Agent trace

Production two-tower improvement sol-0.153.4-max-01

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 139 empty thinking entries; tool calls are shown separately.

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Agent: gpt-5.6-sol · Codex · max effort
Recorded conversation: 49:44 · 145 steps
Private reward: 0.017401 · Pass
Attempt cost: $6.45

Thinking
Model text
Tool call
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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>2026-09-24</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": "2026-09-24",
      "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": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-24",
  "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.

exec command

+00:04

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 baseline and inspect the current negative-sampling path, then make targeted objective changes and compare them on the public backtest. I’ll keep a valid submission under /app/solution/ as soon as possible, and only replace it after a verified improvement.

Tool callexecTool call Returned

Tool call · Input +00:07

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 && sed -n '1,260p' /app/solution/train_current_model.py && sed -n '1,300p' /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

Tool response +00:07

Script completed
Wall time 0.1 seconds
Output:

#!/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,

Harness context token usage record

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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4c1d9c31-45c5-4c60-afde-b1e6722ee86d",
    "process_id": "33190",
    "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 && sed -n '1,260p' /app/solution/train_current_model.py && sed -n '1,300p' /app/solution/two_tower_model.py"
    ],
    "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 && sed -n '1,260p' /app/solution/train_current_model.py && sed -n '1,300p' /app/solution/two_tower_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "#!/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",
    "stderr": "",
    "aggregated_output": "#!/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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4610
    },
    "formatted_output": "#!/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"
  }
}

exec command

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Tool callexecTool call Returned

Tool call · Input +00:10

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

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Script completed
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Output:

/app/solution/two_tower_model.py:27:class TowerConfig:
/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24
/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8
/app/solution/two_tower_model.py:42:    inbatch_correction_power: float = 0.5
/app/solution/two_tower_model.py:43:    inbatch_correction_min: float = 0.25
/app/solution/two_tower_model.py:44:    inbatch_correction_max: float = 4.0
/app/solution/two_tower_model.py:49:class ListenEvents:
/app/solution/two_tower_model.py:57:class ExplicitEvents:
/app/solution/two_tower_model.py:65:def log(message: str) -> None:
/app/solution/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
/app/solution/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
/app/solution/two_tower_model.py:106:def iter_listen_users(
/app/solution/two_tower_model.py:157:def merge_listen_streams(
/app/solution/two_tower_model.py:175:def load_explicit_events(
/app/solution/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:
/app/solution/two_tower_model.py:238:def pack_active_items(
/app/solution/two_tower_model.py:251:def load_inference_state(
/app/solution/two_tower_model.py:284:class SequentialTwoTower(nn.Module):
/app/solution/two_tower_model.py:346:def invalid_negative_mask(
/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,
/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])
/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):
/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(
/app/solution/two_tower_model.py:358:            negatives[:, :column]
/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(
/app/solution/two_tower_model.py:369:    inbatch_count: int,
/app/solution/two_tower_model.py:374:    """Draw uniform and unique-target in-batch negatives."""
/app/solution/two_tower_model.py:382:    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
/app/solution/two_tower_model.py:384:    if inbatch_count:
/app/solution/two_tower_model.py:386:        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
/app/solution/two_tower_model.py:396:            inbatch = unique_targets[columns]
/app/solution/two_tower_model.py:397:        if corrected_count < inbatch_count:
/app/solution/two_tower_model.py:400:                (batch_size, inbatch_count - corrected_count),
/app/solution/two_tower_model.py:404:            inbatch = torch.cat([inbatch, filler], dim=1)
/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
/app/solution/two_tower_model.py:411:        ] = item_correction[inbatch[:, :corrected_count]]
/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)
/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(
/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items
/app/solution/two_tower_model.py:419:            return negatives, correction
/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements
/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(
/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items
/app/solution/two_tower_model.py:432:        raise RuntimeError("mixed negative resampling failed false-negative filter")
/app/solution/two_tower_model.py:433:    return negatives, correction
/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and
/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state
/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
/app/solution/train_current_model.py:20:import argparse
/app/solution/train_current_model.py:41:class Config:
/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536
/app/solution/train_current_model.py:56:    uniform_negatives: int = 24
/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8
/app/solution/train_current_model.py:58:    inbatch_correction_power: float = 0.5
/app/solution/train_current_model.py:59:    inbatch_correction_min: float = 0.25
/app/solution/train_current_model.py:60:    inbatch_correction_max: float = 4.0
/app/solution/train_current_model.py:61:    negative_filter_scope: str = "full-training-history"
/app/solution/train_current_model.py:76:class CandidateSet:
/app/solution/train_current_model.py:99:class SequenceData:
/app/solution/train_current_model.py:127:class TargetPartition:
/app/solution/train_current_model.py:140:def log(message: str) -> None:
/app/solution/train_current_model.py:144:def seed_everything(seed: int) -> None:
/app/solution/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:
/app/solution/train_current_model.py:152:    return base.TowerConfig(
/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,
/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,
/app/solution/train_current_model.py:167:        inbatch_correction_power=config.inbatch_correction_power,
/app/solution/train_current_model.py:168:        inbatch_correction_min=config.inbatch_correction_min,
/app/solution/train_current_model.py:169:        inbatch_correction_max=config.inbatch_correction_max,
/app/solution/train_current_model.py:174:def append_state_snapshot(
/app/solution/train_current_model.py:190:def causal_activity_features(
/app/solution/train_current_model.py:247:def build_sequence_parts(
/app/solution/train_current_model.py:314:def final_user_features(
/app/solution/train_current_model.py:341:def prepare_data(
/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
/app/solution/train_current_model.py:477:        "event_time_feature": "exp-negative-gap-over-60-days",
/app/solution/train_current_model.py:481:def save_cache(
/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
/app/solution/train_current_model.py:511:def load_or_prepare(
/app/solution/train_current_model.py:534:def build_item_correction(
/app/solution/train_current_model.py:562:        config.inbatch_correction_power,
/app/solution/train_current_model.py:567:        config.inbatch_correction_min,
/app/solution/train_current_model.py:568:        config.inbatch_correction_max,
/app/solution/train_current_model.py:572:        f"inbatch correction: min={correction.min():.3f} "
/app/solution/train_current_model.py:578:def build_known_positive_filter(
/app/solution/train_current_model.py:603:        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
/app/solution/train_current_model.py:609:def invalid_full_history_negatives(
/app/solution/train_current_model.py:610:    negatives: np.ndarray,
/app/solution/train_current_model.py:616:    """Return row-level invalid negatives using the complete training prefix."""
/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3
/app/solution/train_current_model.py:618:    bit_columns = negatives & 7
/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]
/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):
/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]
/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(
/app/solution/train_current_model.py:641:    """Draw mixed negatives and reject all known public-training positives."""
/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),
/app/solution/train_current_model.py:651:    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
/app/solution/train_current_model.py:653:    if config.inbatch_negatives:
/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
/app/solution/train_current_model.py:671:            inbatch = unique_targets[columns]
/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:
/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),
/app/solution/train_current_model.py:679:            inbatch = torch.cat([inbatch, filler], dim=1)
/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)
/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count
/app/solution/train_current_model.py:686:        ] = item_correction[inbatch[:, :corrected_count]].numpy()
/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(
/app/solution/train_current_model.py:689:            negatives,
/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),
/app/solution/train_current_model.py:706:        negatives[invalid] = replacements
/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(
/app/solution/train_current_model.py:710:        negatives,
/app/solution/train_current_model.py:717:        raise RuntimeError("full-history negative resampling failed")
/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),
/app/solution/train_current_model.py:724:def draw_training_negatives(
/app/solution/train_current_model.py:736:    if config.negative_filter_scope == "full-training-history":
/app/solution/train_current_model.py:738:            raise RuntimeError("full-history negative filter was not built")
/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(
/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(
/app/solution/train_current_model.py:755:        config.uniform_negatives,
/app/solution/train_current_model.py:756:        config.inbatch_negatives,
/app/solution/train_current_model.py:763:def event_sequence(
/app/solution/train_current_model.py:774:def pool_explicit_states(
/app/solution/train_current_model.py:798:def user_queries(
/app/solution/train_current_model.py:813:def candidate_vectors(
/app/solution/train_current_model.py:816:    negatives: torch.Tensor,
/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])
/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(
/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1
/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors
/app/solution/train_current_model.py:828:def history_windows(
/app/solution/train_current_model.py:845:def corrected_negative_loss(
/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives
/app/solution/train_current_model.py:853:    if config.uniform_negatives:
/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[
/app/solution/train_current_model.py:855:            :, : config.uniform_negatives
/app/solution/train_current_model.py:857:    if config.inbatch_negatives:
/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)
/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (
/app/solution/train_current_model.py:867:def retained_target_window_starts(
/app/solution/train_current_model.py:880:        raise RuntimeError("chronological target partition produced negative counts")
/app/solution/train_current_model.py:888:def chronological_target_partition(
/app/solution/train_current_model.py:914:def load_organic_like_outcomes(
/app/solution/train_current_model.py:955:def build_target_weights(
/app/solution/train_current_model.py:1035:def build_dense_block(
/app/solution/train_current_model.py:1111:def score_target_microbatches(
/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,
/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,
/app/solution/train_current_model.py:1132:        "loss_sum": 0.0,
/app/solution/train_current_model.py:1134:        "negative_sum": 0.0,
/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):
/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)
/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(
/app/solution/train_current_model.py:1165:            negatives[begin:end],
/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(
/app/solution/train_current_model.py:1169:            "bd,bkd->bk", query_vectors, negative_vectors
/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)
/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(
/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config
/app/solution/train_current_model.py:1178:        loss_sum = (
/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0
/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):
/app/solution/train_current_model.py:1182:            raise RuntimeError("dense next-item training produced non-finite loss")
/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()
/app/solution/train_current_model.py:1188:        statistics["loss_sum"] += float(loss_sum.detach().cpu())
/app/solution/train_current_model.py:1190:        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:
/app/solution/train_current_model.py:1203:def run_training_epoch(
/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives
/app/solution/train_current_model.py:1228:    total_loss = 0.0
/app/solution/train_current_model.py:1232:    total_negative = 0.0
/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(
/app/solution/train_current_model.py:1314:                negatives,
/app/solution/train_current_model.py:1315:                negative_weights,
/app/solution/train_current_model.py:1327:            total_loss += statistics["loss_sum"]
/app/solution/train_current_model.py:1331:            total_negative += statistics["negative_sum"]
/app/solution/train_current_model.py:1337:                batch_bce = statistics["loss_sum"] / max(
/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)
/app/solution/train_current_model.py:1351:        "loss": total_loss / max(total_weight, 1.0e-12),
/app/solution/train_current_model.py:1353:        "negative_logit": total_negative / max(total_targets * negative_count, 1),
/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),
/app/solution/train_current_model.py:1364:def evaluate_validation_bce(
/app/solution/train_current_model.py:1388:    total_loss = 0.0
/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(
/app/solution/train_current_model.py:1469:                negatives,
/app/solution/train_current_model.py:1470:                negative_weights,
/app/solution/train_current_model.py:1475:            total_loss += statistics["loss_sum"]
/app/solution/train_current_model.py:1481:        "validation_loss": total_loss / max(total_weight, 1.0e-12),
/app/solution/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
/app/solution/train_current_model.py:1495:def save_epoch_checkpoint(
/app/solution/train_current_model.py:1521:def train_model(
/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == "full-training-history"
/app/solution/train_current_model.py:1571:    best_loss = float("inf")
/app/solution/train_current_model.py:1601:            "negative_filter_scope", "last-100"
/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope
/app/solution/train_current_model.py:1657:            prior.pop("validation_loss", None)
/app/solution/train_current_model.py:1660:                f"{stored_filter_scope}->{config.negative_filter_scope}, "
/app/solution/train_current_model.py:1666:        if "validation_loss" not in prior:
/app/solution/train_current_model.py:1682:        best_loss = float(prior["validation_loss"])
/app/solution/train_current_model.py:1687:            f"loss={best_loss:.6f}"
/app/solution/train_current_model.py:1731:            diagnostics["validation_loss"]
/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta
/app/solution/train_current_model.py:1735:            best_loss = diagnostics["validation_loss"]
/app/solution/train_current_model.py:1754:            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
/app/solution/train_current_model.py:1755:            f"validation_loss={diagnostics['validation_loss']:.5f} "
/app/solution/train_current_model.py:1766:                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
/app/solution/train_current_model.py:1776:        f"loss={best_loss:.6f}"
/app/solution/train_current_model.py:1782:def inference_queries(
/app/solution/train_current_model.py:1861:def rank_candidates(
/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
/app/solution/train_current_model.py:1922:def main() -> int:
/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)
/app/solution/train_current_model.py:1924:    parser.add_argument(
/app/solution/train_current_model.py:1927:    parser.add_argument("--output", type=Path, required=True)
/app/solution/train_current_model.py:1928:    parser.add_argument("--public-dir", type=Path, required=True)
/app/solution/train_current_model.py:1929:    parser.add_argument("--prepared-cache", type=Path)
/app/solution/train_current_model.py:1930:    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
/app/solution/train_current_model.py:1931:    parser.add_argument("--threads", type=int, default=8)
/app/solution/train_current_model.py:1932:    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
/app/solution/train_current_model.py:1933:    parser.add_argument(
/app/solution/train_current_model.py:1936:    parser.add_argument("--epochs", type=int, default=Config.epochs)
/app/solution/train_current_model.py:1937:    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
/app/solution/train_current_model.py:1938:    parser.add_argument(
/app/solution/train_current_model.py:1939:        "--loss-microbatch-size",
/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,
/app/solution/train_current_model.py:1943:    parser.add_argument(
/app/solution/train_current_model.py:1944:        "--negative-filter-scope",
/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,
/app/solution/train_current_model.py:1948:    parser.add_argument(
/app/solution/train_current_model.py:1953:    parser.add_argument(
/app/solution/train_current_model.py:1958:    parser.add_argument(
/app/solution/train_current_model.py:1963:    parser.add_argument(
/app/solution/train_current_model.py:1968:    parser.add_argument(
/app/solution/train_current_model.py:1973:    parser.add_argument(
/app/solution/train_current_model.py:1978:    parser.add_argument(
/app/solution/train_current_model.py:1984:    parser.add_argument(
/app/solution/train_current_model.py:1990:    parser.add_argument(
/app/solution/train_current_model.py:1995:    parser.add_argument(
/app/solution/train_current_model.py:2000:    parser.add_argument("--checkpoint-dir", type=Path)
/app/solution/train_current_model.py:2001:    parser.add_argument("--resume-checkpoint", type=Path)
/app/solution/train_current_model.py:2002:    parser.add_argument("--seed", type=int, default=Config.seed)
/app/solution/train_current_model.py:2003:    parser.add_argument("--ranking-batch-size", type=int, default=64)
/app/solution/train_current_model.py:2004:    parser.add_argument("--prepare-only", action="store_true")
/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1
/app/solution/train_current_model.py:2028:    config = Config(
/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,
/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,
/app/solution/train_current_model.py:2126:                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
/app/solution/train_current_model.py:2129:                    "loss_execution": "gradient-accumulated-microbatches",
/app/solution/train_current_model.py:2136:                    "negative_sampling": (
/app/solution/train_current_model.py:2137:                        "24-uniform-plus-8-propensity-corrected-inbatch"
/app/solution/train_current_model.py:2139:                    "false_negative_filter": config.negative_filter_scope,

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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app/solution/two_tower_model.py:27:class TowerConfig:\n/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24\n/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8\n/app/solution/two_tower_model.py:42:    inbatch_correction_power: float = 0.5\n/app/solution/two_tower_model.py:43:    inbatch_correction_min: float = 0.25\n/app/solution/two_tower_model.py:44:    inbatch_correction_max: float = 4.0\n/app/solution/two_tower_model.py:49:class ListenEvents:\n/app/solution/two_tower_model.py:57:class ExplicitEvents:\n/app/solution/two_tower_model.py:65:def log(message: str) -> None:\n/app/solution/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/app/solution/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/app/solution/two_tower_model.py:106:def iter_listen_users(\n/app/solution/two_tower_model.py:157:def merge_listen_streams(\n/app/solution/two_tower_model.py:175:def load_explicit_events(\n/app/solution/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/app/solution/two_tower_model.py:238:def pack_active_items(\n/app/solution/two_tower_model.py:251:def load_inference_state(\n/app/solution/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/app/solution/two_tower_model.py:346:def invalid_negative_mask(\n/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,\n/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/app/solution/two_tower_model.py:358:            negatives[:, :column]\n/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/app/solution/two_tower_model.py:369:    inbatch_count: int,\n/app/solution/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:382:    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n/app/solution/two_tower_model.py:384:    if inbatch_count:\n/app/solution/two_tower_model.py:386:        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n/app/solution/two_tower_model.py:396:            inbatch = unique_targets[columns]\n/app/solution/two_tower_model.py:397:        if corrected_count < inbatch_count:\n/app/solution/two_tower_model.py:400:                (batch_size, inbatch_count - corrected_count),\n/app/solution/two_tower_model.py:404:            inbatch = torch.cat([inbatch, filler], dim=1)\n/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/app/solution/two_tower_model.py:411:        ] = item_correction[inbatch[:, :corrected_count]]\n/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:419:            return negatives, correction\n/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements\n/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/app/solution/two_tower_model.py:433:    return negatives, correction\n/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/app/solution/train_current_model.py:20:import argparse\n/app/solution/train_current_model.py:41:class Config:\n/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/app/solution/train_current_model.py:56:    uniform_negatives: int = 24\n/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8\n/app/solution/train_current_model.py:58:    inbatch_correction_power: float = 0.5\n/app/solution/train_current_model.py:59:    inbatch_correction_min: float = 0.25\n/app/solution/train_current_model.py:60:    inbatch_correction_max: float = 4.0\n/app/solution/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:76:class CandidateSet:\n/app/solution/train_current_model.py:99:class SequenceData:\n/app/solution/train_current_model.py:127:class TargetPartition:\n/app/solution/train_current_model.py:140:def log(message: str) -> None:\n/app/solution/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/app/solution/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/app/solution/train_current_model.py:152:    return base.TowerConfig(\n/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/app/solution/train_current_model.py:167:        inbatch_correction_power=config.inbatch_correction_power,\n/app/solution/train_current_model.py:168:        inbatch_correction_min=config.inbatch_correction_min,\n/app/solution/train_current_model.py:169:        inbatch_correction_max=config.inbatch_correction_max,\n/app/solution/train_current_model.py:174:def append_state_snapshot(\n/app/solution/train_current_model.py:190:def causal_activity_features(\n/app/solution/train_current_model.py:247:def build_sequence_parts(\n/app/solution/train_current_model.py:314:def final_user_features(\n/app/solution/train_current_model.py:341:def prepare_data(\n/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/app/solution/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:481:def save_cache(\n/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/app/solution/train_current_model.py:511:def load_or_prepare(\n/app/solution/train_current_model.py:534:def build_item_correction(\n/app/solution/train_current_model.py:562:        config.inbatch_correction_power,\n/app/solution/train_current_model.py:567:        config.inbatch_correction_min,\n/app/solution/train_current_model.py:568:        config.inbatch_correction_max,\n/app/solution/train_current_model.py:572:        f\"inbatch correction: min={correction.min():.3f} \"\n/app/solution/train_current_model.py:578:def build_known_positive_filter(\n/app/solution/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/app/solution/train_current_model.py:609:def invalid_full_history_negatives(\n/app/solution/train_current_model.py:610:    negatives: np.ndarray,\n/app/solution/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3\n/app/solution/train_current_model.py:618:    bit_columns = negatives & 7\n/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:651:    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n/app/solution/train_current_model.py:653:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/app/solution/train_current_model.py:671:            inbatch = unique_targets[columns]\n/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/app/solution/train_current_model.py:679:            inbatch = torch.cat([inbatch, filler], dim=1)\n/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/app/solution/train_current_model.py:686:        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:689:            negatives,\n/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:706:        negatives[invalid] = replacements\n/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:710:        negatives,\n/app/solution/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:724:def draw_training_negatives(\n/app/solution/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/app/solution/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/app/solution/train_current_model.py:755:        config.uniform_negatives,\n/app/solution/train_current_model.py:756:        config.inbatch_negatives,\n/app/solution/train_current_model.py:763:def event_sequence(\n/app/solution/train_current_model.py:774:def pool_explicit_states(\n/app/solution/train_current_model.py:798:def user_queries(\n/app/solution/train_current_model.py:813:def candidate_vectors(\n/app/solution/train_current_model.py:816:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors\n/app/solution/train_current_model.py:828:def history_windows(\n/app/solution/train_current_model.py:845:def corrected_negative_loss(\n/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:853:    if config.uniform_negatives:\n/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/app/solution/train_current_model.py:855:            :, : config.uniform_negatives\n/app/solution/train_current_model.py:857:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (\n/app/solution/train_current_model.py:867:def retained_target_window_starts(\n/app/solution/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:888:def chronological_target_partition(\n/app/solution/train_current_model.py:914:def load_organic_like_outcomes(\n/app/solution/train_current_model.py:955:def build_target_weights(\n/app/solution/train_current_model.py:1035:def build_dense_block(\n/app/solution/train_current_model.py:1111:def score_target_microbatches(\n/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/app/solution/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/app/solution/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/app/solution/train_current_model.py:1165:            negatives[begin:end],\n/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(\n/app/solution/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/app/solution/train_current_model.py:1178:        loss_sum = (\n/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/app/solution/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/app/solution/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/app/solution/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/app/solution/train_current_model.py:1203:def run_training_epoch(\n/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:1228:    total_loss = 0.0\n/app/solution/train_current_model.py:1232:    total_negative = 0.0\n/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1314:                negatives,\n/app/solution/train_current_model.py:1315:                negative_weights,\n/app/solution/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/app/solution/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/app/solution/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1364:def evaluate_validation_bce(\n/app/solution/train_current_model.py:1388:    total_loss = 0.0\n/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1469:                negatives,\n/app/solution/train_current_model.py:1470:                negative_weights,\n/app/solution/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/app/solution/train_current_model.py:1495:def save_epoch_checkpoint(\n/app/solution/train_current_model.py:1521:def train_model(\n/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/app/solution/train_current_model.py:1571:    best_loss = float(\"inf\")\n/app/solution/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/app/solution/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/app/solution/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/app/solution/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/app/solution/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/app/solution/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/app/solution/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/app/solution/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/app/solution/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1782:def inference_queries(\n/app/solution/train_current_model.py:1861:def rank_candidates(\n/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/app/solution/train_current_model.py:1922:def main() -> int:\n/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/app/solution/train_current_model.py:1924:    parser.add_argument(\n/app/solution/train_current_model.py:1927:    parser.add_argument(\"--output\", type=Path, required=True)\n/app/solution/train_current_model.py:1928:    parser.add_argument(\"--public-dir\", type=Path, required=True)\n/app/solution/train_current_model.py:1929:    parser.add_argument(\"--prepared-cache\", type=Path)\n/app/solution/train_current_model.py:1930:    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n/app/solution/train_current_model.py:1931:    parser.add_argument(\"--threads\", type=int, default=8)\n/app/solution/train_current_model.py:1932:    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n/app/solution/train_current_model.py:1933:    parser.add_argument(\n/app/solution/train_current_model.py:1936:    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n/app/solution/train_current_model.py:1937:    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n/app/solution/train_current_model.py:1938:    parser.add_argument(\n/app/solution/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/app/solution/train_current_model.py:1943:    parser.add_argument(\n/app/solution/train_current_model.py:1944:        \"--negative-filter-scope\",\n/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/app/solution/train_current_model.py:1948:    parser.add_argument(\n/app/solution/train_current_model.py:1953:    parser.add_argument(\n/app/solution/train_current_model.py:1958:    parser.add_argument(\n/app/solution/train_current_model.py:1963:    parser.add_argument(\n/app/solution/train_current_model.py:1968:    parser.add_argument(\n/app/solution/train_current_model.py:1973:    parser.add_argument(\n/app/solution/train_current_model.py:1978:    parser.add_argument(\n/app/solution/train_current_model.py:1984:    parser.add_argument(\n/app/solution/train_current_model.py:1990:    parser.add_argument(\n/app/solution/train_current_model.py:1995:    parser.add_argument(\n/app/solution/train_current_model.py:2000:    parser.add_argument(\"--checkpoint-dir\", type=Path)\n/app/solution/train_current_model.py:2001:    parser.add_argument(\"--resume-checkpoint\", type=Path)\n/app/solution/train_current_model.py:2002:    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n/app/solution/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/app/solution/train_current_model.py:2004:    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/app/solution/train_current_model.py:2028:    config = Config(\n/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/app/solution/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/app/solution/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/app/solution/train_current_model.py:2136:                    \"negative_sampling\": (\n/app/solution/train_current_model.py:2137:                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n/app/solution/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n",
    "stderr": "",
    "aggregated_output": "/app/solution/two_tower_model.py:27:class TowerConfig:\n/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24\n/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8\n/app/solution/two_tower_model.py:42:    inbatch_correction_power: float = 0.5\n/app/solution/two_tower_model.py:43:    inbatch_correction_min: float = 0.25\n/app/solution/two_tower_model.py:44:    inbatch_correction_max: float = 4.0\n/app/solution/two_tower_model.py:49:class ListenEvents:\n/app/solution/two_tower_model.py:57:class ExplicitEvents:\n/app/solution/two_tower_model.py:65:def log(message: str) -> None:\n/app/solution/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/app/solution/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/app/solution/two_tower_model.py:106:def iter_listen_users(\n/app/solution/two_tower_model.py:157:def merge_listen_streams(\n/app/solution/two_tower_model.py:175:def load_explicit_events(\n/app/solution/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/app/solution/two_tower_model.py:238:def pack_active_items(\n/app/solution/two_tower_model.py:251:def load_inference_state(\n/app/solution/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/app/solution/two_tower_model.py:346:def invalid_negative_mask(\n/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,\n/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/app/solution/two_tower_model.py:358:            negatives[:, :column]\n/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/app/solution/two_tower_model.py:369:    inbatch_count: int,\n/app/solution/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:382:    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n/app/solution/two_tower_model.py:384:    if inbatch_count:\n/app/solution/two_tower_model.py:386:        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n/app/solution/two_tower_model.py:396:            inbatch = unique_targets[columns]\n/app/solution/two_tower_model.py:397:        if corrected_count < inbatch_count:\n/app/solution/two_tower_model.py:400:                (batch_size, inbatch_count - corrected_count),\n/app/solution/two_tower_model.py:404:            inbatch = torch.cat([inbatch, filler], dim=1)\n/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/app/solution/two_tower_model.py:411:        ] = item_correction[inbatch[:, :corrected_count]]\n/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:419:            return negatives, correction\n/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements\n/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/app/solution/two_tower_model.py:433:    return negatives, correction\n/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/app/solution/train_current_model.py:20:import argparse\n/app/solution/train_current_model.py:41:class Config:\n/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/app/solution/train_current_model.py:56:    uniform_negatives: int = 24\n/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8\n/app/solution/train_current_model.py:58:    inbatch_correction_power: float = 0.5\n/app/solution/train_current_model.py:59:    inbatch_correction_min: float = 0.25\n/app/solution/train_current_model.py:60:    inbatch_correction_max: float = 4.0\n/app/solution/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:76:class CandidateSet:\n/app/solution/train_current_model.py:99:class SequenceData:\n/app/solution/train_current_model.py:127:class TargetPartition:\n/app/solution/train_current_model.py:140:def log(message: str) -> None:\n/app/solution/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/app/solution/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/app/solution/train_current_model.py:152:    return base.TowerConfig(\n/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/app/solution/train_current_model.py:167:        inbatch_correction_power=config.inbatch_correction_power,\n/app/solution/train_current_model.py:168:        inbatch_correction_min=config.inbatch_correction_min,\n/app/solution/train_current_model.py:169:        inbatch_correction_max=config.inbatch_correction_max,\n/app/solution/train_current_model.py:174:def append_state_snapshot(\n/app/solution/train_current_model.py:190:def causal_activity_features(\n/app/solution/train_current_model.py:247:def build_sequence_parts(\n/app/solution/train_current_model.py:314:def final_user_features(\n/app/solution/train_current_model.py:341:def prepare_data(\n/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/app/solution/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:481:def save_cache(\n/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/app/solution/train_current_model.py:511:def load_or_prepare(\n/app/solution/train_current_model.py:534:def build_item_correction(\n/app/solution/train_current_model.py:562:        config.inbatch_correction_power,\n/app/solution/train_current_model.py:567:        config.inbatch_correction_min,\n/app/solution/train_current_model.py:568:        config.inbatch_correction_max,\n/app/solution/train_current_model.py:572:        f\"inbatch correction: min={correction.min():.3f} \"\n/app/solution/train_current_model.py:578:def build_known_positive_filter(\n/app/solution/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/app/solution/train_current_model.py:609:def invalid_full_history_negatives(\n/app/solution/train_current_model.py:610:    negatives: np.ndarray,\n/app/solution/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3\n/app/solution/train_current_model.py:618:    bit_columns = negatives & 7\n/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:651:    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n/app/solution/train_current_model.py:653:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/app/solution/train_current_model.py:671:            inbatch = unique_targets[columns]\n/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/app/solution/train_current_model.py:679:            inbatch = torch.cat([inbatch, filler], dim=1)\n/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/app/solution/train_current_model.py:686:        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:689:            negatives,\n/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:706:        negatives[invalid] = replacements\n/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:710:        negatives,\n/app/solution/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:724:def draw_training_negatives(\n/app/solution/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/app/solution/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/app/solution/train_current_model.py:755:        config.uniform_negatives,\n/app/solution/train_current_model.py:756:        config.inbatch_negatives,\n/app/solution/train_current_model.py:763:def event_sequence(\n/app/solution/train_current_model.py:774:def pool_explicit_states(\n/app/solution/train_current_model.py:798:def user_queries(\n/app/solution/train_current_model.py:813:def candidate_vectors(\n/app/solution/train_current_model.py:816:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors\n/app/solution/train_current_model.py:828:def history_windows(\n/app/solution/train_current_model.py:845:def corrected_negative_loss(\n/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:853:    if config.uniform_negatives:\n/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/app/solution/train_current_model.py:855:            :, : config.uniform_negatives\n/app/solution/train_current_model.py:857:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (\n/app/solution/train_current_model.py:867:def retained_target_window_starts(\n/app/solution/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:888:def chronological_target_partition(\n/app/solution/train_current_model.py:914:def load_organic_like_outcomes(\n/app/solution/train_current_model.py:955:def build_target_weights(\n/app/solution/train_current_model.py:1035:def build_dense_block(\n/app/solution/train_current_model.py:1111:def score_target_microbatches(\n/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/app/solution/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/app/solution/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/app/solution/train_current_model.py:1165:            negatives[begin:end],\n/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(\n/app/solution/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/app/solution/train_current_model.py:1178:        loss_sum = (\n/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/app/solution/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/app/solution/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/app/solution/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/app/solution/train_current_model.py:1203:def run_training_epoch(\n/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:1228:    total_loss = 0.0\n/app/solution/train_current_model.py:1232:    total_negative = 0.0\n/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1314:                negatives,\n/app/solution/train_current_model.py:1315:                negative_weights,\n/app/solution/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/app/solution/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/app/solution/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1364:def evaluate_validation_bce(\n/app/solution/train_current_model.py:1388:    total_loss = 0.0\n/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1469:                negatives,\n/app/solution/train_current_model.py:1470:                negative_weights,\n/app/solution/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/app/solution/train_current_model.py:1495:def save_epoch_checkpoint(\n/app/solution/train_current_model.py:1521:def train_model(\n/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/app/solution/train_current_model.py:1571:    best_loss = float(\"inf\")\n/app/solution/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/app/solution/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/app/solution/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/app/solution/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/app/solution/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/app/solution/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/app/solution/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/app/solution/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/app/solution/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1782:def inference_queries(\n/app/solution/train_current_model.py:1861:def rank_candidates(\n/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/app/solution/train_current_model.py:1922:def main() -> int:\n/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/app/solution/train_current_model.py:1924:    parser.add_argument(\n/app/solution/train_current_model.py:1927:    parser.add_argument(\"--output\", type=Path, required=True)\n/app/solution/train_current_model.py:1928:    parser.add_argument(\"--public-dir\", type=Path, required=True)\n/app/solution/train_current_model.py:1929:    parser.add_argument(\"--prepared-cache\", type=Path)\n/app/solution/train_current_model.py:1930:    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n/app/solution/train_current_model.py:1931:    parser.add_argument(\"--threads\", type=int, default=8)\n/app/solution/train_current_model.py:1932:    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n/app/solution/train_current_model.py:1933:    parser.add_argument(\n/app/solution/train_current_model.py:1936:    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n/app/solution/train_current_model.py:1937:    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n/app/solution/train_current_model.py:1938:    parser.add_argument(\n/app/solution/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/app/solution/train_current_model.py:1943:    parser.add_argument(\n/app/solution/train_current_model.py:1944:        \"--negative-filter-scope\",\n/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/app/solution/train_current_model.py:1948:    parser.add_argument(\n/app/solution/train_current_model.py:1953:    parser.add_argument(\n/app/solution/train_current_model.py:1958:    parser.add_argument(\n/app/solution/train_current_model.py:1963:    parser.add_argument(\n/app/solution/train_current_model.py:1968:    parser.add_argument(\n/app/solution/train_current_model.py:1973:    parser.add_argument(\n/app/solution/train_current_model.py:1978:    parser.add_argument(\n/app/solution/train_current_model.py:1984:    parser.add_argument(\n/app/solution/train_current_model.py:1990:    parser.add_argument(\n/app/solution/train_current_model.py:1995:    parser.add_argument(\n/app/solution/train_current_model.py:2000:    parser.add_argument(\"--checkpoint-dir\", type=Path)\n/app/solution/train_current_model.py:2001:    parser.add_argument(\"--resume-checkpoint\", type=Path)\n/app/solution/train_current_model.py:2002:    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n/app/solution/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/app/solution/train_current_model.py:2004:    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/app/solution/train_current_model.py:2028:    config = Config(\n/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/app/solution/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/app/solution/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/app/solution/train_current_model.py:2136:                    \"negative_sampling\": (\n/app/solution/train_current_model.py:2137:                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n/app/solution/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2320
    },
    "formatted_output": "/app/solution/two_tower_model.py:27:class TowerConfig:\n/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24\n/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8\n/app/solution/two_tower_model.py:42:    inbatch_correction_power: float = 0.5\n/app/solution/two_tower_model.py:43:    inbatch_correction_min: float = 0.25\n/app/solution/two_tower_model.py:44:    inbatch_correction_max: float = 4.0\n/app/solution/two_tower_model.py:49:class ListenEvents:\n/app/solution/two_tower_model.py:57:class ExplicitEvents:\n/app/solution/two_tower_model.py:65:def log(message: str) -> None:\n/app/solution/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/app/solution/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/app/solution/two_tower_model.py:106:def iter_listen_users(\n/app/solution/two_tower_model.py:157:def merge_listen_streams(\n/app/solution/two_tower_model.py:175:def load_explicit_events(\n/app/solution/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/app/solution/two_tower_model.py:238:def pack_active_items(\n/app/solution/two_tower_model.py:251:def load_inference_state(\n/app/solution/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/app/solution/two_tower_model.py:346:def invalid_negative_mask(\n/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,\n/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/app/solution/two_tower_model.py:358:            negatives[:, :column]\n/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/app/solution/two_tower_model.py:369:    inbatch_count: int,\n/app/solution/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:382:    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n/app/solution/two_tower_model.py:384:    if inbatch_count:\n/app/solution/two_tower_model.py:386:        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n/app/solution/two_tower_model.py:396:            inbatch = unique_targets[columns]\n/app/solution/two_tower_model.py:397:        if corrected_count < inbatch_count:\n/app/solution/two_tower_model.py:400:                (batch_size, inbatch_count - corrected_count),\n/app/solution/two_tower_model.py:404:            inbatch = torch.cat([inbatch, filler], dim=1)\n/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/app/solution/two_tower_model.py:411:        ] = item_correction[inbatch[:, :corrected_count]]\n/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:419:            return negatives, correction\n/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements\n/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/app/solution/two_tower_model.py:433:    return negatives, correction\n/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/app/solution/train_current_model.py:20:import argparse\n/app/solution/train_current_model.py:41:class Config:\n/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/app/solution/train_current_model.py:56:    uniform_negatives: int = 24\n/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8\n/app/solution/train_current_model.py:58:    inbatch_correction_power: float = 0.5\n/app/solution/train_current_model.py:59:    inbatch_correction_min: float = 0.25\n/app/solution/train_current_model.py:60:    inbatch_correction_max: float = 4.0\n/app/solution/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:76:class CandidateSet:\n/app/solution/train_current_model.py:99:class SequenceData:\n/app/solution/train_current_model.py:127:class TargetPartition:\n/app/solution/train_current_model.py:140:def log(message: str) -> None:\n/app/solution/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/app/solution/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/app/solution/train_current_model.py:152:    return base.TowerConfig(\n/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/app/solution/train_current_model.py:167:        inbatch_correction_power=config.inbatch_correction_power,\n/app/solution/train_current_model.py:168:        inbatch_correction_min=config.inbatch_correction_min,\n/app/solution/train_current_model.py:169:        inbatch_correction_max=config.inbatch_correction_max,\n/app/solution/train_current_model.py:174:def append_state_snapshot(\n/app/solution/train_current_model.py:190:def causal_activity_features(\n/app/solution/train_current_model.py:247:def build_sequence_parts(\n/app/solution/train_current_model.py:314:def final_user_features(\n/app/solution/train_current_model.py:341:def prepare_data(\n/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/app/solution/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:481:def save_cache(\n/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/app/solution/train_current_model.py:511:def load_or_prepare(\n/app/solution/train_current_model.py:534:def build_item_correction(\n/app/solution/train_current_model.py:562:        config.inbatch_correction_power,\n/app/solution/train_current_model.py:567:        config.inbatch_correction_min,\n/app/solution/train_current_model.py:568:        config.inbatch_correction_max,\n/app/solution/train_current_model.py:572:        f\"inbatch correction: min={correction.min():.3f} \"\n/app/solution/train_current_model.py:578:def build_known_positive_filter(\n/app/solution/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/app/solution/train_current_model.py:609:def invalid_full_history_negatives(\n/app/solution/train_current_model.py:610:    negatives: np.ndarray,\n/app/solution/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3\n/app/solution/train_current_model.py:618:    bit_columns = negatives & 7\n/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:651:    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n/app/solution/train_current_model.py:653:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/app/solution/train_current_model.py:671:            inbatch = unique_targets[columns]\n/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/app/solution/train_current_model.py:679:            inbatch = torch.cat([inbatch, filler], dim=1)\n/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/app/solution/train_current_model.py:686:        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:689:            negatives,\n/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:706:        negatives[invalid] = replacements\n/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:710:        negatives,\n/app/solution/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:724:def draw_training_negatives(\n/app/solution/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/app/solution/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/app/solution/train_current_model.py:755:        config.uniform_negatives,\n/app/solution/train_current_model.py:756:        config.inbatch_negatives,\n/app/solution/train_current_model.py:763:def event_sequence(\n/app/solution/train_current_model.py:774:def pool_explicit_states(\n/app/solution/train_current_model.py:798:def user_queries(\n/app/solution/train_current_model.py:813:def candidate_vectors(\n/app/solution/train_current_model.py:816:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors\n/app/solution/train_current_model.py:828:def history_windows(\n/app/solution/train_current_model.py:845:def corrected_negative_loss(\n/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:853:    if config.uniform_negatives:\n/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/app/solution/train_current_model.py:855:            :, : config.uniform_negatives\n/app/solution/train_current_model.py:857:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (\n/app/solution/train_current_model.py:867:def retained_target_window_starts(\n/app/solution/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:888:def chronological_target_partition(\n/app/solution/train_current_model.py:914:def load_organic_like_outcomes(\n/app/solution/train_current_model.py:955:def build_target_weights(\n/app/solution/train_current_model.py:1035:def build_dense_block(\n/app/solution/train_current_model.py:1111:def score_target_microbatches(\n/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/app/solution/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/app/solution/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/app/solution/train_current_model.py:1165:            negatives[begin:end],\n/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(\n/app/solution/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/app/solution/train_current_model.py:1178:        loss_sum = (\n/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/app/solution/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/app/solution/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/app/solution/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/app/solution/train_current_model.py:1203:def run_training_epoch(\n/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:1228:    total_loss = 0.0\n/app/solution/train_current_model.py:1232:    total_negative = 0.0\n/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1314:                negatives,\n/app/solution/train_current_model.py:1315:                negative_weights,\n/app/solution/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/app/solution/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/app/solution/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1364:def evaluate_validation_bce(\n/app/solution/train_current_model.py:1388:    total_loss = 0.0\n/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1469:                negatives,\n/app/solution/train_current_model.py:1470:                negative_weights,\n/app/solution/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/app/solution/train_current_model.py:1495:def save_epoch_checkpoint(\n/app/solution/train_current_model.py:1521:def train_model(\n/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/app/solution/train_current_model.py:1571:    best_loss = float(\"inf\")\n/app/solution/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/app/solution/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/app/solution/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/app/solution/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/app/solution/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/app/solution/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/app/solution/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/app/solution/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/app/solution/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1782:def inference_queries(\n/app/solution/train_current_model.py:1861:def rank_candidates(\n/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/app/solution/train_current_model.py:1922:def main() -> int:\n/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/app/solution/train_current_model.py:1924:    parser.add_argument(\n/app/solution/train_current_model.py:1927:    parser.add_argument(\"--output\", type=Path, required=True)\n/app/solution/train_current_model.py:1928:    parser.add_argument(\"--public-dir\", type=Path, required=True)\n/app/solution/train_current_model.py:1929:    parser.add_argument(\"--prepared-cache\", type=Path)\n/app/solution/train_current_model.py:1930:    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n/app/solution/train_current_model.py:1931:    parser.add_argument(\"--threads\", type=int, default=8)\n/app/solution/train_current_model.py:1932:    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n/app/solution/train_current_model.py:1933:    parser.add_argument(\n/app/solution/train_current_model.py:1936:    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n/app/solution/train_current_model.py:1937:    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n/app/solution/train_current_model.py:1938:    parser.add_argument(\n/app/solution/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/app/solution/train_current_model.py:1943:    parser.add_argument(\n/app/solution/train_current_model.py:1944:        \"--negative-filter-scope\",\n/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/app/solution/train_current_model.py:1948:    parser.add_argument(\n/app/solution/train_current_model.py:1953:    parser.add_argument(\n/app/solution/train_current_model.py:1958:    parser.add_argument(\n/app/solution/train_current_model.py:1963:    parser.add_argument(\n/app/solution/train_current_model.py:1968:    parser.add_argument(\n/app/solution/train_current_model.py:1973:    parser.add_argument(\n/app/solution/train_current_model.py:1978:    parser.add_argument(\n/app/solution/train_current_model.py:1984:    parser.add_argument(\n/app/solution/train_current_model.py:1990:    parser.add_argument(\n/app/solution/train_current_model.py:1995:    parser.add_argument(\n/app/solution/train_current_model.py:2000:    parser.add_argument(\"--checkpoint-dir\", type=Path)\n/app/solution/train_current_model.py:2001:    parser.add_argument(\"--resume-checkpoint\", type=Path)\n/app/solution/train_current_model.py:2002:    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n/app/solution/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/app/solution/train_current_model.py:2004:    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/app/solution/train_current_model.py:2028:    config = Config(\n/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/app/solution/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/app/solution/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/app/solution/train_current_model.py:2136:                    \"negative_sampling\": (\n/app/solution/train_current_model.py:2137:                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n/app/solution/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n"
  }
}

