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Agent trace

Two-tower retriever sol-0.153.4-max-01

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

Thinking text is unavailable for this run. The archive contains 163 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · max effort
Recorded conversation
57:06 · 169 steps
Private reward
0.023952 · Pass
Attempt cost
$5.54
  • 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

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Developer instructions

<skills_instructions>

Skills

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

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

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

<apps_instructions>

Apps (Connectors)

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

03

Developer instructions

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Developer instructions

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

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

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

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

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

They may be addressed as to=/root

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

All agents share the same directory. In detail:

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

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

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

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

04

Developer instructions

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Developer instructions

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

05

User context

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User context

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

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

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-24</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>

Harness context world state
{
  "type": "world_state",
  "full": true,
  "state": {
    "agents_md": {},
    "apps_instructions": true,
    "collaboration_mode": {
      "mode": "default",
      "model": "gpt-5.6-sol",
      "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d"
    },
    "context_window_guidance": "",
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-24",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "managed_developer_instructions": {},
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "persistent_mode": {},
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}
Harness context turn context
{
  "type": "turn_context",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-24",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "max",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "max",
  "summary": "auto"
}
06

Task instructions

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Task instructions

Build a latency-efficient PyTorch two-tower neural-network ranker

Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search.

The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.

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. Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
  2. After selecting the approach, 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").

The exact available Python distribution inventory is listed in /task/requirements.txt. 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 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.

Public tools

/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:

python /task/evaluate_public.py solution/validation.parquet

Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above.

Deliverable

Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.

Leave the code that defines, trains, and applies both towers, along with a concise /app/solution/README.md, in /app/solution/. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline.

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.
  • The environment includes CPU PyTorch 2.8.0. No accelerator is available.
  • You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
07

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.

Model text

I’ll inspect the dataset shapes, schemas, eligibility structure, and evaluator first, then build a scalable event-sampled two-tower baseline. I’ll keep a valid submission checkpointed early, tune only against the public window, and refit the chosen setup with validation events as history for the final artifact.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"pwd && find /task/data -maxdepth 1 -type f -printf '%f\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

/app
candidate_catalog.parquet
target_users.parquet
test_eligible_candidates.npz
test_preference_state.parquet
train_dislikes.parquet
train_likes.parquet
train_listens.parquet
train_undislikes.parquet
train_unlikes.parquet
val_dislikes.parquet
val_likes.parquet
val_listens.parquet
val_undislikes.parquet
val_unlikes.parquet
validation_eligible_candidates.npz
validation_preference_state.parquet
"""Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
#!/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()
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c6522471-cd10-47b2-86be-99ded07904ec",
    "process_id": "16829",
    "command": [
      "/bin/sh",
      "-lc",
      "pwd && find /task/data -maxdepth 1 -type f -printf '%f\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "pwd && find /task/data -maxdepth 1 -type f -printf '%f\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\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 relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\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# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\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 relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\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# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3850
    },
    "formatted_output": "/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\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 relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\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# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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Harness context token usage record
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08

exec command

+00:23
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n  if c in d:\n   s=d[c]\n   print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'same users?',np.array_equal(z['uids'],pd.read_parquet(D/'target_users.parquet').uid))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
ModuleNotFoundError: No module named 'pandas'
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5aba17c3c87d2aeefa64a3812f8fd",
  "usage": {
    "input_tokens": 18817,
    "cached_input_tokens": 14336,
    "cache_write_input_tokens": 0,
    "output_tokens": 426,
    "reasoning_output_tokens": 88,
    "total_tokens": 19243
  },
  "turn_token_usage": {
    "input_tokens": 33393,
    "cached_input_tokens": 25472,
    "cache_write_input_tokens": 0,
    "output_tokens": 733,
    "reasoning_output_tokens": 209,
    "total_tokens": 34126
  },
  "thread_token_usage": {
    "input_tokens": 33393,
    "cached_input_tokens": 25472,
    "cache_write_input_tokens": 0,
    "output_tokens": 733,
    "reasoning_output_tokens": 209,
    "total_tokens": 34126
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1f4c135e-58eb-4835-87ed-ea11e0c49a31",
    "process_id": "54897",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n  if c in d:\n   s=d[c]\n   print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'same users?',np.array_equal(z['uids'],pd.read_parquet(D/'target_users.parquet').uid))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n  if c in d:\n   s=d[c]\n   print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'same users?',np.array_equal(z['uids'],pd.read_parquet(D/'target_users.parquet').uid))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 5270
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\nModuleNotFoundError: No module named 'pandas'\n"
  }
}
09

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.

