = CandidateSet.read(data / candidate_name)\n    if not np.array_equal(eligibility.item_ids, item_ids):\n        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n        base.load_inference_state(\n            data / state_name,\n            item_index,\n            eligibility.uids,\n            n_items,\n        )\n    )\n    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n    # Snapshot zero is the shared empty explicit state.\n    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n    disliked_states = [\n        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n    ]\n    state_counts: list[tuple[int, int]] = [(0, 0)]\n    user_ids: list[int] = []\n    offsets = [0]\n    timestamp_parts: list[np.ndarray] = []\n    item_parts: list[np.ndarray] = []\n    event_parts: list[np.ndarray] = []\n    activity_parts: list[np.ndarray] = []\n    state_parts: list[np.ndarray] = []\n\n    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n    started = time.monotonic()\n    for seen_users, (uid, listens) in enumerate(\n        base.merge_listen_streams(listen_paths, item_index), start=1\n    ):\n        parts = build_sequence_parts(\n            listens,\n            explicit_by_user.get(uid, base.empty_explicit_events()),\n            n_items,\n            liked_states,\n            disliked_states,\n            state_counts,\n        )\n        if parts is None:\n            continue\n        sequence_row = len(user_ids)\n        user_ids.append(uid)\n        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n        item_parts.append(parts[1])\n        event_parts.append(parts[2])\n        activity_parts.append(parts[3])\n        state_parts.append(parts[4])\n        offsets.append(offsets[-1] + len(parts[1]))\n        inference_row = inference_rows.get(uid)\n        if inference_row is not None:\n            inference_sequence_rows[inference_row] = sequence_row\n            inference_user_features[inference_row] = final_user_features(\n                parts[0],\n                parts[1],\n                parts[2],\n                history_end,\n                int(like_counts[inference_row]),\n                int(dislike_counts[inference_row]),\n            )\n        if seen_users % 1_000 == 0:\n            log(\n                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n                f\"states={len(liked_states):,} \"\n                f\"elapsed={time.monotonic() - started:.1f}s\"\n            )\n\n    missing = np.flatnonzero(inference_sequence_rows < 0)\n    if len(missing):\n        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n        inference_user_features[missing, 5] = (\n            np.log1p(dislike_counts[missing]) / 4.0\n        )\n        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n    prepared = SequenceData(\n        item_ids=item_ids,\n        item_features=base.build_item_features(catalog),\n        user_ids=np.asarray(user_ids, dtype=np.int64),\n        offsets=np.asarray(offsets, dtype=np.int64),\n        sequence_timestamps=np.concatenate(timestamp_parts),\n        sequence_items=np.concatenate(item_parts),\n        event_features=np.concatenate(event_parts),\n        activity_features=np.concatenate(activity_parts),\n        state_ids=np.concatenate(state_parts),\n        liked_states=np.stack(liked_states),\n        disliked_states=np.stack(disliked_states),\n        state_counts=np.asarray(state_counts, dtype=np.int32),\n        inference_user_rows=inference_sequence_rows,\n        inference_liked_items=inference_likes,\n        inference_disliked_items=inference_dislikes,\n        inference_user_features=inference_user_features,\n    )\n    log(\n        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n        f\"tokens={len(prepared.sequence_items):,} \"\n        f\"next_item_targets={prepared.target_count:,} \"\n        f\"states={len(prepared.liked_states):,}\"\n    )\n    return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n    return {\n        \"cache_version\": CACHE_VERSION,\n        \"stage\": stage,\n        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n    }\n\n\ndef save_cache(\n    path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n    path.parent.mkdir(parents=True, exist_ok=True)\n    np.savez(\n        path,\n        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n        **prepared.__dict__,\n    )\n    log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n    with np.load(path, allow_pickle=False) as artifact:\n        actual = json.loads(str(artifact[\"metadata\"].item()))\n        expected = cache_metadata(stage, config)\n        if actual != expected:\n            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n        values = {\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_r…9542 tokens truncated…  (len(output_rows), steps),\n                len(prepared.item_ids),\n                dtype=np.uint16,\n            )\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5ba5bf15487d2929e5193255cebf5",
  "usage": {
    "input_tokens": 28176,
    "cached_input_tokens": 21376,
    "cache_write_input_tokens": 0,
    "output_tokens": 151,
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  "turn_token_usage": {
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  }
}

exec command

+00:47

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +00:50

const r = await tools.exec_command({"cmd":"mkdir -p /app/solution && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +00:50

Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"476ffa","wall_time_seconds":0.042024855,"exit_code":1,"original_token_count":45,"output":"Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"}

Harness context token usage record

{
  "type": "token_usage_record",
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d81a65a3-cc62-4476-b66c-8f190c67fa83",
    "process_id": "41046",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 42015145
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"
  }
}

exec command

+00:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The default shell’s python is not the task runtime—it lacks even NumPy. I’m locating the supplied offline environment now, then I’ll rerun the exact baseline there; no model conclusion is being drawn from this setup failure.

Tool callexecTool call Returned

Tool call · Input +01:00

const r = await tools.exec_command({"cmd":"command -v python; command -v python3; which -a python python3 2>/dev/null || true; find /opt /usr/local /venv /env -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; sed -n '1,160p' /task/requirements.txt; ls -la /app /task | head -80","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:00

Script completed
Wall time 0.0 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
/usr/local/bin/python
/usr/local/bin/python3
/usr/bin/python3
/bin/python3
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
/app:
total 16
drwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .
drwxr-xr-x 1 root  root  4096 Sep 25 00:03 ..
drwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution

/task:
total 40
dr-xr-xr-x 1 root root 4096 Sep 24 23:58 .
drwxr-xr-x 1 root root 4096 Sep 25 00:03 ..
-r--r--r-- 1 root root    0 Sep 24 23:56 .keep
dr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model
dr-xr-xr-x 1 root root 4096 Sep 24 23:57 data
-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt

Harness context codex item completed

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    "id": "exec-c43d6b81-e412-491b-96ca-31d831066c63",
    "process_id": "44837",
    "command": [
      "/bin/sh",
      "-lc",
      "command -v python; command -v python3; which -a python python3 2>/dev/null || true; find /opt /usr/local /venv /env -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; sed -n '1,160p' /task/requirements.txt; ls -la /app /task | head -80"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "command -v python; command -v python3; which -a python python3 2>/dev/null || true; find /opt /usr/local /venv /env -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; sed -n '1,160p' /task/requirements.txt; ls -la /app /task | head -80"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root    0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root    0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3360
    },
    "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root    0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n"
  }
}

Harness context token usage record

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exec command

+01:07

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +01:09

const r = await tools.exec_command({"cmd":"python -VV; python -c 'import sys; print(sys.executable); print(\"\\n\".join(sys.path))'; ls -la /usr/local/lib; find / -path '*/site-packages/numpy/__init__.py' -o -path '*/dist-packages/numpy/__init__.py' 2>/dev/null | head -20","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:10

Script completed
Wall time 0.5 seconds
Output:

Python 3.12.13 (main, Aug  5 2026, 01:11:46) [GCC 12.2.0]
/usr/local/bin/python

/usr/local/lib/python312.zip
/usr/local/lib/python3.12
/usr/local/lib/python3.12/lib-dynload
/usr/local/lib/python3.12/site-packages
total 6528
drwxr-xr-x  1 root root    4096 Sep 25 00:01 .
drwxr-xr-x  1 root root    4096 Aug  3 00:00 ..
lrwxrwxrwx  1 root root      20 Aug  5 01:13 libpython3.12.so -> libpython3.12.so.1.0
-rwxr-xr-x  1 root root 6642968 Aug  5 01:13 libpython3.12.so.1.0
-rwxr-xr-x  1 root root   14000 Aug  5 01:13 libpython3.so
drwxr-xr-x  2 root root    4096 Aug  5 01:13 pkgconfig
drwxr-xr-x  3 root root    4096 Sep 25 00:01 python3.11
drwxr-xr-x 38 root root    4096 Aug  5 01:13 python3.12
/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py

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Harness context codex item completed

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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py\n",
    "stderr": "",
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    "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py\n"
  }
}

exec command

+01:16

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +01:17

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -c 'import numpy,pandas,torch,pyarrow; print(numpy.__version__, pandas.__version__, torch.__version__, pyarrow.__version__)' && ls -la /opt/task-venv/bin/python*","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +01:21

Script completed
Wall time 4.0 seconds
Output:

2.2.6 2.3.3 2.8.0+cpu 18.1.0
lrwxrwxrwx 1 root root 21 Sep 24 23:58 /opt/task-venv/bin/python -> /usr/local/bin/python
lrwxrwxrwx 1 root root  6 Sep 24 23:58 /opt/task-venv/bin/python3 -> python
lrwxrwxrwx 1 root root  6 Sep 24 23:58 /opt/task-venv/bin/python3.12 -> python

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}

exec command

+01:28

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +01:30

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +01:31

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"3a6e3d","wall_time_seconds":1.001855524,"session_id":74144,"original_token_count":0,"output":""}

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exec command

+01:36

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +01:37

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +01:42

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"af47b5","wall_time_seconds":5.001029982,"session_id":74144,"original_token_count":29,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\n"}

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exec command

+01:47

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +01:49

const r = await tools.exec_command({"cmd":"sed -n '798,1038p' /task/current_model/train_current_model.py; sed -n '1035,1210p' /task/current_model/train_current_model.py; sed -n '1203,1368p' /task/current_model/train_current_model.py; sed -n '1364,1782p' /task/current_model/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":50000});
text(r.output);

Tool response +01:50

Script completed
Wall time 0.0 seconds
Output:

def user_queries(
    model: base.SequentialTwoTower,
    sequence_vectors: torch.Tensor,
    liked_vectors: torch.Tensor,
    disliked_vectors: torch.Tensor,
    user_features: torch.Tensor,
) -> torch.Tensor:
    return model.user_tower(
        torch.cat(
            [sequence_vectors, liked_vectors, disliked_vectors, user_features],
            dim=1,
        )
    )


def candidate_vectors(
    model: base.SequentialTwoTower,
    positives: torch.Tensor,
    negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    all_items = torch.cat([positives, negatives.reshape(-1)])
    unique_items, inverse = torch.unique(all_items, return_inverse=True)
    encoded = model.encode_items(unique_items)
    positive_vectors = encoded[inverse[: len(positives)]]
    negative_vectors = encoded[inverse[len(positives) :]].reshape(
        len(positives), negatives.shape[1], -1
    )
    return positive_vectors, negative_vectors


def history_windows(
    sequence: np.ndarray,
    start: int,
    count: int,
    width: int,
    padding_item: int,
) -> np.ndarray:
    base_start = max(0, start - width + 1)
    segment = sequence[base_start : start + count]
    padded = np.concatenate(
        [np.full(width - 1, padding_item, dtype=np.uint16), segment]
    )
    windows = np.lib.stride_tricks.sliding_window_view(padded, width)
    offset = start - base_start
    return windows[offset : offset + count].copy()


def corrected_negative_loss(
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
    raw = F.softplus(logits)
    total = config.uniform_negatives + config.inbatch_negatives
    result = torch.zeros(len(logits), device=logits.device)
    if config.uniform_negatives:
        result += config.uniform_negatives * raw[
            :, : config.uniform_negatives
        ].mean(dim=1)
    if config.inbatch_negatives:
        selected = slice(config.uniform_negatives, total)
        weights = correction[:, selected]
        result += config.inbatch_negatives * (
            (raw[:, selected] * weights).sum(dim=1)
            / weights.sum(dim=1).clamp_min(1.0e-6)
        )
    return result / total


def retained_target_window_starts(
    total_targets: np.ndarray,
    validation_counts: np.ndarray,
    training_counts: np.ndarray,
) -> np.ndarray:
    """Return the discarded prefix before the retained chronological suffix."""
    training_region_ends = total_targets - validation_counts
    window_starts = training_region_ends - training_counts
    if (
        np.any(window_starts < 0)
        or np.any(training_counts < 0)
        or np.any(validation_counts < 0)
    ):
        raise RuntimeError("chronological target partition produced negative counts")
    if not np.array_equal(
        window_starts + training_counts + validation_counts, total_targets
    ):
        raise RuntimeError("chronological target window is not a final contiguous suffix")
    return window_starts


def chronological_target_partition(
    prepared: SequenceData,
    validation_fraction: float,
    max_train_targets_per_user: int,
    max_validation_targets_per_user: int,
) -> TargetPartition:
    """Select a recent contiguous window with a final out-of-time BCE suffix."""
    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
    validation = np.floor(totals * validation_fraction).astype(np.int64)
    validation[(totals >= 2) & (validation == 0)] = 1
    if max_validation_targets_per_user:
        validation = np.minimum(validation, max_validation_targets_per_user)

    history_training = totals - validation
    training = history_training.copy()
    if max_train_targets_per_user:
        training = np.minimum(training, max_train_targets_per_user)
    window_starts = retained_target_window_starts(totals, validation, training)
    return TargetPartition(
        window_starts=window_starts,
        training_counts=training,
        validation_counts=validation,
        history_training_counts=history_training,
    )


def load_organic_like_outcomes(
    public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
    """Load mapped organic-like timestamps from public fitting history."""
    prefixes = ("train",) if stage == "validation" else ("train", "val")
    frames = [
        pd.read_parquet(
            public / "data" / f"{prefix}_likes.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic"],
        )
        for prefix in prefixes
    ]
    likes = pd.concat(frames, ignore_index=True)
    likes = likes[likes.is_organic == 1].copy()
    mapped = pd.Index(item_ids).get_indexer(
        likes.item_id.to_numpy(dtype=np.int64)
    )
    likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
    likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
    likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
    nested: dict[int, dict[int, list[int]]] = {}
    for raw_uid, raw_timestamp, raw_item in likes.itertuples(
        index=False, name=None
    ):
        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
            int(raw_timestamp)
        )
    result = {
        uid: {
            item: np.asarray(timestamps, dtype=np.int64)
            for item, timestamps in items.items()
        }
        for uid, items in nested.items()
    }
    log(
        f"loaded organic-like outcomes: rows={len(likes):,} "
        f"users={len(result):,}"
    )
    return result


def build_target_weights(
    public: Path,
    stage: str,
    prepared: SequenceData,
    training_counts: np.ndarray,
    config: Config,
) -> np.ndarray:
    """Build nonzero per-token weights without dropping adjacent targets."""
    if config.target_weighting == "uniform":
        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
    else:
        weights = np.full(
            len(prepared.sequence_items),
            config.ordinary_target_weight,
            dtype=np.float32,
        )
        organic_strong = (
            (prepared.event_features[:, 0] > 0.5)
            & (prepared.event_features[:, 1] >= 0.8)
        )
        weights[organic_strong] = config.organic_strong_target_weight
        if config.target_weighting == "discovery-graded":
            like_outcomes = load_organic_like_outcomes(
                public, stage, prepared.item_ids
            )
            history_end = (
                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
            )
            horizon = config.discovery_horizon_days * base.DAY
            grade_three = 0
            for user_row, uid in enumerate(prepared.user_ids):
                start = int(prepared.offsets[user_row])
                end = int(prepared.offsets[user_row + 1])
                if end - start < 2:
                    continue
                last_training_target = start + int(training_counts[user_row])
                first_validation_target = last_training_target + 1
                split_timestamp = (
                    int(prepared.sequence_timestamps[first_validation_target])
                    if first_validation_target < end
                    else history_end
                )
                user_likes = like_outcomes.get(int(uid), {})
                seen = {int(prepared.sequence_items[start])}
                for position in range(start + 1, end):
                    item = int(prepared.sequence_items[position])
                    novel = item not in seen
                    seen.add(item)
                    if not novel:
                        # Repeats remain training examples, but they cannot be
                        # eligible discovery outcomes at the corresponding
                        # cutoff and therefore keep only the ordinary weight.
                        weights[position] = config.ordinary_target_weight
                        continue
                    timestamps = user_likes.get(item)
                    if timestamps is None:
                        continue
                    timestamp = int(prepared.sequence_timestamps[position])
                    upper = min(
                        timestamp + horizon,
                        split_timestamp
                        if position <= last_training_target
                        else history_end,
                    )
                    outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
                        weights[position] = config.organic_like_target_weight
                        grade_three += 1
            log(
                f"grade-3 first-encounter targets={grade_three:,} "
                f"horizon_days={config.discovery_horizon_days}"
            )
    log(
        f"target weighting={config.target_weighting} "
        f"min={weights.min():.3f} mean={weights.mean():.3f} "
        f"max={weights.max():.3f}"
    )
    return weights


def build_dense_block(
    prepared: SequenceData,
    target_weights: np.ndarray,
    users: np.ndarray,
def build_dense_block(
    prepared: SequenceData,
    target_weights: np.ndarray,
    users: np.ndarray,
    window_starts: np.ndarray,
    process_counts: np.ndarray,
    score_starts: np.ndarray,
    start: int,
    steps: int,
    config: Config,
) -> tuple[np.ndarray, ...]:
    """Build one causal sequence block and mark the target rows to score."""
    batch_size = len(users)
    padding_item = len(prepared.item_ids)
    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
    targets = np.zeros((batch_size, steps), dtype=np.uint16)
    weights = np.ones((batch_size, steps), dtype=np.float32)
    scored = np.zeros((batch_size, steps), dtype=bool)
    histories = np.full(
        (batch_size, steps, config.bptt_steps),
        padding_item,
        dtype=np.uint16,
    )
    processed = np.zeros(batch_size, dtype=np.int32)

    lengths = prepared.lengths
    for row, user_row in enumerate(users):
        count = min(steps, max(int(process_counts[row]) - start, 0))
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        window_start = int(window_starts[row])
        absolute_start = window_start + start
        positions = slice(
            offset + absolute_start,
            offset + absolute_start + count,
        )
        target_positions = slice(
            offset + absolute_start + 1,
            offset + absolute_start + count + 1,
        )
        input_items[row, :count] = prepared.sequence_items[positions]
        event_features[row, :count] = prepared.event_features[positions]
        activity_features[row, :count] = prepared.activity_features[positions]
        state_ids[row, :count] = prepared.state_ids[positions]
        targets[row, :count] = prepared.sequence_items[target_positions]
        weights[row, :count] = target_weights[target_positions]
        score_from = min(count, max(int(score_starts[row]) - start, 0))
        scored[row, score_from:count] = True
        processed[row] = count
        user_sequence = prepared.sequence_items[
            offset : offset + int(lengths[user_row])
        ]
        histories[row, :count] = history_windows(
            user_sequence,
            absolute_start,
            count,
            config.bptt_steps,
            padding_item,
        )
    return (
        input_items,
        event_features,
        activity_features,
        state_ids,
        targets,
        scored,
        histories,
        processed,
        weights,
    )


def score_target_microbatches(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    sequence_vectors: torch.Tensor,
    flat_states: np.ndarray,
    flat_activity: np.ndarray,
    positive_items: torch.Tensor,
    negatives: torch.Tensor,
    negative_weights: torch.Tensor,
    sample_weights: np.ndarray,
    device: torch.device,
    backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
    """Compute one logical block's exact BCE in bounded-memory slices."""
    target_count = len(positive_items)
    total_weight = float(sample_weights.sum())
    if total_weight <= 0:
        raise RuntimeError("target block has no positive sample weight")
    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
    statistics = {
        "loss_sum": 0.0,
        "positive_sum": 0.0,
        "negative_sum": 0.0,
        "pair_correct": 0.0,
        "weight_sum": 0.0,
    }
    for begin in range(0, target_count, config.loss_microbatch_size):
        end = min(begin + config.loss_microbatch_size, target_count)
        selected_states = flat_states[begin:end]
        if backward:
            sequence_input = (
                sequence_vectors[begin:end].detach().requires_grad_(True)
            )
        else:
            sequence_input = sequence_vectors[begin:end]
        liked_vectors, disliked_vectors, _ = pool_explicit_states(
            model, prepared, selected_states, device
        )
        counts = prepared.state_counts[selected_states]
        numeric = np.zeros((end - begin, 6), dtype=np.float32)
        numeric[:, :4] = flat_activity[begin:end]
        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
        query_vectors = user_queries(
            model,
            sequence_input,
            liked_vectors,
            disliked_vectors,
            torch.from_numpy(numeric).to(device),
        )
        positive_vectors, negative_vectors = candidate_vectors(
            model,
            positive_items[begin:end],
            negatives[begin:end],
        )
        positive_logits = (query_vectors * positive_vectors).sum(dim=1)
        negative_logits = torch.einsum(
            "bd,bkd->bk", query_vectors, negative_vectors
        )
        positive_loss = F.softplus(-positive_logits)
        negative_loss = corrected_negative_loss(
            negative_logits, negative_weights[begin:end], config
        )
        selected_weights = torch.from_numpy(
            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        loss_sum = (
            selected_weights * (positive_loss + negative_loss) / 2.0
        ).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
            sequence_gradient[begin:end] = sequence_input.grad
        statistics["loss_sum"] += float(loss_sum.detach().cpu())
        statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
        statistics["pair_correct"] += float(
            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
        )
        statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    target_counts: np.ndarray,
def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    target_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
) -> dict[str, float]:
    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
    group_order = torch.randperm(len(groups), generator=generator).numpy()
    negative_count = config.uniform_negatives + config.inbatch_negatives
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()

    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=True,
            )
            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()

            count = len(positive_items)
            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1
            if batches % 25 == 0:
                release_accelerator_cache(device)
            if batches % 250 == 0:
                batch_bce = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct
        / max(total_targets * negative_count, 1),
        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    started = time.monotonic()

    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=False,
            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,
        "validation_seconds": time.monotonic() - started,
    }


def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
    return {
        name: value.detach().cpu().clone()
        for name, value in model.state_dict().items()
    }


def save_epoch_checkpoint(
    checkpoint_dir: Path,
    epoch: int,
    model_state: dict[str, torch.Tensor],
    config: Config,
    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
            "diagnostics": diagnostics,
            "model_state": model_state,
            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path


def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        stage,
        prepared,
        partition.history_training_counts,
        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
    history: list[dict[str, float]] = []
    best_loss = float("inf")
    best_epoch = 0
    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
        )
        stored_config = payload.get("config", {})
        architecture_fields = (
            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
        improved = (
            diagnostics["validation_loss"]
            < best_loss - config.early_stopping_min_delta
        )
        if improved:
            best_loss = diagnostics["validation_loss"]
            best_epoch = epoch
            best_state = current_state
            epochs_without_improvement = 0
            if checkpoint_dir is not None:
                torch.save(
                    {
                        "epoch": epoch,
                        "config": asdict(config),
                        "diagnostics": diagnostics,
                        "model_state": current_state,
                        "optimizer_state": optimizer.state_dict(),
                        "generator_state": generator.get_state(),
                    },
                    checkpoint_dir / "best.pt",
                )
        else:
            epochs_without_improvement += 1
        log(
            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
            f"validation_loss={diagnostics['validation_loss']:.5f} "
            f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
            f"train_targets={int(diagnostics['targets']):,} "
            f"validation_targets={int(diagnostics['validation_targets']):,} "
            f"train_seconds={diagnostics['seconds']:.1f} "
            f"validation_seconds={diagnostics['validation_seconds']:.1f}"
        )
        release_accelerator_cache(device)
        if epochs_without_improvement >= config.early_stopping_patience:
            log(
                f"early stopping after epoch={epoch}; "
                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
            )
            break

    if best_state is None:
        raise RuntimeError("training did not produce a valid checkpoint")
    model.load_state_dict(best_state)
    model.to(device).eval()
    log(
        f"restored best validation-BCE checkpoint: epoch={best_epoch} "
        f"loss={best_loss:.6f}"
    )
    return model, history


@torch.inference_mode()
def inference_queries(

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5ea3621d-5a60-450d-a0ee-e5e13faaacf0",
    "process_id": "31190",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '798,1038p' /task/current_model/train_current_model.py; sed -n '1035,1210p' /task/current_model/train_current_model.py; sed -n '1203,1368p' /task/current_model/train_current_model.py; sed -n '1364,1782p' /task/current_model/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '798,1038p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1035,1210p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1203,1368p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1364,1782p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "def user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n",
    "stderr": "",
    "aggregated_output": "def user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4050
    },
    "formatted_output": "def user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n"
  }
}

Harness context token usage record

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  }
}

exec command

+01:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +01:58

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +02:03

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"8081a3","wall_time_seconds":5.001527297,"session_id":74144,"original_token_count":67,"output":"prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\n"}

Harness context token usage record

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  }
}

exec command

+02:12

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +02:20

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data'\ncat=pd.read_parquet(D+'/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\ntgt=pd.read_parquet(D+'/target_users.parquet')\nprint('catalog', cat.shape, cat.columns.tolist())\nprint(cat.head().to_string(index=False))\nprint('targets', tgt.shape, tgt.head().to_string(index=False))\nfor st in ['validation','test']:\n z=np.load(f'{D}/{st}_eligible_candidates.npz')\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n c=masks.sum(1)\n print(st, z['uids'].shape, masks.shape, 'eligible min/mean/max',c.min(),c.mean(),c.max())\n# public truth rank/profile\nuids=set(tgt.uid.astype(int))\nstrong=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=strong[(strong.uid.isin(uids))&(strong.is_organic==1)&(strong.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=likes[(likes.uid.isin(uids))&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\nitemrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nprint('val strong raw',len(strong),'likes',len(likes),'users',strong.uid.nunique(),likes.uid.nunique())\nfor name,x in [('strong',strong),('likes',likes)]:\n rr=np.array([itemrank.get(int(i),np.nan) for i in x.item_id]); rr=rr[np.isfinite(rr)]\n print(name,'catalog events',len(rr),'rank quantiles',np.quantile(rr,[0,.1,.25,.5,.75,.9,1]).astype(int))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response +02:22

Script completed
Wall time 2.5 seconds
Output:

catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
 item_id  positive_listens  organic_positive_listens  popularity_rank
 5862961             26615                     20620                1
 6901374             25536                     17347                2
 5635052             24691                     17194                3
 3542184             24075                     17699                4
 9378983             24036                     18195                5
targets (8456, 3)  uid  recent_positive_listens  history_positive_listens
 100                    844.0                      2604
 200                     50.0                       286
 300                    168.0                       359
 600                   1985.0                      2423
 700                   1776.0                      6114
validation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000
test (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000
val strong raw 255608 likes 17195 users 6325 2714
strong catalog events 170791 rank quantiles [    1   207   996  4079 11258 20059 30000]
likes catalog events 8528 rank quantiles [    1   442  1648  5554 13506 21728 29989]

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-18e185bd-0f7f-4832-a7e5-b55a0bc5d0e3",
    "process_id": "5549",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data'\ncat=pd.read_parquet(D+'/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\ntgt=pd.read_parquet(D+'/target_users.parquet')\nprint('catalog', cat.shape, cat.columns.tolist())\nprint(cat.head().to_string(index=False))\nprint('targets', tgt.shape, tgt.head().to_string(index=False))\nfor st in ['validation','test']:\n z=np.load(f'{D}/{st}_eligible_candidates.npz')\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n c=masks.sum(1)\n print(st, z['uids'].shape, masks.shape, 'eligible min/mean/max',c.min(),c.mean(),c.max())\n# public truth rank/profile\nuids=set(tgt.uid.astype(int))\nstrong=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=strong[(strong.uid.isin(uids))&(strong.is_organic==1)&(strong.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=likes[(likes.uid.isin(uids))&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\nitemrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nprint('val strong raw',len(strong),'likes',len(likes),'users',strong.uid.nunique(),likes.uid.nunique())\nfor name,x in [('strong',strong),('likes',likes)]:\n rr=np.array([itemrank.get(int(i),np.nan) for i in x.item_id]); rr=rr[np.isfinite(rr)]\n print(name,'catalog events',len(rr),'rank quantiles',np.quantile(rr,[0,.1,.25,.5,.75,.9,1]).astype(int))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data'\ncat=pd.read_parquet(D+'/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\ntgt=pd.read_parquet(D+'/target_users.parquet')\nprint('catalog', cat.shape, cat.columns.tolist())\nprint(cat.head().to_string(index=False))\nprint('targets', tgt.shape, tgt.head().to_string(index=False))\nfor st in ['validation','test']:\n z=np.load(f'{D}/{st}_eligible_candidates.npz')\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n c=masks.sum(1)\n print(st, z['uids'].shape, masks.shape, 'eligible min/mean/max',c.min(),c.mean(),c.max())\n# public truth rank/profile\nuids=set(tgt.uid.astype(int))\nstrong=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=strong[(strong.uid.isin(uids))&(strong.is_organic==1)&(strong.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=likes[(likes.uid.isin(uids))&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\nitemrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nprint('val strong raw',len(strong),'likes',len(likes),'users',strong.uid.nunique(),likes.uid.nunique())\nfor name,x in [('strong',strong),('likes',likes)]:\n rr=np.array([itemrank.get(int(i),np.nan) for i in x.item_id]); rr=rr[np.isfinite(rr)]\n print(name,'catalog events',len(rr),'rank quantiles',np.quantile(rr,[0,.1,.25,.5,.75,.9,1]).astype(int))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\ntargets (8456, 3)  uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\n 600                   1985.0                      2423\n 700                   1776.0                      6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [    1   207   996  4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [    1   442  1648  5554 13506 21728 29989]\n",
    "stderr": "",
    "aggregated_output": "catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\ntargets (8456, 3)  uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\n 600                   1985.0                      2423\n 700                   1776.0                      6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [    1   207   996  4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [    1   442  1648  5554 13506 21728 29989]\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 359904203
    },
    "formatted_output": "catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\ntargets (8456, 3)  uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\n 600                   1985.0                      2423\n 700                   1776.0                      6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [    1   207   996  4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [    1   442  1648  5554 13506 21728 29989]\n"
  }
}

exec command

+02:26

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +02:31

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD='/task/data'; t=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); g=validation_truth(e,t)\nvals=[(u,i,v) for u,z in g.items() for i,v in z.items()]\nprint('scored users',len(g),'relevant pairs',len(vals),'grades',pd.Series([v for _,_,v in vals]).value_counts().to_dict())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ranks=dict(zip(cat.item_id,cat.popularity_rank))\nfor gr in [1,3]:\n x=np.array([ranks[i] for _,i,v in vals if v==gr]); print(gr,len(x),np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int))\nprint('rels/user',pd.Series([len(x) for x in g.values()]).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":6000});
text(r.output);

Tool response +02:34

Script completed
Wall time 3.1 seconds
Output:

scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}
1 33352 [    1   408  1693  5545 13274 21656 30000]
3 4417 [    2   618  2107  6371 14181 21801 29989]
rels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}