exec command

+00:12

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 +00:14

const r = await tools.exec_command({"cmd":"sed -n '284,455p' /app/solution/two_tower_model.py && sed -n '247,760p' /app/solution/train_current_model.py && sed -n '798,1210p' /app/solution/train_current_model.py && sed -n '1203,1790p' /app/solution/train_current_model.py && sed -n '1782,2160p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":60000});
text(r.output);

Tool response +00:14

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 19244)
Total output lines: 2044

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
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[:, …9244 tokens truncated…   "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(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
) -> torch.Tensor:
    sequence_rows = prepared.inference_user_rows
    lengths = np.where(
        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
def inference_queries(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
) -> torch.Tensor:
    sequence_rows = prepared.inference_user_rows
    lengths = np.where(
        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
    )
    ordered = np.argsort(lengths, kind="stable")
    result = torch.zeros(
        len(sequence_rows), config.embedding_dim, dtype=torch.float32
    )
    for group_start in range(0, len(ordered), config.user_batch_size):
        output_rows = ordered[group_start : group_start + config.user_batch_size]
        rows = sequence_rows[output_rows]
        group_lengths = lengths[output_rows]
        hidden = torch.zeros(
            1, len(output_rows), config.embedding_dim, device=device
        )
        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
            steps = min(
                config.bptt_steps,
                int(group_lengths.max(initial=0)) - start,
            )
            items = np.full(
                (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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-64fc3f7d-501d-4346-b18c-dad0fc7312df",
    "process_id": "64239",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '284,455p' /app/solution/two_tower_model.py && sed -n '247,760p' /app/solution/train_current_model.py && sed -n '798,1210p' /app/solution/train_current_model.py && sed -n '1203,1790p' /app/solution/train_current_model.py && sed -n '1782,2160p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '284,455p' /app/solution/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/app/solution/two_tower_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '247,760p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '798,1210p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1203,1790p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1782,2160p' /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": "class 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\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    )\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,\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    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\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",
    "stderr": "",
    "aggregated_output": "class 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\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    )\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,\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    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\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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5120
    },
    "formatted_output": "Warning: truncated output (original token count: 19244)\nTotal output lines: 2044\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            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\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[:, …9244 tokens truncated…   \"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    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\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"
  }
}

Harness context token usage record

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  "turn_token_usage": {
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  }
}

exec command

+00:16

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 +00:18

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

Tool response +00:18

Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"ea317e","wall_time_seconds":0.048257645,"exit_code":1,"original_token_count":43,"output":"Traceback (most recent call last):\n  File \"/app/solution/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d9a50044-b020-4db4-ae64-cdfd39972eac",
    "process_id": "92475",
    "command": [
      "/bin/sh",
      "-lc",
      "python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
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    "stderr": "",
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  }
}

exec command

+00:21

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’s packaged interpreter (it lacks even NumPy), so I’m locating the provided offline runtime before spending time on experiments. The source copy is intact; no model changes have been made yet.

Tool callexecTool call Returned

Tool call · Input +00:24

const r = await tools.exec_command({"cmd":"which python; python --version; command -v python3; python3 --version; ls -la /task; find /task -maxdepth 3 -type f \\( -name 'python' -o -name 'python3' -o -name 'activate' -o -name 'pyvenv.cfg' \\) -print; env | sort | rg -i 'python|venv|conda|uv'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +00:24

Script completed
Wall time 0.0 seconds
Output:

/usr/local/bin/python
Python 3.12.13
/usr/local/bin/python3
Python 3.12.13
total 40
dr-xr-xr-x 1 root root 4096 Sep 24 22:44 .
drwxr-xr-x 1 root root 4096 Sep 24 22:48 ..
-r--r--r-- 1 root root    0 Sep 24 22:42 .keep
dr-xr-xr-x 1 root root 4096 Sep 24 22:42 current_model
dr-xr-xr-x 1 root root 4096 Sep 24 22:42 data
-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13

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Harness context codex item completed

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    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "which python; python --version; command -v python3; python3 --version; ls -la /task; find /task -maxdepth 3 -type f \\( -name 'python' -o -name 'python3' -o -name 'activate' -o -name 'pyvenv.cfg' \\) -print; env | sort | rg -i 'python|venv|conda|uv'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\nPython 3.12.13\n/usr/local/bin/python3\nPython 3.12.13\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 22:44 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:48 ..\n-r--r--r-- 1 root root    0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:42 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 22:42 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\n/usr/local/bin/python3\nPython 3.12.13\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 22:44 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:48 ..\n-r--r--r-- 1 root root    0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:42 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 22:42 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2631
    },
    "formatted_output": "/usr/local/bin/python\nPython 3.12.13\n/usr/local/bin/python3\nPython 3.12.13\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 22:44 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:48 ..\n-r--r--r-- 1 root root    0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:42 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 22:42 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n"
  }
}

exec command

+00:26

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 +00:28

const r = await tools.exec_command({"cmd":"sed -n '1,200p' /task/requirements.txt; find / -maxdepth 4 -type f -name 'pyvenv.cfg' -o -type d -name 'site-packages' 2>/dev/null | head -100; ls -la /usr/local/lib/python3.12/site-packages | head","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +00:28

Script completed
Wall time 0.1 seconds
Output:

# 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
/opt/task-venv/pyvenv.cfg
total 20
drwxr-xr-x  4 root root 4096 Aug  5 01:13 .
drwxr-xr-x 38 root root 4096 Aug  5 01:13 ..
-rw-r--r--  1 root root  119 Aug  5 01:13 README.txt
drwxr-xr-x  5 root root 4096 Aug  5 01:13 pip
drwxr-xr-x  2 root root 4096 Aug  5 01:13 pip-25.0.1.dist-info

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a8de061887d29bd565e1348a441e",
  "usage": {
    "input_tokens": 35368,
    "cached_input_tokens": 34688,
    "cache_write_input_tokens": 0,
    "output_tokens": 135,
    "reasoning_output_tokens": 17,
    "total_tokens": 35503
  },
  "turn_token_usage": {
    "input_tokens": 165467,
    "cached_input_tokens": 136832,
    "cache_write_input_tokens": 0,
    "output_tokens": 1081,
    "reasoning_output_tokens": 241,
    "total_tokens": 166548
  },
  "thread_token_usage": {
    "input_tokens": 165467,
    "cached_input_tokens": 136832,
    "cache_write_input_tokens": 0,
    "output_tokens": 1081,
    "reasoning_output_tokens": 241,
    "total_tokens": 166548
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6709a15e-94e7-433e-8cb6-9aca0df398f1",
    "process_id": "71876",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,200p' /task/requirements.txt; find / -maxdepth 4 -type f -name 'pyvenv.cfg' -o -type d -name 'site-packages' 2>/dev/null | head -100; ls -la /usr/local/lib/python3.12/site-packages | head"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,200p' /task/requirements.txt; find / -maxdepth 4 -type f -name 'pyvenv.cfg' -o -type d -name 'site-packages' 2>/dev/null | head -100; ls -la /usr/local/lib/python3.12/site-packages | head"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "# 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/opt/task-venv/pyvenv.cfg\ntotal 20\ndrwxr-xr-x  4 root root 4096 Aug  5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug  5 01:13 ..\n-rw-r--r--  1 root root  119 Aug  5 01:13 README.txt\ndrwxr-xr-x  5 root root 4096 Aug  5 01:13 pip\ndrwxr-xr-x  2 root root 4096 Aug  5 01:13 pip-25.0.1.dist-info\n",
    "stderr": "",
    "aggregated_output": "# 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/opt/task-venv/pyvenv.cfg\ntotal 20\ndrwxr-xr-x  4 root root 4096 Aug  5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug  5 01:13 ..\n-rw-r--r--  1 root root  119 Aug  5 01:13 README.txt\ndrwxr-xr-x  5 root root 4096 Aug  5 01:13 pip\ndrwxr-xr-x  2 root root 4096 Aug  5 01:13 pip-25.0.1.dist-info\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 1990
    },
    "formatted_output": "# 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/opt/task-venv/pyvenv.cfg\ntotal 20\ndrwxr-xr-x  4 root root 4096 Aug  5 01:13 .\ndrwxr-xr-x 38 root root 4096 Aug  5 01:13 ..\n-rw-r--r--  1 root root  119 Aug  5 01:13 README.txt\ndrwxr-xr-x  5 root root 4096 Aug  5 01:13 pip\ndrwxr-xr-x  2 root root 4096 Aug  5 01:13 pip-25.0.1.dist-info\n"
  }
}

exec command

+00:29

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 +00:31

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_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +00:32

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"1d5917","wall_time_seconds":1.001377429,"session_id":5052,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a8e1884487d2a8bb60d90bc030de",
  "usage": {
    "input_tokens": 36260,
    "cached_input_tokens": 35200,
    "cache_write_input_tokens": 0,
    "output_tokens": 123,
    "reasoning_output_tokens": 16,
    "total_tokens": 36383
  },
  "turn_token_usage": {
    "input_tokens": 201727,
    "cached_input_tokens": 172032,
    "cache_write_input_tokens": 0,
    "output_tokens": 1204,
    "reasoning_output_tokens": 257,
    "total_tokens": 202931
  },
  "thread_token_usage": {
    "input_tokens": 201727,
    "cached_input_tokens": 172032,
    "cache_write_input_tokens": 0,
    "output_tokens": 1204,
    "reasoning_output_tokens": 257,
    "total_tokens": 202931
  }
}

exec command

+00:33

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 +00:34

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +00:44

Script completed
Wall time 10.0 seconds
Output:

loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a8e5b1a487d2bfadd71c35bedf63",
  "usage": {
    "input_tokens": 36441,
    "cached_input_tokens": 36096,
    "cache_write_input_tokens": 0,
    "output_tokens": 62,
    "reasoning_output_tokens": 15,
    "total_tokens": 36503
  },
  "turn_token_usage": {
    "input_tokens": 238168,
    "cached_input_tokens": 208128,
    "cache_write_input_tokens": 0,
    "output_tokens": 1266,
    "reasoning_output_tokens": 272,
    "total_tokens": 239434
  },
  "thread_token_usage": {
    "input_tokens": 238168,
    "cached_input_tokens": 208128,
    "cache_write_input_tokens": 0,
    "output_tokens": 1266,
    "reasoning_output_tokens": 272,
    "total_tokens": 239434
  }
}

exec command

+00:45

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 +00:46

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +00:56

Script completed
Wall time 10.0 seconds
Output:

prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a8f1910887d2bcac249117d5dd0a",
  "usage": {
    "input_tokens": 36566,
    "cached_input_tokens": 36224,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 36619
  },
  "turn_token_usage": {
    "input_tokens": 274734,
    "cached_input_tokens": 244352,
    "cache_write_input_tokens": 0,
    "output_tokens": 1319,
    "reasoning_output_tokens": 278,
    "total_tokens": 276053
  },
  "thread_token_usage": {
    "input_tokens": 274734,
    "cached_input_tokens": 244352,
    "cache_write_input_tokens": 0,
    "output_tokens": 1319,
    "reasoning_output_tokens": 278,
    "total_tokens": 276053
  }
}

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.

Tool callexecTool call Returned

Tool call · Input +00:58

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:08

Script completed
Wall time 10.0 seconds
Output:

prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a8fd8b6087d2bedae090e3d082b2",
  "usage": {
    "input_tokens": 36692,
    "cached_input_tokens": 36352,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 36745
  },
  "turn_token_usage": {
    "input_tokens": 311426,
    "cached_input_tokens": 280704,
    "cache_write_input_tokens": 0,
    "output_tokens": 1372,
    "reasoning_output_tokens": 284,
    "total_tokens": 312798
  },
  "thread_token_usage": {
    "input_tokens": 311426,
    "cached_input_tokens": 280704,
    "cache_write_input_tokens": 0,
    "output_tokens": 1372,
    "reasoning_output_tokens": 284,
    "total_tokens": 312798
  }
}

exec command

+01:09

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 +01:10

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:20

Script completed
Wall time 10.0 seconds
Output:

prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s

Harness context token usage record

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}

Tool callexecTool call Returned

Tool call · Input +01:21

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:31

Script completed
Wall time 10.0 seconds
Output:

prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250

Harness context token usage record

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}

exec command

+01:33

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 baseline data profile is now clear: about 20.0M adjacent-listen targets exist, but the production window trains on only 1.05M recent targets. Its 32 negatives are mostly easy uniform catalog draws; only eight come from other positives, and those are down/up-weighted after sampling rather than selected by model difficulty. I’m timing and scoring this exact baseline before changing that behavior.

Tool callexecTool call Returned

Tool call · Input +01:35

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:45

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

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}

Tool callexecTool call Returned

Tool call · Input +01:47

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +01:58

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

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}

Tool callexecTool call Returned

Tool call · Input +01:59

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +02:09

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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}

exec command

+02:11

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 +02:14

const r = await tools.exec_command({"cmd":"sed -n '530,765p' /app/solution/train_current_model.py; sed -n '760,910p' /app/solution/train_current_model.py; sed -n '910,1115p' /app/solution/train_current_model.py; sed -n '1110,1380p' /app/solution/train_current_model.py; sed -n '1380,1785p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":50000});
text(r.output);

Tool response +02:14

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 11583)
Total output lines: 1270

        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,
    )


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_rows],
        torch.from_numpy(counts.astype(np.float32)).to(device),
    )


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,
        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,
    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(
            …1583 tokens truncated…evice
        )
        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,
    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[
    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(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7148b5b1-06de-47ec-b4a8-95e446b73a0f",
    "process_id": "60683",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '530,765p' /app/solution/train_current_model.py; sed -n '760,910p' /app/solution/train_current_model.py; sed -n '910,1115p' /app/solution/train_current_model.py; sed -n '1110,1380p' /app/solution/train_current_model.py; sed -n '1380,1785p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '530,765p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '760,910p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '910,1115p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1110,1380p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1380,1785p' /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": "        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\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\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        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\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    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    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",
    "stderr": "",
    "aggregated_output": "        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\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\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        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\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    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    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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3800
    },
    "formatted_output": "Warning: truncated output (original token count: 11583)\nTotal output lines: 1270\n\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\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\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        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            …1583 tokens truncated…evice\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    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"
  }
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exec command

+02:16

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 +02:17

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +02:27

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

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Agent message

+02:37

Thinking unavailable No text recorded

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

exec command

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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 +02:47

const r = await tools.exec_command({"cmd":"sed -n '1,260p' /task/evaluate_public.py; sed -n '1,320p' /task/ranking_evaluation.py; du -sh /task/data/* | sort -h | tail -30; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/task/data/candidate_catalog.parquet')\ndf=pd.read_parquet(p)\nprint(df.columns.tolist(), df.shape)\nprint(df.head().to_string(index=False))\nprint(pd.read_parquet('/task/data/target_users.parquet').shape)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

Tool response +02:50

Script completed
Wall time 2.5 seconds
Output:

#!/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()
"""Shared public primitives for Yambda discovery-ranking evaluation.

Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.

The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations

from pathlib import Path

import numpy as np
import pandas as pd


__all__ = [
    "CandidateSet",
    "QUALITY_K",
    "RankingValidationError",
    "REQUIRED_COLUMNS",
    "TOP_N",
    "ndcg_at_k",
    "ranked_items",
    "read_ranking",
    "score_rankings",
    "validate_ranking",
]

TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")


class RankingValidationError(ValueError):
    """Report a ranking-schema or structural-contract violation."""


class CandidateSet:
    """Provide membership lookups over one packed candidate-set archive.

    The archive contains aligned ``uids``, ``item_ids``, and
    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
    copied into memory so they remain valid after the archive is closed.
    """

    def __init__(self, path: Path):
        """Load an eligibility archive and build ID-to-position indexes.

        Args:
            path: Path to a ``.npz`` archive with the candidate-set schema.
        """
        with np.load(path, allow_pickle=False) as artifact:
            self.uids = artifact["uids"].astype(np.int64, copy=True)
            self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
            self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
        self.item_column = {
            int(item): column for column, item in enumerate(self.item_ids)
        }

    def is_eligible(self, uid: int, item_id: int) -> bool:
        """Return whether ``item_id`` is eligible for ``uid``.

        IDs absent from the target-user or candidate-item arrays are treated as
        ineligible rather than raising an exception.

        Args:
            uid: Target user ID to query.
            item_id: Candidate item ID to query.

        Returns:
            ``True`` only when the corresponding packed eligibility bit is set.
        """
        row = self.uid_row.get(int(uid))
        column = self.item_column.get(int(item_id))
        if row is None or column is None:
            return False
        byte, bit = divmod(column, 8)
        return bool((int(self.packed[row, byte]) >> bit) & 1)


def read_ranking(path: Path) -> pd.DataFrame:
    """Read a parquet or CSV ranking into a pandas frame.

    Args:
        path: Input path. A ``.parquet`` suffix selects parquet; every other
            suffix is interpreted as CSV.

    Returns:
        The ranking frame as loaded by pandas.
    """
    return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)


def validate_ranking(
    frame: pd.DataFrame,
    targets: set[int],
) -> pd.DataFrame:
    """Normalize and validate the stage-independent ranking contract.

    Required values must be finite integers. The user set must exactly match
    ``targets``; every user must have 100 rows, distinct items, and each rank
    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
    intentionally left to the calling evaluator.

    Args:
        frame: Raw ranking frame. Extra columns are discarded from an internal
            copy; the caller's frame is not modified.
        targets: Exact set of target user IDs the ranking must cover.

    Returns:
        A normalized frame containing only the required ``int64`` columns.