Model text

The default shell’s python is not the benchmark environment despite the supplied inventory. I’m locating the task’s pinned interpreter and will use that read-only environment for all training and evaluation.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"command -v python; command -v python3; python --version; python3 --version; find /task -maxdepth 3 -type f \\( -name python -o -name 'python3*' \\) -print 2>/dev/null | head -30; ls -la /task | sed -n '1,160p'; env | sort | sed -n '1,160p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
Python 3.12.13
Python 3.12.13
total 36
dr-xr-xr-x 1 root root 4096 Sep 24 22:56 .
drwxr-xr-x 1 root root 4096 Sep 24 23:00 ..
-r--r--r-- 1 root root    0 Sep 24 22:42 .keep
dr-xr-xr-x 1 root root 4096 Sep 24 22:43 data
-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt
CODEX_CI=1
CODEX_HOME=/tmp/codex-home
CODEX_MANAGED_BY_NPM=1
CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex
CODEX_SESSION_ID=01a0d5a6-369b-76d1-953a-8c850355d141
CODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141
COLORTERM=
GH_PAGER=cat
GIT_PAGER=cat
GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305
HOME=/home/agent
HOSTNAME=e2af0984d070
LANG=C.UTF-8
LC_ALL=C.UTF-8
LC_CTYPE=C.UTF-8
NO_COLOR=1
NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin
NVM_CD_FLAGS=
NVM_DIR=/home/agent/.nvm
NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node
PAGER=cat
PATH=/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/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games
PWD=/app
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13
SHLVL=1
TERM=dumb
_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-785f357a-2812-4bd1-a402-d4f80ea54264",
    "process_id": "63438",
    "command": [
      "/bin/sh",
      "-lc",
      "command -v python; command -v python3; python --version; python3 --version; find /task -maxdepth 3 -type f \\( -name python -o -name 'python3*' \\) -print 2>/dev/null | head -30; ls -la /task | sed -n '1,160p'; env | sort | sed -n '1,160p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "command -v python; command -v python3; python --version; python3 --version; find /task -maxdepth 3 -type f \\( -name python -o -name 'python3*' \\) -print 2>/dev/null | head -30; ls -la /task | sed -n '1,160p'; env | sort | sed -n '1,160p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root    0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/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/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root    0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/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/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4660
    },
    "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root    0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/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/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5abb099cc87d29bf53ffcbdf9f9e1",
  "usage": {
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    "cached_input_tokens": 18688,
    "cache_write_input_tokens": 0,
    "output_tokens": 206,
    "reasoning_output_tokens": 27,
    "total_tokens": 19505
  },
  "turn_token_usage": {
    "input_tokens": 52692,
    "cached_input_tokens": 44160,
    "cache_write_input_tokens": 0,
    "output_tokens": 939,
    "reasoning_output_tokens": 236,
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  },
  "thread_token_usage": {
    "input_tokens": 52692,
    "cached_input_tokens": 44160,
    "cache_write_input_tokens": 0,
    "output_tokens": 939,
    "reasoning_output_tokens": 236,
    "total_tokens": 53631
  }
}
10

exec command

+00:43
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"python -m pip list --format=columns | sed -n '1,200p'; find / -type f -path '*/site-packages/pandas/__init__.py' -o -type f -path '*/bin/python' 2>/dev/null | head -100","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 1.2 seconds
Output:

Package Version
------- -------
pip     25.0.1
/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5abb9235487d29a4dbfd9f2c54526",
  "usage": {
    "input_tokens": 20175,
    "cached_input_tokens": 19072,
    "cache_write_input_tokens": 0,
    "output_tokens": 121,
    "reasoning_output_tokens": 15,
    "total_tokens": 20296
  },
  "turn_token_usage": {
    "input_tokens": 72867,
    "cached_input_tokens": 63232,
    "cache_write_input_tokens": 0,
    "output_tokens": 1060,
    "reasoning_output_tokens": 251,
    "total_tokens": 73927
  },
  "thread_token_usage": {
    "input_tokens": 72867,
    "cached_input_tokens": 63232,
    "cache_write_input_tokens": 0,
    "output_tokens": 1060,
    "reasoning_output_tokens": 251,
    "total_tokens": 73927
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-93b0cf2e-c982-4933-aacf-2dbfab43ef09",
    "process_id": "83793",
    "command": [
      "/bin/sh",
      "-lc",
      "python -m pip list --format=columns | sed -n '1,200p'; find / -type f -path '*/site-packages/pandas/__init__.py' -o -type f -path '*/bin/python' 2>/dev/null | head -100"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python -m pip list --format=columns | sed -n '1,200p'; find / -type f -path '*/site-packages/pandas/__init__.py' -o -type f -path '*/bin/python' 2>/dev/null | head -100"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Package Version\n------- -------\npip     25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n",
    "stderr": "",
    "aggregated_output": "Package Version\n------- -------\npip     25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 61696484
    },
    "formatted_output": "Package Version\n------- -------\npip     25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n"
  }
}
11

