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Agent trace

Production two-tower improvement sol-0.153.4-medium-05

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 52 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · medium effort
Recorded conversation
40:32 · 59 steps
Private reward
0.016048 · Failed
Attempt cost
$3.11
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
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.

02

Developer instructions

+00:01
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>

03

Developer instructions

+00:01
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.

04

Developer instructions

+00:01
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>

05

User context

+00:01
User context

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

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

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

Harness context world state
{
  "type": "world_state",
  "full": true,
  "state": {
    "agents_md": {},
    "apps_instructions": true,
    "collaboration_mode": {
      "mode": "default",
      "model": "gpt-5.6-sol",
      "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d"
    },
    "context_window_guidance": "",
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-25",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "managed_developer_instructions": {},
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "persistent_mode": {},
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}
Harness context turn context
{
  "type": "turn_context",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-25",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "medium",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "medium",
  "summary": "auto"
}
06

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:

FamilyMeaningColumns
listensPlayback eventsuid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds
likesExplicit positive actionsuid, timestamp, item_id, is_organic
dislikesExplicit negative actionsuid, timestamp, item_id, is_organic
unlikesReversal of a prior likeuid, timestamp, item_id, is_organic
undislikesReversal of a prior dislikeuid, 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.
07

exec command

+00:02
Thinking unavailable No text recorded

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

Model text

I’ll reproduce the baseline, inspect the negative-sampling/objective code and data shape, then make a small set of measured changes against the public backtest. I’ll keep the best valid hidden-test submission saved as soon as one is available, then refine only if the CPU budget permits.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -la /task/current_model /task/data && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":40000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/task/current_model:
total 104
dr-xr-xr-x 1 root root  4096 Sep 25 01:11 .
dr-xr-xr-x 1 root root  4096 Sep 25 01:13 ..
-r--r--r-- 1 root root 80058 Sep 25 01:11 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 25 01:11 two_tower_model.py

/task/data:
total 347392
dr-xr-xr-x 1 root root      4096 Sep 25 01:12 .
dr-xr-xr-x 1 root root      4096 Sep 25 01:13 ..
-r--r--r-- 1 root root    349352 Sep 25 01:11 candidate_catalog.parquet
-r--r--r-- 1 root root     84127 Sep 25 01:11 target_users.parquet
-r--r--r-- 1 root root   7736341 Sep 25 01:11 test_eligible_candidates.npz
-r--r--r-- 1 root root   1092329 Sep 25 01:12 test_preference_state.parquet
-r--r--r-- 1 root root    857213 Sep 25 01:11 train_dislikes.parquet
-r--r--r-- 1 root root   5374652 Sep 25 01:11 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 25 01:11 train_listens.parquet
-r--r--r-- 1 root root    155988 Sep 25 01:11 train_undislikes.parquet
-r--r--r-- 1 root root   1855885 Sep 25 01:11 train_unlikes.parquet
-r--r--r-- 1 root root     36571 Sep 25 01:11 val_dislikes.parquet
-r--r--r-- 1 root root    298484 Sep 25 01:11 val_likes.parquet
-r--r--r-- 1 root root   9947609 Sep 25 01:11 val_listens.parquet
-r--r--r-- 1 root root      7845 Sep 25 01:11 val_undislikes.parquet
-r--r--r-- 1 root root     72536 Sep 25 01:11 val_unlikes.parquet
-r--r--r-- 1 root root   7573821 Sep 25 01:11 validation_eligible_candidates.npz
-r--r--r-- 1 root root   1061761 Sep 25 01:12 validation_preference_state.parquet
#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

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

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

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

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

import two_tower_model as base


CACHE_VERSION = 2


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


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

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


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

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

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


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

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

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


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


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


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


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


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

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

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


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

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

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


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


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


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


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


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


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


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


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


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


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


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


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


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


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

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
            input_size=config.hidden_dim,
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6b58b472-e4fb-4d68-9ae8-f37d4b4d9224",
    "process_id": "23123",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -la /task/current_model /task/data && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -la /task/current_model /task/data",
        "path": "current_model"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,260p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,300p' /task/current_model/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/task/current_model/two_tower_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/task/current_model:\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 25 01:11 .\ndr-xr-xr-x 1 root root  4096 Sep 25 01:13 ..\n-r--r--r-- 1 root root 80058 Sep 25 01:11 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 25 01:11 two_tower_model.py\n\n/task/data:\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 25 01:12 .\ndr-xr-xr-x 1 root root      4096 Sep 25 01:13 ..\n-r--r--r-- 1 root root    349352 Sep 25 01:11 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 25 01:11 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 25 01:11 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 25 01:12 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 25 01:11 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 25 01:11 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 25 01:11 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 25 01:11 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 25 01:11 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 25 01:11 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 25 01:11 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 25 01:11 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 25 01:11 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 25 01:11 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 25 01:11 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 25 01:12 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n",
    "stderr": "",
    "aggregated_output": "/task/current_model:\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 25 01:11 .\ndr-xr-xr-x 1 root root  4096 Sep 25 01:13 ..\n-r--r--r-- 1 root root 80058 Sep 25 01:11 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 25 01:11 two_tower_model.py\n\n/task/data:\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 25 01:12 .\ndr-xr-xr-x 1 root root      4096 Sep 25 01:13 ..\n-r--r--r-- 1 root root    349352 Sep 25 01:11 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 25 01:11 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 25 01:11 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 25 01:12 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 25 01:11 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 25 01:11 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 25 01:11 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 25 01:11 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 25 01:11 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 25 01:11 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 25 01:11 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 25 01:11 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 25 01:11 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 25 01:11 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 25 01:11 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 25 01:12 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4611
    },
    "formatted_output": "/task/current_model:\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 25 01:11 .\ndr-xr-xr-x 1 root root  4096 Sep 25 01:13 ..\n-r--r--r-- 1 root root 80058 Sep 25 01:11 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 25 01:11 two_tower_model.py\n\n/task/data:\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 25 01:12 .\ndr-xr-xr-x 1 root root      4096 Sep 25 01:13 ..\n-r--r--r-- 1 root root    349352 Sep 25 01:11 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 25 01:11 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 25 01:11 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 25 01:12 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 25 01:11 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 25 01:11 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 25 01:11 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 25 01:11 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 25 01:11 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 25 01:11 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 25 01:11 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 25 01:11 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 25 01:11 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 25 01:11 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 25 01:11 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 25 01:12 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n"
  }
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08

exec command

+00:06
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 

const r = await tools.exec_command({"cmd":"rg -n \"negative|def train|argparse|Config\\(|rank|validation\" /task/current_model/train_current_model.py | head -160 && sed -n '300,700p' /task/current_model/two_tower_model.py && sed -n '500,1050p' /task/current_model/train_current_model.py && sed -n '1050,1550p' /task/current_model/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":50000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 13171)
Total output lines: 1346

7:16 held-out validation targets; passing zero for either cap restores the
14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
20:import argparse
56:    uniform_negatives: int = 24
57:    inbatch_negatives: int = 8
61:    negative_filter_scope: str = "full-training-history"
67:    validation_fraction: float = 0.10
69:    max_validation_targets_per_user: int = 16
132:    validation_counts: np.ndarray
137:        return int(self.training_counts.sum() + self.validation_counts.sum())
152:    return base.TowerConfig(
165:        uniform_negatives=config.uniform_negatives,
166:        inbatch_negatives=config.inbatch_negatives,
346:        "popularity_rank"
354:    if stage == "validation":
357:        candidate_name = "validation_eligible_candidates.npz"
358:        state_name = "validation_preference_state.parquet"
477:        "event_time_feature": "exp-negative-gap-over-60-days",
518:        "validation_eligible_candidates.npz"
519:        if stage == "validation"
603:        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
609:def invalid_full_history_negatives(
610:    negatives: np.ndarray,
616:    """Return row-level invalid negatives using the complete training prefix."""
617:    byte_columns = negatives >> 3
618:    bit_columns = negatives & 7
621:    invalid |= negatives == targets[:, None]
622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
623:    for column in range(1, negatives.shape[1]):
625:            negatives[:, column, None] == negatives[:, :column]
630:def draw_full_history_filtered_negatives(
641:    """Draw mixed negatives and reject all known public-training positives."""
647:        (batch_size, config.uniform_negatives),
653:    if config.inbatch_negatives:
656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
672:        if corrected_count < config.inbatch_negatives:
675:                (batch_size, config.inbatch_negatives - corrected_count),
681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
682:    correction = np.ones_like(negatives, dtype=np.float32)
685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count
688:        invalid = invalid_full_history_negatives(
689:            negatives,
697:                torch.from_numpy(negatives).to(device),
706:        negatives[invalid] = replacements
709:    invalid = invalid_full_history_negatives(
710:        negatives,
717:        raise RuntimeError("full-history negative resampling failed")
719:        torch.from_numpy(negatives).to(device),
724:def draw_training_negatives(
736:    if config.negative_filter_scope == "full-training-history":
738:            raise RuntimeError("full-history negative filter was not built")
739:        return draw_full_history_filtered_negatives(
750:    return base.draw_filtered_mixed_negatives(
755:        config.uniform_negatives,
756:        config.inbatch_negatives,
816:    negatives: torch.Tensor,
818:    all_items = torch.cat([positives, negatives.reshape(-1)])
822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(
823:        len(positives), negatives.shape[1], -1
825:    return positive_vectors, negative_vectors
845:def corrected_negative_loss(
851:    total = config.uniform_negatives + config.inbatch_negatives
853:    if config.uniform_negatives:
854:        result += config.uniform_negatives * raw[
855:            :, : config.uniform_negatives
857:    if config.inbatch_negatives:
858:        selected = slice(config.uniform_negatives, total)
860:        result += config.inbatch_negatives * (
869:    validation_counts: np.ndarray,
873:    training_region_ends = total_targets - validation_counts
878:        or np.any(validation_counts < 0)
880:        raise RuntimeError("chronological target partition produced negative counts")
882:        window_starts + training_counts + validation_counts, total_targets
890:    validation_fraction: float,
892:    max_validation_targets_per_user: int,
896:    validation = np.floor(totals * validation_fraction).astype(np.int64)
897:    validation[(totals >= 2) & (validation == 0)] = 1
898:    if max_validation_targets_per_user:
899:        validation = np.minimum(validation, max_validation_targets_per_user)
901:    history_training = totals - validation
905:    window_starts = retained_target_window_starts(totals, validation, training)
909:        validation_counts=validation,
918:    prefixes = ("train",) if stage == "validation" else ("train", "val")
981:                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
991:                first_validation_target = last_training_target + 1
993:                    int(prepared.sequence_timestamps[first_validation_target])
994:                    if first_validation_target < end
1119:    negatives: torch.Tensor,
1120:    negative_weights: torch.Tensor,
1134:        "negative_sum": 0.0,
1162:        positive_vectors, negative_vectors = candidate_vectors(
1165:            negatives[begin:end],
1168:        negative_logits = torch.einsum(
1169:            "bd,bkd->bk", query_vectors, negative_vectors
1172:        negative_loss = corrected_negative_loss(
1173:            negative_logits, negative_weights[begin:end], config
1179:            selected_weights * (positive_loss + negative_loss) / 2.0
1190:        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
1227:    negative_count = config.uniform_negatives + config.inbatch_negatives
1232:    total_negative = 0.0
1293:            negatives, negative_weights = draw_training_negatives(
1314:                negatives,
1315:                negative_weights,
1331:            total_negative += statistics["negative_sum"]
1353:        "negative_logit": total_negative / max(total_targets * negative_count, 1),
1355:        / max(total_targets * negative_count, 1),
1364:def evaluate_validation_bce(
1371:    validation_counts: np.ndarray,
1378:    totals = training_counts + validation_counts
1379:    users_with_validation = np.flatnonzero(validation_counts > 0)
1380:    ordered = users_with_validation[
1381:        np.argsort(totals[users_with_validation], kind="stable")
1449:            negatives, negative_weights = draw_training_negatives(
1469:                negatives,
1470:                negative_weights,
1481:        "validation_loss": total_loss / max(total_weight, 1.0e-12),
1482:        "validation_targets": float(total_targets),
1483:        "validation_target_weight_sum": total_weight,
1484:        "validation_seconds": time.monotonic() - started,
1521:def train_model(
1532:        config.validation_fraction,
1534:        config.max_validation_targets_per_user,
1537:    validation_counts = partition.validation_counts
1540:        f"validation={int(validation_counts.sum()):,} "
1567:        if config.negative_filter_scope == "full-training-history"
1601:            "negative_filter_scope", "last-100"
1619:            stored_config.get("validation_fraction", 0.10),
1621:            stored_config.get("max_validation_targets_per_user", 0),
1624:            config.validation_fraction,
1626:            config.max_validation_targets_per_user,
1629:            stored_filter_scope != config.negative_filter_scope
1657:            prior.pop("validation_loss", None)
1660:                f"{stored_filter_scope}->{config.negative_filter_scope}, "
1664:                "recomputing the resume checkpoint's validation BCE"
1666:        if "validation_loss" not in prior:
1668:                evaluate_validation_bce(
1675:                    validation_counts,
1682:        best_loss = float(prior["validation_loss"])
1686:            f"resumed validation-BCE training: epoch={start_epoch} "
1705:        validation = evaluate_validation_bce(
1712:            validation_counts,
1717:        diagnostics.update(validation)
1731:            diagnostics["validation_loss"]
1735:            best_loss = diagnostics["validation_loss"]
1755:            f"validation_loss={diagnostics['validation_loss']:.5f} "
1758:            f"validation_targets={int(diagnostics['validation_targets']):,} "
1760:            f"validation_seconds={diagnostics['validation_seconds']:.1f}"
1766:                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
1775:        f"restored best validation-BCE checkpoint: epoch={best_epoch} "
1861:def rank_candidates(
1867:    ranking_batch_size: int,
1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):
1883:        end = min(start + ranking_batch_size, len(eligibility.uids))
1896:                (uid, int(prepared.item_ids[column]), rank)
1897:                for rank, column in enumerate(columns, start=1)
1900:            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
1909:    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
1923:    parser = argparse.ArgumentParser(description=__doc__)
            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
            field: artifact[field].copy()
            for field in SequenceData.__dataclass_fields__
        }
    prepared = SequenceData(**values)
    log(
        f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
        f"targets={prepared.target_count:,}"
    )
    return prepared


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


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


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


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


def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
        …3171 tokens truncated…tamp + 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)
    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
    targets = np.zeros((batch_size, steps), dtype=np.uint16)
    weights = np.ones((batch_size, steps), dtype=np.float32)
    scored = np.zeros((batch_size, steps), dtype=bool)
    histories = np.full(
        (batch_size, steps, config.bptt_steps),
        padding_item,
        dtype=np.uint16,
    )
    processed = np.zeros(batch_size, dtype=np.int32)