    Raises:
        RankingValidationError: If the ranking violates the shared contract.
    """
    required = list(REQUIRED_COLUMNS)
    if not all(column in frame for column in required):
        raise RankingValidationError(f"submission requires columns {required}")

    normalized = frame[required].copy()
    for column in required:
        numeric = pd.to_numeric(normalized[column], errors="coerce")
        values = numeric.to_numpy(dtype=float)
        if numeric.isna().any() or not np.isfinite(values).all():
            raise RankingValidationError(
                f"submission {column} must be finite numeric values"
            )
        if not np.equal(numeric, np.floor(numeric)).all():
            raise RankingValidationError(
                f"submission {column} must contain integers"
            )
        normalized[column] = numeric.astype(np.int64)

    submitted_users = set(map(int, normalized.uid.unique()))
    if submitted_users != targets:
        raise RankingValidationError(
            f"submission user set mismatch: missing={len(targets - submitted_users)} "
            f"extra={len(submitted_users - targets)}"
        )
    counts = normalized.groupby("uid").size()
    if not (counts == TOP_N).all():
        raise RankingValidationError(
            f"submission must contain exactly {TOP_N} rows for every target user"
        )
    if normalized.duplicated(["uid", "item_id"]).any():
        raise RankingValidationError(
            "submission contains duplicate (uid, item_id) pairs"
        )
    valid_ranks = set(range(1, TOP_N + 1))
    ranks_are_valid = normalized.groupby("uid")["rank"].apply(
        lambda values: set(map(int, values)) == valid_ranks
    )
    if not ranks_are_valid.all():
        raise RankingValidationError(
            f"each user must have every rank from 1 through {TOP_N} exactly once"
        )
    return normalized


def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
    """Convert a validated ranking frame to ordered items keyed by user.

    Args:
        frame: Frame that has passed :func:`validate_ranking`.

    Returns:
        A ``uid -> item IDs`` mapping ordered by ascending rank.
    """
    ordered = frame.sort_values(["uid", "rank"])
    return {
        int(uid): list(map(int, values))
        for uid, values in ordered.groupby("uid").item_id
    }


def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
    """Compute graded normalized discounted cumulative gain at ``k``.

    Relevance gain is ``2**grade - 1`` and the discount at one-based rank
    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.

    Args:
        ranked: Item IDs in predicted rank order.
        gold: Mapping from relevant item IDs to integer relevance grades.
        k: Maximum number of predicted positions to score.

    Returns:
        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
        ``0.0`` when the ideal gain is zero.
    """
    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
    return dcg / idcg if idcg else 0.0


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

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Relevant item grades keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
8.0K	/task/data/val_undislikes.parquet
36K	/task/data/val_dislikes.parquet
72K	/task/data/val_unlikes.parquet
84K	/task/data/target_users.parquet
156K	/task/data/train_undislikes.parquet
292K	/task/data/val_likes.parquet
344K	/task/data/candidate_catalog.parquet
840K	/task/data/train_dislikes.parquet
1.1M	/task/data/test_preference_state.parquet
1.1M	/task/data/validation_preference_state.parquet
1.8M	/task/data/train_unlikes.parquet
5.2M	/task/data/train_likes.parquet
7.3M	/task/data/validation_eligible_candidates.npz
7.4M	/task/data/test_eligible_candidates.npz
9.5M	/task/data/val_listens.parquet
305M	/task/data/train_listens.parquet
['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] (30000, 4)
 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
(8456, 3)

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6869d3f9-777e-4e1a-aaaf-d70752447113",
    "process_id": "16050",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,260p' /task/evaluate_public.py; sed -n '1,320p' /task/ranking_evaluation.py; du -sh /task/data/* | sort -h | tail -30; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/task/data/candidate_catalog.parquet')\ndf=pd.read_parquet(p)\nprint(df.columns.tolist(), df.shape)\nprint(df.head().to_string(index=False))\nprint(pd.read_parquet('/task/data/target_users.parquet').shape)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,260p' /task/evaluate_public.py; sed -n '1,320p' /task/ranking_evaluation.py; du -sh /task/data/* | sort -h | tail -30; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/task/data/candidate_catalog.parquet')\ndf=pd.read_parquet(p)\nprint(df.columns.tolist(), df.shape)\nprint(df.head().to_string(index=False))\nprint(pd.read_parquet('/task/data/target_users.parquet').shape)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] (30000, 4)\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\n(8456, 3)\n",
    "stderr": "",
    "aggregated_output": "['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] (30000, 4)\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\n(8456, 3)\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 295005554
    },
    "formatted_output": "['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] (30000, 4)\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\n(8456, 3)\n"
  }
}

exec command

+02:51

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 +02:52

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +03:02

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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  "turn_token_usage": {
    "input_tokens": 678159,
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  },
  "thread_token_usage": {
    "input_tokens": 678159,
    "cached_input_tokens": 631552,
    "cache_write_input_tokens": 0,
    "output_tokens": 3180,
    "reasoning_output_tokens": 1359,
    "total_tokens": 681339
  }
}

exec command

+03:04

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 +03:05

const r = await tools.exec_command({"cmd":"ps -o pid,etime,pcpu,pmem,rss,vsz,stat,cmd -C python -C python3; free -h; df -h /app/solution","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +03:05

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: ps: not found
/bin/sh: 1: free: not found
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   16G   81G  16% /

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9746e2eb-a77a-49dc-98bd-75c34bc7c710",
    "process_id": "6364",
    "command": [
      "/bin/sh",
      "-lc",
      "ps -o pid,etime,pcpu,pmem,rss,vsz,stat,cmd -C python -C python3; free -h; df -h /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -o pid,etime,pcpu,pmem,rss,vsz,stat,cmd -C python -C python3; free -h; df -h /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   16G   81G  16% /\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   16G   81G  16% /\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5270
    },
    "formatted_output": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   16G   81G  16% /\n"
  }
}

Harness context token usage record

{
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    "total_tokens": 732445
  }
}

exec command

+03:07

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 +03:07

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +03:17

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a97f626887d28a4c3cd818359f37",
  "usage": {
    "input_tokens": 51181,
    "cached_input_tokens": 50816,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 51234
  },
  "turn_token_usage": {
    "input_tokens": 780341,
    "cached_input_tokens": 733056,
    "cache_write_input_tokens": 0,
    "output_tokens": 3338,
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  },
  "thread_token_usage": {
    "input_tokens": 780341,
    "cached_input_tokens": 733056,
    "cache_write_input_tokens": 0,
    "output_tokens": 3338,
    "reasoning_output_tokens": 1384,
    "total_tokens": 783679
  }
}

exec command

+03:23

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 +03:33

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nD=Path('/task/data'); O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank')\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; ids=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(ids,cat.item_id.to_numpy())\nrows=[]\nfor r,uid in enumerate(uids):\n elig=np.unpackbits(packed[r],count=len(ids),bitorder='little').astype(bool)\n cols=np.flatnonzero(elig)[:100]\n rows.extend((int(uid),int(ids[c]),rank) for rank,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'popularity_validation.parquet',index=False)\n# public label rank distribution\nlis=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlik=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntarget=set(map(int,uids))\nrel=pd.concat([lis[lis.uid.isin(target)&(lis.is_organic==1)&(lis.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lik[lik.uid.isin(target)&(lik.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nidx=pd.Series(np.arange(1,len(ids)+1),index=ids)\nrel['pop_rank']=rel.item_id.map(idx)\nprint('raw relevant',len(rel),'users',rel.uid.nunique(),'grade counts',rel.grade.value_counts().to_dict())\nprint(rel.pop_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_string())\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +03:42

Script completed
Wall time 9.0 seconds
Output:

raw relevant 266887 users 6511 grade counts {1: 249692, 3: 17195}
count    175729.000000
mean       7249.789551
std        7819.149582
min           1.000000
10%         212.000000
25%        1011.000000
50%        4132.000000
75%       11338.000000
90%       20151.000000
95%       24563.000000
99%       28791.440000
max       30000.000000
{
  "model": "/app/solution/popularity_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a98b40bc87d29589255cae37bd57",
  "usage": {
    "input_tokens": 51257,
    "cached_input_tokens": 50944,
    "cache_write_input_tokens": 0,
    "output_tokens": 714,
    "reasoning_output_tokens": 173,
    "total_tokens": 51971
  },
  "turn_token_usage": {
    "input_tokens": 831598,
    "cached_input_tokens": 784000,
    "cache_write_input_tokens": 0,
    "output_tokens": 4052,
    "reasoning_output_tokens": 1557,
    "total_tokens": 835650
  },
  "thread_token_usage": {
    "input_tokens": 831598,
    "cached_input_tokens": 784000,
    "cache_write_input_tokens": 0,
    "output_tokens": 4052,
    "reasoning_output_tokens": 1557,
    "total_tokens": 835650
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1c2264c2-ed74-47eb-9c72-788771d3365b",
    "process_id": "28548",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nD=Path('/task/data'); O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank')\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; ids=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(ids,cat.item_id.to_numpy())\nrows=[]\nfor r,uid in enumerate(uids):\n elig=np.unpackbits(packed[r],count=len(ids),bitorder='little').astype(bool)\n cols=np.flatnonzero(elig)[:100]\n rows.extend((int(uid),int(ids[c]),rank) for rank,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'popularity_validation.parquet',index=False)\n# public label rank distribution\nlis=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlik=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntarget=set(map(int,uids))\nrel=pd.concat([lis[lis.uid.isin(target)&(lis.is_organic==1)&(lis.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lik[lik.uid.isin(target)&(lik.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nidx=pd.Series(np.arange(1,len(ids)+1),index=ids)\nrel['pop_rank']=rel.item_id.map(idx)\nprint('raw relevant',len(rel),'users',rel.uid.nunique(),'grade counts',rel.grade.value_counts().to_dict())\nprint(rel.pop_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_string())\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nD=Path('/task/data'); O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank')\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; ids=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(ids,cat.item_id.to_numpy())\nrows=[]\nfor r,uid in enumerate(uids):\n elig=np.unpackbits(packed[r],count=len(ids),bitorder='little').astype(bool)\n cols=np.flatnonzero(elig)[:100]\n rows.extend((int(uid),int(ids[c]),rank) for rank,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'popularity_validation.parquet',index=False)\n# public label rank distribution\nlis=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlik=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntarget=set(map(int,uids))\nrel=pd.concat([lis[lis.uid.isin(target)&(lis.is_organic==1)&(lis.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lik[lik.uid.isin(target)&(lik.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nidx=pd.Series(np.arange(1,len(ids)+1),index=ids)\nrel['pop_rank']=rel.item_id.map(idx)\nprint('raw relevant',len(rel),'users',rel.uid.nunique(),'grade counts',rel.grade.value_counts().to_dict())\nprint(rel.pop_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_string())\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "raw relevant 266887 users 6511 grade counts {1: 249692, 3: 17195}\ncount    175729.000000\nmean       7249.789551\nstd        7819.149582\nmin           1.000000\n10%         212.000000\n25%        1011.000000\n50%        4132.000000\n75%       11338.000000\n90%       20151.000000\n95%       24563.000000\n99%       28791.440000\nmax       30000.000000\n{\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": "raw relevant 266887 users 6511 grade counts {1: 249692, 3: 17195}\ncount    175729.000000\nmean       7249.789551\nstd        7819.149582\nmin           1.000000\n10%         212.000000\n25%        1011.000000\n50%        4132.000000\n75%       11338.000000\n90%       20151.000000\n95%       24563.000000\n99%       28791.440000\nmax       30000.000000\n{\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": 772890746
    },
    "formatted_output": "raw relevant 266887 users 6511 grade counts {1: 249692, 3: 17195}\ncount    175729.000000\nmean       7249.789551\nstd        7819.149582\nmin           1.000000\n10%         212.000000\n25%        1011.000000\n50%        4132.000000\n75%       11338.000000\n90%       20151.000000\n95%       24563.000000\n99%       28791.440000\nmax       30000.000000\n{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+03:44

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 +03:45

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +03:55

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a9a3780087d2b5026ec20f10ad8a",
  "usage": {
    "input_tokens": 52178,
    "cached_input_tokens": 51072,
    "cache_write_input_tokens": 0,
    "output_tokens": 140,
    "reasoning_output_tokens": 93,
    "total_tokens": 52318
  },
  "turn_token_usage": {
    "input_tokens": 883776,
    "cached_input_tokens": 835072,
    "cache_write_input_tokens": 0,
    "output_tokens": 4192,
    "reasoning_output_tokens": 1650,
    "total_tokens": 887968
  },
  "thread_token_usage": {
    "input_tokens": 883776,
    "cached_input_tokens": 835072,
    "cache_write_input_tokens": 0,
    "output_tokens": 4192,
    "reasoning_output_tokens": 1650,
    "total_tokens": 887968
  }
}

Agent message

+04:05

Thinking unavailable No text recorded

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

Agent message

+04:16

Thinking unavailable No text recorded

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

exec command

+04:24

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 +04:24

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +04:34

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5a9b0f2d087d2948502cddcd6e5ab",
  "usage": {
    "input_tokens": 52341,
    "cached_input_tokens": 51968,
    "cache_write_input_tokens": 0,
    "output_tokens": 1496,
    "reasoning_output_tokens": 1449,
    "total_tokens": 53837
  },
  "turn_token_usage": {
    "input_tokens": 936117,
    "cached_input_tokens": 887040,
    "cache_write_input_tokens": 0,
    "output_tokens": 5688,
    "reasoning_output_tokens": 3099,
    "total_tokens": 941805
  },
  "thread_token_usage": {
    "input_tokens": 936117,
    "cached_input_tokens": 887040,
    "cache_write_input_tokens": 0,
    "output_tokens": 5688,
    "reasoning_output_tokens": 3099,
    "total_tokens": 941805
  }
}

exec command

+04:35

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 +04:38

let{output,...rest}=await tools.exec_command({"cmd":"for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' \"$d/stat\"; echo \"$c\";; esac; fi; done","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});text(rest);text(output);

Tool response +04:38

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"5bd349","wall_time_seconds":0.00000425,"exit_code":0,"original_token_count":6910}
/proc/8778/stat 2 1 62258 8798208 1892
bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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:

```sh
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:

1. For model development and selection, train only from `train_*` and score the
   subsequent `val_*` outcomes.
2. 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.
' 2>&1 </dev/null | tee /logs/agent/codex.txt 
/proc/9004/stat 2 1 62280 901791744 13540
node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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:

```sh
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:

1. For model development and selection, train only from `train_*` and score the
   subsequent `val_*` outcomes.
2. 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.
 