Harness context token usage record

{
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  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
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  }
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ac13f981-e2c2-49c2-8094-6f3830f8215f",
    "process_id": "76216",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD='/task/data'; t=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); g=validation_truth(e,t)\nvals=[(u,i,v) for u,z in g.items() for i,v in z.items()]\nprint('scored users',len(g),'relevant pairs',len(vals),'grades',pd.Series([v for _,_,v in vals]).value_counts().to_dict())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ranks=dict(zip(cat.item_id,cat.popularity_rank))\nfor gr in [1,3]:\n x=np.array([ranks[i] for _,i,v in vals if v==gr]); print(gr,len(x),np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int))\nprint('rels/user',pd.Series([len(x) for x in g.values()]).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD='/task/data'; t=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); g=validation_truth(e,t)\nvals=[(u,i,v) for u,z in g.items() for i,v in z.items()]\nprint('scored users',len(g),'relevant pairs',len(vals),'grades',pd.Series([v for _,_,v in vals]).value_counts().to_dict())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ranks=dict(zip(cat.item_id,cat.popularity_rank))\nfor gr in [1,3]:\n x=np.array([ranks[i] for _,i,v in vals if v==gr]); print(gr,len(x),np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int))\nprint('rels/user',pd.Series([len(x) for x in g.values()]).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [    1   408  1693  5545 13274 21656 30000]\n3 4417 [    2   618  2107  6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n",
    "stderr": "",
    "aggregated_output": "scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [    1   408  1693  5545 13274 21656 30000]\n3 4417 [    2   618  2107  6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 907586075
    },
    "formatted_output": "scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [    1   408  1693  5545 13274 21656 30000]\n3 4417 [    2   618  2107  6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n"
  }
}

exec command

+02:39

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +02:40

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +02:45

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"213d39","wall_time_seconds":5.00152362,"session_id":74144,"original_token_count":215,"output":"prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Harness context token usage record

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}

Agent message

+03:00

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

exec command

+03:05

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +03:06

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +03:11

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"8cfdfe","wall_time_seconds":5.001984843,"session_id":74144,"original_token_count":0,"output":""}

Harness context token usage record

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}

exec command

+03:15

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +03:16

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +03:21

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"3b2353","wall_time_seconds":5.001816902,"session_id":74144,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
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  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
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  }
}

exec command

+03:22

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +03:23

const r = await tools.exec_command({"cmd":"ps -o pid,etimes,%cpu,%mem,rss,vsz,cmd -C python","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +03:23

Script completed
Wall time 0.1 seconds
Output:

/bin/sh: 1: ps: not found

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-824299cf-8aaa-4f2a-b3fc-2d12797a430f",
    "process_id": "55719",
    "command": [
      "/bin/sh",
      "-lc",
      "ps -o pid,etimes,%cpu,%mem,rss,vsz,cmd -C python"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -o pid,etimes,%cpu,%mem,rss,vsz,cmd -C python"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/bin/sh: 1: ps: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\n",
    "exit_code": 127,
    "duration": {
      "secs": 0,
      "nanos": 4620
    },
    "formatted_output": "/bin/sh: 1: ps: not found\n"
  }
}

Harness context token usage record

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}

Agent message

+03:38

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Agent message

+03:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

exec command

+03:54

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +03:55

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +04:00

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"e88b2e","wall_time_seconds":5.000836899,"session_id":74144,"original_token_count":0,"output":""}

Harness context token usage record

{
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}

exec command

+04:05

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +04:06

const r = await tools.exec_command({"cmd":"du -h /app/solution/* 2>/dev/null | sort -h; ls -lh /app/solution","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +04:06

Script completed
Wall time 0.1 seconds
Output:

0	/app/solution/production_validation_run.json
16K	/app/solution/two_tower_model.py
80K	/app/solution/train_current_model.py
511M	/app/solution/validation_sequences.npz
total 511M
-rw-r--r-- 1 agent agent    0 Sep 25 00:04 production_validation_run.json
-r--r--r-- 1 agent agent  79K Sep 25 00:04 train_current_model.py
-r--r--r-- 1 agent agent  16K Sep 25 00:04 two_tower_model.py
-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
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      "/bin/sh",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "du -h /app/solution/* 2>/dev/null | sort -h; ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0\t/app/solution/production_validation_run.json\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\ntotal 511M\n-rw-r--r-- 1 agent agent    0 Sep 25 00:04 production_validation_run.json\n-r--r--r-- 1 agent agent  79K Sep 25 00:04 train_current_model.py\n-r--r--r-- 1 agent agent  16K Sep 25 00:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz\n",
    "stderr": "",
    "aggregated_output": "0\t/app/solution/production_validation_run.json\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\ntotal 511M\n-rw-r--r-- 1 agent agent    0 Sep 25 00:04 production_validation_run.json\n-r--r--r-- 1 agent agent  79K Sep 25 00:04 train_current_model.py\n-r--r--r-- 1 agent agent  16K Sep 25 00:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3820
    },
    "formatted_output": "0\t/app/solution/production_validation_run.json\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\ntotal 511M\n-rw-r--r-- 1 agent agent    0 Sep 25 00:04 production_validation_run.json\n-r--r--r-- 1 agent agent  79K Sep 25 00:04 train_current_model.py\n-r--r--r-- 1 agent agent  16K Sep 25 00:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz\n"
  }
}

exec command

+04:09

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Tool callexecTool call Returned

Tool call · Input +04:10

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +04:15

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"65caca","wall_time_seconds":5.001924901,"session_id":74144,"original_token_count":0,"output":""}

Harness context token usage record

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Agent message

+04:29

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Agent message

+04:42

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Agent message

+04:53

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Agent message

+05:06

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Agent message

+05:18

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Agent message

+05:30

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Agent message

+05:39

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exec command

+05:44

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Model text

The baseline’s main weakness is now concrete: 24/32 negatives are catalog-uniform (mostly easy tail tracks), while only 8 come from observed positives, and those 8 are inverse-propensity downweighted. I’m adding a fixed-size hard-negative component mined from the item tower after a one-epoch warm-up, mixed with uniform and in-batch examples so compute per training row stays at 32 negatives.

Tool callexecTool call Returned

Tool call · Input +05:49

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower retriever.\n@@\n-The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+The model is a pure two-tower retriever: no user-ID embedding, no output\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  After a one-epoch warm-up, the negative mix combines uniform,\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n+keeping the production total of 32 negatives per target.  Every selected\n+adjacent target retains a nonzero weight. Only files under the supplied public\n+directory are read.\n@@\n class Config:\n@@\n-    epochs: int = 1\n+    epochs: int = 2\n@@\n-    uniform_negatives: int = 24\n+    uniform_negatives: int = 8\n     inbatch_negatives: int = 8\n+    hard_negatives: int = 16\n+    hard_negative_pool_size: int = 64\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +05:49

Script failed
Wall time 0.0 seconds
Output:

Script error:
Exit code: 1
Wall time: 0 seconds
Output:
Failed to write file /app/solution/train_current_model.py

Harness context codex item completed

{
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    "type": "FileChange",
    "id": "exec-bb6237f8-6357-4a38-b98a-708efebac6d3",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1,3 +1,3 @@\n #!/usr/bin/env python3\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower retriever.\n \n@@ -12,6 +12,8 @@\n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  After a one-epoch warm-up, the negative mix combines uniform,\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n+keeping the production total of 32 negatives per target.  Every selected\n+adjacent target retains a nonzero weight. Only files under the supplied public\n+directory are read.\n \"\"\"\n@@ -50,3 +52,3 @@\n     dropout: float = 0.05\n-    epochs: int = 1\n+    epochs: int = 2\n     learning_rate: float = 1.0e-3\n@@ -55,4 +57,6 @@\n     loss_microbatch_size: int = 65_536\n-    uniform_negatives: int = 24\n+    uniform_negatives: int = 8\n     inbatch_negatives: int = 8\n+    hard_negatives: int = 16\n+    hard_negative_pool_size: int = 64\n     inbatch_correction_power: float = 0.5\n",
        "move_path": null
      }
    },
    "status": "failed",
    "stdout": "",
    "stderr": "Failed to write file /app/solution/train_current_model.py\n"
  }
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Harness context token usage record

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exec command

+05:54

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Tool callexecTool call Returned

Tool call · Input +05:54

const r = await tools.exec_command({"cmd":"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response +05:54

Script completed
Wall time 0.0 seconds
Output:

Harness context token usage record

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Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py"
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exec command

+05:57

Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input +06:03

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower retriever.\n@@\n-The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+The model is a pure two-tower retriever: no user-ID embedding, no output\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  After a one-epoch warm-up, the negative mix combines uniform,\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n+keeping the production total of 32 negatives per target.  Every selected\n+adjacent target retains a nonzero weight. Only files under the supplied public\n+directory are read.\n@@\n class Config:\n@@\n-    epochs: int = 1\n+    epochs: int = 2\n@@\n-    uniform_negatives: int = 24\n+    uniform_negatives: int = 8\n     inbatch_negatives: int = 8\n+    hard_negatives: int = 16\n+    hard_negative_pool_size: int = 64\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +06:03

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-dbee8b8e-ae38-43f3-9960-176aecf6d27a",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1,3 +1,3 @@\n #!/usr/bin/env python3\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower retriever.\n \n@@ -12,6 +12,8 @@\n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  After a one-epoch warm-up, the negative mix combines uniform,\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n+keeping the production total of 32 negatives per target.  Every selected\n+adjacent target retains a nonzero weight. Only files under the supplied public\n+directory are read.\n \"\"\"\n@@ -50,3 +52,3 @@\n     dropout: float = 0.05\n-    epochs: int = 1\n+    epochs: int = 2\n     learning_rate: float = 1.0e-3\n@@ -55,4 +57,6 @@\n     loss_microbatch_size: int = 65_536\n-    uniform_negatives: int = 24\n+    uniform_negatives: int = 8\n     inbatch_negatives: int = 8\n+    hard_negatives: int = 16\n+    hard_negative_pool_size: int = 64\n     inbatch_correction_power: float = 0.5\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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exec command

+06:09

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:10

const r = await tools.exec_command({"cmd":"sed -n '520,765p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":22000});
text(r.output);

Tool response +06:10

Script completed
Wall time 0.1 seconds
Output:

) -> tuple[SequenceData, CandidateSet]:
    candidate_name = (
        "validation_eligible_candidates.npz"
        if stage == "validation"
        else "test_eligible_candidates.npz"
    )
    eligibility = CandidateSet.read(public / "data" / candidate_name)
    if cache is not None and cache.is_file():
        prepared = load_cache(cache, stage, config)
        if not np.array_equal(prepared.item_ids, eligibility.item_ids):
            raise RuntimeError("cached catalog differs from candidate artifact")
        return prepared, eligibility
    prepared, built_eligibility = prepare_data(public, stage, config)
    if cache is not None:
        save_cache(cache, prepared, stage, config)
    return prepared, built_eligibility


def build_item_correction(
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
    raw = np.power(
        (1.0 / len(counts)) / probabilities,
        config.inbatch_correction_power,
    )
    normalization = float(np.sum(counts * raw) / max(total, 1.0))
    correction = np.clip(
        raw / max(normalization, 1.0e-12),
        config.inbatch_correction_min,
        config.inbatch_correction_max,
    ).astype(np.float32)
    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
    log(
        f"inbatch correction: min={correction.min():.3f} "
        f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
    )
    return torch.from_numpy(correction)


def build_known_positive_filter(
    prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
    """Pack each user's complete public-training positive-item set."""
    width = (len(prepared.item_ids) + 7) // 8
    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
    retained = 0
    for user_row, target_count in enumerate(training_counts):
        if not target_count:
            continue
        offset = int(prepared.offsets[user_row])
        # T adjacent targets consume sequence positions 0 through T.  The
        # chronological BCE suffix starts at T + 1 and is intentionally absent.
        items = np.unique(
            prepared.sequence_items[
                offset : offset + int(target_count) + 1
            ].astype(np.int64)
        )
        byte_columns = items >> 3
        bit_values = np.left_shift(
            np.uint8(1), (items & 7).astype(np.uint8)
        )
        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
        retained += len(items)
    log(
        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
        f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
    )
    return packed


def invalid_full_history_negatives(
    negatives: np.ndarray,
    targets: np.ndarray,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray,
    liked_items: np.ndarray,
) -> np.ndarray:
    """Return row-level invalid negatives using the complete training prefix."""
    byte_columns = negatives >> 3
    bit_columns = negatives & 7
    known = known_positive_packed[user_rows[:, None], byte_columns]
    invalid = ((known >> bit_columns) & 1).astype(bool)
    invalid |= negatives == targets[:, None]
    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= (
            negatives[:, column, None] == negatives[:, :column]
        ).any(axis=1)
    return invalid


def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:
        correction[
            :, config.uniform_negatives : config.uniform_negatives + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]].numpy()
    for _ in range(32):
        invalid = invalid_full_history_negatives(
            negatives,
            targets_cpu,
            user_rows,
            known_positive_packed,
            liked_cpu,
        )
        if not np.any(invalid):
            return (
                torch.from_numpy(negatives).to(device),
                torch.from_numpy(correction).to(device),
            )
        replacements = torch.randint(
            n_items,
            (int(invalid.sum()),),
            generator=generator,
            device="cpu",
        ).numpy()
        negatives[invalid] = replacements
        # Rejected in-batch items become ordinary uniform draws.
        correction[invalid] = 1.0
    invalid = invalid_full_history_negatives(
        negatives,
        targets_cpu,
        user_rows,
        known_positive_packed,
        liked_cpu,
    )
    if np.any(invalid):
        raise RuntimeError("full-history negative resampling failed")
    return (
        torch.from_numpy(negatives).to(device),
        torch.from_numpy(correction).to(device),
    )


def draw_training_negatives(
    positive_items: torch.Tensor,
    known_history: torch.Tensor,
    liked_items: torch.Tensor,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
    prepared: SequenceData,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    if config.negative_filter_scope == "full-training-history":
        if known_positive_packed is None:
            raise RuntimeError("full-history negative filter was not built")
        return draw_full_history_filtered_negatives(
            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
    return base.draw_filtered_mixed_negatives(
        positive_items,
        known_history,
        liked_items,
        len(prepared.item_ids),
        config.uniform_negatives,
        config.inbatch_negatives,
        item_correction,
        generator,
        device,
    )

Harness context token usage record

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  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
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  "usage": {
    "input_tokens": 58029,
    "cached_input_tokens": 57472,
    "cache_write_input_tokens": 0,
    "output_tokens": 239,
    "reasoning_output_tokens": 175,
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  },
  "turn_token_usage": {
    "input_tokens": 1066046,
    "cached_input_tokens": 1014912,
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  "thread_token_usage": {
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7515276d-f558-4327-8fd6-438650c93262",
    "process_id": "67927",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '520,765p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '520,765p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": ") -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n",
    "stderr": "",
    "aggregated_output": ") -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3930
    },
    "formatted_output": ") -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n"
  }
}

exec command

+06:13

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:31

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n def build_item_correction(\n@@\n     return torch.from_numpy(correction)\n \n \[email protected]_grad()\n+def build_hard_negative_lookup(\n+    model: base.SequentialTwoTower,\n+    n_items: int,\n+    pool_size: int,\n+    device: torch.device,\n+) -> np.ndarray:\n+    \"\"\"Mine a compact nearest-neighbor pool in the serving item-vector space.\n+\n+    Mining is performed between epochs and never changes the serving score.  A\n+    small row block bounds memory while exact dot products preserve the same\n+    notion of confusability used by retrieval.\n+    \"\"\"\n+    if pool_size < 1 or pool_size >= n_items:\n+        raise ValueError(\"hard-negative pool size must be in [1, n_items)\")\n+    started = time.monotonic()\n+    model.eval()\n+    parts: list[torch.Tensor] = []\n+    for start in range(0, n_items, 2_048):\n+        rows = torch.arange(\n+            start,\n+            min(start + 2_048, n_items),\n+            dtype=torch.long,\n+            device=device,\n+        )\n+        parts.append(model.encode_items(rows).cpu())\n+    vectors = torch.cat(parts)\n+    lookup = np.empty((n_items, pool_size), dtype=np.uint16)\n+    score_block = 256\n+    for start in range(0, n_items, score_block):\n+        end = min(start + score_block, n_items)\n+        scores = vectors[start:end] @ vectors.T\n+        local_rows = torch.arange(end - start)\n+        scores[local_rows, torch.arange(start, end)] = -torch.inf\n+        lookup[start:end] = torch.topk(\n+            scores, k=pool_size, dim=1, sorted=True\n+        ).indices.numpy().astype(np.uint16, copy=False)\n+    log(\n+        f\"mined item-tower hard-negative lookup: items={n_items:,} \"\n+        f\"pool={pool_size} seconds={time.monotonic() - started:.1f}\"\n+    )\n+    return lookup\n+\n+\n def build_known_positive_filter(\n@@\n def draw_full_history_filtered_negatives(\n@@\n     config: Config,\n     item_correction: torch.Tensor,\n+    hard_negative_lookup: np.ndarray | None,\n     generator: torch.Generator,\n@@\n-    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n+    hard = torch.empty((batch_size, 0), dtype=torch.long)\n+    if config.hard_negatives:\n+        if hard_negative_lookup is None:\n+            # Epoch one is an intentional baseline warm-up.  Keeping the total\n+            # at 32 also makes its compute directly comparable to production.\n+            hard = torch.randint(\n+                n_items,\n+                (batch_size, config.hard_negatives),\n+                generator=generator,\n+                device=\"cpu\",\n+            )\n+        else:\n+            pool_size = hard_negative_lookup.shape[1]\n+            priorities = torch.rand(\n+                (batch_size, pool_size), generator=generator\n+            )\n+            columns = torch.topk(\n+                priorities, k=config.hard_negatives, dim=1\n+            ).indices.numpy()\n+            hard = torch.from_numpy(\n+                hard_negative_lookup[targets_cpu[:, None], columns].astype(\n+                    np.int64, copy=False\n+                )\n+            )\n+\n+    negatives = torch.cat([uniform, inbatch, hard], dim=1).numpy()\n@@\n def draw_training_negatives(\n@@\n     config: Config,\n     item_correction: torch.Tensor,\n+    hard_negative_lookup: np.ndarray | None,\n     generator: torch.Generator,\n@@\n             config,\n             item_correction,\n+            hard_negative_lookup,\n             generator,\n@@\n-    return base.draw_filtered_mixed_negatives(\n+    if config.hard_negatives:\n+        raise RuntimeError(\n+            \"hard negatives require --negative-filter-scope full-training-history\"\n+        )\n+    return base.draw_filtered_mixed_negatives(\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +06:31

Script completed
Wall time 0.1 seconds
Output:

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Harness context codex item completed

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  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-e99f8683-10c8-482f-9baa-84d61efd1a44",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -581,2 +581,46 @@\n \[email protected]_grad()\n+def build_hard_negative_lookup(\n+    model: base.SequentialTwoTower,\n+    n_items: int,\n+    pool_size: int,\n+    device: torch.device,\n+) -> np.ndarray:\n+    \"\"\"Mine a compact nearest-neighbor pool in the serving item-vector space.\n+\n+    Mining is performed between epochs and never changes the serving score.  A\n+    small row block bounds memory while exact dot products preserve the same\n+    notion of confusability used by retrieval.\n+    \"\"\"\n+    if pool_size < 1 or pool_size >= n_items:\n+        raise ValueError(\"hard-negative pool size must be in [1, n_items)\")\n+    started = time.monotonic()\n+    model.eval()\n+    parts: list[torch.Tensor] = []\n+    for start in range(0, n_items, 2_048):\n+        rows = torch.arange(\n+            start,\n+            min(start + 2_048, n_items),\n+            dtype=torch.long,\n+            device=device,\n+        )\n+        parts.append(model.encode_items(rows).cpu())\n+    vectors = torch.cat(parts)\n+    lookup = np.empty((n_items, pool_size), dtype=np.uint16)\n+    score_block = 256\n+    for start in range(0, n_items, score_block):\n+        end = min(start + score_block, n_items)\n+        scores = vectors[start:end] @ vectors.T\n+        local_rows = torch.arange(end - start)\n+        scores[local_rows, torch.arange(start, end)] = -torch.inf\n+        lookup[start:end] = torch.topk(\n+            scores, k=pool_size, dim=1, sorted=True\n+        ).indices.numpy().astype(np.uint16, copy=False)\n+    log(\n+        f\"mined item-tower hard-negative lookup: items={n_items:,} \"\n+        f\"pool={pool_size} seconds={time.monotonic() - started:.1f}\"\n+    )\n+    return lookup\n+\n+\n def build_known_positive_filter(\n@@ -641,2 +685,3 @@\n     item_correction: torch.Tensor,\n+    hard_negative_lookup: np.ndarray | None,\n     generator: torch.Generator,\n@@ -684,3 +729,28 @@\n \n-    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n+    hard = torch.empty((batch_size, 0), dtype=torch.long)\n+    if config.hard_negatives:\n+        if hard_negative_lookup is None:\n+            # Epoch one is an intentional baseline warm-up.  Keeping the total\n+            # at 32 also makes its compute directly comparable to production.\n+            hard = torch.randint(\n+                n_items,\n+                (batch_size, config.hard_negatives),\n+                generator=generator,\n+                device=\"cpu\",\n+            )\n+        else:\n+            pool_size = hard_negative_lookup.shape[1]\n+            priorities = torch.rand(\n+                (batch_size, pool_size), generator=generator\n+            )\n+            columns = torch.topk(\n+                priorities, k=config.hard_negatives, dim=1\n+            ).indices.numpy()\n+            hard = torch.from_numpy(\n+                hard_negative_lookup[targets_cpu[:, None], columns].astype(\n+                    np.int64, copy=False\n+                )\n+            )\n+\n+    negatives = torch.cat([uniform, inbatch, hard], dim=1).numpy()\n     correction = np.ones_like(negatives, dtype=np.float32)\n@@ -736,2 +806,3 @@\n     item_correction: torch.Tensor,\n+    hard_negative_lookup: np.ndarray | None,\n     generator: torch.Generator,\n@@ -750,2 +821,3 @@\n             item_correction,\n+            hard_negative_lookup,\n             generator,\n@@ -753,2 +825,6 @@\n         )\n+    if config.hard_negatives:\n+        raise RuntimeError(\n+            \"hard negatives require --negative-filter-scope full-training-history\"\n+        )\n     return base.draw_filtered_mixed_negatives(\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

exec command

+06:34

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:39

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n def corrected_negative_loss(\n@@\n-    total = config.uniform_negatives + config.inbatch_negatives\n+    total = (\n+        config.uniform_negatives\n+        + config.inbatch_negatives\n+        + config.hard_negatives\n+    )\n@@\n     if config.inbatch_negatives:\n-        selected = slice(config.uniform_negatives, total)\n+        selected = slice(\n+            config.uniform_negatives,\n+            config.uniform_negatives + config.inbatch_negatives,\n+        )\n         weights = correction[:, selected]\n         result += config.inbatch_negatives * (\n             (raw[:, selected] * weights).sum(dim=1)\n             / weights.sum(dim=1).clamp_min(1.0e-6)\n         )\n+    if config.hard_negatives:\n+        selected = slice(\n+            config.uniform_negatives + config.inbatch_negatives,\n+            total,\n+        )\n+        result += config.hard_negatives * raw[:, selected].mean(dim=1)\n     return result / total\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +06:39

Script completed
Wall time 0.1 seconds
Output:

{}

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Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-d3e65aa9-9b84-417a-9032-c9d93b607f61",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -930,3 +930,7 @@\n     raw = F.softplus(logits)\n-    total = config.uniform_negatives + config.inbatch_negatives\n+    total = (\n+        config.uniform_negatives\n+        + config.inbatch_negatives\n+        + config.hard_negatives\n+    )\n     result = torch.zeros(len(logits), device=logits.device)\n@@ -937,3 +941,6 @@\n     if config.inbatch_negatives:\n-        selected = slice(config.uniform_negatives, total)\n+        selected = slice(\n+            config.uniform_negatives,\n+            config.uniform_negatives + config.inbatch_negatives,\n+        )\n         weights = correction[:, selected]\n@@ -943,2 +950,8 @@\n         )\n+    if config.hard_negatives:\n+        selected = slice(\n+            config.uniform_negatives + config.inbatch_negatives,\n+            total,\n+        )\n+        result += config.hard_negatives * raw[:, selected].mean(dim=1)\n     return result / total\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

exec command

+06:42

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:43

const r = await tools.exec_command({"cmd":"rg -n \"draw_training_negatives\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,\" /app/solution/train_current_model.py | sed -n '1,160p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":6000});
text(r.output);

Tool response +06:43

Script completed
Wall time 0.1 seconds
Output:

798:def draw_training_negatives(
821:            item_correction,
837:        item_correction,
1296:def run_training_epoch(
1320:    negative_count = config.uniform_negatives + config.inbatch_negatives
1386:            negatives, negative_weights = draw_training_negatives(
1394:                item_correction,
1457:def evaluate_validation_bce(
1542:            negatives, negative_weights = draw_training_negatives(
1550:                item_correction,
1769:                    item_correction,
1792:            item_correction,
1806:            item_correction,

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-27a3c302-ba6a-4123-ae7f-0545e4d68360",
    "process_id": "90420",
    "command": [
      "/bin/sh",
      "-lc",
      "rg -n \"draw_training_negatives\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,\" /app/solution/train_current_model.py | sed -n '1,160p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "search",
        "cmd": "rg -n \"draw_training_negatives\\\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,\" /app/solution/train_current_model.py",
        "query": "draw_training_negatives\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,",
        "path": "train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "798:def draw_training_negatives(\n821:            item_correction,\n837:        item_correction,\n1296:def run_training_epoch(\n1320:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1386:            negatives, negative_weights = draw_training_negatives(\n1394:                item_correction,\n1457:def evaluate_validation_bce(\n1542:            negatives, negative_weights = draw_training_negatives(\n1550:                item_correction,\n1769:                    item_correction,\n1792:            item_correction,\n1806:            item_correction,\n",
    "stderr": "",
    "aggregated_output": "798:def draw_training_negatives(\n821:            item_correction,\n837:        item_correction,\n1296:def run_training_epoch(\n1320:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1386:            negatives, negative_weights = draw_training_negatives(\n1394:                item_correction,\n1457:def evaluate_validation_bce(\n1542:            negatives, negative_weights = draw_training_negatives(\n1550:                item_correction,\n1769:                    item_correction,\n1792:            item_correction,\n1806:            item_correction,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4360
    },
    "formatted_output": "798:def draw_training_negatives(\n821:            item_correction,\n837:        item_correction,\n1296:def run_training_epoch(\n1320:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1386:            negatives, negative_weights = draw_training_negatives(\n1394:                item_correction,\n1457:def evaluate_validation_bce(\n1542:            negatives, negative_weights = draw_training_negatives(\n1550:                item_correction,\n1769:                    item_correction,\n1792:            item_correction,\n1806:            item_correction,\n"
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exec command

+06:46

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:47

const r = await tools.exec_command({"cmd":"sed -n '1288,1410p' /app/solution/train_current_model.py; sed -n '1448,1560p' /app/solution/train_current_model.py; sed -n '1680,1820p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":24000});
text(r.output);

Tool response +06:47

Script completed
Wall time 0.1 seconds
Output:

    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    target_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
) -> dict[str, float]:
    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
    group_order = torch.randperm(len(groups), generator=generator).numpy()
    negative_count = config.uniform_negatives + config.inbatch_negatives
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()

    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
        / max(total_targets * negative_count, 1),
        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    started = time.monotonic()

    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6e50df1b-9787-4dbd-a185-6fee39a2ce40",
    "process_id": "18818",
    "command": [
      "/bin/sh",
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      "sed -n '1288,1410p' /app/solution/train_current_model.py; sed -n '1448,1560p' /app/solution/train_current_model.py; sed -n '1680,1820p' /app/solution/train_current_model.py"
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n",
    "stderr": "",
    "aggregated_output": "    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3610
    },
    "formatted_output": "    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n"
  }
}

exec command

+06:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:54

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n def run_training_epoch(\n@@\n     item_correction: torch.Tensor,\n     known_positive_packed: np.ndarray | None,\n+    hard_negative_lookup: np.ndarray | None,\n     generator: torch.Generator,\n@@\n-    negative_count = config.uniform_negatives + config.inbatch_negatives\n+    negative_count = (\n+        config.uniform_negatives\n+        + config.inbatch_negatives\n+        + config.hard_negatives\n+    )\n@@\n                 config,\n                 item_correction,\n+                hard_negative_lookup,\n                 generator,\n@@\n def evaluate_validation_bce(\n@@\n     item_correction: torch.Tensor,\n     known_positive_packed: np.ndarray | None,\n+    hard_negative_lookup: np.ndarray | None,\n     device: torch.device,\n@@\n                 config,\n                 item_correction,\n+                hard_negative_lookup,\n                 generator,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +06:54

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-a0c9b9da-e73c-4cd5-acfc-1306331c7d7e",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1305,2 +1305,3 @@\n     known_positive_packed: np.ndarray | None,\n+    hard_negative_lookup: np.ndarray | None,\n     generator: torch.Generator,\n@@ -1319,3 +1320,7 @@\n     group_order = torch.randperm(len(groups), generator=generator).numpy()\n-    negative_count = config.uniform_negatives + config.inbatch_negatives\n+    negative_count = (\n+        config.uniform_negatives\n+        + config.inbatch_negatives\n+        + config.hard_negatives\n+    )\n     total_loss = 0.0\n@@ -1394,2 +1399,3 @@\n                 item_correction,\n+                hard_negative_lookup,\n                 generator,\n@@ -1466,2 +1472,3 @@\n     known_positive_packed: np.ndarray | None,\n+    hard_negative_lookup: np.ndarray | None,\n     device: torch.device,\n@@ -1550,2 +1557,3 @@\n                 item_correction,\n+                hard_negative_lookup,\n                 generator,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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  }
}

exec command

+06:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +06:59

const r = await tools.exec_command({"cmd":"sed -n '1600,1845p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":26000});
text(r.output);

Tool response +06:59

Script completed
Wall time 0.1 seconds
Output:

    config: Config,
    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
            "diagnostics": diagnostics,
            "model_state": model_state,
            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path


def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        stage,
        prepared,
        partition.history_training_counts,
        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
    history: list[dict[str, float]] = []
    best_loss = float("inf")
    best_epoch = 0
    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
        )
        stored_config = payload.get("config", {})
        architecture_fields = (
            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
        improved = (
            diagnostics["validation_loss"]
            < best_loss - config.early_stopping_min_delta
        )
        if improved:
            best_loss = diagnostics["validation_loss"]
            best_epoch = epoch
            best_state = current_state
            epochs_without_improvement = 0
            if checkpoint_dir is not None:
                torch.save(
                    {
                        "epoch": epoch,
                        "config": asdict(config),
                        "diagnostics": diagnostics,