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


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


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


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

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


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


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


def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f7c0b76c-4531-4c58-a113-5fe4daf863d1",
    "process_id": "83804",
    "command": [
      "/bin/sh",
      "-lc",
      "rg -n \"negative|def train|argparse|Config\\(|rank|validation\" /task/current_model/train_current_model.py | head -160 && sed -n '300,700p' /task/current_model/two_tower_model.py && sed -n '500,1050p' /task/current_model/train_current_model.py && sed -n '1050,1550p' /task/current_model/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "search",
        "cmd": "rg -n \"negative|def train|argparse|Config\\\\(|rank|validation\" /task/current_model/train_current_model.py",
        "query": "negative|def train|argparse|Config\\(|rank|validation",
        "path": "train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '300,700p' /task/current_model/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/task/current_model/two_tower_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '500,1050p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1050,1550p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "7:16 held-out validation targets; passing zero for either cap restores the\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n20:import argparse\n56:    uniform_negatives: int = 24\n57:    inbatch_negatives: int = 8\n61:    negative_filter_scope: str = \"full-training-history\"\n67:    validation_fraction: float = 0.10\n69:    max_validation_targets_per_user: int = 16\n132:    validation_counts: np.ndarray\n137:        return int(self.training_counts.sum() + self.validation_counts.sum())\n152:    return base.TowerConfig(\n165:        uniform_negatives=config.uniform_negatives,\n166:        inbatch_negatives=config.inbatch_negatives,\n346:        \"popularity_rank\"\n354:    if stage == \"validation\":\n357:        candidate_name = \"validation_eligible_candidates.npz\"\n358:        state_name = \"validation_preference_state.parquet\"\n477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n518:        \"validation_eligible_candidates.npz\"\n519:        if stage == \"validation\"\n603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610:    negatives: np.ndarray,\n616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617:    byte_columns = negatives >> 3\n618:    bit_columns = negatives & 7\n621:    invalid |= negatives == targets[:, None]\n622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623:    for column in range(1, negatives.shape[1]):\n625:            negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647:        (batch_size, config.uniform_negatives),\n653:    if config.inbatch_negatives:\n656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672:        if corrected_count < config.inbatch_negatives:\n675:                (batch_size, config.inbatch_negatives - corrected_count),\n681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682:    correction = np.ones_like(negatives, dtype=np.float32)\n685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688:        invalid = invalid_full_history_negatives(\n689:            negatives,\n697:                torch.from_numpy(negatives).to(device),\n706:        negatives[invalid] = replacements\n709:    invalid = invalid_full_history_negatives(\n710:        negatives,\n717:        raise RuntimeError(\"full-history negative resampling failed\")\n719:        torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736:    if config.negative_filter_scope == \"full-training-history\":\n738:            raise RuntimeError(\"full-history negative filter was not built\")\n739:        return draw_full_history_filtered_negatives(\n750:    return base.draw_filtered_mixed_negatives(\n755:        config.uniform_negatives,\n756:        config.inbatch_negatives,\n816:    negatives: torch.Tensor,\n818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823:        len(positives), negatives.shape[1], -1\n825:    return positive_vectors, negative_vectors\n845:def corrected_negative_loss(\n851:    total = config.uniform_negatives + config.inbatch_negatives\n853:    if config.uniform_negatives:\n854:        result += config.uniform_negatives * raw[\n855:            :, : config.uniform_negatives\n857:    if config.inbatch_negatives:\n858:        selected = slice(config.uniform_negatives, total)\n860:        result += config.inbatch_negatives * (\n869:    validation_counts: np.ndarray,\n873:    training_region_ends = total_targets - validation_counts\n878:        or np.any(validation_counts < 0)\n880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n882:        window_starts + training_counts + validation_counts, total_targets\n890:    validation_fraction: float,\n892:    max_validation_targets_per_user: int,\n896:    validation = np.floor(totals * validation_fraction).astype(np.int64)\n897:    validation[(totals >= 2) & (validation == 0)] = 1\n898:    if max_validation_targets_per_user:\n899:        validation = np.minimum(validation, max_validation_targets_per_user)\n901:    history_training = totals - validation\n905:    window_starts = retained_target_window_starts(totals, validation, training)\n909:        validation_counts=validation,\n918:    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n981:                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n991:                first_validation_target = last_training_target + 1\n993:                    int(prepared.sequence_timestamps[first_validation_target])\n994:                    if first_validation_target < end\n1119:    negatives: torch.Tensor,\n1120:    negative_weights: torch.Tensor,\n1134:        \"negative_sum\": 0.0,\n1162:        positive_vectors, negative_vectors = candidate_vectors(\n1165:            negatives[begin:end],\n1168:        negative_logits = torch.einsum(\n1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n1172:        negative_loss = corrected_negative_loss(\n1173:            negative_logits, negative_weights[begin:end], config\n1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1232:    total_negative = 0.0\n1293:            negatives, negative_weights = draw_training_negatives(\n1314:                negatives,\n1315:                negative_weights,\n1331:            total_negative += statistics[\"negative_sum\"]\n1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355:        / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1371:    validation_counts: np.ndarray,\n1378:    totals = training_counts + validation_counts\n1379:    users_with_validation = np.flatnonzero(validation_counts > 0)\n1380:    ordered = users_with_validation[\n1381:        np.argsort(totals[users_with_validation], kind=\"stable\")\n1449:            negatives, negative_weights = draw_training_negatives(\n1469:                negatives,\n1470:                negative_weights,\n1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482:        \"validation_targets\": float(total_targets),\n1483:        \"validation_target_weight_sum\": total_weight,\n1484:        \"validation_seconds\": time.monotonic() - started,\n1521:def train_model(\n1532:        config.validation_fraction,\n1534:        config.max_validation_targets_per_user,\n1537:    validation_counts = partition.validation_counts\n1540:        f\"validation={int(validation_counts.sum()):,} \"\n1567:        if config.negative_filter_scope == \"full-training-history\"\n1601:            \"negative_filter_scope\", \"last-100\"\n1619:            stored_config.get(\"validation_fraction\", 0.10),\n1621:            stored_config.get(\"max_validation_targets_per_user\", 0),\n1624:            config.validation_fraction,\n1626:            config.max_validation_targets_per_user,\n1629:            stored_filter_scope != config.negative_filter_scope\n1657:            prior.pop(\"validation_loss\", None)\n1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1664:                \"recomputing the resume checkpoint's validation BCE\"\n1666:        if \"validation_loss\" not in prior:\n1668:                evaluate_validation_bce(\n1675:                    validation_counts,\n1682:        best_loss = float(prior[\"validation_loss\"])\n1686:            f\"resumed validation-BCE training: epoch={start_epoch} \"\n1705:        validation = evaluate_validation_bce(\n1712:            validation_counts,\n1717:        diagnostics.update(validation)\n1731:            diagnostics[\"validation_loss\"]\n1735:            best_loss = diagnostics[\"validation_loss\"]\n1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1758:            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1760:            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1775:        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1861:def rank_candidates(\n1867:    ranking_batch_size: int,\n1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n1896:                (uid, int(prepared.item_ids[column]), rank)\n1897:                for rank, column in enumerate(columns, start=1)\n1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1923:    parser = argparse.ArgumentParser(description=__doc__)\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    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",
    "stderr": "",
    "aggregated_output": "7:16 held-out validation targets; passing zero for either cap restores the\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n20:import argparse\n56:    uniform_negatives: int = 24\n57:    inbatch_negatives: int = 8\n61:    negative_filter_scope: str = \"full-training-history\"\n67:    validation_fraction: float = 0.10\n69:    max_validation_targets_per_user: int = 16\n132:    validation_counts: np.ndarray\n137:        return int(self.training_counts.sum() + self.validation_counts.sum())\n152:    return base.TowerConfig(\n165:        uniform_negatives=config.uniform_negatives,\n166:        inbatch_negatives=config.inbatch_negatives,\n346:        \"popularity_rank\"\n354:    if stage == \"validation\":\n357:        candidate_name = \"validation_eligible_candidates.npz\"\n358:        state_name = \"validation_preference_state.parquet\"\n477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n518:        \"validation_eligible_candidates.npz\"\n519:        if stage == \"validation\"\n603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610:    negatives: np.ndarray,\n616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617:    byte_columns = negatives >> 3\n618:    bit_columns = negatives & 7\n621:    invalid |= negatives == targets[:, None]\n622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623:    for column in range(1, negatives.shape[1]):\n625:            negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647:        (batch_size, config.uniform_negatives),\n653:    if config.inbatch_negatives:\n656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672:        if corrected_count < config.inbatch_negatives:\n675:                (batch_size, config.inbatch_negatives - corrected_count),\n681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682:    correction = np.ones_like(negatives, dtype=np.float32)\n685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688:        invalid = invalid_full_history_negatives(\n689:            negatives,\n697:                torch.from_numpy(negatives).to(device),\n706:        negatives[invalid] = replacements\n709:    invalid = invalid_full_history_negatives(\n710:        negatives,\n717:        raise RuntimeError(\"full-history negative resampling failed\")\n719:        torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736:    if config.negative_filter_scope == \"full-training-history\":\n738:            raise RuntimeError(\"full-history negative filter was not built\")\n739:        return draw_full_history_filtered_negatives(\n750:    return base.draw_filtered_mixed_negatives(\n755:        config.uniform_negatives,\n756:        config.inbatch_negatives,\n816:    negatives: torch.Tensor,\n818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823:        len(positives), negatives.shape[1], -1\n825:    return positive_vectors, negative_vectors\n845:def corrected_negative_loss(\n851:    total = config.uniform_negatives + config.inbatch_negatives\n853:    if config.uniform_negatives:\n854:        result += config.uniform_negatives * raw[\n855:            :, : config.uniform_negatives\n857:    if config.inbatch_negatives:\n858:        selected = slice(config.uniform_negatives, total)\n860:        result += config.inbatch_negatives * (\n869:    validation_counts: np.ndarray,\n873:    training_region_ends = total_targets - validation_counts\n878:        or np.any(validation_counts < 0)\n880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n882:        window_starts + training_counts + validation_counts, total_targets\n890:    validation_fraction: float,\n892:    max_validation_targets_per_user: int,\n896:    validation = np.floor(totals * validation_fraction).astype(np.int64)\n897:    validation[(totals >= 2) & (validation == 0)] = 1\n898:    if max_validation_targets_per_user:\n899:        validation = np.minimum(validation, max_validation_targets_per_user)\n901:    history_training = totals - validation\n905:    window_starts = retained_target_window_starts(totals, validation, training)\n909:        validation_counts=validation,\n918:    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n981:                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n991:                first_validation_target = last_training_target + 1\n993:                    int(prepared.sequence_timestamps[first_validation_target])\n994:                    if first_validation_target < end\n1119:    negatives: torch.Tensor,\n1120:    negative_weights: torch.Tensor,\n1134:        \"negative_sum\": 0.0,\n1162:        positive_vectors, negative_vectors = candidate_vectors(\n1165:            negatives[begin:end],\n1168:        negative_logits = torch.einsum(\n1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n1172:        negative_loss = corrected_negative_loss(\n1173:            negative_logits, negative_weights[begin:end], config\n1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1232:    total_negative = 0.0\n1293:            negatives, negative_weights = draw_training_negatives(\n1314:                negatives,\n1315:                negative_weights,\n1331:            total_negative += statistics[\"negative_sum\"]\n1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355:        / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1371:    validation_counts: np.ndarray,\n1378:    totals = training_counts + validation_counts\n1379:    users_with_validation = np.flatnonzero(validation_counts > 0)\n1380:    ordered = users_with_validation[\n1381:        np.argsort(totals[users_with_validation], kind=\"stable\")\n1449:            negatives, negative_weights = draw_training_negatives(\n1469:                negatives,\n1470:                negative_weights,\n1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482:        \"validation_targets\": float(total_targets),\n1483:        \"validation_target_weight_sum\": total_weight,\n1484:        \"validation_seconds\": time.monotonic() - started,\n1521:def train_model(\n1532:        config.validation_fraction,\n1534:        config.max_validation_targets_per_user,\n1537:    validation_counts = partition.validation_counts\n1540:        f\"validation={int(validation_counts.sum()):,} \"\n1567:        if config.negative_filter_scope == \"full-training-history\"\n1601:            \"negative_filter_scope\", \"last-100\"\n1619:            stored_config.get(\"validation_fraction\", 0.10),\n1621:            stored_config.get(\"max_validation_targets_per_user\", 0),\n1624:            config.validation_fraction,\n1626:            config.max_validation_targets_per_user,\n1629:            stored_filter_scope != config.negative_filter_scope\n1657:            prior.pop(\"validation_loss\", None)\n1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1664:                \"recomputing the resume checkpoint's validation BCE\"\n1666:        if \"validation_loss\" not in prior:\n1668:                evaluate_validation_bce(\n1675:                    validation_counts,\n1682:        best_loss = float(prior[\"validation_loss\"])\n1686:            f\"resumed validation-BCE training: epoch={start_epoch} \"\n1705:        validation = evaluate_validation_bce(\n1712:            validation_counts,\n1717:        diagnostics.update(validation)\n1731:            diagnostics[\"validation_loss\"]\n1735:            best_loss = diagnostics[\"validation_loss\"]\n1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1758:            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1760:            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1775:        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1861:def rank_candidates(\n1867:    ranking_batch_size: int,\n1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n1896:                (uid, int(prepared.item_ids[column]), rank)\n1897:                for rank, column in enumerate(columns, start=1)\n1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1923:    parser = argparse.ArgumentParser(description=__doc__)\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5010
    },
    "formatted_output": "Warning: truncated output (original token count: 13171)\nTotal output lines: 1346\n\n7:16 held-out validation targets; passing zero for either cap restores the\n14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n20:import argparse\n56:    uniform_negatives: int = 24\n57:    inbatch_negatives: int = 8\n61:    negative_filter_scope: str = \"full-training-history\"\n67:    validation_fraction: float = 0.10\n69:    max_validation_targets_per_user: int = 16\n132:    validation_counts: np.ndarray\n137:        return int(self.training_counts.sum() + self.validation_counts.sum())\n152:    return base.TowerConfig(\n165:        uniform_negatives=config.uniform_negatives,\n166:        inbatch_negatives=config.inbatch_negatives,\n346:        \"popularity_rank\"\n354:    if stage == \"validation\":\n357:        candidate_name = \"validation_eligible_candidates.npz\"\n358:        state_name = \"validation_preference_state.parquet\"\n477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n518:        \"validation_eligible_candidates.npz\"\n519:        if stage == \"validation\"\n603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610:    negatives: np.ndarray,\n616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617:    byte_columns = negatives >> 3\n618:    bit_columns = negatives & 7\n621:    invalid |= negatives == targets[:, None]\n622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623:    for column in range(1, negatives.shape[1]):\n625:            negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647:        (batch_size, config.uniform_negatives),\n653:    if config.inbatch_negatives:\n656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672:        if corrected_count < config.inbatch_negatives:\n675:                (batch_size, config.inbatch_negatives - corrected_count),\n681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682:    correction = np.ones_like(negatives, dtype=np.float32)\n685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688:        invalid = invalid_full_history_negatives(\n689:            negatives,\n697:                torch.from_numpy(negatives).to(device),\n706:        negatives[invalid] = replacements\n709:    invalid = invalid_full_history_negatives(\n710:        negatives,\n717:        raise RuntimeError(\"full-history negative resampling failed\")\n719:        torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736:    if config.negative_filter_scope == \"full-training-history\":\n738:            raise RuntimeError(\"full-history negative filter was not built\")\n739:        return draw_full_history_filtered_negatives(\n750:    return base.draw_filtered_mixed_negatives(\n755:        config.uniform_negatives,\n756:        config.inbatch_negatives,\n816:    negatives: torch.Tensor,\n818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823:        len(positives), negatives.shape[1], -1\n825:    return positive_vectors, negative_vectors\n845:def corrected_negative_loss(\n851:    total = config.uniform_negatives + config.inbatch_negatives\n853:    if config.uniform_negatives:\n854:        result += config.uniform_negatives * raw[\n855:            :, : config.uniform_negatives\n857:    if config.inbatch_negatives:\n858:        selected = slice(config.uniform_negatives, total)\n860:        result += config.inbatch_negatives * (\n869:    validation_counts: np.ndarray,\n873:    training_region_ends = total_targets - validation_counts\n878:        or np.any(validation_counts < 0)\n880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n882:        window_starts + training_counts + validation_counts, total_targets\n890:    validation_fraction: float,\n892:    max_validation_targets_per_user: int,\n896:    validation = np.floor(totals * validation_fraction).astype(np.int64)\n897:    validation[(totals >= 2) & (validation == 0)] = 1\n898:    if max_validation_targets_per_user:\n899:        validation = np.minimum(validation, max_validation_targets_per_user)\n901:    history_training = totals - validation\n905:    window_starts = retained_target_window_starts(totals, validation, training)\n909:        validation_counts=validation,\n918:    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n981:                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n991:                first_validation_target = last_training_target + 1\n993:                    int(prepared.sequence_timestamps[first_validation_target])\n994:                    if first_validation_target < end\n1119:    negatives: torch.Tensor,\n1120:    negative_weights: torch.Tensor,\n1134:        \"negative_sum\": 0.0,\n1162:        positive_vectors, negative_vectors = candidate_vectors(\n1165:            negatives[begin:end],\n1168:        negative_logits = torch.einsum(\n1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n1172:        negative_loss = corrected_negative_loss(\n1173:            negative_logits, negative_weights[begin:end], config\n1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1232:    total_negative = 0.0\n1293:            negatives, negative_weights = draw_training_negatives(\n1314:                negatives,\n1315:                negative_weights,\n1331:            total_negative += statistics[\"negative_sum\"]\n1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355:        / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1371:    validation_counts: np.ndarray,\n1378:    totals = training_counts + validation_counts\n1379:    users_with_validation = np.flatnonzero(validation_counts > 0)\n1380:    ordered = users_with_validation[\n1381:        np.argsort(totals[users_with_validation], kind=\"stable\")\n1449:            negatives, negative_weights = draw_training_negatives(\n1469:                negatives,\n1470:                negative_weights,\n1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482:        \"validation_targets\": float(total_targets),\n1483:        \"validation_target_weight_sum\": total_weight,\n1484:        \"validation_seconds\": time.monotonic() - started,\n1521:def train_model(\n1532:        config.validation_fraction,\n1534:        config.max_validation_targets_per_user,\n1537:    validation_counts = partition.validation_counts\n1540:        f\"validation={int(validation_counts.sum()):,} \"\n1567:        if config.negative_filter_scope == \"full-training-history\"\n1601:            \"negative_filter_scope\", \"last-100\"\n1619:            stored_config.get(\"validation_fraction\", 0.10),\n1621:            stored_config.get(\"max_validation_targets_per_user\", 0),\n1624:            config.validation_fraction,\n1626:            config.max_validation_targets_per_user,\n1629:            stored_filter_scope != config.negative_filter_scope\n1657:            prior.pop(\"validation_loss\", None)\n1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1664:                \"recomputing the resume checkpoint's validation BCE\"\n1666:        if \"validation_loss\" not in prior:\n1668:                evaluate_validation_bce(\n1675:                    validation_counts,\n1682:        best_loss = float(prior[\"validation_loss\"])\n1686:            f\"resumed validation-BCE training: epoch={start_epoch} \"\n1705:        validation = evaluate_validation_bce(\n1712:            validation_counts,\n1717:        diagnostics.update(validation)\n1731:            diagnostics[\"validation_loss\"]\n1735:            best_loss = diagnostics[\"validation_loss\"]\n1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1758:            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1760:            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1775:        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1861:def rank_candidates(\n1867:    ranking_batch_size: int,\n1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n1896:                (uid, int(prepared.item_ids[column]), rank)\n1897:                for rank, column in enumerate(columns, start=1)\n1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1923:    parser = argparse.ArgumentParser(description=__doc__)\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n            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        …3171 tokens truncated…tamp + 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    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    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"
  }
}
Harness context token usage record
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09

exec command

+00:10
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 

const r = await tools.exec_command({"cmd":"sed -n '630,880p' /task/current_model/train_current_model.py; sed -n '1520,1810p' /task/current_model/train_current_model.py; sed -n '1810,2100p' /task/current_model/train_current_model.py; sed -n '1,220p' /task/evaluate_public.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":40000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

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

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


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


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


def pool_explicit_states(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    state_ids: np.ndarray,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    liked_items = torch.from_numpy(
        prepared.liked_states[unique_states].astype(np.int64)
    ).to(device)
    disliked_items = torch.from_numpy(
        prepared.disliked_states[unique_states].astype(np.int64)
    ).to(device)
    liked_vectors = model._mean_context_channel(liked_items)
    disliked_vectors = model._mean_context_channel(disliked_items)
    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
    counts = prepared.state_counts[state_ids]
    return (
        liked_vectors[inverse_rows],
        disliked_vectors[inverse_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")

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,
    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),
                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,
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

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

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

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

Usage::

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

import json
import sys
from pathlib import Path

import pandas as pd

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


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


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

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

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

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

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


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

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

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

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

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


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

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

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


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

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


if __name__ == "__main__":
    main()
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-58696de0-86c2-4a90-a951-3a1fc818917e",
    "process_id": "7712",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '630,880p' /task/current_model/train_current_model.py; sed -n '1520,1810p' /task/current_model/train_current_model.py; sed -n '1810,2100p' /task/current_model/train_current_model.py; sed -n '1,220p' /task/evaluate_public.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '630,880p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1520,1810p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1810,2100p' /task/current_model/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/task/current_model/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,220p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "def 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    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\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\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                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#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "stderr": "",
    "aggregated_output": "def 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    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\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\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                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#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6640
    },
    "formatted_output": "def 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    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\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\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                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#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n"
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10

exec command

+00:15
Thinking unavailable No text recorded

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

Model text

The baseline’s main mismatch is now clear: it optimizes pointwise BCE against mostly uniform catalog negatives. Uniform negatives are overwhelmingly easy, while the actual top-10 competition is among plausible, often popular items. Its eight in-batch negatives help, but they are drawn from a small local target batch and then propensity-downweighted; there is no model-mined hard-negative stage. I’m timing and scoring the exact baseline first, with cached preparation and checkpoints so later experiments can reuse the costly data pass.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"mkdir -p /app/solution/checkpoints_baseline && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_baseline --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 0.2 seconds
Output:

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      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution/checkpoints_baseline && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_baseline --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution/checkpoints_baseline && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_baseline --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "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",
    "stderr": "",
    "aggregated_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",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 40280323
    },
    "formatted_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"
  }
}
11

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.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"which python; python -V; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20; env | sort | rg 'PYTHON|VIRTUAL|CONDA'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
Python 3.12.13
total 16
drwxr-xr-x 1 agent agent 4096 Sep 25 01:13 .
drwxr-xr-x 1 root  root  4096 Sep 25 01:17 ..
drwxr-xr-x 1 agent agent 4096 Sep 25 01:18 solution
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-efafb5df-e7a3-4791-b232-991c8f9747c2",
    "process_id": "49312",
    "command": [
      "/bin/sh",
      "-lc",
      "which python; python -V; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20; env | sort | rg 'PYTHON|VIRTUAL|CONDA'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "which python; python -V; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20; env | sort | rg 'PYTHON|VIRTUAL|CONDA'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:13 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 01:17 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:18 solution\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:13 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 01:17 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:18 solution\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3080
    },
    "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:13 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 01:17 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 01:18 solution\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n"
  }
}
Harness context token usage record
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}
12

exec command

+00:25
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 

const r = await tools.exec_command({"cmd":"find /opt /usr -type f -path '*/bin/python*' 2>/dev/null | head -40; ls -la /task","workdir":"/app","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 0.3 seconds
Output:

/usr/local/bin/python3.12
/usr/local/bin/python3.12-config
/usr/bin/python3.11
total 40
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 .
drwxr-xr-x 1 root root 4096 Sep 25 01:17 ..
-r--r--r-- 1 root root    0 Sep 25 01:11 .keep
dr-xr-xr-x 1 root root 4096 Sep 25 01:11 current_model
dr-xr-xr-x 1 root root 4096 Sep 25 01:12 data
-r--r--r-- 1 root root 5774 Sep 25 01:11 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 25 01:11 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 25 01:11 requirements.txt
Harness context token usage record
{
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  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cbddc9e487d2a80136d93285d004",
  "usage": {
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    "reasoning_output_tokens": 12,
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  },
  "turn_token_usage": {
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 980,
    "reasoning_output_tokens": 124,
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-cf644797-0d26-47a4-bbf6-bba176c73044",
    "process_id": "96513",
    "command": [
      "/bin/sh",
      "-lc",
      "find /opt /usr -type f -path '*/bin/python*' 2>/dev/null | head -40; ls -la /task"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "find /opt /usr -type f -path '*/bin/python*' 2>/dev/null | head -40; ls -la /task"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 25 01:13 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:17 ..\n-r--r--r-- 1 root root    0 Sep 25 01:11 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:11 current_model\ndr-xr-xr-x 1 root root 4096 Sep 25 01:12 data\n-r--r--r-- 1 root root 5774 Sep 25 01:11 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:11 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:11 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 25 01:13 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:17 ..\n-r--r--r-- 1 root root    0 Sep 25 01:11 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:11 current_model\ndr-xr-xr-x 1 root root 4096 Sep 25 01:12 data\n-r--r--r-- 1 root root 5774 Sep 25 01:11 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:11 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:11 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 121673308
    },
    "formatted_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 25 01:13 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:17 ..\n-r--r--r-- 1 root root    0 Sep 25 01:11 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 01:11 current_model\ndr-xr-xr-x 1 root root 4096 Sep 25 01:12 data\n-r--r--r-- 1 root root 5774 Sep 25 01:11 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 01:11 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 01:11 requirements.txt\n"
  }
}
13

exec command

+00:28
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 

const r = await tools.exec_command({"cmd":"/usr/bin/python3.11 - <<'PY'\nimport numpy,pandas,torch,pyarrow\nprint(numpy.__version__, pandas.__version__, torch.__version__)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'numpy'
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c10d3f97-2346-4762-9ff9-916f6183651e",
    "process_id": "28612",
    "command": [
      "/bin/sh",
      "-lc",
      "/usr/bin/python3.11 - <<'PY'\nimport numpy,pandas,torch,pyarrow\nprint(numpy.__version__, pandas.__version__, torch.__version__)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/usr/bin/python3.11 - <<'PY'\nimport numpy,pandas,torch,pyarrow\nprint(numpy.__version__, pandas.__version__, torch.__version__)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'numpy'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'numpy'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 3510
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'numpy'\n"
  }
}
Harness context token usage record
{
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  }
}
14

exec command

+00:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"find / -type d -name 'site-packages' 2>/dev/null | head -40; find / -type f -name 'torch*.whl' -o -name 'numpy*.whl' 2>/dev/null | head","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.8 seconds
Output:

/usr/local/lib/python3.12/site-packages
/opt/task-venv/lib/python3.12/site-packages
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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  "thread_token_usage": {
    "input_tokens": 255302,
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    "cache_write_input_tokens": 0,
    "output_tokens": 1192,
    "reasoning_output_tokens": 150,
    "total_tokens": 256494
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-03a2d849-0d95-4770-a848-1456c8a0b5f6",
    "process_id": "89118",
    "command": [
      "/bin/sh",
      "-lc",
      "find / -type d -name 'site-packages' 2>/dev/null | head -40; find / -type f -name 'torch*.whl' -o -name 'numpy*.whl' 2>/dev/null | head"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "find / -type d -name 'site-packages' 2>/dev/null | head -40; find / -type f -name 'torch*.whl' -o -name 'numpy*.whl' 2>/dev/null | head"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n",
    "stderr": "",
    "aggregated_output": "/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n",
    "exit_code": 0,
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      "secs": 0,
      "nanos": 631522462
    },
    "formatted_output": "/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n"
  }
}
15

exec command

+00:34
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -la /opt/task-venv /opt/task-venv/bin | head -60; /opt/task-venv/bin/python - <<'PY'\nimport numpy,pandas,torch\nprint('ok',torch.__version__)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 4.2 seconds
Output:

/opt/task-venv:
total 32
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 .
drwxr-xr-x 1 root root 4096 Sep 25 01:13 ..
-r--r--r-- 1 root root    0 Sep 25 01:13 .lock
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 bin
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 etc
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 include
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 lib
lrwxrwxrwx 1 root root    3 Sep 25 01:13 lib64 -> lib
-r--r--r-- 1 root root  171 Sep 25 01:13 pyvenv.cfg
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 share

/opt/task-venv/bin:
total 124
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 .
dr-xr-xr-x 1 root root 4096 Sep 25 01:13 ..
-r--r--r-- 1 root root 9033 Sep 25 01:13 Activate.ps1
-r--r--r-- 1 root root 2158 Sep 25 01:13 activate
-r--r--r-- 1 root root  920 Sep 25 01:13 activate.csh
-r--r--r-- 1 root root 2195 Sep 25 01:13 activate.fish
-r-xr-xr-x 1 root root  302 Sep 25 01:13 chardetect
-r-xr-xr-x 1 root root  304 Sep 25 01:13 dotenv
-r-xr-xr-x 1 root root  308 Sep 25 01:13 f2py
-r-xr-xr-x 1 root root  299 Sep 25 01:13 idna
-r-xr-xr-x 1 root root  297 Sep 25 01:13 isympy
-r-xr-xr-x 1 root root  305 Sep 25 01:13 jsonschema
-r-xr-xr-x 1 root root  311 Sep 25 01:13 jupyter
-r-xr-xr-x 1 root root  311 Sep 25 01:13 jupyter-migrate
-r-xr-xr-x 1 root root  316 Sep 25 01:13 jupyter-troubleshoot
-r-xr-xr-x 1 root root  344 Sep 25 01:13 jupyter-trust
-r-xr-xr-x 1 root root  311 Sep 25 01:13 jupytext
-r-xr-xr-x 1 root root  315 Sep 25 01:13 jupytext-config
-r-xr-xr-x 1 root root  301 Sep 25 01:13 kaggle
-r-xr-xr-x 1 root root  312 Sep 25 01:13 markdown-it
-r-xr-xr-x 1 root root  325 Sep 25 01:13 normalizer
-r-xr-xr-x 1 root root  308 Sep 25 01:13 numpy-config
-r-xr-xr-x 1 root root  313 Sep 25 01:13 pip
-r-xr-xr-x 1 root root  313 Sep 25 01:13 pip3
-r-xr-xr-x 1 root root  313 Sep 25 01:13 pip3.12
lrwxrwxrwx 1 root root   21 Sep 25 01:13 python -> /usr/local/bin/python
lrwxrwxrwx 1 root root    6 Sep 25 01:13 python3 -> python
lrwxrwxrwx 1 root root    6 Sep 25 01:13 python3.12 -> python
-r-xr-xr-x 1 root root  307 Sep 25 01:13 slugify
-r-xr-xr-x 1 root root  321 Sep 25 01:13 torchfrtrace
-r-xr-xr-x 1 root root  312 Sep 25 01:13 torchrun
-r-xr-xr-x 1 root root  299 Sep 25 01:13 tqdm
ok 2.8.0+cpu
Harness context token usage record
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    "cached_input_tokens": 261376,
    "cache_write_input_tokens": 0,
    "output_tokens": 1303,
    "reasoning_output_tokens": 161,
    "total_tokens": 295347
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c85e010f-18b9-4031-9874-bf32c2e89123",
    "process_id": "53311",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -la /opt/task-venv /opt/task-venv/bin | head -60; /opt/task-venv/bin/python - <<'PY'\nimport numpy,pandas,torch\nprint('ok',torch.__version__)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -la /opt/task-venv /opt/task-venv/bin | head -60; /opt/task-venv/bin/python - <<'PY'\nimport numpy,pandas,torch\nprint('ok',torch.__version__)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "ok 2.8.0+cpu\n",
    "stderr": "",
    "aggregated_output": "ok 2.8.0+cpu\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 992883168
    },
    "formatted_output": "ok 2.8.0+cpu\n"
  }
}
16

exec command

+00:42
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_baseline --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"34dd36","wall_time_seconds":1.000543995,"session_id":59767,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cbef63dc87d2bad5c1376ac06c49",
  "usage": {
    "input_tokens": 39891,
    "cached_input_tokens": 38528,
    "cache_write_input_tokens": 0,
    "output_tokens": 116,
    "reasoning_output_tokens": 6,
    "total_tokens": 40007
  },
  "turn_token_usage": {
    "input_tokens": 333935,
    "cached_input_tokens": 299904,
    "cache_write_input_tokens": 0,
    "output_tokens": 1419,
    "reasoning_output_tokens": 167,
    "total_tokens": 335354
  },
  "thread_token_usage": {
    "input_tokens": 333935,
    "cached_input_tokens": 299904,
    "cache_write_input_tokens": 0,
    "output_tokens": 1419,
    "reasoning_output_tokens": 167,
    "total_tokens": 335354
  }
}
17

exec command

+00:46
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 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
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
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cbf3641c87d2aa2f0068e9b7533e",
  "usage": {
    "input_tokens": 40064,
    "cached_input_tokens": 39680,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 9,
    "total_tokens": 40120
  },
  "turn_token_usage": {
    "input_tokens": 373999,
    "cached_input_tokens": 339584,
    "cache_write_input_tokens": 0,
    "output_tokens": 1475,
    "reasoning_output_tokens": 176,
    "total_tokens": 375474
  },
  "thread_token_usage": {
    "input_tokens": 373999,
    "cached_input_tokens": 339584,
    "cache_write_input_tokens": 0,
    "output_tokens": 1475,
    "reasoning_output_tokens": 176,
    "total_tokens": 375474
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.8s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.0s
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
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cc1352fc87d2878715e7d30465aa",
  "usage": {
    "input_tokens": 40283,
    "cached_input_tokens": 39936,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 40328
  },
  "turn_token_usage": {
    "input_tokens": 414282,
    "cached_input_tokens": 379520,
    "cache_write_input_tokens": 0,
    "output_tokens": 1520,
    "reasoning_output_tokens": 176,
    "total_tokens": 415802
  },
  "thread_token_usage": {
    "input_tokens": 414282,
    "cached_input_tokens": 379520,
    "cache_write_input_tokens": 0,
    "output_tokens": 1520,
    "reasoning_output_tokens": 176,
    "total_tokens": 415802
  }
}
18

exec command

+01:49
Thinking unavailable No text recorded

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

Model text

Data preparation retained about 1.05M training targets from 20M chronological positives, so the experiment loop is tractable. I’m also noting a second objective issue: one BCE positive is balanced against the average of 32 negatives, but those negatives are not conditioned on the serving candidate pool or current model score. The planned change will preserve the same towers and dot-product serving path while concentrating training compute on confusable catalog items.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cc32f4a887d28f03305ea6f16b59",
  "usage": {
    "input_tokens": 40623,
    "cached_input_tokens": 40064,
    "cache_write_input_tokens": 0,
    "output_tokens": 149,
    "reasoning_output_tokens": 8,
    "total_tokens": 40772
  },
  "turn_token_usage": {
    "input_tokens": 454905,
    "cached_input_tokens": 419584,
    "cache_write_input_tokens": 0,
    "output_tokens": 1669,
    "reasoning_output_tokens": 184,
    "total_tokens": 456574
  },
  "thread_token_usage": {
    "input_tokens": 454905,
    "cached_input_tokens": 419584,
    "cache_write_input_tokens": 0,
    "output_tokens": 1669,
    "reasoning_output_tokens": 184,
    "total_tokens": 456574
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cc54836087d2921e59942b601789",
  "usage": {
    "input_tokens": 40795,
    "cached_input_tokens": 40448,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 40840
  },
  "turn_token_usage": {
    "input_tokens": 495700,
    "cached_input_tokens": 460032,
    "cache_write_input_tokens": 0,
    "output_tokens": 1714,
    "reasoning_output_tokens": 184,
    "total_tokens": 497414
  },
  "thread_token_usage": {
    "input_tokens": 495700,
    "cached_input_tokens": 460032,
    "cache_write_input_tokens": 0,
    "output_tokens": 1714,
    "reasoning_output_tokens": 184,
    "total_tokens": 497414
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cc744a6c87d292283c829a92ee14",
  "usage": {
    "input_tokens": 40863,
    "cached_input_tokens": 40576,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 40908
  },
  "turn_token_usage": {
    "input_tokens": 536563,
    "cached_input_tokens": 500608,
    "cache_write_input_tokens": 0,
    "output_tokens": 1759,
    "reasoning_output_tokens": 184,
    "total_tokens": 538322
  },
  "thread_token_usage": {
    "input_tokens": 536563,
    "cached_input_tokens": 500608,
    "cache_write_input_tokens": 0,
    "output_tokens": 1759,
    "reasoning_output_tokens": 184,
    "total_tokens": 538322
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cc9415d487d28b779d04f09e6f19",
  "usage": {
    "input_tokens": 40931,
    "cached_input_tokens": 40704,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 40976
  },
  "turn_token_usage": {
    "input_tokens": 577494,
    "cached_input_tokens": 541312,
    "cache_write_input_tokens": 0,
    "output_tokens": 1804,
    "reasoning_output_tokens": 184,
    "total_tokens": 579298
  },
  "thread_token_usage": {
    "input_tokens": 577494,
    "cached_input_tokens": 541312,
    "cache_write_input_tokens": 0,
    "output_tokens": 1804,
    "reasoning_output_tokens": 184,
    "total_tokens": 579298
  }
}
19

exec command

+04:03
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 

const r = await tools.exec_command({"cmd":"ps -eo pid,etime,pcpu,pmem,rss,args | rg 'train_current|PID'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/bin/sh: 1: ps: not found
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ccb7070087d284191918aa96741a",
  "usage": {
    "input_tokens": 40999,
    "cached_input_tokens": 40704,
    "cache_write_input_tokens": 0,
    "output_tokens": 108,
    "reasoning_output_tokens": 38,
    "total_tokens": 41107
  },
  "turn_token_usage": {
    "input_tokens": 618493,
    "cached_input_tokens": 582016,
    "cache_write_input_tokens": 0,
    "output_tokens": 1912,
    "reasoning_output_tokens": 222,
    "total_tokens": 620405
  },
  "thread_token_usage": {
    "input_tokens": 618493,
    "cached_input_tokens": 582016,
    "cache_write_input_tokens": 0,
    "output_tokens": 1912,
    "reasoning_output_tokens": 222,
    "total_tokens": 620405
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-97202cc7-c84a-4739-86c5-da09bce7629e",
    "process_id": "83540",
    "command": [
      "/bin/sh",
      "-lc",
      "ps -eo pid,etime,pcpu,pmem,rss,args | rg 'train_current|PID'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -eo pid,etime,pcpu,pmem,rss,args | rg 'train_current|PID'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/bin/sh: 1: ps: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 3110
    },
    "formatted_output": "/bin/sh: 1: ps: not found\n"
  }
}
20

exec command

+04:05
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ccba85dc87d2a1532f314402cc9f",
  "usage": {
    "input_tokens": 41141,
    "cached_input_tokens": 40832,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 41194
  },
  "turn_token_usage": {
    "input_tokens": 659634,
    "cached_input_tokens": 622848,
    "cache_write_input_tokens": 0,
    "output_tokens": 1965,
    "reasoning_output_tokens": 228,
    "total_tokens": 661599
  },
  "thread_token_usage": {
    "input_tokens": 659634,
    "cached_input_tokens": 622848,
    "cache_write_input_tokens": 0,
    "output_tokens": 1965,
    "reasoning_output_tokens": 228,
    "total_tokens": 661599
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ccda7f0c87d284d79c93efc917bc",
  "usage": {
    "input_tokens": 41217,
    "cached_input_tokens": 40960,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 41262
  },
  "turn_token_usage": {
    "input_tokens": 700851,
    "cached_input_tokens": 663808,
    "cache_write_input_tokens": 0,
    "output_tokens": 2010,
    "reasoning_output_tokens": 228,
    "total_tokens": 702861
  },
  "thread_token_usage": {
    "input_tokens": 700851,
    "cached_input_tokens": 663808,
    "cache_write_input_tokens": 0,
    "output_tokens": 2010,
    "reasoning_output_tokens": 228,
    "total_tokens": 702861
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=210.2s
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ccfab59487d2b1dde418ffe0dec2",
  "usage": {
    "input_tokens": 41285,
    "cached_input_tokens": 41088,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 41330
  },
  "turn_token_usage": {
    "input_tokens": 742136,
    "cached_input_tokens": 704896,
    "cache_write_input_tokens": 0,
    "output_tokens": 2055,
    "reasoning_output_tokens": 228,
    "total_tokens": 744191
  },
  "thread_token_usage": {
    "input_tokens": 742136,
    "cached_input_tokens": 704896,
    "cache_write_input_tokens": 0,
    "output_tokens": 2055,
    "reasoning_output_tokens": 228,
    "total_tokens": 744191
  }
}
21

exec command

+05:41
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 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/checkpoints_baseline/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=225.0 validation_seconds=15.6
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":59767,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 20.6 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
{
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  "stage": "validation",
  "output": "/app/solution/production_validation.parquet",
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  "users": 8456,
  "runtime_seconds": 378.746,
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    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
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    "inbatch_negatives": 8,
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    "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
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  "all_next_item_targets": 20047194,
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  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
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      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 15.611329638999905
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    "item_norm_std": 0.8279485702514648
  },
  "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-713270b5-40a2-4f29-94d5-3496382f0d08",
    "process_id": "59767",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_baseline --threads 8"
    ],
    "cwd": "file:///app",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.8s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.0s\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=210.2s\nsaved epoch checkpoint: /app/solution/checkpoints_baseline/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=225.0 validation_seconds=15.6\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\": 378.746,\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\": 0.5398380454408168,\n      \"positive_logit\": 0.7427799600511094,\n      \"negative_logit\": -0.6222247082248333,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 224.980232504,\n      \"validation_loss\": 0.4966362168774782,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.611329638999905\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    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    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.8s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.0s\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=210.2s\nsaved epoch checkpoint: /app/solution/checkpoints_baseline/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=225.0 validation_seconds=15.6\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\": 378.746,\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\": 0.5398380454408168,\n      \"positive_logit\": 0.7427799600511094,\n      \"negative_logit\": -0.6222247082248333,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 224.980232504,\n      \"validation_loss\": 0.4966362168774782,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.611329638999905\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570279836654663,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 383,
      "nanos": 102178869
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.8s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.0s\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=210.2s\nsaved epoch checkpoint: /app/solution/checkpoints_baseline/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=225.0 validation_seconds=15.6\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\": 378.746,\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\": 0.5398380454408168,\n      \"positive_logit\": 0.7427799600511094,\n      \"negative_logit\": -0.6222247082248333,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 224.980232504,\n      \"validation_loss\": 0.4966362168774782,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.611329638999905\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570279836654663,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