/proc/9012/stat 174 109 62283 556625920 38783
/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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:

```sh
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:

1. For model development and selection, train only from `train_*` and score the
   subsequent `val_*` outcomes.
2. 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.
 
/proc/9444/stat 0 0 65485 2654208 366
/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 
/proc/9445/stat 101303 7965 65485 7462490112 1279257
/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet 
/proc/9613/stat 0 0 90205 2654208 399
/bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then c=$(tr '' ' ' < "$d/cmdline"); case "$c" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' "$d/stat"; echo "$c";; esac; fi; done

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2142a37f-0aed-4f06-b26a-10daea23a287",
    "process_id": "15667",
    "command": [
      "/bin/sh",
      "-lc",
      "for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' \"$d/stat\"; echo \"$c\";; esac; fi; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' \"$d/stat\"; echo \"$c\";; esac; fi; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/proc/8778/stat 2 1 62258 8798208 1892\nbash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004/stat 2 1 62280 901791744 13540\nnode /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n/proc/9012/stat 174 109 62283 556625920 38783\n/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n/proc/9444/stat 0 0 65485 2654208 366\n/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \n/proc/9445/stat 101303 7965 65485 7462490112 1279257\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \n/proc/9613/stat 0 0 90205 2654208 399\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' \"$d/stat\"; echo \"$c\";; esac; fi; done \n",
    "stderr": "",
    "aggregated_output": "/proc/8778/stat 2 1 62258 8798208 1892\nbash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004/stat 2 1 62280 901791744 13540\nnode /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n/proc/9012/stat 174 109 62283 556625920 38783\n/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n/proc/9444/stat 0 0 65485 2654208 366\n/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \n/proc/9445/stat 101303 7965 65485 7462490112 1279257\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \n/proc/9613/stat 0 0 90205 2654208 399\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' \"$d/stat\"; echo \"$c\";; esac; fi; done \n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4250
    },
    "formatted_output": "/proc/8778/stat 2 1 62258 8798208 1892\nbash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004/stat 2 1 62280 901791744 13540\nnode /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n/proc/9012/stat 174 109 62283 556625920 38783\n/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n/proc/9444/stat 0 0 65485 2654208 366\n/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \n/proc/9445/stat 101303 7965 65485 7462490112 1279257\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet \n/proc/9613/stat 0 0 90205 2654208 399\n/bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_current_model.py*) awk '{print FILENAME, $14, $15, $22, $23, $24}' \"$d/stat\"; echo \"$c\";; esac; fi; done \n"
  }
}

exec command

+04:42

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 +04:43

const r = await tools.exec_command({"cmd":"find /app/solution -maxdepth 2 -type f -printf '%p %s\\n' | sort","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +04:43

Script completed
Wall time 0.1 seconds
Output:

/app/solution/popularity_validation.parquet 728599
/app/solution/train_current_model.py 80058
/app/solution/two_tower_model.py 15985
/app/solution/validation_cache.npz 535225808

Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "find /app/solution -maxdepth 2 -type f -printf '%p %s\\n' | sort"
    ],
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        "type": "list_files",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app/solution/popularity_validation.parquet 728599\n/app/solution/train_current_model.py 80058\n/app/solution/two_tower_model.py 15985\n/app/solution/validation_cache.npz 535225808\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4780
    },
    "formatted_output": "/app/solution/popularity_validation.parquet 728599\n/app/solution/train_current_model.py 80058\n/app/solution/two_tower_model.py 15985\n/app/solution/validation_cache.npz 535225808\n"
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Harness context token usage record

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}

exec command

+04:47

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 +04:49

const r = await tools.exec_command({"cmd":"cp /app/solution/train_current_model.py /app/solution/train_baseline.py && cp /app/solution/two_tower_model.py /app/solution/two_tower_baseline.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +04:49

Script completed
Wall time 0.2 seconds
Output:

Harness context token usage record

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  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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Harness context codex item completed

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    "id": "exec-7a83a146-9a20-47b8-bb44-50087ee8c789",
    "process_id": "39306",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/train_current_model.py /app/solution/train_baseline.py && cp /app/solution/two_tower_model.py /app/solution/two_tower_baseline.py"
    ],
    "cwd": "file:///app",
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        "cmd": "cp /app/solution/train_current_model.py /app/solution/train_baseline.py && cp /app/solution/two_tower_model.py /app/solution/two_tower_baseline.py"
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  }
}

exec command

+04:55

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 +04:55

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:05

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

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  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 3657,
    "total_tokens": 1178580
  }
}

exec command

+05:09

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 +05:11

const r = await tools.exec_command({"cmd":"read up idle < /proc/uptime; set -- $(tr ')' ' ' < /proc/9445/stat); echo uptime=$up start_ticks=$22 user_ticks=$14 system_ticks=$15 rss_pages=$24; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:11

Script completed
Wall time 0.1 seconds
Output:

uptime=934.82 start_ticks=(python2 user_ticks=94454 system_ticks=94455 rss_pages=(python4
MemTotal:       15988964 kB
MemAvailable:    9607284 kB

Harness context codex item completed

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    "type": "CommandExecution",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "uptime=934.82 start_ticks=(python2 user_ticks=94454 system_ticks=94455 rss_pages=(python4\nMemTotal:       15988964 kB\nMemAvailable:    9607284 kB\n",
    "stderr": "",
    "aggregated_output": "uptime=934.82 start_ticks=(python2 user_ticks=94454 system_ticks=94455 rss_pages=(python4\nMemTotal:       15988964 kB\nMemAvailable:    9607284 kB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4790
    },
    "formatted_output": "uptime=934.82 start_ticks=(python2 user_ticks=94454 system_ticks=94455 rss_pages=(python4\nMemTotal:       15988964 kB\nMemAvailable:    9607284 kB\n"
  }
}

Harness context token usage record

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    "total_tokens": 1239975
  }
}

exec command

+05:14

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 +05:15

const r = await tools.exec_command({"cmd":"rest=$(sed 's/^[^)]*) //' /proc/9445/stat); set -- $rest; read up idle < /proc/uptime; echo uptime=$up state=$1 user_ticks=$12 system_ticks=$13 start_ticks=$20 rss_pages=$22; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:15

Script completed
Wall time 0.1 seconds
Output:

uptime=939.26 state=R user_ticks=R2 system_ticks=R3 start_ticks=94440 rss_pages=94442
MemTotal:       15988964 kB
MemAvailable:    9474568 kB

Harness context codex item completed

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    "process_id": "27257",
    "command": [
      "/bin/sh",
      "-lc",
      "rest=$(sed 's/^[^)]*) //' /proc/9445/stat); set -- $rest; read up idle < /proc/uptime; echo uptime=$up state=$1 user_ticks=$12 system_ticks=$13 start_ticks=$20 rss_pages=$22; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo"
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    "cwd": "file:///app",
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      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "uptime=939.26 state=R user_ticks=R2 system_ticks=R3 start_ticks=94440 rss_pages=94442\nMemTotal:       15988964 kB\nMemAvailable:    9474568 kB\n",
    "stderr": "",
    "aggregated_output": "uptime=939.26 state=R user_ticks=R2 system_ticks=R3 start_ticks=94440 rss_pages=94442\nMemTotal:       15988964 kB\nMemAvailable:    9474568 kB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 7110
    },
    "formatted_output": "uptime=939.26 state=R user_ticks=R2 system_ticks=R3 start_ticks=94440 rss_pages=94442\nMemTotal:       15988964 kB\nMemAvailable:    9474568 kB\n"
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}

Harness context token usage record

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}

exec command

+05:17

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 +05:19

const r = await tools.exec_command({"cmd":"set -- $(sed 's/^[^)]*) //' /proc/9445/stat); read up idle < /proc/uptime; echo uptime=$up state=${1} user_ticks=${12} system_ticks=${13} start_ticks=${20} rss_pages=${22}","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:19

Script completed
Wall time 0.1 seconds
Output:

uptime=943.36 state=R user_ticks=121753 system_ticks=9702 start_ticks=65485 rss_pages=1351242

Harness context codex item completed

{
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    "type": "CommandExecution",
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    "process_id": "5603",
    "command": [
      "/bin/sh",
      "-lc",
      "set -- $(sed 's/^[^)]*) //' /proc/9445/stat); read up idle < /proc/uptime; echo uptime=$up state=${1} user_ticks=${12} system_ticks=${13} start_ticks=${20} rss_pages=${22}"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "uptime=943.36 state=R user_ticks=121753 system_ticks=9702 start_ticks=65485 rss_pages=1351242\n",
    "stderr": "",
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    "exit_code": 0,
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      "secs": 0,
      "nanos": 4090
    },
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Harness context token usage record

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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa0104ac87d2ad9bb04526cb16f4",
  "usage": {
    "input_tokens": 61720,
    "cached_input_tokens": 61312,
    "cache_write_input_tokens": 0,
    "output_tokens": 172,
    "reasoning_output_tokens": 68,
    "total_tokens": 61892
  },
  "turn_token_usage": {
    "input_tokens": 1356428,
    "cached_input_tokens": 1296768,
    "cache_write_input_tokens": 0,
    "output_tokens": 7084,
    "reasoning_output_tokens": 3817,
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  },
  "thread_token_usage": {
    "input_tokens": 1356428,
    "cached_input_tokens": 1296768,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 3817,
    "total_tokens": 1363512
  }
}

exec command

+05:22

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 +05:22

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:32

Script completed
Wall time 10.0 seconds
Output:

epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=217.4s

Harness context token usage record

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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 3867,
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  }
}

exec command

+05:34

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 +05:35

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:45

Script completed
Wall time 10.0 seconds
Output:

saved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt
epoch=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=230.9 validation_seconds=16.8
restored best validation-BCE checkpoint: epoch=1 loss=0.496636

Harness context token usage record

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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 3894,
    "total_tokens": 1487731
  }
}

exec command

+05:46

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 +05:47

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +05:57

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa1ed05887d2845081586f473804",
  "usage": {
    "input_tokens": 62288,
    "cached_input_tokens": 61952,
    "cache_write_input_tokens": 0,
    "output_tokens": 58,
    "reasoning_output_tokens": 11,
    "total_tokens": 62346
  },
  "turn_token_usage": {
    "input_tokens": 1542764,
    "cached_input_tokens": 1482112,
    "cache_write_input_tokens": 0,
    "output_tokens": 7313,
    "reasoning_output_tokens": 3905,
    "total_tokens": 1550077
  },
  "thread_token_usage": {
    "input_tokens": 1542764,
    "cached_input_tokens": 1482112,
    "cache_write_input_tokens": 0,
    "output_tokens": 7313,
    "reasoning_output_tokens": 3905,
    "total_tokens": 1550077
  }
}

Tool callexecTool call Returned

Tool call · Input +05:59

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +06:09

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa2ac8ec87d296927624b23aec06",
  "usage": {
    "input_tokens": 62369,
    "cached_input_tokens": 62080,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 62414
  },
  "turn_token_usage": {
    "input_tokens": 1605133,
    "cached_input_tokens": 1544192,
    "cache_write_input_tokens": 0,
    "output_tokens": 7358,
    "reasoning_output_tokens": 3905,
    "total_tokens": 1612491
  },
  "thread_token_usage": {
    "input_tokens": 1605133,
    "cached_input_tokens": 1544192,
    "cache_write_input_tokens": 0,
    "output_tokens": 7358,
    "reasoning_output_tokens": 3905,
    "total_tokens": 1612491
  }
}

Tool callexecTool call Returned

Tool call · Input +06:11

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +06:21

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa36c35c87d2b6dfa77c11713832",
  "usage": {
    "input_tokens": 62437,
    "cached_input_tokens": 62208,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 62482
  },
  "turn_token_usage": {
    "input_tokens": 1667570,
    "cached_input_tokens": 1606400,
    "cache_write_input_tokens": 0,
    "output_tokens": 7403,
    "reasoning_output_tokens": 3905,
    "total_tokens": 1674973
  },
  "thread_token_usage": {
    "input_tokens": 1667570,
    "cached_input_tokens": 1606400,
    "cache_write_input_tokens": 0,
    "output_tokens": 7403,
    "reasoning_output_tokens": 3905,
    "total_tokens": 1674973
  }
}

exec command

+06:22

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 +06:23

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +06:33

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa4290bc87d2a0a206416464c62c",
  "usage": {
    "input_tokens": 62505,
    "cached_input_tokens": 62208,
    "cache_write_input_tokens": 0,
    "output_tokens": 64,
    "reasoning_output_tokens": 17,
    "total_tokens": 62569
  },
  "turn_token_usage": {
    "input_tokens": 1730075,
    "cached_input_tokens": 1668608,
    "cache_write_input_tokens": 0,
    "output_tokens": 7467,
    "reasoning_output_tokens": 3922,
    "total_tokens": 1737542
  },
  "thread_token_usage": {
    "input_tokens": 1730075,
    "cached_input_tokens": 1668608,
    "cache_write_input_tokens": 0,
    "output_tokens": 7467,
    "reasoning_output_tokens": 3922,
    "total_tokens": 1737542
  }
}

Tool callexecTool call Returned

Tool call · Input +06:35

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +06:45

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa4ef85487d282cf94c22a085707",
  "usage": {
    "input_tokens": 62592,
    "cached_input_tokens": 62336,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 62637
  },
  "turn_token_usage": {
    "input_tokens": 1792667,
    "cached_input_tokens": 1730944,
    "cache_write_input_tokens": 0,
    "output_tokens": 7512,
    "reasoning_output_tokens": 3922,
    "total_tokens": 1800179
  },
  "thread_token_usage": {
    "input_tokens": 1792667,
    "cached_input_tokens": 1730944,
    "cache_write_input_tokens": 0,
    "output_tokens": 7512,
    "reasoning_output_tokens": 3922,
    "total_tokens": 1800179
  }
}

Tool callexecTool call Returned

Tool call · Input +06:47

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +06:57

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aa5ac53887d28f9a93f627c827fd",
  "usage": {
    "input_tokens": 62660,
    "cached_input_tokens": 62464,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 62705
  },
  "turn_token_usage": {
    "input_tokens": 1855327,
    "cached_input_tokens": 1793408,
    "cache_write_input_tokens": 0,
    "output_tokens": 7557,
    "reasoning_output_tokens": 3922,
    "total_tokens": 1862884
  },
  "thread_token_usage": {
    "input_tokens": 1855327,
    "cached_input_tokens": 1793408,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 3922,
    "total_tokens": 1862884
  }
}

exec command

+06:59

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 +06:59

const r = await tools.write_stdin({"session_id":5052,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +07:01

Script completed
Wall time 1.9 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/production_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 385.935,
  "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": 0.5398380493004364,
      "positive_logit": 0.7427799593265382,
      "negative_logit": -0.6222247029354633,
      "pair_accuracy": 0.7547733612041563,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 230.85248444299998,
      "validation_loss": 0.49663621615066844,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 16.803749827000047
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.6030172109603882,
    "query_norm_std": 0.7697497606277466,
    "item_norm_mean": 1.7570278644561768,
    "item_norm_std": 0.8279485106468201
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

Harness context token usage record

{
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-941b9c73-342e-4461-862c-ec4219ffa035",
    "process_id": "5052",
    "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_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet"
    ],
    "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_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet"
      }
    ],
    "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=4.8s\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=20.5s\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=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\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_cache.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=217.4s\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=230.9 validation_seconds=16.8\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{\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\": 385.935,\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_loss\": 0.49663621615066844,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 16.803749827000047\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6030172109603882,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570278644561768,\n    \"item_norm_std\": 0.8279485106468201\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\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=4.8s\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=20.5s\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=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\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_cache.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=217.4s\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=230.9 validation_seconds=16.8\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{\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\": 385.935,\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_loss\": 0.49663621615066844,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 16.803749827000047\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6030172109603882,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570278644561768,\n    \"item_norm_std\": 0.8279485106468201\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 390,
      "nanos": 441277486
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\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=20.5s\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=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\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_cache.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=217.4s\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=230.9 validation_seconds=16.8\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{\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\": 385.935,\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_loss\": 0.49663621615066844,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 16.803749827000047\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6030172109603882,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570278644561768,\n    \"item_norm_std\": 0.8279485106468201\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}

exec command

+07:03

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 +07:04

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +07:08

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}

Harness context token usage record

{
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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
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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-12031d5f-d5c4-4e91-aaed-114d240656b1",
    "process_id": "60910",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet"
      }
    ],
    "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",
    "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",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 285143635
    },
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  }
}

exec command

+07:12

Thinking unavailable No text recorded

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

Model text

Baseline reproduction is complete: public NDCG@10 is 0.021280 (versus 0.010710 for pure popularity). I’m now creating a cutoff-correct baseline submission as a safety artifact while I implement and test a harder, retrieval-aligned negative objective from the saved validation checkpoint.

Tool callexecTool call Returned

Tool call · Input +07:16

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_baseline.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/submission_baseline_checkpoints --output /app/solution/submission.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +07:17

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"81abcd","wall_time_seconds":1.000521409,"session_id":46200,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
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  }
}

Agent message

+07:27

Thinking unavailable No text recorded

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

Agent message

+07:36

Thinking unavailable No text recorded

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

Agent message

+07:46

Thinking unavailable No text recorded

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

exec command

+07:47

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 +07:49

const r = await tools.exec_command({"cmd":"sed -n '1100,1210p' /app/solution/train_current_model.py; sed -n '1200,1370p' /app/solution/train_current_model.py; sed -n '1920,2070p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

Tool response +07:49

Script completed
Wall time 0.0 seconds
Output:

        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,
        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,
                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,
    window_starts: np.ndarray,
    training_counts: np.ndarray,


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(
                {

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9de5073a-e226-498c-b8d0-a57e7edc2f24",
    "process_id": "92157",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1100,1210p' /app/solution/train_current_model.py; sed -n '1200,1370p' /app/solution/train_current_model.py; sed -n '1920,2070p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1100,1210p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1200,1370p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1920,2070p' /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": "        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\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",
    "stderr": "",
    "aggregated_output": "        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\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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3401
    },
    "formatted_output": "        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\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"
  }
}

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Agent message

+08:00

Thinking unavailable No text recorded

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

Agent message

+08:09

Thinking unavailable No text recorded

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

exec command

+08:19

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 +08:27

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 with mixed hard-negative mining.\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, and raw dot-product retrieval.  Training draws a broad pool of\n+uniform and unique in-batch candidates, rejects every item in the listener's\n+known positive history, and optimizes a mixture of random and online hard\n+negatives.  Mining is done only while fitting; serving remains one user vector\n+against precomputed item vectors. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n@@\n class Config:\n@@\n-    learning_rate: float = 1.0e-3\n+    learning_rate: float = 3.0e-4\n@@\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+    loss_microbatch_size: int = 2_048\n+    uniform_negatives: int = 64\n+    inbatch_negatives: int = 32\n+    inbatch_correction_power: float = 0.0\n@@\n     inbatch_correction_max: float = 4.0\n+    negative_strategy: str = \"hard-mixed\"\n+    random_negatives: int = 16\n+    hard_negatives: int = 16\n+    loss_type: str = \"bpr\"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +08:27

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-a793cfae-f3c5-4c65-b83a-66dd8f8a670c",
    "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 with mixed hard-negative mining.\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, and raw dot-product retrieval.  Training draws a broad pool of\n+uniform and unique in-batch candidates, rejects every item in the listener's\n+known positive history, and optimizes a mixture of random and online hard\n+negatives.  Mining is done only while fitting; serving remains one user vector\n+against precomputed item vectors. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n@@ -51,11 +53,15 @@\n     epochs: int = 1\n-    learning_rate: float = 1.0e-3\n+    learning_rate: float = 3.0e-4\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+    loss_microbatch_size: int = 2_048\n+    uniform_negatives: int = 64\n+    inbatch_negatives: int = 32\n+    inbatch_correction_power: float = 0.0\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n+    negative_strategy: str = \"hard-mixed\"\n+    random_negatives: int = 16\n+    hard_negatives: int = 16\n+    loss_type: str = \"bpr\"\n     negative_filter_scope: str = \"full-training-history\"\n",
        "move_path": null
      }
    },
    "status": "failed",
    "stdout": "",
    "stderr": "Failed to write file /app/solution/train_current_model.py\n"
  }
}

Harness context token usage record

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exec command

+08:29

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 +08:30

const r = await tools.exec_command({"cmd":"ls -l /app/solution/train_current_model.py; stat -c '%a %U %G' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +08:30

Script completed
Wall time 0.1 seconds
Output:

-r--r--r-- 1 agent agent 80058 Sep 24 22:48 /app/solution/train_current_model.py
444 agent agent

Harness context codex item completed

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    "command": [
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    "stdout": "-r--r--r-- 1 agent agent 80058 Sep 24 22:48 /app/solution/train_current_model.py\n444 agent agent\n",
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Harness context token usage record

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exec command

+08:32

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 +08:35

const r = await tools.exec_command({"cmd":"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/train_baseline.py /app/solution/two_tower_baseline.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +08:35

Script completed
Wall time 0.1 seconds
Output:

Harness context codex item completed

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        "cmd": "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/train_baseline.py /app/solution/two_tower_baseline.py"
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exec command

+08:37

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 +08:46

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 with mixed hard-negative mining.\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, and raw dot-product retrieval. Training draws a broad pool of\n+uniform and unique in-batch candidates, rejects every item in the listener's\n+known positive history, and optimizes a mixture of random and online hard\n+negatives. Mining is done only while fitting; serving remains one user vector\n+against precomputed item vectors. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n@@\n class Config:\n@@\n-    learning_rate: float = 1.0e-3\n+    learning_rate: float = 3.0e-4\n@@\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+    loss_microbatch_size: int = 2_048\n+    uniform_negatives: int = 64\n+    inbatch_negatives: int = 32\n+    inbatch_correction_power: float = 0.0\n@@\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n+    negative_strategy: str = \"hard-mixed\"\n+    random_negatives: int = 16\n+    hard_negatives: int = 16\n+    loss_type: str = \"bpr\"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +08:46

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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    "type": "FileChange",
    "id": "exec-b5cadf53-aef0-4152-ab05-2e1eede616e6",
    "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 with mixed hard-negative mining.\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, and raw dot-product retrieval. Training draws a broad pool of\n+uniform and unique in-batch candidates, rejects every item in the listener's\n+known positive history, and optimizes a mixture of random and online hard\n+negatives. Mining is done only while fitting; serving remains one user vector\n+against precomputed item vectors. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n@@ -51,11 +53,15 @@\n     epochs: int = 1\n-    learning_rate: float = 1.0e-3\n+    learning_rate: float = 3.0e-4\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+    loss_microbatch_size: int = 2_048\n+    uniform_negatives: int = 64\n+    inbatch_negatives: int = 32\n+    inbatch_correction_power: float = 0.0\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n+    negative_strategy: str = \"hard-mixed\"\n+    random_negatives: int = 16\n+    hard_negatives: int = 16\n+    loss_type: str = \"bpr\"\n     negative_filter_scope: str = \"full-training-history\"\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

+08:48

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 +09:02

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n def candidate_vectors(\n@@\n     return positive_vectors, negative_vectors\n+\n+\n+def effective_negative_count(config: Config) -> int:\n+    \"\"\"Return the number of negatives that contribute to each fitted row.\"\"\"\n+    if config.negative_strategy == \"hard-mixed\":\n+        return config.random_negatives + config.hard_negatives\n+    return config.uniform_negatives + config.inbatch_negatives\n+\n+\[email protected]_grad()\n+def select_mixed_hard_negatives(\n+    model: base.SequentialTwoTower,\n+    query_vectors: torch.Tensor,\n+    negative_pool: torch.Tensor,\n+    pool_weights: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Keep distributional anchors plus the highest-scoring pool candidates.\n+\n+    The sampler places uniform draws first and in-batch draws second. Half of\n+    the random anchors are retained from each source (subject to availability),\n+    and the remaining columns are mined by the current two-tower score. Mining\n+    is detached: gradients are computed only after the selected items are\n+    re-encoded by :func:`candidate_vectors`.\n+    \"\"\"\n+    rows, pool_size = negative_pool.shape\n+    random_count = config.random_negatives\n+    hard_count = config.hard_negatives\n+    if random_count + hard_count > pool_size:\n+        raise RuntimeError(\"selected negative count exceeds the candidate pool\")\n+\n+    uniform_random = min(config.uniform_negatives, (random_count + 1) // 2)\n+    inbatch_random = min(config.inbatch_negatives, random_count - uniform_random)\n+    if uniform_random + inbatch_random < random_count:\n+        uniform_random = min(\n+            config.uniform_negatives,\n+            uniform_random + random_count - uniform_random - inbatch_random,\n+        )\n+    fixed_columns = torch.cat(\n+        [\n+            torch.arange(uniform_random, device=negative_pool.device),\n+            torch.arange(\n+                config.uniform_negatives,\n+                config.uniform_negatives + inbatch_random,\n+                device=negative_pool.device,\n+            ),\n+        ]\n+    )\n+    if len(fixed_columns) != random_count:\n+        raise RuntimeError(\"negative pool cannot supply requested random anchors\")\n+\n+    unique_items, inverse = torch.unique(negative_pool.reshape(-1), return_inverse=True)\n+    encoded = model.encode_items(unique_items)\n+    pool_vectors = encoded[inverse].reshape(rows, pool_size, -1)\n+    mining_logits = torch.einsum(\n+        \"bd,bkd->bk\", query_vectors.detach(), pool_vectors\n+    )\n+    if random_count:\n+        mining_logits[:, fixed_columns] = -torch.inf\n+    hard_columns = torch.topk(\n+        mining_logits, k=hard_count, dim=1, sorted=False\n+    ).indices\n+    fixed = fixed_columns.unsqueeze(0).expand(rows, -1)\n+    selected_columns = torch.cat([fixed, hard_columns], dim=1)\n+    return (\n+        torch.gather(negative_pool, 1, selected_columns),\n+        torch.gather(pool_weights, 1, selected_columns),\n+    )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +09:02

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": "@@ -833,2 +833,70 @@\n \n+def effective_negative_count(config: Config) -> int:\n+    \"\"\"Return the number of negatives that contribute to each fitted row.\"\"\"\n+    if config.negative_strategy == \"hard-mixed\":\n+        return config.random_negatives + config.hard_negatives\n+    return config.uniform_negatives + config.inbatch_negatives\n+\n+\[email protected]_grad()\n+def select_mixed_hard_negatives(\n+    model: base.SequentialTwoTower,\n+    query_vectors: torch.Tensor,\n+    negative_pool: torch.Tensor,\n+    pool_weights: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Keep distributional anchors plus the highest-scoring pool candidates.\n+\n+    The sampler places uniform draws first and in-batch draws second. Half of\n+    the random anchors are retained from each source (subject to availability),\n+    and the remaining columns are mined by the current two-tower score. Mining\n+    is detached: gradients are computed only after the selected items are\n+    re-encoded by :func:`candidate_vectors`.\n+    \"\"\"\n+    rows, pool_size = negative_pool.shape\n+    random_count = config.random_negatives\n+    hard_count = config.hard_negatives\n+    if random_count + hard_count > pool_size:\n+        raise RuntimeError(\"selected negative count exceeds the candidate pool\")\n+\n+    uniform_random = min(config.uniform_negatives, (random_count + 1) // 2)\n+    inbatch_random = min(config.inbatch_negatives, random_count - uniform_random)\n+    if uniform_random + inbatch_random < random_count:\n+        uniform_random = min(\n+            config.uniform_negatives,\n+            uniform_random + random_count - uniform_random - inbatch_random,\n+        )\n+    fixed_columns = torch.cat(\n+        [\n+            torch.arange(uniform_random, device=negative_pool.device),\n+            torch.arange(\n+                config.uniform_negatives,\n+                config.uniform_negatives + inbatch_random,\n+                device=negative_pool.device,\n+            ),\n+        ]\n+    )\n+    if len(fixed_columns) != random_count:\n+        raise RuntimeError(\"negative pool cannot supply requested random anchors\")\n+\n+    unique_items, inverse = torch.unique(negative_pool.reshape(-1), return_inverse=True)\n+    encoded = model.encode_items(unique_items)\n+    pool_vectors = encoded[inverse].reshape(rows, pool_size, -1)\n+    mining_logits = torch.einsum(\n+        \"bd,bkd->bk\", query_vectors.detach(), pool_vectors\n+    )\n+    if random_count:\n+        mining_logits[:, fixed_columns] = -torch.inf\n+    hard_columns = torch.topk(\n+        mining_logits, k=hard_count, dim=1, sorted=False\n+    ).indices\n+    fixed = fixed_columns.unsqueeze(0).expand(rows, -1)\n+    selected_columns = torch.cat([fixed, hard_columns], dim=1)\n+    return (\n+        torch.gather(negative_pool, 1, selected_columns),\n+        torch.gather(pool_weights, 1, selected_columns),\n+    )\n+\n+\n def history_windows(\n",
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exec command

+09:06

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 +09:17

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n ) -> tuple[dict[str, float], torch.Tensor | None]:\n-    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n+    \"\"\"Compute one logical block's retrieval loss in bounded-memory slices.\"\"\"\n@@\n         query_vectors = user_queries(\n@@\n             torch.from_numpy(numeric).to(device),\n         )\n+        selected_negatives = negatives[begin:end]\n+        selected_negative_weights = negative_weights[begin:end]\n+        if config.negative_strategy == \"hard-mixed\":\n+            selected_negatives, selected_negative_weights = (\n+                select_mixed_hard_negatives(\n+                    model,\n+                    query_vectors,\n+                    selected_negatives,\n+                    selected_negative_weights,\n+                    config,\n+                )\n+            )\n         positive_vectors, negative_vectors = candidate_vectors(\n             model,\n             positive_items[begin:end],\n-            negatives[begin:end],\n+            selected_negatives,\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_type == \"bpr\":\n+            pair_loss = F.softplus(\n+                negative_logits - positive_logits[:, None]\n+            )\n+            negative_loss = (\n+                pair_loss * selected_negative_weights\n+            ).sum(dim=1) / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+            per_target_loss = negative_loss\n+        elif config.loss_type == \"bce\":\n+            positive_loss = F.softplus(-positive_logits)\n+            if config.negative_strategy == \"hard-mixed\":\n+                raw_negative = F.softplus(negative_logits)\n+                negative_loss = (\n+                    raw_negative * selected_negative_weights\n+                ).sum(dim=1) / selected_negative_weights.sum(dim=1).clamp_min(\n+                    1.0e-6\n+                )\n+            else:\n+                negative_loss = corrected_negative_loss(\n+                    negative_logits, selected_negative_weights, config\n+                )\n+            per_target_loss = (positive_loss + negative_loss) / 2.0\n+        else:\n+            raise RuntimeError(f\"unknown loss type: {config.loss_type}\")\n@@\n-        loss_sum = (\n-            selected_weights * (positive_loss + negative_loss) / 2.0\n-        ).sum()\n+        loss_sum = (selected_weights * per_target_loss).sum()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +09:17

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-be56be2b-3413-44a1-8ec2-5d32e0321f75",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1198,3 +1198,3 @@\n ) -> tuple[dict[str, float], torch.Tensor | None]:\n-    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n+    \"\"\"Compute one logical block's retrieval loss in bounded-memory slices.\"\"\"\n     target_count = len(positive_items)\n@@ -1235,2 +1235,14 @@\n         )\n+        selected_negatives = negatives[begin:end]\n+        selected_negative_weights = negative_weights[begin:end]\n+        if config.negative_strategy == \"hard-mixed\":\n+            selected_negatives, selected_negative_weights = (\n+                select_mixed_hard_negatives(\n+                    model,\n+                    query_vectors,\n+                    selected_negatives,\n+                    selected_negative_weights,\n+                    config,\n+                )\n+            )\n         positive_vectors, negative_vectors = candidate_vectors(\n@@ -1238,3 +1250,3 @@\n             positive_items[begin:end],\n-            negatives[begin:end],\n+            selected_negatives,\n         )\n@@ -1244,6 +1256,26 @@\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_type == \"bpr\":\n+            pair_loss = F.softplus(\n+                negative_logits - positive_logits[:, None]\n+            )\n+            negative_loss = (\n+                pair_loss * selected_negative_weights\n+            ).sum(dim=1) / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+            per_target_loss = negative_loss\n+        elif config.loss_type == \"bce\":\n+            positive_loss = F.softplus(-positive_logits)\n+            if config.negative_strategy == \"hard-mixed\":\n+                raw_negative = F.softplus(negative_logits)\n+                negative_loss = (\n+                    raw_negative * selected_negative_weights\n+                ).sum(dim=1) / selected_negative_weights.sum(dim=1).clamp_min(\n+                    1.0e-6\n+                )\n+            else:\n+                negative_loss = corrected_negative_loss(\n+                    negative_logits, selected_negative_weights, config\n+                )\n+            per_target_loss = (positive_loss + negative_loss) / 2.0\n+        else:\n+            raise RuntimeError(f\"unknown loss type: {config.loss_type}\")\n         selected_weights = torch.from_numpy(\n@@ -1251,5 +1283,3 @@\n         ).to(device)\n-        loss_sum = (\n-            selected_weights * (positive_loss + negative_loss) / 2.0\n-        ).sum()\n+        loss_sum = (selected_weights * per_target_loss).sum()\n         if not bool(torch.isfinite(loss_sum)):\n",
        "move_path": null
      }
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    "stderr": ""
  }
}

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}

exec command

+09:19

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 +09:21

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

Tool response +09:21

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-6911ae96-aa8e-48fa-bd00-f3558f61d3f2",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1330,3 +1330,3 @@\n     group_order = torch.randperm(len(groups), generator=generator).numpy()\n-    negative_count = config.uniform_negatives + config.inbatch_negatives\n+    negative_count = effective_negative_count(config)\n     total_loss = 0.0\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

+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 +09:27

const r = await tools.exec_command({"cmd":"rg -n \"negative_count =|uniform_negatives \\+ config.inbatch|negative_strategy|loss_type|random_negatives|hard_negatives\" /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +09:27

Script completed
Wall time 0.0 seconds
Output:

63:    negative_strategy: str = "hard-mixed"
64:    random_negatives: int = 16
65:    hard_negatives: int = 16
66:    loss_type: str = "bpr"
836:    if config.negative_strategy == "hard-mixed":
837:        return config.random_negatives + config.hard_negatives
838:    return config.uniform_negatives + config.inbatch_negatives
842:def select_mixed_hard_negatives(
858:    random_count = config.random_negatives
859:    hard_count = config.hard_negatives
925:    total = config.uniform_negatives + config.inbatch_negatives
1238:        if config.negative_strategy == "hard-mixed":
1240:                select_mixed_hard_negatives(
1257:        if config.loss_type == "bpr":
1265:        elif config.loss_type == "bce":
1267:            if config.negative_strategy == "hard-mixed":
1280:            raise RuntimeError(f"unknown loss type: {config.loss_type}")
1331:    negative_count = effective_negative_count(config)

Harness context codex item completed

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    "stdout": "63:    negative_strategy: str = \"hard-mixed\"\n64:    random_negatives: int = 16\n65:    hard_negatives: int = 16\n66:    loss_type: str = \"bpr\"\n836:    if config.negative_strategy == \"hard-mixed\":\n837:        return config.random_negatives + config.hard_negatives\n838:    return config.uniform_negatives + config.inbatch_negatives\n842:def select_mixed_hard_negatives(\n858:    random_count = config.random_negatives\n859:    hard_count = config.hard_negatives\n925:    total = config.uniform_negatives + config.inbatch_negatives\n1238:        if config.negative_strategy == \"hard-mixed\":\n1240:                select_mixed_hard_negatives(\n1257:        if config.loss_type == \"bpr\":\n1265:        elif config.loss_type == \"bce\":\n1267:            if config.negative_strategy == \"hard-mixed\":\n1280:            raise RuntimeError(f\"unknown loss type: {config.loss_type}\")\n1331:    negative_count = effective_negative_count(config)\n",
    "stderr": "",
    "aggregated_output": "63:    negative_strategy: str = \"hard-mixed\"\n64:    random_negatives: int = 16\n65:    hard_negatives: int = 16\n66:    loss_type: str = \"bpr\"\n836:    if config.negative_strategy == \"hard-mixed\":\n837:        return config.random_negatives + config.hard_negatives\n838:    return config.uniform_negatives + config.inbatch_negatives\n842:def select_mixed_hard_negatives(\n858:    random_count = config.random_negatives\n859:    hard_count = config.hard_negatives\n925:    total = config.uniform_negatives + config.inbatch_negatives\n1238:        if config.negative_strategy == \"hard-mixed\":\n1240:                select_mixed_hard_negatives(\n1257:        if config.loss_type == \"bpr\":\n1265:        elif config.loss_type == \"bce\":\n1267:            if config.negative_strategy == \"hard-mixed\":\n1280:            raise RuntimeError(f\"unknown loss type: {config.loss_type}\")\n1331:    negative_count = effective_negative_count(config)\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6490
    },
    "formatted_output": "63:    negative_strategy: str = \"hard-mixed\"\n64:    random_negatives: int = 16\n65:    hard_negatives: int = 16\n66:    loss_type: str = \"bpr\"\n836:    if config.negative_strategy == \"hard-mixed\":\n837:        return config.random_negatives + config.hard_negatives\n838:    return config.uniform_negatives + config.inbatch_negatives\n842:def select_mixed_hard_negatives(\n858:    random_count = config.random_negatives\n859:    hard_count = config.hard_negatives\n925:    total = config.uniform_negatives + config.inbatch_negatives\n1238:        if config.negative_strategy == \"hard-mixed\":\n1240:                select_mixed_hard_negatives(\n1257:        if config.loss_type == \"bpr\":\n1265:        elif config.loss_type == \"bce\":\n1267:            if config.negative_strategy == \"hard-mixed\":\n1280:            raise RuntimeError(f\"unknown loss type: {config.loss_type}\")\n1331:    negative_count = effective_negative_count(config)\n"
  }
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exec command

+09:30

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 +09:41

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+        \"--negative-strategy\",\n+        choices=(\"hard-mixed\", \"mixed\"),\n+        default=Config.negative_strategy,\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+        \"--random-negatives\", type=int, default=Config.random_negatives\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\", type=int, default=Config.hard_negatives\n+    )\n+    parser.add_argument(\n+        \"--loss-type\", choices=(\"bpr\", \"bce\"), default=Config.loss_type\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.random_negatives < 0\n+        or args.hard_negatives < 0\n     ):\n         parser.error(\"training counts, learning rate, and patience are invalid\")\n+    negative_pool_size = args.uniform_negatives + args.inbatch_negatives\n+    if negative_pool_size < 1:\n+        parser.error(\"at least one negative must be drawn\")\n+    if args.negative_strategy == \"hard-mixed\" and (\n+        args.hard_negatives < 1\n+        or args.random_negatives + args.hard_negatives > negative_pool_size\n+    ):\n+        parser.error(\"hard/random negative selection does not fit in the pool\")\n@@\n         learning_rate=args.learning_rate,\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_strategy=args.negative_strategy,\n+        uniform_negatives=args.uniform_negatives,\n+        inbatch_negatives=args.inbatch_negatives,\n+        random_negatives=args.random_negatives,\n+        hard_negatives=args.hard_negatives,\n+        loss_type=args.loss_type,\n         negative_filter_scope=args.negative_filter_scope,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +09:41

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

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  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-1dd5de79-628a-4b9a-9b21-f7ae53e45762",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2047,2 +2047,22 @@\n     parser.add_argument(\n+        \"--negative-strategy\",\n+        choices=(\"hard-mixed\", \"mixed\"),\n+        default=Config.negative_strategy,\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+        \"--random-negatives\", type=int, default=Config.random_negatives\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\", type=int, default=Config.hard_negatives\n+    )\n+    parser.add_argument(\n+        \"--loss-type\", choices=(\"bpr\", \"bce\"), default=Config.loss_type\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2124,4 +2144,16 @@\n         or args.max_validation_targets_per_user < 0\n+        or args.uniform_negatives < 0\n+        or args.inbatch_negatives < 0\n+        or args.random_negatives < 0\n+        or args.hard_negatives < 0\n     ):\n         parser.error(\"training counts, learning rate, and patience are invalid\")\n+    negative_pool_size = args.uniform_negatives + args.inbatch_negatives\n+    if negative_pool_size < 1:\n+        parser.error(\"at least one negative must be drawn\")\n+    if args.negative_strategy == \"hard-mixed\" and (\n+        args.hard_negatives < 1\n+        or args.random_negatives + args.hard_negatives > negative_pool_size\n+    ):\n+        parser.error(\"hard/random negative selection does not fit in the pool\")\n     if not 0.0 < args.validation_fraction < 1.0:\n@@ -2137,2 +2169,8 @@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_strategy=args.negative_strategy,\n+        uniform_negatives=args.uniform_negatives,\n+        inbatch_negatives=args.inbatch_negatives,\n+        random_negatives=args.random_negatives,\n+        hard_negatives=args.hard_negatives,\n+        loss_type=args.loss_type,\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

+09:43

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 +09:45

const r = await tools.exec_command({"cmd":"sed -n '1660,1755p' /app/solution/train_current_model.py; sed -n '2190,2255p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":16000});
text(r.output);

Tool response +09:45

Script completed
Wall time 0.1 seconds
Output:

        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}"
            )
    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(
            {

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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "        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    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",
    "stderr": "",
    "aggregated_output": "        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    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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4680
    },
    "formatted_output": "        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    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"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ab0b04c487d2bf1df95c67ac6c05",
  "usage": {
    "input_tokens": 75291,
    "cached_input_tokens": 74496,
    "cache_write_input_tokens": 0,
    "output_tokens": 129,
    "reasoning_output_tokens": 43,
    "total_tokens": 75420
  },
  "turn_token_usage": {
    "input_tokens": 2840099,
    "cached_input_tokens": 2763136,
    "cache_write_input_tokens": 0,
    "output_tokens": 15032,
    "reasoning_output_tokens": 7544,
    "total_tokens": 2855131
  },
  "thread_token_usage": {
    "input_tokens": 2840099,
    "cached_input_tokens": 2763136,
    "cache_write_input_tokens": 0,
    "output_tokens": 15032,
    "reasoning_output_tokens": 7544,
    "total_tokens": 2855131
  }
}

exec command

+09:49

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 +09:56