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-8dcf4705-177e-4dbc-a283-d1c896f10800",
    "process_id": "56486",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1600,1845p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1600,1845p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n",
    "stderr": "",
    "aggregated_output": "    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4190
    },
    "formatted_output": "    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bbdfcc9487d2b4922f32407e7ff5",
  "usage": {
    "input_tokens": 65017,
    "cached_input_tokens": 64512,
    "cache_write_input_tokens": 0,
    "output_tokens": 97,
    "reasoning_output_tokens": 31,
    "total_tokens": 65114
  },
  "turn_token_usage": {
    "input_tokens": 1440774,
    "cached_input_tokens": 1381504,
    "cache_write_input_tokens": 0,
    "output_tokens": 12816,
    "reasoning_output_tokens": 7447,
    "total_tokens": 1453590
  },
  "thread_token_usage": {
    "input_tokens": 1440774,
    "cached_input_tokens": 1381504,
    "cache_write_input_tokens": 0,
    "output_tokens": 12816,
    "reasoning_output_tokens": 7447,
    "total_tokens": 1453590
  }
}

exec command

+07:10

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +07:24

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     epochs_without_improvement = 0\n     start_epoch = 0\n+    hard_negative_lookup: np.ndarray | None = None\n@@\n         current_window_values = (\n@@\n             config.max_validation_targets_per_user,\n         )\n+        stored_negative_values = (\n+            stored_config.get(\"uniform_negatives\", 24),\n+            stored_config.get(\"inbatch_negatives\", 8),\n+            stored_config.get(\"hard_negatives\", 0),\n+            stored_config.get(\"hard_negative_pool_size\", 64),\n+        )\n+        current_negative_values = (\n+            config.uniform_negatives,\n+            config.inbatch_negatives,\n+            config.hard_negatives,\n+            config.hard_negative_pool_size,\n+        )\n         objective_changed = (\n             stored_filter_scope != config.negative_filter_scope\n             or stored_target_weighting != config.target_weighting\n             or stored_weight_values != current_weight_values\n             or stored_window_values != current_window_values\n+            or stored_negative_values != current_negative_values\n         )\n         model.load_state_dict(payload[\"model_state\"])\n+        if config.hard_negatives:\n+            hard_negative_lookup = build_hard_negative_lookup(\n+                model,\n+                len(prepared.item_ids),\n+                config.hard_negative_pool_size,\n+                device,\n+            )\n@@\n         prior = {\n@@\n         }\n-        if objective_changed:\n+        if objective_changed or config.hard_negatives:\n             prior.pop(\"validation_loss\", None)\n+        if objective_changed:\n             log(\n@@\n                 \"target weighting \"\n                 f\"{stored_target_weighting}->{config.target_weighting}, \"\n                 f\"target window {stored_window_values}->{current_window_values}; \"\n+                f\"negative mix {stored_negative_values}->{current_negative_values}; \"\n                 \"recomputing the resume checkpoint's validation BCE\"\n             )\n@@\n                     item_correction,\n                     known_positive_packed,\n+                    hard_negative_lookup,\n                     device,\n@@\n \n     for epoch in range(start_epoch + 1, config.epochs + 1):\n+        if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n+            hard_negative_lookup = build_hard_negative_lookup(\n+                model,\n+                len(prepared.item_ids),\n+                config.hard_negative_pool_size,\n+                device,\n+            )\n+            reference = evaluate_validation_bce(\n+                model,\n+                prepared,\n+                config,\n+                target_weights,\n+                partition.window_starts,\n+                training_counts,\n+                validation_counts,\n+                item_correction,\n+                known_positive_packed,\n+                hard_negative_lookup,\n+                device,\n+            )\n+            best_loss = float(reference[\"validation_loss\"])\n+            best_epoch = epoch - 1\n+            best_state = snapshot_model(model)\n+            epochs_without_improvement = 0\n+            log(\n+                \"hard-negative warm-up complete: \"\n+                f\"reference_epoch={best_epoch} validation_loss={best_loss:.6f}\"\n+            )\n         diagnostics = run_training_epoch(\n@@\n             item_correction,\n             known_positive_packed,\n+            hard_negative_lookup,\n             generator,\n@@\n             item_correction,\n             known_positive_packed,\n+            hard_negative_lookup,\n             device,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +07:24

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-20dd1e82-05ae-4c84-a3b1-79667700e273",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1676,2 +1676,3 @@\n     start_epoch = 0\n+    hard_negative_lookup: np.ndarray | None = None\n \n@@ -1728,2 +1729,14 @@\n         )\n+        stored_negative_values = (\n+            stored_config.get(\"uniform_negatives\", 24),\n+            stored_config.get(\"inbatch_negatives\", 8),\n+            stored_config.get(\"hard_negatives\", 0),\n+            stored_config.get(\"hard_negative_pool_size\", 64),\n+        )\n+        current_negative_values = (\n+            config.uniform_negatives,\n+            config.inbatch_negatives,\n+            config.hard_negatives,\n+            config.hard_negative_pool_size,\n+        )\n         objective_changed = (\n@@ -1733,4 +1746,12 @@\n             or stored_window_values != current_window_values\n+            or stored_negative_values != current_negative_values\n         )\n         model.load_state_dict(payload[\"model_state\"])\n+        if config.hard_negatives:\n+            hard_negative_lookup = build_hard_negative_lookup(\n+                model,\n+                len(prepared.item_ids),\n+                config.hard_negative_pool_size,\n+                device,\n+            )\n         if \"optimizer_state\" in payload and not objective_changed:\n@@ -1756,4 +1777,5 @@\n         }\n+        if objective_changed or config.hard_negatives:\n+            prior.pop(\"validation_loss\", None)\n         if objective_changed:\n-            prior.pop(\"validation_loss\", None)\n             log(\n@@ -1764,2 +1786,3 @@\n                 f\"target window {stored_window_values}->{current_window_values}; \"\n+                f\"negative mix {stored_negative_values}->{current_negative_values}; \"\n                 \"recomputing the resume checkpoint's validation BCE\"\n@@ -1778,2 +1801,3 @@\n                     known_positive_packed,\n+                    hard_negative_lookup,\n                     device,\n@@ -1791,2 +1815,30 @@\n     for epoch in range(start_epoch + 1, config.epochs + 1):\n+        if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n+            hard_negative_lookup = build_hard_negative_lookup(\n+                model,\n+                len(prepared.item_ids),\n+                config.hard_negative_pool_size,\n+                device,\n+            )\n+            reference = evaluate_validation_bce(\n+                model,\n+                prepared,\n+                config,\n+                target_weights,\n+                partition.window_starts,\n+                training_counts,\n+                validation_counts,\n+                item_correction,\n+                known_positive_packed,\n+                hard_negative_lookup,\n+                device,\n+            )\n+            best_loss = float(reference[\"validation_loss\"])\n+            best_epoch = epoch - 1\n+            best_state = snapshot_model(model)\n+            epochs_without_improvement = 0\n+            log(\n+                \"hard-negative warm-up complete: \"\n+                f\"reference_epoch={best_epoch} validation_loss={best_loss:.6f}\"\n+            )\n         diagnostics = run_training_epoch(\n@@ -1801,2 +1853,3 @@\n             known_positive_packed,\n+            hard_negative_lookup,\n             generator,\n@@ -1815,2 +1868,3 @@\n             known_positive_packed,\n+            hard_negative_lookup,\n             device,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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exec command

+07:26

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +07:27

const r = await tools.exec_command({"cmd":"sed -n '2010,2215p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":24000});
text(r.output);

Tool response +07:27

Script completed
Wall time 0.0 seconds
Output:

        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


@torch.inference_mode()
def rank_candidates(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
    model.eval()
    queries = inference_queries(model, prepared, config, device)
    item_parts: list[torch.Tensor] = []
    for start in range(0, len(prepared.item_ids), 2_048):
        rows = torch.arange(
            start,
            min(start + 2_048, len(prepared.item_ids)),
            dtype=torch.long,
            device=device,
        )
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
    rows: list[tuple[int, int, int]] = []
    for start in range(0, len(eligibility.uids), ranking_batch_size):
        end = min(start + ranking_batch_size, len(eligibility.uids))
        scores = queries[start:end] @ items.T
        eligible = np.unpackbits(
            eligibility.packed[start:end],
            axis=1,
            count=len(prepared.item_ids),
            bitorder="little",
        ).astype(bool, copy=False)
        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
        for local_row, columns in enumerate(selected):
            uid = int(eligibility.uids[start + local_row])
            rows.extend(
                (uid, int(prepared.item_ids[column]), rank)
                for rank, column in enumerate(columns, start=1)
            )
        if end == len(eligibility.uids) or end % 1_024 == 0:
            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
    diagnostics = {
        "query_norm_mean": float(query_norm.mean()),
        "query_norm_std": float(query_norm.std()),
        "item_norm_mean": float(item_norm.mean()),
        "item_norm_std": float(item_norm.std()),
    }
    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics


def write_ranking(frame: pd.DataFrame, output: Path) -> None:
    output.parent.mkdir(parents=True, exist_ok=True)
    if output.suffix == ".parquet":
        frame.to_parquet(output, index=False, compression="zstd")
    elif output.suffix == ".csv":
        frame.to_csv(output, index=False)
    else:
        raise ValueError("--output must end in .parquet or .csv")


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
    )
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--public-dir", type=Path, required=True)
    parser.add_argument("--prepared-cache", type=Path)
    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
    parser.add_argument("--threads", type=int, default=8)
    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
    parser.add_argument(
        "--user-batch-size", type=int, default=Config.user_batch_size
    )
    parser.add_argument("--epochs", type=int, default=Config.epochs)
    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
    parser.add_argument(
        "--loss-microbatch-size",
        type=int,
        default=Config.loss_microbatch_size,
    )
    parser.add_argument(
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--target-weighting",
        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
        "--organic-strong-target-weight",
        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
    parser.add_argument(
        "--discovery-horizon-days",
        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
    parser.add_argument(
        "--max-train-targets-per-user",
        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
        help="retain this many final validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )
    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1
        or args.early_stopping_patience < 1
        or args.early_stopping_min_delta < 0
        or args.ordinary_target_weight <= 0
        or args.organic_strong_target_weight <= 0
        or args.organic_like_target_weight <= 0
        or args.discovery_horizon_days < 1
        or args.max_train_targets_per_user < 0
        or args.max_validation_targets_per_user < 0
    ):
        parser.error("training counts, learning rate, and patience are invalid")
    if not 0.0 < args.validation_fraction < 1.0:
        parser.error("validation fraction must be strictly between zero and one")
    public = args.public_dir.resolve()
    if not (public / "data" / "candidate_catalog.parquet").is_file():
        parser.error(f"public task data not found under {public}")
    config = Config(
        bptt_steps=args.bptt_steps,
        user_batch_size=args.user_batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate,
        loss_microbatch_size=args.loss_microbatch_size,
        negative_filter_scope=args.negative_filter_scope,
        target_weighting=args.target_weighting,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
        organic_like_target_weight=args.organic_like_target_weight,
        discovery_horizon_days=args.discovery_horizon_days,
        validation_fraction=args.validation_fraction,
        max_train_targets_per_user=args.max_train_targets_per_user,
        max_validation_targets_per_user=args.max_validation_targets_per_user,
        early_stopping_patience=args.early_stopping_patience,
        early_stopping_min_delta=args.early_stopping_min_delta,
        seed=args.seed,
    )
    torch.set_num_threads(args.threads)
    torch.set_num_interop_threads(1)
    seed_everything(config.seed)
    device = torch.device(args.device)
    if device.type == "mps" and not torch.backends.mps.is_available():
        parser.error("MPS was requested but is unavailable")

    started = time.monotonic()
    prepared, eligibility = load_or_prepare(
        public,
        args.stage,
        config,
        args.prepared_cache.resolve() if args.prepared_cache else None,
    )

Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-ea55d23c-1f08-4fe5-a828-9ec5cdad6687",
    "process_id": "54899",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '2010,2215p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '2010,2215p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n",
    "stderr": "",
    "aggregated_output": "        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4540
    },
    "formatted_output": "        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n"
  }
}

Harness context token usage record

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}

exec command

+07:30

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +07:39

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     parser.add_argument(\n         \"--loss-microbatch-size\",\n         type=int,\n         default=Config.loss_microbatch_size,\n     )\n+    parser.add_argument(\n+        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n+    )\n+    parser.add_argument(\n+        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\", type=int, default=Config.hard_negatives\n+    )\n+    parser.add_argument(\n+        \"--hard-negative-pool-size\",\n+        type=int,\n+        default=Config.hard_negative_pool_size,\n+    )\n@@\n         or args.max_train_targets_per_user < 0\n         or args.max_validation_targets_per_user < 0\n+        or args.uniform_negatives < 0\n+        or args.inbatch_negatives < 0\n+        or args.hard_negatives < 0\n+        or args.hard_negative_pool_size < args.hard_negatives\n+        or args.hard_negative_pool_size >= 30_000\n+        or (\n+            args.uniform_negatives\n+            + args.inbatch_negatives\n+            + args.hard_negatives\n+            < 1\n+        )\n     ):\n         parser.error(\"training counts, learning rate, and patience are invalid\")\n@@\n     if not 0.0 < args.validation_fraction < 1.0:\n         parser.error(\"validation fraction must be strictly between zero and one\")\n+    if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":\n+        parser.error(\"hard negatives require full-training-history filtering\")\n@@\n         learning_rate=args.learning_rate,\n         loss_microbatch_size=args.loss_microbatch_size,\n+        uniform_negatives=args.uniform_negatives,\n+        inbatch_negatives=args.inbatch_negatives,\n+        hard_negatives=args.hard_negatives,\n+        hard_negative_pool_size=args.hard_negative_pool_size,\n         negative_filter_scope=args.negative_filter_scope,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +07:39

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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    "type": "FileChange",
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    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2098,2 +2098,16 @@\n     parser.add_argument(\n+        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n+    )\n+    parser.add_argument(\n+        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\", type=int, default=Config.hard_negatives\n+    )\n+    parser.add_argument(\n+        \"--hard-negative-pool-size\",\n+        type=int,\n+        default=Config.hard_negative_pool_size,\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2175,2 +2189,13 @@\n         or args.max_validation_targets_per_user < 0\n+        or args.uniform_negatives < 0\n+        or args.inbatch_negatives < 0\n+        or args.hard_negatives < 0\n+        or args.hard_negative_pool_size < args.hard_negatives\n+        or args.hard_negative_pool_size >= 30_000\n+        or (\n+            args.uniform_negatives\n+            + args.inbatch_negatives\n+            + args.hard_negatives\n+            < 1\n+        )\n     ):\n@@ -2179,2 +2204,4 @@\n         parser.error(\"validation fraction must be strictly between zero and one\")\n+    if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":\n+        parser.error(\"hard negatives require full-training-history filtering\")\n     public = args.public_dir.resolve()\n@@ -2188,2 +2215,6 @@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        uniform_negatives=args.uniform_negatives,\n+        inbatch_negatives=args.inbatch_negatives,\n+        hard_negatives=args.hard_negatives,\n+        hard_negative_pool_size=args.hard_negative_pool_size,\n         negative_filter_scope=args.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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exec command

+07:41

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +07:43

const r = await tools.exec_command({"cmd":"sed -n '2250,2325p' /app/solution/train_current_model.py; rg -n \"evaluate_validation_bce\\(|run_training_epoch\\(|draw_training_negatives\\(\" /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":8000});
text(r.output);

Tool response +07:43

Script completed
Wall time 0.1 seconds
Output:

        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    if args.prepare_only:
        print(
            json.dumps(
                {
                    "stage": args.stage,
                    "sequence_users": len(prepared.user_ids),
                    "sequence_tokens": len(prepared.sequence_items),
                    "next_item_targets": prepared.target_count,
                    "selected_training_targets": int(
                        target_partition.training_counts.sum()
                    ),
                    "selected_validation_targets": int(
                        target_partition.validation_counts.sum()
                    ),
                    "discarded_old_targets": int(
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
                indent=2,
            )
        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
        device,
        args.ranking_batch_size,
    )
    write_ranking(ranking, args.output.resolve())
    print(
        json.dumps(
            {
                "baseline": "yambda-windowed-sequence-two-tower-base",
                "stage": args.stage,
                "output": str(args.output.resolve()),
                "rows": len(ranking),
                "users": int(ranking.uid.nunique()),
                "runtime_seconds": round(time.monotonic() - started, 3),
                "public_inputs_only": True,
                "architecture": {
                    "family": "pure-windowed-sequence-two-tower",
                    "sequence_training": (
                        "every-adjacent-target-within-recent-chronological-window"
                    ),
                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
                    "bptt_hidden_state": "carried-and-detached",
                    "model_selection": "chronological-validation-BCE-early-stopping",
                    "loss_execution": "gradient-accumulated-microbatches",
                    "user_id_embedding": False,
                    "user_mlp": "582-512-256-192",
                    "item_mlp": "196-512-256-192",
                    "normalization": "none",
                    "score": "raw-dot-product",
                    "objective": "sample-weighted-BCE-at-every-timestep",
                    "negative_sampling": (
                        "24-uniform-plus-8-propensity-corrected-inbatch"
                    ),
                    "false_negative_filter": config.negative_filter_scope,
798:def draw_training_negatives(
1296:def run_training_epoch(
1391:            negatives, negative_weights = draw_training_negatives(
1463:def evaluate_validation_bce(
1549:            negatives, negative_weights = draw_training_negatives(
1792:                evaluate_validation_bce(
1823:            reference = evaluate_validation_bce(
1844:        diagnostics = run_training_epoch(
1859:        validation = evaluate_validation_bce(

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1a6f882c-0b81-4231-ba7c-1b9830a0244b",
    "process_id": "74270",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '2250,2325p' /app/solution/train_current_model.py; rg -n \"evaluate_validation_bce\\(|run_training_epoch\\(|draw_training_negatives\\(\" /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '2250,2325p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "search",
        "cmd": "rg -n \"evaluate_validation_bce\\\\(|run_training_epoch\\\\(|draw_training_negatives\\\\(\" /app/solution/train_current_model.py",
        "query": "evaluate_validation_bce\\(|run_training_epoch\\(|draw_training_negatives\\(",
        "path": "train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391:            negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549:            negatives, negative_weights = draw_training_negatives(\n1792:                evaluate_validation_bce(\n1823:            reference = evaluate_validation_bce(\n1844:        diagnostics = run_training_epoch(\n1859:        validation = evaluate_validation_bce(\n",
    "stderr": "",
    "aggregated_output": "        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391:            negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549:            negatives, negative_weights = draw_training_negatives(\n1792:                evaluate_validation_bce(\n1823:            reference = evaluate_validation_bce(\n1844:        diagnostics = run_training_epoch(\n1859:        validation = evaluate_validation_bce(\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4581
    },
    "formatted_output": "        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391:            negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549:            negatives, negative_weights = draw_training_negatives(\n1792:                evaluate_validation_bce(\n1823:            reference = evaluate_validation_bce(\n1844:        diagnostics = run_training_epoch(\n1859:        validation = evaluate_validation_bce(\n"
  }
}

Harness context token usage record

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}

exec command

+07:45

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +07:48

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n@@\n                     \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n-                    \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n-                    ),\n+                    \"negative_sampling\": {\n+                        \"uniform\": config.uniform_negatives,\n+                        \"propensity_corrected_inbatch\": config.inbatch_negatives,\n+                        \"item_tower_hard\": config.hard_negatives,\n+                        \"hard_pool\": config.hard_negative_pool_size,\n+                    },\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +07:48

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-435916ee-0429-4315-9c36-82233ece6c16",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2299,3 +2299,3 @@\n             {\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n                 \"stage\": args.stage,\n@@ -2321,5 +2321,8 @@\n                     \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n-                    \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n-                    ),\n+                    \"negative_sampling\": {\n+                        \"uniform\": config.uniform_negatives,\n+                        \"propensity_corrected_inbatch\": config.inbatch_negatives,\n+                        \"item_tower_hard\": config.hard_negatives,\n+                        \"hard_pool\": config.hard_negative_pool_size,\n+                    },\n                     \"false_negative_filter\": config.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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}

exec command

+07:51

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Harness context codex item completed

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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.6 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.6 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "exit_code": 0,
    "duration": {
      "secs": 380,
      "nanos": 885933313
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_sequences.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.6 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"
  }
}

Tool callexecTool call Returned

Tool call · Input +07:52

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,160p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +07:56

Script completed
Wall time 4.2 seconds
Output:

usage: train_current_model.py [-h] [--stage {validation,submission}] --output
                              OUTPUT --public-dir PUBLIC_DIR
                              [--prepared-cache PREPARED_CACHE]
                              [--device {cpu,mps}] [--threads THREADS]
                              [--bptt-steps BPTT_STEPS]
                              [--user-batch-size USER_BATCH_SIZE]
                              [--epochs EPOCHS]
                              [--learning-rate LEARNING_RATE]
                              [--loss-microbatch-size LOSS_MICROBATCH_SIZE]
                              [--uniform-negatives UNIFORM_NEGATIVES]
                              [--inbatch-negatives INBATCH_NEGATIVES]
                              [--hard-negatives HARD_NEGATIVES]
                              [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]
                              [--negative-filter-scope {full-training-history,last-100}]
                              [--target-weighting {discovery-graded,organic-strong,uniform}]
                              [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]
                              [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]
                              [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]
                              [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]
                              [--validation-fraction VALIDATION_FRACTION]
                              [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]
                              [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]
                              [--early-stopping-patience EARLY_STOPPING_PATIENCE]
                              [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]
                              [--checkpoint-dir CHECKPOINT_DIR]
                              [--resume-checkpoint RESUME_CHECKPOINT]
                              [--seed SEED]
                              [--ranking-batch-size RANKING_BATCH_SIZE]
                              [--prepare-only]

Train the Yambda windowed two-tower retriever. Every catalog listen with at
least 50 percent completion is a sequence token. Within each selected
chronological window, every adjacent target contributes a loss. The fast
default retains each user's most recent 128 training targets and 16 held-out
validation targets; passing zero for either cap restores the uncapped behavior
for that side of the split. A 100-step truncated-BPTT block computes all of
its timestep losses and carries the detached GRU hidden state into the next
block. The model is a pure two-tower retriever: no user-ID embedding, no
output normalization, raw dot-product retrieval, and sample-weighted binary
cross entropy. After a one-epoch warm-up, the negative mix combines uniform,
propensity-corrected in-batch, and item-tower-neighbor hard negatives while
keeping the production total of 32 negatives per target. Every selected
adjacent target retains a nonzero weight. Only files under the supplied public
directory are read.

options:
  -h, --help            show this help message and exit
  --stage {validation,submission}
  --output OUTPUT
  --public-dir PUBLIC_DIR
  --prepared-cache PREPARED_CACHE
  --device {cpu,mps}
  --threads THREADS
  --bptt-steps BPTT_STEPS
  --user-batch-size USER_BATCH_SIZE
  --epochs EPOCHS
  --learning-rate LEARNING_RATE
  --loss-microbatch-size LOSS_MICROBATCH_SIZE
  --uniform-negatives UNIFORM_NEGATIVES
  --inbatch-negatives INBATCH_NEGATIVES
  --hard-negatives HARD_NEGATIVES
  --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE
  --negative-filter-scope {full-training-history,last-100}
  --target-weighting {discovery-graded,organic-strong,uniform}
  --ordinary-target-weight ORDINARY_TARGET_WEIGHT
  --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT
  --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT
  --discovery-horizon-days DISCOVERY_HORIZON_DAYS
  --validation-fraction VALIDATION_FRACTION
  --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER
                        retain this many recent pre-validation targets per
                        user; 0 is uncapped
  --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER
                        retain this many final validation targets per user; 0
                        is uncapped
  --early-stopping-patience EARLY_STOPPING_PATIENCE
  --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA
  --checkpoint-dir CHECKPOINT_DIR
  --resume-checkpoint RESUME_CHECKPOINT
  --seed SEED
  --ranking-batch-size RANKING_BATCH_SIZE
  --prepare-only

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bc1676e087d2b9538c3c1269238e",
  "usage": {
    "input_tokens": 71677,
    "cached_input_tokens": 71296,
    "cache_write_input_tokens": 0,
    "output_tokens": 122,
    "reasoning_output_tokens": 16,
    "total_tokens": 71799
  },
  "turn_token_usage": {
    "input_tokens": 1859390,
    "cached_input_tokens": 1792512,
    "cache_write_input_tokens": 0,
    "output_tokens": 15189,
    "reasoning_output_tokens": 7968,
    "total_tokens": 1874579
  },
  "thread_token_usage": {
    "input_tokens": 1859390,
    "cached_input_tokens": 1792512,
    "cache_write_input_tokens": 0,
    "output_tokens": 15189,
    "reasoning_output_tokens": 7968,
    "total_tokens": 1874579
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-00356ba2-8b8d-4af1-87a4-a9a8f9237fda",
    "process_id": "92247",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,160p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,160p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n                              OUTPUT --public-dir PUBLIC_DIR\n                              [--prepared-cache PREPARED_CACHE]\n                              [--device {cpu,mps}] [--threads THREADS]\n                              [--bptt-steps BPTT_STEPS]\n                              [--user-batch-size USER_BATCH_SIZE]\n                              [--epochs EPOCHS]\n                              [--learning-rate LEARNING_RATE]\n                              [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n                              [--uniform-negatives UNIFORM_NEGATIVES]\n                              [--inbatch-negatives INBATCH_NEGATIVES]\n                              [--hard-negatives HARD_NEGATIVES]\n                              [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n                              [--negative-filter-scope {full-training-history,last-100}]\n                              [--target-weighting {discovery-graded,organic-strong,uniform}]\n                              [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n                              [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n                              [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n                              [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n                              [--validation-fraction VALIDATION_FRACTION]\n                              [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n                              [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n                              [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n                              [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n                              [--checkpoint-dir CHECKPOINT_DIR]\n                              [--resume-checkpoint RESUME_CHECKPOINT]\n                              [--seed SEED]\n                              [--ranking-batch-size RANKING_BATCH_SIZE]\n                              [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n  -h, --help            show this help message and exit\n  --stage {validation,submission}\n  --output OUTPUT\n  --public-dir PUBLIC_DIR\n  --prepared-cache PREPARED_CACHE\n  --device {cpu,mps}\n  --threads THREADS\n  --bptt-steps BPTT_STEPS\n  --user-batch-size USER_BATCH_SIZE\n  --epochs EPOCHS\n  --learning-rate LEARNING_RATE\n  --loss-microbatch-size LOSS_MICROBATCH_SIZE\n  --uniform-negatives UNIFORM_NEGATIVES\n  --inbatch-negatives INBATCH_NEGATIVES\n  --hard-negatives HARD_NEGATIVES\n  --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n  --negative-filter-scope {full-training-history,last-100}\n  --target-weighting {discovery-graded,organic-strong,uniform}\n  --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n  --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n  --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n  --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n  --validation-fraction VALIDATION_FRACTION\n  --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n                        retain this many recent pre-validation targets per\n                        user; 0 is uncapped\n  --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n                        retain this many final validation targets per user; 0\n                        is uncapped\n  --early-stopping-patience EARLY_STOPPING_PATIENCE\n  --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n  --checkpoint-dir CHECKPOINT_DIR\n  --resume-checkpoint RESUME_CHECKPOINT\n  --seed SEED\n  --ranking-batch-size RANKING_BATCH_SIZE\n  --prepare-only\n",
    "stderr": "",
    "aggregated_output": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n                              OUTPUT --public-dir PUBLIC_DIR\n                              [--prepared-cache PREPARED_CACHE]\n                              [--device {cpu,mps}] [--threads THREADS]\n                              [--bptt-steps BPTT_STEPS]\n                              [--user-batch-size USER_BATCH_SIZE]\n                              [--epochs EPOCHS]\n                              [--learning-rate LEARNING_RATE]\n                              [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n                              [--uniform-negatives UNIFORM_NEGATIVES]\n                              [--inbatch-negatives INBATCH_NEGATIVES]\n                              [--hard-negatives HARD_NEGATIVES]\n                              [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n                              [--negative-filter-scope {full-training-history,last-100}]\n                              [--target-weighting {discovery-graded,organic-strong,uniform}]\n                              [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n                              [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n                              [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n                              [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n                              [--validation-fraction VALIDATION_FRACTION]\n                              [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n                              [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n                              [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n                              [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n                              [--checkpoint-dir CHECKPOINT_DIR]\n                              [--resume-checkpoint RESUME_CHECKPOINT]\n                              [--seed SEED]\n                              [--ranking-batch-size RANKING_BATCH_SIZE]\n                              [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n  -h, --help            show this help message and exit\n  --stage {validation,submission}\n  --output OUTPUT\n  --public-dir PUBLIC_DIR\n  --prepared-cache PREPARED_CACHE\n  --device {cpu,mps}\n  --threads THREADS\n  --bptt-steps BPTT_STEPS\n  --user-batch-size USER_BATCH_SIZE\n  --epochs EPOCHS\n  --learning-rate LEARNING_RATE\n  --loss-microbatch-size LOSS_MICROBATCH_SIZE\n  --uniform-negatives UNIFORM_NEGATIVES\n  --inbatch-negatives INBATCH_NEGATIVES\n  --hard-negatives HARD_NEGATIVES\n  --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n  --negative-filter-scope {full-training-history,last-100}\n  --target-weighting {discovery-graded,organic-strong,uniform}\n  --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n  --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n  --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n  --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n  --validation-fraction VALIDATION_FRACTION\n  --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n                        retain this many recent pre-validation targets per\n                        user; 0 is uncapped\n  --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n                        retain this many final validation targets per user; 0\n                        is uncapped\n  --early-stopping-patience EARLY_STOPPING_PATIENCE\n  --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n  --checkpoint-dir CHECKPOINT_DIR\n  --resume-checkpoint RESUME_CHECKPOINT\n  --seed SEED\n  --ranking-batch-size RANKING_BATCH_SIZE\n  --prepare-only\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 32217844
    },
    "formatted_output": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n                              OUTPUT --public-dir PUBLIC_DIR\n                              [--prepared-cache PREPARED_CACHE]\n                              [--device {cpu,mps}] [--threads THREADS]\n                              [--bptt-steps BPTT_STEPS]\n                              [--user-batch-size USER_BATCH_SIZE]\n                              [--epochs EPOCHS]\n                              [--learning-rate LEARNING_RATE]\n                              [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n                              [--uniform-negatives UNIFORM_NEGATIVES]\n                              [--inbatch-negatives INBATCH_NEGATIVES]\n                              [--hard-negatives HARD_NEGATIVES]\n                              [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n                              [--negative-filter-scope {full-training-history,last-100}]\n                              [--target-weighting {discovery-graded,organic-strong,uniform}]\n                              [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n                              [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n                              [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n                              [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n                              [--validation-fraction VALIDATION_FRACTION]\n                              [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n                              [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n                              [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n                              [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n                              [--checkpoint-dir CHECKPOINT_DIR]\n                              [--resume-checkpoint RESUME_CHECKPOINT]\n                              [--seed SEED]\n                              [--ranking-batch-size RANKING_BATCH_SIZE]\n                              [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n  -h, --help            show this help message and exit\n  --stage {validation,submission}\n  --output OUTPUT\n  --public-dir PUBLIC_DIR\n  --prepared-cache PREPARED_CACHE\n  --device {cpu,mps}\n  --threads THREADS\n  --bptt-steps BPTT_STEPS\n  --user-batch-size USER_BATCH_SIZE\n  --epochs EPOCHS\n  --learning-rate LEARNING_RATE\n  --loss-microbatch-size LOSS_MICROBATCH_SIZE\n  --uniform-negatives UNIFORM_NEGATIVES\n  --inbatch-negatives INBATCH_NEGATIVES\n  --hard-negatives HARD_NEGATIVES\n  --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n  --negative-filter-scope {full-training-history,last-100}\n  --target-weighting {discovery-graded,organic-strong,uniform}\n  --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n  --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n  --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n  --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n  --validation-fraction VALIDATION_FRACTION\n  --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n                        retain this many recent pre-validation targets per\n                        user; 0 is uncapped\n  --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n                        retain this many final validation targets per user; 0\n                        is uncapped\n  --early-stopping-patience EARLY_STOPPING_PATIENCE\n  --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n  --checkpoint-dir CHECKPOINT_DIR\n  --resume-checkpoint RESUME_CHECKPOINT\n  --seed SEED\n  --ranking-batch-size RANKING_BATCH_SIZE\n  --prepare-only\n"
  }
}

exec command

+07:59

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +08:00

const r = await tools.write_stdin({"session_id":74144,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +08:00

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"243e5a","wall_time_seconds":0.00000225,"exit_code":0,"original_token_count":155,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.6 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

Harness context token usage record

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  }
}

exec command

+08:02

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +08:04

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet && sed -n '1,260p' /app/solution/production_validation_run.json && ls -lh /app/solution/baseline_checkpoints","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +08:07

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/production_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 376.675,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.539838048100336,
      "positive_logit": 0.7427799626849259,
      "negative_logit": -0.6222247025677434,
      "pair_accuracy": 0.7547733612041563,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 218.5692425829999,
      "validation_loss": 0.4966362165382877,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 17.11805218400002
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.603016972541809,
    "query_norm_std": 0.7697497606277466,
    "item_norm_mean": 1.7570281028747559,
    "item_norm_std": 0.8279485702514648
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
total 289M
-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt
-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-238036ff-81c4-4a31-b946-97661394c632",
    "process_id": "84134",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet && sed -n '1,260p' /app/solution/production_validation_run.json && ls -lh /app/solution/baseline_checkpoints"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet && sed -n '1,260p' /app/solution/production_validation_run.json && ls -lh /app/solution/baseline_checkpoints"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 376.675,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_loss\": 0.4966362165382877,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 17.11805218400002\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 376.675,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_loss\": 0.4966362165382877,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 17.11805218400002\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 197006522
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 376.675,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_loss\": 0.4966362165382877,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 17.11805218400002\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n"
  }
}

exec command

+08:11

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The exact public baseline is 0.021280 NDCG@10 over 4,263 scored users. I’ve preserved its ranking and checkpoint. The first controlled experiment will warm-start that exact model and change only the negative mix: 8 uniform, 8 corrected in-batch, and 16 item-tower-neighbor negatives drawn from each positive’s top-64 confusable items.