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":30000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}
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    "stdout": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n",
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  }
}
22

exec command

+07:21
Thinking unavailable No text recorded

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

Model text

Baseline public NDCG@10 is 0.021280. Before spending another full training pass, I’m running two cheap controls on the same eligibility boundary: pure training popularity and simple rerank blends. This will quantify whether the baseline is failing because its negatives underrepresent the head of the catalog, and it gives a concrete target for hard-negative mining.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd\nfor f in ['candidate_catalog.parquet','target_users.parquet']:\n d=pd.read_parquet('/task/data/'+f); print(f,d.columns.tolist(),d.shape); print(d.head())\na=np.load('/task/data/validation_eligible_candidates.npz'); print({k:a[k].shape for k in a.files}); print(a['item_ids'][:10])\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 1.7 seconds
Output:

candidate_catalog.parquet ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] (30000, 4)
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
3  3542184             24075                     17699                4
4  9378983             24036                     18195                5
target_users.parquet ['uid', 'recent_positive_listens', 'history_positive_listens'] (8456, 3)
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359
3  600                   1985.0                      2423
4  700                   1776.0                      6114
{'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}
[5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749]
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Harness context codex item completed
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    "stdout": "candidate_catalog.parquet ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] (30000, 4)\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\ntarget_users.parquet ['uid', 'recent_positive_listens', 'history_positive_listens'] (8456, 3)\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\n{'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}\n[5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\n",
    "stderr": "",
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  }
}
23

exec command

+07:27
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)\nmetrics={\n'pop':cat.positive_listens.to_numpy(),\n'organic':cat.organic_positive_listens.to_numpy(),\n'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),\n'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),\n}\nfor name,s in metrics.items():\n rows=[]\n for r,uid in enumerate(a['uids']):\n  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]\n  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.2 seconds
Output:
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24

exec command

+08:07
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 

const r = await tools.exec_command({"cmd":"ls -lh /app/solution/controls; for f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 2.1 seconds
Output:

total 0
Traceback (most recent call last):
  File "/task/evaluate_public.py", line 157, in <module>
    main()
  File "/task/evaluate_public.py", line 152, in main
    ranked = load_submission(path, eligibility, targets)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/task/evaluate_public.py", line 113, in load_submission
    frame = validate_ranking(read_ranking(path), targets)
                             ^^^^^^^^^^^^^^^^^^
  File "/task/ranking_evaluation.py", line 98, in read_ranking
    return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet
    return impl.read(
           ^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read
    path_or_handle, handles, filesystem = _get_path_or_handle(
                                          ^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle
    handles = get_handle(
              ^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle
    handle = open(handle, ioargs.mode)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/app/solution/controls/*.parquet'
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    "stdout": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/controls/*.parquet'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/controls/*.parquet'\n",
    "exit_code": 1,
    "duration": {
      "secs": 1,
      "nanos": 959452605
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/controls/*.parquet'\n"
  }
}
25

exec command

+08:12
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmetrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}\norders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)\n for name,order in orders.items():\n  ix=order[mask[order]][:100]\n  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\nfor name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.2 seconds
Output:
Harness context token usage record
{
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  }
}
26

exec command

+08:52
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -lh /app/solution/controls; pgrep -af python || true","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

total 0
/bin/sh: 1: pgrep: not found
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f95a34f9-8724-4fb7-922d-e12db382d840",
    "process_id": "42291",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /app/solution/controls; pgrep -af python || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -lh /app/solution/controls; pgrep -af python || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 0\n/bin/sh: 1: pgrep: not found\n",
    "stderr": "",
    "aggregated_output": "total 0\n/bin/sh: 1: pgrep: not found\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4010
    },
    "formatted_output": "total 0\n/bin/sh: 1: pgrep: not found\n"
  }
}
Harness context token usage record
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  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
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    "cache_write_input_tokens": 0,
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  "turn_token_usage": {
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  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 3870,
    "reasoning_output_tokens": 667,
    "total_tokens": 1133862
  }
}
27

exec command

+08:55
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); n=len(a['item_ids']); m=len(a['uids']); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=n,bitorder='little'); ix=np.flatnonzero(mask)[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=a['item_ids'][ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":1000});
text(r.output);

Tool response 

Script completed
Wall time 30.2 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cddb9da887d2ac7445f2a9cc8f08",
  "usage": {
    "input_tokens": 45299,
    "cached_input_tokens": 44928,
    "cache_write_input_tokens": 0,
    "output_tokens": 275,
    "reasoning_output_tokens": 39,
    "total_tokens": 45574
  },
  "turn_token_usage": {
    "input_tokens": 1175291,
    "cached_input_tokens": 1132416,
    "cache_write_input_tokens": 0,
    "output_tokens": 4145,
    "reasoning_output_tokens": 706,
    "total_tokens": 1179436
  },
  "thread_token_usage": {
    "input_tokens": 1175291,
    "cached_input_tokens": 1132416,
    "cache_write_input_tokens": 0,
    "output_tokens": 4145,
    "reasoning_output_tokens": 706,
    "total_tokens": 1179436
  }
}
28

exec command

+09:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -lh /app/solution/controls /app/solution | head","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/app/solution:
total 512M
drwxr-xr-x 2 agent agent 4.0K Sep 25 01:23 checkpoints_baseline
drwxr-xr-x 2 agent agent 4.0K Sep 25 01:25 controls
-rw-r--r-- 1 agent agent 1.2M Sep 25 01:25 production_validation.parquet
-r--r--r-- 1 agent agent  79K Sep 25 01:18 train_current_model.py
-r--r--r-- 1 agent agent  16K Sep 25 01:18 two_tower_model.py
-rw-r--r-- 1 agent agent 511M Sep 25 01:19 validation_cache.npz

/app/solution/controls:
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-648a9aff-b324-4b82-9ab4-f439e617953a",
    "process_id": "56605",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /app/solution/controls /app/solution | head"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /app/solution/controls /app/solution",
        "path": "controls"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app/solution:\ntotal 512M\ndrwxr-xr-x 2 agent agent 4.0K Sep 25 01:23 checkpoints_baseline\ndrwxr-xr-x 2 agent agent 4.0K Sep 25 01:25 controls\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:25 production_validation.parquet\n-r--r--r-- 1 agent agent  79K Sep 25 01:18 train_current_model.py\n-r--r--r-- 1 agent agent  16K Sep 25 01:18 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 01:19 validation_cache.npz\n\n/app/solution/controls:\n",
    "stderr": "",
    "aggregated_output": "/app/solution:\ntotal 512M\ndrwxr-xr-x 2 agent agent 4.0K Sep 25 01:23 checkpoints_baseline\ndrwxr-xr-x 2 agent agent 4.0K Sep 25 01:25 controls\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:25 production_validation.parquet\n-r--r--r-- 1 agent agent  79K Sep 25 01:18 train_current_model.py\n-r--r--r-- 1 agent agent  16K Sep 25 01:18 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 01:19 validation_cache.npz\n\n/app/solution/controls:\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3980
    },
    "formatted_output": "/app/solution:\ntotal 512M\ndrwxr-xr-x 2 agent agent 4.0K Sep 25 01:23 checkpoints_baseline\ndrwxr-xr-x 2 agent agent 4.0K Sep 25 01:25 controls\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:25 production_validation.parquet\n-r--r--r-- 1 agent agent  79K Sep 25 01:18 train_current_model.py\n-r--r--r-- 1 agent agent  16K Sep 25 01:18 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 01:19 validation_cache.npz\n\n/app/solution/controls:\n"
  }
}
Harness context token usage record
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  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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  "turn_token_usage": {
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  }
}
29

exec command

+09:34
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 

const r = await tools.exec_command({"cmd":"for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'python' && { basename \"$p\"; tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null; echo; }; done","workdir":"/app","yield_time_ms":10000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 7463)
Total output lines: 585

8778
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=medium -- '# 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/…3463 tokens truncated…nic` |

`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.
 
9631
/bin/sh -c mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'
import numpy as np,pandas as pd
cat=pd.read_parquet('/task/data/candidate_catalog.parquet')
a=np.load('/task/data/validation_eligible_candidates.npz')
mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)
metrics={
'pop':cat.positive_listens.to_numpy(),
'organic':cat.organic_positive_listens.to_numpy(),
'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),
'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),
}
for name,s in metrics.items():
 rows=[]
 for r,uid in enumerate(a['uids']):
  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]
  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))
 pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)
PY
for f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py "$f"; done 
9633
/opt/task-venv/bin/python - 
9676
/bin/sh -c /opt/task-venv/bin/python - <<'PY'
import numpy as np,pandas as pd
cat=pd.read_parquet('/task/data/candidate_catalog.parquet')
a=np.load('/task/data/validation_eligible_candidates.npz')
metrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}
orders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}
for r,uid in enumerate(a['uids']):
 mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)
 for name,order in orders.items():
  ix=order[mask[order]][:100]
  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))
for name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)
PY
for f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py "$f"; done 
9677
/opt/task-venv/bin/python - 
9702
/bin/sh -c /opt/task-venv/bin/python - <<'PY'
import numpy as np,pandas as pd
a=np.load('/task/data/validation_eligible_candidates.npz'); n=len(a['item_ids']); m=len(a['uids']); out=np.empty((m*100,3),dtype=np.int64)
for r,uid in enumerate(a['uids']):
 mask=np.unpackbits(a['packed_eligible'][r],count=n,bitorder='little'); ix=np.flatnonzero(mask)[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=a['item_ids'][ix]; out[sl,2]=np.arange(1,101)
pd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)
PY 
9703
/opt/task-venv/bin/python - 
9726
/bin/sh -c for p in /proc/[0-9]*; do tr '\0' ' ' < "$p/cmdline" 2>/dev/null | rg -q 'python' && { basename "$p"; tr '\0' ' ' < "$p/cmdline" 2>/dev/null; echo; }; done
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0218a355-b30c-4668-a659-bae3393d986e",
    "process_id": "96397",
    "command": [
      "/bin/sh",
      "-lc",
      "for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'python' && { basename \"$p\"; tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null; echo; }; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'python' && { basename \"$p\"; tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null; echo; }; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "8778\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=medium -- '# 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 \n9004\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=medium -- # 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 \n9012\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=medium -- # 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 \n9631\n/bin/sh -c mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)\nmetrics={\n'pop':cat.positive_listens.to_numpy(),\n'organic':cat.organic_positive_listens.to_numpy(),\n'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),\n'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),\n}\nfor name,s in metrics.items():\n rows=[]\n for r,uid in enumerate(a['uids']):\n  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]\n  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done \n9633\n/opt/task-venv/bin/python - \n9676\n/bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmetrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}\norders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)\n for name,order in orders.items():\n  ix=order[mask[order]][:100]\n  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\nfor name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done \n9677\n/opt/task-venv/bin/python - \n9702\n/bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); n=len(a['item_ids']); m=len(a['uids']); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=n,bitorder='little'); ix=np.flatnonzero(mask)[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=a['item_ids'][ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY \n9703\n/opt/task-venv/bin/python - \n9726\n/bin/sh -c for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'python' && { basename \"$p\"; tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null; echo; }; done \n",
    "stderr": "",
    "aggregated_output": "8778\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=medium -- '# 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 \n9004\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=medium -- # 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 \n9012\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=medium -- # 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 \n9631\n/bin/sh -c mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)\nmetrics={\n'pop':cat.positive_listens.to_numpy(),\n'organic':cat.organic_positive_listens.to_numpy(),\n'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),\n'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),\n}\nfor name,s in metrics.items():\n rows=[]\n for r,uid in enumerate(a['uids']):\n  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]\n  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done \n9633\n/opt/task-venv/bin/python - \n9676\n/bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmetrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}\norders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)\n for name,order in orders.items():\n  ix=order[mask[order]][:100]\n  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\nfor name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done \n9677\n/opt/task-venv/bin/python - \n9702\n/bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); n=len(a['item_ids']); m=len(a['uids']); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=n,bitorder='little'); ix=np.flatnonzero(mask)[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=a['item_ids'][ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY \n9703\n/opt/task-venv/bin/python - \n9726\n/bin/sh -c for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'python' && { basename \"$p\"; tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null; echo; }; done \n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5560
    },
    "formatted_output": "8778\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=medium -- '# 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 \n9004\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=medium -- # 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 \n9012\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=medium -- # 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 \n9631\n/bin/sh -c mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)\nmetrics={\n'pop':cat.positive_listens.to_numpy(),\n'organic':cat.organic_positive_listens.to_numpy(),\n'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),\n'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),\n}\nfor name,s in metrics.items():\n rows=[]\n for r,uid in enumerate(a['uids']):\n  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]\n  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done \n9633\n/opt/task-venv/bin/python - \n9676\n/bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmetrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}\norders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)\n for name,order in orders.items():\n  ix=order[mask[order]][:100]\n  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\nfor name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done \n9677\n/opt/task-venv/bin/python - \n9702\n/bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); n=len(a['item_ids']); m=len(a['uids']); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=n,bitorder='little'); ix=np.flatnonzero(mask)[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=a['item_ids'][ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY \n9703\n/opt/task-venv/bin/python - \n9726\n/bin/sh -c for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'python' && { basename \"$p\"; tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null; echo; }; done \n"
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Harness context token usage record
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30

exec command

+09:38
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 

const r = await tools.exec_command({"cmd":"kill 9631 9633 9676 9677 9702 9703 2>/dev/null || true; awk '/MemAvailable|MemTotal/{print}' /proc/meminfo","workdir":"/app","yield_time_ms":10000,"max_output_tokens":1000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

MemTotal:       15988972 kB
MemAvailable:   14434608 kB
Harness context codex item completed
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      "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmetrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}\norders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)\n for name,order in orders.items():\n  ix=order[mask[order]][:100]\n  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\nfor name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmetrics={'pop':cat.positive_listens.to_numpy(),'organic':cat.organic_positive_listens.to_numpy(),'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1))}\norders={k:np.argsort(v)[::-1] for k,v in metrics.items()}; rows={k:[] for k in metrics}\nfor r,uid in enumerate(a['uids']):\n mask=np.unpackbits(a['packed_eligible'][r],count=len(a['item_ids']),bitorder='little').astype(bool)\n for name,order in orders.items():\n  ix=order[mask[order]][:100]\n  rows[name].extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\nfor name,x in rows.items(): pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 143,
    "duration": {
      "secs": 80,
      "nanos": 513292884
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-86544c5f-4b0b-486d-b5cf-f8ee0b776bac",
    "process_id": "86081",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)\nmetrics={\n'pop':cat.positive_listens.to_numpy(),\n'organic':cat.organic_positive_listens.to_numpy(),\n'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),\n'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),\n}\nfor name,s in metrics.items():\n rows=[]\n for r,uid in enumerate(a['uids']):\n  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]\n  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution/controls && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\na=np.load('/task/data/validation_eligible_candidates.npz')\nmask=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').astype(bool)\nmetrics={\n'pop':cat.positive_listens.to_numpy(),\n'organic':cat.organic_positive_listens.to_numpy(),\n'organic_ratio':cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1),\n'pop_sqrt_ratio':np.sqrt(cat.positive_listens.to_numpy())*(cat.organic_positive_listens.to_numpy()/np.maximum(cat.positive_listens.to_numpy(),1)),\n}\nfor name,s in metrics.items():\n rows=[]\n for r,uid in enumerate(a['uids']):\n  z=np.where(mask[r],s,-np.inf); ix=np.argpartition(z,-100)[-100:]; ix=ix[np.argsort(z[ix])[::-1]]\n  rows.extend((int(uid),int(a['item_ids'][j]),k+1) for k,j in enumerate(ix))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/'+name+'.parquet',index=False)\nPY\nfor f in /app/solution/controls/*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 143,
    "duration": {
      "secs": 124,
      "nanos": 952698896
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
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  "item": {
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    "process_id": "33891",
    "command": [
      "/bin/sh",
      "-lc",
      "kill 9631 9633 9676 9677 9702 9703 2>/dev/null || true; awk '/MemAvailable|MemTotal/{print}' /proc/meminfo"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "kill 9631 9633 9676 9677 9702 9703 2>/dev/null || true; awk '/MemAvailable|MemTotal/{print}' /proc/meminfo"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "MemTotal:       15988972 kB\nMemAvailable:   14434608 kB\n",
    "stderr": "",
    "aggregated_output": "MemTotal:       15988972 kB\nMemAvailable:   14434608 kB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4400
    },
    "formatted_output": "MemTotal:       15988972 kB\nMemAvailable:   14434608 kB\n"
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}
Harness context token usage record
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31

exec command

+09:41
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); uids=a['uids']; items=a['item_ids']; packed=a['packed_eligible']; n=len(items); m=len(uids); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(uids):\n ix=np.flatnonzero(np.unpackbits(packed[r],count=n,bitorder='little'))[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=items[ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/controls/pop.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 5.8 seconds
Output:

{
  "model": "/app/solution/controls/pop.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-b682d28c-c371-4651-a7c9-a12c206880a1",
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    "command": [
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      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); uids=a['uids']; items=a['item_ids']; packed=a['packed_eligible']; n=len(items); m=len(uids); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(uids):\n ix=np.flatnonzero(np.unpackbits(packed[r],count=n,bitorder='little'))[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=items[ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/controls/pop.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\na=np.load('/task/data/validation_eligible_candidates.npz'); uids=a['uids']; items=a['item_ids']; packed=a['packed_eligible']; n=len(items); m=len(uids); out=np.empty((m*100,3),dtype=np.int64)\nfor r,uid in enumerate(uids):\n ix=np.flatnonzero(np.unpackbits(packed[r],count=n,bitorder='little'))[:100]; sl=slice(r*100,(r+1)*100); out[sl,0]=uid; out[sl,1]=items[ix]; out[sl,2]=np.arange(1,101)\npd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet('/app/solution/controls/pop.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/controls/pop.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/controls/pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/controls/pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 633380677
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/controls/pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
32

exec command

+09:55
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 

let{output,...v}=await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np=pd.read_parquet('/app/solution/production_validation.parquet'); c=pd.read_parquet('/task/data/candidate_catalog.parquet'); x=p.merge(c,on='item_id')\nfor b in [-20,-10,-5,-2,-1,1,2,5,10,20]:\n # smaller score wins: original rank + b * standardized log popularity rank within candidates globally\n x['s']=x['rank']+b*np.log1p(x['popularity_rank'])\n y=x.sort_values(['uid','s','rank']).copy(); y['rank']=y.groupby('uid').cumcount()+1\n y[['uid','item_id','rank']].to_parquet(f'/app/solution/controls/blend_{b}.parquet',index=False)\nPY\nfor f in /app/solution/controls/blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":6000});text(v);text(output);

Tool response 

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"322439","wall_time_seconds":30.000953614,"session_id":60478,"original_token_count":307}
{   "model": "/app/solution/controls/blend_-1.parquet",   "scores": {     "quality_ndcg@10": 0.022029,     "quality_users": 4263   } } /app/solution/controls/blend_-1.parquet
{   "model": "/app/solution/controls/blend_-10.parquet",   "scores": {     "quality_ndcg@10": 0.020657,     "quality_users": 4263   } } /app/solution/controls/blend_-10.parquet
{   "model": "/app/solution/controls/blend_-2.parquet",   "scores": {     "quality_ndcg@10": 0.022495,     "quality_users": 4263   } } /app/solution/controls/blend_-2.parquet
{   "model": "/app/solution/controls/blend_-20.parquet",   "scores": {     "quality_ndcg@10": 0.019966,     "quality_users": 4263   } } /app/solution/controls/blend_-20.parquet
{   "model": "/app/solution/controls/blend_-5.parquet",   "scores": {     "quality_ndcg@10": 0.021393,     "quality_users": 4263   } } /app/solution/controls/blend_-5.parquet
{   "model": "/app/solution/controls/blend_1.parquet",   "scores": {     "quality_ndcg@10": 0.019788,     "quality_users": 4263   } } /app/solution/controls/blend_1.parquet
{   "model": "/app/solution/controls/blend_10.parquet",   "scores": {     "quality_ndcg@10": 0.012864,     "quality_users": 4263   } } /app/solution/controls/blend_10.parquet
Harness context token usage record
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33

exec command

+10:32
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 

const r = await tools.write_stdin({"session_id":60478,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 4.3 seconds
Output:

{   "model": "/app/solution/controls/blend_2.parquet",   "scores": {     "quality_ndcg@10": 0.018357,     "quality_users": 4263   } } /app/solution/controls/blend_2.parquet
{   "model": "/app/solution/controls/blend_20.parquet",   "scores": {     "quality_ndcg@10": 0.011625,     "quality_users": 4263   } } /app/solution/controls/blend_20.parquet
{   "model": "/app/solution/controls/blend_5.parquet",   "scores": {     "quality_ndcg@10": 0.014809,     "quality_users": 4263   } } /app/solution/controls/blend_5.parquet
Harness context token usage record
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-07c81dd2-acbb-4599-93ab-3b4dce18737b",
    "process_id": "60478",
    "command": [
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      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np=pd.read_parquet('/app/solution/production_validation.parquet'); c=pd.read_parquet('/task/data/candidate_catalog.parquet'); x=p.merge(c,on='item_id')\nfor b in [-20,-10,-5,-2,-1,1,2,5,10,20]:\n # smaller score wins: original rank + b * standardized log popularity rank within candidates globally\n x['s']=x['rank']+b*np.log1p(x['popularity_rank'])\n y=x.sort_values(['uid','s','rank']).copy(); y['rank']=y.groupby('uid').cumcount()+1\n y[['uid','item_id','rank']].to_parquet(f'/app/solution/controls/blend_{b}.parquet',index=False)\nPY\nfor f in /app/solution/controls/blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo \"$f\"; done"
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{   \"model\": \"/app/solution/controls/blend_-1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022029,     \"quality_users\": 4263   } } /app/solution/controls/blend_-1.parquet\n{   \"model\": \"/app/solution/controls/blend_-10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020657,     \"quality_users\": 4263   } } /app/solution/controls/blend_-10.parquet\n{   \"model\": \"/app/solution/controls/blend_-2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022495,     \"quality_users\": 4263   } } /app/solution/controls/blend_-2.parquet\n{   \"model\": \"/app/solution/controls/blend_-20.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019966,     \"quality_users\": 4263   } } /app/solution/controls/blend_-20.parquet\n{   \"model\": \"/app/solution/controls/blend_-5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.021393,     \"quality_users\": 4263   } } /app/solution/controls/blend_-5.parquet\n{   \"model\": \"/app/solution/controls/blend_1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019788,     \"quality_users\": 4263   } } /app/solution/controls/blend_1.parquet\n{   \"model\": \"/app/solution/controls/blend_10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012864,     \"quality_users\": 4263   } } /app/solution/controls/blend_10.parquet\n{   \"model\": \"/app/solution/controls/blend_2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018357,     \"quality_users\": 4263   } } /app/solution/controls/blend_2.parquet\n{   \"model\": \"/app/solution/controls/blend_20.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011625,     \"quality_users\": 4263   } } /app/solution/controls/blend_20.parquet\n{   \"model\": \"/app/solution/controls/blend_5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.014809,     \"quality_users\": 4263   } } /app/solution/controls/blend_5.parquet\n",
    "stderr": "",
    "aggregated_output": "{   \"model\": \"/app/solution/controls/blend_-1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022029,     \"quality_users\": 4263   } } /app/solution/controls/blend_-1.parquet\n{   \"model\": \"/app/solution/controls/blend_-10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020657,     \"quality_users\": 4263   } } /app/solution/controls/blend_-10.parquet\n{   \"model\": \"/app/solution/controls/blend_-2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022495,     \"quality_users\": 4263   } } /app/solution/controls/blend_-2.parquet\n{   \"model\": \"/app/solution/controls/blend_-20.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019966,     \"quality_users\": 4263   } } /app/solution/controls/blend_-20.parquet\n{   \"model\": \"/app/solution/controls/blend_-5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.021393,     \"quality_users\": 4263   } } /app/solution/controls/blend_-5.parquet\n{   \"model\": \"/app/solution/controls/blend_1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019788,     \"quality_users\": 4263   } } /app/solution/controls/blend_1.parquet\n{   \"model\": \"/app/solution/controls/blend_10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012864,     \"quality_users\": 4263   } } /app/solution/controls/blend_10.parquet\n{   \"model\": \"/app/solution/controls/blend_2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018357,     \"quality_users\": 4263   } } /app/solution/controls/blend_2.parquet\n{   \"model\": \"/app/solution/controls/blend_20.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011625,     \"quality_users\": 4263   } } /app/solution/controls/blend_20.parquet\n{   \"model\": \"/app/solution/controls/blend_5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.014809,     \"quality_users\": 4263   } } /app/solution/controls/blend_5.parquet\n",
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    "formatted_output": "{   \"model\": \"/app/solution/controls/blend_-1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022029,     \"quality_users\": 4263   } } /app/solution/controls/blend_-1.parquet\n{   \"model\": \"/app/solution/controls/blend_-10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020657,     \"quality_users\": 4263   } } /app/solution/controls/blend_-10.parquet\n{   \"model\": \"/app/solution/controls/blend_-2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022495,     \"quality_users\": 4263   } } /app/solution/controls/blend_-2.parquet\n{   \"model\": \"/app/solution/controls/blend_-20.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019966,     \"quality_users\": 4263   } } /app/solution/controls/blend_-20.parquet\n{   \"model\": \"/app/solution/controls/blend_-5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.021393,     \"quality_users\": 4263   } } /app/solution/controls/blend_-5.parquet\n{   \"model\": \"/app/solution/controls/blend_1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019788,     \"quality_users\": 4263   } } /app/solution/controls/blend_1.parquet\n{   \"model\": \"/app/solution/controls/blend_10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012864,     \"quality_users\": 4263   } } /app/solution/controls/blend_10.parquet\n{   \"model\": \"/app/solution/controls/blend_2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018357,     \"quality_users\": 4263   } } /app/solution/controls/blend_2.parquet\n{   \"model\": \"/app/solution/controls/blend_20.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011625,     \"quality_users\": 4263   } } /app/solution/controls/blend_20.parquet\n{   \"model\": \"/app/solution/controls/blend_5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.014809,     \"quality_users\": 4263   } } /app/solution/controls/blend_5.parquet\n"
  }
}
34

Agent message

+10:48
Thinking unavailable No text recorded

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

35

exec command

+10:54
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 controls support the diagnosis: pure popularity is much worse (0.010710), while mildly demoting very popular items inside the model’s retrieved set raises NDCG to 0.022495. That is consistent with easy uniform negatives letting head items remain overcompetitive. I’m implementing a genuine two-tower hard-negative step: draw a larger, false-negative-filtered pool, score it with the current towers, retain the highest-scoring negatives for BCE, and keep the corrected in-batch negatives. This changes training only; serving remains one user vector, precomputed item vectors, and dot products.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    hard_pool_multiplier: int = 3\n@@\n def draw_full_history_filtered_negatives(\n@@\n-    uniform = torch.randint(\n+    # Oversample catalog negatives here.  The loss keeps only the currently\n+    # highest-scoring subset (see score_target_microbatches), giving useful\n+    # model-mined negatives without changing the serving towers.\n+    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier\n+    uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_pool),\n@@\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_pool : uniform_pool + corrected_count\n@@\n def score_target_microbatches(\n@@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n+        pool_count = config.uniform_negatives * config.hard_pool_multiplier\n+        if config.hard_pool_multiplier > 1 and config.uniform_negatives:\n+            # Detach selection as in standard sampled hard-negative mining;\n+            # gradients still flow through the selected logits and item tower.\n+            hard_columns = torch.topk(\n+                negative_logits[:, :pool_count].detach(),\n+                k=config.uniform_negatives,\n+                dim=1,\n+            ).indices\n+            hard_logits = negative_logits[:, :pool_count].gather(1, hard_columns)\n+            hard_weights = negative_weights[begin:end, :pool_count].gather(\n+                1, hard_columns\n+            )\n+            negative_logits = torch.cat(\n+                [hard_logits, negative_logits[:, pool_count:]], dim=1\n+            )\n+            negative_weights_for_loss = torch.cat(\n+                [hard_weights, negative_weights[begin:end, pool_count:]], dim=1\n+            )\n+        else:\n+            negative_weights_for_loss = negative_weights[begin:end]\n         positive_loss = F.softplus(-positive_logits)\n         negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n+            negative_logits, negative_weights_for_loss, config\n@@\n         stored_weight_values = (\n@@\n         current_weight_values = (\n@@\n         objective_changed = (\n@@\n             or stored_window_values != current_window_values\n+            or stored_config.get(\"hard_pool_multiplier\", 1)\n+            != config.hard_pool_multiplier\n@@\n     parser.add_argument(\n         \"--loss-microbatch-size\",\n@@\n     )\n+    parser.add_argument(\n+        \"--hard-pool-multiplier\",\n+        type=int,\n+        default=Config.hard_pool_multiplier,\n+        help=\"draw this many uniform candidates per retained hard negative\",\n+    )\n@@\n         or args.loss_microbatch_size < 1\n+        or args.hard_pool_multiplier < 1\n@@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        hard_pool_multiplier=args.hard_pool_multiplier,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

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
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-55a6a15d-0238-4ed6-ac00-5028d3c2a086",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -57,2 +57,3 @@\n     inbatch_negatives: int = 8\n+    hard_pool_multiplier: int = 3\n     inbatch_correction_power: float = 0.5\n@@ -644,5 +645,9 @@\n     batch_size = len(targets_cpu)\n+    # Oversample catalog negatives here.  The loss keeps only the currently\n+    # highest-scoring subset (see score_target_microbatches), giving useful\n+    # model-mined negatives without changing the serving towers.\n+    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_pool),\n         generator=generator,\n@@ -684,3 +689,3 @@\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_pool : uniform_pool + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n@@ -1170,5 +1175,26 @@\n         )\n+        pool_count = config.uniform_negatives * config.hard_pool_multiplier\n+        if config.hard_pool_multiplier > 1 and config.uniform_negatives:\n+            # Detach selection as in standard sampled hard-negative mining;\n+            # gradients still flow through the selected logits and item tower.\n+            hard_columns = torch.topk(\n+                negative_logits[:, :pool_count].detach(),\n+                k=config.uniform_negatives,\n+                dim=1,\n+            ).indices\n+            hard_logits = negative_logits[:, :pool_count].gather(1, hard_columns)\n+            hard_weights = negative_weights[begin:end, :pool_count].gather(\n+                1, hard_columns\n+            )\n+            negative_logits = torch.cat(\n+                [hard_logits, negative_logits[:, pool_count:]], dim=1\n+            )\n+            negative_weights_for_loss = torch.cat(\n+                [hard_weights, negative_weights[begin:end, pool_count:]], dim=1\n+            )\n+        else:\n+            negative_weights_for_loss = negative_weights[begin:end]\n         positive_loss = F.softplus(-positive_logits)\n         negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n+            negative_logits, negative_weights_for_loss, config\n         )\n@@ -1632,2 +1658,4 @@\n             or stored_window_values != current_window_values\n+            or stored_config.get(\"hard_pool_multiplier\", 1)\n+            != config.hard_pool_multiplier\n         )\n@@ -1943,2 +1971,8 @@\n     parser.add_argument(\n+        \"--hard-pool-multiplier\",\n+        type=int,\n+        default=Config.hard_pool_multiplier,\n+        help=\"draw this many uniform candidates per retained hard negative\",\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2012,2 +2046,3 @@\n         or args.loss_microbatch_size < 1\n+        or args.hard_pool_multiplier < 1\n         or args.early_stopping_patience < 1\n@@ -2033,2 +2068,3 @@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        hard_pool_multiplier=args.hard_pool_multiplier,\n         negative_filter_scope=args.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "failed",
    "stdout": "",
    "stderr": "Failed to write file /app/solution/train_current_model.py\n"
  }
}
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36

exec command

+11:14
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 

const r = await tools.exec_command({"cmd":"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":1000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 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"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
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Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    hard_pool_multiplier: int = 3\n@@\n-    uniform = torch.randint(\n+    # Oversample catalog negatives here. The loss keeps only the currently\n+    # highest-scoring subset, producing model-mined training competition.\n+    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier\n+    uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_pool),\n@@\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_pool : uniform_pool + corrected_count\n@@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n+        pool_count = config.uniform_negatives * config.hard_pool_multiplier\n+        if config.hard_pool_multiplier > 1 and config.uniform_negatives:\n+            # Selection is non-differentiable; selected logits retain their\n+            # gradients into both towers, as in sampled hard-negative mining.\n+            hard_columns = torch.topk(\n+                negative_logits[:, :pool_count].detach(),\n+                k=config.uniform_negatives,\n+                dim=1,\n+            ).indices\n+            hard_logits = negative_logits[:, :pool_count].gather(1, hard_columns)\n+            hard_weights = negative_weights[begin:end, :pool_count].gather(\n+                1, hard_columns\n+            )\n+            negative_logits = torch.cat(\n+                [hard_logits, negative_logits[:, pool_count:]], dim=1\n+            )\n+            negative_weights_for_loss = torch.cat(\n+                [hard_weights, negative_weights[begin:end, pool_count:]], dim=1\n+            )\n+        else:\n+            negative_weights_for_loss = negative_weights[begin:end]\n         positive_loss = F.softplus(-positive_logits)\n         negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n+            negative_logits, negative_weights_for_loss, config\n@@\n         objective_changed = (\n@@\n             or stored_window_values != current_window_values\n+            or stored_config.get(\"hard_pool_multiplier\", 1)\n+            != config.hard_pool_multiplier\n@@\n     parser.add_argument(\n         \"--loss-microbatch-size\",\n@@\n     )\n+    parser.add_argument(\n+        \"--hard-pool-multiplier\",\n+        type=int,\n+        default=Config.hard_pool_multiplier,\n+        help=\"draw this many uniform candidates per retained hard negative\",\n+    )\n@@\n         or args.loss_microbatch_size < 1\n+        or args.hard_pool_multiplier < 1\n@@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        hard_pool_multiplier=args.hard_pool_multiplier,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-d96b0f8d-6399-41c6-951c-f3f024264ca0",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -57,2 +57,3 @@\n     inbatch_negatives: int = 8\n+    hard_pool_multiplier: int = 3\n     inbatch_correction_power: float = 0.5\n@@ -644,5 +645,8 @@\n     batch_size = len(targets_cpu)\n+    # Oversample catalog negatives here. The loss keeps only the currently\n+    # highest-scoring subset, producing model-mined training competition.\n+    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_pool),\n         generator=generator,\n@@ -684,3 +688,3 @@\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_pool : uniform_pool + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n@@ -1170,5 +1174,26 @@\n         )\n+        pool_count = config.uniform_negatives * config.hard_pool_multiplier\n+        if config.hard_pool_multiplier > 1 and config.uniform_negatives:\n+            # Selection is non-differentiable; selected logits retain their\n+            # gradients into both towers, as in sampled hard-negative mining.\n+            hard_columns = torch.topk(\n+                negative_logits[:, :pool_count].detach(),\n+                k=config.uniform_negatives,\n+                dim=1,\n+            ).indices\n+            hard_logits = negative_logits[:, :pool_count].gather(1, hard_columns)\n+            hard_weights = negative_weights[begin:end, :pool_count].gather(\n+                1, hard_columns\n+            )\n+            negative_logits = torch.cat(\n+                [hard_logits, negative_logits[:, pool_count:]], dim=1\n+            )\n+            negative_weights_for_loss = torch.cat(\n+                [hard_weights, negative_weights[begin:end, pool_count:]], dim=1\n+            )\n+        else:\n+            negative_weights_for_loss = negative_weights[begin:end]\n         positive_loss = F.softplus(-positive_logits)\n         negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n+            negative_logits, negative_weights_for_loss, config\n         )\n@@ -1632,2 +1657,4 @@\n             or stored_window_values != current_window_values\n+            or stored_config.get(\"hard_pool_multiplier\", 1)\n+            != config.hard_pool_multiplier\n         )\n@@ -1943,2 +1970,8 @@\n     parser.add_argument(\n+        \"--hard-pool-multiplier\",\n+        type=int,\n+        default=Config.hard_pool_multiplier,\n+        help=\"draw this many uniform candidates per retained hard negative\",\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2012,2 +2045,3 @@\n         or args.loss_microbatch_size < 1\n+        or args.hard_pool_multiplier < 1\n         or args.early_stopping_patience < 1\n@@ -2033,2 +2067,3 @@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        hard_pool_multiplier=args.hard_pool_multiplier,\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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  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ce690b7c87d28165655b280b6398",
  "usage": {
    "input_tokens": 53487,
    "cached_input_tokens": 53248,
    "cache_write_input_tokens": 0,
    "output_tokens": 711,
    "reasoning_output_tokens": 0,
    "total_tokens": 54198
  },
  "turn_token_usage": {
    "input_tokens": 1627151,
    "cached_input_tokens": 1574656,
    "cache_write_input_tokens": 0,
    "output_tokens": 7782,
    "reasoning_output_tokens": 1801,
    "total_tokens": 1634933
  },
  "thread_token_usage": {
    "input_tokens": 1627151,
    "cached_input_tokens": 1574656,
    "cache_write_input_tokens": 0,
    "output_tokens": 7782,
    "reasoning_output_tokens": 1801,
    "total_tokens": 1634933
  }
}
37

exec command

+11:38
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && mkdir -p /app/solution/checkpoints_hard && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_hard --resume-checkpoint /app/solution/checkpoints_baseline/epoch-01.pt --epochs 2 --hard-pool-multiplier 3 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"8e3448","wall_time_seconds":1.000991793,"session_id":21589,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ce778de887d287b4d178f62a4aaa",
  "usage": {
    "input_tokens": 54222,
    "cached_input_tokens": 53248,
    "cache_write_input_tokens": 0,
    "output_tokens": 233,
    "reasoning_output_tokens": 60,
    "total_tokens": 54455
  },
  "turn_token_usage": {
    "input_tokens": 1681373,
    "cached_input_tokens": 1627904,
    "cache_write_input_tokens": 0,
    "output_tokens": 8015,
    "reasoning_output_tokens": 1861,
    "total_tokens": 1689388
  },
  "thread_token_usage": {
    "input_tokens": 1681373,
    "cached_input_tokens": 1627904,
    "cache_write_input_tokens": 0,
    "output_tokens": 8015,
    "reasoning_output_tokens": 1861,
    "total_tokens": 1689388
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 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=0.250 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
using a fresh AdamW optimizer after loading /app/solution/checkpoints_baseline/epoch-01.pt
BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5ce815ab487d2a7a36354c04925aa",
  "usage": {
    "input_tokens": 54513,
    "cached_input_tokens": 54016,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 54558
  },
  "turn_token_usage": {
    "input_tokens": 1735886,
    "cached_input_tokens": 1681920,
    "cache_write_input_tokens": 0,
    "output_tokens": 8060,
    "reasoning_output_tokens": 1861,
    "total_tokens": 1743946
  },
  "thread_token_usage": {
    "input_tokens": 1735886,
    "cached_input_tokens": 1681920,
    "cache_write_input_tokens": 0,
    "output_tokens": 8060,
    "reasoning_output_tokens": 1861,
    "total_tokens": 1743946
  }
}
38

exec command

+12:12
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 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

resumed validation-BCE training: epoch=1 loss=0.658425
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cea133c887d28978e54261b4f0b9",
  "usage": {
    "input_tokens": 54795,
    "cached_input_tokens": 54272,
    "cache_write_input_tokens": 0,
    "output_tokens": 60,
    "reasoning_output_tokens": 13,
    "total_tokens": 54855
  },
  "turn_token_usage": {
    "input_tokens": 1790681,
    "cached_input_tokens": 1736192,
    "cache_write_input_tokens": 0,
    "output_tokens": 8120,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1798801
  },
  "thread_token_usage": {
    "input_tokens": 1790681,
    "cached_input_tokens": 1736192,
    "cache_write_input_tokens": 0,
    "output_tokens": 8120,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1798801
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cec13f9487d2920eaf447f617004",
  "usage": {
    "input_tokens": 54895,
    "cached_input_tokens": 54656,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 54940
  },
  "turn_token_usage": {
    "input_tokens": 1845576,
    "cached_input_tokens": 1790848,
    "cache_write_input_tokens": 0,
    "output_tokens": 8165,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1853741
  },
  "thread_token_usage": {
    "input_tokens": 1845576,
    "cached_input_tokens": 1790848,
    "cache_write_input_tokens": 0,
    "output_tokens": 8165,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1853741
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cee111b887d2b39b2e2f26367502",
  "usage": {
    "input_tokens": 54963,
    "cached_input_tokens": 54656,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 55008
  },
  "turn_token_usage": {
    "input_tokens": 1900539,
    "cached_input_tokens": 1845504,
    "cache_write_input_tokens": 0,
    "output_tokens": 8210,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1908749
  },
  "thread_token_usage": {
    "input_tokens": 1900539,
    "cached_input_tokens": 1845504,
    "cache_write_input_tokens": 0,
    "output_tokens": 8210,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1908749
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cf01369487d2b46500c2466a3b18",
  "usage": {
    "input_tokens": 55031,
    "cached_input_tokens": 54784,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 55076
  },
  "turn_token_usage": {
    "input_tokens": 1955570,
    "cached_input_tokens": 1900288,
    "cache_write_input_tokens": 0,
    "output_tokens": 8255,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1963825
  },
  "thread_token_usage": {
    "input_tokens": 1955570,
    "cached_input_tokens": 1900288,
    "cache_write_input_tokens": 0,
    "output_tokens": 8255,
    "reasoning_output_tokens": 1874,
    "total_tokens": 1963825
  }
}
39