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\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+        stored_negative_values = (\n+            stored_config.get(\"negative_strategy\", \"mixed\"),\n+            stored_config.get(\"uniform_negatives\", 24),\n+            stored_config.get(\"inbatch_negatives\", 8),\n+            stored_config.get(\"inbatch_correction_power\", 0.5),\n+            stored_config.get(\"random_negatives\", 0),\n+            stored_config.get(\"hard_negatives\", 0),\n+            stored_config.get(\"loss_type\", \"bce\"),\n+        )\n+        current_negative_values = (\n+            config.negative_strategy,\n+            config.uniform_negatives,\n+            config.inbatch_negatives,\n+            config.inbatch_correction_power,\n+            config.random_negatives,\n+            config.hard_negatives,\n+            config.loss_type,\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                 \"target weighting \"\n                 f\"{stored_target_weighting}->{config.target_weighting}, \"\n+                f\"negatives {stored_negative_values}->{current_negative_values}, \"\n                 f\"target window {stored_window_values}->{current_window_values}; \"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +09:56

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ab0ee90087d2b9333a7f1e8731d4",
  "usage": {
    "input_tokens": 76573,
    "cached_input_tokens": 75136,
    "cache_write_input_tokens": 0,
    "output_tokens": 427,
    "reasoning_output_tokens": 27,
    "total_tokens": 77000
  },
  "turn_token_usage": {
    "input_tokens": 2916672,
    "cached_input_tokens": 2838272,
    "cache_write_input_tokens": 0,
    "output_tokens": 15459,
    "reasoning_output_tokens": 7571,
    "total_tokens": 2932131
  },
  "thread_token_usage": {
    "input_tokens": 2916672,
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    "cache_write_input_tokens": 0,
    "output_tokens": 15459,
    "reasoning_output_tokens": 7571,
    "total_tokens": 2932131
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-7e147f4a-bad9-4497-8c11-6d09f08c98ef",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1731,2 +1731,20 @@\n         )\n+        stored_negative_values = (\n+            stored_config.get(\"negative_strategy\", \"mixed\"),\n+            stored_config.get(\"uniform_negatives\", 24),\n+            stored_config.get(\"inbatch_negatives\", 8),\n+            stored_config.get(\"inbatch_correction_power\", 0.5),\n+            stored_config.get(\"random_negatives\", 0),\n+            stored_config.get(\"hard_negatives\", 0),\n+            stored_config.get(\"loss_type\", \"bce\"),\n+        )\n+        current_negative_values = (\n+            config.negative_strategy,\n+            config.uniform_negatives,\n+            config.inbatch_negatives,\n+            config.inbatch_correction_power,\n+            config.random_negatives,\n+            config.hard_negatives,\n+            config.loss_type,\n+        )\n         objective_changed = (\n@@ -1736,2 +1754,3 @@\n             or stored_window_values != current_window_values\n+            or stored_negative_values != current_negative_values\n         )\n@@ -1766,2 +1785,3 @@\n                 f\"{stored_target_weighting}->{config.target_weighting}, \"\n+                f\"negatives {stored_negative_values}->{current_negative_values}, \"\n                 f\"target window {stored_window_values}->{current_window_values}; \"\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

exec command

+09:58

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 +09:59

const r = await tools.exec_command({"cmd":"sed -n '2250,2325p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +09:59

Script completed
Wall time 0.1 seconds
Output:

                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__":

Harness context codex item completed

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      "/bin/sh",
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      "sed -n '2250,2325p' /app/solution/train_current_model.py"
    ],
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    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '2250,2325p' /app/solution/train_current_model.py",
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        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "                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",
    "stderr": "",
    "aggregated_output": "                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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4170
    },
    "formatted_output": "                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"
  }
}

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}

exec command

+10:00

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 +10:04

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-two-tower-hard-negative\",\n@@\n-                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n+                    \"model_selection\": \"chronological-validation-loss-early-stopping\",\n@@\n-                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n-                    \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n-                    ),\n+                    \"objective\": f\"sample-weighted-{config.loss_type}\",\n+                    \"negative_sampling\": config.negative_strategy,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +10:04

Script completed
Wall time 0.1 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-8f878d68-5ed1-44b2-b821-98e4f23128f1",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2275,3 +2275,3 @@\n             {\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-two-tower-hard-negative\",\n                 \"stage\": args.stage,\n@@ -2289,3 +2289,3 @@\n                     \"bptt_hidden_state\": \"carried-and-detached\",\n-                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n+                    \"model_selection\": \"chronological-validation-loss-early-stopping\",\n                     \"loss_execution\": \"gradient-accumulated-microbatches\",\n@@ -2296,6 +2296,4 @@\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+                    \"objective\": f\"sample-weighted-{config.loss_type}\",\n+                    \"negative_sampling\": config.negative_strategy,\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

+10:07

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 +10:09

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":16000});
text(r.output);

Tool response +10:15

Script completed
Wall time 5.9 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]
                              [--negative-strategy {hard-mixed,mixed}]
                              [--uniform-negatives UNIFORM_NEGATIVES]
                              [--inbatch-negatives INBATCH_NEGATIVES]
                              [--random-negatives RANDOM_NEGATIVES]
                              [--hard-negatives HARD_NEGATIVES]
                              [--loss-type {bpr,bce}]
                              [--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 with mixed hard-negative mining. 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, and raw dot-product retrieval. Training draws a broad
pool of uniform and unique in-batch candidates, rejects every item in the
listener's known positive history, and optimizes a mixture of random and
online hard negatives. Mining is done only while fitting; serving remains one
user vector against precomputed item vectors. 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
  --negative-strategy {hard-mixed,mixed}
  --uniform-negatives UNIFORM_NEGATIVES
  --inbatch-negatives INBATCH_NEGATIVES
  --random-negatives RANDOM_NEGATIVES
  --hard-negatives HARD_NEGATIVES
  --loss-type {bpr,bce}
  --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

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-33d76a9b-9779-4dde-993b-23a6e4178b94",
    "process_id": "90047",
    "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'"
    ],
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      {
        "type": "unknown",
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    "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                              [--negative-strategy {hard-mixed,mixed}]\n                              [--uniform-negatives UNIFORM_NEGATIVES]\n                              [--inbatch-negatives INBATCH_NEGATIVES]\n                              [--random-negatives RANDOM_NEGATIVES]\n                              [--hard-negatives HARD_NEGATIVES]\n                              [--loss-type {bpr,bce}]\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 with mixed hard-negative mining. Every\ncatalog listen with at least 50 percent completion is a sequence token. Within\neach selected chronological window, every adjacent target contributes a loss.\nThe fast default retains each user's most recent 128 training targets and 16\nheld-out validation targets; passing zero for either cap restores the uncapped\nbehavior for that side of the split. A 100-step truncated-BPTT block computes\nall of its timestep losses and carries the detached GRU hidden state into the\nnext block. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, and raw dot-product retrieval. Training draws a broad\npool of uniform and unique in-batch candidates, rejects every item in the\nlistener's known positive history, and optimizes a mixture of random and\nonline hard negatives. Mining is done only while fitting; serving remains one\nuser vector against precomputed item vectors. Every selected adjacent target\nretains a nonzero weight. Only files under the supplied public directory are\nread.\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  --negative-strategy {hard-mixed,mixed}\n  --uniform-negatives UNIFORM_NEGATIVES\n  --inbatch-negatives INBATCH_NEGATIVES\n  --random-negatives RANDOM_NEGATIVES\n  --hard-negatives HARD_NEGATIVES\n  --loss-type {bpr,bce}\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                              [--negative-strategy {hard-mixed,mixed}]\n                              [--uniform-negatives UNIFORM_NEGATIVES]\n                              [--inbatch-negatives INBATCH_NEGATIVES]\n                              [--random-negatives RANDOM_NEGATIVES]\n                              [--hard-negatives HARD_NEGATIVES]\n                              [--loss-type {bpr,bce}]\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 with mixed hard-negative mining. Every\ncatalog listen with at least 50 percent completion is a sequence token. Within\neach selected chronological window, every adjacent target contributes a loss.\nThe fast default retains each user's most recent 128 training targets and 16\nheld-out validation targets; passing zero for either cap restores the uncapped\nbehavior for that side of the split. A 100-step truncated-BPTT block computes\nall of its timestep losses and carries the detached GRU hidden state into the\nnext block. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, and raw dot-product retrieval. Training draws a broad\npool of uniform and unique in-batch candidates, rejects every item in the\nlistener's known positive history, and optimizes a mixture of random and\nonline hard negatives. Mining is done only while fitting; serving remains one\nuser vector against precomputed item vectors. Every selected adjacent target\nretains a nonzero weight. Only files under the supplied public directory are\nread.\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  --negative-strategy {hard-mixed,mixed}\n  --uniform-negatives UNIFORM_NEGATIVES\n  --inbatch-negatives INBATCH_NEGATIVES\n  --random-negatives RANDOM_NEGATIVES\n  --hard-negatives HARD_NEGATIVES\n  --loss-type {bpr,bce}\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": 5,
      "nanos": 705687117
    },
    "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                              [--negative-strategy {hard-mixed,mixed}]\n                              [--uniform-negatives UNIFORM_NEGATIVES]\n                              [--inbatch-negatives INBATCH_NEGATIVES]\n                              [--random-negatives RANDOM_NEGATIVES]\n                              [--hard-negatives HARD_NEGATIVES]\n                              [--loss-type {bpr,bce}]\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 with mixed hard-negative mining. Every\ncatalog listen with at least 50 percent completion is a sequence token. Within\neach selected chronological window, every adjacent target contributes a loss.\nThe fast default retains each user's most recent 128 training targets and 16\nheld-out validation targets; passing zero for either cap restores the uncapped\nbehavior for that side of the split. A 100-step truncated-BPTT block computes\nall of its timestep losses and carries the detached GRU hidden state into the\nnext block. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, and raw dot-product retrieval. Training draws a broad\npool of uniform and unique in-batch candidates, rejects every item in the\nlistener's known positive history, and optimizes a mixture of random and\nonline hard negatives. Mining is done only while fitting; serving remains one\nuser vector against precomputed item vectors. Every selected adjacent target\nretains a nonzero weight. Only files under the supplied public directory are\nread.\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  --negative-strategy {hard-mixed,mixed}\n  --uniform-negatives UNIFORM_NEGATIVES\n  --inbatch-negatives INBATCH_NEGATIVES\n  --random-negatives RANDOM_NEGATIVES\n  --hard-negatives HARD_NEGATIVES\n  --loss-type {bpr,bce}\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

+10:18

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 +10:19

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +10:29

Script completed
Wall time 10.0 seconds
Output:

loaded explicit events: rows=746,504 users=8,307
prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s
prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.3s
prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s
prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s
prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s
prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s
prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s
prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.7s
prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s
target users without qualifying catalog sequence=6
prepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322
saved public-derived full-sequence cache: /app/solution/submission_cache.npz
chronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750

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Agent message

+10:41

Thinking unavailable No text recorded

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

exec command

+10:49

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 +10:50

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-    loss_microbatch_size: int = 2_048\n+    loss_microbatch_size: int = 8_192\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +10:50

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-e443ac7f-b235-4ab7-878d-d03acd378884",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -56,3 +56,3 @@\n     gradient_clip: float = 2.0\n-    loss_microbatch_size: int = 2_048\n+    loss_microbatch_size: int = 8_192\n     uniform_negatives: int = 64\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

+11:01

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 +11:05

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, torch\nimport train_current_model as t\nimport two_tower_model as b\ncfg=t.Config(embedding_dim=8,hidden_dim=8,user_hidden_1=16,user_hidden_2=8,item_hidden_1=16,item_hidden_2=8,uniform_negatives=6,inbatch_negatives=4,random_negatives=4,hard_negatives=3)\nm=b.SequentialTwoTower(np.zeros((100,4),np.float32),t.model_config(cfg))\npool=torch.stack([torch.randperm(100)[:10] for _ in range(7)])\nw=torch.ones_like(pool,dtype=torch.float32)\nq=torch.randn(7,8)\nx,xw=t.select_mixed_hard_negatives(m,q,pool,w,cfg)\nassert x.shape==(7,7) and xw.shape==(7,7)\nassert all(len(set(row.tolist()))==7 for row in x)\nprint('hard-selection-smoke-ok',x.shape)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +11:11

Script completed
Wall time 6.0 seconds
Output:

hard-selection-smoke-ok torch.Size([7, 7])

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-363fa27a-04a7-4343-9d07-4e6588abca76",
    "process_id": "53788",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, torch\nimport train_current_model as t\nimport two_tower_model as b\ncfg=t.Config(embedding_dim=8,hidden_dim=8,user_hidden_1=16,user_hidden_2=8,item_hidden_1=16,item_hidden_2=8,uniform_negatives=6,inbatch_negatives=4,random_negatives=4,hard_negatives=3)\nm=b.SequentialTwoTower(np.zeros((100,4),np.float32),t.model_config(cfg))\npool=torch.stack([torch.randperm(100)[:10] for _ in range(7)])\nw=torch.ones_like(pool,dtype=torch.float32)\nq=torch.randn(7,8)\nx,xw=t.select_mixed_hard_negatives(m,q,pool,w,cfg)\nassert x.shape==(7,7) and xw.shape==(7,7)\nassert all(len(set(row.tolist()))==7 for row in x)\nprint('hard-selection-smoke-ok',x.shape)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, torch\nimport train_current_model as t\nimport two_tower_model as b\ncfg=t.Config(embedding_dim=8,hidden_dim=8,user_hidden_1=16,user_hidden_2=8,item_hidden_1=16,item_hidden_2=8,uniform_negatives=6,inbatch_negatives=4,random_negatives=4,hard_negatives=3)\nm=b.SequentialTwoTower(np.zeros((100,4),np.float32),t.model_config(cfg))\npool=torch.stack([torch.randperm(100)[:10] for _ in range(7)])\nw=torch.ones_like(pool,dtype=torch.float32)\nq=torch.randn(7,8)\nx,xw=t.select_mixed_hard_negatives(m,q,pool,w,cfg)\nassert x.shape==(7,7) and xw.shape==(7,7)\nassert all(len(set(row.tolist()))==7 for row in x)\nprint('hard-selection-smoke-ok',x.shape)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "hard-selection-smoke-ok torch.Size([7, 7])\n",
    "stderr": "",
    "aggregated_output": "hard-selection-smoke-ok torch.Size([7, 7])\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 836665206
    },
    "formatted_output": "hard-selection-smoke-ok torch.Size([7, 7])\n"
  }
}

exec command

+11:15

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 +11:16

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +11:26

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ab652a8487d29c10d84a3fc20d44",
  "usage": {
    "input_tokens": 81583,
    "cached_input_tokens": 80640,
    "cache_write_input_tokens": 0,
    "output_tokens": 58,
    "reasoning_output_tokens": 11,
    "total_tokens": 81641
  },
  "turn_token_usage": {
    "input_tokens": 3470611,
    "cached_input_tokens": 3385984,
    "cache_write_input_tokens": 0,
    "output_tokens": 17915,
    "reasoning_output_tokens": 9201,
    "total_tokens": 3488526
  },
  "thread_token_usage": {
    "input_tokens": 3470611,
    "cached_input_tokens": 3385984,
    "cache_write_input_tokens": 0,
    "output_tokens": 17915,
    "reasoning_output_tokens": 9201,
    "total_tokens": 3488526
  }
}

Agent message

+11:39

Thinking unavailable No text recorded

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

exec command

+11:41

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 +11:42

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +11:52

Script completed
Wall time 10.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ab7378c887d29d0e0b0c8468165f",
  "usage": {
    "input_tokens": 81664,
    "cached_input_tokens": 81408,
    "cache_write_input_tokens": 0,
    "output_tokens": 700,
    "reasoning_output_tokens": 653,
    "total_tokens": 82364
  },
  "turn_token_usage": {
    "input_tokens": 3552275,
    "cached_input_tokens": 3467392,
    "cache_write_input_tokens": 0,
    "output_tokens": 18615,
    "reasoning_output_tokens": 9854,
    "total_tokens": 3570890
  },
  "thread_token_usage": {
    "input_tokens": 3552275,
    "cached_input_tokens": 3467392,
    "cache_write_input_tokens": 0,
    "output_tokens": 18615,
    "reasoning_output_tokens": 9854,
    "total_tokens": 3570890
  }
}

Agent message

+12:03

Thinking unavailable No text recorded

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

Model text

The safety refit is training now. The improved trainer has passed a smoke test: it draws 96 filtered candidates per target (64 catalog-uniform, 32 in-batch), retains 16 distributional anchors, mines the 16 highest-scoring remaining candidates with detached selection, and applies a pairwise ranking loss. Mining is training-only, so request-time scoring remains the original batched dot product.

exec command

+12:03

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 +12:03

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +12:13

Script completed
Wall time 10.0 seconds
Output:

epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=220.9s

Harness context token usage record

{
  "type": "token_usage_record",
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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ab8dcb7087d2953884727cbc9de7",
  "usage": {
    "input_tokens": 82387,
    "cached_input_tokens": 81536,
    "cache_write_input_tokens": 0,
    "output_tokens": 207,
    "reasoning_output_tokens": 71,
    "total_tokens": 82594
  },
  "turn_token_usage": {
    "input_tokens": 3634662,
    "cached_input_tokens": 3548928,
    "cache_write_input_tokens": 0,
    "output_tokens": 18822,
    "reasoning_output_tokens": 9925,
    "total_tokens": 3653484
  },
  "thread_token_usage": {
    "input_tokens": 3634662,
    "cached_input_tokens": 3548928,
    "cache_write_input_tokens": 0,
    "output_tokens": 18822,
    "reasoning_output_tokens": 9925,
    "total_tokens": 3653484
  }
}

exec command

+12:20

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 +12:20

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +12:30

Script completed
Wall time 10.0 seconds
Output:

saved epoch checkpoint: /app/solution/submission_baseline_checkpoints/epoch-01.pt

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aba266fc87d2a34e58ec0e302f01",
  "usage": {
    "input_tokens": 82653,
    "cached_input_tokens": 82176,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 82706
  },
  "turn_token_usage": {
    "input_tokens": 3717315,
    "cached_input_tokens": 3631104,
    "cache_write_input_tokens": 0,
    "output_tokens": 18875,
    "reasoning_output_tokens": 9931,
    "total_tokens": 3736190
  },
  "thread_token_usage": {
    "input_tokens": 3717315,
    "cached_input_tokens": 3631104,
    "cache_write_input_tokens": 0,
    "output_tokens": 18875,
    "reasoning_output_tokens": 9931,
    "total_tokens": 3736190
  }
}

Tool callexecTool call Returned

Tool call · Input +12:36

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +12:46

Script completed
Wall time 10.0 seconds
Output:

epoch=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=236.3 validation_seconds=17.8
restored best validation-BCE checkpoint: epoch=1 loss=0.497949

Harness context token usage record

{
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5abb44aa887d28498a9208ef96ead",
  "usage": {
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    "cached_input_tokens": 82432,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 0,
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  },
  "turn_token_usage": {
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    "cached_input_tokens": 3713536,
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  },
  "thread_token_usage": {
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    "cached_input_tokens": 3713536,
    "cache_write_input_tokens": 0,
    "output_tokens": 18920,
    "reasoning_output_tokens": 9931,
    "total_tokens": 3818984
  }
}

exec command

+12:51

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 +12:52

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +13:22

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5abc392e087d291260385e996fd2b",
  "usage": {
    "input_tokens": 82887,
    "cached_input_tokens": 82560,
    "cache_write_input_tokens": 0,
    "output_tokens": 77,
    "reasoning_output_tokens": 30,
    "total_tokens": 82964
  },
  "turn_token_usage": {
    "input_tokens": 3882951,
    "cached_input_tokens": 3796096,
    "cache_write_input_tokens": 0,
    "output_tokens": 18997,
    "reasoning_output_tokens": 9961,
    "total_tokens": 3901948
  },
  "thread_token_usage": {
    "input_tokens": 3882951,
    "cached_input_tokens": 3796096,
    "cache_write_input_tokens": 0,
    "output_tokens": 18997,
    "reasoning_output_tokens": 9961,
    "total_tokens": 3901948
  }
}

Tool callexecTool call Returned

Tool call · Input +13:28

const r = await tools.write_stdin({"session_id":46200,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +13:58

Script completed
Wall time 29.3 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "submission",
  "output": "/app/solution/submission.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 397.277,
  "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": 20712971,
  "all_next_item_targets": 20703818,
  "selected_training_targets": 1067606,
  "selected_validation_targets": 131997,
  "discarded_old_targets": 19504215,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5383531189748144,
      "positive_logit": 0.7500170016807174,
      "negative_logit": -0.6300986844154737,
      "pair_accuracy": 0.7552423658634365,
      "targets": 1067606.0,
      "target_weight_sum": 641383.25,
      "batches": 270.0,
      "seconds": 236.25973453100005,
      "validation_loss": 0.4979487093045201,
      "validation_targets": 131997.0,
      "validation_target_weight_sum": 80328.75,
      "validation_seconds": 17.841581375000032
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.6518515348434448,
    "query_norm_std": 0.8145267367362976,
    "item_norm_mean": 1.9033515453338623,
    "item_norm_std": 0.9598208069801331
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

Harness context token usage record

{
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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5abe77b4487d2b1f719a048c1a908",
  "usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 0,
    "total_tokens": 83032
  },
  "turn_token_usage": {
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    "cached_input_tokens": 3878784,
    "cache_write_input_tokens": 0,
    "output_tokens": 19042,
    "reasoning_output_tokens": 9961,
    "total_tokens": 3984980
  },
  "thread_token_usage": {
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    "cached_input_tokens": 3878784,
    "cache_write_input_tokens": 0,
    "output_tokens": 19042,
    "reasoning_output_tokens": 9961,
    "total_tokens": 3984980
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-86ce8851-0a7a-4407-a9cf-85a6e5c8c316",
    "process_id": "46200",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_baseline.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/submission_baseline_checkpoints --output /app/solution/submission.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_baseline.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/submission_baseline_checkpoints --output /app/solution/submission.parquet"
      }
    ],
    "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=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.3s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.7s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\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_cache.npz\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=220.9s\nsaved epoch checkpoint: /app/solution/submission_baseline_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=236.3 validation_seconds=17.8\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{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 397.277,\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\": 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\": 0.5383531189748144,\n      \"positive_logit\": 0.7500170016807174,\n      \"negative_logit\": -0.6300986844154737,\n      \"pair_accuracy\": 0.7552423658634365,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 236.25973453100005,\n      \"validation_loss\": 0.4979487093045201,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 17.841581375000032\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6518515348434448,\n    \"query_norm_std\": 0.8145267367362976,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208069801331\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\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=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.3s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.7s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\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_cache.npz\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=220.9s\nsaved epoch checkpoint: /app/solution/submission_baseline_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=236.3 validation_seconds=17.8\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{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 397.277,\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\": 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\": 0.5383531189748144,\n      \"positive_logit\": 0.7500170016807174,\n      \"negative_logit\": -0.6300986844154737,\n      \"pair_accuracy\": 0.7552423658634365,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 236.25973453100005,\n      \"validation_loss\": 0.4979487093045201,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 17.841581375000032\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6518515348434448,\n    \"query_norm_std\": 0.8145267367362976,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208069801331\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 401,
      "nanos": 783196275
    },
    "formatted_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.3s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.7s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\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_cache.npz\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=220.9s\nsaved epoch checkpoint: /app/solution/submission_baseline_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=236.3 validation_seconds=17.8\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{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 397.277,\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\": 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\": 0.5383531189748144,\n      \"positive_logit\": 0.7500170016807174,\n      \"negative_logit\": -0.6300986844154737,\n      \"pair_accuracy\": 0.7552423658634365,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 236.25973453100005,\n      \"validation_loss\": 0.4979487093045201,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 17.841581375000032\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6518515348434448,\n    \"query_norm_std\": 0.8145267367362976,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208069801331\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}

exec command

+14:06

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 +14:08

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_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +14:09

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"8fdbf6","wall_time_seconds":1.000850316,"session_id":61356,"original_token_count":0,"output":""}

Harness context token usage record

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    "total_tokens": 4069301
  }
}

exec command

+14:17

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 +14:18

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +14:28

Script completed
Wall time 10.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt
BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('hard-mixed', 64, 32, 0.0, 16, 16, 'bpr'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE

Harness context token usage record

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  }
}

exec command

+14:35

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 +14:35

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +15:06

Script completed
Wall time 30.0 seconds
Output:

resumed validation-BCE training: epoch=1 loss=0.733431

Harness context token usage record

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  }
}

exec command

+15:11

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 +15:13

const r = await tools.exec_command({"cmd":"for d in /proc/[0-9]*; do [ -r \"$d/cmdline\" ] || continue; c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' \"$d/stat\"); echo pid=$(basename \"$d\") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd=\"$c\";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +15:14

Script completed
Wall time 0.1 seconds
Output:

pid=9928 state=S user_ticks=0 system_ticks=0 rss_pages=399 cmd=/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet 
pid=9929 state=R user_ticks=29477 system_ticks=2449 rss_pages=864531 cmd=/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet 
pid=9964 state=S user_ticks=0 system_ticks=0 rss_pages=411 cmd=/bin/sh -c for d in /proc/[0-9]*; do [ -r "$d/cmdline" ] || continue; c=$(tr '' ' ' < "$d/cmdline"); case "$c" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' "$d/stat"); echo pid=$(basename "$d") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd="$c";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo 
MemTotal:       15988964 kB
MemAvailable:   11330336 kB

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fa91979a-eaa7-4cfc-83af-3723cc00866a",
    "process_id": "53604",
    "command": [
      "/bin/sh",
      "-lc",
      "for d in /proc/[0-9]*; do [ -r \"$d/cmdline\" ] || continue; c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' \"$d/stat\"); echo pid=$(basename \"$d\") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd=\"$c\";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for d in /proc/[0-9]*; do [ -r \"$d/cmdline\" ] || continue; c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' \"$d/stat\"); echo pid=$(basename \"$d\") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd=\"$c\";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "pid=9928 state=S user_ticks=0 system_ticks=0 rss_pages=399 cmd=/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet \npid=9929 state=R user_ticks=29477 system_ticks=2449 rss_pages=864531 cmd=/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet \npid=9964 state=S user_ticks=0 system_ticks=0 rss_pages=411 cmd=/bin/sh -c for d in /proc/[0-9]*; do [ -r \"$d/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' \"$d/stat\"); echo pid=$(basename \"$d\") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd=\"$c\";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo \nMemTotal:       15988964 kB\nMemAvailable:   11330336 kB\n",
    "stderr": "",
    "aggregated_output": "pid=9928 state=S user_ticks=0 system_ticks=0 rss_pages=399 cmd=/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet \npid=9929 state=R user_ticks=29477 system_ticks=2449 rss_pages=864531 cmd=/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet \npid=9964 state=S user_ticks=0 system_ticks=0 rss_pages=411 cmd=/bin/sh -c for d in /proc/[0-9]*; do [ -r \"$d/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' \"$d/stat\"); echo pid=$(basename \"$d\") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd=\"$c\";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo \nMemTotal:       15988964 kB\nMemAvailable:   11330336 kB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3131
    },
    "formatted_output": "pid=9928 state=S user_ticks=0 system_ticks=0 rss_pages=399 cmd=/bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet \npid=9929 state=R user_ticks=29477 system_ticks=2449 rss_pages=864531 cmd=/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet \npid=9964 state=S user_ticks=0 system_ticks=0 rss_pages=411 cmd=/bin/sh -c for d in /proc/[0-9]*; do [ -r \"$d/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *hard_bpr_validation*) set -- $(sed 's/^[^)]*) //' \"$d/stat\"); echo pid=$(basename \"$d\") state=${1} user_ticks=${12} system_ticks=${13} rss_pages=${22} cmd=\"$c\";; esac; done; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo \nMemTotal:       15988964 kB\nMemAvailable:   11330336 kB\n"
  }
}

Agent message

+15:28

Thinking unavailable No text recorded

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

exec command

+15:29

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 +15:29

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +15:59

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ac579f6087d2b317c6ef4e2b6882",
  "usage": {
    "input_tokens": 85500,
    "cached_input_tokens": 84608,
    "cache_write_input_tokens": 0,
    "output_tokens": 583,
    "reasoning_output_tokens": 536,
    "total_tokens": 86083
  },
  "turn_token_usage": {
    "input_tokens": 4389482,
    "cached_input_tokens": 4299008,
    "cache_write_input_tokens": 0,
    "output_tokens": 20214,
    "reasoning_output_tokens": 10691,
    "total_tokens": 4409696
  },
  "thread_token_usage": {
    "input_tokens": 4389482,
    "cached_input_tokens": 4299008,
    "cache_write_input_tokens": 0,
    "output_tokens": 20214,
    "reasoning_output_tokens": 10691,
    "total_tokens": 4409696
  }
}

Tool callexecTool call Returned

Tool call · Input +16:02

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +16:32

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ac84fd1087d2ab8360cfb7701f48",
  "usage": {
    "input_tokens": 86106,
    "cached_input_tokens": 85376,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 86151
  },
  "turn_token_usage": {
    "input_tokens": 4475588,
    "cached_input_tokens": 4384384,
    "cache_write_input_tokens": 0,
    "output_tokens": 20259,
    "reasoning_output_tokens": 10691,
    "total_tokens": 4495847
  },
  "thread_token_usage": {
    "input_tokens": 4475588,
    "cached_input_tokens": 4384384,
    "cache_write_input_tokens": 0,
    "output_tokens": 20259,
    "reasoning_output_tokens": 10691,
    "total_tokens": 4495847
  }
}

exec command

+16:34

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 +16:37

const r = await tools.exec_command({"cmd":"set -- $(sed 's/^[^)]*) //' /proc/9929/stat); read up idle < /proc/uptime; echo elapsed_seconds=$(/bin/awk -v u=\"$up\" -v s=\"${20}\" 'BEGIN {printf \"%.1f\",u-s/100}') cpu_seconds=$(/bin/awk -v a=\"${12}\" -v b=\"${13}\" 'BEGIN {printf \"%.1f\",(a+b)/100}') rss_gib=$(/bin/awk -v r=\"${22}\" 'BEGIN {printf \"%.2f\",r*4096/1073741824}')","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +16:37

Script completed
Wall time 0.1 seconds
Output:

elapsed_seconds=149.2 cpu_seconds=696.5 rss_gib=4.35

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e3260647-4602-418e-921c-5d7b9ff0af70",
    "process_id": "44690",
    "command": [
      "/bin/sh",
      "-lc",
      "set -- $(sed 's/^[^)]*) //' /proc/9929/stat); read up idle < /proc/uptime; echo elapsed_seconds=$(/bin/awk -v u=\"$up\" -v s=\"${20}\" 'BEGIN {printf \"%.1f\",u-s/100}') cpu_seconds=$(/bin/awk -v a=\"${12}\" -v b=\"${13}\" 'BEGIN {printf \"%.1f\",(a+b)/100}') rss_gib=$(/bin/awk -v r=\"${22}\" 'BEGIN {printf \"%.2f\",r*4096/1073741824}')"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "set -- $(sed 's/^[^)]*) //' /proc/9929/stat); read up idle < /proc/uptime; echo elapsed_seconds=$(/bin/awk -v u=\"$up\" -v s=\"${20}\" 'BEGIN {printf \"%.1f\",u-s/100}') cpu_seconds=$(/bin/awk -v a=\"${12}\" -v b=\"${13}\" 'BEGIN {printf \"%.1f\",(a+b)/100}') rss_gib=$(/bin/awk -v r=\"${22}\" 'BEGIN {printf \"%.2f\",r*4096/1073741824}')"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "elapsed_seconds=149.2 cpu_seconds=696.5 rss_gib=4.35\n",
    "stderr": "",
    "aggregated_output": "elapsed_seconds=149.2 cpu_seconds=696.5 rss_gib=4.35\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3089
    },
    "formatted_output": "elapsed_seconds=149.2 cpu_seconds=696.5 rss_gib=4.35\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5aca5c70c87d29169c7ba7913dedf",
  "usage": {
    "input_tokens": 86174,
    "cached_input_tokens": 85888,
    "cache_write_input_tokens": 0,
    "output_tokens": 194,
    "reasoning_output_tokens": 13,
    "total_tokens": 86368
  },
  "turn_token_usage": {
    "input_tokens": 4561762,
    "cached_input_tokens": 4470272,
    "cache_write_input_tokens": 0,
    "output_tokens": 20453,
    "reasoning_output_tokens": 10704,
    "total_tokens": 4582215
  },
  "thread_token_usage": {
    "input_tokens": 4561762,
    "cached_input_tokens": 4470272,
    "cache_write_input_tokens": 0,
    "output_tokens": 20453,
    "reasoning_output_tokens": 10704,
    "total_tokens": 4582215
  }
}

Agent message

+16:46

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 valid cutoff-correct baseline submission.parquet is now safely saved. The first enhanced backtest is fine-tuning from the identical baseline checkpoint; its pre-update hard-pair loss is 0.7334, confirming that the old sampler’s good BCE hides many high-scoring competitors—the exact failure mode the new objective targets.

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 +16:49

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +17:19

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5acab44dc87d288ec06fab32a6822",
  "usage": {
    "input_tokens": 86411,
    "cached_input_tokens": 86016,
    "cache_write_input_tokens": 0,
    "output_tokens": 417,
    "reasoning_output_tokens": 295,
    "total_tokens": 86828
  },
  "turn_token_usage": {
    "input_tokens": 4648173,
    "cached_input_tokens": 4556288,
    "cache_write_input_tokens": 0,
    "output_tokens": 20870,
    "reasoning_output_tokens": 10999,
    "total_tokens": 4669043
  },
  "thread_token_usage": {
    "input_tokens": 4648173,
    "cached_input_tokens": 4556288,
    "cache_write_input_tokens": 0,
    "output_tokens": 20870,
    "reasoning_output_tokens": 10999,
    "total_tokens": 4669043
  }
}

Tool callexecTool call Returned

Tool call · Input +17:21

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +17:51

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5acd49ed487d288f4227b9284bfe1",
  "usage": {
    "input_tokens": 86851,
    "cached_input_tokens": 86272,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 86896
  },
  "turn_token_usage": {
    "input_tokens": 4735024,
    "cached_input_tokens": 4642560,
    "cache_write_input_tokens": 0,
    "output_tokens": 20915,
    "reasoning_output_tokens": 10999,
    "total_tokens": 4755939
  },
  "thread_token_usage": {
    "input_tokens": 4735024,
    "cached_input_tokens": 4642560,
    "cache_write_input_tokens": 0,
    "output_tokens": 20915,
    "reasoning_output_tokens": 10999,
    "total_tokens": 4755939
  }
}

exec command

+17:53

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 +17:54

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +18:24

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5acf5368c87d2b26304f96c7cd4ed",
  "usage": {
    "input_tokens": 86919,
    "cached_input_tokens": 86656,
    "cache_write_input_tokens": 0,
    "output_tokens": 58,
    "reasoning_output_tokens": 11,
    "total_tokens": 86977
  },
  "turn_token_usage": {
    "input_tokens": 4821943,
    "cached_input_tokens": 4729216,
    "cache_write_input_tokens": 0,
    "output_tokens": 20973,
    "reasoning_output_tokens": 11010,
    "total_tokens": 4842916
  },
  "thread_token_usage": {
    "input_tokens": 4821943,
    "cached_input_tokens": 4729216,
    "cache_write_input_tokens": 0,
    "output_tokens": 20973,
    "reasoning_output_tokens": 11010,
    "total_tokens": 4842916
  }
}

exec command

+18:28

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 +18:31

const r = await tools.exec_command({"cmd":"set -- $(sed 's/^[^)]*) //' /proc/9929/stat); read up idle < /proc/uptime; echo elapsed_seconds=$(/bin/awk -v u=\"$up\" -v s=\"${20}\" 'BEGIN {printf \"%.1f\",u-s/100}') cpu_seconds=$(/bin/awk -v a=\"${12}\" -v b=\"${13}\" 'BEGIN {printf \"%.1f\",(a+b)/100}') rss_gib=$(/bin/awk -v r=\"${22}\" 'BEGIN {printf \"%.2f\",r*4096/1073741824}')","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +18:31

Script completed
Wall time 0.0 seconds
Output:

elapsed_seconds=263.0 cpu_seconds=1216.7 rss_gib=5.42

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-104479f5-bb4c-4784-abb9-4b8d23c37c4a",
    "process_id": "13241",
    "command": [
      "/bin/sh",
      "-lc",
      "set -- $(sed 's/^[^)]*) //' /proc/9929/stat); read up idle < /proc/uptime; echo elapsed_seconds=$(/bin/awk -v u=\"$up\" -v s=\"${20}\" 'BEGIN {printf \"%.1f\",u-s/100}') cpu_seconds=$(/bin/awk -v a=\"${12}\" -v b=\"${13}\" 'BEGIN {printf \"%.1f\",(a+b)/100}') rss_gib=$(/bin/awk -v r=\"${22}\" 'BEGIN {printf \"%.2f\",r*4096/1073741824}')"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "set -- $(sed 's/^[^)]*) //' /proc/9929/stat); read up idle < /proc/uptime; echo elapsed_seconds=$(/bin/awk -v u=\"$up\" -v s=\"${20}\" 'BEGIN {printf \"%.1f\",u-s/100}') cpu_seconds=$(/bin/awk -v a=\"${12}\" -v b=\"${13}\" 'BEGIN {printf \"%.1f\",(a+b)/100}') rss_gib=$(/bin/awk -v r=\"${22}\" 'BEGIN {printf \"%.2f\",r*4096/1073741824}')"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "elapsed_seconds=263.0 cpu_seconds=1216.7 rss_gib=5.42\n",
    "stderr": "",
    "aggregated_output": "elapsed_seconds=263.0 cpu_seconds=1216.7 rss_gib=5.42\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5641
    },
    "formatted_output": "elapsed_seconds=263.0 cpu_seconds=1216.7 rss_gib=5.42\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ad16051c87d28504a4464222433d",
  "usage": {
    "input_tokens": 87000,
    "cached_input_tokens": 86784,
    "cache_write_input_tokens": 0,
    "output_tokens": 197,
    "reasoning_output_tokens": 16,
    "total_tokens": 87197
  },
  "turn_token_usage": {
    "input_tokens": 4908943,
    "cached_input_tokens": 4816000,
    "cache_write_input_tokens": 0,
    "output_tokens": 21170,
    "reasoning_output_tokens": 11026,
    "total_tokens": 4930113
  },
  "thread_token_usage": {
    "input_tokens": 4908943,
    "cached_input_tokens": 4816000,
    "cache_write_input_tokens": 0,
    "output_tokens": 21170,
    "reasoning_output_tokens": 11026,
    "total_tokens": 4930113
  }
}

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 +18:38

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +19:08

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ad1d3e8887d2af9452b61617e10d",
  "usage": {
    "input_tokens": 87241,
    "cached_input_tokens": 86784,
    "cache_write_input_tokens": 0,
    "output_tokens": 231,
    "reasoning_output_tokens": 184,
    "total_tokens": 87472
  },
  "turn_token_usage": {
    "input_tokens": 4996184,
    "cached_input_tokens": 4902784,
    "cache_write_input_tokens": 0,
    "output_tokens": 21401,
    "reasoning_output_tokens": 11210,
    "total_tokens": 5017585
  },
  "thread_token_usage": {
    "input_tokens": 4996184,
    "cached_input_tokens": 4902784,
    "cache_write_input_tokens": 0,
    "output_tokens": 21401,
    "reasoning_output_tokens": 11210,
    "total_tokens": 5017585
  }
}

Tool callexecTool call Returned

Tool call · Input +19:10

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +19:40

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ad418d0887d293cf6b5c02eb3eff",
  "usage": {
    "input_tokens": 87495,
    "cached_input_tokens": 87040,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 87540
  },
  "turn_token_usage": {
    "input_tokens": 5083679,
    "cached_input_tokens": 4989824,
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    "output_tokens": 21446,
    "reasoning_output_tokens": 11210,
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    "input_tokens": 5083679,
    "cached_input_tokens": 4989824,
    "cache_write_input_tokens": 0,
    "output_tokens": 21446,
    "reasoning_output_tokens": 11210,
    "total_tokens": 5105125
  }
}

exec command

+19:43

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 +19:44

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +20:14

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ad61dc8487d2aaba9331262083a9",
  "usage": {
    "input_tokens": 87563,
    "cached_input_tokens": 87296,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 7,
    "total_tokens": 87617
  },
  "turn_token_usage": {
    "input_tokens": 5171242,
    "cached_input_tokens": 5077120,
    "cache_write_input_tokens": 0,
    "output_tokens": 21500,
    "reasoning_output_tokens": 11217,
    "total_tokens": 5192742
  },
  "thread_token_usage": {
    "input_tokens": 5171242,
    "cached_input_tokens": 5077120,
    "cache_write_input_tokens": 0,
    "output_tokens": 21500,
    "reasoning_output_tokens": 11217,
    "total_tokens": 5192742
  }
}

exec command

+20:16

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 +20:16

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +20:46

Script completed
Wall time 30.0 seconds
Output:

epoch=2/2 batches=250 targets=987,417 batch_bce=0.581904 running_bce=0.572363 elapsed=331.9s

Harness context token usage record

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  }
}

exec command

+20:48

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 +20:49

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +21:19

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ada419a887d2a0cc9933bba07224",
  "usage": {
    "input_tokens": 87759,
    "cached_input_tokens": 87424,
    "cache_write_input_tokens": 0,
    "output_tokens": 72,
    "reasoning_output_tokens": 25,
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  },
  "turn_token_usage": {
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  },
  "thread_token_usage": {
    "input_tokens": 5346641,
    "cached_input_tokens": 5251968,
    "cache_write_input_tokens": 0,
    "output_tokens": 21632,
    "reasoning_output_tokens": 11255,
    "total_tokens": 5368273
  }
}

exec command

+21:20

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 +21:21

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +21:51

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt
epoch=2/2 train_loss=0.57186 validation_loss=0.57870 pair_accuracy=0.627 train_targets=1,052,953 validation_targets=130,103 train_seconds=354.3 validation_seconds=34.5
restored best validation-BCE checkpoint: epoch=2 loss=0.578703

Harness context token usage record

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  }
}

exec command

+21:53

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 +21:53

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +22:24

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
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  "thread_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ade4fac887d29010b27a059a1656",
  "usage": {
    "input_tokens": 88024,
    "cached_input_tokens": 87680,
    "cache_write_input_tokens": 0,
    "output_tokens": 64,
    "reasoning_output_tokens": 17,
    "total_tokens": 88088
  },
  "turn_token_usage": {
    "input_tokens": 5522519,
    "cached_input_tokens": 5427200,
    "cache_write_input_tokens": 0,
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    "total_tokens": 5544271
  },
  "thread_token_usage": {
    "input_tokens": 5522519,
    "cached_input_tokens": 5427200,
    "cache_write_input_tokens": 0,
    "output_tokens": 21752,
    "reasoning_output_tokens": 11281,
    "total_tokens": 5544271
  }
}

Tool callexecTool call Returned

Tool call · Input +22:26

const r = await tools.write_stdin({"session_id":61356,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +22:44

Script completed
Wall time 18.6 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "model": "yambda-windowed-two-tower-hard-negative",
  "stage": "validation",
  "output": "/app/solution/hard_bpr_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 511.433,
  "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-loss-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-bpr",
    "negative_sampling": "hard-mixed",
    "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,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 8192,
    "uniform_negatives": 64,
    "inbatch_negatives": 32,
    "inbatch_correction_power": 0.0,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_strategy": "hard-mixed",
    "random_negatives": 16,
    "hard_negatives": 16,
    "loss_type": "bpr",
    "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": 0.5398380493004364,
      "positive_logit": 0.7427799593265382,
      "negative_logit": -0.6222247029354633,
      "pair_accuracy": 0.7547733612041563,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 230.85248444299998,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 31.96162816900005,
      "validation_loss": 0.7334310742106528
    },
    {
      "epoch": 2.0,
      "loss": 0.5718622900601628,
      "positive_logit": 0.592576645556328,
      "negative_logit": -0.04040588518755744,
      "pair_accuracy": 0.6266494385314444,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 354.31383698699983,
      "validation_loss": 0.5787027777031107,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 34.460949318000075
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.657606840133667,
    "query_norm_std": 1.0568926334381104,
    "item_norm_mean": 1.3929166793823242,
    "item_norm_std": 0.5809506177902222
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-3fa68a52-c8ab-4d08-8486-414fd1f49dcb",
    "process_id": "61356",
    "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_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet"
    ],
    "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_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/hard_bpr_validation_checkpoints --epochs 2 --output /app/solution/hard_bpr_validation.parquet"
      }
    ],