Tool callexecTool call Returned

Tool call · Input +08:15

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/hard_checkpoints --output /app/solution/hard_validation.parquet > /app/solution/hard_validation_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +08:16

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c27bd2","wall_time_seconds":1.001959384,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

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}

exec command

+08:18

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +08:19

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +08:24

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"024e1f","wall_time_seconds":5.001871626,"session_id":71321,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Harness context token usage record

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}

exec command

+08:25

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +08:26

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +08:31

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"807658","wall_time_seconds":5.000906454,"session_id":71321,"original_token_count":107,"output":"mined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\n"}

Harness context token usage record

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exec command

+08:33

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +08:34

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +08:39

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"f71153","wall_time_seconds":5.002023595,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

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}

exec command

+08:51

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +08:58

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nhits=[]\nfor uid,item,rank in r.itertuples(index=False,name=None):\n gr=g.get(int(uid),{}).get(int(item),0)\n if gr: hits.append((int(uid),int(item),int(rank),gr))\nh=pd.DataFrame(hits,columns=['uid','item','rank','grade'])\nprint('top100 hits',len(h),'users',h.uid.nunique(),'top10 hits',len(h[h['rank']<=10]),'top10 users',h[h['rank']<=10].uid.nunique())\nprint(h.groupby('grade')['rank'].agg(['count','mean','median']).to_string())\nprint('grade top10',h[h['rank']<=10].grade.value_counts().to_dict())\n# candidate global popularity rank recall among truth and ranked\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id,cat.popularity_rank))\nh['poprank']=[pr[x] for x in h.item]\nprint('hit poprank',h.groupby('grade').poprank.median().to_dict())\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":8000});
text(r.output);

Tool response +09:02

Script completed
Wall time 3.9 seconds
Output:

top100 hits 2619 users 1079 top10 hits 644 top10 users 378
       count       mean  median
grade                          
1       2314  37.986603    32.0
3        305  38.062295    32.0
grade top10 {1: 564, 3: 80}
hit poprank {1: 312.5, 3: 618.0}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-62d5aab7-83e0-4bed-9f7d-c0b56119257d",
    "process_id": "72033",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nhits=[]\nfor uid,item,rank in r.itertuples(index=False,name=None):\n gr=g.get(int(uid),{}).get(int(item),0)\n if gr: hits.append((int(uid),int(item),int(rank),gr))\nh=pd.DataFrame(hits,columns=['uid','item','rank','grade'])\nprint('top100 hits',len(h),'users',h.uid.nunique(),'top10 hits',len(h[h['rank']<=10]),'top10 users',h[h['rank']<=10].uid.nunique())\nprint(h.groupby('grade')['rank'].agg(['count','mean','median']).to_string())\nprint('grade top10',h[h['rank']<=10].grade.value_counts().to_dict())\n# candidate global popularity rank recall among truth and ranked\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id,cat.popularity_rank))\nh['poprank']=[pr[x] for x in h.item]\nprint('hit poprank',h.groupby('grade').poprank.median().to_dict())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nhits=[]\nfor uid,item,rank in r.itertuples(index=False,name=None):\n gr=g.get(int(uid),{}).get(int(item),0)\n if gr: hits.append((int(uid),int(item),int(rank),gr))\nh=pd.DataFrame(hits,columns=['uid','item','rank','grade'])\nprint('top100 hits',len(h),'users',h.uid.nunique(),'top10 hits',len(h[h['rank']<=10]),'top10 users',h[h['rank']<=10].uid.nunique())\nprint(h.groupby('grade')['rank'].agg(['count','mean','median']).to_string())\nprint('grade top10',h[h['rank']<=10].grade.value_counts().to_dict())\n# candidate global popularity rank recall among truth and ranked\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id,cat.popularity_rank))\nh['poprank']=[pr[x] for x in h.item]\nprint('hit poprank',h.groupby('grade').poprank.median().to_dict())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "top100 hits 2619 users 1079 top10 hits 644 top10 users 378\n       count       mean  median\ngrade                          \n1       2314  37.986603    32.0\n3        305  38.062295    32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n",
    "stderr": "",
    "aggregated_output": "top100 hits 2619 users 1079 top10 hits 644 top10 users 378\n       count       mean  median\ngrade                          \n1       2314  37.986603    32.0\n3        305  38.062295    32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 682882347
    },
    "formatted_output": "top100 hits 2619 users 1079 top10 hits 644 top10 users 378\n       count       mean  median\ngrade                          \n1       2314  37.986603    32.0\n3        305  38.062295    32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n"
  }
}

exec command

+09:08

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +09:13

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     for start in range(0, n_items, score_block):\n@@\n         lookup[start:end] = torch.topk(\n             scores, k=pool_size, dim=1, sorted=True\n         ).indices.numpy().astype(np.uint16, copy=False)\n+    sampled = lookup[:, :: max(pool_size // 8, 1)].reshape(-1).astype(np.int64)\n     log(\n         f\"mined item-tower hard-negative lookup: items={n_items:,} \"\n-        f\"pool={pool_size} seconds={time.monotonic() - started:.1f}\"\n+        f\"pool={pool_size} unique={len(np.unique(lookup)):,} \"\n+        f\"sampled_popularity_rank_median={float(np.median(sampled + 1)):.0f} \"\n+        f\"seconds={time.monotonic() - started:.1f}\"\n     )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +09:13

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-a5a4be26-9777-4930-bf18-01552e032abd",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -618,5 +618,8 @@\n         ).indices.numpy().astype(np.uint16, copy=False)\n+    sampled = lookup[:, :: max(pool_size // 8, 1)].reshape(-1).astype(np.int64)\n     log(\n         f\"mined item-tower hard-negative lookup: items={n_items:,} \"\n-        f\"pool={pool_size} seconds={time.monotonic() - started:.1f}\"\n+        f\"pool={pool_size} unique={len(np.unique(lookup)):,} \"\n+        f\"sampled_popularity_rank_median={float(np.median(sampled + 1)):.0f} \"\n+        f\"seconds={time.monotonic() - started:.1f}\"\n     )\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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    "cache_write_input_tokens": 0,
    "output_tokens": 17370,
    "reasoning_output_tokens": 8974,
    "total_tokens": 2474126
  }
}

exec command

+09:17

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +09:17

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +09:22

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"176898","wall_time_seconds":5.001526965,"session_id":71321,"original_token_count":14,"output":"resumed validation-BCE training: epoch=1 loss=0.749658\n"}

Harness context token usage record

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  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
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}

exec command

+09:25

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +09:28

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank)); r=pd.read_parquet('/app/solution/production_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\nfor k in [1,10,100]:\n x=p[r['rank'].to_numpy()<=k]; print(k, 'poprank q',np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int), 'unique',len(np.unique(x)))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +09:31

Script completed
Wall time 2.4 seconds
Output:

1 poprank q [    1     4     4    22  2395  7317 29868] unique 161
10 poprank q [    1     5    14   111  2124  7317 29868] unique 592
100 poprank q [    1    33   102   413  2627  9774 29992] unique 2575

Harness context token usage record

{
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  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2a8cdf90-09a1-4628-9521-4e9b290660df",
    "process_id": "60781",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank)); r=pd.read_parquet('/app/solution/production_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\nfor k in [1,10,100]:\n x=p[r['rank'].to_numpy()<=k]; print(k, 'poprank q',np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int), 'unique',len(np.unique(x)))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank)); r=pd.read_parquet('/app/solution/production_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\nfor k in [1,10,100]:\n x=p[r['rank'].to_numpy()<=k]; print(k, 'poprank q',np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int), 'unique',len(np.unique(x)))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "1 poprank q [    1     4     4    22  2395  7317 29868] unique 161\n10 poprank q [    1     5    14   111  2124  7317 29868] unique 592\n100 poprank q [    1    33   102   413  2627  9774 29992] unique 2575\n",
    "stderr": "",
    "aggregated_output": "1 poprank q [    1     4     4    22  2395  7317 29868] unique 161\n10 poprank q [    1     5    14   111  2124  7317 29868] unique 592\n100 poprank q [    1    33   102   413  2627  9774 29992] unique 2575\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 195806433
    },
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  }
}

Agent message

+09:45

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

exec command

+09:47

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

While the hard-negative epoch trains, I’m placing a schema- and eligibility-valid popularity fallback at the required submission path. It is only deadline insurance and will be replaced by the refitted two-tower ranking once the winning objective is selected.

Tool callexecTool call Returned

Tool call · Input +09:56

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nout=Path('/app/solution/submission.parquet')\nwith np.load('/task/data/test_eligible_candidates.npz',allow_pickle=False) as z:\n    uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\nrows=[]\nfor row,uid in enumerate(uids):\n    eligible=np.unpackbits(packed[row],count=len(items),bitorder='little').astype(bool)\n    chosen=np.flatnonzero(eligible)[:100]\n    rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(chosen,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False,compression='zstd')\nprint(out, len(rows), len(uids))\nPY\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); f=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print({'rows':len(f),'users':f.uid.nunique(),'eligible':True})\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +10:03

Script completed
Wall time 7.3 seconds
Output:

/app/solution/submission.parquet 845600 8456
{'rows': 845600, 'users': 8456, 'eligible': True}

Harness context token usage record

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Harness context codex item completed

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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nout=Path('/app/solution/submission.parquet')\nwith np.load('/task/data/test_eligible_candidates.npz',allow_pickle=False) as z:\n    uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\nrows=[]\nfor row,uid in enumerate(uids):\n    eligible=np.unpackbits(packed[row],count=len(items),bitorder='little').astype(bool)\n    chosen=np.flatnonzero(eligible)[:100]\n    rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(chosen,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False,compression='zstd')\nprint(out, len(rows), len(uids))\nPY\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); f=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print({'rows':len(f),'users':f.uid.nunique(),'eligible':True})\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nout=Path('/app/solution/submission.parquet')\nwith np.load('/task/data/test_eligible_candidates.npz',allow_pickle=False) as z:\n    uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\nrows=[]\nfor row,uid in enumerate(uids):\n    eligible=np.unpackbits(packed[row],count=len(items),bitorder='little').astype(bool)\n    chosen=np.flatnonzero(eligible)[:100]\n    rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(chosen,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False,compression='zstd')\nprint(out, len(rows), len(uids))\nPY\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); f=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print({'rows':len(f),'users':f.uid.nunique(),'eligible':True})\nPY"
      }
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    "stderr": "",
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}

exec command

+10:05

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +10:06

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +10:11

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"05b90a","wall_time_seconds":5.001427367,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+10:25

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +10:25

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +10:30

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"9248a1","wall_time_seconds":5.001960591,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+10:33

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +10:37

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/validation_eligible_candidates.npz',allow_pickle=False) as z:\n u=z['uids']; items=z['item_ids']; p=z['packed_eligible']\n rows=[]\n for r,uid in enumerate(u):\n  ok=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\n  ix=np.flatnonzero(ok)[:100]\n  rows.extend((int(uid),int(items[i]),j) for j,i in enumerate(ix,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\nPYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +10:46

Script completed
Wall time 8.6 seconds
Output:

{
  "model": "/app/solution/popularity_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}

Harness context token usage record

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Harness context codex item completed

{
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    "type": "CommandExecution",
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      "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/validation_eligible_candidates.npz',allow_pickle=False) as z:\n u=z['uids']; items=z['item_ids']; p=z['packed_eligible']\n rows=[]\n for r,uid in enumerate(u):\n  ok=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\n  ix=np.flatnonzero(ok)[:100]\n  rows.extend((int(uid),int(items[i]),j) for j,i in enumerate(ix,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\nPYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet"
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 8,
      "nanos": 325045177
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

Agent message

+10:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

exec command

+11:00

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The preserved baseline also shows the expected failure mode: its top-10 recommendations have median catalog popularity rank 111, whereas public relevant items have median rank around 5,500. The hard pool is designed to make those high-scoring distractors contribute gradient instead of letting easy uniform tail negatives dominate. I’m also preparing a pairwise variant so the same mined examples can train the ordering directly if pointwise BCE plateaus.

Tool callexecTool call Returned

Tool call · Input +11:09

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     hard_negatives: int = 16\n     hard_negative_pool_size: int = 64\n+    loss_objective: str = \"pairwise\"\n@@\n def corrected_negative_loss(\n     logits: torch.Tensor,\n     correction: torch.Tensor,\n     config: Config,\n+    positive_logits: torch.Tensor | None = None,\n ) -> torch.Tensor:\n-    raw = F.softplus(logits)\n+    raw = F.softplus(\n+        logits\n+        if positive_logits is None\n+        else logits - positive_logits[:, None]\n+    )\n@@\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n-        )\n+        if config.loss_objective == \"pointwise-bce\":\n+            positive_loss = F.softplus(-positive_logits)\n+            negative_loss = corrected_negative_loss(\n+                negative_logits, negative_weights[begin:end], config\n+            )\n+            row_loss = (positive_loss + negative_loss) / 2.0\n+        elif config.loss_objective == \"pairwise\":\n+            row_loss = corrected_negative_loss(\n+                negative_logits,\n+                negative_weights[begin:end],\n+                config,\n+                positive_logits,\n+            )\n+        else:\n+            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n@@\n-        loss_sum = (\n-            selected_weights * (positive_loss + negative_loss) / 2.0\n-        ).sum()\n+        loss_sum = (selected_weights * row_loss).sum()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +11:09

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -61,2 +61,3 @@\n     hard_negative_pool_size: int = 64\n+    loss_objective: str = \"pairwise\"\n     inbatch_correction_power: float = 0.5\n@@ -931,4 +932,9 @@\n     config: Config,\n+    positive_logits: torch.Tensor | None = None,\n ) -> torch.Tensor:\n-    raw = F.softplus(logits)\n+    raw = F.softplus(\n+        logits\n+        if positive_logits is None\n+        else logits - positive_logits[:, None]\n+    )\n     total = (\n@@ -1266,6 +1272,17 @@\n         )\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n-        )\n+        if config.loss_objective == \"pointwise-bce\":\n+            positive_loss = F.softplus(-positive_logits)\n+            negative_loss = corrected_negative_loss(\n+                negative_logits, negative_weights[begin:end], config\n+            )\n+            row_loss = (positive_loss + negative_loss) / 2.0\n+        elif config.loss_objective == \"pairwise\":\n+            row_loss = corrected_negative_loss(\n+                negative_logits,\n+                negative_weights[begin:end],\n+                config,\n+                positive_logits,\n+            )\n+        else:\n+            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n         selected_weights = torch.from_numpy(\n@@ -1273,5 +1290,3 @@\n         ).to(device)\n-        loss_sum = (\n-            selected_weights * (positive_loss + negative_loss) / 2.0\n-        ).sum()\n+        loss_sum = (selected_weights * row_loss).sum()\n         if not bool(torch.isfinite(loss_sum)):\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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exec command

+11:11

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:14

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     parser.add_argument(\n         \"--hard-negative-pool-size\",\n         type=int,\n         default=Config.hard_negative_pool_size,\n     )\n+    parser.add_argument(\n+        \"--loss-objective\",\n+        choices=(\"pointwise-bce\", \"pairwise\"),\n+        default=Config.loss_objective,\n+    )\n@@\n         hard_negatives=args.hard_negatives,\n         hard_negative_pool_size=args.hard_negative_pool_size,\n+        loss_objective=args.loss_objective,\n         negative_filter_scope=args.negative_filter_scope,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +11:14

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

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    "type": "FileChange",
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    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2130,2 +2130,7 @@\n     parser.add_argument(\n+        \"--loss-objective\",\n+        choices=(\"pointwise-bce\", \"pairwise\"),\n+        default=Config.loss_objective,\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2237,2 +2242,3 @@\n         hard_negative_pool_size=args.hard_negative_pool_size,\n+        loss_objective=args.loss_objective,\n         negative_filter_scope=args.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
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exec command

+11:16

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:20

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n         current_negative_values = (\n@@\n             config.hard_negative_pool_size,\n         )\n+        stored_loss_objective = stored_config.get(\n+            \"loss_objective\", \"pointwise-bce\"\n+        )\n         objective_changed = (\n@@\n             or stored_window_values != current_window_values\n             or stored_negative_values != current_negative_values\n+            or stored_loss_objective != config.loss_objective\n@@\n                 f\"target window {stored_window_values}->{current_window_values}; \"\n                 f\"negative mix {stored_negative_values}->{current_negative_values}; \"\n+                f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n                 \"recomputing the resume checkpoint's validation BCE\"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +11:20

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

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        "type": "update",
        "unified_diff": "@@ -1759,2 +1759,5 @@\n         )\n+        stored_loss_objective = stored_config.get(\n+            \"loss_objective\", \"pointwise-bce\"\n+        )\n         objective_changed = (\n@@ -1765,2 +1768,3 @@\n             or stored_negative_values != current_negative_values\n+            or stored_loss_objective != config.loss_objective\n         )\n@@ -1805,2 +1809,3 @@\n                 f\"negative mix {stored_negative_values}->{current_negative_values}; \"\n+                f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n                 \"recomputing the resume checkpoint's validation BCE\"\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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exec command

+11:22

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:24

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n+                    \"objective\": (\n+                        f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n+                    ),\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +11:24

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

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        "type": "update",
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        "move_path": null
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exec command

+11:30

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:31

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && rg -n \"loss_objective|hard_negative_lookup\" /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":8000});
text(r.output);

Tool response +11:31

Script completed
Wall time 0.2 seconds
Output:

62:    loss_objective: str = "pairwise"
584:def build_hard_negative_lookup(
690:    hard_negative_lookup: np.ndarray | None,
736:        if hard_negative_lookup is None:
746:            pool_size = hard_negative_lookup.shape[1]
754:                hard_negative_lookup[targets_cpu[:, None], columns].astype(
811:    hard_negative_lookup: np.ndarray | None,
826:            hard_negative_lookup,
1273:        if config.loss_objective == "pointwise-bce":
1279:        elif config.loss_objective == "pairwise":
1287:            raise RuntimeError(f"unknown loss objective {config.loss_objective}")
1324:    hard_negative_lookup: np.ndarray | None,
1418:                hard_negative_lookup,
1491:    hard_negative_lookup: np.ndarray | None,
1576:                hard_negative_lookup,
1695:    hard_negative_lookup: np.ndarray | None = None
1760:        stored_loss_objective = stored_config.get(
1761:            "loss_objective", "pointwise-bce"
1769:            or stored_loss_objective != config.loss_objective
1773:            hard_negative_lookup = build_hard_negative_lookup(
1810:                f"loss {stored_loss_objective}->{config.loss_objective}; "
1825:                    hard_negative_lookup,
1839:        if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:
1840:            hard_negative_lookup = build_hard_negative_lookup(
1856:                hard_negative_lookup,
1877:            hard_negative_lookup,
1892:            hard_negative_lookup,
2138:        default=Config.loss_objective,
2248:        loss_objective=args.loss_objective,
2351:                        f"sample-weighted-{config.loss_objective}-at-every-timestep"

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    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
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    "parsed_cmd": [
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "62:    loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690:    hard_negative_lookup: np.ndarray | None,\n736:        if hard_negative_lookup is None:\n746:            pool_size = hard_negative_lookup.shape[1]\n754:                hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811:    hard_negative_lookup: np.ndarray | None,\n826:            hard_negative_lookup,\n1273:        if config.loss_objective == \"pointwise-bce\":\n1279:        elif config.loss_objective == \"pairwise\":\n1287:            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324:    hard_negative_lookup: np.ndarray | None,\n1418:                hard_negative_lookup,\n1491:    hard_negative_lookup: np.ndarray | None,\n1576:                hard_negative_lookup,\n1695:    hard_negative_lookup: np.ndarray | None = None\n1760:        stored_loss_objective = stored_config.get(\n1761:            \"loss_objective\", \"pointwise-bce\"\n1769:            or stored_loss_objective != config.loss_objective\n1773:            hard_negative_lookup = build_hard_negative_lookup(\n1810:                f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825:                    hard_negative_lookup,\n1839:        if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840:            hard_negative_lookup = build_hard_negative_lookup(\n1856:                hard_negative_lookup,\n1877:            hard_negative_lookup,\n1892:            hard_negative_lookup,\n2138:        default=Config.loss_objective,\n2248:        loss_objective=args.loss_objective,\n2351:                        f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n",
    "stderr": "",
    "aggregated_output": "62:    loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690:    hard_negative_lookup: np.ndarray | None,\n736:        if hard_negative_lookup is None:\n746:            pool_size = hard_negative_lookup.shape[1]\n754:                hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811:    hard_negative_lookup: np.ndarray | None,\n826:            hard_negative_lookup,\n1273:        if config.loss_objective == \"pointwise-bce\":\n1279:        elif config.loss_objective == \"pairwise\":\n1287:            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324:    hard_negative_lookup: np.ndarray | None,\n1418:                hard_negative_lookup,\n1491:    hard_negative_lookup: np.ndarray | None,\n1576:                hard_negative_lookup,\n1695:    hard_negative_lookup: np.ndarray | None = None\n1760:        stored_loss_objective = stored_config.get(\n1761:            \"loss_objective\", \"pointwise-bce\"\n1769:            or stored_loss_objective != config.loss_objective\n1773:            hard_negative_lookup = build_hard_negative_lookup(\n1810:                f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825:                    hard_negative_lookup,\n1839:        if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840:            hard_negative_lookup = build_hard_negative_lookup(\n1856:                hard_negative_lookup,\n1877:            hard_negative_lookup,\n1892:            hard_negative_lookup,\n2138:        default=Config.loss_objective,\n2248:        loss_objective=args.loss_objective,\n2351:                        f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n",
    "exit_code": 0,
    "duration": {
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      "nanos": 19526810
    },
    "formatted_output": "62:    loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690:    hard_negative_lookup: np.ndarray | None,\n736:        if hard_negative_lookup is None:\n746:            pool_size = hard_negative_lookup.shape[1]\n754:                hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811:    hard_negative_lookup: np.ndarray | None,\n826:            hard_negative_lookup,\n1273:        if config.loss_objective == \"pointwise-bce\":\n1279:        elif config.loss_objective == \"pairwise\":\n1287:            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324:    hard_negative_lookup: np.ndarray | None,\n1418:                hard_negative_lookup,\n1491:    hard_negative_lookup: np.ndarray | None,\n1576:                hard_negative_lookup,\n1695:    hard_negative_lookup: np.ndarray | None = None\n1760:        stored_loss_objective = stored_config.get(\n1761:            \"loss_objective\", \"pointwise-bce\"\n1769:            or stored_loss_objective != config.loss_objective\n1773:            hard_negative_lookup = build_hard_negative_lookup(\n1810:                f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825:                    hard_negative_lookup,\n1839:        if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840:            hard_negative_lookup = build_hard_negative_lookup(\n1856:                hard_negative_lookup,\n1877:            hard_negative_lookup,\n1892:            hard_negative_lookup,\n2138:        default=Config.loss_objective,\n2248:        loss_objective=args.loss_objective,\n2351:                        f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n"
  }
}

exec command

+11:33

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:34

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +11:39

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"9e4c95","wall_time_seconds":5.001977281,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bcf5400887d2a6bf9ea04352ff85",
  "usage": {
    "input_tokens": 82284,
    "cached_input_tokens": 81408,
    "cache_write_input_tokens": 0,
    "output_tokens": 67,
    "reasoning_output_tokens": 19,
    "total_tokens": 82351
  },
  "turn_token_usage": {
    "input_tokens": 3412612,
    "cached_input_tokens": 3263360,
    "cache_write_input_tokens": 0,
    "output_tokens": 21797,
    "reasoning_output_tokens": 11196,
    "total_tokens": 3434409
  },
  "thread_token_usage": {
    "input_tokens": 3412612,
    "cached_input_tokens": 3263360,
    "cache_write_input_tokens": 0,
    "output_tokens": 21797,
    "reasoning_output_tokens": 11196,
    "total_tokens": 3434409
  }
}

exec command

+11:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:50

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +11:55

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"d1a659","wall_time_seconds":5.001939831,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bcfcae3c87d283bfa0d7b7e5397f",
  "usage": {
    "input_tokens": 82410,
    "cached_input_tokens": 82048,
    "cache_write_input_tokens": 0,
    "output_tokens": 519,
    "reasoning_output_tokens": 471,
    "total_tokens": 82929
  },
  "turn_token_usage": {
    "input_tokens": 3495022,
    "cached_input_tokens": 3345408,
    "cache_write_input_tokens": 0,
    "output_tokens": 22316,
    "reasoning_output_tokens": 11667,
    "total_tokens": 3517338
  },
  "thread_token_usage": {
    "input_tokens": 3495022,
    "cached_input_tokens": 3345408,
    "cache_write_input_tokens": 0,
    "output_tokens": 22316,
    "reasoning_output_tokens": 11667,
    "total_tokens": 3517338
  }
}

exec command

+11:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +11:58

const r = await tools.exec_command({"cmd":"df -h /app 2>/dev/null || true; du -sh /app/solution","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response +11:58

Script completed
Wall time 0.1 seconds
Output:

Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   16G   81G  17% /
802M	/app/solution

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd0cb6dc87d2bf4af9140c8aa289",
  "usage": {
    "input_tokens": 82987,
    "cached_input_tokens": 82176,
    "cache_write_input_tokens": 0,
    "output_tokens": 133,
    "reasoning_output_tokens": 66,
    "total_tokens": 83120
  },
  "turn_token_usage": {
    "input_tokens": 3578009,
    "cached_input_tokens": 3427584,
    "cache_write_input_tokens": 0,
    "output_tokens": 22449,
    "reasoning_output_tokens": 11733,
    "total_tokens": 3600458
  },
  "thread_token_usage": {
    "input_tokens": 3578009,
    "cached_input_tokens": 3427584,
    "cache_write_input_tokens": 0,
    "output_tokens": 22449,
    "reasoning_output_tokens": 11733,
    "total_tokens": 3600458
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-3f9d6f7d-0977-42ac-a03d-10978ddc51fc",
    "process_id": "81031",
    "command": [
      "/bin/sh",
      "-lc",
      "df -h /app 2>/dev/null || true; du -sh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "df -h /app 2>/dev/null || true; du -sh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   16G   81G  17% /\n802M\t/app/solution\n",
    "stderr": "",
    "aggregated_output": "Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   16G   81G  17% /\n802M\t/app/solution\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4260
    },
    "formatted_output": "Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   16G   81G  17% /\n802M\t/app/solution\n"
  }
}

exec command

+12:00

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:01

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +12:06

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"91bb1d","wall_time_seconds":5.00183554,"session_id":71321,"original_token_count":24,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd105e5087d282fdcc72257c0bf1",
  "usage": {
    "input_tokens": 83180,
    "cached_input_tokens": 82816,
    "cache_write_input_tokens": 0,
    "output_tokens": 72,
    "reasoning_output_tokens": 24,
    "total_tokens": 83252
  },
  "turn_token_usage": {
    "input_tokens": 3661189,
    "cached_input_tokens": 3510400,
    "cache_write_input_tokens": 0,
    "output_tokens": 22521,
    "reasoning_output_tokens": 11757,
    "total_tokens": 3683710
  },
  "thread_token_usage": {
    "input_tokens": 3661189,
    "cached_input_tokens": 3510400,
    "cache_write_input_tokens": 0,
    "output_tokens": 22521,
    "reasoning_output_tokens": 11757,
    "total_tokens": 3683710
  }
}

exec command

+12:08

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:08

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +12:13

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"3f76b1","wall_time_seconds":5.001402589,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd181d4487d2998442464ff3cc56",
  "usage": {
    "input_tokens": 83345,
    "cached_input_tokens": 82944,
    "cache_write_input_tokens": 0,
    "output_tokens": 60,
    "reasoning_output_tokens": 12,
    "total_tokens": 83405
  },
  "turn_token_usage": {
    "input_tokens": 3744534,
    "cached_input_tokens": 3593344,
    "cache_write_input_tokens": 0,
    "output_tokens": 22581,
    "reasoning_output_tokens": 11769,
    "total_tokens": 3767115
  },
  "thread_token_usage": {
    "input_tokens": 3744534,
    "cached_input_tokens": 3593344,
    "cache_write_input_tokens": 0,
    "output_tokens": 22581,
    "reasoning_output_tokens": 11769,
    "total_tokens": 3767115
  }
}

exec command

+12:16

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:16

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +12:21

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"28220e","wall_time_seconds":5.001909972,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd1f6cd087d2b1642fdc2b1d7686",
  "usage": {
    "input_tokens": 83464,
    "cached_input_tokens": 83200,
    "cache_write_input_tokens": 0,
    "output_tokens": 57,
    "reasoning_output_tokens": 9,
    "total_tokens": 83521
  },
  "turn_token_usage": {
    "input_tokens": 3827998,
    "cached_input_tokens": 3676544,
    "cache_write_input_tokens": 0,
    "output_tokens": 22638,
    "reasoning_output_tokens": 11778,
    "total_tokens": 3850636
  },
  "thread_token_usage": {
    "input_tokens": 3827998,
    "cached_input_tokens": 3676544,
    "cache_write_input_tokens": 0,
    "output_tokens": 22638,
    "reasoning_output_tokens": 11778,
    "total_tokens": 3850636
  }
}

exec command

+12:23

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:24

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +12:29

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"4f697e","wall_time_seconds":5.001426667,"session_id":71321,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd277c4487d28108fa961d87dfe1",
  "usage": {
    "input_tokens": 83578,
    "cached_input_tokens": 83328,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 83632
  },
  "turn_token_usage": {
    "input_tokens": 3911576,
    "cached_input_tokens": 3759872,
    "cache_write_input_tokens": 0,
    "output_tokens": 22692,
    "reasoning_output_tokens": 11784,
    "total_tokens": 3934268
  },
  "thread_token_usage": {
    "input_tokens": 3911576,
    "cached_input_tokens": 3759872,
    "cache_write_input_tokens": 0,
    "output_tokens": 22692,
    "reasoning_output_tokens": 11784,
    "total_tokens": 3934268
  }
}

exec command

+12:33

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:34

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +12:39

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"bbc8af","wall_time_seconds":5.001197764,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd2ecedc87d28a986641fe97dc78",
  "usage": {
    "input_tokens": 83778,
    "cached_input_tokens": 83456,
    "cache_write_input_tokens": 0,
    "output_tokens": 83,
    "reasoning_output_tokens": 35,
    "total_tokens": 83861
  },
  "turn_token_usage": {
    "input_tokens": 3995354,
    "cached_input_tokens": 3843328,
    "cache_write_input_tokens": 0,
    "output_tokens": 22775,
    "reasoning_output_tokens": 11819,
    "total_tokens": 4018129
  },
  "thread_token_usage": {
    "input_tokens": 3995354,
    "cached_input_tokens": 3843328,
    "cache_write_input_tokens": 0,
    "output_tokens": 22775,
    "reasoning_output_tokens": 11819,
    "total_tokens": 4018129
  }
}

exec command

+12:44

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:45

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +12:50

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"ad1285","wall_time_seconds":5.001963,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd39544487d281c5882f69130a9a",
  "usage": {
    "input_tokens": 83918,
    "cached_input_tokens": 83584,
    "cache_write_input_tokens": 0,
    "output_tokens": 196,
    "reasoning_output_tokens": 148,
    "total_tokens": 84114
  },
  "turn_token_usage": {
    "input_tokens": 4079272,
    "cached_input_tokens": 3926912,
    "cache_write_input_tokens": 0,
    "output_tokens": 22971,
    "reasoning_output_tokens": 11967,
    "total_tokens": 4102243
  },
  "thread_token_usage": {
    "input_tokens": 4079272,
    "cached_input_tokens": 3926912,
    "cache_write_input_tokens": 0,
    "output_tokens": 22971,
    "reasoning_output_tokens": 11967,
    "total_tokens": 4102243
  }
}

exec command

+12:54

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +12:55

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +13:00

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"c40296","wall_time_seconds":5.002015708,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd43f3f487d29c54258c5c186b42",
  "usage": {
    "input_tokens": 84170,
    "cached_input_tokens": 83712,
    "cache_write_input_tokens": 0,
    "output_tokens": 214,
    "reasoning_output_tokens": 166,
    "total_tokens": 84384
  },
  "turn_token_usage": {
    "input_tokens": 4163442,
    "cached_input_tokens": 4010624,
    "cache_write_input_tokens": 0,
    "output_tokens": 23185,
    "reasoning_output_tokens": 12133,
    "total_tokens": 4186627
  },
  "thread_token_usage": {
    "input_tokens": 4163442,
    "cached_input_tokens": 4010624,
    "cache_write_input_tokens": 0,
    "output_tokens": 23185,
    "reasoning_output_tokens": 12133,
    "total_tokens": 4186627
  }
}

exec command

+13:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +13:03

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +13:08

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"6b96c9","wall_time_seconds":5.001842022,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5bd4e205087d2a4207ca64bd9b53d",
  "usage": {
    "input_tokens": 84441,
    "cached_input_tokens": 83968,
    "cache_write_input_tokens": 0,
    "output_tokens": 104,
    "reasoning_output_tokens": 56,
    "total_tokens": 84545
  },
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exec command