exec command

+00:52
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 -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python - <<'PY'\nimport pandas, torch, pyarrow, sklearn\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,sklearn.__version__)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 5.6 seconds
Output:

total 124
dr-xr-xr-x 1 root root 4096 Sep 24 22:56 .
dr-xr-xr-x 1 root root 4096 Sep 24 22:56 ..
-r--r--r-- 1 root root 9033 Sep 24 22:56 Activate.ps1
-r--r--r-- 1 root root 2158 Sep 24 22:56 activate
-r--r--r-- 1 root root  920 Sep 24 22:56 activate.csh
-r--r--r-- 1 root root 2195 Sep 24 22:56 activate.fish
-r-xr-xr-x 1 root root  302 Sep 24 22:56 chardetect
-r-xr-xr-x 1 root root  304 Sep 24 22:56 dotenv
-r-xr-xr-x 1 root root  308 Sep 24 22:56 f2py
-r-xr-xr-x 1 root root  299 Sep 24 22:56 idna
-r-xr-xr-x 1 root root  297 Sep 24 22:56 isympy
-r-xr-xr-x 1 root root  305 Sep 24 22:56 jsonschema
-r-xr-xr-x 1 root root  311 Sep 24 22:56 jupyter
-r-xr-xr-x 1 root root  311 Sep 24 22:56 jupyter-migrate
-r-xr-xr-x 1 root root  316 Sep 24 22:56 jupyter-troubleshoot
-r-xr-xr-x 1 root root  344 Sep 24 22:56 jupyter-trust
-r-xr-xr-x 1 root root  311 Sep 24 22:56 jupytext
-r-xr-xr-x 1 root root  315 Sep 24 22:56 jupytext-config
-r-xr-xr-x 1 root root  301 Sep 24 22:56 kaggle
-r-xr-xr-x 1 root root  312 Sep 24 22:56 markdown-it
-r-xr-xr-x 1 root root  325 Sep 24 22:56 normalizer
-r-xr-xr-x 1 root root  308 Sep 24 22:56 numpy-config
-r-xr-xr-x 1 root root  313 Sep 24 22:56 pip
-r-xr-xr-x 1 root root  313 Sep 24 22:56 pip3
-r-xr-xr-x 1 root root  313 Sep 24 22:56 pip3.12
lrwxrwxrwx 1 root root   21 Sep 24 22:56 python -> /usr/local/bin/python
lrwxrwxrwx 1 root root    6 Sep 24 22:56 python3 -> python
lrwxrwxrwx 1 root root    6 Sep 24 22:56 python3.12 -> python
-r-xr-xr-x 1 root root  307 Sep 24 22:56 slugify
-r-xr-xr-x 1 root root  321 Sep 24 22:56 torchfrtrace
-r-xr-xr-x 1 root root  312 Sep 24 22:56 torchrun
-r-xr-xr-x 1 root root  299 Sep 24 22:56 tqdm
Python 3.12.13
2.3.3 2.8.0+cpu 18.1.0 1.7.2
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Harness context codex item completed
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    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python - <<'PY'\nimport pandas, torch, pyarrow, sklearn\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,sklearn.__version__)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python - <<'PY'\nimport pandas, torch, pyarrow, sklearn\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,sklearn.__version__)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "2.3.3 2.8.0+cpu 18.1.0 1.7.2\n",
    "stderr": "",
    "aggregated_output": "2.3.3 2.8.0+cpu 18.1.0 1.7.2\n",
    "exit_code": 0,
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      "secs": 5,
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}
12

exec command

+01:05
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.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.astype(str).to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n  if c in d:\n   s=d[c]\n   print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'targets match set?',set(z['uids'])==set(pd.read_parquet(D/'target_users.parquet').uid))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response