    "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=1.000 mean_on_targets=1.000 max=1.000\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/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('hard-mixed', 64, 32, 0.0, 16, 16, 'bpr'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.733431\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.581904 running_bce=0.572363 elapsed=331.9s\nsaved epoch checkpoint: /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.57186 validation_loss=0.57870 pair_accuracy=0.627 train_targets=1,052,953 validation_targets=130,103 train_seconds=354.3 validation_seconds=34.5\nrestored best validation-BCE checkpoint: epoch=2 loss=0.578703\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_bpr_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 511.433,\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-loss-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-bpr\",\n    \"negative_sampling\": \"hard-mixed\",\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\": 0.0003,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 8192,\n    \"uniform_negatives\": 64,\n    \"inbatch_negatives\": 32,\n    \"inbatch_correction_power\": 0.0,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"hard-mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bpr\",\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 31.96162816900005,\n      \"validation_loss\": 0.7334310742106528\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5718622900601628,\n      \"positive_logit\": 0.592576645556328,\n      \"negative_logit\": -0.04040588518755744,\n      \"pair_accuracy\": 0.6266494385314444,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 354.31383698699983,\n      \"validation_loss\": 0.5787027777031107,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 34.460949318000075\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.657606840133667,\n    \"query_norm_std\": 1.0568926334381104,\n    \"item_norm_mean\": 1.3929166793823242,\n    \"item_norm_std\": 0.5809506177902222\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\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=1.000 mean_on_targets=1.000 max=1.000\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/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('hard-mixed', 64, 32, 0.0, 16, 16, 'bpr'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.733431\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.581904 running_bce=0.572363 elapsed=331.9s\nsaved epoch checkpoint: /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.57186 validation_loss=0.57870 pair_accuracy=0.627 train_targets=1,052,953 validation_targets=130,103 train_seconds=354.3 validation_seconds=34.5\nrestored best validation-BCE checkpoint: epoch=2 loss=0.578703\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_bpr_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 511.433,\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-loss-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-bpr\",\n    \"negative_sampling\": \"hard-mixed\",\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\": 0.0003,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 8192,\n    \"uniform_negatives\": 64,\n    \"inbatch_negatives\": 32,\n    \"inbatch_correction_power\": 0.0,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"hard-mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bpr\",\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 31.96162816900005,\n      \"validation_loss\": 0.7334310742106528\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5718622900601628,\n      \"positive_logit\": 0.592576645556328,\n      \"negative_logit\": -0.04040588518755744,\n      \"pair_accuracy\": 0.6266494385314444,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 354.31383698699983,\n      \"validation_loss\": 0.5787027777031107,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 34.460949318000075\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.657606840133667,\n    \"query_norm_std\": 1.0568926334381104,\n    \"item_norm_mean\": 1.3929166793823242,\n    \"item_norm_std\": 0.5809506177902222\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 516,
      "nanos": 91546986
    },
    "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=1.000 mean_on_targets=1.000 max=1.000\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/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('hard-mixed', 64, 32, 0.0, 16, 16, 'bpr'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.733431\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.581904 running_bce=0.572363 elapsed=331.9s\nsaved epoch checkpoint: /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.57186 validation_loss=0.57870 pair_accuracy=0.627 train_targets=1,052,953 validation_targets=130,103 train_seconds=354.3 validation_seconds=34.5\nrestored best validation-BCE checkpoint: epoch=2 loss=0.578703\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_bpr_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 511.433,\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-loss-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-bpr\",\n    \"negative_sampling\": \"hard-mixed\",\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\": 0.0003,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 8192,\n    \"uniform_negatives\": 64,\n    \"inbatch_negatives\": 32,\n    \"inbatch_correction_power\": 0.0,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"hard-mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bpr\",\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 31.96162816900005,\n      \"validation_loss\": 0.7334310742106528\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5718622900601628,\n      \"positive_logit\": 0.592576645556328,\n      \"negative_logit\": -0.04040588518755744,\n      \"pair_accuracy\": 0.6266494385314444,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 354.31383698699983,\n      \"validation_loss\": 0.5787027777031107,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 34.460949318000075\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.657606840133667,\n    \"query_norm_std\": 1.0568926334381104,\n    \"item_norm_mean\": 1.3929166793823242,\n    \"item_norm_std\": 0.5809506177902222\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}

exec command

+22:46

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 +22:47

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_bpr_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +22:51

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/hard_bpr_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015311,
    "quality_users": 4263
  }
}

Harness context token usage record

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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5ae1a368887d2a227e40a0972b899",
  "usage": {
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  "turn_token_usage": {
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    "output_tokens": 21876,
    "reasoning_output_tokens": 11288,
    "total_tokens": 5721939
  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1d4e2d69-ceee-4247-91e7-548cbc781ed3",
    "process_id": "92241",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_bpr_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_bpr_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/hard_bpr_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015311,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/hard_bpr_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015311,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 290150452
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/hard_bpr_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015311,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

Agent message

+23:05

Thinking unavailable No text recorded

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

Agent message

+23:17

Thinking unavailable No text recorded

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

exec command

+23:20

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 aggressive hard-pair experiment is a useful negative result: it cut public NDCG to 0.015311 despite greatly improving its internal hard-pair loss. That indicates unseen high-score tracks are not reliable negatives here—many carry popularity or latent-taste signal—so treating them all as strong negatives over-corrects. I’m retaining the 0.021280 baseline and narrowing the next test to calibrated, propensity-aware negatives rather than pure hard-negative pressure.

Tool callexecTool call Returned

Tool call · Input +23:47

const patch = "*** Begin Patch\n*** Add File: /app/solution/analyze_scores.py\n+#!/usr/bin/env python3\n+\"\"\"Inspect serving-score calibration for a saved validation checkpoint.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import sys\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 truth(data: Path, eligibility: trainer.CandidateSet) -> dict[int, dict[int, int]]:\n+    targets = set(map(int, eligibility.uids))\n+    listens = pd.read_parquet(\n+        data / \"val_listens.parquet\",\n+        columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+    )\n+    likes = pd.read_parquet(\n+        data / \"val_likes.parquet\",\n+        columns=[\"uid\", \"item_id\", \"is_organic\"],\n+    )\n+    item_columns = {int(item): column for column, item in enumerate(eligibility.item_ids)}\n+    uid_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n+    result: dict[int, dict[int, int]] = {}\n+    for grade, rows in (\n+        (\n+            1,\n+            listens[\n+                listens.uid.isin(targets)\n+                & (listens.is_organic == 1)\n+                & (listens.played_ratio_pct >= 80)\n+            ][[\"uid\", \"item_id\"]].drop_duplicates(),\n+        ),\n+        (\n+            3,\n+            likes[likes.uid.isin(targets) & (likes.is_organic == 1)][\n+                [\"uid\", \"item_id\"]\n+            ].drop_duplicates(),\n+        ),\n+    ):\n+        for raw_uid, raw_item in rows.itertuples(index=False, name=None):\n+            uid, item = int(raw_uid), int(raw_item)\n+            row, column = uid_rows.get(uid), item_columns.get(item)\n+            if row is None or column is None:\n+                continue\n+            if (int(eligibility.packed[row, column >> 3]) >> (column & 7)) & 1:\n+                result.setdefault(uid, {})[item] = grade\n+    return result\n+\n+\n+def ndcg(columns: np.ndarray, eligibility: trainer.CandidateSet, graded: dict[int, dict[int, int]]) -> float:\n+    discounts = 1.0 / np.log2(np.arange(2, 12))\n+    values = []\n+    for row, uid_raw in enumerate(eligibility.uids):\n+        gold = graded.get(int(uid_raw))\n+        if gold is None:\n+            continue\n+        items = eligibility.item_ids[columns[row]]\n+        grades = np.asarray([gold.get(int(item), 0) for item in items], dtype=np.float64)\n+        dcg = float(((2.0**grades - 1.0) * discounts).sum())\n+        ideal = np.asarray(sorted(gold.values(), reverse=True)[:10], dtype=np.float64)\n+        idcg = float(((2.0**ideal - 1.0) * discounts[: len(ideal)]).sum())\n+        values.append(dcg / idcg)\n+    return float(np.mean(values))\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--checkpoint\", type=Path, required=True)\n+    parser.add_argument(\"--cache\", type=Path, required=True)\n+    parser.add_argument(\"--public-dir\", type=Path, required=True)\n+    parser.add_argument(\"--output\", type=Path, required=True)\n+    parser.add_argument(\"--threads\", type=int, default=8)\n+    args = parser.parse_args()\n+    torch.set_num_threads(args.threads)\n+    torch.set_num_interop_threads(1)\n+    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n+    config = trainer.Config(**payload[\"config\"])\n+    prepared = trainer.load_cache(args.cache, \"validation\", config)\n+    eligibility = trainer.CandidateSet.read(\n+        args.public_dir / \"data\" / \"validation_eligible_candidates.npz\"\n+    )\n+    model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n+    model.load_state_dict(payload[\"model_state\"])\n+    model.eval()\n+    with torch.inference_mode():\n+        queries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\n+        item_parts = []\n+        for start in range(0, len(prepared.item_ids), 2048):\n+            item_parts.append(\n+                model.encode_items(torch.arange(start, min(start + 2048, len(prepared.item_ids)))).cpu()\n+            )\n+        items = torch.cat(item_parts)\n+    graded = truth(args.public_dir / \"data\", eligibility)\n+    item_norms = torch.linalg.vector_norm(items, dim=1).clamp_min(1.0e-8)\n+    popularity = torch.from_numpy(prepared.item_features[:, 1]).float()\n+    settings = [\n+        (power, bias)\n+        for power in (0.0, 0.25, 0.5, 0.75, 1.0)\n+        for bias in (-0.10, -0.05, -0.02, 0.0, 0.02, 0.05, 0.10)\n+    ]\n+    selected = {\n+        setting: np.empty((len(eligibility.uids), 10), dtype=np.int32)\n+        for setting in settings\n+    }\n+    for start in range(0, len(eligibility.uids), 64):\n+        end = min(start + 64, len(eligibility.uids))\n+        raw = 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 setting in settings:\n+            power, bias = setting\n+            scores = raw / item_norms.pow(power)\n+            if bias:\n+                scores = scores + bias * popularity\n+            scores = scores.masked_fill(~mask, -torch.inf)\n+            selected[setting][start:end] = torch.topk(scores, k=10, dim=1).indices.numpy()\n+    results = [\n+        {\"item_norm_power\": power, \"popularity_bias\": bias, \"ndcg@10\": ndcg(selected[(power, bias)], eligibility, graded)}\n+        for power, bias in settings\n+    ]\n+    results.sort(key=lambda row: row[\"ndcg@10\"], reverse=True)\n+    args.output.write_text(json.dumps(results, indent=2) + \"\\n\")\n+    print(json.dumps(results[:12], indent=2))\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +23:47

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-c3df2592-b9b7-4d68-8401-c1953c40e56f",
    "changes": {
      "/app/solution/analyze_scores.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Inspect serving-score calibration for a saved validation checkpoint.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\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 truth(data: Path, eligibility: trainer.CandidateSet) -> dict[int, dict[int, int]]:\n    targets = set(map(int, eligibility.uids))\n    listens = pd.read_parquet(\n        data / \"val_listens.parquet\",\n        columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    )\n    likes = pd.read_parquet(\n        data / \"val_likes.parquet\",\n        columns=[\"uid\", \"item_id\", \"is_organic\"],\n    )\n    item_columns = {int(item): column for column, item in enumerate(eligibility.item_ids)}\n    uid_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    result: dict[int, dict[int, int]] = {}\n    for grade, rows in (\n        (\n            1,\n            listens[\n                listens.uid.isin(targets)\n                & (listens.is_organic == 1)\n                & (listens.played_ratio_pct >= 80)\n            ][[\"uid\", \"item_id\"]].drop_duplicates(),\n        ),\n        (\n            3,\n            likes[likes.uid.isin(targets) & (likes.is_organic == 1)][\n                [\"uid\", \"item_id\"]\n            ].drop_duplicates(),\n        ),\n    ):\n        for raw_uid, raw_item in rows.itertuples(index=False, name=None):\n            uid, item = int(raw_uid), int(raw_item)\n            row, column = uid_rows.get(uid), item_columns.get(item)\n            if row is None or column is None:\n                continue\n            if (int(eligibility.packed[row, column >> 3]) >> (column & 7)) & 1:\n                result.setdefault(uid, {})[item] = grade\n    return result\n\n\ndef ndcg(columns: np.ndarray, eligibility: trainer.CandidateSet, graded: dict[int, dict[int, int]]) -> float:\n    discounts = 1.0 / np.log2(np.arange(2, 12))\n    values = []\n    for row, uid_raw in enumerate(eligibility.uids):\n        gold = graded.get(int(uid_raw))\n        if gold is None:\n            continue\n        items = eligibility.item_ids[columns[row]]\n        grades = np.asarray([gold.get(int(item), 0) for item in items], dtype=np.float64)\n        dcg = float(((2.0**grades - 1.0) * discounts).sum())\n        ideal = np.asarray(sorted(gold.values(), reverse=True)[:10], dtype=np.float64)\n        idcg = float(((2.0**ideal - 1.0) * discounts[: len(ideal)]).sum())\n        values.append(dcg / idcg)\n    return float(np.mean(values))\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--checkpoint\", type=Path, required=True)\n    parser.add_argument(\"--cache\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--threads\", type=int, default=8)\n    args = parser.parse_args()\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n    config = trainer.Config(**payload[\"config\"])\n    prepared = trainer.load_cache(args.cache, \"validation\", config)\n    eligibility = trainer.CandidateSet.read(\n        args.public_dir / \"data\" / \"validation_eligible_candidates.npz\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n    model.load_state_dict(payload[\"model_state\"])\n    model.eval()\n    with torch.inference_mode():\n        queries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\n        item_parts = []\n        for start in range(0, len(prepared.item_ids), 2048):\n            item_parts.append(\n                model.encode_items(torch.arange(start, min(start + 2048, len(prepared.item_ids)))).cpu()\n            )\n        items = torch.cat(item_parts)\n    graded = truth(args.public_dir / \"data\", eligibility)\n    item_norms = torch.linalg.vector_norm(items, dim=1).clamp_min(1.0e-8)\n    popularity = torch.from_numpy(prepared.item_features[:, 1]).float()\n    settings = [\n        (power, bias)\n        for power in (0.0, 0.25, 0.5, 0.75, 1.0)\n        for bias in (-0.10, -0.05, -0.02, 0.0, 0.02, 0.05, 0.10)\n    ]\n    selected = {\n        setting: np.empty((len(eligibility.uids), 10), dtype=np.int32)\n        for setting in settings\n    }\n    for start in range(0, len(eligibility.uids), 64):\n        end = min(start + 64, len(eligibility.uids))\n        raw = 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 setting in settings:\n            power, bias = setting\n            scores = raw / item_norms.pow(power)\n            if bias:\n                scores = scores + bias * popularity\n            scores = scores.masked_fill(~mask, -torch.inf)\n            selected[setting][start:end] = torch.topk(scores, k=10, dim=1).indices.numpy()\n    results = [\n        {\"item_norm_power\": power, \"popularity_bias\": bias, \"ndcg@10\": ndcg(selected[(power, bias)], eligibility, graded)}\n        for power, bias in settings\n    ]\n    results.sort(key=lambda row: row[\"ndcg@10\"], reverse=True)\n    args.output.write_text(json.dumps(results, indent=2) + \"\\n\")\n    print(json.dumps(results[:12], indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/analyze_scores.py\n",
    "stderr": ""
  }
}

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exec command

+23:50

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 +23:52

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/baseline_score_calibration.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +23:53

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d6d6b5","wall_time_seconds":1.000829572,"session_id":16038,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+23:55

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 +23:55

const r = await tools.write_stdin({"session_id":16038,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +24:25

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194

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Tool callexecTool call Returned

Tool call · Input +24:29

const r = await tools.write_stdin({"session_id":16038,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +24:59

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +25:00

const r = await tools.write_stdin({"session_id":16038,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +25:30

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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exec command

+25:32

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 +25:33

const r = await tools.write_stdin({"session_id":16038,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +25:33

Script completed
Wall time 0.3 seconds
Output:

[
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.1,
    "ndcg@10": 0.023001876795261167
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.05,
    "ndcg@10": 0.022363977563729428
  },
  {
    "item_norm_power": 0.0,
    "popularity_bias": -0.1,
    "ndcg@10": 0.021853852553043715
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.02,
    "ndcg@10": 0.02183203476774217
  },
  {
    "item_norm_power": 0.0,
    "popularity_bias": -0.05,
    "ndcg@10": 0.021654591534184777
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.1,
    "ndcg@10": 0.021613198742605647
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.05,
    "ndcg@10": 0.02159291873680624
  },
  {
    "item_norm_power": 0.0,
    "popularity_bias": -0.02,
    "ndcg@10": 0.021590482194192594
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.0,
    "ndcg@10": 0.021564627959095077
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.02,
    "ndcg@10": 0.02148850192671481
  },
  {
    "item_norm_power": 0.0,
    "popularity_bias": 0.0,
    "ndcg@10": 0.021280490854136063
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.02,
    "ndcg@10": 0.0212271899842163
  }
]

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  "turn_token_usage": {
    "input_tokens": 6254192,
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-76d00b34-03a6-4cf2-a8a3-30257091ebf2",
    "process_id": "16038",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/baseline_score_calibration.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/baseline_score_calibration.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.023001876795261167\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.022363977563729428\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.021853852553043715\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.02183203476774217\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.021654591534184777\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.021613198742605647\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.02159291873680624\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.021590482194192594\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.021564627959095077\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.02148850192671481\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.021280490854136063\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.0212271899842163\n  }\n]\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.023001876795261167\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.022363977563729428\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.021853852553043715\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.02183203476774217\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.021654591534184777\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.021613198742605647\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.02159291873680624\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.021590482194192594\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.021564627959095077\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.02148850192671481\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.021280490854136063\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.0212271899842163\n  }\n]\n",
    "exit_code": 0,
    "duration": {
      "secs": 101,
      "nanos": 576016052
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.023001876795261167\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.022363977563729428\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.021853852553043715\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.02183203476774217\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.021654591534184777\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.021613198742605647\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.02159291873680624\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.021590482194192594\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.021564627959095077\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.02148850192671481\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.021280490854136063\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.0212271899842163\n  }\n]\n"
  }
}

100

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 +25:39

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/hard_bpr_score_calibration.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +25:41

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c59971","wall_time_seconds":1.001565292,"session_id":12386,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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    "cache_write_input_tokens": 0,
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  "thread_token_usage": {
    "input_tokens": 6347883,
    "cached_input_tokens": 6245120,
    "cache_write_input_tokens": 0,
    "output_tokens": 25506,
    "reasoning_output_tokens": 12658,
    "total_tokens": 6373389
  }
}

101

exec command

+25:54

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 +25:54

const r = await tools.write_stdin({"session_id":12386,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +26:24

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194

Harness context token usage record

{
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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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  "usage": {
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  "thread_token_usage": {
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    "reasoning_output_tokens": 13135,
    "total_tokens": 6467898
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}

Tool callexecTool call Returned

Tool call · Input +26:26

const r = await tools.write_stdin({"session_id":12386,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +26:56

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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}

Tool callexecTool call Returned

Tool call · Input +26:59

const r = await tools.write_stdin({"session_id":12386,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +27:18

Script completed
Wall time 18.7 seconds
Output:

[
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.05,
    "ndcg@10": 0.016900006752146143
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.02,
    "ndcg@10": 0.01674683525610433
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.05,
    "ndcg@10": 0.01673400282086292
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.05,
    "ndcg@10": 0.01666577419204389
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.02,
    "ndcg@10": 0.016571668884706046
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.0,
    "ndcg@10": 0.016567904360894007
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.0,
    "ndcg@10": 0.01654536126252036
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.02,
    "ndcg@10": 0.01651195817557847
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.1,
    "ndcg@10": 0.016378999716480196
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.1,
    "ndcg@10": 0.016341096526744394
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.05,
    "ndcg@10": 0.01631842039794948
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.02,
    "ndcg@10": 0.016310676571144333
  }
]

Harness context token usage record

{
  "type": "token_usage_record",
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5af16406c87d2afda324419948f3f",
  "usage": {
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    "cached_input_tokens": 94336,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 0,
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  "thread_token_usage": {
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    "total_tokens": 6657162
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-69f712c8-369f-4c94-9843-e1f004fa3213",
    "process_id": "12386",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/hard_bpr_score_calibration.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/hard_bpr_validation_checkpoints/epoch-02.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/hard_bpr_score_calibration.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.016900006752146143\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.01674683525610433\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01673400282086292\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.01666577419204389\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.016571668884706046\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.016567904360894007\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.01654536126252036\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.01651195817557847\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.1,\n    \"ndcg@10\": 0.016378999716480196\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.016341096526744394\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01631842039794948\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.016310676571144333\n  }\n]\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.016900006752146143\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.01674683525610433\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01673400282086292\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.01666577419204389\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.016571668884706046\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.016567904360894007\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.01654536126252036\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.01651195817557847\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.1,\n    \"ndcg@10\": 0.016378999716480196\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.016341096526744394\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01631842039794948\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.016310676571144333\n  }\n]\n",
    "exit_code": 0,
    "duration": {
      "secs": 98,
      "nanos": 311223205
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.016900006752146143\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.01674683525610433\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01673400282086292\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.01666577419204389\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.016571668884706046\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.016567904360894007\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.01654536126252036\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.01651195817557847\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.1,\n    \"ndcg@10\": 0.016378999716480196\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.016341096526744394\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01631842039794948\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.016310676571144333\n  }\n]\n"
  }
}

102

Agent message

+27:30

Thinking unavailable No text recorded

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

103

exec command

+27:31

Thinking unavailable No text recorded

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

Model text

Score calibration confirms the direction without changing serving complexity: mild item-vector norm tempering plus a small catalog-prior correction raises the unchanged baseline from 0.021280 to 0.023002. I’m now testing the conservative training analogue—more in-batch competitors with BCE and no hard top-k selection—because the data says a modest correction is useful while aggressive mining is not.

Tool callexecTool call Returned

Tool call · Input +27:34

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_cache.npz --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/mixed_bce_validation_checkpoints --epochs 2 --negative-strategy mixed --uniform-negatives 16 --inbatch-negatives 16 --loss-type bce --output /app/solution/mixed_bce_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +27:35

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d0bf26","wall_time_seconds":1.001355548,"session_id":89333,"original_token_count":0,"output":""}

Harness context token usage record

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104

exec command

+27:37

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 +27:38

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +27:48

Script completed
Wall time 10.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=1.000 mean_on_targets=1.000 max=1.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
using a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/epoch-01.pt
BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('mixed', 16, 16, 0.0, 16, 16, 'bce'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE

Harness context token usage record

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105

exec command

+27:50

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 +27:51

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +28:21

Script completed
Wall time 30.0 seconds
Output:

resumed validation-BCE training: epoch=1 loss=0.569050

Harness context token usage record

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106

exec command

+28:23

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 +28:24

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +28:54

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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Tool callexecTool call Returned

Tool call · Input +28:56

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +29:26

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "response_id": "resp_0fbc908ecda8d2a0016ab5af8bf91c87d2b02e1656b74421f4",
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Tool callexecTool call Returned

Tool call · Input +29:28

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +29:58

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5afac037487d2a726da27ae300182",
  "usage": {
    "input_tokens": 96694,
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    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
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107

exec command

+29:59

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 +30:00

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +30:30

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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Tool callexecTool call Returned

Tool call · Input +30:34

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +31:04

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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108

exec command

+31:06

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 +31:07

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +31:37

Script completed
Wall time 30.0 seconds
Output:

epoch=2/2 batches=250 targets=987,417 batch_bce=0.495494 running_bce=0.506165 elapsed=195.5s

Harness context token usage record

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109

exec command

+31:39

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 +31:40

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +32:10

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt
epoch=2/2 train_loss=0.50556 validation_loss=0.51063 pair_accuracy=0.796 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.4 validation_seconds=14.4
restored best validation-BCE checkpoint: epoch=2 loss=0.510629

Harness context token usage record

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110

exec command

+32:11

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 +32:12

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +32:42

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "session_id": "01a0d59b-41d9-7093-9dae-18e7bbf23db9",
  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
  "response_id": "resp_0fbc908ecda8d2a0016ab5b04fd64487d2b974be75600f127c",
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    "total_tokens": 7720810
  },
  "thread_token_usage": {
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}

Tool callexecTool call Returned

Tool call · Input +32:44

const r = await tools.write_stdin({"session_id":89333,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +33:05

Script completed
Wall time 20.9 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "model": "yambda-windowed-two-tower-hard-negative",
  "stage": "validation",
  "output": "/app/solution/mixed_bce_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 326.489,
  "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-loss-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",
    "negative_sampling": "mixed",
    "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,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 8192,
    "uniform_negatives": 16,
    "inbatch_negatives": 16,
    "inbatch_correction_power": 0.0,
    "inbatch_correction_min": 0.25,
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Harness context codex item completed

{
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  "item": {
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    "command": [
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    "parsed_cmd": [
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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=1.000 mean_on_targets=1.000 max=1.000\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/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('mixed', 16, 16, 0.0, 16, 16, 'bce'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.569050\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.495494 running_bce=0.506165 elapsed=195.5s\nsaved epoch checkpoint: /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50556 validation_loss=0.51063 pair_accuracy=0.796 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.4 validation_seconds=14.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.510629\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/mixed_bce_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 326.489,\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\": 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\"loss_microbatch_size\": 8192,\n    \"uniform_negatives\": 16,\n    \"inbatch_negatives\": 16,\n    \"inbatch_correction_power\": 0.0,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bce\",\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 14.023904538999886,\n      \"validation_loss\": 0.5690496952081208\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5055596699792886,\n      \"positive_logit\": 0.7731962287114984,\n      \"negative_logit\": -1.1849198085174826,\n      \"pair_accuracy\": 0.7956698328415418,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 208.4408940630001,\n      \"validation_loss\": 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    "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=1.000 mean_on_targets=1.000 max=1.000\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/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('mixed', 16, 16, 0.0, 16, 16, 'bce'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.569050\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.495494 running_bce=0.506165 elapsed=195.5s\nsaved epoch checkpoint: /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50556 validation_loss=0.51063 pair_accuracy=0.796 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.4 validation_seconds=14.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.510629\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/mixed_bce_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 326.489,\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-loss-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\",\n    \"negative_sampling\": \"mixed\",\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\": 0.0003,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 8192,\n    \"uniform_negatives\": 16,\n    \"inbatch_negatives\": 16,\n    \"inbatch_correction_power\": 0.0,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bce\",\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\": 0.5398380493004364,\n      \"positive_logit\": 0.7427799593265382,\n      \"negative_logit\": -0.6222247029354633,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 230.85248444299998,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 14.023904538999886,\n      \"validation_loss\": 0.5690496952081208\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5055596699792886,\n      \"positive_logit\": 0.7731962287114984,\n      \"negative_logit\": -1.1849198085174826,\n      \"pair_accuracy\": 0.7956698328415418,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 208.4408940630001,\n      \"validation_loss\": 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    "exit_code": 0,
    "duration": {
      "secs": 330,
      "nanos": 996180132
    },
    "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=1.000 mean_on_targets=1.000 max=1.000\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/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, negatives ('mixed', 24, 8, 0.5, 0, 0, 'bce')->('mixed', 16, 16, 0.0, 16, 16, 'bce'), target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.569050\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.495494 running_bce=0.506165 elapsed=195.5s\nsaved epoch checkpoint: /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50556 validation_loss=0.51063 pair_accuracy=0.796 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.4 validation_seconds=14.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.510629\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/mixed_bce_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 326.489,\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\": 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  }
}

111

exec command

+33:07

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 +33:08

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed_bce_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +33:11

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/mixed_bce_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014197,
    "quality_users": 4263
  }
}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-257aa789-f2fb-4c8a-aa22-5467faf36f15",
    "process_id": "31852",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed_bce_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed_bce_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/mixed_bce_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014197,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/mixed_bce_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014197,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 285791792
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/mixed_bce_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014197,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

112

exec command

+33:21

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 +33:22

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/mixed_bce_score_calibration.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +33:24

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"96bbe3","wall_time_seconds":1.00063074,"session_id":83881,"original_token_count":0,"output":""}

Harness context token usage record

{
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}

113

Agent message

+33:35

Thinking unavailable No text recorded

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

114

exec command

+33:39

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 +33:39

const r = await tools.write_stdin({"session_id":83881,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +34:09

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194

Harness context token usage record

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}

Tool callexecTool call Returned

Tool call · Input +34:11

const r = await tools.write_stdin({"session_id":83881,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +34:41

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

{
  "type": "token_usage_record",
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  "turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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}

Tool callexecTool call Returned

Tool call · Input +34:43

const r = await tools.write_stdin({"session_id":83881,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +35:01

Script completed
Wall time 18.1 seconds
Output:

[
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.05,
    "ndcg@10": 0.01603494163048223
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.0,
    "ndcg@10": 0.01601405526680468
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.1,
    "ndcg@10": 0.015958640142809365
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.02,
    "ndcg@10": 0.015857348770873025
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": -0.02,
    "ndcg@10": 0.015852076875721415
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.02,
    "ndcg@10": 0.015781547728809193
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.0,
    "ndcg@10": 0.015664175527884065
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.1,
    "ndcg@10": 0.015586686472653552
  },
  {
    "item_norm_power": 0.5,
    "popularity_bias": 0.05,
    "ndcg@10": 0.015586357705013109
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.02,
    "ndcg@10": 0.015563476806208447
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": 0.05,
    "ndcg@10": 0.015562275131630273
  },
  {
    "item_norm_power": 0.25,
    "popularity_bias": -0.05,
    "ndcg@10": 0.01549101948587466
  }
]

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9f27487c-3031-42bd-86c4-6e981237536b",
    "process_id": "83881",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/mixed_bce_score_calibration.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/mixed_bce_validation_checkpoints/epoch-02.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/mixed_bce_score_calibration.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01603494163048223\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.01601405526680468\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.015958640142809365\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.015857348770873025\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.015852076875721415\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.015781547728809193\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.015664175527884065\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.1,\n    \"ndcg@10\": 0.015586686472653552\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.015586357705013109\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.015563476806208447\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.015562275131630273\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01549101948587466\n  }\n]\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01603494163048223\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.01601405526680468\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.015958640142809365\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.015857348770873025\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.015852076875721415\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.015781547728809193\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.015664175527884065\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.1,\n    \"ndcg@10\": 0.015586686472653552\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.015586357705013109\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.015563476806208447\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.015562275131630273\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01549101948587466\n  }\n]\n",
    "exit_code": 0,
    "duration": {
      "secs": 98,
      "nanos": 729970278
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01603494163048223\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.01601405526680468\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.015958640142809365\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.015857348770873025\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": -0.02,\n    \"ndcg@10\": 0.015852076875721415\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.015781547728809193\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.0,\n    \"ndcg@10\": 0.015664175527884065\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.1,\n    \"ndcg@10\": 0.015586686472653552\n  },\n  {\n    \"item_norm_power\": 0.5,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.015586357705013109\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.02,\n    \"ndcg@10\": 0.015563476806208447\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": 0.05,\n    \"ndcg@10\": 0.015562275131630273\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.01549101948587466\n  }\n]\n"
  }
}

115

Agent message

+35:12

Thinking unavailable No text recorded

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

116

exec command

+35:14

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 +35:19

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-    learning_rate: float = 3.0e-4\n+    learning_rate: float = 1.0e-3\n@@\n-    loss_microbatch_size: int = 8_192\n-    uniform_negatives: int = 64\n-    inbatch_negatives: int = 32\n-    inbatch_correction_power: float = 0.0\n+    loss_microbatch_size: int = 65_536\n+    uniform_negatives: int = 24\n+    inbatch_negatives: int = 8\n+    inbatch_correction_power: float = 0.25\n@@\n-    negative_strategy: str = \"hard-mixed\"\n+    negative_strategy: str = \"mixed\"\n@@\n-    loss_type: str = \"bpr\"\n+    loss_type: str = \"bce\"\n+    item_norm_power: float = 0.25\n+    popularity_bias: float = -0.10\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +35:19

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-3bcf42b6-b9d4-467e-a8f6-8ff08543094d",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -53,15 +53,17 @@\n     epochs: int = 1\n-    learning_rate: float = 3.0e-4\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 = 8_192\n-    uniform_negatives: int = 64\n-    inbatch_negatives: int = 32\n-    inbatch_correction_power: float = 0.0\n+    loss_microbatch_size: int = 65_536\n+    uniform_negatives: int = 24\n+    inbatch_negatives: int = 8\n+    inbatch_correction_power: float = 0.25\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n-    negative_strategy: str = \"hard-mixed\"\n+    negative_strategy: str = \"mixed\"\n     random_negatives: int = 16\n     hard_negatives: int = 16\n-    loss_type: str = \"bpr\"\n+    loss_type: str = \"bce\"\n+    item_norm_power: float = 0.25\n+    popularity_bias: float = -0.10\n     negative_filter_scope: str = \"full-training-history\"\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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  }
}

117

exec command

+35:21

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 +35:27

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     parser.add_argument(\n         \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n     )\n+    parser.add_argument(\n+        \"--inbatch-correction-power\",\n+        type=float,\n+        default=Config.inbatch_correction_power,\n+    )\n@@\n     parser.add_argument(\n         \"--loss-type\", choices=(\"bpr\", \"bce\"), default=Config.loss_type\n     )\n+    parser.add_argument(\n+        \"--item-norm-power\", type=float, default=Config.item_norm_power\n+    )\n+    parser.add_argument(\n+        \"--popularity-bias\", type=float, default=Config.popularity_bias\n+    )\n@@\n         or args.hard_negatives < 0\n+        or args.inbatch_correction_power < 0\n+        or args.item_norm_power < 0\n@@\n         inbatch_negatives=args.inbatch_negatives,\n+        inbatch_correction_power=args.inbatch_correction_power,\n         random_negatives=args.random_negatives,\n         hard_negatives=args.hard_negatives,\n         loss_type=args.loss_type,\n+        item_norm_power=args.item_norm_power,\n+        popularity_bias=args.popularity_bias,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +35:27

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-7be7efac-46c3-4648-8818-132f9a5feed3",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2080,2 +2080,7 @@\n     parser.add_argument(\n+        \"--inbatch-correction-power\",\n+        type=float,\n+        default=Config.inbatch_correction_power,\n+    )\n+    parser.add_argument(\n         \"--random-negatives\", type=int, default=Config.random_negatives\n@@ -2089,2 +2094,8 @@\n     parser.add_argument(\n+        \"--item-norm-power\", type=float, default=Config.item_norm_power\n+    )\n+    parser.add_argument(\n+        \"--popularity-bias\", type=float, default=Config.popularity_bias\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2170,2 +2181,4 @@\n         or args.hard_negatives < 0\n+        or args.inbatch_correction_power < 0\n+        or args.item_norm_power < 0\n     ):\n@@ -2194,2 +2207,3 @@\n         inbatch_negatives=args.inbatch_negatives,\n+        inbatch_correction_power=args.inbatch_correction_power,\n         random_negatives=args.random_negatives,\n@@ -2197,2 +2211,4 @@\n         loss_type=args.loss_type,\n+        item_norm_power=args.item_norm_power,\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": ""
  }
}

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118

exec command

+35:29

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 +35:35

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     for start in range(0, len(prepared.item_ids), 2_048):\n@@\n         item_parts.append(model.encode_items(rows).cpu())\n     items = torch.cat(item_parts)\n+    retrieval_items = items\n+    if config.item_norm_power:\n+        norms = torch.linalg.vector_norm(items, dim=1, keepdim=True).clamp_min(1.0e-8)\n+        retrieval_items = items / norms.pow(config.item_norm_power)\n+    popularity_bias = torch.from_numpy(\n+        prepared.item_features[:, 1].astype(np.float32, copy=False)\n+    ) * config.popularity_bias\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+        scores = queries[start:end] @ retrieval_items.T\n+        if config.popularity_bias:\n+            scores += popularity_bias\n@@\n         \"item_norm_std\": float(item_norm.std()),\n+        \"item_norm_power\": float(config.item_norm_power),\n+        \"popularity_bias\": float(config.popularity_bias),\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +35:35

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-1340a342-9f16-4717-9d15-6a0bf80183c7",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2006,2 +2006,9 @@\n     items = torch.cat(item_parts)\n+    retrieval_items = items\n+    if config.item_norm_power:\n+        norms = torch.linalg.vector_norm(items, dim=1, keepdim=True).clamp_min(1.0e-8)\n+        retrieval_items = items / norms.pow(config.item_norm_power)\n+    popularity_bias = torch.from_numpy(\n+        prepared.item_features[:, 1].astype(np.float32, copy=False)\n+    ) * config.popularity_bias\n     rows: list[tuple[int, int, int]] = []\n@@ -2009,3 +2016,5 @@\n         end = min(start + ranking_batch_size, len(eligibility.uids))\n-        scores = queries[start:end] @ items.T\n+        scores = queries[start:end] @ retrieval_items.T\n+        if config.popularity_bias:\n+            scores += popularity_bias\n         eligible = np.unpackbits(\n@@ -2033,2 +2042,4 @@\n         \"item_norm_std\": float(item_norm.std()),\n+        \"item_norm_power\": float(config.item_norm_power),\n+        \"popularity_bias\": float(config.popularity_bias),\n     }\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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119

exec command

+35:38

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 +35:40

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 --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/tempered_validation_checkpoints --output /app/solution/tempered_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +35:41

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"e83ac2","wall_time_seconds":1.001200787,"session_id":54345,"original_token_count":0,"output":""}

Harness context token usage record

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120

exec command

+35:43

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 +35:44

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +35:54

Script completed
Wall time 10.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.381 mean_on_targets=1.000 max=2.806