+13:12

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +13:12

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +13:42

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e75ae2","wall_time_seconds":30.000818385,"session_id":71321,"original_token_count":0,"output":""}

Harness context token usage record

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Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
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    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/hard_checkpoints --output /app/solution/hard_validation.parquet > /app/solution/hard_validation_run.json"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nmined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.749658\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\nsaved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nmined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.749658\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\nsaved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "exit_code": 0,
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      "secs": 332,
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    },
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  }
}

Agent message

+13:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The pointwise hard-negative epoch has converged cleanly: its held-out objective fell from 0.74966 to 0.43771, with pair accuracy rising to 0.846. The model is now computing the full public ranking; the public NDCG—not this internal loss—will decide whether it remains the leader.

exec command

+13:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +13:49

const r = await tools.write_stdin({"session_id":71321,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +13:49

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"f0b3df","wall_time_seconds":0.0000023,"exit_code":0,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

Harness context token usage record

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exec command

+13:51

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +13:52

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet && sed -n '1,260p' /app/solution/hard_validation_run.json","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +13:56

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/hard_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.0109,
    "quality_users": 4263
  }
}
{
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  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 328.196,
  "public_inputs_only": true,
  "architecture": {
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    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
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  "config": {
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Harness context codex item completed

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    "source": "unified_exec_startup",
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    "stdout": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.0109,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 328.196,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform\": 8,\n      \"propensity_corrected_inbatch\": 8,\n      \"item_tower_hard\": 16,\n      \"hard_pool\": 64\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 8,\n    \"inbatch_negatives\": 8,\n    \"hard_negatives\": 16,\n    \"hard_negative_pool_size\": 64,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 14.392733400999987,\n      \"validation_loss\": 0.7496576998776704\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.47361474907688017,\n      \"positive_logit\": 0.8771473302352399,\n      \"negative_logit\": -1.7027217965618686,\n      \"pair_accuracy\": 0.8463514931340715,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.8691298199999,\n      \"validation_loss\": 0.43770692986345666,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 13.397850344000062\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5817008018493652,\n    \"query_norm_std\": 0.42488959431648254,\n    \"item_norm_mean\": 2.509742498397827,\n    \"item_norm_std\": 0.9850362539291382\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.0109,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 328.196,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform\": 8,\n      \"propensity_corrected_inbatch\": 8,\n      \"item_tower_hard\": 16,\n      \"hard_pool\": 64\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 8,\n    \"inbatch_negatives\": 8,\n    \"hard_negatives\": 16,\n    \"hard_negative_pool_size\": 64,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 14.392733400999987,\n      \"validation_loss\": 0.7496576998776704\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.47361474907688017,\n      \"positive_logit\": 0.8771473302352399,\n      \"negative_logit\": -1.7027217965618686,\n      \"pair_accuracy\": 0.8463514931340715,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.8691298199999,\n      \"validation_loss\": 0.43770692986345666,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 13.397850344000062\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5817008018493652,\n    \"query_norm_std\": 0.42488959431648254,\n    \"item_norm_mean\": 2.509742498397827,\n    \"item_norm_std\": 0.9850362539291382\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 206733767
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.0109,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 328.196,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform\": 8,\n      \"propensity_corrected_inbatch\": 8,\n      \"item_tower_hard\": 16,\n      \"hard_pool\": 64\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 8,\n    \"inbatch_negatives\": 8,\n    \"hard_negatives\": 16,\n    \"hard_negative_pool_size\": 64,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 14.392733400999987,\n      \"validation_loss\": 0.7496576998776704\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.47361474907688017,\n      \"positive_logit\": 0.8771473302352399,\n      \"negative_logit\": -1.7027217965618686,\n      \"pair_accuracy\": 0.8463514931340715,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.8691298199999,\n      \"validation_loss\": 0.43770692986345666,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 13.397850344000062\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5817008018493652,\n    \"query_norm_std\": 0.42488959431648254,\n    \"item_norm_mean\": 2.509742498397827,\n    \"item_norm_std\": 0.9850362539291382\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}

Agent message

+13:59

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

That first hard-negative design is rejected: public NDCG fell to 0.010900 despite a much better sampled validation loss. Item-to-item nearest neighbors are too semantically close to the positive; treating many as absolute negatives destroys useful collaborative neighborhoods. This is a useful diagnostic—the internal sampled BCE is not a reliable selector when the negative distribution changes. I’m switching to user-query hard mining with a gentler pairwise loss, which penalizes only violations relative to the positive rather than forcing every neighbor’s absolute logit below zero.

Agent message

+14:11

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Agent message

+14:20

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Agent message

+14:29

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

exec command

+14:33

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The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +14:35

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-    uniform_negatives: int = 8\n-    inbatch_negatives: int = 8\n-    hard_negatives: int = 16\n+    uniform_negatives: int = 16\n+    inbatch_negatives: int = 48\n+    hard_negatives: int = 0\n     hard_negative_pool_size: int = 64\n+    query_random_negatives: int = 8\n+    query_hard_negatives: int = 24\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +14:35

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-95a58540-97d6-4ada-b852-135cc93d9ecd",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -57,6 +57,8 @@\n     loss_microbatch_size: int = 65_536\n-    uniform_negatives: int = 8\n-    inbatch_negatives: int = 8\n-    hard_negatives: int = 16\n+    uniform_negatives: int = 16\n+    inbatch_negatives: int = 48\n+    hard_negatives: int = 0\n     hard_negative_pool_size: int = 64\n+    query_random_negatives: int = 8\n+    query_hard_negatives: int = 24\n     loss_objective: str = \"pairwise\"\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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exec command

+14:37

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +14:41

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-entropy.  After a one-epoch warm-up, the negative mix combines uniform,\n-propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n-keeping the production total of 32 negatives per target.  Every selected\n-adjacent target retains a nonzero weight. Only files under the supplied public\n-directory are read.\n+entropy. The improved sampler draws a 64-item mixture of uniform and\n+propensity-corrected in-batch candidates, keeps eight random examples for\n+coverage, and mines the 24 highest-scoring remaining examples per user query.\n+The loss therefore retains the production total of 32 negatives per target.\n+Every selected adjacent target retains a nonzero weight. Only files under the\n+supplied public directory are read.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +14:41

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "type": "codex_item_completed",
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    "type": "FileChange",
    "id": "exec-d036be12-9323-4847-a3c1-ddfa5f93c292",
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        "type": "update",
        "unified_diff": "@@ -13,7 +13,8 @@\n normalization, raw dot-product retrieval, and sample-weighted binary cross\n-entropy.  After a one-epoch warm-up, the negative mix combines uniform,\n-propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n-keeping the production total of 32 negatives per target.  Every selected\n-adjacent target retains a nonzero weight. Only files under the supplied public\n-directory are read.\n+entropy. The improved sampler draws a 64-item mixture of uniform and\n+propensity-corrected in-batch candidates, keeps eight random examples for\n+coverage, and mines the 24 highest-scoring remaining examples per user query.\n+The loss therefore retains the production total of 32 negatives per target.\n+Every selected adjacent target retains a nonzero weight. Only files under the\n+supplied public directory are read.\n \"\"\"\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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exec command

+14:43

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +14:45

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     raw = F.softplus(\n         logits\n         if positive_logits is None\n         else logits - positive_logits[:, None]\n     )\n+    if config.query_hard_negatives:\n+        return (raw * correction).sum(dim=1) / correction.sum(\n+            dim=1\n+        ).clamp_min(1.0e-6)\n     total = (\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +14:45

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-8cab8322-74f3-41a7-8149-58f17d33b575",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -942,2 +942,6 @@\n     )\n+    if config.query_hard_negatives:\n+        return (raw * correction).sum(dim=1) / correction.sum(\n+            dim=1\n+        ).clamp_min(1.0e-6)\n     total = (\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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}

exec command

+14:47

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +14:48

const r = await tools.exec_command({"cmd":"sed -n '1245,1305p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":8000});
text(r.output);

Tool response +14:48

Script completed
Wall time 0.0 seconds
Output:

        "weight_sum": 0.0,
    }
    for begin in range(0, target_count, config.loss_microbatch_size):
        end = min(begin + config.loss_microbatch_size, target_count)
        selected_states = flat_states[begin:end]
        if backward:
            sequence_input = (
                sequence_vectors[begin:end].detach().requires_grad_(True)
            )
        else:
            sequence_input = sequence_vectors[begin:end]
        liked_vectors, disliked_vectors, _ = pool_explicit_states(
            model, prepared, selected_states, device
        )
        counts = prepared.state_counts[selected_states]
        numeric = np.zeros((end - begin, 6), dtype=np.float32)
        numeric[:, :4] = flat_activity[begin:end]
        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
        query_vectors = user_queries(
            model,
            sequence_input,
            liked_vectors,
            disliked_vectors,
            torch.from_numpy(numeric).to(device),
        )
        positive_vectors, negative_vectors = candidate_vectors(
            model,
            positive_items[begin:end],
            negatives[begin:end],
        )
        positive_logits = (query_vectors * positive_vectors).sum(dim=1)
        negative_logits = torch.einsum(
            "bd,bkd->bk", query_vectors, negative_vectors
        )
        if config.loss_objective == "pointwise-bce":
            positive_loss = F.softplus(-positive_logits)
            negative_loss = corrected_negative_loss(
                negative_logits, negative_weights[begin:end], config
            )
            row_loss = (positive_loss + negative_loss) / 2.0
        elif config.loss_objective == "pairwise":
            row_loss = corrected_negative_loss(
                negative_logits,
                negative_weights[begin:end],
                config,
                positive_logits,
            )
        else:
            raise RuntimeError(f"unknown loss objective {config.loss_objective}")
        selected_weights = torch.from_numpy(
            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        loss_sum = (selected_weights * row_loss).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
            sequence_gradient[begin:end] = sequence_input.grad

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-a847c5c5-9b79-4e90-b84d-2c536c74f0f9",
    "process_id": "85359",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1245,1305p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1245,1305p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        if config.loss_objective == \"pointwise-bce\":\n            positive_loss = F.softplus(-positive_logits)\n            negative_loss = corrected_negative_loss(\n                negative_logits, negative_weights[begin:end], config\n            )\n            row_loss = (positive_loss + negative_loss) / 2.0\n        elif config.loss_objective == \"pairwise\":\n            row_loss = corrected_negative_loss(\n                negative_logits,\n                negative_weights[begin:end],\n                config,\n                positive_logits,\n            )\n        else:\n            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (selected_weights * row_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n",
    "stderr": "",
    "aggregated_output": "        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        if config.loss_objective == \"pointwise-bce\":\n            positive_loss = F.softplus(-positive_logits)\n            negative_loss = corrected_negative_loss(\n                negative_logits, negative_weights[begin:end], config\n            )\n            row_loss = (positive_loss + negative_loss) / 2.0\n        elif config.loss_objective == \"pairwise\":\n            row_loss = corrected_negative_loss(\n                negative_logits,\n                negative_weights[begin:end],\n                config,\n                positive_logits,\n            )\n        else:\n            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (selected_weights * row_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6930
    },
    "formatted_output": "        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        if config.loss_objective == \"pointwise-bce\":\n            positive_loss = F.softplus(-positive_logits)\n            negative_loss = corrected_negative_loss(\n                negative_logits, negative_weights[begin:end], config\n            )\n            row_loss = (positive_loss + negative_loss) / 2.0\n        elif config.loss_objective == \"pairwise\":\n            row_loss = corrected_negative_loss(\n                negative_logits,\n                negative_weights[begin:end],\n                config,\n                positive_logits,\n            )\n        else:\n            raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (selected_weights * row_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n"
  }
}

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  }
}

exec command

+14:53

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +14:59

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n+        selected_negative_weights = negative_weights[begin:end]\n+        if config.query_hard_negatives:\n+            random_count = config.query_random_negatives\n+            hard_columns = torch.topk(\n+                negative_logits[:, random_count:],\n+                k=config.query_hard_negatives,\n+                dim=1,\n+                sorted=False,\n+            ).indices + random_count\n+            random_columns = torch.arange(\n+                random_count, device=device\n+            )[None, :].expand(end - begin, -1)\n+            selected_columns = torch.cat(\n+                [random_columns, hard_columns], dim=1\n+            )\n+            negative_logits = negative_logits.gather(1, selected_columns)\n+            selected_negative_weights = selected_negative_weights.gather(\n+                1, selected_columns\n+            )\n         if config.loss_objective == \"pointwise-bce\":\n             positive_loss = F.softplus(-positive_logits)\n             negative_loss = corrected_negative_loss(\n-                negative_logits, negative_weights[begin:end], config\n+                negative_logits, selected_negative_weights, config\n             )\n@@\n                 negative_logits,\n-                negative_weights[begin:end],\n+                selected_negative_weights,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +14:59

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-756c4cdf-2b43-4493-aa54-8dffdc125c99",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1279,2 +1279,21 @@\n         )\n+        selected_negative_weights = negative_weights[begin:end]\n+        if config.query_hard_negatives:\n+            random_count = config.query_random_negatives\n+            hard_columns = torch.topk(\n+                negative_logits[:, random_count:],\n+                k=config.query_hard_negatives,\n+                dim=1,\n+                sorted=False,\n+            ).indices + random_count\n+            random_columns = torch.arange(\n+                random_count, device=device\n+            )[None, :].expand(end - begin, -1)\n+            selected_columns = torch.cat(\n+                [random_columns, hard_columns], dim=1\n+            )\n+            negative_logits = negative_logits.gather(1, selected_columns)\n+            selected_negative_weights = selected_negative_weights.gather(\n+                1, selected_columns\n+            )\n         if config.loss_objective == \"pointwise-bce\":\n@@ -1282,3 +1301,3 @@\n             negative_loss = corrected_negative_loss(\n-                negative_logits, negative_weights[begin:end], config\n+                negative_logits, selected_negative_weights, config\n             )\n@@ -1288,3 +1307,3 @@\n                 negative_logits,\n-                negative_weights[begin:end],\n+                selected_negative_weights,\n                 config,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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}

exec command

+15:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:05

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     negative_count = (\n-        config.uniform_negatives\n-        + config.inbatch_negatives\n-        + config.hard_negatives\n+        config.query_random_negatives + config.query_hard_negatives\n+        if config.query_hard_negatives\n+        else config.uniform_negatives\n+        + config.inbatch_negatives\n+        + config.hard_negatives\n     )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +15:05

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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    "type": "FileChange",
    "id": "exec-f51680bd-9906-4240-91bc-344a0e6a5db2",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1365,3 +1365,5 @@\n     negative_count = (\n-        config.uniform_negatives\n+        config.query_random_negatives + config.query_hard_negatives\n+        if config.query_hard_negatives\n+        else config.uniform_negatives\n         + config.inbatch_negatives\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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100

exec command

+15:09

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:15

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     parser.add_argument(\n         \"--hard-negative-pool-size\",\n         type=int,\n         default=Config.hard_negative_pool_size,\n     )\n+    parser.add_argument(\n+        \"--query-random-negatives\",\n+        type=int,\n+        default=Config.query_random_negatives,\n+    )\n+    parser.add_argument(\n+        \"--query-hard-negatives\",\n+        type=int,\n+        default=Config.query_hard_negatives,\n+    )\n@@\n         or args.hard_negative_pool_size >= 30_000\n+        or args.query_random_negatives < 0\n+        or args.query_hard_negatives < 0\n+        or args.query_random_negatives > args.uniform_negatives\n+        or args.query_hard_negatives\n+        > (\n+            args.uniform_negatives\n+            + args.inbatch_negatives\n+            + args.hard_negatives\n+            - args.query_random_negatives\n+        )\n@@\n         hard_negatives=args.hard_negatives,\n         hard_negative_pool_size=args.hard_negative_pool_size,\n+        query_random_negatives=args.query_random_negatives,\n+        query_hard_negatives=args.query_hard_negatives,\n         loss_objective=args.loss_objective,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +15:15

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-5ea4c2fc-ddeb-45a7-bfda-3d061bc1b6d5",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2163,2 +2163,12 @@\n     parser.add_argument(\n+        \"--query-random-negatives\",\n+        type=int,\n+        default=Config.query_random_negatives,\n+    )\n+    parser.add_argument(\n+        \"--query-hard-negatives\",\n+        type=int,\n+        default=Config.query_hard_negatives,\n+    )\n+    parser.add_argument(\n         \"--loss-objective\",\n@@ -2250,2 +2260,12 @@\n         or args.hard_negative_pool_size >= 30_000\n+        or args.query_random_negatives < 0\n+        or args.query_hard_negatives < 0\n+        or args.query_random_negatives > args.uniform_negatives\n+        or args.query_hard_negatives\n+        > (\n+            args.uniform_negatives\n+            + args.inbatch_negatives\n+            + args.hard_negatives\n+            - args.query_random_negatives\n+        )\n         or (\n@@ -2275,2 +2295,4 @@\n         hard_negative_pool_size=args.hard_negative_pool_size,\n+        query_random_negatives=args.query_random_negatives,\n+        query_hard_negatives=args.query_hard_negatives,\n         loss_objective=args.loss_objective,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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101

exec command

+15:19

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:22

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n         stored_negative_values = (\n             stored_config.get(\"uniform_negatives\", 24),\n             stored_config.get(\"inbatch_negatives\", 8),\n             stored_config.get(\"hard_negatives\", 0),\n             stored_config.get(\"hard_negative_pool_size\", 64),\n+            stored_config.get(\"query_random_negatives\", 0),\n+            stored_config.get(\"query_hard_negatives\", 0),\n         )\n@@\n             config.hard_negatives,\n             config.hard_negative_pool_size,\n+            config.query_random_negatives,\n+            config.query_hard_negatives,\n         )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +15:22

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-2fc9c1df-5e8f-49cc-b6d2-b9292aacf8ca",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1780,2 +1780,4 @@\n             stored_config.get(\"hard_negative_pool_size\", 64),\n+            stored_config.get(\"query_random_negatives\", 0),\n+            stored_config.get(\"query_hard_negatives\", 0),\n         )\n@@ -1786,2 +1788,4 @@\n             config.hard_negative_pool_size,\n+            config.query_random_negatives,\n+            config.query_hard_negatives,\n         )\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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}

102

exec command

+15:24

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:28

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n                     \"negative_sampling\": {\n-                        \"uniform\": config.uniform_negatives,\n-                        \"propensity_corrected_inbatch\": config.inbatch_negatives,\n-                        \"item_tower_hard\": config.hard_negatives,\n-                        \"hard_pool\": config.hard_negative_pool_size,\n+                        \"uniform_pool\": config.uniform_negatives,\n+                        \"propensity_corrected_inbatch_pool\": (\n+                            config.inbatch_negatives\n+                        ),\n+                        \"item_neighbor_pool\": config.hard_negatives,\n+                        \"random_retained\": config.query_random_negatives,\n+                        \"query_hard_retained\": config.query_hard_negatives,\n                     },\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +15:28

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2407,6 +2407,9 @@\n                     \"negative_sampling\": {\n-                        \"uniform\": config.uniform_negatives,\n-                        \"propensity_corrected_inbatch\": config.inbatch_negatives,\n-                        \"item_tower_hard\": config.hard_negatives,\n-                        \"hard_pool\": config.hard_negative_pool_size,\n+                        \"uniform_pool\": config.uniform_negatives,\n+                        \"propensity_corrected_inbatch_pool\": (\n+                            config.inbatch_negatives\n+                        ),\n+                        \"item_neighbor_pool\": config.hard_negatives,\n+                        \"random_retained\": config.query_random_negatives,\n+                        \"query_hard_retained\": config.query_hard_negatives,\n                     },\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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}

103

exec command

+15:32

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:33

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help >/dev/null","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +15:37

Script completed
Wall time 4.1 seconds
Output:

Harness context token usage record

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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help >/dev/null"
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      "nanos": 979313048
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}

104

exec command

+15:45

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:46

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/query_pairwise_checkpoints --output /app/solution/query_pairwise_validation.parquet > /app/solution/query_pairwise_validation_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +15:48

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"a5dd37","wall_time_seconds":1.000754974,"session_id":75600,"original_token_count":0,"output":""}

Harness context token usage record

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}

105

exec command

+15:50

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:51

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +15:56

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"44358b","wall_time_seconds":5.000932994,"session_id":75600,"original_token_count":49,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\n"}

Harness context token usage record

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106

exec command

+15:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +15:58

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +16:03

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"c3b8b2","wall_time_seconds":5.001888686,"session_id":75600,"original_token_count":154,"output":"inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 48, 0, 64, 8, 24); loss pointwise-bce->pairwise; recomputing the resume checkpoint's validation BCE\n"}

Harness context token usage record

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  }
}

107

exec command

+16:06

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +16:07

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +16:37

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"8bfa44","wall_time_seconds":30.000853666,"session_id":75600,"original_token_count":14,"output":"resumed validation-BCE training: epoch=1 loss=0.690025\n"}

Harness context token usage record

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}

108

exec command

+16:39

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +16:42

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank));\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\n print(name,'top10 popularity rank',np.quantile(p[r['rank'].to_numpy()<=10],[.1,.25,.5,.75,.9]).astype(int),'unique',len(np.unique(p[r['rank'].to_numpy()<=10])))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +16:45

Script completed
Wall time 2.5 seconds
Output:

production top10 popularity rank [   5   14  111 2124 7317] unique 592
hard top10 popularity rank [ 195  377 1342 3595 8300] unique 595

Harness context token usage record

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Harness context codex item completed

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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "production top10 popularity rank [   5   14  111 2124 7317] unique 592\nhard top10 popularity rank [ 195  377 1342 3595 8300] unique 595\n",
    "stderr": "",
    "aggregated_output": "production top10 popularity rank [   5   14  111 2124 7317] unique 592\nhard top10 popularity rank [ 195  377 1342 3595 8300] unique 595\n",
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      "secs": 2,
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}

109

exec command

+16:48

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +16:56

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),t)\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); h=[(u,i,k,g.get(u,{}).get(i,0)) for u,i,k in r.itertuples(index=False,name=None) if g.get(u,{}).get(i,0)]; x=pd.DataFrame(h,columns=['u','i','rank','grade']); y=x[x['rank']<=10]; print(name,'top100',len(x),x.u.nunique(),'top10',len(y),y.u.nunique(),y.grade.value_counts().to_dict())\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +16:59

Script completed
Wall time 3.6 seconds
Output:

production top100 2619 1079 top10 644 378 {1: 564, 3: 80}
hard top100 1803 962 top10 357 283 {1: 312, 3: 45}

Harness context token usage record

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Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-d5f55a5b-84f3-4a6f-8652-00a90eb81f3a",
    "process_id": "45349",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),t)\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); h=[(u,i,k,g.get(u,{}).get(i,0)) for u,i,k in r.itertuples(index=False,name=None) if g.get(u,{}).get(i,0)]; x=pd.DataFrame(h,columns=['u','i','rank','grade']); y=x[x['rank']<=10]; print(name,'top100',len(x),x.u.nunique(),'top10',len(y),y.u.nunique(),y.grade.value_counts().to_dict())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),t)\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); h=[(u,i,k,g.get(u,{}).get(i,0)) for u,i,k in r.itertuples(index=False,name=None) if g.get(u,{}).get(i,0)]; x=pd.DataFrame(h,columns=['u','i','rank','grade']); y=x[x['rank']<=10]; print(name,'top100',len(x),x.u.nunique(),'top10',len(y),y.u.nunique(),y.grade.value_counts().to_dict())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "production top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n",
    "stderr": "",
    "aggregated_output": "production top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 423067156
    },
    "formatted_output": "production top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n"
  }
}

110

Agent message

+17:10

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

111

exec command

+17:17

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +17:22

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nfor beta in [-16,-8,-4,-2,-1,0,1,2,4,8,16]:\n ranked={}\n for uid,x in r.groupby('uid',sort=False):\n  s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id]))\n  ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'])\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +17:32

Script completed
Wall time 10.2 seconds
Output:

Harness context token usage record

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  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
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    "output_tokens": 30463,
    "reasoning_output_tokens": 16054,
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  }
}

112

exec command

+17:37

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7824beac-6128-4b49-9c58-fea695309f60",
    "process_id": "78927",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nfor beta in [-16,-8,-4,-2,-1,0,1,2,4,8,16]:\n ranked={}\n for uid,x in r.groupby('uid',sort=False):\n  s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id]))\n  ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nfor beta in [-16,-8,-4,-2,-1,0,1,2,4,8,16]:\n ranked={}\n for uid,x in r.groupby('uid',sort=False):\n  s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id]))\n  ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "-16 0.011951\n-8 0.013554\n-4 0.015507\n-2 0.018381\n-1 0.019763\n0 0.02128\n1 0.021974\n2 0.02239\n4 0.021683\n8 0.020468\n16 0.020423\n",
    "stderr": "",
    "aggregated_output": "-16 0.011951\n-8 0.013554\n-4 0.015507\n-2 0.018381\n-1 0.019763\n0 0.02128\n1 0.021974\n2 0.02239\n4 0.021683\n8 0.020468\n16 0.020423\n",
    "exit_code": 0,
    "duration": {
      "secs": 18,
      "nanos": 296955606
    },
    "formatted_output": "-16 0.011951\n-8 0.013554\n-4 0.015507\n-2 0.018381\n-1 0.019763\n0 0.02128\n1 0.021974\n2 0.02239\n4 0.021683\n8 0.020468\n16 0.020423\n"
  }
}

Tool callexecTool call Returned

Tool call · Input +17:43

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\ngroups=list(r.groupby('uid',sort=False))\nfor beta in [-8,-4,-2,-1,0,1,2,4,8]:\n ranked={}\n for uid,x in groups:\n  vals=np.array([pr[int(i)] for i in x.item_id])\n  s=-x['rank'].to_numpy(float)+beta*np.log(vals)\n  ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g),flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":6000});
text(JSON.stringify(r));

Tool response +17:54

Script completed
Wall time 11.2 seconds
Output:

{"chunk_id":"ccc58f","wall_time_seconds":11.044941463,"exit_code":0,"original_token_count":125,"output":"-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n"}

Harness context token usage record

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-dd8b46b8-8da0-43d6-a954-d13906f7f111",
    "process_id": "62118",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\ngroups=list(r.groupby('uid',sort=False))\nfor beta in [-8,-4,-2,-1,0,1,2,4,8]:\n ranked={}\n for uid,x in groups:\n  vals=np.array([pr[int(i)] for i in x.item_id])\n  s=-x['rank'].to_numpy(float)+beta*np.log(vals)\n  ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g),flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\ngroups=list(r.groupby('uid',sort=False))\nfor beta in [-8,-4,-2,-1,0,1,2,4,8]:\n ranked={}\n for uid,x in groups:\n  vals=np.array([pr[int(i)] for i in x.item_id])\n  s=-x['rank'].to_numpy(float)+beta*np.log(vals)\n  ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g),flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n",
    "stderr": "",
    "aggregated_output": "-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n",
    "exit_code": 0,
    "duration": {
      "secs": 11,
      "nanos": 45157838
    },
    "formatted_output": "-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n"
  }
}

113

exec command

+18:00

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +18:05

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); r=pd.read_parquet('/app/solution/production_validation.parquet'); groups=list(r.groupby('uid',sort=False))\nfor beta in [1.25,1.5,1.75,2,2.25,2.5,2.75,3,3.5]:\n ranked={}\n for uid,x in groups:\n  s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id])); ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'],flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":4000});
text(JSON.stringify(r));

Tool response +18:17

Script completed
Wall time 11.2 seconds
Output:

{"chunk_id":"33e13b","wall_time_seconds":11.014744139,"exit_code":0,"original_token_count":29,"output":"1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n"}

Harness context token usage record

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-23e5bb67-e33b-43c8-9ca7-adf92398e9b6",
    "process_id": "58474",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); r=pd.read_parquet('/app/solution/production_validation.parquet'); groups=list(r.groupby('uid',sort=False))\nfor beta in [1.25,1.5,1.75,2,2.25,2.5,2.75,3,3.5]:\n ranked={}\n for uid,x in groups:\n  s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id])); ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'],flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); r=pd.read_parquet('/app/solution/production_validation.parquet'); groups=list(r.groupby('uid',sort=False))\nfor beta in [1.25,1.5,1.75,2,2.25,2.5,2.75,3,3.5]:\n ranked={}\n for uid,x in groups:\n  s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id])); ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'],flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n",
    "stderr": "",
    "aggregated_output": "1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n",
    "exit_code": 0,
    "duration": {
      "secs": 11,
      "nanos": 15031376
    },
    "formatted_output": "1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n"
  }
}

114

exec command

+18:27

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +18:28

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +18:33

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"b7c9fa","wall_time_seconds":5.001939951,"session_id":75600,"original_token_count":0,"output":""}

Harness context token usage record

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  }
}

115

exec command

+18:37

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +18:37

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +19:07

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"63a532","wall_time_seconds":30.001633423,"session_id":75600,"original_token_count":0,"output":""}

Harness context token usage record

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}

116

Agent message

+19:09

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

A cheap diagnostic confirms the sampler diagnosis independently: mildly demoting globally popular items within the baseline’s existing top 100 raises public NDCG from 0.021280 to 0.022716 (+6.7%). That means the baseline’s easy-negative mix is indeed leaving a popularity shortcut. I’ll use this only as a calibration check; the active experiment is training query-specific hard negatives so the correction remains inside the two-tower objective.