exec command

+14:21
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 hard pool behaves as intended on the frozen baseline: validation BCE rises from 0.4966 under easy sampling to 0.6584 when each row must compete against the hardest 24 of 72 filtered catalog draws. That is expected—the objective is harder, not degraded. The fine-tuning pass is now reducing this serving-relevant loss; once ranked, I’ll use public NDCG rather than BCE to decide whether it ships.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cf23198c87d2a86c2f109bc57ff3",
  "usage": {
    "input_tokens": 55099,
    "cached_input_tokens": 54912,
    "cache_write_input_tokens": 0,
    "output_tokens": 151,
    "reasoning_output_tokens": 7,
    "total_tokens": 55250
  },
  "turn_token_usage": {
    "input_tokens": 2010669,
    "cached_input_tokens": 1955200,
    "cache_write_input_tokens": 0,
    "output_tokens": 8406,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2019075
  },
  "thread_token_usage": {
    "input_tokens": 2010669,
    "cached_input_tokens": 1955200,
    "cache_write_input_tokens": 0,
    "output_tokens": 8406,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2019075
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cf44b12887d2aa49fa70d761ebb0",
  "usage": {
    "input_tokens": 55273,
    "cached_input_tokens": 54912,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 55318
  },
  "turn_token_usage": {
    "input_tokens": 2065942,
    "cached_input_tokens": 2010112,
    "cache_write_input_tokens": 0,
    "output_tokens": 8451,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2074393
  },
  "thread_token_usage": {
    "input_tokens": 2065942,
    "cached_input_tokens": 2010112,
    "cache_write_input_tokens": 0,
    "output_tokens": 8451,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2074393
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cf65825887d2a42a957b484ac775",
  "usage": {
    "input_tokens": 55341,
    "cached_input_tokens": 55040,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 55386
  },
  "turn_token_usage": {
    "input_tokens": 2121283,
    "cached_input_tokens": 2065152,
    "cache_write_input_tokens": 0,
    "output_tokens": 8496,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2129779
  },
  "thread_token_usage": {
    "input_tokens": 2121283,
    "cached_input_tokens": 2065152,
    "cache_write_input_tokens": 0,
    "output_tokens": 8496,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2129779
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cf87b78087d2bd65fcb01f4f314a",
  "usage": {
    "input_tokens": 55409,
    "cached_input_tokens": 55168,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 55454
  },
  "turn_token_usage": {
    "input_tokens": 2176692,
    "cached_input_tokens": 2120320,
    "cache_write_input_tokens": 0,
    "output_tokens": 8541,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2185233
  },
  "thread_token_usage": {
    "input_tokens": 2176692,
    "cached_input_tokens": 2120320,
    "cache_write_input_tokens": 0,
    "output_tokens": 8541,
    "reasoning_output_tokens": 1881,
    "total_tokens": 2185233
  }
}
40

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 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cfa7419487d2a525cf94188e296c",
  "usage": {
    "input_tokens": 55477,
    "cached_input_tokens": 55168,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 9,
    "total_tokens": 55533
  },
  "turn_token_usage": {
    "input_tokens": 2232169,
    "cached_input_tokens": 2175488,
    "cache_write_input_tokens": 0,
    "output_tokens": 8597,
    "reasoning_output_tokens": 1890,
    "total_tokens": 2240766
  },
  "thread_token_usage": {
    "input_tokens": 2232169,
    "cached_input_tokens": 2175488,
    "cache_write_input_tokens": 0,
    "output_tokens": 8597,
    "reasoning_output_tokens": 1890,
    "total_tokens": 2240766
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

epoch=2/2 batches=250 targets=987,417 batch_bce=0.614314 running_bce=0.583348 elapsed=312.9s
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cfc709a087d29dbde632643c75e0",
  "usage": {
    "input_tokens": 55556,
    "cached_input_tokens": 55296,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 55601
  },
  "turn_token_usage": {
    "input_tokens": 2287725,
    "cached_input_tokens": 2230784,
    "cache_write_input_tokens": 0,
    "output_tokens": 8642,
    "reasoning_output_tokens": 1890,
    "total_tokens": 2296367
  },
  "thread_token_usage": {
    "input_tokens": 2287725,
    "cached_input_tokens": 2230784,
    "cache_write_input_tokens": 0,
    "output_tokens": 8642,
    "reasoning_output_tokens": 1890,
    "total_tokens": 2296367
  }
}
41

exec command

+17:37
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5cfe6a71087d2b7ac1738e0c31b8d",
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/checkpoints_hard/epoch-02.pt
epoch=2/2 train_loss=0.58317 validation_loss=0.56996 pair_accuracy=0.669 train_targets=1,052,953 validation_targets=130,103 train_seconds=334.0 validation_seconds=26.8
restored best validation-BCE checkpoint: epoch=2 loss=0.569964
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":21589,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 28.0 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
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  "runtime_seconds": 481.309,
  "public_inputs_only": true,
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    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "hard_pool_multiplier": 3,
    "inbatch_correction_power": 0.5,
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    "ordinary_target_weight": 0.25,
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Harness context codex item completed
{
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  "item": {
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    "parsed_cmd": [
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        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && mkdir -p /app/solution/checkpoints_hard && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/checkpoints_hard --resume-checkpoint /app/solution/checkpoints_baseline/epoch-01.pt --epochs 2 --hard-pool-multiplier 3 --threads 8"
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    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/checkpoints_baseline/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.658425\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.614314 running_bce=0.583348 elapsed=312.9s\nsaved epoch checkpoint: /app/solution/checkpoints_hard/epoch-02.pt\nepoch=2/2 train_loss=0.58317 validation_loss=0.56996 pair_accuracy=0.669 train_targets=1,052,953 validation_targets=130,103 train_seconds=334.0 validation_seconds=26.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.569964\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/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 481.309,\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\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_pool_multiplier\": 3,\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\": 0.5398380454408168,\n      \"positive_logit\": 0.7427799600511094,\n      \"negative_logit\": -0.6222247082248333,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 224.980232504,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 26.569713051999997,\n      \"validation_loss\": 0.6584245743169987\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5831651328828212,\n      \"positive_logit\": 0.5792760509437772,\n      \"negative_logit\": -0.41204229690517835,\n      \"pair_accuracy\": 0.6693351934986652,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 334.0359183119999,\n      \"validation_loss\": 0.5699637239732036,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 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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=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/checkpoints_baseline/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.658425\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.614314 running_bce=0.583348 elapsed=312.9s\nsaved epoch checkpoint: /app/solution/checkpoints_hard/epoch-02.pt\nepoch=2/2 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    "exit_code": 0,
    "duration": {
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      "nanos": 700049962
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nusing a fresh AdamW optimizer after loading /app/solution/checkpoints_baseline/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.658425\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.614314 running_bce=0.583348 elapsed=312.9s\nsaved epoch checkpoint: /app/solution/checkpoints_hard/epoch-02.pt\nepoch=2/2 train_loss=0.58317 validation_loss=0.56996 pair_accuracy=0.669 train_targets=1,052,953 validation_targets=130,103 train_seconds=334.0 validation_seconds=26.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.569964\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/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 481.309,\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\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_pool_multiplier\": 3,\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\": 0.5398380454408168,\n      \"positive_logit\": 0.7427799600511094,\n      \"negative_logit\": -0.6222247082248333,\n      \"pair_accuracy\": 0.7547733612041563,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 224.980232504,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 26.569713051999997,\n      \"validation_loss\": 0.6584245743169987\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5831651328828212,\n      \"positive_logit\": 0.5792760509437772,\n      \"negative_logit\": -0.41204229690517835,\n      \"pair_accuracy\": 0.6693351934986652,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 334.0359183119999,\n      \"validation_loss\": 0.5699637239732036,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 26.79947858499986\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5197328329086304,\n    \"query_norm_std\": 0.8509312868118286,\n    \"item_norm_mean\": 1.650304913520813,\n    \"item_norm_std\": 0.5119697451591492\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/hard_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.022305,
    "quality_users": 4263
  }
}
Harness context token usage record
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}
Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-7d4b880c-8514-4a57-b056-1db1f4d86dbf",
    "process_id": "67467",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.022305,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.022305,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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  }
}
42

exec command

+19:52
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np=pd.read_parquet('/app/solution/hard_validation.parquet'); c=pd.read_parquet('/task/data/candidate_catalog.parquet'); x=p.merge(c,on='item_id')\nfor b in [-0.5,-1,-1.5,-2,-3]:\n x['s']=x['rank']+b*np.log1p(x['popularity_rank']); y=x.sort_values(['uid','s','rank']).copy(); y['rank']=y.groupby('uid').cumcount()+1\n y[['uid','item_id','rank']].to_parquet(f'/app/solution/controls/hard_blend_{b}.parquet',index=False)\nPY\nfor f in /app/solution/controls/hard_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo \"$f\"; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"9de163","wall_time_seconds":1.000484042,"session_id":96625,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":96625,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 16.3 seconds
Output:

{   "model": "/app/solution/controls/hard_blend_-0.5.parquet",   "scores": {     "quality_ndcg@10": 0.023174,     "quality_users": 4263   } } /app/solution/controls/hard_blend_-0.5.parquet
{   "model": "/app/solution/controls/hard_blend_-1.5.parquet",   "scores": {     "quality_ndcg@10": 0.024147,     "quality_users": 4263   } } /app/solution/controls/hard_blend_-1.5.parquet
{   "model": "/app/solution/controls/hard_blend_-1.parquet",   "scores": {     "quality_ndcg@10": 0.023495,     "quality_users": 4263   } } /app/solution/controls/hard_blend_-1.parquet
{   "model": "/app/solution/controls/hard_blend_-2.parquet",   "scores": {     "quality_ndcg@10": 0.024249,     "quality_users": 4263   } } /app/solution/controls/hard_blend_-2.parquet
{   "model": "/app/solution/controls/hard_blend_-3.parquet",   "scores": {     "quality_ndcg@10": 0.02442,     "quality_users": 4263   } } /app/solution/controls/hard_blend_-3.parquet
Harness context token usage record
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}
Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-23ddadbc-a6ff-4c26-a4c9-57940cac9918",
    "process_id": "96625",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np=pd.read_parquet('/app/solution/hard_validation.parquet'); c=pd.read_parquet('/task/data/candidate_catalog.parquet'); x=p.merge(c,on='item_id')\nfor b in [-0.5,-1,-1.5,-2,-3]:\n x['s']=x['rank']+b*np.log1p(x['popularity_rank']); y=x.sort_values(['uid','s','rank']).copy(); y['rank']=y.groupby('uid').cumcount()+1\n y[['uid','item_id','rank']].to_parquet(f'/app/solution/controls/hard_blend_{b}.parquet',index=False)\nPY\nfor f in /app/solution/controls/hard_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{   \"model\": \"/app/solution/controls/hard_blend_-0.5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.023174,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-0.5.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-1.5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024147,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-1.5.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.023495,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-1.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024249,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-2.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.02442,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-3.parquet\n",
    "stderr": "",
    "aggregated_output": "{   \"model\": \"/app/solution/controls/hard_blend_-0.5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.023174,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-0.5.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-1.5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024147,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-1.5.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.023495,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-1.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024249,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-2.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.02442,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-3.parquet\n",
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    "formatted_output": "{   \"model\": \"/app/solution/controls/hard_blend_-0.5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.023174,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-0.5.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-1.5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024147,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-1.5.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-1.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.023495,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-1.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-2.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024249,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-2.parquet\n{   \"model\": \"/app/solution/controls/hard_blend_-3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.02442,     \"quality_users\": 4263   } } /app/solution/controls/hard_blend_-3.parquet\n"
  }
}
43

exec command

+20:17
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\np=pd.read_parquet('/app/solution/hard_validation.parquet'); c=pd.read_parquet('/task/data/candidate_catalog.parquet'); x=p.merge(c,on='item_id')\nfor b in [-4,-5,-6,-8]:\n x['s']=x['rank']+b*np.log1p(x['popularity_rank']); y=x.sort_values(['uid','s','rank']).copy(); y['rank']=y.groupby('uid').cumcount()+1\n y[['uid','item_id','rank']].to_parquet(f'/app/solution/controls/hard_blend_{b}.parquet',index=False)\nPY\nfor b in -4 -5 -6 -8; do f=/app/solution/controls/hard_blend_${b}.parquet; /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"8c644c","wall_time_seconds":1.000920217,"session_id":13790,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":13790,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 13.0 seconds
Output:

{   "model": "/app/solution/controls/hard_blend_-4.parquet",   "scores": {     "quality_ndcg@10": 0.024851,     "quality_users": 4263   } } 
{   "model": "/app/solution/controls/hard_blend_-5.parquet",   "scores": {     "quality_ndcg@10": 0.024889,     "quality_users": 4263   } } 
{   "model": "/app/solution/controls/hard_blend_-6.parquet",   "scores": {     "quality_ndcg@10": 0.024334,     "quality_users": 4263   } } 
{   "model": "/app/solution/controls/hard_blend_-8.parquet",   "scores": {     "quality_ndcg@10": 0.024698,     "quality_users": 4263   } }
Harness context token usage record
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Harness context codex item completed
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    "stdout": "{   \"model\": \"/app/solution/controls/hard_blend_-4.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024851,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/controls/hard_blend_-5.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024889,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/controls/hard_blend_-6.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024334,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/controls/hard_blend_-8.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024698,     \"quality_users\": 4263   } } \n",
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  }
}
44

exec command

+20:47
Thinking unavailable No text recorded

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

Model text

Hard-negative fine-tuning improves the actual public metric to 0.022305 (+4.8% over production). A fixed item-side novelty prior is complementary and still exactly representable by the two-tower contract (append one precomputed item coordinate and a constant user coordinate). I’m calibrating that prior on raw dot-product scores now; the earlier rank-only diagnostic reached 0.024889, but I’ll ship only the score-space version so the serving contract remains clean.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/calibrate_prior.py\n+#!/usr/bin/env python3\n+\"\"\"Emit validation rankings for several item-side prior strengths.\"\"\"\n+import sys\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+import torch\n+\n+sys.path.insert(0, str(Path(__file__).parent))\n+import train_current_model as trainer\n+import two_tower_model as base\n+\n+public = Path(\"/task\")\n+config = trainer.Config(epochs=2, hard_pool_multiplier=3)\n+trainer.seed_everything(config.seed)\n+torch.set_num_threads(8)\n+prepared, eligibility = trainer.load_or_prepare(\n+    public, \"validation\", config, Path(\"/app/solution/validation_cache.npz\")\n+)\n+model = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n+payload = torch.load(\n+    \"/app/solution/checkpoints_hard/epoch-02.pt\", map_location=\"cpu\", weights_only=False\n+)\n+model.load_state_dict(payload[\"model_state\"])\n+model.eval()\n+queries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\n+parts = []\n+with torch.inference_mode():\n+    for start in range(0, len(prepared.item_ids), 2048):\n+        parts.append(model.encode_items(torch.arange(start, min(start + 2048, len(prepared.item_ids)))))\n+items = torch.cat(parts)\n+prior = torch.log1p(torch.arange(len(prepared.item_ids), dtype=torch.float32) + 1.0)\n+for alpha in (0.0, 0.01, 0.02, 0.04, 0.08, 0.12, 0.2):\n+    output = np.empty((len(eligibility.uids) * 100, 3), dtype=np.int64)\n+    for start in range(0, len(eligibility.uids), 64):\n+        end = min(start + 64, len(eligibility.uids))\n+        scores = queries[start:end] @ items.T + alpha * prior\n+        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(items), bitorder=\"little\").astype(bool)\n+        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\n+        selected = torch.topk(scores, k=100, dim=1).indices.numpy()\n+        for local, cols in enumerate(selected):\n+            row = start + local\n+            sl = slice(row * 100, (row + 1) * 100)\n+            output[sl, 0] = eligibility.uids[row]\n+            output[sl, 1] = prepared.item_ids[cols]\n+            output[sl, 2] = np.arange(1, 101)\n+    pd.DataFrame(output, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n+        f\"/app/solution/controls/prior_{alpha}.parquet\", index=False\n+    )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
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    "id": "exec-e6983127-b673-4ea5-8b09-b6b3cac9330c",
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      "/app/solution/calibrate_prior.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Emit validation rankings for several item-side prior strengths.\"\"\"\nimport sys\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path.insert(0, str(Path(__file__).parent))\nimport train_current_model as trainer\nimport two_tower_model as base\n\npublic = Path(\"/task\")\nconfig = trainer.Config(epochs=2, hard_pool_multiplier=3)\ntrainer.seed_everything(config.seed)\ntorch.set_num_threads(8)\nprepared, eligibility = trainer.load_or_prepare(\n    public, \"validation\", config, Path(\"/app/solution/validation_cache.npz\")\n)\nmodel = base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\npayload = torch.load(\n    \"/app/solution/checkpoints_hard/epoch-02.pt\", map_location=\"cpu\", weights_only=False\n)\nmodel.load_state_dict(payload[\"model_state\"])\nmodel.eval()\nqueries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\nparts = []\nwith torch.inference_mode():\n    for start in range(0, len(prepared.item_ids), 2048):\n        parts.append(model.encode_items(torch.arange(start, min(start + 2048, len(prepared.item_ids)))))\nitems = torch.cat(parts)\nprior = torch.log1p(torch.arange(len(prepared.item_ids), dtype=torch.float32) + 1.0)\nfor alpha in (0.0, 0.01, 0.02, 0.04, 0.08, 0.12, 0.2):\n    output = np.empty((len(eligibility.uids) * 100, 3), dtype=np.int64)\n    for start in range(0, len(eligibility.uids), 64):\n        end = min(start + 64, len(eligibility.uids))\n        scores = queries[start:end] @ items.T + alpha * prior\n        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(items), bitorder=\"little\").astype(bool)\n        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)\n        selected = torch.topk(scores, k=100, dim=1).indices.numpy()\n        for local, cols in enumerate(selected):\n            row = start + local\n            sl = slice(row * 100, (row + 1) * 100)\n            output[sl, 0] = eligibility.uids[row]\n            output[sl, 1] = prepared.item_ids[cols]\n            output[sl, 2] = np.arange(1, 101)\n    pd.DataFrame(output, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n        f\"/app/solution/controls/prior_{alpha}.parquet\", index=False\n    )\n"
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/calibrate_prior.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":19328,"chars":"","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response 

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 

const r = await tools.write_stdin({"session_id":19328,"chars":"","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":19328,"chars":"","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 22.0 seconds
Output:
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}
45

exec command

+22:38
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 

const r = await tools.exec_command({"cmd":"for f in /app/solution/controls/prior_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":7336,"chars":"","yield_time_ms":30000,"max_output_tokens":6000});
text(r.output);

Tool response 

Script completed
Wall time 19.3 seconds
Output:

{   "model": "/app/solution/controls/prior_0.0.parquet",   "scores": {     "quality_ndcg@10": 0.022305,     "quality_users": 4263   } } 
{   "model": "/app/solution/controls/prior_0.01.parquet",   "scores": {     "quality_ndcg@10": 0.022664,     "quality_users": 4263   } } 
{   "model": "/app/solution/controls/prior_0.02.parquet",   "scores": {     "quality_ndcg@10": 0.022948,     "quality_users": 4263   } } 
{   "model": "/app/solution/controls/prior_0.04.parquet",   "scores": {     "quality_ndcg@10": 0.02312,     "quality_users": 4263   } } 
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{   "model": "/app/solution/controls/prior_0.12.parquet",   "scores": {     "quality_ndcg@10": 0.024357,     "quality_users": 4263   } } 
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}
46

exec command

+23:05
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/calibrate_prior.py\n@@\n-for alpha in (0.0, 0.01, 0.02, 0.04, 0.08, 0.12, 0.2):\n+for alpha in (0.3, 0.4, 0.6, 0.8):\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
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    "id": "exec-9bc48bf0-a974-4694-81f0-98e396e40bf7",
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/calibrate_prior.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":94048,"chars":"","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":94048,"chars":"","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":94048,"chars":"","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 20.2 seconds
Output:
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"for b in 0.3 0.4 0.6 0.8; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/controls/prior_${b}.parquet | tr '\\n' ' '; echo; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":3888,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 7.4 seconds
Output:

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{   "model": "/app/solution/controls/prior_0.4.parquet",   "scores": {     "quality_ndcg@10": 0.025271,     "quality_users": 4263   } } 
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      "nanos": 458865355
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    "formatted_output": "{   \"model\": \"/app/solution/controls/prior_0.3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.02585,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/controls/prior_0.4.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025271,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/controls/prior_0.6.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024138,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/controls/prior_0.8.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019656,     \"quality_users\": 4263   } } \n"
  }
}
47

exec command

+25:00
Thinking unavailable No text recorded

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

Model text

The score-space calibration peaks at 0.025850 NDCG@10, a 21.5% relative lift over production. The selected term is a small novelty prior based only on catalog popularity rank; it is an item-only scalar and can be folded into the precomputed item vector with a constant query coordinate, so request-time behavior is still a batchable two-tower dot product. I’m integrating that setting, then doing the required submission-stage refit on train + val.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     hard_pool_multiplier: int = 3\n+    item_prior_weight: float = 0.30\n@@\n def rank_candidates(\n@@\n-        scores = queries[start:end] @ items.T\n+        scores = queries[start:end] @ items.T\n+        # Catalog order is training-positive popularity order.  This fixed\n+        # item-side novelty coordinate is equivalent to appending\n+        # [1] to every query and [w*log(1+rank)] to every offline item vector.\n+        if config.item_prior_weight:\n+            novelty = torch.log1p(\n+                torch.arange(1, len(prepared.item_ids) + 1, dtype=scores.dtype)\n+            )\n+            scores += config.item_prior_weight * novelty\n@@\n-        \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n+        \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-corrected-inbatch\",\n@@\n     parser.add_argument(\n         \"--hard-pool-multiplier\",\n@@\n     )\n+    parser.add_argument(\n+        \"--item-prior-weight\",\n+        type=float,\n+        default=Config.item_prior_weight,\n+        help=\"item-side log popularity-rank (novelty) score coordinate\",\n+    )\n@@\n         loss_microbatch_size=args.loss_microbatch_size,\n         hard_pool_multiplier=args.hard_pool_multiplier,\n+        item_prior_weight=args.item_prior_weight,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script failed
Wall time 0.0 seconds
Output:

Script error:
apply_patch verification failed: Failed to find expected lines in /app/solution/train_current_model.py:
        "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
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48

exec command

+25:12
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 

const r = await tools.exec_command({"cmd":"rg -n 'negative_sampling|hard_pool|scores = queries|loss_microbatch_size=args' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

58:    hard_pool_multiplier: int = 3
648:    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier
1175:        pool_count = config.uniform_negatives * config.hard_pool_multiplier
1176:        if config.hard_pool_multiplier > 1 and config.uniform_negatives:
1658:            or stored_config.get("hard_pool_multiplier", 1)
1659:            != config.hard_pool_multiplier
1911:        scores = queries[start:end] @ items.T
1973:        default=Config.hard_pool_multiplier,
2046:        or args.hard_pool_multiplier < 1
2067:        loss_microbatch_size=args.loss_microbatch_size,
2068:        hard_pool_multiplier=args.hard_pool_multiplier,
2171:                    "negative_sampling": (
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    "aggregated_output": "58:    hard_pool_multiplier: int = 3\n648:    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier\n1175:        pool_count = config.uniform_negatives * config.hard_pool_multiplier\n1176:        if config.hard_pool_multiplier > 1 and config.uniform_negatives:\n1658:            or stored_config.get(\"hard_pool_multiplier\", 1)\n1659:            != config.hard_pool_multiplier\n1911:        scores = queries[start:end] @ items.T\n1973:        default=Config.hard_pool_multiplier,\n2046:        or args.hard_pool_multiplier < 1\n2067:        loss_microbatch_size=args.loss_microbatch_size,\n2068:        hard_pool_multiplier=args.hard_pool_multiplier,\n2171:                    \"negative_sampling\": (\n",
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"sed -n '1955,1995p' /app/solution/train_current_model.py; sed -n '2058,2080p' /app/solution/train_current_model.py; sed -n '2158,2182p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

    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(
        "--hard-pool-multiplier",
        type=int,
        default=Config.hard_pool_multiplier,
        help="draw this many uniform candidates per retained hard negative",
    )
    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.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,
        hard_pool_multiplier=args.hard_pool_multiplier,
        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,
                    "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()
                ),
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      }
    ],
    "source": "unified_exec_startup",
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    "stdout": "    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        \"--hard-pool-multiplier\",\n        type=int,\n        default=Config.hard_pool_multiplier,\n        help=\"draw this many uniform candidates per retained hard negative\",\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.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        hard_pool_multiplier=args.hard_pool_multiplier,\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                    \"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",
    "stderr": "",
    "aggregated_output": "    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        \"--hard-pool-multiplier\",\n        type=int,\n        default=Config.hard_pool_multiplier,\n        help=\"draw this many uniform candidates per retained hard negative\",\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.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        hard_pool_multiplier=args.hard_pool_multiplier,\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                    \"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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3760
    },
    "formatted_output": "    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        \"--hard-pool-multiplier\",\n        type=int,\n        default=Config.hard_pool_multiplier,\n        help=\"draw this many uniform candidates per retained hard negative\",\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.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        hard_pool_multiplier=args.hard_pool_multiplier,\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                    \"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"
  }
}
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}
49

exec command

+25:18
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 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     hard_pool_multiplier: int = 3\n+    item_prior_weight: float = 0.30\n@@\n-        scores = queries[start:end] @ items.T\n+        scores = queries[start:end] @ items.T\n+        # A fixed item-only coordinate: equivalently append 1 to each query\n+        # and w*log(1+popularity_rank) to each precomputed item vector.\n+        if config.item_prior_weight:\n+            novelty = torch.log1p(\n+                torch.arange(1, len(prepared.item_ids) + 1, dtype=scores.dtype)\n+            )\n+            scores += config.item_prior_weight * novelty\n@@\n     parser.add_argument(\n         \"--hard-pool-multiplier\",\n@@\n     )\n+    parser.add_argument(\n+        \"--item-prior-weight\",\n+        type=float,\n+        default=Config.item_prior_weight,\n+        help=\"item-side log popularity-rank novelty coordinate\",\n+    )\n@@\n         hard_pool_multiplier=args.hard_pool_multiplier,\n+        item_prior_weight=args.item_prior_weight,\n@@\n-                    \"score\": \"raw-dot-product\",\n+                    \"score\": \"dot-product-with-item-side-novelty-coordinate\",\n@@\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"hardest-24-of-72-uniform-plus-8-corrected-inbatch\"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-c8d85527-cc1d-43de-9235-880a6fb849b5",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -58,2 +58,3 @@\n     hard_pool_multiplier: int = 3\n+    item_prior_weight: float = 0.30\n     inbatch_correction_power: float = 0.5\n@@ -1911,2 +1912,9 @@\n         scores = queries[start:end] @ items.T\n+        # A fixed item-only coordinate: equivalently append 1 to each query\n+        # and w*log(1+popularity_rank) to each precomputed item vector.\n+        if config.item_prior_weight:\n+            novelty = torch.log1p(\n+                torch.arange(1, len(prepared.item_ids) + 1, dtype=scores.dtype)\n+            )\n+            scores += config.item_prior_weight * novelty\n         eligible = np.unpackbits(\n@@ -1976,2 +1984,8 @@\n     parser.add_argument(\n+        \"--item-prior-weight\",\n+        type=float,\n+        default=Config.item_prior_weight,\n+        help=\"item-side log popularity-rank novelty coordinate\",\n+    )\n+    parser.add_argument(\n         \"--negative-filter-scope\",\n@@ -2068,2 +2082,3 @@\n         hard_pool_multiplier=args.hard_pool_multiplier,\n+        item_prior_weight=args.item_prior_weight,\n         negative_filter_scope=args.negative_filter_scope,\n@@ -2168,6 +2183,6 @@\n                     \"normalization\": \"none\",\n-                    \"score\": \"raw-dot-product\",\n+                    \"score\": \"dot-product-with-item-side-novelty-coordinate\",\n                     \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                     \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"hardest-24-of-72-uniform-plus-8-corrected-inbatch\"\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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}
50

exec command

+25:28
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && mkdir -p /app/solution/checkpoints_final_base /app/solution/checkpoints_final_hard && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/checkpoints_final_base --hard-pool-multiplier 1 --item-prior-weight 0.30 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"b66616","wall_time_seconds":1.000990258,"session_id":97526,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 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=10.4s
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
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

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=47.7s
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
Harness context token usage record
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51

exec command

+26:41
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 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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}
52

exec command

+28:20

Model text

The final refit is using the correct stage boundary: all train_* + val_* events are observed history, and test eligibility excludes both windows. I’m first saving a valid one-epoch submission checkpoint, then applying the selected hard-negative fine-tune and overwriting it only after the improved artifact passes the structural eligibility checks.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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    "reasoning_output_tokens": 0,
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=205.5s
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/checkpoints_final_base/epoch-01.pt
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=220.8 validation_seconds=19.6
restored best validation-BCE checkpoint: epoch=1 loss=0.497949
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d30d47c487d2a0a8c74bfd67a7a0",
  "usage": {
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  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":97526,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 21.4 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": 383.151,
  "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": "dot-product-with-item-side-novelty-coordinate",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "hardest-24-of-72-uniform-plus-8-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,
    "hard_pool_multiplier": 1,
    "item_prior_weight": 0.3,
    "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": [
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      "epoch": 1.0,
      "loss": 0.5383531185941673,
      "positive_logit": 0.7500170017057294,
      "negative_logit": -0.6300986818338857,
      "pair_accuracy": 0.7552423073212402,
      "targets": 1067606.0,
      "target_weight_sum": 641383.25,
      "batches": 270.0,
      "seconds": 220.81805216199973,
      "validation_loss": 0.49794869614425086,
      "validation_targets": 131997.0,
      "validation_target_weight_sum": 80328.75,
      "validation_seconds": 19.595205439999972
    }
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  "embedding_diagnostics": {
    "query_norm_mean": 1.6518515348434448,
    "query_norm_std": 0.8145268559455872,
    "item_norm_mean": 1.9033515453338623,
    "item_norm_std": 0.9598208665847778
  },
  "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-0cb95400-839d-43a0-8f00-ab51a017de90",
    "process_id": "97526",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && mkdir -p /app/solution/checkpoints_final_base /app/solution/checkpoints_final_hard && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/checkpoints_final_base --hard-pool-multiplier 1 --item-prior-weight 0.30 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && mkdir -p /app/solution/checkpoints_final_base /app/solution/checkpoints_final_hard && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/checkpoints_final_base --hard-pool-multiplier 1 --item-prior-weight 0.30 --threads 8"
      }
    ],
    "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=10.4s\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=47.7s\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=205.5s\nsaved epoch checkpoint: /app/solution/checkpoints_final_base/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=220.8 validation_seconds=19.6\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\": 383.151,\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\": \"dot-product-with-item-side-novelty-coordinate\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-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    \"hard_pool_multiplier\": 1,\n    \"item_prior_weight\": 0.3,\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\": 0.5383531185941673,\n      \"positive_logit\": 0.7500170017057294,\n      \"negative_logit\": -0.6300986818338857,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.81805216199973,\n      \"validation_loss\": 0.49794869614425086,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 19.595205439999972\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6518515348434448,\n    \"query_norm_std\": 0.8145268559455872,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\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=10.4s\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=47.7s\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=205.5s\nsaved epoch checkpoint: /app/solution/checkpoints_final_base/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=220.8 validation_seconds=19.6\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\": 383.151,\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\": \"dot-product-with-item-side-novelty-coordinate\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-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    \"hard_pool_multiplier\": 1,\n    \"item_prior_weight\": 0.3,\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\": 0.5383531185941673,\n      \"positive_logit\": 0.7500170017057294,\n      \"negative_logit\": -0.6300986818338857,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.81805216199973,\n      \"validation_loss\": 0.49794869614425086,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 19.595205439999972\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6518515348434448,\n    \"query_norm_std\": 0.8145268559455872,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 387,
      "nanos": 534838254
    },
    "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=10.4s\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=47.7s\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=205.5s\nsaved epoch checkpoint: /app/solution/checkpoints_final_base/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=220.8 validation_seconds=19.6\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\": 383.151,\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\": \"dot-product-with-item-side-novelty-coordinate\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-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    \"hard_pool_multiplier\": 1,\n    \"item_prior_weight\": 0.3,\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\": 0.5383531185941673,\n      \"positive_logit\": 0.7500170017057294,\n      \"negative_logit\": -0.6300986818338857,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.81805216199973,\n      \"validation_loss\": 0.49794869614425086,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 19.595205439999972\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6518515348434448,\n    \"query_norm_std\": 0.8145268559455872,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
53

exec command

+32:01
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 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/checkpoints_final_hard --resume-checkpoint /app/solution/checkpoints_final_base/epoch-01.pt --epochs 2 --hard-pool-multiplier 3 --item-prior-weight 0.30 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(JSON.stringify(r));

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"545310","wall_time_seconds":1.000752331,"session_id":56887,"original_token_count":0,"output":""}
Harness context token usage record
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  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d344dec087d29e55d1ada526b750",
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  },
  "turn_token_usage": {
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  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 14099,
    "reasoning_output_tokens": 2850,
    "total_tokens": 4834871
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

loaded full-sequence cache: tokens=20,712,971 targets=20,703,818
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
using a fresh AdamW optimizer after loading /app/solution/checkpoints_final_base/epoch-01.pt
BCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE
Harness context token usage record
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  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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  "usage": {
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    "cache_write_input_tokens": 0,
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  "thread_token_usage": {
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

resumed validation-BCE training: epoch=1 loss=0.680394
Harness context token usage record
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  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d36b9f9887d2b65b39af4ec5136f",
  "usage": {
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 2850,
    "total_tokens": 4970295
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d38c883087d295e94f5bd0408d67",
  "usage": {
    "input_tokens": 67893,
    "cached_input_tokens": 67584,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 67938
  },
  "turn_token_usage": {
    "input_tokens": 5023999,
    "cached_input_tokens": 4947072,
    "cache_write_input_tokens": 0,
    "output_tokens": 14234,
    "reasoning_output_tokens": 2850,
    "total_tokens": 5038233
  },
  "thread_token_usage": {
    "input_tokens": 5023999,
    "cached_input_tokens": 4947072,
    "cache_write_input_tokens": 0,
    "output_tokens": 14234,
    "reasoning_output_tokens": 2850,
    "total_tokens": 5038233
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d3acfb3487d2b61b8e64293f9ee7",
  "usage": {
    "input_tokens": 67961,
    "cached_input_tokens": 67712,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 68006
  },
  "turn_token_usage": {
    "input_tokens": 5091960,
    "cached_input_tokens": 5014784,
    "cache_write_input_tokens": 0,
    "output_tokens": 14279,
    "reasoning_output_tokens": 2850,
    "total_tokens": 5106239
  },
  "thread_token_usage": {
    "input_tokens": 5091960,
    "cached_input_tokens": 5014784,
    "cache_write_input_tokens": 0,
    "output_tokens": 14279,
    "reasoning_output_tokens": 2850,
    "total_tokens": 5106239
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d3cceaa487d291a7d11386639b5e",
  "usage": {
    "input_tokens": 68029,
    "cached_input_tokens": 67840,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 68074
  },
  "turn_token_usage": {
    "input_tokens": 5159989,
    "cached_input_tokens": 5082624,
    "cache_write_input_tokens": 0,
    "output_tokens": 14324,
    "reasoning_output_tokens": 2850,
    "total_tokens": 5174313
  },
  "thread_token_usage": {
    "input_tokens": 5159989,
    "cached_input_tokens": 5082624,
    "cache_write_input_tokens": 0,
    "output_tokens": 14324,
    "reasoning_output_tokens": 2850,
    "total_tokens": 5174313
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d3edcb9887d2a3de318bfc635791",
  "usage": {
    "input_tokens": 68097,
    "cached_input_tokens": 67840,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
    "total_tokens": 68142
  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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}
54

exec command

+35:52
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 submission is already saved. The selected final pass is now training against the harder objective; its frozen starting checkpoint has hard-pool BCE 0.6804 on the later chronological split. After completion I’ll validate exact users, ranks, distinctness, integer types, and per-user test eligibility directly from the submitted file.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

epoch=2/2 batches=250 targets=994,228 batch_bce=0.521620 running_bce=0.582711 elapsed=283.1s
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
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  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:

saved epoch checkpoint: /app/solution/checkpoints_final_hard/epoch-02.pt
epoch=2/2 train_loss=0.58109 validation_loss=0.58024 pair_accuracy=0.671 train_targets=1,067,606 validation_targets=131,997 train_seconds=303.5 validation_seconds=26.1
restored best validation-BCE checkpoint: epoch=2 loss=0.580239
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 30.0 seconds
Output:
Harness context token usage record
{
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  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":56887,"chars":"","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 23.1 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
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "submission",
  "output": "/app/solution/submission.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 445.129,
  "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": "dot-product-with-item-side-novelty-coordinate",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "hardest-24-of-72-uniform-plus-8-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": 2,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "hard_pool_multiplier": 3,
    "item_prior_weight": 0.3,
    "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": [
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      "epoch": 1.0,
      "loss": 0.5383531185941673,
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      "negative_logit": -0.6300986818338857,
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      "seconds": 220.81805216199973,
      "validation_targets": 131997.0,
      "validation_target_weight_sum": 80328.75,
      "validation_seconds": 26.929757490000156,
      "validation_loss": 0.6803942896417029
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    {
      "epoch": 2.0,
      "loss": 0.5810943002424908,
      "positive_logit": 0.5913247774711043,
      "negative_logit": -0.4150816630035099,
      "pair_accuracy": 0.6711956763543854,
      "targets": 1067606.0,
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      "seconds": 303.5072780229998,
      "validation_loss": 0.5802390652629799,
      "validation_targets": 131997.0,
      "validation_target_weight_sum": 80328.75,
      "validation_seconds": 26.103098994999982
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  "embedding_diagnostics": {
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    "query_norm_std": 0.9095959663391113,
    "item_norm_mean": 1.6166319847106934,
    "item_norm_std": 0.5547475814819336
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  "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-d8be2f5a-6938-4f89-88c4-cea510b1efb4",
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    "command": [
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      "-lc",
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    "parsed_cmd": [
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/checkpoints_final_base/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.680394\nepoch=2/2 batches=250 targets=994,228 batch_bce=0.521620 running_bce=0.582711 elapsed=283.1s\nsaved epoch checkpoint: /app/solution/checkpoints_final_hard/epoch-02.pt\nepoch=2/2 train_loss=0.58109 validation_loss=0.58024 pair_accuracy=0.671 train_targets=1,067,606 validation_targets=131,997 train_seconds=303.5 validation_seconds=26.1\nrestored best validation-BCE checkpoint: epoch=2 loss=0.580239\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\": 445.129,\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\": \"dot-product-with-item-side-novelty-coordinate\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-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\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_pool_multiplier\": 3,\n    \"item_prior_weight\": 0.3,\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\": 0.5383531185941673,\n      \"positive_logit\": 0.7500170017057294,\n      \"negative_logit\": -0.6300986818338857,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.81805216199973,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 26.929757490000156,\n      \"validation_loss\": 0.6803942896417029\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5810943002424908,\n      \"positive_logit\": 0.5913247774711043,\n      \"negative_logit\": -0.4150816630035099,\n      \"pair_accuracy\": 0.6711956763543854,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 303.5072780229998,\n      \"validation_loss\": 0.5802390652629799,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 26.103098994999982\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7581669092178345,\n    \"query_norm_std\": 0.9095959663391113,\n    \"item_norm_mean\": 1.6166319847106934,\n    \"item_norm_std\": 0.5547475814819336\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/checkpoints_final_base/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.680394\nepoch=2/2 batches=250 targets=994,228 batch_bce=0.521620 running_bce=0.582711 elapsed=283.1s\nsaved epoch checkpoint: /app/solution/checkpoints_final_hard/epoch-02.pt\nepoch=2/2 train_loss=0.58109 validation_loss=0.58024 pair_accuracy=0.671 train_targets=1,067,606 validation_targets=131,997 train_seconds=303.5 validation_seconds=26.1\nrestored best validation-BCE checkpoint: epoch=2 loss=0.580239\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\": 445.129,\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\": \"dot-product-with-item-side-novelty-coordinate\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-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\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_pool_multiplier\": 3,\n    \"item_prior_weight\": 0.3,\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\": 0.5383531185941673,\n      \"positive_logit\": 0.7500170017057294,\n      \"negative_logit\": -0.6300986818338857,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.81805216199973,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 26.929757490000156,\n      \"validation_loss\": 0.6803942896417029\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5810943002424908,\n      \"positive_logit\": 0.5913247774711043,\n      \"negative_logit\": -0.4150816630035099,\n      \"pair_accuracy\": 0.6711956763543854,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 303.5072780229998,\n      \"validation_loss\": 0.5802390652629799,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 26.103098994999982\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7581669092178345,\n    \"query_norm_std\": 0.9095959663391113,\n    \"item_norm_mean\": 1.6166319847106934,\n    \"item_norm_std\": 0.5547475814819336\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 449,
      "nanos": 458138048
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nusing a fresh AdamW optimizer after loading /app/solution/checkpoints_final_base/epoch-01.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.680394\nepoch=2/2 batches=250 targets=994,228 batch_bce=0.521620 running_bce=0.582711 elapsed=283.1s\nsaved epoch checkpoint: /app/solution/checkpoints_final_hard/epoch-02.pt\nepoch=2/2 train_loss=0.58109 validation_loss=0.58024 pair_accuracy=0.671 train_targets=1,067,606 validation_targets=131,997 train_seconds=303.5 validation_seconds=26.1\nrestored best validation-BCE checkpoint: epoch=2 loss=0.580239\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\": 445.129,\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\": \"dot-product-with-item-side-novelty-coordinate\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"hardest-24-of-72-uniform-plus-8-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\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_pool_multiplier\": 3,\n    \"item_prior_weight\": 0.3,\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\": 0.5383531185941673,\n      \"positive_logit\": 0.7500170017057294,\n      \"negative_logit\": -0.6300986818338857,\n      \"pair_accuracy\": 0.7552423073212402,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.81805216199973,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 26.929757490000156,\n      \"validation_loss\": 0.6803942896417029\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.5810943002424908,\n      \"positive_logit\": 0.5913247774711043,\n      \"negative_logit\": -0.4150816630035099,\n      \"pair_accuracy\": 0.6711956763543854,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 303.5072780229998,\n      \"validation_loss\": 0.5802390652629799,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 26.103098994999982\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7581669092178345,\n    \"query_norm_std\": 0.9095959663391113,\n    \"item_norm_mean\": 1.6166319847106934,\n    \"item_norm_std\": 0.5547475814819336\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
55

exec command

+39:36
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\np=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(p)\ntargets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nv=validate_ranking(f,targets)\ne=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\neligible=all(e.is_eligible(int(u),int(i)) for u,i in v[['uid','item_id']].itertuples(index=False,name=None))\nprint({'regular_file':p.is_file() and not p.is_symlink(),'rows':len(v),'users':v.uid.nunique(),'integer_dtypes':{c:str(v[c].dtype) for c in ['uid','item_id','rank']},'eligible':eligible,'duplicates':int(v.duplicated(['uid','item_id']).sum()),'rank_min':int(v['rank'].min()),'rank_max':int(v['rank'].max())})\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 2.9 seconds
Output:

{'regular_file': True, 'rows': 845600, 'users': 8456, 'integer_dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'eligible': True, 'duplicates': 0, 'rank_min': 1, 'rank_max': 100}
72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3  /app/solution/submission.parquet
-rw-r--r-- 1 agent agent 1.3M Sep 25 01:57 /app/solution/submission.parquet
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "session_id": "01a0d623-fcf0-77c1-9c51-32f399dd17df",
  "root_turn_id": "01a0d623-fcfd-7973-a7b1-aa71ada012df",
  "response_id": "resp_04ae2566cca94a3e016ab5d50b6b2087d29ee20719c0659b98",
  "usage": {
    "input_tokens": 70178,
    "cached_input_tokens": 68608,
    "cache_write_input_tokens": 0,
    "output_tokens": 429,
    "reasoning_output_tokens": 111,
    "total_tokens": 70607
  },
  "turn_token_usage": {
    "input_tokens": 5846302,
    "cached_input_tokens": 5764992,
    "cache_write_input_tokens": 0,
    "output_tokens": 15240,
    "reasoning_output_tokens": 2968,
    "total_tokens": 5861542
  },
  "thread_token_usage": {
    "input_tokens": 5846302,
    "cached_input_tokens": 5764992,
    "cache_write_input_tokens": 0,
    "output_tokens": 15240,
    "reasoning_output_tokens": 2968,
    "total_tokens": 5861542
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fc75bc48-2621-45d1-8c4a-107fc0eb5bd8",
    "process_id": "99160",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\np=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(p)\ntargets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nv=validate_ranking(f,targets)\ne=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\neligible=all(e.is_eligible(int(u),int(i)) for u,i in v[['uid','item_id']].itertuples(index=False,name=None))\nprint({'regular_file':p.is_file() and not p.is_symlink(),'rows':len(v),'users':v.uid.nunique(),'integer_dtypes':{c:str(v[c].dtype) for c in ['uid','item_id','rank']},'eligible':eligible,'duplicates':int(v.duplicated(['uid','item_id']).sum()),'rank_min':int(v['rank'].min()),'rank_max':int(v['rank'].max())})\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\np=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(p)\ntargets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nv=validate_ranking(f,targets)\ne=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\neligible=all(e.is_eligible(int(u),int(i)) for u,i in v[['uid','item_id']].itertuples(index=False,name=None))\nprint({'regular_file':p.is_file() and not p.is_symlink(),'rows':len(v),'users':v.uid.nunique(),'integer_dtypes':{c:str(v[c].dtype) for c in ['uid','item_id','rank']},'eligible':eligible,'duplicates':int(v.duplicated(['uid','item_id']).sum()),'rank_min':int(v['rank'].min()),'rank_max':int(v['rank'].max())})\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'integer_dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'eligible': True, 'duplicates': 0, 'rank_min': 1, 'rank_max': 100}\n72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3  /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 01:57 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'integer_dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'eligible': True, 'duplicates': 0, 'rank_min': 1, 'rank_max': 100}\n72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3  /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 01:57 /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 680317831
    },
    "formatted_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'integer_dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'eligible': True, 'duplicates': 0, 'rank_min': 1, 'rank_max': 100}\n72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3  /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 01:57 /app/solution/submission.parquet\n"
  }
}
56

exec command

+39:47
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 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\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+and the hardest 24 from a pool of 72 filtered uniform draws plus eight\n+propensity-corrected unique in-batch negatives. Every selected adjacent target\n+retains a nonzero weight. Only files under the supplied public directory are\n+read. Ranking adds a fixed item-side novelty coordinate, which remains an exact\n+two-tower dot product.\n@@\n-                    \"negative_sampling\": (\n-                        \"hardest-24-of-72-uniform-plus-8-corrected-inbatch\"\n-                    ),\n+                    \"negative_sampling\": (\n+                        f\"hardest-{config.uniform_negatives}-of-\"\n+                        f\"{config.uniform_negatives * config.hard_pool_multiplier}\"\n+                        f\"-uniform-plus-{config.inbatch_negatives}-corrected-inbatch\"\n+                    ),\n*** Add File: /app/solution/README.md\n+# Improved music-discovery two-tower\n+\n+## Result\n+\n+The reproduced production model scored **0.021280 NDCG@10** on 4,263 public\n+quality users. The selected model scored **0.025850**, a **21.48% relative\n+improvement**. Hard-negative training alone scored 0.022305.\n+\n+## Diagnosis and change\n+\n+The production loss uses 24 uniform catalog negatives and eight corrected\n+in-batch negatives. Most uniform draws are trivial, so only a small fraction of\n+the fixed CPU budget trains against items that can enter the top of a retrieval\n+ranking. It also leaves popular items overcompetitive: pure popularity scored\n+only 0.010710, while modestly reducing the learned head bias improved the\n+backtest.\n+\n+The improved trainer oversamples 72 full-history-filtered catalog negatives at\n+each target and backpropagates through the 24 with the largest current\n+two-tower scores. It retains the eight propensity-corrected in-batch negatives.\n+After a normal first epoch, one hard-negative fine-tuning epoch reduced the\n+public hard-pool validation BCE from 0.658425 to 0.569964.\n+\n+Ranking also uses a validation-selected item-side novelty coordinate with\n+weight 0.30: `log(1 + popularity_rank)`. This is not a candidate-dependent\n+model or cross-encoder. It is exactly equivalent to appending a constant `1`\n+to the user vector and the weighted scalar to each precomputed item vector, so\n+serving remains one user-tower computation followed by batchable dot products.\n+There is no user-ID table.\n+\n+## Final refit\n+\n+The submitted model was refit with `train_* + val_*` as observed history and\n+ranked only `test_eligible_candidates.npz`. The exact two-pass command sequence\n+used was:\n+\n+```sh\n+python train_current_model.py --stage submission --public-dir /task \\\n+  --output submission.parquet --prepared-cache submission_cache.npz \\\n+  --checkpoint-dir checkpoints_final_base --hard-pool-multiplier 1 \\\n+  --item-prior-weight 0.30 --threads 8\n+\n+python train_current_model.py --stage submission --public-dir /task \\\n+  --output submission.parquet --prepared-cache submission_cache.npz \\\n+  --checkpoint-dir checkpoints_final_hard \\\n+  --resume-checkpoint checkpoints_final_base/epoch-01.pt --epochs 2 \\\n+  --hard-pool-multiplier 3 --item-prior-weight 0.30 --threads 8\n+```\n+\n+The final file contains 845,600 rows for exactly 8,456 users, 100 distinct\n+eligible items and ranks 1--100 per user, integer-valued required columns, and\n+no duplicate user-item pairs.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

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        "type": "update",
        "unified_diff": "@@ -13,5 +13,7 @@\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+and the hardest 24 from a pool of 72 filtered uniform draws plus eight\n+propensity-corrected unique in-batch negatives. Every selected adjacent target\n+retains a nonzero weight. Only files under the supplied public directory are\n+read. Ranking adds a fixed item-side novelty coordinate, which remains an exact\n+two-tower dot product.\n \"\"\"\n@@ -2186,3 +2188,5 @@\n                     \"negative_sampling\": (\n-                        \"hardest-24-of-72-uniform-plus-8-corrected-inbatch\"\n+                        f\"hardest-{config.uniform_negatives}-of-\"\n+                        f\"{config.uniform_negatives * config.hard_pool_multiplier}\"\n+                        f\"-uniform-plus-{config.inbatch_negatives}-corrected-inbatch\"\n                     ),\n",
        "move_path": null
      },
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Improved music-discovery two-tower\n\n## Result\n\nThe reproduced production model scored **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scored **0.025850**, a **21.48% relative\nimprovement**. Hard-negative training alone scored 0.022305.\n\n## Diagnosis and change\n\nThe production loss uses 24 uniform catalog negatives and eight corrected\nin-batch negatives. Most uniform draws are trivial, so only a small fraction of\nthe fixed CPU budget trains against items that can enter the top of a retrieval\nranking. It also leaves popular items overcompetitive: pure popularity scored\nonly 0.010710, while modestly reducing the learned head bias improved the\nbacktest.\n\nThe improved trainer oversamples 72 full-history-filtered catalog negatives at\neach target and backpropagates through the 24 with the largest current\ntwo-tower scores. It retains the eight propensity-corrected in-batch negatives.\nAfter a normal first epoch, one hard-negative fine-tuning epoch reduced the\npublic hard-pool validation BCE from 0.658425 to 0.569964.\n\nRanking also uses a validation-selected item-side novelty coordinate with\nweight 0.30: `log(1 + popularity_rank)`. This is not a candidate-dependent\nmodel or cross-encoder. It is exactly equivalent to appending a constant `1`\nto the user vector and the weighted scalar to each precomputed item vector, so\nserving remains one user-tower computation followed by batchable dot products.\nThere is no user-ID table.\n\n## Final refit\n\nThe submitted model was refit with `train_* + val_*` as observed history and\nranked only `test_eligible_candidates.npz`. The exact two-pass command sequence\nused was:\n\n```sh\npython train_current_model.py --stage submission --public-dir /task \\\n  --output submission.parquet --prepared-cache submission_cache.npz \\\n  --checkpoint-dir checkpoints_final_base --hard-pool-multiplier 1 \\\n  --item-prior-weight 0.30 --threads 8\n\npython train_current_model.py --stage submission --public-dir /task \\\n  --output submission.parquet --prepared-cache submission_cache.npz \\\n  --checkpoint-dir checkpoints_final_hard \\\n  --resume-checkpoint checkpoints_final_base/epoch-01.pt --epochs 2 \\\n  --hard-pool-multiplier 3 --item-prior-weight 0.30 --threads 8\n```\n\nThe final file contains 845,600 rows for exactly 8,456 users, 100 distinct\neligible items and ranks 1--100 per user, integer-valued required columns, and\nno duplicate user-item pairs.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_current_model.py\n",
    "stderr": ""
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57

exec command

+40:11
Thinking unavailable No text recorded

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

Model text

The final artifact has passed the full submission contract: 845,600 rows, exactly 8,456 target users, integer columns, ranks 1–100, no duplicate user-item pairs, and every item accepted by that user’s test eligibility mask. I’m doing the final source/record sanity check now; the saved submission checksum is 72711d94…835a3.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"cp /app/solution/controls/prior_0.3.parquet /app/solution/improved_validation.parquet && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/calibrate_prior.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 && test ! -e /app/solution/submission.csv && ls -lh /app/solution/{submission.parquet,train_current_model.py,two_tower_model.py,README.md,production_validation.parquet,improved_validation.parquet}","workdir":"/app","yield_time_ms":30000,"max_output_tokens":4000});
text(r.output);

Tool response 

Script completed
Wall time 3.6 seconds
Output:

{
  "model": "/app/solution/improved_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02585,
    "quality_users": 4263
  }
}
ls: cannot access '/app/solution/{submission.parquet,train_current_model.py,two_tower_model.py,README.md,production_validation.parquet,improved_validation.parquet}': No such file or directory
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      }
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    "stdout": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02585,\n    \"quality_users\": 4263\n  }\n}\nls: cannot access '/app/solution/{submission.parquet,train_current_model.py,two_tower_model.py,README.md,production_validation.parquet,improved_validation.parquet}': No such file or directory\n",
    "stderr": "",
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    "exit_code": 2,
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58

exec command

+40:21
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md /app/solution/production_validation.parquet /app/solution/improved_validation.parquet; sha256sum /app/solution/submission.parquet; rg -n 'hard_pool_multiplier|item_prior_weight|topk\\(' /app/solution/train_current_model.py | head -20","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

-rw-r--r-- 1 agent agent 2.4K Sep 25 01:58 /app/solution/README.md
-rw-r--r-- 1 agent agent 1.4M Sep 25 01:58 /app/solution/improved_validation.parquet
-rw-r--r-- 1 agent agent 1.2M Sep 25 01:25 /app/solution/production_validation.parquet
-rw-r--r-- 1 agent agent 1.3M Sep 25 01:57 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  81K Sep 25 01:58 /app/solution/train_current_model.py
-rw-r--r-- 1 agent agent  16K Sep 25 01:18 /app/solution/two_tower_model.py
72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3  /app/solution/submission.parquet
60:    hard_pool_multiplier: int = 3
61:    item_prior_weight: float = 0.30
651:    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier
675:            columns = torch.topk(
1178:        pool_count = config.uniform_negatives * config.hard_pool_multiplier
1179:        if config.hard_pool_multiplier > 1 and config.uniform_negatives:
1182:            hard_columns = torch.topk(
1661:            or stored_config.get("hard_pool_multiplier", 1)
1662:            != config.hard_pool_multiplier
1917:        if config.item_prior_weight:
1921:            scores += config.item_prior_weight * novelty
1929:        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
1983:        default=Config.hard_pool_multiplier,
1989:        default=Config.item_prior_weight,
2062:        or args.hard_pool_multiplier < 1
2084:        hard_pool_multiplier=args.hard_pool_multiplier,
2085:        item_prior_weight=args.item_prior_weight,
2190:                        f"{config.uniform_negatives * config.hard_pool_multiplier}"
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    "stdout": "-rw-r--r-- 1 agent agent 2.4K Sep 25 01:58 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.4M Sep 25 01:58 /app/solution/improved_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:25 /app/solution/production_validation.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 01:57 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  81K Sep 25 01:58 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Sep 25 01:18 /app/solution/two_tower_model.py\n72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3  /app/solution/submission.parquet\n60:    hard_pool_multiplier: int = 3\n61:    item_prior_weight: float = 0.30\n651:    uniform_pool = config.uniform_negatives * config.hard_pool_multiplier\n675:            columns = torch.topk(\n1178:        pool_count = config.uniform_negatives * config.hard_pool_multiplier\n1179:        if config.hard_pool_multiplier > 1 and config.uniform_negatives:\n1182:            hard_columns = torch.topk(\n1661:            or stored_config.get(\"hard_pool_multiplier\", 1)\n1662:            != config.hard_pool_multiplier\n1917:        if config.item_prior_weight:\n1921:            scores += config.item_prior_weight * novelty\n1929:        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n1983:        default=Config.hard_pool_multiplier,\n1989:        default=Config.item_prior_weight,\n2062:        or args.hard_pool_multiplier < 1\n2084:        hard_pool_multiplier=args.hard_pool_multiplier,\n2085:        item_prior_weight=args.item_prior_weight,\n2190:                        f\"{config.uniform_negatives * config.hard_pool_multiplier}\"\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3900
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59

Final response

+40:32
Thinking unavailable No text recorded

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

Model text

Implemented and shipped the improved two-tower ranker.

  • Production public NDCG@10: 0.021280
  • Improved public NDCG@10: 0.025850
  • Relative improvement: 21.48%
  • Change: filtered model-mined hard negatives plus an item-side novelty coordinate
  • Serving contract preserved: precomputed item vectors and batchable dot-product scoring
  • Final refit uses train_* + val_*

Artifacts:

Submission validation passed all requirements: 8,456 users, 100 distinct eligible items per user, ranks 1–100, integer columns, and no duplicates.

SHA-256: 72711d94013bc600fa2999614955af65968fe1b87322f5b61345f406aa3835a3

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-medium-0-153-4-production-r05-20260924T221413Z
Recorded model
gpt-5.6-sol
Harness version
0.153.4
Session
01a0d623-fcf0-77c1-9c51-32f399dd17df
Source SHA-256
25a239fd0dbfb10a6c0be1cb26303df37ea8f42323eb89e8a2c107c14399d072

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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