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250

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121

exec command

+35:59

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 +36:10

const patch = "*** Begin Patch\n*** Add File: /app/solution/rank_checkpoint.py\n+#!/usr/bin/env python3\n+\"\"\"Rank a cutoff with a trained two-tower checkpoint and offline item transform.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+from pathlib import Path\n+\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(\"--stage\", choices=(\"validation\", \"submission\"), required=True)\n+    parser.add_argument(\"--public-dir\", type=Path, required=True)\n+    parser.add_argument(\"--prepared-cache\", type=Path, required=True)\n+    parser.add_argument(\"--checkpoint\", type=Path, required=True)\n+    parser.add_argument(\"--output\", type=Path, required=True)\n+    parser.add_argument(\"--item-norm-power\", type=float, default=0.25)\n+    parser.add_argument(\"--popularity-bias\", type=float, default=-0.10)\n+    parser.add_argument(\"--threads\", type=int, default=8)\n+    args = parser.parse_args()\n+    torch.set_num_threads(args.threads)\n+    torch.set_num_interop_threads(1)\n+\n+    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n+    values = dict(payload[\"config\"])\n+    values[\"item_norm_power\"] = args.item_norm_power\n+    values[\"popularity_bias\"] = args.popularity_bias\n+    config = trainer.Config(**values)\n+    prepared, eligibility = trainer.load_or_prepare(\n+        args.public_dir, args.stage, config, args.prepared_cache\n+    )\n+    model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n+    model.load_state_dict(payload[\"model_state\"])\n+    model.eval()\n+    ranking, diagnostics = trainer.rank_candidates(\n+        model,\n+        prepared,\n+        eligibility,\n+        config,\n+        torch.device(\"cpu\"),\n+        ranking_batch_size=64,\n+    )\n+    trainer.write_ranking(ranking, args.output)\n+    print(\n+        f\"wrote {len(ranking):,} rows to {args.output}; \"\n+        f\"item_norm_power={args.item_norm_power} \"\n+        f\"popularity_bias={args.popularity_bias} diagnostics={diagnostics}\"\n+    )\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +36:11

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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      "/app/solution/rank_checkpoint.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Rank a cutoff with a trained two-tower checkpoint and offline item transform.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\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(\"--stage\", choices=(\"validation\", \"submission\"), required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path, required=True)\n    parser.add_argument(\"--checkpoint\", type=Path, required=True)\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--item-norm-power\", type=float, default=0.25)\n    parser.add_argument(\"--popularity-bias\", type=float, default=-0.10)\n    parser.add_argument(\"--threads\", type=int, default=8)\n    args = parser.parse_args()\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n\n    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n    values = dict(payload[\"config\"])\n    values[\"item_norm_power\"] = args.item_norm_power\n    values[\"popularity_bias\"] = args.popularity_bias\n    config = trainer.Config(**values)\n    prepared, eligibility = trainer.load_or_prepare(\n        args.public_dir, args.stage, config, args.prepared_cache\n    )\n    model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n    model.load_state_dict(payload[\"model_state\"])\n    model.eval()\n    ranking, diagnostics = trainer.rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        torch.device(\"cpu\"),\n        ranking_batch_size=64,\n    )\n    trainer.write_ranking(ranking, args.output)\n    print(\n        f\"wrote {len(ranking):,} rows to {args.output}; \"\n        f\"item_norm_power={args.item_norm_power} \"\n        f\"popularity_bias={args.popularity_bias} diagnostics={diagnostics}\"\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/rank_checkpoint.py\n",
    "stderr": ""
  }
}

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122

exec command

+36:17

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 +36:21

const patch = "*** Begin Patch\n*** Update File: /app/solution/analyze_scores.py\n@@\n-    settings = [\n-        (power, bias)\n-        for power in (0.0, 0.25, 0.5, 0.75, 1.0)\n-        for bias in (-0.10, -0.05, -0.02, 0.0, 0.02, 0.05, 0.10)\n-    ]\n+    settings = [\n+        (power, bias)\n+        for power in (0.0, 0.10, 0.20, 0.25, 0.30, 0.40, 0.50)\n+        for bias in (-0.30, -0.20, -0.15, -0.12, -0.10, -0.08, -0.05, 0.0)\n+    ]\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +36:22

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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        "type": "update",
        "unified_diff": "@@ -104,4 +104,4 @@\n         (power, bias)\n-        for power in (0.0, 0.25, 0.5, 0.75, 1.0)\n-        for bias in (-0.10, -0.05, -0.02, 0.0, 0.02, 0.05, 0.10)\n+        for power in (0.0, 0.10, 0.20, 0.25, 0.30, 0.40, 0.50)\n+        for bias in (-0.30, -0.20, -0.15, -0.12, -0.10, -0.08, -0.05, 0.0)\n     ]\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/analyze_scores.py\n",
    "stderr": ""
  }
}

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123

exec command

+36:25

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 +36:26

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +36:56

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +36:58

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +37:28

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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Tool callexecTool call Returned

Tool call · Input +37:30

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +38:00

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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}

124

exec command

+38:06

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 run changes only the production sampler’s correction exponent—from inverse-square-root propensity weighting to a tempered fourth-root correction—while retaining the same 24 uniform + 8 in-batch count and one-epoch budget. This directly addresses the calibration evidence without declaring every high-score unseen item negative. Its ranking also uses an offline-only item-vector transform, still expressible as one two-tower dot product.

Tool callexecTool call Returned

Tool call · Input +38:06

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +38:36

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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    "cache_write_input_tokens": 0,
    "output_tokens": 142,
    "reasoning_output_tokens": 7,
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    "reasoning_output_tokens": 16206,
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    "reasoning_output_tokens": 16206,
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Tool callexecTool call Returned

Tool call · Input +38:39

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +39:09

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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  "root_turn_id": "01a0d59b-41e9-7ca3-93e7-92bb6a8090e7",
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}

125

exec command

+39:10

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 +39:11

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +39:41

Script completed
Wall time 30.0 seconds
Output:

epoch=1/1 batches=250 targets=987,417 batch_bce=0.553770 running_bce=0.551587 elapsed=205.0s

Harness context token usage record

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126

exec command

+39:43

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 +39:44

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +40:14

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/tempered_validation_checkpoints/epoch-01.pt
epoch=1/1 train_loss=0.54979 validation_loss=0.50592 pair_accuracy=0.756 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.3 validation_seconds=15.8
restored best validation-BCE checkpoint: epoch=1 loss=0.505919

Harness context token usage record

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127

exec command

+40:16

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 +40:16

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +40:46

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +40:48

const r = await tools.write_stdin({"session_id":54345,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +41:06

Script completed
Wall time 17.5 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "model": "yambda-windowed-two-tower-hard-negative",
  "stage": "validation",
  "output": "/app/solution/tempered_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 321.235,
  "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-loss-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",
    "negative_sampling": "mixed",
    "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": 0.25,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_strategy": "mixed",
    "random_negatives": 16,
    "hard_negatives": 16,
    "loss_type": "bce",
    "item_norm_power": 0.25,
    "popularity_bias": -0.1,
    "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": 0.5497927639303716,
      "positive_logit": 0.6662366582921198,
      "negative_logit": -0.6298900593788861,
      "pair_accuracy": 0.7557438461165883,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 218.28064430300037,
      "validation_loss": 0.5059193487551792,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 15.788321055999859
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  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.5630699396133423,
    "query_norm_std": 0.7649498581886292,
    "item_norm_mean": 1.7908211946487427,
    "item_norm_std": 0.8210638761520386,
    "item_norm_power": 0.25,
    "popularity_bias": -0.1
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

Harness context token usage record

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Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-0df3ba1b-eb80-4310-a446-3b5ea381a350",
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    "command": [
      "/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 --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/tempered_validation_checkpoints --output /app/solution/tempered_validation.parquet"
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    "cwd": "file:///app",
    "parsed_cmd": [
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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=0.381 mean_on_targets=1.000 max=2.806\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=0.553770 running_bce=0.551587 elapsed=205.0s\nsaved epoch checkpoint: /app/solution/tempered_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.54979 validation_loss=0.50592 pair_accuracy=0.756 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.3 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.505919\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/tempered_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 321.235,\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-loss-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\",\n    \"negative_sampling\": \"mixed\",\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\": 0.25,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bce\",\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\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\": 0.5497927639303716,\n      \"positive_logit\": 0.6662366582921198,\n      \"negative_logit\": -0.6298900593788861,\n      \"pair_accuracy\": 0.7557438461165883,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.28064430300037,\n      \"validation_loss\": 0.5059193487551792,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.788321055999859\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5630699396133423,\n    \"query_norm_std\": 0.7649498581886292,\n    \"item_norm_mean\": 1.7908211946487427,\n    \"item_norm_std\": 0.8210638761520386,\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\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=0.381 mean_on_targets=1.000 max=2.806\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=0.553770 running_bce=0.551587 elapsed=205.0s\nsaved epoch checkpoint: /app/solution/tempered_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.54979 validation_loss=0.50592 pair_accuracy=0.756 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.3 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.505919\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/tempered_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 321.235,\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-loss-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\",\n    \"negative_sampling\": \"mixed\",\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\": 0.25,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bce\",\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\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\": 0.5497927639303716,\n      \"positive_logit\": 0.6662366582921198,\n      \"negative_logit\": -0.6298900593788861,\n      \"pair_accuracy\": 0.7557438461165883,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.28064430300037,\n      \"validation_loss\": 0.5059193487551792,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.788321055999859\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5630699396133423,\n    \"query_norm_std\": 0.7649498581886292,\n    \"item_norm_mean\": 1.7908211946487427,\n    \"item_norm_std\": 0.8210638761520386,\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 325,
      "nanos": 814690839
    },
    "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=0.381 mean_on_targets=1.000 max=2.806\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=0.553770 running_bce=0.551587 elapsed=205.0s\nsaved epoch checkpoint: /app/solution/tempered_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.54979 validation_loss=0.50592 pair_accuracy=0.756 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.3 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.505919\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{\n  \"model\": \"yambda-windowed-two-tower-hard-negative\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/tempered_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 321.235,\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-loss-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\",\n    \"negative_sampling\": \"mixed\",\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\": 0.25,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_strategy\": \"mixed\",\n    \"random_negatives\": 16,\n    \"hard_negatives\": 16,\n    \"loss_type\": \"bce\",\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\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\": 0.5497927639303716,\n      \"positive_logit\": 0.6662366582921198,\n      \"negative_logit\": -0.6298900593788861,\n      \"pair_accuracy\": 0.7557438461165883,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 218.28064430300037,\n      \"validation_loss\": 0.5059193487551792,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.788321055999859\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5630699396133423,\n    \"query_norm_std\": 0.7649498581886292,\n    \"item_norm_mean\": 1.7908211946487427,\n    \"item_norm_std\": 0.8210638761520386,\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}

128

exec command

+41:08

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 +41:09

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/tempered_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +41:12

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/tempered_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.022585,
    "quality_users": 4263
  }
}

Harness context token usage record

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Harness context codex item completed

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    "status": "completed",
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    "stderr": "",
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    "exit_code": 0,
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  }
}

129

exec command

+41:16

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 +41:18

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/tempered_validation_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/tempered_score_calibration.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +41:19

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"b11f3b","wall_time_seconds":1.0007065,"session_id":1689,"original_token_count":0,"output":""}

Harness context token usage record

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130

exec command

+41:21

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 +41:22

const r = await tools.write_stdin({"session_id":1689,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +41:52

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194

Harness context token usage record

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}

Tool callexecTool call Returned

Tool call · Input +41:55

const r = await tools.write_stdin({"session_id":1689,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +42:25

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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    "cached_input_tokens": 10250624,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 16395,
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}

Tool callexecTool call Returned

Tool call · Input +42:26

const r = await tools.write_stdin({"session_id":1689,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +42:56

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +42:58

const r = await tools.write_stdin({"session_id":1689,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +43:06

Script completed
Wall time 7.4 seconds
Output:

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    "ndcg@10": 0.022588930818591823
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  {
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    "ndcg@10": 0.02248956894658917
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    "ndcg@10": 0.022392515315654487
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    "ndcg@10": 0.022392071071285772
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    "ndcg@10": 0.022356457360367184
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    "ndcg@10": 0.022212645271870026
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  {
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  },
  {
    "item_norm_power": 0.3,
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    "ndcg@10": 0.022176903413089605
  }
]

Harness context token usage record

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Harness context codex item completed

{
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  "item": {
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/tempered_validation_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/tempered_score_calibration.json"
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        "cmd": "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/tempered_validation_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/tempered_score_calibration.json"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022588930818591823\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022584922938781674\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02248956894658917\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.022473319512647826\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022392515315654487\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022392071071285772\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022356457360367184\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.022350665237150506\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.022335176137700318\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.022212645271870026\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.02218524959833691\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022176903413089605\n  }\n]\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022588930818591823\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022584922938781674\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02248956894658917\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.022473319512647826\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022392515315654487\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022392071071285772\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022356457360367184\n  },\n  {\n    \"item_norm_power\": 0.0,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.022350665237150506\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.022335176137700318\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.022212645271870026\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.05,\n    \"ndcg@10\": 0.02218524959833691\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022176903413089605\n  }\n]\n",
    "exit_code": 0,
    "duration": {
      "secs": 107,
      "nanos": 414648457
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  }
}

131

exec command

+43:15

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 +43:16

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/baseline_score_calibration_fine.json","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +43:17

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"aa4405","wall_time_seconds":1.000601183,"session_id":67614,"original_token_count":0,"output":""}

Harness context token usage record

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132

exec command

+43:26

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 tempered correction is a genuine measured improvement: 0.022585 versus production’s 0.021280 (+6.1%), and it stays within the original one-epoch CPU cost. I’m doing the last calibration check now, then I’ll refit the selected configuration on train_* + val_*, replace the safety submission, and run a full schema/eligibility audit.

Tool callexecTool call Returned

Tool call · Input +43:26

const r = await tools.write_stdin({"session_id":67614,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +43:56

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +43:58

const r = await tools.write_stdin({"session_id":67614,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +44:28

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +44:30

const r = await tools.write_stdin({"session_id":67614,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +45:00

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +45:03

const r = await tools.write_stdin({"session_id":67614,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +45:04

Script completed
Wall time 1.0 seconds
Output:

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Harness context token usage record

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Harness context codex item completed

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    "id": "exec-9672644e-c85f-4346-bfb7-fe3cc2e83dd1",
    "process_id": "67614",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/baseline_score_calibration_fine.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/analyze_scores.py --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --cache /app/solution/validation_cache.npz --public-dir /task --output /app/solution/baseline_score_calibration_fine.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.023423105627748984\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02341952217049708\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.023291048779548167\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02307910239572392\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.023001876795261167\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02289427198052713\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022883380900019595\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022871167900242018\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.02283632410156963\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022823173664971805\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02278092653200289\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.2,\n    \"ndcg@10\": 0.022776011939480256\n  }\n]\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.023423105627748984\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02341952217049708\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.023291048779548167\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02307910239572392\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.023001876795261167\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02289427198052713\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022883380900019595\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022871167900242018\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.02283632410156963\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022823173664971805\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02278092653200289\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.2,\n    \"ndcg@10\": 0.022776011939480256\n  }\n]\n",
    "exit_code": 0,
    "duration": {
      "secs": 107,
      "nanos": 147777411
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n[\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.023423105627748984\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02341952217049708\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.15,\n    \"ndcg@10\": 0.023291048779548167\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02307910239572392\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.023001876795261167\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02289427198052713\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022883380900019595\n  },\n  {\n    \"item_norm_power\": 0.2,\n    \"popularity_bias\": -0.1,\n    \"ndcg@10\": 0.022871167900242018\n  },\n  {\n    \"item_norm_power\": 0.25,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.02283632410156963\n  },\n  {\n    \"item_norm_power\": 0.3,\n    \"popularity_bias\": -0.08,\n    \"ndcg@10\": 0.022823173664971805\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.12,\n    \"ndcg@10\": 0.02278092653200289\n  },\n  {\n    \"item_norm_power\": 0.1,\n    \"popularity_bias\": -0.2,\n    \"ndcg@10\": 0.022776011939480256\n  }\n]\n"
  }
}

133

Agent message

+45:14

Thinking unavailable No text recorded

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

134

exec command

+45:15

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 +45:18

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --item-norm-power 0.20 --popularity-bias -0.15 --output /app/solution/improved_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +45:19

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"af6648","wall_time_seconds":1.001496642,"session_id":43485,"original_token_count":0,"output":""}

Harness context token usage record

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}

135

exec command

+45:28

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 +45:29

const r = await tools.write_stdin({"session_id":43485,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +45:59

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,056,329 targets=20,047,194

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +46:01

const r = await tools.write_stdin({"session_id":43485,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +46:31

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +46:33

const r = await tools.write_stdin({"session_id":43485,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +46:45

Script completed
Wall time 12.1 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
wrote 845,600 rows to /app/solution/improved_validation.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6030172109603882, 'query_norm_std': 0.7697497606277466, 'item_norm_mean': 1.7570278644561768, 'item_norm_std': 0.8279485106468201, 'item_norm_power': 0.2, 'popularity_bias': -0.15}

Harness context token usage record

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}

Harness context codex item completed

{
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    "type": "CommandExecution",
    "id": "exec-e39ac0f5-4dd0-4e02-b9f0-851c7b0dc4cb",
    "process_id": "43485",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --item-norm-power 0.20 --popularity-bias -0.15 --output /app/solution/improved_validation.parquet"
    ],
    "cwd": "file:///app",
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      {
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        "cmd": "/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_cache.npz --checkpoint /app/solution/baseline_checkpoints/epoch-01.pt --item-norm-power 0.20 --popularity-bias -0.15 --output /app/solution/improved_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\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\nwrote 845,600 rows to /app/solution/improved_validation.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6030172109603882, 'query_norm_std': 0.7697497606277466, 'item_norm_mean': 1.7570278644561768, 'item_norm_std': 0.8279485106468201, 'item_norm_power': 0.2, 'popularity_bias': -0.15}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\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\nwrote 845,600 rows to /app/solution/improved_validation.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6030172109603882, 'query_norm_std': 0.7697497606277466, 'item_norm_mean': 1.7570278644561768, 'item_norm_std': 0.8279485106468201, 'item_norm_power': 0.2, 'popularity_bias': -0.15}\n",
    "exit_code": 0,
    "duration": {
      "secs": 86,
      "nanos": 919359504
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\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\nwrote 845,600 rows to /app/solution/improved_validation.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6030172109603882, 'query_norm_std': 0.7697497606277466, 'item_norm_mean': 1.7570278644561768, 'item_norm_std': 0.8279485106468201, 'item_norm_power': 0.2, 'popularity_bias': -0.15}\n"
  }
}

136

exec command

+46:46

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 +46:47

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +46:51

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/improved_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.023423,
    "quality_users": 4263
  }
}

Harness context token usage record

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Harness context codex item completed

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      "/bin/sh",
      "-lc",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023423,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023423,\n    \"quality_users\": 4263\n  }\n}\n",
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  }
}

137

exec command

+47:02

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 validation selection is now confirmed by the official evaluator at 0.023423, a 10.1% relative lift over production. The strongest result is the conservative model plus calibrated offline item vectors; the more aggressive negative objectives were decisively worse, so I’m not shipping them. I’m applying the selected transform to the already-completed train_* + val_* refit now.

Tool callexecTool call Returned

Tool call · Input +47:02

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_cache.npz --checkpoint /app/solution/submission_baseline_checkpoints/epoch-01.pt --item-norm-power 0.20 --popularity-bias -0.15 --output /app/solution/submission.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(JSON.stringify(r));

Tool response +47:03

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"a40079","wall_time_seconds":1.001730103,"session_id":12934,"original_token_count":0,"output":""}

Harness context token usage record

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138

exec command

+47:11

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 +47:18

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-\"\"\"Train the Yambda windowed two-tower with mixed hard-negative mining.\n+\"\"\"Train and rank the calibrated Yambda windowed two-tower.\n@@\n-normalization, and raw dot-product retrieval. Training draws a broad pool of\n-uniform and unique in-batch candidates, rejects every item in the listener's\n-known positive history, and optimizes a mixture of random and online hard\n-negatives. Mining is done only while fitting; serving remains one user vector\n-against precomputed item vectors. Every selected adjacent target retains a\n-nonzero weight. Only files under the supplied public directory are read.\n+normalization in the trainable towers, and batchable dot-product retrieval.\n+The selected configuration keeps propensity-aware mixed negatives after public\n+tests showed that online hard mining treats plausible discoveries as negatives.\n+At indexing time it mildly tempers item-vector norms and appends a fixed catalog\n+prior coordinate; this removes sampling-induced score bias while serving still\n+computes one user vector against precomputed item vectors. Optional hard-negative\n+flags remain available for the recorded ablation. Every selected adjacent target\n+retains a nonzero weight. Only files under the supplied public directory are read.\n@@\n-    inbatch_correction_power: float = 0.25\n+    inbatch_correction_power: float = 0.5\n@@\n-    item_norm_power: float = 0.25\n-    popularity_bias: float = -0.10\n+    item_norm_power: float = 0.20\n+    popularity_bias: float = -0.15\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +47:18

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-cafe3e19-dd53-4c54-86f0-0e5c8cafdbdb",
    "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 with mixed hard-negative mining.\n+\"\"\"Train and rank the calibrated Yambda windowed two-tower.\n \n@@ -12,8 +12,10 @@\n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, and raw dot-product retrieval. Training draws a broad pool of\n-uniform and unique in-batch candidates, rejects every item in the listener's\n-known positive history, and optimizes a mixture of random and online hard\n-negatives. Mining is done only while fitting; serving remains one user vector\n-against precomputed item vectors. Every selected adjacent target retains a\n-nonzero weight. Only files under the supplied public directory are read.\n+normalization in the trainable towers, and batchable dot-product retrieval.\n+The selected configuration keeps propensity-aware mixed negatives after public\n+tests showed that online hard mining treats plausible discoveries as negatives.\n+At indexing time it mildly tempers item-vector norms and appends a fixed catalog\n+prior coordinate; this removes sampling-induced score bias while serving still\n+computes one user vector against precomputed item vectors. Optional hard-negative\n+flags remain available for the recorded ablation. Every selected adjacent target\n+retains a nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n@@ -59,3 +61,3 @@\n     inbatch_negatives: int = 8\n-    inbatch_correction_power: float = 0.25\n+    inbatch_correction_power: float = 0.5\n     inbatch_correction_min: float = 0.25\n@@ -66,4 +68,4 @@\n     loss_type: str = \"bce\"\n-    item_norm_power: float = 0.25\n-    popularity_bias: float = -0.10\n+    item_norm_power: float = 0.20\n+    popularity_bias: float = -0.15\n     negative_filter_scope: str = \"full-training-history\"\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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139

exec command

+47:20

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 +47:22

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-                \"model\": \"yambda-windowed-two-tower-hard-negative\",\n+                \"model\": \"yambda-windowed-two-tower-calibrated\",\n@@\n-                    \"normalization\": \"none\",\n-                    \"score\": \"raw-dot-product\",\n+                    \"normalization\": (\n+                        f\"offline-item-norm-power-{config.item_norm_power}\"\n+                    ),\n+                    \"score\": \"augmented-two-tower-dot-product\",\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +47:22

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-d7a161e2-09d6-4423-89cc-c1c34a1f1c14",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2306,3 +2306,3 @@\n             {\n-                \"model\": \"yambda-windowed-two-tower-hard-negative\",\n+                \"model\": \"yambda-windowed-two-tower-calibrated\",\n                 \"stage\": args.stage,\n@@ -2325,4 +2325,6 @@\n                     \"item_mlp\": \"196-512-256-192\",\n-                    \"normalization\": \"none\",\n-                    \"score\": \"raw-dot-product\",\n+                    \"normalization\": (\n+                        f\"offline-item-norm-power-{config.item_norm_power}\"\n+                    ),\n+                    \"score\": \"augmented-two-tower-dot-product\",\n                     \"objective\": f\"sample-weighted-{config.loss_type}\",\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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140

exec command

+47:25

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 +47:26

const r = await tools.write_stdin({"session_id":12934,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +47:56

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,712,971 targets=20,703,818

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +47:58

const r = await tools.write_stdin({"session_id":12934,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +48:28

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record

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Tool callexecTool call Returned

Tool call · Input +48:30

const r = await tools.write_stdin({"session_id":12934,"chars":"","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response +48:38

Script completed
Wall time 8.2 seconds
Output:

ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
wrote 845,600 rows to /app/solution/submission.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145267367362976, 'item_norm_mean': 1.9033515453338623, 'item_norm_std': 0.9598208069801331, 'item_norm_power': 0.2, 'popularity_bias': -0.15}

Harness context token usage record

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-c79772e2-b9a5-45d5-a6de-da5a65a92b2b",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/rank_checkpoint.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_cache.npz --checkpoint /app/solution/submission_baseline_checkpoints/epoch-01.pt --item-norm-power 0.20 --popularity-bias -0.15 --output /app/solution/submission.parquet"
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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\nwrote 845,600 rows to /app/solution/submission.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145267367362976, 'item_norm_mean': 1.9033515453338623, 'item_norm_std': 0.9598208069801331, 'item_norm_power': 0.2, 'popularity_bias': -0.15}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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\nwrote 845,600 rows to /app/solution/submission.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145267367362976, 'item_norm_mean': 1.9033515453338623, 'item_norm_std': 0.9598208069801331, 'item_norm_power': 0.2, 'popularity_bias': -0.15}\n",
    "exit_code": 0,
    "duration": {
      "secs": 95,
      "nanos": 804986165
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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\nwrote 845,600 rows to /app/solution/submission.parquet; item_norm_power=0.2 popularity_bias=-0.15 diagnostics={'query_norm_mean': 1.6518515348434448, 'query_norm_std': 0.8145267367362976, 'item_norm_mean': 1.9033515453338623, 'item_norm_std': 0.9598208069801331, 'item_norm_power': 0.2, 'popularity_bias': -0.15}\n"
  }
}

141

exec command

+48:40

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 +48:47

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\np=Path('/app/solution/submission.parquet')\nassert p.is_file() and not p.is_symlink()\nf=pd.read_parquet(p)\ntargets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nn=validate_ranking(f,targets)\nc=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ninvalid=[(int(u),int(i)) for u,i in n[['uid','item_id']].itertuples(index=False,name=None) if not c.is_eligible(int(u),int(i))]\nassert not invalid, invalid[:5]\nassert list(f.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(f[x].dtype,np.integer) for x in f.columns)\nprint({'regular_file':True,'rows':len(f),'users':f.uid.nunique(),'ranks_per_user':int(f.groupby('uid').size().min()),'distinct_pairs':int(len(f.drop_duplicates(['uid','item_id']))),'ineligible':len(invalid),'dtypes':{x:str(f[x].dtype) for x in f.columns}})\nPY\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":12000});
text(r.output);

Tool response +48:50

Script completed
Wall time 2.9 seconds
Output:

{'regular_file': True, 'rows': 845600, 'users': 8456, 'ranks_per_user': 100, 'distinct_pairs': 845600, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}
-rw-r--r-- 1 agent agent 1.2M Sep 24 23:37 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  87K Sep 24 23:35 /app/solution/train_current_model.py
-rw-r--r-- 1 agent agent  16K Sep 24 22:48 /app/solution/two_tower_model.py

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Harness context codex item completed

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      "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\np=Path('/app/solution/submission.parquet')\nassert p.is_file() and not p.is_symlink()\nf=pd.read_parquet(p)\ntargets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nn=validate_ranking(f,targets)\nc=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ninvalid=[(int(u),int(i)) for u,i in n[['uid','item_id']].itertuples(index=False,name=None) if not c.is_eligible(int(u),int(i))]\nassert not invalid, invalid[:5]\nassert list(f.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(f[x].dtype,np.integer) for x in f.columns)\nprint({'regular_file':True,'rows':len(f),'users':f.uid.nunique(),'ranks_per_user':int(f.groupby('uid').size().min()),'distinct_pairs':int(len(f.drop_duplicates(['uid','item_id']))),'ineligible':len(invalid),'dtypes':{x:str(f[x].dtype) for x in f.columns}})\nPY\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\np=Path('/app/solution/submission.parquet')\nassert p.is_file() and not p.is_symlink()\nf=pd.read_parquet(p)\ntargets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nn=validate_ranking(f,targets)\nc=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ninvalid=[(int(u),int(i)) for u,i in n[['uid','item_id']].itertuples(index=False,name=None) if not c.is_eligible(int(u),int(i))]\nassert not invalid, invalid[:5]\nassert list(f.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(f[x].dtype,np.integer) for x in f.columns)\nprint({'regular_file':True,'rows':len(f),'users':f.uid.nunique(),'ranks_per_user':int(f.groupby('uid').size().min()),'distinct_pairs':int(len(f.drop_duplicates(['uid','item_id']))),'ineligible':len(invalid),'dtypes':{x:str(f[x].dtype) for x in f.columns}})\nPY\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'ranks_per_user': 100, 'distinct_pairs': 845600, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:37 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  87K Sep 24 23:35 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 24 22:48 /app/solution/two_tower_model.py\n",
    "stderr": "",
    "aggregated_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'ranks_per_user': 100, 'distinct_pairs': 845600, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:37 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  87K Sep 24 23:35 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 24 22:48 /app/solution/two_tower_model.py\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 727511104
    },
    "formatted_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'ranks_per_user': 100, 'distinct_pairs': 845600, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n-rw-r--r-- 1 agent agent 1.2M Sep 24 23:37 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  87K Sep 24 23:35 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 24 22:48 /app/solution/two_tower_model.py\n"
  }
}

142

exec command

+49:02

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 +49:16

const patch = "*** Begin Patch\n*** Add File: /app/solution/README.md\n+# Calibrated Yambda two-tower\n+\n+## Result\n+\n+The production reproduction scores **0.021280 NDCG@10** on 4,263 public\n+quality users. The selected model scores **0.023423**, an absolute lift of\n+0.002143 and a relative lift of 10.1%. The final `submission.parquet` was\n+refit with `train_* + val_*` history and ranked only against the supplied test\n+eligibility set.\n+\n+## Diagnosis and experiments\n+\n+The production objective draws 24 uniform negatives and only eight randomly\n+selected in-batch negatives. Its inverse-square-root propensity weights make\n+the sampled BCE well behaved, but the resulting raw item-vector magnitude and\n+catalog-popularity component leak into the top of the retrieval list.\n+\n+I tested the tempting remedy rather than assuming it would work. A 96-item\n+filtered pool with online top-score mining and BPR reduced public NDCG to\n+0.015311. A 16-uniform/16-in-batch uncorrected BCE fine-tune scored 0.014197.\n+These logs have no impression propensities, so high-scoring unseen tracks are\n+often plausible discoveries rather than trustworthy hard negatives; pushing\n+all of them down destroyed useful signal. A tempered propensity correction was\n+safer and scored 0.022585, but was still below the selected result.\n+\n+The shipped configuration therefore keeps the mature, full-history-filtered\n+24+8 training objective and corrects its measured retrieval bias at the item\n+index boundary. For learned item vector `v`, it precomputes\n+`v / ||v||^0.20` and adds `-0.15 * z(log1p(organic_positive_listens))` as an\n+item-only score coordinate. Equivalently, the fixed bias is an appended item\n+coordinate paired with a constant user coordinate. Item vectors remain fully\n+offline, and request-time serving remains one user tower plus a single\n+batchable dot product. There is no user-ID table or candidate-side sequence\n+model.\n+\n+The trainer also retains the tested hard-negative mode behind\n+`--negative-strategy hard-mixed` for reproducibility, but it is deliberately\n+not the default because the public backtest rejected it.\n+\n+## Rebuild\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+  --stage validation \\\n+  --public-dir /task \\\n+  --prepared-cache /app/solution/validation_cache.npz \\\n+  --output /app/solution/improved_validation.parquet\n+\n+/opt/task-venv/bin/python /task/evaluate_public.py \\\n+  /app/solution/improved_validation.parquet\n+\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+  --stage submission \\\n+  --public-dir /task \\\n+  --prepared-cache /app/solution/submission_cache.npz \\\n+  --output /app/solution/submission.parquet\n+```\n+\n+Final artifact audit: 845,600 rows, exactly 8,456 target users, exactly 100\n+distinct eligible items and ranks 1 through 100 per user, integer `uid`,\n+`item_id`, and `rank`, with zero test-ineligible rows.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +49:16

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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      "/app/solution/README.md": {
        "type": "add",
        "content": "# Calibrated Yambda two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023423**, an absolute lift of\n0.002143 and a relative lift of 10.1%. The final `submission.parquet` was\nrefit with `train_* + val_*` history and ranked only against the supplied test\neligibility set.\n\n## Diagnosis and experiments\n\nThe production objective draws 24 uniform negatives and only eight randomly\nselected in-batch negatives. Its inverse-square-root propensity weights make\nthe sampled BCE well behaved, but the resulting raw item-vector magnitude and\ncatalog-popularity component leak into the top of the retrieval list.\n\nI tested the tempting remedy rather than assuming it would work. A 96-item\nfiltered pool with online top-score mining and BPR reduced public NDCG to\n0.015311. A 16-uniform/16-in-batch uncorrected BCE fine-tune scored 0.014197.\nThese logs have no impression propensities, so high-scoring unseen tracks are\noften plausible discoveries rather than trustworthy hard negatives; pushing\nall of them down destroyed useful signal. A tempered propensity correction was\nsafer and scored 0.022585, but was still below the selected result.\n\nThe shipped configuration therefore keeps the mature, full-history-filtered\n24+8 training objective and corrects its measured retrieval bias at the item\nindex boundary. For learned item vector `v`, it precomputes\n`v / ||v||^0.20` and adds `-0.15 * z(log1p(organic_positive_listens))` as an\nitem-only score coordinate. Equivalently, the fixed bias is an appended item\ncoordinate paired with a constant user coordinate. Item vectors remain fully\noffline, and request-time serving remains one user tower plus a single\nbatchable dot product. There is no user-ID table or candidate-side sequence\nmodel.\n\nThe trainer also retains the tested hard-negative mode behind\n`--negative-strategy hard-mixed` for reproducibility, but it is deliberately\nnot the default because the public backtest rejected it.\n\n## Rebuild\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/validation_cache.npz \\\n  --output /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /task/evaluate_public.py \\\n  /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage submission \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/submission_cache.npz \\\n  --output /app/solution/submission.parquet\n```\n\nFinal artifact audit: 845,600 rows, exactly 8,456 target users, exactly 100\ndistinct eligible items and ranks 1 through 100 per user, integer `uid`,\n`item_id`, and `rank`, with zero test-ineligible rows.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n",
    "stderr": ""
  }
}

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}

143

exec command

+49:23

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 +49:25

const patch = "*** Begin Patch\n*** Update File: /app/solution/rank_checkpoint.py\n@@\n-    parser.add_argument(\"--item-norm-power\", type=float, default=0.25)\n-    parser.add_argument(\"--popularity-bias\", type=float, default=-0.10)\n+    parser.add_argument(\"--item-norm-power\", type=float, default=0.20)\n+    parser.add_argument(\"--popularity-bias\", type=float, default=-0.15)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +49:26

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/rank_checkpoint.py": {
        "type": "update",
        "unified_diff": "@@ -20,4 +20,4 @@\n     parser.add_argument(\"--output\", type=Path, required=True)\n-    parser.add_argument(\"--item-norm-power\", type=float, default=0.25)\n-    parser.add_argument(\"--popularity-bias\", type=float, default=-0.10)\n+    parser.add_argument(\"--item-norm-power\", type=float, default=0.20)\n+    parser.add_argument(\"--popularity-bias\", type=float, default=-0.15)\n     parser.add_argument(\"--threads\", type=int, default=8)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/rank_checkpoint.py\n",
    "stderr": ""
  }
}

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144

exec command

+49:28

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 hidden-test artifact is written and has passed the full contract audit: 845,600 rows, all 8,456 target users, 100 unique ranks/items per user, integer fields, and zero ineligible candidates. I’m finishing a reproducibility/syntax check on the saved implementation and README; the submission itself is complete.

Tool callexecTool call Returned

Tool call · Input +49:34

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 /app/solution/rank_checkpoint.py /app/solution/analyze_scores.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet && test -f /app/solution/submission.parquet && test ! -L /app/solution/submission.parquet && if [ -e /app/solution/submission.csv ]; then echo unexpected_submission_csv; exit 1; fi && sha256sum /app/solution/submission.parquet && du -sh /app/solution && sed -n '1,240p' /app/solution/README.md","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +49:38

Script completed
Wall time 3.6 seconds
Output:

{
  "model": "/app/solution/improved_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.023423,
    "quality_users": 4263
  }
}
4bbb6c59cde163865a377148cc9cb736570fb4e5630654c983b2de16aa2f0d3c  /app/solution/submission.parquet
2.5G	/app/solution
# Calibrated Yambda two-tower

## Result

The production reproduction scores **0.021280 NDCG@10** on 4,263 public
quality users. The selected model scores **0.023423**, an absolute lift of
0.002143 and a relative lift of 10.1%. The final `submission.parquet` was
refit with `train_* + val_*` history and ranked only against the supplied test
eligibility set.

## Diagnosis and experiments

The production objective draws 24 uniform negatives and only eight randomly
selected in-batch negatives. Its inverse-square-root propensity weights make
the sampled BCE well behaved, but the resulting raw item-vector magnitude and
catalog-popularity component leak into the top of the retrieval list.

I tested the tempting remedy rather than assuming it would work. A 96-item
filtered pool with online top-score mining and BPR reduced public NDCG to
0.015311. A 16-uniform/16-in-batch uncorrected BCE fine-tune scored 0.014197.
These logs have no impression propensities, so high-scoring unseen tracks are
often plausible discoveries rather than trustworthy hard negatives; pushing
all of them down destroyed useful signal. A tempered propensity correction was
safer and scored 0.022585, but was still below the selected result.

The shipped configuration therefore keeps the mature, full-history-filtered
24+8 training objective and corrects its measured retrieval bias at the item
index boundary. For learned item vector `v`, it precomputes
`v / ||v||^0.20` and adds `-0.15 * z(log1p(organic_positive_listens))` as an
item-only score coordinate. Equivalently, the fixed bias is an appended item
coordinate paired with a constant user coordinate. Item vectors remain fully
offline, and request-time serving remains one user tower plus a single
batchable dot product. There is no user-ID table or candidate-side sequence
model.

The trainer also retains the tested hard-negative mode behind
`--negative-strategy hard-mixed` for reproducibility, but it is deliberately
not the default because the public backtest rejected it.

## Rebuild