117

exec command

+19:12

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +19:13

const r = await tools.exec_command({"cmd":"ls -lh /app/solution/query_pairwise_checkpoints 2>/dev/null || true; du -sh /app/solution/query_pairwise_checkpoints 2>/dev/null || true","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response +19:14

Script completed
Wall time 0.1 seconds
Output:

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e7d6d593-75df-4cfb-b02c-056fc7c91dba",
    "process_id": "50649",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /app/solution/query_pairwise_checkpoints 2>/dev/null || true; du -sh /app/solution/query_pairwise_checkpoints 2>/dev/null || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -lh /app/solution/query_pairwise_checkpoints 2>/dev/null || true; du -sh /app/solution/query_pairwise_checkpoints 2>/dev/null || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4000
    },
    "formatted_output": ""
  }
}

Harness context token usage record

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118

Agent message

+19:27

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

119

exec command

+19:31

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +19:49

const patch = "*** Begin Patch\n*** Add File: /app/solution/sweep_popularity_bias.py\n+#!/usr/bin/env python3\n+\"\"\"Development-only sweep of a static item-side popularity calibration.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+\n+import train_current_model as trainer\n+import two_tower_model as base\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--checkpoint\", type=Path, required=True)\n+    parser.add_argument(\"--prepared-cache\", type=Path, required=True)\n+    parser.add_argument(\"--public-dir\", type=Path, required=True)\n+    parser.add_argument(\"--output-dir\", type=Path, required=True)\n+    parser.add_argument(\n+        \"--biases\",\n+        type=float,\n+        nargs=\"+\",\n+        default=(0.0, 0.025, 0.05, 0.1, 0.2, 0.3, 0.5),\n+    )\n+    parser.add_argument(\"--threads\", type=int, default=8)\n+    args = parser.parse_args()\n+\n+    torch.set_num_threads(args.threads)\n+    torch.set_num_interop_threads(1)\n+    config = trainer.Config()\n+    prepared = trainer.load_cache(args.prepared_cache, \"validation\", config)\n+    eligibility = trainer.CandidateSet.read(\n+        args.public_dir / \"data\" / \"validation_eligible_candidates.npz\"\n+    )\n+    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n+    model = base.SequentialTwoTower(\n+        prepared.item_features, trainer.model_config(config)\n+    )\n+    model.load_state_dict(payload[\"model_state\"])\n+    model.eval()\n+\n+    with torch.inference_mode():\n+        queries = trainer.inference_queries(\n+            model, prepared, config, torch.device(\"cpu\")\n+        )\n+        item_parts = []\n+        for start in range(0, len(prepared.item_ids), 2_048):\n+            rows = torch.arange(start, min(start + 2_048, len(prepared.item_ids)))\n+            item_parts.append(model.encode_items(rows))\n+        items = torch.cat(item_parts)\n+\n+        selections = {\n+            bias: np.empty((len(eligibility.uids), base.TOP_N), dtype=np.uint16)\n+            for bias in args.biases\n+        }\n+        item_popularity = torch.from_numpy(prepared.item_features[:, 0])\n+        for start in range(0, len(eligibility.uids), 64):\n+            end = min(start + 64, len(eligibility.uids))\n+            raw_scores = queries[start:end] @ items.T\n+            eligible = np.unpackbits(\n+                eligibility.packed[start:end],\n+                axis=1,\n+                count=len(prepared.item_ids),\n+                bitorder=\"little\",\n+            ).astype(bool, copy=False)\n+            mask = torch.from_numpy(eligible)\n+            for bias in args.biases:\n+                scores = raw_scores - float(bias) * item_popularity\n+                scores = scores.masked_fill(~mask, -torch.inf)\n+                selections[bias][start:end] = torch.topk(\n+                    scores, k=base.TOP_N, dim=1\n+                ).indices.numpy().astype(np.uint16)\n+\n+    args.output_dir.mkdir(parents=True, exist_ok=True)\n+    for bias, selected in selections.items():\n+        frame = pd.DataFrame(\n+            {\n+                \"uid\": np.repeat(eligibility.uids, base.TOP_N),\n+                \"item_id\": prepared.item_ids[selected.reshape(-1)],\n+                \"rank\": np.tile(np.arange(1, base.TOP_N + 1), len(eligibility.uids)),\n+            }\n+        )\n+        label = str(bias).replace(\"-\", \"m\").replace(\".\", \"p\")\n+        frame.to_parquet(\n+            args.output_dir / f\"bias_{label}.parquet\",\n+            index=False,\n+            compression=\"zstd\",\n+        )\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +19:50

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-75fd9d27-2d4e-4d67-ba07-dcc343b91dbd",
    "changes": {
      "/app/solution/sweep_popularity_bias.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Development-only sweep of a static item-side popularity calibration.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nimport train_current_model as trainer\nimport two_tower_model as base\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--checkpoint\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--output-dir\", type=Path, required=True)\n    parser.add_argument(\n        \"--biases\",\n        type=float,\n        nargs=\"+\",\n        default=(0.0, 0.025, 0.05, 0.1, 0.2, 0.3, 0.5),\n    )\n    parser.add_argument(\"--threads\", type=int, default=8)\n    args = parser.parse_args()\n\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    config = trainer.Config()\n    prepared = trainer.load_cache(args.prepared_cache, \"validation\", config)\n    eligibility = trainer.CandidateSet.read(\n        args.public_dir / \"data\" / \"validation_eligible_candidates.npz\"\n    )\n    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n    model = base.SequentialTwoTower(\n        prepared.item_features, trainer.model_config(config)\n    )\n    model.load_state_dict(payload[\"model_state\"])\n    model.eval()\n\n    with torch.inference_mode():\n        queries = trainer.inference_queries(\n            model, prepared, config, torch.device(\"cpu\")\n        )\n        item_parts = []\n        for start in range(0, len(prepared.item_ids), 2_048):\n            rows = torch.arange(start, min(start + 2_048, len(prepared.item_ids)))\n            item_parts.append(model.encode_items(rows))\n        items = torch.cat(item_parts)\n\n        selections = {\n            bias: np.empty((len(eligibility.uids), base.TOP_N), dtype=np.uint16)\n            for bias in args.biases\n        }\n        item_popularity = torch.from_numpy(prepared.item_features[:, 0])\n        for start in range(0, len(eligibility.uids), 64):\n            end = min(start + 64, len(eligibility.uids))\n            raw_scores = queries[start:end] @ items.T\n            eligible = np.unpackbits(\n                eligibility.packed[start:end],\n                axis=1,\n                count=len(prepared.item_ids),\n                bitorder=\"little\",\n            ).astype(bool, copy=False)\n            mask = torch.from_numpy(eligible)\n            for bias in args.biases:\n                scores = raw_scores - float(bias) * item_popularity\n                scores = scores.masked_fill(~mask, -torch.inf)\n                selections[bias][start:end] = torch.topk(\n                    scores, k=base.TOP_N, dim=1\n                ).indices.numpy().astype(np.uint16)\n\n    args.output_dir.mkdir(parents=True, exist_ok=True)\n    for bias, selected in selections.items():\n        frame = pd.DataFrame(\n            {\n                \"uid\": np.repeat(eligibility.uids, base.TOP_N),\n                \"item_id\": prepared.item_ids[selected.reshape(-1)],\n                \"rank\": np.tile(np.arange(1, base.TOP_N + 1), len(eligibility.uids)),\n            }\n        )\n        label = str(bias).replace(\"-\", \"m\").replace(\".\", \"p\")\n        frame.to_parquet(\n            args.output_dir / f\"bias_{label}.parquet\",\n            index=False,\n            compression=\"zstd\",\n        )\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/sweep_popularity_bias.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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120

exec command

+19:52

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +19:53

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +20:23

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c8758d","wall_time_seconds":30.00024452,"session_id":75600,"original_token_count":0,"output":""}

Harness context token usage record

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}

121

exec command

+20:27

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +20:28

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +20:58

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"64fa01","wall_time_seconds":30.001358218,"session_id":75600,"original_token_count":24,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.542237 running_bce=0.547834 elapsed=273.1s\n"}

Harness context token usage record

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122

exec command

+21:00

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +21:01

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +21:31

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d8493e","wall_time_seconds":30.001845655,"session_id":75600,"original_token_count":77,"output":"saved epoch checkpoint: /app/solution/query_pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.54652 validation_loss=0.52864 pair_accuracy=0.623 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.0 validation_seconds=17.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.528635\n"}

Harness context token usage record

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123

exec command

+21:33

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +21:34

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +22:04

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b45968","wall_time_seconds":30.00168489,"session_id":75600,"original_token_count":0,"output":""}

Harness context token usage record

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124

exec command

+22:06

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +22:07

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +22:37

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"190994","wall_time_seconds":30.001915164,"session_id":75600,"original_token_count":0,"output":""}

Harness context token usage record

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Harness context codex item completed

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    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 48, 0, 64, 8, 24); loss pointwise-bce->pairwise; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.690025\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.542237 running_bce=0.547834 elapsed=273.1s\nsaved epoch checkpoint: /app/solution/query_pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.54652 validation_loss=0.52864 pair_accuracy=0.623 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.0 validation_seconds=17.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.528635\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
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    "exit_code": 0,
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      "secs": 416,
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    },
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  }
}

125

Agent message

+22:47

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The query-mined pairwise epoch also converged internally (0.6900 → 0.5286) and kept exactly 32 loss negatives from a 64-item pool. Ranking inference is underway. I’m treating the earlier loss/quality mismatch as a hard guardrail: only public NDCG can promote this checkpoint.

126

exec command

+22:47

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +22:47

const r = await tools.write_stdin({"session_id":75600,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +22:47

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"ec469f","wall_time_seconds":0.00000141,"exit_code":0,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

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127

exec command

+22:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +22:51

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/query_pairwise_validation.parquet && sed -n '1,260p' /app/solution/query_pairwise_validation_run.json","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +22:54

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/query_pairwise_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.020273,
    "quality_users": 4263
  }
}
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  "stage": "validation",
  "output": "/app/solution/query_pairwise_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 412.496,
  "public_inputs_only": true,
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    "bptt_hidden_state": "carried-and-detached",
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    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-pairwise-at-every-timestep",
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      "item_neighbor_pool": 0,
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    },
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
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    "organic_like_target_weight": 3.0,
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    "stdout": "{\n  \"model\": \"/app/solution/query_pairwise_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.020273,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/query_pairwise_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 412.496,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-pairwise-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform_pool\": 16,\n      \"propensity_corrected_inbatch_pool\": 48,\n      \"item_neighbor_pool\": 0,\n      \"random_retained\": 8,\n      \"query_hard_retained\": 24\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 16,\n    \"inbatch_negatives\": 48,\n    \"hard_negatives\": 0,\n    \"hard_negative_pool_size\": 64,\n    \"query_random_negatives\": 8,\n    \"query_hard_negatives\": 24,\n    \"loss_objective\": \"pairwise\",\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.539838048100336,\n      \"positive_logit\": 0.7427799626849259,\n      \"negative_logit\": -0.6222247025677434,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.5692425829999,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 18.021699814000158,\n      \"validation_loss\": 0.6900251174726226\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5465189422087784,\n      \"positive_logit\": 0.8022641865617705,\n      \"negative_logit\": 0.13663982700910554,\n      \"pair_accuracy\": 0.6231171999130065,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 288.951905763,\n      \"validation_loss\": 0.5286354844009227,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 17.83157643300001\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.3673927783966064,\n    \"query_norm_std\": 0.5522235631942749,\n    \"item_norm_mean\": 1.6735552549362183,\n    \"item_norm_std\": 0.5771517157554626\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
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  }
}

128

exec command

+23:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Query-aware mining is much safer but still slightly below baseline (0.020273 vs. 0.021280), so it is not promoted. The lesson is that 24 mined examples are too dominant under one full fine-tuning epoch. I’m keeping the baseline checkpoint as leader and testing two conservative corrections next: fewer hard examples at a lower learning rate, and an item-side popularity calibration expressed as part of the two-tower dot product.

Tool callexecTool call Returned

Tool call · Input +23:07

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep --biases 0 0.01 0.02 0.03 0.05 0.075 0.1 0.15 0.2 0.3 0.5","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +23:08

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"a25cee","wall_time_seconds":1.000660067,"session_id":69840,"original_token_count":0,"output":""}

Harness context token usage record

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129

exec command

+23:13

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +23:14

const r = await tools.write_stdin({"session_id":69840,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +23:44

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"8dee5d","wall_time_seconds":30.000720153,"session_id":69840,"original_token_count":17,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n"}

Harness context token usage record

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130

exec command

+23:46

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +23:47

const r = await tools.write_stdin({"session_id":69840,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +24:17

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"acff39","wall_time_seconds":30.001487877,"session_id":69840,"original_token_count":0,"output":""}

Harness context token usage record

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131

exec command

+24:20

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +24:21

const r = await tools.write_stdin({"session_id":69840,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +24:36

Script completed
Wall time 15.6 seconds
Output:

{"chunk_id":"32eff6","wall_time_seconds":15.5412018,"exit_code":0,"original_token_count":0,"output":""}

Harness context token usage record

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Harness context codex item completed

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      }
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  }
}

132

exec command

+24:39

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +24:41

const r = await tools.exec_command({"cmd":"for f in /app/solution/bias_sweep/*.parquet; do PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +25:11

Script completed
Wall time 30.2 seconds
Output:

{
  "model": "/app/solution/bias_sweep/bias_0p0.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/bias_sweep/bias_0p01.parquet",
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    "quality_ndcg@10": 0.02158,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/bias_sweep/bias_0p02.parquet",
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    "quality_ndcg@10": 0.021671,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/bias_sweep/bias_0p03.parquet",
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    "quality_users": 4263
  }
}
{
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    "quality_users": 4263
  }
}
{
  "model": "/app/solution/bias_sweep/bias_0p075.parquet",
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    "quality_ndcg@10": 0.022222,
    "quality_users": 4263
  }
}
{
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    "quality_users": 4263
  }
}
{
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    "quality_users": 4263
  }
}
{
  "model": "/app/solution/bias_sweep/bias_0p2.parquet",
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    "quality_users": 4263
  }
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133

exec command

+25:15

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +25:17

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p3.parquet && PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p5.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response +25:24

Script completed
Wall time 6.7 seconds
Output:

{
  "model": "/app/solution/bias_sweep/bias_0p3.parquet",
  "scores": {
    "quality_ndcg@10": 0.022594,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/bias_sweep/bias_0p5.parquet",
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    "quality_users": 4263
  }
}

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Harness context codex item completed

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134

exec command

+25:27

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The exact dot-product calibration is materially better: subtracting 0.20 × the standardized log-popularity item feature raises public NDCG to 0.023208 (+9.1%) while leaving user computation and indexed retrieval unchanged. This is representable by one appended constant user dimension and one precomputed item dimension. I’m narrowing that coefficient and then testing whether conservative hard-negative fine-tuning adds value beyond it.

Tool callexecTool call Returned

Tool call · Input +25:32

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep_fine --biases 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +25:33

Script completed
Wall time 1.2 seconds
Output:

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135

exec command

+25:37

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +25:38

const r = await tools.write_stdin({"session_id":11678,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +26:08

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"1608b8","wall_time_seconds":30.001231441,"session_id":11678,"original_token_count":17,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n"}

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136

exec command

+26:10

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +26:11

const r = await tools.write_stdin({"session_id":11678,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +26:41

Script completed
Wall time 30.0 seconds
Output:

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Harness context token usage record

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137

exec command

+26:42

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +26:43

const r = await tools.write_stdin({"session_id":11678,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +26:57

Script completed
Wall time 13.9 seconds
Output:

{"chunk_id":"4f020d","wall_time_seconds":13.860632111,"exit_code":0,"original_token_count":0,"output":""}

Harness context token usage record

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    "cache_write_input_tokens": 0,
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}

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-1a4706dd-d43a-4b36-b1ae-2cde1ea7863d",
    "process_id": "11678",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep_fine --biases 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep_fine --biases 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n",
    "exit_code": 0,
    "duration": {
      "secs": 85,
      "nanos": 381134306
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n"
  }
}

138

exec command

+27:01

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +27:05

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nfor f in sorted(Path('/app/solution/bias_sweep_fine').glob('*.parquet')):\n x=pd.read_parquet(f).sort_values(['uid','rank']); ranked={int(u):g.item_id.astype(int).tolist() for u,g in x.groupby('uid',sort=False)}; print(f.stem,score_rankings(ranked,truth)['quality_ndcg@10'])\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response +27:14

Script completed
Wall time 9.3 seconds
Output:

bias_0p16 0.023157
bias_0p17 0.023061
bias_0p18 0.023004
bias_0p19 0.023064
bias_0p2 0.023208
bias_0p21 0.023069
bias_0p22 0.02278
bias_0p23 0.022631
bias_0p24 0.022761
bias_0p25 0.022735

Harness context token usage record

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-a93285eb-1343-4755-8391-970fdb3224e0",
    "process_id": "17645",
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      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nfor f in sorted(Path('/app/solution/bias_sweep_fine').glob('*.parquet')):\n x=pd.read_parquet(f).sort_values(['uid','rank']); ranked={int(u):g.item_id.astype(int).tolist() for u,g in x.groupby('uid',sort=False)}; print(f.stem,score_rankings(ranked,truth)['quality_ndcg@10'])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nfor f in sorted(Path('/app/solution/bias_sweep_fine').glob('*.parquet')):\n x=pd.read_parquet(f).sort_values(['uid','rank']); ranked={int(u):g.item_id.astype(int).tolist() for u,g in x.groupby('uid',sort=False)}; print(f.stem,score_rankings(ranked,truth)['quality_ndcg@10'])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "bias_0p16 0.023157\nbias_0p17 0.023061\nbias_0p18 0.023004\nbias_0p19 0.023064\nbias_0p2 0.023208\nbias_0p21 0.023069\nbias_0p22 0.02278\nbias_0p23 0.022631\nbias_0p24 0.022761\nbias_0p25 0.022735\n",
    "stderr": "",
    "aggregated_output": "bias_0p16 0.023157\nbias_0p17 0.023061\nbias_0p18 0.023004\nbias_0p19 0.023064\nbias_0p2 0.023208\nbias_0p21 0.023069\nbias_0p22 0.02278\nbias_0p23 0.022631\nbias_0p24 0.022761\nbias_0p25 0.022735\n",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 163713173
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  }
}

139

exec command

+27:24

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +27:27

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/mixed16_checkpoints --output /app/solution/mixed16_validation.parquet --uniform-negatives 16 --inbatch-negatives 16 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --learning-rate 0.0003 --epochs 2 > /app/solution/mixed16_validation_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +27:29

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"bf2259","wall_time_seconds":1.000984635,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

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}

140

exec command

+27:30

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +27:36

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     query_hard_negatives: int = 24\n     loss_objective: str = \"pairwise\"\n+    popularity_bias: float = 0.20\n@@\n def rank_candidates(\n@@\n-        scores = queries[start:end] @ items.T\n+        scores = queries[start:end] @ items.T\n+        # This is still a pure dot product: it is equivalent to appending 1\n+        # to every user vector and -bias * popularity to each item vector.\n+        if config.popularity_bias:\n+            scores -= config.popularity_bias * torch.from_numpy(\n+                prepared.item_features[:, 0]\n+            )\n@@\n     parser.add_argument(\n         \"--loss-objective\",\n@@\n         default=Config.loss_objective,\n     )\n+    parser.add_argument(\n+        \"--popularity-bias\", type=float, default=Config.popularity_bias\n+    )\n@@\n         query_hard_negatives=args.query_hard_negatives,\n         loss_objective=args.loss_objective,\n+        popularity_bias=args.popularity_bias,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +27:36

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-f1a4f9dc-eee2-403b-a300-c5c8e231243e",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -65,2 +65,3 @@\n     loss_objective: str = \"pairwise\"\n+    popularity_bias: float = 0.20\n     inbatch_correction_power: float = 0.5\n@@ -2094,2 +2095,8 @@\n         scores = queries[start:end] @ items.T\n+        # This is still a pure dot product: it is equivalent to appending 1\n+        # to every user vector and -bias * popularity to each item vector.\n+        if config.popularity_bias:\n+            scores -= config.popularity_bias * torch.from_numpy(\n+                prepared.item_features[:, 0]\n+            )\n         eligible = np.unpackbits(\n@@ -2182,2 +2189,5 @@\n     parser.add_argument(\n+        \"--popularity-bias\", type=float, default=Config.popularity_bias\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2302,2 +2312,3 @@\n         loss_objective=args.loss_objective,\n+        popularity_bias=args.popularity_bias,\n         negative_filter_scope=args.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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  }
}

141

exec command

+27:39

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +27:40

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n         or args.query_hard_negatives < 0\n+        or not np.isfinite(args.popularity_bias)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +27:40

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-222aab34-17ba-47ed-b0dd-5801b2a4f5e1",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2276,2 +2276,3 @@\n         or args.query_hard_negatives < 0\n+        or not np.isfinite(args.popularity_bias)\n         or args.query_random_negatives > args.uniform_negatives\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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}

142

exec command

+27:42

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +27:44

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n                     \"normalization\": \"none\",\n-                    \"score\": \"raw-dot-product\",\n+                    \"score\": \"augmented-dot-product-with-item-popularity-bias\",\n+                    \"popularity_bias\": config.popularity_bias,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +27:44

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

{
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  "item": {
    "type": "FileChange",
    "id": "exec-fc2030e9-cf27-49f9-93e8-085cbf54a6fd",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2414,3 +2414,4 @@\n                     \"normalization\": \"none\",\n-                    \"score\": \"raw-dot-product\",\n+                    \"score\": \"augmented-dot-product-with-item-popularity-bias\",\n+                    \"popularity_bias\": config.popularity_bias,\n                     \"objective\": (\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
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}

Harness context token usage record

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}

143

exec command

+27:48

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +27:49

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +27:54

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"1bbc65","wall_time_seconds":5.000800646,"session_id":90974,"original_token_count":217,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\n"}

Harness context token usage record

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}

144

exec command

+28:01

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +28:02

const r = await tools.exec_command({"cmd":"date -u +%H:%M:%S","workdir":"/app","yield_time_ms":10000,"max_output_tokens":1000});
text(r.output);

Tool response +28:02

Script completed
Wall time 0.1 seconds
Output:

00:31:15

Harness context codex item completed

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    "type": "CommandExecution",
    "id": "exec-609ed3da-1612-44ad-8de5-ff08b70594a2",
    "process_id": "52502",
    "command": [
      "/bin/sh",
      "-lc",
      "date -u +%H:%M:%S"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "date -u +%H:%M:%S"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "00:31:15\n",
    "stderr": "",
    "aggregated_output": "00:31:15\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6491
    },
    "formatted_output": "00:31:15\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c0cbc67887d2ae8acf1c1113c5cd",
  "usage": {
    "input_tokens": 106736,
    "cached_input_tokens": 106112,
    "cache_write_input_tokens": 0,
    "output_tokens": 368,
    "reasoning_output_tokens": 311,
    "total_tokens": 107104
  },
  "turn_token_usage": {
    "input_tokens": 9090980,
    "cached_input_tokens": 8907008,
    "cache_write_input_tokens": 0,
    "output_tokens": 38723,
    "reasoning_output_tokens": 19893,
    "total_tokens": 9129703
  },
  "thread_token_usage": {
    "input_tokens": 9090980,
    "cached_input_tokens": 8907008,
    "cache_write_input_tokens": 0,
    "output_tokens": 38723,
    "reasoning_output_tokens": 19893,
    "total_tokens": 9129703
  }
}

145

exec command

+28:07

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The coefficient search is stable enough to select 0.20 (0.023208, +9.1%). A conservative 16-uniform/16-in-batch fine-tune is now running at one-third the original learning rate; unlike the rejected miners, it changes only the mix of genuine observed-positive negatives. In parallel I’m building the cutoff-correct train+val sequence cache so the final refit can start immediately after this decision.

Tool callexecTool call Returned

Tool call · Input +28:11

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --prepare-only --output /app/solution/unused_prepare_only.parquet > /app/solution/submission_prepare.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(JSON.stringify(r));

Tool response +28:12

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"845eab","wall_time_seconds":1.00170292,"session_id":30598,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c0d3fea487d2bb81813bf5f57dc9",
  "usage": {
    "input_tokens": 107133,
    "cached_input_tokens": 106496,
    "cache_write_input_tokens": 0,
    "output_tokens": 422,
    "reasoning_output_tokens": 214,
    "total_tokens": 107555
  },
  "turn_token_usage": {
    "input_tokens": 9198113,
    "cached_input_tokens": 9013504,
    "cache_write_input_tokens": 0,
    "output_tokens": 39145,
    "reasoning_output_tokens": 20107,
    "total_tokens": 9237258
  },
  "thread_token_usage": {
    "input_tokens": 9198113,
    "cached_input_tokens": 9013504,
    "cache_write_input_tokens": 0,
    "output_tokens": 39145,
    "reasoning_output_tokens": 20107,
    "total_tokens": 9237258
  }
}

146

exec command

+28:15

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +28:16

const r = await tools.write_stdin({"session_id":30598,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +28:21

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"f76971","wall_time_seconds":5.001879848,"session_id":30598,"original_token_count":13,"output":"loaded explicit events: rows=746,504 users=8,307\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c0de03a887d2aafa81ed26eac865",
  "usage": {
    "input_tokens": 107612,
    "cached_input_tokens": 107008,
    "cache_write_input_tokens": 0,
    "output_tokens": 68,
    "reasoning_output_tokens": 20,
    "total_tokens": 107680
  },
  "turn_token_usage": {
    "input_tokens": 9305725,
    "cached_input_tokens": 9120512,
    "cache_write_input_tokens": 0,
    "output_tokens": 39213,
    "reasoning_output_tokens": 20127,
    "total_tokens": 9344938
  },
  "thread_token_usage": {
    "input_tokens": 9305725,
    "cached_input_tokens": 9120512,
    "cache_write_input_tokens": 0,
    "output_tokens": 39213,
    "reasoning_output_tokens": 20127,
    "total_tokens": 9344938
  }
}

147

exec command

+28:22

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +28:23

const r = await tools.write_stdin({"session_id":30598,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +28:53

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"40091e","wall_time_seconds":30.002340554,"session_id":30598,"original_token_count":67,"output":"prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=31.2s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c0e68ae087d2a16cd074ff51b5ad",
  "usage": {
    "input_tokens": 107751,
    "cached_input_tokens": 107392,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 8,
    "total_tokens": 107807
  },
  "turn_token_usage": {
    "input_tokens": 9413476,
    "cached_input_tokens": 9227904,
    "cache_write_input_tokens": 0,
    "output_tokens": 39269,
    "reasoning_output_tokens": 20135,
    "total_tokens": 9452745
  },
  "thread_token_usage": {
    "input_tokens": 9413476,
    "cached_input_tokens": 9227904,
    "cache_write_input_tokens": 0,
    "output_tokens": 39269,
    "reasoning_output_tokens": 20135,
    "total_tokens": 9452745
  }
}

148

exec command

+28:55

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +28:55

const r = await tools.write_stdin({"session_id":30598,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +29:25

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"1eccc4","wall_time_seconds":30.001894695,"session_id":30598,"original_token_count":68,"output":"prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c106f43487d280da21a20e3300ac",
  "usage": {
    "input_tokens": 107963,
    "cached_input_tokens": 107520,
    "cache_write_input_tokens": 0,
    "output_tokens": 62,
    "reasoning_output_tokens": 14,
    "total_tokens": 108025
  },
  "turn_token_usage": {
    "input_tokens": 9521439,
    "cached_input_tokens": 9335424,
    "cache_write_input_tokens": 0,
    "output_tokens": 39331,
    "reasoning_output_tokens": 20149,
    "total_tokens": 9560770
  },
  "thread_token_usage": {
    "input_tokens": 9521439,
    "cached_input_tokens": 9335424,
    "cache_write_input_tokens": 0,
    "output_tokens": 39331,
    "reasoning_output_tokens": 20149,
    "total_tokens": 9560770
  }
}

149

exec command

+29:28

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +29:29

const r = await tools.write_stdin({"session_id":30598,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +29:33

Script completed
Wall time 4.1 seconds
Output:

{"chunk_id":"3e13b6","wall_time_seconds":4.135349045,"exit_code":0,"original_token_count":78,"output":"prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=69.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c127663c87d2ab59f1da5443512e",
  "usage": {
    "input_tokens": 108182,
    "cached_input_tokens": 107776,
    "cache_write_input_tokens": 0,
    "output_tokens": 70,
    "reasoning_output_tokens": 22,
    "total_tokens": 108252
  },
  "turn_token_usage": {
    "input_tokens": 9629621,
    "cached_input_tokens": 9443200,
    "cache_write_input_tokens": 0,
    "output_tokens": 39401,
    "reasoning_output_tokens": 20171,
    "total_tokens": 9669022
  },
  "thread_token_usage": {
    "input_tokens": 9629621,
    "cached_input_tokens": 9443200,
    "cache_write_input_tokens": 0,
    "output_tokens": 39401,
    "reasoning_output_tokens": 20171,
    "total_tokens": 9669022
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ccfe1735-1649-40ba-8336-9bd33a6d1c78",
    "process_id": "30598",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --prepare-only --output /app/solution/unused_prepare_only.parquet > /app/solution/submission_prepare.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --prepare-only --output /app/solution/unused_prepare_only.parquet > /app/solution/submission_prepare.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=31.2s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=69.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=31.2s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=69.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\n",
    "exit_code": 0,
    "duration": {
      "secs": 81,
      "nanos": 805277780
    },
    "formatted_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=31.2s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=69.6s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_sequences.npz\n"
  }
}

150

exec command

+29:34

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +29:35

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +29:40

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"da2d0b","wall_time_seconds":5.001216852,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c12ebdfc87d2bf637d5da6bc0182",
  "usage": {
    "input_tokens": 108394,
    "cached_input_tokens": 108032,
    "cache_write_input_tokens": 0,
    "output_tokens": 58,
    "reasoning_output_tokens": 10,
    "total_tokens": 108452
  },
  "turn_token_usage": {
    "input_tokens": 9738015,
    "cached_input_tokens": 9551232,
    "cache_write_input_tokens": 0,
    "output_tokens": 39459,
    "reasoning_output_tokens": 20181,
    "total_tokens": 9777474
  },
  "thread_token_usage": {
    "input_tokens": 9738015,
    "cached_input_tokens": 9551232,
    "cache_write_input_tokens": 0,
    "output_tokens": 39459,
    "reasoning_output_tokens": 20181,
    "total_tokens": 9777474
  }
}

151

exec command

+29:46

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +29:46

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +30:16

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"fd3e6b","wall_time_seconds":30.001820755,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c1361c7087d2b03981604b19969b",
  "usage": {
    "input_tokens": 108511,
    "cached_input_tokens": 108160,
    "cache_write_input_tokens": 0,
    "output_tokens": 62,
    "reasoning_output_tokens": 14,
    "total_tokens": 108573
  },
  "turn_token_usage": {
    "input_tokens": 9846526,
    "cached_input_tokens": 9659392,
    "cache_write_input_tokens": 0,
    "output_tokens": 39521,
    "reasoning_output_tokens": 20195,
    "total_tokens": 9886047
  },
  "thread_token_usage": {
    "input_tokens": 9846526,
    "cached_input_tokens": 9659392,
    "cache_write_input_tokens": 0,
    "output_tokens": 39521,
    "reasoning_output_tokens": 20195,
    "total_tokens": 9886047
  }
}

152

exec command

+30:18

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +30:19

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +30:49

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"eae44f","wall_time_seconds":30.001127494,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c159ddfc87d29a3851c6cafa2a30",
  "usage": {
    "input_tokens": 108632,
    "cached_input_tokens": 108288,
    "cache_write_input_tokens": 0,
    "output_tokens": 82,
    "reasoning_output_tokens": 34,
    "total_tokens": 108714
  },
  "turn_token_usage": {
    "input_tokens": 9955158,
    "cached_input_tokens": 9767680,
    "cache_write_input_tokens": 0,
    "output_tokens": 39603,
    "reasoning_output_tokens": 20229,
    "total_tokens": 9994761
  },
  "thread_token_usage": {
    "input_tokens": 9955158,
    "cached_input_tokens": 9767680,
    "cache_write_input_tokens": 0,
    "output_tokens": 39603,
    "reasoning_output_tokens": 20229,
    "total_tokens": 9994761
  }
}

153

Agent message

+30:51

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The final-stage cache is ready and correctly includes train_* + val_* history: 9,153 sequence users and 20.7M next-item targets. The conservative validation fine-tune is still running after sharing CPU with cache construction; the required submission path remains protected by the valid fallback until the refitted model replaces it.

154

exec command

+30:53

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +30:53

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +31:23

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e23581","wall_time_seconds":30.001357533,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
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}

155

exec command

+31:25

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +31:26

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +31:56

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d55ed4","wall_time_seconds":30.001486607,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

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}

156

exec command

+31:58

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +32:00

const r = await tools.exec_command({"cmd":"date -u +%H:%M:%S; ls -lh /app/solution/mixed16_checkpoints 2>/dev/null || true","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response +32:00

Script completed
Wall time 0.1 seconds
Output:

00:35:13

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-79471df3-9ca3-488d-a66b-d8a6d9fada40",
    "process_id": "35918",
    "command": [
      "/bin/sh",
      "-lc",
      "date -u +%H:%M:%S; ls -lh /app/solution/mixed16_checkpoints 2>/dev/null || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "date -u +%H:%M:%S; ls -lh /app/solution/mixed16_checkpoints 2>/dev/null || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "00:35:13\n",
    "stderr": "",
    "aggregated_output": "00:35:13\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4760
    },
    "formatted_output": "00:35:13\n"
  }
}

Harness context token usage record

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}

157

exec command

+32:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +32:04

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +32:34

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"97f42c","wall_time_seconds":30.001443788,"session_id":90974,"original_token_count":99,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\n"}

Harness context token usage record

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}

158

exec command

+32:39

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +32:40

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +33:10

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"bb55c9","wall_time_seconds":30.001828023,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

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}

159

exec command

+33:12

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +33:12

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +33:43

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"4c6aa0","wall_time_seconds":30.00104711,"session_id":90974,"original_token_count":0,"output":""}

Harness context token usage record

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}

160

exec command

+33:45

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +33:46

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +33:47

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"3a367e","wall_time_seconds":1.2970973670000001,"exit_code":0,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

Harness context token usage record

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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5fd9bc01-9878-4c3a-80ae-9a497e66e243",
    "process_id": "90974",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/mixed16_checkpoints --output /app/solution/mixed16_validation.parquet --uniform-negatives 16 --inbatch-negatives 16 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --learning-rate 0.0003 --epochs 2 > /app/solution/mixed16_validation_run.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/mixed16_checkpoints --output /app/solution/mixed16_validation.parquet --uniform-negatives 16 --inbatch-negatives 16 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --learning-rate 0.0003 --epochs 2 > /app/solution/mixed16_validation_run.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "exit_code": 0,
    "duration": {
      "secs": 379,
      "nanos": 419145671
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"
  }
}

161

exec command

+33:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +33:51

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed16_validation.parquet && sed -n '1,240p' /app/solution/mixed16_validation_run.json","workdir":"/app","yield_time_ms":10000,"max_output_tokens":18000});
text(r.output);

Tool response +33:54

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/mixed16_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.019112,
    "quality_users": 4263
  }
}
{
  "model": "yambda-windowed-sequence-two-tower-hard-negative",
  "stage": "validation",
  "output": "/app/solution/mixed16_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 375.102,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-pointwise-bce-at-every-timestep",
    "negative_sampling": {
      "uniform_pool": 16,
      "propensity_corrected_inbatch_pool": 16,
      "item_neighbor_pool": 0,
      "random_retained": 0,
      "query_hard_retained": 0
    },
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 2,
    "learning_rate": 0.0003,
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Harness context codex item completed

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  }
}

162

Agent message

+34:05

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

163

exec command

+34:09

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The conservative second-epoch mix also fails (0.019112), confirming that simply continuing the mature checkpoint is the problem: the sampled held-out loss improves while discovery degrades. I’m running one final controlled sampler from scratch: preserve all 24 production uniform negatives, replace the 8 random in-batch negatives with the 8 hardest of 40 filtered in-batch candidates, and keep the original one-epoch pointwise objective. This isolates negative selection without extra epochs or an objective change.

Tool callexecTool call Returned

Tool call · Input +34:14

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet --uniform-negatives 24 --inbatch-negatives 40 --hard-negatives 0 --query-random-negatives 24 --query-hard-negatives 8 --loss-objective pointwise-bce --popularity-bias 0 --learning-rate 0.001 --epochs 1 > /app/solution/semihard_validation_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +34:15

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"4b8b41","wall_time_seconds":1.00119798,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

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164

exec command

+34:18

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +34:19

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +34:24

Script completed
Wall time 5.0 seconds
Output:

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Harness context token usage record

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165

exec command

+34:26

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +34:27

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +34:32

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"34b326","wall_time_seconds":5.000855143,"session_id":2832,"original_token_count":55,"output":"inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c251c8b887d2806299292742d758",
  "usage": {
    "input_tokens": 112871,
    "cached_input_tokens": 112512,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 112925
  },
  "turn_token_usage": {
    "input_tokens": 11167902,
    "cached_input_tokens": 10974208,
    "cache_write_input_tokens": 0,
    "output_tokens": 41679,
    "reasoning_output_tokens": 21383,
    "total_tokens": 11209581
  },
  "thread_token_usage": {
    "input_tokens": 11167902,
    "cached_input_tokens": 10974208,
    "cache_write_input_tokens": 0,
    "output_tokens": 41679,
    "reasoning_output_tokens": 21383,
    "total_tokens": 11209581
  }
}

166

Agent message

+34:44

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

167

exec command

+34:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +34:49

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +35:19

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"09ee17","wall_time_seconds":30.001082582,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c2598ca487d2b9ab978f0b979eb7",
  "usage": {
    "input_tokens": 113054,
    "cached_input_tokens": 112640,
    "cache_write_input_tokens": 0,
    "output_tokens": 853,
    "reasoning_output_tokens": 805,
    "total_tokens": 113907
  },
  "turn_token_usage": {
    "input_tokens": 11280956,
    "cached_input_tokens": 11086848,
    "cache_write_input_tokens": 0,
    "output_tokens": 42532,
    "reasoning_output_tokens": 22188,
    "total_tokens": 11323488
  },
  "thread_token_usage": {
    "input_tokens": 11280956,
    "cached_input_tokens": 11086848,
    "cache_write_input_tokens": 0,
    "output_tokens": 42532,
    "reasoning_output_tokens": 22188,
    "total_tokens": 11323488
  }
}

168

exec command

+35:22

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +35:22

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +35:52

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d443c0","wall_time_seconds":30.000769042,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c289650487d29675383e45337eef",
  "usage": {
    "input_tokens": 113964,
    "cached_input_tokens": 112896,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 7,
    "total_tokens": 114019
  },
  "turn_token_usage": {
    "input_tokens": 11394920,
    "cached_input_tokens": 11199744,
    "cache_write_input_tokens": 0,
    "output_tokens": 42587,
    "reasoning_output_tokens": 22195,
    "total_tokens": 11437507
  },
  "thread_token_usage": {
    "input_tokens": 11394920,
    "cached_input_tokens": 11199744,
    "cache_write_input_tokens": 0,
    "output_tokens": 42587,
    "reasoning_output_tokens": 22195,
    "total_tokens": 11437507
  }
}

169

Agent message

+35:58

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The final sampler experiment is training from a fresh initialization, so it avoids the misleading “better second epoch” pattern seen in both rejected fine-tunes. The submission-stage cache and fallback artifact are already secure; once this public ranking lands, I’ll immediately refit only the winning configuration and run a full eligibility/schema audit.

170

exec command

+35:58

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +35:58

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +36:28

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"8d209f","wall_time_seconds":30.001640586,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c2aa5ce887d2a26ff91a4be89b35",
  "usage": {
    "input_tokens": 114077,
    "cached_input_tokens": 113792,
    "cache_write_input_tokens": 0,
    "output_tokens": 146,
    "reasoning_output_tokens": 27,
    "total_tokens": 114223
  },
  "turn_token_usage": {
    "input_tokens": 11508997,
    "cached_input_tokens": 11313536,
    "cache_write_input_tokens": 0,
    "output_tokens": 42733,
    "reasoning_output_tokens": 22222,
    "total_tokens": 11551730
  },
  "thread_token_usage": {
    "input_tokens": 11508997,
    "cached_input_tokens": 11313536,
    "cache_write_input_tokens": 0,
    "output_tokens": 42733,
    "reasoning_output_tokens": 22222,
    "total_tokens": 11551730
  }
}

171

exec command

+36:30

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +36:31

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +37:01

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"77bf48","wall_time_seconds":30.001470338,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c2ce5b0c87d2b2374079df4388f2",
  "usage": {
    "input_tokens": 114281,
    "cached_input_tokens": 113920,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 7,
    "total_tokens": 114336
  },
  "turn_token_usage": {
    "input_tokens": 11623278,
    "cached_input_tokens": 11427456,
    "cache_write_input_tokens": 0,
    "output_tokens": 42788,
    "reasoning_output_tokens": 22229,
    "total_tokens": 11666066
  },
  "thread_token_usage": {
    "input_tokens": 11623278,
    "cached_input_tokens": 11427456,
    "cache_write_input_tokens": 0,
    "output_tokens": 42788,
    "reasoning_output_tokens": 22229,
    "total_tokens": 11666066
  }
}

172

exec command

+37:05

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +37:05

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +37:35

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ef45f6","wall_time_seconds":30.001935493,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c2ee9dc487d28418fbbef96ae6bd",
  "usage": {
    "input_tokens": 114393,
    "cached_input_tokens": 114048,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 7,
    "total_tokens": 114448
  },
  "turn_token_usage": {
    "input_tokens": 11737671,
    "cached_input_tokens": 11541504,
    "cache_write_input_tokens": 0,
    "output_tokens": 42843,
    "reasoning_output_tokens": 22236,
    "total_tokens": 11780514
  },
  "thread_token_usage": {
    "input_tokens": 11737671,
    "cached_input_tokens": 11541504,
    "cache_write_input_tokens": 0,
    "output_tokens": 42843,
    "reasoning_output_tokens": 22236,
    "total_tokens": 11780514
  }
}

173

exec command

+37:37

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +37:38

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +38:08

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e13204","wall_time_seconds":30.001668686,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c3114eb487d2bca277c050562700",
  "usage": {
    "input_tokens": 114506,
    "cached_input_tokens": 114176,
    "cache_write_input_tokens": 0,
    "output_tokens": 64,
    "reasoning_output_tokens": 16,
    "total_tokens": 114570
  },
  "turn_token_usage": {
    "input_tokens": 11852177,
    "cached_input_tokens": 11655680,
    "cache_write_input_tokens": 0,
    "output_tokens": 42907,
    "reasoning_output_tokens": 22252,
    "total_tokens": 11895084
  },
  "thread_token_usage": {
    "input_tokens": 11852177,
    "cached_input_tokens": 11655680,
    "cache_write_input_tokens": 0,
    "output_tokens": 42907,
    "reasoning_output_tokens": 22252,
    "total_tokens": 11895084
  }
}

174

exec command

+38:09

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +38:10

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +38:40

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"4d6138","wall_time_seconds":30.000705554,"session_id":2832,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.612418 running_bce=0.595345 elapsed=242.8s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c331b3e487d2aa1b8c9ed4db4411",
  "usage": {
    "input_tokens": 114627,
    "cached_input_tokens": 114304,
    "cache_write_input_tokens": 0,
    "output_tokens": 65,
    "reasoning_output_tokens": 17,
    "total_tokens": 114692
  },
  "turn_token_usage": {
    "input_tokens": 11966804,
    "cached_input_tokens": 11769984,
    "cache_write_input_tokens": 0,
    "output_tokens": 42972,
    "reasoning_output_tokens": 22269,
    "total_tokens": 12009776
  },
  "thread_token_usage": {
    "input_tokens": 11966804,
    "cached_input_tokens": 11769984,
    "cache_write_input_tokens": 0,
    "output_tokens": 42972,
    "reasoning_output_tokens": 22269,
    "total_tokens": 12009776
  }
}

175

exec command

+38:42

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +38:43

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +39:13

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"eeadce","wall_time_seconds":30.001382369,"session_id":2832,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.59416 validation_loss=0.56073 pair_accuracy=0.655 train_targets=1,052,953 validation_targets=130,103 train_seconds=259.1 validation_seconds=19.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.560735\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c352403087d2ae825ab3c2d11c74",
  "usage": {
    "input_tokens": 114785,
    "cached_input_tokens": 114432,
    "cache_write_input_tokens": 0,
    "output_tokens": 66,
    "reasoning_output_tokens": 18,
    "total_tokens": 114851
  },
  "turn_token_usage": {
    "input_tokens": 12081589,
    "cached_input_tokens": 11884416,
    "cache_write_input_tokens": 0,
    "output_tokens": 43038,
    "reasoning_output_tokens": 22287,
    "total_tokens": 12124627
  },
  "thread_token_usage": {
    "input_tokens": 12081589,
    "cached_input_tokens": 11884416,
    "cache_write_input_tokens": 0,
    "output_tokens": 43038,
    "reasoning_output_tokens": 22287,
    "total_tokens": 12124627
  }
}

176

exec command

+39:15

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The from-scratch semi-hard run completed normally: one epoch, the same 1.053M training targets, and 32 retained negatives per target. Its sampled validation loss is intentionally higher because the negatives are harder; I’m waiting on the public ranking, which is the relevant comparison.

Tool callexecTool call Returned

Tool call · Input +39:17

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +39:47

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"95bd78","wall_time_seconds":30.00169659,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c372f3ec87d2a93a450b90fe904d",
  "usage": {
    "input_tokens": 114998,
    "cached_input_tokens": 114560,
    "cache_write_input_tokens": 0,
    "output_tokens": 124,
    "reasoning_output_tokens": 11,
    "total_tokens": 115122
  },
  "turn_token_usage": {
    "input_tokens": 12196587,
    "cached_input_tokens": 11998976,
    "cache_write_input_tokens": 0,
    "output_tokens": 43162,
    "reasoning_output_tokens": 22298,
    "total_tokens": 12239749
  },
  "thread_token_usage": {
    "input_tokens": 12196587,
    "cached_input_tokens": 11998976,
    "cache_write_input_tokens": 0,
    "output_tokens": 43162,
    "reasoning_output_tokens": 22298,
    "total_tokens": 12239749
  }
}

177

exec command

+39:48

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +39:49

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +40:19

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e0da0c","wall_time_seconds":30.001808209,"session_id":2832,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c394a25887d287ab26de56fbfda4",
  "usage": {
    "input_tokens": 115179,
    "cached_input_tokens": 114816,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 7,
    "total_tokens": 115234
  },
  "turn_token_usage": {
    "input_tokens": 12311766,
    "cached_input_tokens": 12113792,
    "cache_write_input_tokens": 0,
    "output_tokens": 43217,
    "reasoning_output_tokens": 22305,
    "total_tokens": 12354983
  },
  "thread_token_usage": {
    "input_tokens": 12311766,
    "cached_input_tokens": 12113792,
    "cache_write_input_tokens": 0,
    "output_tokens": 43217,
    "reasoning_output_tokens": 22305,
    "total_tokens": 12354983
  }
}

178

exec command

+40:21

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +40:21

const r = await tools.write_stdin({"session_id":2832,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +40:25

Script completed
Wall time 3.7 seconds
Output:

{"chunk_id":"b08e44","wall_time_seconds":3.716906738,"exit_code":0,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
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Harness context codex item completed

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        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet --uniform-negatives 24 --inbatch-negatives 40 --hard-negatives 0 --query-random-negatives 24 --query-hard-negatives 8 --loss-objective pointwise-bce --popularity-bias 0 --learning-rate 0.001 --epochs 1 > /app/solution/semihard_validation_run.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.612418 running_bce=0.595345 elapsed=242.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.59416 validation_loss=0.56073 pair_accuracy=0.655 train_targets=1,052,953 validation_targets=130,103 train_seconds=259.1 validation_seconds=19.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.560735\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "stderr": "",
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  }
}

179

exec command

+40:27

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +40:28

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet && date -u +%H:%M:%S","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response +40:31

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/semihard_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01865,
    "quality_users": 4263
  }
}
00:43:45

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180

exec command

+40:36

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Semi-hard mining is also rejected (0.018650). The evidence is consistent: on these implicit-feedback logs, unobserved “hard” tracks are often plausible positives, so harder sampled negatives improve surrogate separation but hurt discovery. The measured winner remains the production objective plus the principled item-side popularity correction (0.023208, +9.1%). I’m refitting that winner on train_* + val_* now and will overwrite the fallback submission.

Tool callexecTool call Returned

Tool call · Input +40:41

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet --epochs 1 --uniform-negatives 24 --inbatch-negatives 8 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --popularity-bias 0.20 --learning-rate 0.001 > /app/solution/submission_run.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +40:42

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"61b1f2","wall_time_seconds":1.001320644,"session_id":97979,"original_token_count":0,"output":""}

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181

exec command

+40:51

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +40:58

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-entropy. The improved sampler draws a 64-item mixture of uniform and\n-propensity-corrected in-batch candidates, keeps eight random examples for\n-coverage, and mines the 24 highest-scoring remaining examples per user query.\n-The loss therefore retains the production total of 32 negatives per target.\n-Every selected adjacent target retains a nonzero weight. Only files under the\n-supplied public directory are read.\n+entropy. The trainer can evaluate query-mined and item-neighbor negatives, but\n+the selected public-backtest configuration retains the robust 24 uniform plus\n+eight propensity-corrected in-batch examples: harder unobserved tracks behaved\n+as false negatives.  It corrects the popularity prior induced by this sampled\n+objective with one static item-side score coordinate, which remains an indexed\n+two-tower dot product. Every selected adjacent target retains a nonzero weight.\n+Only files under the supplied public directory are read.\n@@\n-    epochs: int = 2\n+    epochs: int = 1\n@@\n-    uniform_negatives: int = 16\n-    inbatch_negatives: int = 48\n+    uniform_negatives: int = 24\n+    inbatch_negatives: int = 8\n@@\n-    query_random_negatives: int = 8\n-    query_hard_negatives: int = 24\n-    loss_objective: str = \"pairwise\"\n+    query_random_negatives: int = 0\n+    query_hard_negatives: int = 0\n+    loss_objective: str = \"pointwise-bce\"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +40:58

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-354af9ff-899f-470c-b632-169a19627f27",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -13,8 +13,9 @@\n normalization, raw dot-product retrieval, and sample-weighted binary cross\n-entropy. The improved sampler draws a 64-item mixture of uniform and\n-propensity-corrected in-batch candidates, keeps eight random examples for\n-coverage, and mines the 24 highest-scoring remaining examples per user query.\n-The loss therefore retains the production total of 32 negatives per target.\n-Every selected adjacent target retains a nonzero weight. Only files under the\n-supplied public directory are read.\n+entropy. The trainer can evaluate query-mined and item-neighbor negatives, but\n+the selected public-backtest configuration retains the robust 24 uniform plus\n+eight propensity-corrected in-batch examples: harder unobserved tracks behaved\n+as false negatives.  It corrects the popularity prior induced by this sampled\n+objective with one static item-side score coordinate, which remains an indexed\n+two-tower dot product. Every selected adjacent target retains a nonzero weight.\n+Only files under the supplied public directory are read.\n \"\"\"\n@@ -53,3 +54,3 @@\n     dropout: float = 0.05\n-    epochs: int = 2\n+    epochs: int = 1\n     learning_rate: float = 1.0e-3\n@@ -58,9 +59,9 @@\n     loss_microbatch_size: int = 65_536\n-    uniform_negatives: int = 16\n-    inbatch_negatives: int = 48\n+    uniform_negatives: int = 24\n+    inbatch_negatives: int = 8\n     hard_negatives: int = 0\n     hard_negative_pool_size: int = 64\n-    query_random_negatives: int = 8\n-    query_hard_negatives: int = 24\n-    loss_objective: str = \"pairwise\"\n+    query_random_negatives: int = 0\n+    query_hard_negatives: int = 0\n+    loss_objective: str = \"pointwise-bce\"\n     popularity_bias: float = 0.20\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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182

exec command

+41:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +41:04

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-                \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-calibrated\",\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +41:04

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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        "type": "update",
        "unified_diff": "@@ -2395,3 +2395,3 @@\n             {\n-                \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-calibrated\",\n                 \"stage\": args.stage,\n",
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183

exec command

+41:07

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +41:08

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +41:13

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"e277b4","wall_time_seconds":5.00184556,"session_id":97979,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n"}

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184

exec command

+41:20

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +41:34

const patch = "*** Begin Patch\n*** Add File: /app/solution/README.md\n+# Calibrated Yambda two-tower\n+\n+## Result\n+\n+The reproduced production model scores **0.021280 NDCG@10** on 4,263 public\n+quality users.  The selected model scores **0.023208** (**+9.1% relative**) on\n+the same frozen validation boundary.\n+\n+| validation model | NDCG@10 |\n+| --- | ---: |\n+| production, 24 uniform + 8 corrected in-batch negatives | 0.021280 |\n+| item-neighbor hard-negative fine-tune | 0.010900 |\n+| query-hard pairwise fine-tune | 0.020273 |\n+| from-scratch semi-hard in-batch mining | 0.018650 |\n+| **selected: production objective + item-side calibration** | **0.023208** |\n+\n+## Diagnosis and change\n+\n+The 24 catalog-uniform negatives are overwhelmingly easy tail items.  The\n+model consequently learns a strong positive-popularity prior: its public\n+top-10 has median catalog popularity rank 111, versus roughly 5,500 for\n+eligible relevant outcomes.  Only eight negatives come from observed positive\n+targets, and their inverse-propensity weights intentionally weaken the most\n+popular examples.\n+\n+I tested filtered hard negatives rather than assuming they would help.  All\n+variants improved their own sampled held-out loss, but reduced public NDCG.\n+With interaction rather than impression logs, high-scoring unobserved tracks\n+are frequently plausible positives, so aggressive hard mining creates false\n+negatives and erodes the collaborative item geometry.\n+\n+The selected fix therefore retains the robust production sampler and removes\n+its measured popularity shortcut at scoring time:\n+\n+```text\n+score(u, i) = user_vector(u) dot item_vector(i)\n+              - 0.20 * standardized_log_positive_listens(i)\n+```\n+\n+This is still an indexed two-tower dot product: append `1` to the user vector\n+and append the precomputed negative popularity term to the item vector.  It\n+requires no user-ID table, cross-encoder, or per-candidate sequence work.  The\n+coefficient was selected only on the public validation window.  The final\n+artifact was then refit with `train_* + val_*` history and test-cutoff\n+eligibility/state.\n+\n+## Reproduce the final artifact\n+\n+From this directory, using the supplied offline environment:\n+\n+```sh\n+/opt/task-venv/bin/python train_current_model.py \\\n+  --stage submission \\\n+  --public-dir /task \\\n+  --prepared-cache /app/solution/submission_sequences.npz \\\n+  --checkpoint-dir /app/solution/final_checkpoints \\\n+  --output /app/solution/submission.parquet\n+```\n+\n+The defaults are the selected configuration: one epoch, 24 uniform plus 8\n+propensity-corrected in-batch negatives, pointwise BCE, full-history\n+false-negative filtering, organic-strong target weights, and popularity bias\n+0.20.  Optional hard-negative switches remain in the script so the rejected\n+experiments are reproducible.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +41:34

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-cfd2410b-f815-4e16-bd1b-2184eadf9722",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Calibrated Yambda two-tower\n\n## Result\n\nThe reproduced production model scores **0.021280 NDCG@10** on 4,263 public\nquality users.  The selected model scores **0.023208** (**+9.1% relative**) on\nthe same frozen validation boundary.\n\n| validation model | NDCG@10 |\n| --- | ---: |\n| production, 24 uniform + 8 corrected in-batch negatives | 0.021280 |\n| item-neighbor hard-negative fine-tune | 0.010900 |\n| query-hard pairwise fine-tune | 0.020273 |\n| from-scratch semi-hard in-batch mining | 0.018650 |\n| **selected: production objective + item-side calibration** | **0.023208** |\n\n## Diagnosis and change\n\nThe 24 catalog-uniform negatives are overwhelmingly easy tail items.  The\nmodel consequently learns a strong positive-popularity prior: its public\ntop-10 has median catalog popularity rank 111, versus roughly 5,500 for\neligible relevant outcomes.  Only eight negatives come from observed positive\ntargets, and their inverse-propensity weights intentionally weaken the most\npopular examples.\n\nI tested filtered hard negatives rather than assuming they would help.  All\nvariants improved their own sampled held-out loss, but reduced public NDCG.\nWith interaction rather than impression logs, high-scoring unobserved tracks\nare frequently plausible positives, so aggressive hard mining creates false\nnegatives and erodes the collaborative item geometry.\n\nThe selected fix therefore retains the robust production sampler and removes\nits measured popularity shortcut at scoring time:\n\n```text\nscore(u, i) = user_vector(u) dot item_vector(i)\n              - 0.20 * standardized_log_positive_listens(i)\n```\n\nThis is still an indexed two-tower dot product: append `1` to the user vector\nand append the precomputed negative popularity term to the item vector.  It\nrequires no user-ID table, cross-encoder, or per-candidate sequence work.  The\ncoefficient was selected only on the public validation window.  The final\nartifact was then refit with `train_* + val_*` history and test-cutoff\neligibility/state.\n\n## Reproduce the final artifact\n\nFrom this directory, using the supplied offline environment:\n\n```sh\n/opt/task-venv/bin/python train_current_model.py \\\n  --stage submission \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/submission_sequences.npz \\\n  --checkpoint-dir /app/solution/final_checkpoints \\\n  --output /app/solution/submission.parquet\n```\n\nThe defaults are the selected configuration: one epoch, 24 uniform plus 8\npropensity-corrected in-batch negatives, pointwise BCE, full-history\nfalse-negative filtering, organic-strong target weights, and popularity bias\n0.20.  Optional hard-negative switches remain in the script so the rejected\nexperiments are reproducible.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n",
    "stderr": ""
  }
}

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185

exec command

+41:42

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +41:48

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n @torch.inference_mode()\n+def calibrated_retrieval_scores(\n+    queries: torch.Tensor,\n+    items: torch.Tensor,\n+    item_features: np.ndarray,\n+    popularity_bias: float,\n+) -> torch.Tensor:\n+    \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n+    scores = queries @ items.T\n+    if popularity_bias:\n+        popularity = torch.as_tensor(\n+            item_features[:, 0], device=scores.device, dtype=scores.dtype\n+        )\n+        scores -= popularity_bias * popularity\n+    return scores\n+\n+\[email protected]_mode()\n def rank_candidates(\n@@\n-        scores = queries[start:end] @ items.T\n-        # This is still a pure dot product: it is equivalent to appending 1\n-        # to every user vector and -bias * popularity to each item vector.\n-        if config.popularity_bias:\n-            scores -= config.popularity_bias * torch.from_numpy(\n-                prepared.item_features[:, 0]\n-            )\n+        # This is still a pure dot product: it is equivalent to appending 1\n+        # to every user vector and -bias * popularity to each item vector.\n+        scores = calibrated_retrieval_scores(\n+            queries[start:end],\n+            items,\n+            prepared.item_features,\n+            config.popularity_bias,\n+        )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +41:48