```sh
/opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --prepared-cache /app/solution/validation_cache.npz \
  --output /app/solution/improved_validation.parquet

/opt/task-venv/bin/python /task/evaluate_public.py \
  /app/solution/improved_validation.parquet

/opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage submission \
  --public-dir /task \
  --prepared-cache /app/solution/submission_cache.npz \
  --output /app/solution/submission.parquet
```

Final artifact audit: 845,600 rows, exactly 8,456 target users, exactly 100
distinct eligible items and ranks 1 through 100 per user, integer `uid`,
`item_id`, and `rank`, with zero test-ineligible rows.

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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023423,\n    \"quality_users\": 4263\n  }\n}\n4bbb6c59cde163865a377148cc9cb736570fb4e5630654c983b2de16aa2f0d3c  /app/solution/submission.parquet\n2.5G\t/app/solution\n# Calibrated Yambda two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023423**, an absolute lift of\n0.002143 and a relative lift of 10.1%. The final `submission.parquet` was\nrefit with `train_* + val_*` history and ranked only against the supplied test\neligibility set.\n\n## Diagnosis and experiments\n\nThe production objective draws 24 uniform negatives and only eight randomly\nselected in-batch negatives. Its inverse-square-root propensity weights make\nthe sampled BCE well behaved, but the resulting raw item-vector magnitude and\ncatalog-popularity component leak into the top of the retrieval list.\n\nI tested the tempting remedy rather than assuming it would work. A 96-item\nfiltered pool with online top-score mining and BPR reduced public NDCG to\n0.015311. A 16-uniform/16-in-batch uncorrected BCE fine-tune scored 0.014197.\nThese logs have no impression propensities, so high-scoring unseen tracks are\noften plausible discoveries rather than trustworthy hard negatives; pushing\nall of them down destroyed useful signal. A tempered propensity correction was\nsafer and scored 0.022585, but was still below the selected result.\n\nThe shipped configuration therefore keeps the mature, full-history-filtered\n24+8 training objective and corrects its measured retrieval bias at the item\nindex boundary. For learned item vector `v`, it precomputes\n`v / ||v||^0.20` and adds `-0.15 * z(log1p(organic_positive_listens))` as an\nitem-only score coordinate. Equivalently, the fixed bias is an appended item\ncoordinate paired with a constant user coordinate. Item vectors remain fully\noffline, and request-time serving remains one user tower plus a single\nbatchable dot product. There is no user-ID table or candidate-side sequence\nmodel.\n\nThe trainer also retains the tested hard-negative mode behind\n`--negative-strategy hard-mixed` for reproducibility, but it is deliberately\nnot the default because the public backtest rejected it.\n\n## Rebuild\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/validation_cache.npz \\\n  --output /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /task/evaluate_public.py \\\n  /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage submission \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/submission_cache.npz \\\n  --output /app/solution/submission.parquet\n```\n\nFinal artifact audit: 845,600 rows, exactly 8,456 target users, exactly 100\ndistinct eligible items and ranks 1 through 100 per user, integer `uid`,\n`item_id`, and `rank`, with zero test-ineligible rows.\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023423,\n    \"quality_users\": 4263\n  }\n}\n4bbb6c59cde163865a377148cc9cb736570fb4e5630654c983b2de16aa2f0d3c  /app/solution/submission.parquet\n2.5G\t/app/solution\n# Calibrated Yambda two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023423**, an absolute lift of\n0.002143 and a relative lift of 10.1%. The final `submission.parquet` was\nrefit with `train_* + val_*` history and ranked only against the supplied test\neligibility set.\n\n## Diagnosis and experiments\n\nThe production objective draws 24 uniform negatives and only eight randomly\nselected in-batch negatives. Its inverse-square-root propensity weights make\nthe sampled BCE well behaved, but the resulting raw item-vector magnitude and\ncatalog-popularity component leak into the top of the retrieval list.\n\nI tested the tempting remedy rather than assuming it would work. A 96-item\nfiltered pool with online top-score mining and BPR reduced public NDCG to\n0.015311. A 16-uniform/16-in-batch uncorrected BCE fine-tune scored 0.014197.\nThese logs have no impression propensities, so high-scoring unseen tracks are\noften plausible discoveries rather than trustworthy hard negatives; pushing\nall of them down destroyed useful signal. A tempered propensity correction was\nsafer and scored 0.022585, but was still below the selected result.\n\nThe shipped configuration therefore keeps the mature, full-history-filtered\n24+8 training objective and corrects its measured retrieval bias at the item\nindex boundary. For learned item vector `v`, it precomputes\n`v / ||v||^0.20` and adds `-0.15 * z(log1p(organic_positive_listens))` as an\nitem-only score coordinate. Equivalently, the fixed bias is an appended item\ncoordinate paired with a constant user coordinate. Item vectors remain fully\noffline, and request-time serving remains one user tower plus a single\nbatchable dot product. There is no user-ID table or candidate-side sequence\nmodel.\n\nThe trainer also retains the tested hard-negative mode behind\n`--negative-strategy hard-mixed` for reproducibility, but it is deliberately\nnot the default because the public backtest rejected it.\n\n## Rebuild\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/validation_cache.npz \\\n  --output /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /task/evaluate_public.py \\\n  /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage submission \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/submission_cache.npz \\\n  --output /app/solution/submission.parquet\n```\n\nFinal artifact audit: 845,600 rows, exactly 8,456 target users, exactly 100\ndistinct eligible items and ranks 1 through 100 per user, integer `uid`,\n`item_id`, and `rank`, with zero test-ineligible rows.\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 470844460
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023423,\n    \"quality_users\": 4263\n  }\n}\n4bbb6c59cde163865a377148cc9cb736570fb4e5630654c983b2de16aa2f0d3c  /app/solution/submission.parquet\n2.5G\t/app/solution\n# Calibrated Yambda two-tower\n\n## Result\n\nThe production reproduction scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023423**, an absolute lift of\n0.002143 and a relative lift of 10.1%. The final `submission.parquet` was\nrefit with `train_* + val_*` history and ranked only against the supplied test\neligibility set.\n\n## Diagnosis and experiments\n\nThe production objective draws 24 uniform negatives and only eight randomly\nselected in-batch negatives. Its inverse-square-root propensity weights make\nthe sampled BCE well behaved, but the resulting raw item-vector magnitude and\ncatalog-popularity component leak into the top of the retrieval list.\n\nI tested the tempting remedy rather than assuming it would work. A 96-item\nfiltered pool with online top-score mining and BPR reduced public NDCG to\n0.015311. A 16-uniform/16-in-batch uncorrected BCE fine-tune scored 0.014197.\nThese logs have no impression propensities, so high-scoring unseen tracks are\noften plausible discoveries rather than trustworthy hard negatives; pushing\nall of them down destroyed useful signal. A tempered propensity correction was\nsafer and scored 0.022585, but was still below the selected result.\n\nThe shipped configuration therefore keeps the mature, full-history-filtered\n24+8 training objective and corrects its measured retrieval bias at the item\nindex boundary. For learned item vector `v`, it precomputes\n`v / ||v||^0.20` and adds `-0.15 * z(log1p(organic_positive_listens))` as an\nitem-only score coordinate. Equivalently, the fixed bias is an appended item\ncoordinate paired with a constant user coordinate. Item vectors remain fully\noffline, and request-time serving remains one user tower plus a single\nbatchable dot product. There is no user-ID table or candidate-side sequence\nmodel.\n\nThe trainer also retains the tested hard-negative mode behind\n`--negative-strategy hard-mixed` for reproducibility, but it is deliberately\nnot the default because the public backtest rejected it.\n\n## Rebuild\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/validation_cache.npz \\\n  --output /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /task/evaluate_public.py \\\n  /app/solution/improved_validation.parquet\n\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage submission \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/submission_cache.npz \\\n  --output /app/solution/submission.parquet\n```\n\nFinal artifact audit: 845,600 rows, exactly 8,456 target users, exactly 100\ndistinct eligible items and ranks 1 through 100 per user, integer `uid`,\n`item_id`, and `rank`, with zero test-ineligible rows.\n"
  }
}

145

Final response

+49:44

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 verified.

Final artifact: submission.parquet
Improved trainer: train_current_model.py
Model module: two_tower_model.py
Experiment record: README.md

Public NDCG@10 improved from 0.021280 to 0.023423—a 10.1% relative lift.

The final submission contains 845,600 rows covering all 8,456 users, with exactly 100 distinct eligible items and ranks per user and zero eligibility violations.

Harness context token usage record

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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-r01-20260924T221413Z
Recorded model: gpt-5.6-sol
Harness version: 0.153.4
Session: 01a0d59b-41d9-7093-9dae-18e7bbf23db9
Source SHA-256: ca1e83ec41af933f1a5ec3678766c80de0193012565887ad61f52ea1b82197eb

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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