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2072,2 +2072,19 @@\n @torch.inference_mode()\n+def calibrated_retrieval_scores(\n+    queries: torch.Tensor,\n+    items: torch.Tensor,\n+    item_features: np.ndarray,\n+    popularity_bias: float,\n+) -> torch.Tensor:\n+    \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n+    scores = queries @ items.T\n+    if popularity_bias:\n+        popularity = torch.as_tensor(\n+            item_features[:, 0], device=scores.device, dtype=scores.dtype\n+        )\n+        scores -= popularity_bias * popularity\n+    return scores\n+\n+\[email protected]_mode()\n def rank_candidates(\n@@ -2095,9 +2112,10 @@\n         end = min(start + ranking_batch_size, len(eligibility.uids))\n-        scores = queries[start:end] @ items.T\n         # This is still a pure dot product: it is equivalent to appending 1\n         # to every user vector and -bias * popularity to each item vector.\n-        if config.popularity_bias:\n-            scores -= config.popularity_bias * torch.from_numpy(\n-                prepared.item_features[:, 0]\n-            )\n+        scores = calibrated_retrieval_scores(\n+            queries[start:end],\n+            items,\n+            prepared.item_features,\n+            config.popularity_bias,\n+        )\n         eligible = np.unpackbits(\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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186

exec command

+41:50

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +42:01

const patch = "*** Begin Patch\n*** Add File: /app/solution/test_improvements.py\n+from __future__ import annotations\n+\n+import unittest\n+\n+import numpy as np\n+import torch\n+\n+import train_current_model as trainer\n+\n+\n+class ImprovementTests(unittest.TestCase):\n+    def test_calibration_is_an_augmented_dot_product(self) -> None:\n+        queries = torch.tensor([[1.0, 2.0], [-1.0, 0.5]])\n+        items = torch.tensor([[2.0, 1.0], [0.5, -2.0], [1.0, 3.0]])\n+        features = np.zeros((3, 4), dtype=np.float32)\n+        features[:, 0] = np.array([-1.0, 0.25, 2.0], dtype=np.float32)\n+        bias = 0.2\n+\n+        actual = trainer.calibrated_retrieval_scores(\n+            queries, items, features, bias\n+        )\n+        augmented_queries = torch.cat(\n+            [queries, torch.ones((len(queries), 1))], dim=1\n+        )\n+        augmented_items = torch.cat(\n+            [items, torch.from_numpy((-bias * features[:, 0])[:, None])], dim=1\n+        )\n+        expected = augmented_queries @ augmented_items.T\n+        torch.testing.assert_close(actual, expected)\n+\n+    def test_full_history_filter_rejects_known_and_duplicate_items(self) -> None:\n+        packed = np.zeros((1, 2), dtype=np.uint8)\n+        packed[0, 0] = np.uint8(1 << 3)\n+        negatives = np.array([[3, 5, 5, 7]], dtype=np.int64)\n+        invalid = trainer.invalid_full_history_negatives(\n+            negatives=negatives,\n+            targets=np.array([7], dtype=np.int64),\n+            user_rows=np.array([0], dtype=np.int64),\n+            known_positive_packed=packed,\n+            liked_items=np.array([[11]], dtype=np.int64),\n+        )\n+        np.testing.assert_array_equal(\n+            invalid, np.array([[True, False, True, True]])\n+        )\n+\n+\n+if __name__ == \"__main__\":\n+    unittest.main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +42:01

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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    "changes": {
      "/app/solution/test_improvements.py": {
        "type": "add",
        "content": "from __future__ import annotations\n\nimport unittest\n\nimport numpy as np\nimport torch\n\nimport train_current_model as trainer\n\n\nclass ImprovementTests(unittest.TestCase):\n    def test_calibration_is_an_augmented_dot_product(self) -> None:\n        queries = torch.tensor([[1.0, 2.0], [-1.0, 0.5]])\n        items = torch.tensor([[2.0, 1.0], [0.5, -2.0], [1.0, 3.0]])\n        features = np.zeros((3, 4), dtype=np.float32)\n        features[:, 0] = np.array([-1.0, 0.25, 2.0], dtype=np.float32)\n        bias = 0.2\n\n        actual = trainer.calibrated_retrieval_scores(\n            queries, items, features, bias\n        )\n        augmented_queries = torch.cat(\n            [queries, torch.ones((len(queries), 1))], dim=1\n        )\n        augmented_items = torch.cat(\n            [items, torch.from_numpy((-bias * features[:, 0])[:, None])], dim=1\n        )\n        expected = augmented_queries @ augmented_items.T\n        torch.testing.assert_close(actual, expected)\n\n    def test_full_history_filter_rejects_known_and_duplicate_items(self) -> None:\n        packed = np.zeros((1, 2), dtype=np.uint8)\n        packed[0, 0] = np.uint8(1 << 3)\n        negatives = np.array([[3, 5, 5, 7]], dtype=np.int64)\n        invalid = trainer.invalid_full_history_negatives(\n            negatives=negatives,\n            targets=np.array([7], dtype=np.int64),\n            user_rows=np.array([0], dtype=np.int64),\n            known_positive_packed=packed,\n            liked_items=np.array([[11]], dtype=np.int64),\n        )\n        np.testing.assert_array_equal(\n            invalid, np.array([[True, False, True, True]])\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/test_improvements.py\n",
    "stderr": ""
  }
}

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187

exec command

+42:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +42:04

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution /opt/task-venv/bin/python -m unittest -v /app/solution/test_improvements.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":6000});
text(r.output);

Tool response +42:10

Script completed
Wall time 5.4 seconds
Output:

test_calibration_is_an_augmented_dot_product (solution.test_improvements.ImprovementTests.test_calibration_is_an_augmented_dot_product) ... ok
test_full_history_filter_rejects_known_and_duplicate_items (solution.test_improvements.ImprovementTests.test_full_history_filter_rejects_known_and_duplicate_items) ... ok

----------------------------------------------------------------------
Ran 2 tests in 0.018s

OK

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      "-lc",
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    "stderr": "",
    "aggregated_output": "test_calibration_is_an_augmented_dot_product (solution.test_improvements.ImprovementTests.test_calibration_is_an_augmented_dot_product) ... ok\ntest_full_history_filter_rejects_known_and_duplicate_items (solution.test_improvements.ImprovementTests.test_full_history_filter_rejects_known_and_duplicate_items) ... ok\n\n----------------------------------------------------------------------\nRan 2 tests in 0.018s\n\nOK\n",
    "exit_code": 0,
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  }
}

188

exec command

+42:12

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +42:13

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +42:43

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c418ea","wall_time_seconds":30.001938563,"session_id":97979,"original_token_count":0,"output":""}

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189

exec command

+42:45

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +42:46

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +43:16

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d4afcb","wall_time_seconds":30.000994608,"session_id":97979,"original_token_count":0,"output":""}

Harness context token usage record

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}

190

Agent message

+43:24

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The selected implementation is now documented and tested. Two local tests verify that the popularity correction is exactly an augmented dot product and that full-history/duplicate false negatives remain filtered. The final refit is the only long-running step left; afterward I’ll validate all 845,600 rows against the test eligibility service.

191

exec command

+43:24

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +43:24

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +43:54

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"1e41b5","wall_time_seconds":30.001849353,"session_id":97979,"original_token_count":0,"output":""}

Harness context token usage record

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}

192

exec command

+43:57

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +43:57

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +44:27

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"41df87","wall_time_seconds":30.002823831,"session_id":97979,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\n"}

Harness context token usage record

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193

exec command

+44:30

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +44:30

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +45:00

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"69fcce","wall_time_seconds":30.00168008,"session_id":97979,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\n"}

Harness context token usage record

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}

194

exec command

+45:03

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +45:03

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +45:33

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"7b7d00","wall_time_seconds":30.00292476,"session_id":97979,"original_token_count":0,"output":""}

Harness context token usage record

{
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}

195

exec command

+45:36

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +45:37

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +46:07

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6fd9c6","wall_time_seconds":30.00189869,"session_id":97979,"original_token_count":0,"output":""}

Harness context token usage record

{
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}

196

exec command

+46:09

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input +46:09

const r = await tools.write_stdin({"session_id":97979,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +46:12

Script completed
Wall time 2.8 seconds
Output:

{"chunk_id":"43fe09","wall_time_seconds":2.794439509,"exit_code":0,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

Harness context token usage record

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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9adb7b32-cae0-4c7a-bcdc-4d6391007a95",
    "process_id": "97979",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet --epochs 1 --uniform-negatives 24 --inbatch-negatives 8 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --popularity-bias 0.20 --learning-rate 0.001 > /app/solution/submission_run.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet --epochs 1 --uniform-negatives 24 --inbatch-negatives 8 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --popularity-bias 0.20 --learning-rate 0.001 > /app/solution/submission_run.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n",
    "exit_code": 0,
    "duration": {
      "secs": 330,
      "nanos": 815945004
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"
  }
}

197

exec command

+46:16

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The final refit has completed and the calibrated model has replaced the fallback at /app/solution/submission.parquet. I’m doing the last exhaustive audit now: regular-file status, required integer columns, exact user coverage, 100 unique items and ranks per user, and all 845,600 user–item pairs accepted by test eligibility.

Tool callexecTool call Returned

Tool call · Input +46:26

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); path=Path('/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nframe=pd.read_parquet(path)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nchecked=validate_ranking(frame,targets)\neligibility=CandidateSet(D/'test_eligible_candidates.npz')\naccepted=sum(eligibility.is_eligible(int(u),int(i)) for u,i in checked[['uid','item_id']].itertuples(index=False,name=None))\nassert accepted==len(checked)\nassert list(frame.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\nraw=path.read_bytes()\nprint({\n 'regular_file': True,\n 'rows': len(frame),\n 'users': frame.uid.nunique(),\n 'items_per_user_min': int(frame.groupby('uid').item_id.nunique().min()),\n 'items_per_user_max': int(frame.groupby('uid').item_id.nunique().max()),\n 'rank_min': int(frame['rank'].min()),\n 'rank_max': int(frame['rank'].max()),\n 'eligible_pairs': accepted,\n 'columns': list(frame.columns),\n 'dtypes': {c:str(frame[c].dtype) for c in frame.columns},\n 'sha256': hashlib.sha256(raw).hexdigest(),\n 'bytes': len(raw),\n})\nPY\nsed -n '1,280p' /app/solution/submission_run.json\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md","workdir":"/app","yield_time_ms":30000,"max_output_tokens":24000});
text(r.output);

Tool response +46:29

Script completed
Wall time 2.8 seconds
Output:

{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}
{
  "model": "yambda-windowed-sequence-two-tower-hard-negative",
  "stage": "submission",
  "output": "/app/solution/submission.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 326.608,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "augmented-dot-product-with-item-popularity-bias",
    "popularity_bias": 0.2,
    "objective": "sample-weighted-pointwise-bce-at-every-timestep",
    "negative_sampling": {
      "uniform_pool": 24,
      "propensity_corrected_inbatch_pool": 8,
      "item_neighbor_pool": 0,
      "random_retained": 0,
      "query_hard_retained": 0
    },
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "hard_negatives": 0,
    "hard_negative_pool_size": 64,
    "query_random_negatives": 0,
    "query_hard_negatives": 0,
    "loss_objective": "pointwise-bce",
    "popularity_bias": 0.2,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20712971,
  "all_next_item_targets": 20703818,
  "selected_training_targets": 1067606,
  "selected_validation_targets": 131997,
  "discarded_old_targets": 19504215,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5383531182848916,
      "positive_logit": 0.7500169938662775,
      "negative_logit": -0.6300986730886449,
      "pair_accuracy": 0.7552423073212402,
      "targets": 1067606.0,
      "target_weight_sum": 641383.25,
      "batches": 270.0,
      "seconds": 222.40853657900016,
      "validation_loss": 0.49794868927621677,
      "validation_targets": 131997.0,
      "validation_target_weight_sum": 80328.75,
      "validation_seconds": 15.82028488099968
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.651851773262024,
    "query_norm_std": 0.8145269751548767,
    "item_norm_mean": 1.9033515453338623,
    "item_norm_std": 0.9598209857940674
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md
-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  90K Sep 25 00:45 /app/solution/train_current_model.py
-rw-r--r-- 1 agent agent  16K Sep 25 00:04 /app/solution/two_tower_model.py

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c51649b887d29fe9d18c558aaf6f",
  "usage": {
    "input_tokens": 121394,
    "cached_input_tokens": 120960,
    "cache_write_input_tokens": 0,
    "output_tokens": 710,
    "reasoning_output_tokens": 149,
    "total_tokens": 122104
  },
  "turn_token_usage": {
    "input_tokens": 14570395,
    "cached_input_tokens": 14362624,
    "cache_write_input_tokens": 0,
    "output_tokens": 48622,
    "reasoning_output_tokens": 23912,
    "total_tokens": 14619017
  },
  "thread_token_usage": {
    "input_tokens": 14570395,
    "cached_input_tokens": 14362624,
    "cache_write_input_tokens": 0,
    "output_tokens": 48622,
    "reasoning_output_tokens": 23912,
    "total_tokens": 14619017
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-46db45fd-73b6-4ec0-9b3f-75cdcbc35ade",
    "process_id": "34988",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); path=Path('/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nframe=pd.read_parquet(path)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nchecked=validate_ranking(frame,targets)\neligibility=CandidateSet(D/'test_eligible_candidates.npz')\naccepted=sum(eligibility.is_eligible(int(u),int(i)) for u,i in checked[['uid','item_id']].itertuples(index=False,name=None))\nassert accepted==len(checked)\nassert list(frame.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\nraw=path.read_bytes()\nprint({\n 'regular_file': True,\n 'rows': len(frame),\n 'users': frame.uid.nunique(),\n 'items_per_user_min': int(frame.groupby('uid').item_id.nunique().min()),\n 'items_per_user_max': int(frame.groupby('uid').item_id.nunique().max()),\n 'rank_min': int(frame['rank'].min()),\n 'rank_max': int(frame['rank'].max()),\n 'eligible_pairs': accepted,\n 'columns': list(frame.columns),\n 'dtypes': {c:str(frame[c].dtype) for c in frame.columns},\n 'sha256': hashlib.sha256(raw).hexdigest(),\n 'bytes': len(raw),\n})\nPY\nsed -n '1,280p' /app/solution/submission_run.json\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); path=Path('/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nframe=pd.read_parquet(path)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nchecked=validate_ranking(frame,targets)\neligibility=CandidateSet(D/'test_eligible_candidates.npz')\naccepted=sum(eligibility.is_eligible(int(u),int(i)) for u,i in checked[['uid','item_id']].itertuples(index=False,name=None))\nassert accepted==len(checked)\nassert list(frame.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\nraw=path.read_bytes()\nprint({\n 'regular_file': True,\n 'rows': len(frame),\n 'users': frame.uid.nunique(),\n 'items_per_user_min': int(frame.groupby('uid').item_id.nunique().min()),\n 'items_per_user_max': int(frame.groupby('uid').item_id.nunique().max()),\n 'rank_min': int(frame['rank'].min()),\n 'rank_max': int(frame['rank'].max()),\n 'eligible_pairs': accepted,\n 'columns': list(frame.columns),\n 'dtypes': {c:str(frame[c].dtype) for c in frame.columns},\n 'sha256': hashlib.sha256(raw).hexdigest(),\n 'bytes': len(raw),\n})\nPY\nsed -n '1,280p' /app/solution/submission_run.json\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 326.608,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"augmented-dot-product-with-item-popularity-bias\",\n    \"popularity_bias\": 0.2,\n    \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform_pool\": 24,\n      \"propensity_corrected_inbatch_pool\": 8,\n      \"item_neighbor_pool\": 0,\n      \"random_retained\": 0,\n      \"query_hard_retained\": 0\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negatives\": 0,\n    \"hard_negative_pool_size\": 64,\n    \"query_random_negatives\": 0,\n    \"query_hard_negatives\": 0,\n    \"loss_objective\": \"pointwise-bce\",\n    \"popularity_bias\": 0.2,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531182848916,\n      \"positive_logit\": 0.7500169938662775,\n      \"negative_logit\": -0.6300986730886449,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 222.40853657900016,\n      \"validation_loss\": 0.49794868927621677,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 15.82028488099968\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269751548767,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598209857940674\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 25 00:04 /app/solution/two_tower_model.py\n",
    "stderr": "",
    "aggregated_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 326.608,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"augmented-dot-product-with-item-popularity-bias\",\n    \"popularity_bias\": 0.2,\n    \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform_pool\": 24,\n      \"propensity_corrected_inbatch_pool\": 8,\n      \"item_neighbor_pool\": 0,\n      \"random_retained\": 0,\n      \"query_hard_retained\": 0\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negatives\": 0,\n    \"hard_negative_pool_size\": 64,\n    \"query_random_negatives\": 0,\n    \"query_hard_negatives\": 0,\n    \"loss_objective\": \"pointwise-bce\",\n    \"popularity_bias\": 0.2,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531182848916,\n      \"positive_logit\": 0.7500169938662775,\n      \"negative_logit\": -0.6300986730886449,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 222.40853657900016,\n      \"validation_loss\": 0.49794868927621677,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 15.82028488099968\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269751548767,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598209857940674\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 25 00:04 /app/solution/two_tower_model.py\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 661356770
    },
    "formatted_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n  \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 326.608,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"augmented-dot-product-with-item-popularity-bias\",\n    \"popularity_bias\": 0.2,\n    \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n    \"negative_sampling\": {\n      \"uniform_pool\": 24,\n      \"propensity_corrected_inbatch_pool\": 8,\n      \"item_neighbor_pool\": 0,\n      \"random_retained\": 0,\n      \"query_hard_retained\": 0\n    },\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negatives\": 0,\n    \"hard_negative_pool_size\": 64,\n    \"query_random_negatives\": 0,\n    \"query_hard_negatives\": 0,\n    \"loss_objective\": \"pointwise-bce\",\n    \"popularity_bias\": 0.2,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531182848916,\n      \"positive_logit\": 0.7500169938662775,\n      \"negative_logit\": -0.6300986730886449,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 222.40853657900016,\n      \"validation_loss\": 0.49794868927621677,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 15.82028488099968\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269751548767,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598209857940674\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 25 00:04 /app/solution/two_tower_model.py\n"
  }
}

198

Agent message

+46:40

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

199

Agent message

+46:49

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

200

exec command

+46:54

Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input +46:56

const r = await tools.exec_command({"cmd":"sed -n '1,90p' /app/solution/train_current_model.py; sed -n '2025,2095p' /app/solution/train_current_model.py; sed -n '2110,2280p' /app/solution/train_current_model.py; find /app/solution -maxdepth 1 -type l -print; test ! -e /app/solution/submission.csv","workdir":"/app","yield_time_ms":10000,"max_output_tokens":22000});
text(r.output);

Tool response +46:56

Script completed
Wall time 0.0 seconds
Output:

#!/usr/bin/env python3
"""Train the Yambda windowed two-tower retriever.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, and sample-weighted binary cross
entropy. The trainer can evaluate query-mined and item-neighbor negatives, but
the selected public-backtest configuration retains the robust 24 uniform plus
eight propensity-corrected in-batch examples: harder unobserved tracks behaved
as false negatives.  It corrects the popularity prior induced by this sampled
objective with one static item-side score coordinate, which remains an indexed
two-tower dot product. Every selected adjacent target retains a nonzero weight.
Only files under the supplied public directory are read.
"""
from __future__ import annotations

import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    hard_negatives: int = 0
    hard_negative_pool_size: int = 64
    query_random_negatives: int = 0
    query_hard_negatives: int = 0
    loss_objective: str = "pointwise-bce"
    popularity_bias: float = 0.20
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)
class CandidateSet:
    uids: np.ndarray
    item_ids: np.ndarray
    packed: np.ndarray

            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
            counts = np.zeros(len(output_rows), dtype=np.int32)
            for local_row, sequence_row in enumerate(rows):
                if sequence_row < 0:
                    continue
                count = min(steps, max(int(group_lengths[local_row]) - start, 0))
                if not count:
                    continue
                offset = int(prepared.offsets[sequence_row])
                selected = slice(offset + start, offset + start + count)
                items[local_row, :count] = prepared.sequence_items[selected]
                features[local_row, :count] = prepared.event_features[selected]
                counts[local_row] = count
            outputs, _ = event_sequence(
                model,
                torch.from_numpy(items.astype(np.int64)).to(device),
                torch.from_numpy(features.astype(np.float32)).to(device),
                hidden,
            )
            next_rows: list[torch.Tensor] = []
            for local_row, count in enumerate(counts):
                next_rows.append(
                    outputs[local_row, count - 1]
                    if count
                    else hidden[0, local_row]
                )
            hidden = torch.stack(next_rows).unsqueeze(0)

        liked_items = torch.from_numpy(
            prepared.inference_liked_items[output_rows].astype(np.int64)
        ).to(device)
        disliked_items = torch.from_numpy(
            prepared.inference_disliked_items[output_rows].astype(np.int64)
        ).to(device)
        queries = user_queries(
            model,
            hidden.squeeze(0),
            model._mean_context_channel(liked_items),
            model._mean_context_channel(disliked_items),
            torch.from_numpy(
                prepared.inference_user_features[output_rows].astype(np.float32)
            ).to(device),
        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


@torch.inference_mode()
def calibrated_retrieval_scores(
    queries: torch.Tensor,
    items: torch.Tensor,
    item_features: np.ndarray,
    popularity_bias: float,
) -> torch.Tensor:
    """Return the augmented two-tower dot product used by indexed serving."""
    scores = queries @ items.T
    if popularity_bias:
        popularity = torch.as_tensor(
            item_features[:, 0], device=scores.device, dtype=scores.dtype
        )
        scores -= popularity_bias * popularity
    return scores


@torch.inference_mode()
def rank_candidates(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    rows: list[tuple[int, int, int]] = []
    for start in range(0, len(eligibility.uids), ranking_batch_size):
        end = min(start + ranking_batch_size, len(eligibility.uids))
        # This is still a pure dot product: it is equivalent to appending 1
        # to every user vector and -bias * popularity to each item vector.
        scores = calibrated_retrieval_scores(
            queries[start:end],
            items,
            prepared.item_features,
            config.popularity_bias,
        )
        eligible = np.unpackbits(
            eligibility.packed[start:end],
            axis=1,
            count=len(prepared.item_ids),
            bitorder="little",
        ).astype(bool, copy=False)
        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
        for local_row, columns in enumerate(selected):
            uid = int(eligibility.uids[start + local_row])
            rows.extend(
                (uid, int(prepared.item_ids[column]), rank)
                for rank, column in enumerate(columns, start=1)
            )
        if end == len(eligibility.uids) or end % 1_024 == 0:
            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
    diagnostics = {
        "query_norm_mean": float(query_norm.mean()),
        "query_norm_std": float(query_norm.std()),
        "item_norm_mean": float(item_norm.mean()),
        "item_norm_std": float(item_norm.std()),
    }
    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics


def write_ranking(frame: pd.DataFrame, output: Path) -> None:
    output.parent.mkdir(parents=True, exist_ok=True)
    if output.suffix == ".parquet":
        frame.to_parquet(output, index=False, compression="zstd")
    elif output.suffix == ".csv":
        frame.to_csv(output, index=False)
    else:
        raise ValueError("--output must end in .parquet or .csv")


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
    )
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--public-dir", type=Path, required=True)
    parser.add_argument("--prepared-cache", type=Path)
    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
    parser.add_argument("--threads", type=int, default=8)
    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
    parser.add_argument(
        "--user-batch-size", type=int, default=Config.user_batch_size
    )
    parser.add_argument("--epochs", type=int, default=Config.epochs)
    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
    parser.add_argument(
        "--loss-microbatch-size",
        type=int,
        default=Config.loss_microbatch_size,
    )
    parser.add_argument(
        "--uniform-negatives", type=int, default=Config.uniform_negatives
    )
    parser.add_argument(
        "--inbatch-negatives", type=int, default=Config.inbatch_negatives
    )
    parser.add_argument(
        "--hard-negatives", type=int, default=Config.hard_negatives
    )
    parser.add_argument(
        "--hard-negative-pool-size",
        type=int,
        default=Config.hard_negative_pool_size,
    )
    parser.add_argument(
        "--query-random-negatives",
        type=int,
        default=Config.query_random_negatives,
    )
    parser.add_argument(
        "--query-hard-negatives",
        type=int,
        default=Config.query_hard_negatives,
    )
    parser.add_argument(
        "--loss-objective",
        choices=("pointwise-bce", "pairwise"),
        default=Config.loss_objective,
    )
    parser.add_argument(
        "--popularity-bias", type=float, default=Config.popularity_bias
    )
    parser.add_argument(
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--target-weighting",
        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
        "--organic-strong-target-weight",
        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
    parser.add_argument(
        "--discovery-horizon-days",
        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
    parser.add_argument(
        "--max-train-targets-per-user",
        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
        help="retain this many final validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )
    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-67c9b2e7-643b-43e5-92c6-1c789ce3591f",
    "process_id": "25522",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,90p' /app/solution/train_current_model.py; sed -n '2025,2095p' /app/solution/train_current_model.py; sed -n '2110,2280p' /app/solution/train_current_model.py; find /app/solution -maxdepth 1 -type l -print; test ! -e /app/solution/submission.csv"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,90p' /app/solution/train_current_model.py; sed -n '2025,2095p' /app/solution/train_current_model.py; sed -n '2110,2280p' /app/solution/train_current_model.py; find /app/solution -maxdepth 1 -type l -print; test ! -e /app/solution/submission.csv"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives.  It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    hard_negatives: int = 0\n    hard_negative_pool_size: int = 64\n    query_random_negatives: int = 0\n    query_hard_negatives: int = 0\n    loss_objective: str = \"pointwise-bce\"\n    popularity_bias: float = 0.20\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef calibrated_retrieval_scores(\n    queries: torch.Tensor,\n    items: torch.Tensor,\n    item_features: np.ndarray,\n    popularity_bias: float,\n) -> torch.Tensor:\n    \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n    scores = queries @ items.T\n    if popularity_bias:\n        popularity = torch.as_tensor(\n            item_features[:, 0], device=scores.device, dtype=scores.dtype\n        )\n        scores -= popularity_bias * popularity\n    return scores\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        # This is still a pure dot product: it is equivalent to appending 1\n        # to every user vector and -bias * popularity to each item vector.\n        scores = calibrated_retrieval_scores(\n            queries[start:end],\n            items,\n            prepared.item_features,\n            config.popularity_bias,\n        )\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n    )\n    parser.add_argument(\n        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n    )\n    parser.add_argument(\n        \"--hard-negatives\", type=int, default=Config.hard_negatives\n    )\n    parser.add_argument(\n        \"--hard-negative-pool-size\",\n        type=int,\n        default=Config.hard_negative_pool_size,\n    )\n    parser.add_argument(\n        \"--query-random-negatives\",\n        type=int,\n        default=Config.query_random_negatives,\n    )\n    parser.add_argument(\n        \"--query-hard-negatives\",\n        type=int,\n        default=Config.query_hard_negatives,\n    )\n    parser.add_argument(\n        \"--loss-objective\",\n        choices=(\"pointwise-bce\", \"pairwise\"),\n        default=Config.loss_objective,\n    )\n    parser.add_argument(\n        \"--popularity-bias\", type=float, default=Config.popularity_bias\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n",
    "stderr": "",
    "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives.  It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    hard_negatives: int = 0\n    hard_negative_pool_size: int = 64\n    query_random_negatives: int = 0\n    query_hard_negatives: int = 0\n    loss_objective: str = \"pointwise-bce\"\n    popularity_bias: float = 0.20\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef calibrated_retrieval_scores(\n    queries: torch.Tensor,\n    items: torch.Tensor,\n    item_features: np.ndarray,\n    popularity_bias: float,\n) -> torch.Tensor:\n    \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n    scores = queries @ items.T\n    if popularity_bias:\n        popularity = torch.as_tensor(\n            item_features[:, 0], device=scores.device, dtype=scores.dtype\n        )\n        scores -= popularity_bias * popularity\n    return scores\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        # This is still a pure dot product: it is equivalent to appending 1\n        # to every user vector and -bias * popularity to each item vector.\n        scores = calibrated_retrieval_scores(\n            queries[start:end],\n            items,\n            prepared.item_features,\n            config.popularity_bias,\n        )\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n    )\n    parser.add_argument(\n        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n    )\n    parser.add_argument(\n        \"--hard-negatives\", type=int, default=Config.hard_negatives\n    )\n    parser.add_argument(\n        \"--hard-negative-pool-size\",\n        type=int,\n        default=Config.hard_negative_pool_size,\n    )\n    parser.add_argument(\n        \"--query-random-negatives\",\n        type=int,\n        default=Config.query_random_negatives,\n    )\n    parser.add_argument(\n        \"--query-hard-negatives\",\n        type=int,\n        default=Config.query_hard_negatives,\n    )\n    parser.add_argument(\n        \"--loss-objective\",\n        choices=(\"pointwise-bce\", \"pairwise\"),\n        default=Config.loss_objective,\n    )\n    parser.add_argument(\n        \"--popularity-bias\", type=float, default=Config.popularity_bias\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3260
    },
    "formatted_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives.  It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    hard_negatives: int = 0\n    hard_negative_pool_size: int = 64\n    query_random_negatives: int = 0\n    query_hard_negatives: int = 0\n    loss_objective: str = \"pointwise-bce\"\n    popularity_bias: float = 0.20\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef calibrated_retrieval_scores(\n    queries: torch.Tensor,\n    items: torch.Tensor,\n    item_features: np.ndarray,\n    popularity_bias: float,\n) -> torch.Tensor:\n    \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n    scores = queries @ items.T\n    if popularity_bias:\n        popularity = torch.as_tensor(\n            item_features[:, 0], device=scores.device, dtype=scores.dtype\n        )\n        scores -= popularity_bias * popularity\n    return scores\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        # This is still a pure dot product: it is equivalent to appending 1\n        # to every user vector and -bias * popularity to each item vector.\n        scores = calibrated_retrieval_scores(\n            queries[start:end],\n            items,\n            prepared.item_features,\n            config.popularity_bias,\n        )\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n    )\n    parser.add_argument(\n        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n    )\n    parser.add_argument(\n        \"--hard-negatives\", type=int, default=Config.hard_negatives\n    )\n    parser.add_argument(\n        \"--hard-negative-pool-size\",\n        type=int,\n        default=Config.hard_negative_pool_size,\n    )\n    parser.add_argument(\n        \"--query-random-negatives\",\n        type=int,\n        default=Config.query_random_negatives,\n    )\n    parser.add_argument(\n        \"--query-hard-negatives\",\n        type=int,\n        default=Config.query_hard_negatives,\n    )\n    parser.add_argument(\n        \"--loss-objective\",\n        choices=(\"pointwise-bce\", \"pairwise\"),\n        default=Config.loss_objective,\n    )\n    parser.add_argument(\n        \"--popularity-bias\", type=float, default=Config.popularity_bias\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c5272fe487d2af3f4232733a036b",
  "usage": {
    "input_tokens": 123473,
    "cached_input_tokens": 121216,
    "cache_write_input_tokens": 0,
    "output_tokens": 1415,
    "reasoning_output_tokens": 1283,
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  "turn_token_usage": {
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    "cached_input_tokens": 14483840,
    "cache_write_input_tokens": 0,
    "output_tokens": 50037,
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  "thread_token_usage": {
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}

201

Agent message

+47:08

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

202

Final response

+47:13

Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Completed and validated.

Public NDCG@10: 0.021280 → 0.023208 (+9.1%).
Final submission: submission.parquet
Improved trainer: train_current_model.py
Experiment record: README.md
Tests: test_improvements.py

Final audit passed: 845,600 rows, exactly 8,456 users, 100 distinct eligible items and ranks 1–100 per user, all integer-valued columns, and no rejected test candidate pairs.

Harness context token usage record

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  "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b",
  "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732",
  "response_id": "resp_09e7a180cbf96934016ab5c542bd4087d2be0ea1bf3db2168b",
  "usage": {
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    "cached_input_tokens": 123264,
    "cache_write_input_tokens": 0,
    "output_tokens": 954,
    "reasoning_output_tokens": 803,
    "total_tokens": 128661
  },
  "turn_token_usage": {
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}

About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt: sol-max-0-153-4-production-r03-20260924T221413Z
Recorded model: gpt-5.6-sol
Harness version: 0.153.4
Session: 01a0d5df-8eec-7470-8ab0-ef79e6e12f3b
Source SHA-256: 5011b9603ca9b2b9bd1fa5de6588c6d1f9970b931417fabedc59cd682edb4bfb

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. System instructions present in this archive are included above.

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