Script completed
Wall time 5.5 seconds
Output:


 candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}
[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]
item_id nunique 30000 min 206 max 9390294

 target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}
[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]
uid nunique 8456 min 100 max 1000000

 test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}
[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]
uid nunique 7092 min 100 max 1000000
item_id nunique 28863 min 206 max 9390294
state nunique 2 min disliked max liked

 train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]
uid nunique 5732 min 100 max 1000000
item_id nunique 50876 min 105 max 9390541
timestamp nunique 67779 min 75 max 24786580
is_organic nunique 2 min 0 max 1

 train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]
uid nunique 8144 min 100 max 1000000
item_id nunique 172303 min 26 max 9390522
timestamp nunique 562774 min 80 max 24786790
is_organic nunique 2 min 0 max 1

 train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}
[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]
uid nunique 9238 min 100 max 1000000
item_id nunique 845395 min 22 max 9390623
timestamp nunique 4806896 min 0 max 24786795
is_organic nunique 2 min 0 max 1
played_ratio_pct nunique 160 min 0 max 159

 train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]
uid nunique 2769 min 100 max 999900
item_id nunique 14725 min 178 max 9390240
timestamp nunique 6956 min 15950 max 24785395
is_organic nunique 2 min 0 max 1

 train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]
uid nunique 6197 min 100 max 1000000
item_id nunique 113072 min 26 max 9390601
timestamp nunique 87635 min 250 max 24786570
is_organic nunique 2 min 0 max 1

 val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]
uid nunique 955 min 1600 max 998800
item_id nunique 3135 min 651 max 9388630
timestamp nunique 2386 min 24789920 max 25393280
is_organic nunique 2 min 0 max 1

 val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]
uid nunique 4114 min 100 max 999900
item_id nunique 22902 min 209 max 9390461
timestamp nunique 19790 min 24788815 max 25393390
is_organic nunique 2 min 0 max 1

 val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}
[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]
uid nunique 8196 min 100 max 1000000
item_id nunique 191747 min 50 max 9390541
timestamp nunique 119851 min 24788600 max 25393395
is_organic nunique 2 min 0 max 1
played_ratio_pct nunique 160 min 0 max 159

 val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]
uid nunique 205 min 900 max 998800
item_id nunique 579 min 15149 max 9385279
timestamp nunique 242 min 24810715 max 25393035
is_organic nunique 2 min 0 max 1

 val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}
[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]
uid nunique 1313 min 1100 max 998800
item_id nunique 8080 min 1928 max 9390163
timestamp nunique 2941 min 24789005 max 25393385
is_organic nunique 2 min 0 max 1

 validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}
[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]
uid nunique 7038 min 100 max 1000000
item_id nunique 28814 min 206 max 9390294
state nunique 2 min disliked max liked

 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
eligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] targets match set? True

 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
eligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] targets match set? True
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-eaaa3677-52c2-4c98-b5e7-0f1ef980581b",
    "process_id": "59650",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.astype(str).to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n  if c in d:\n   s=d[c]\n   print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'targets match set?',set(z['uids'])==set(pd.read_parquet(D/'target_users.parquet').uid))\nPY"
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    "cwd": "file:///app",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] targets match set? True\n",
    "stderr": "",
    "aggregated_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] targets match set? True\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 349052852
    },
    "formatted_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] targets match set? True\n"
  }
}
13

exec command

+01:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"free -h; nproc; df -h /app; du -h /task/data/* | sort -h | tail -20","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: free: not found
8
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   15G   82G  16% /
8.0K	/task/data/val_undislikes.parquet
36K	/task/data/val_dislikes.parquet
72K	/task/data/val_unlikes.parquet
84K	/task/data/target_users.parquet
156K	/task/data/train_undislikes.parquet
292K	/task/data/val_likes.parquet
344K	/task/data/candidate_catalog.parquet
840K	/task/data/train_dislikes.parquet
1.1M	/task/data/test_preference_state.parquet
1.1M	/task/data/validation_preference_state.parquet
1.8M	/task/data/train_unlikes.parquet
5.2M	/task/data/train_likes.parquet
7.3M	/task/data/validation_eligible_candidates.npz
7.4M	/task/data/test_eligible_candidates.npz
9.5M	/task/data/val_listens.parquet
305M	/task/data/train_listens.parquet
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d36bcfa4-3020-4bca-8072-8f50dfa38a7a",
    "process_id": "75456",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h; nproc; df -h /app; du -h /task/data/* | sort -h | tail -20"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h; nproc; df -h /app; du -h /task/data/* | sort -h | tail -20"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: free: not found\n8\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: free: not found\n8\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3780
    },
    "formatted_output": "/bin/sh: 1: free: not found\n8\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n"
  }
}
Harness context token usage record
{
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  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5abe0594887d2a35f785b7a0fef4b",
  "usage": {
    "input_tokens": 24619,
    "cached_input_tokens": 21120,
    "cache_write_input_tokens": 0,
    "output_tokens": 404,
    "reasoning_output_tokens": 329,
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  },
  "turn_token_usage": {
    "input_tokens": 139152,
    "cached_input_tokens": 124544,
    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 139152,
    "cached_input_tokens": 124544,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 621,
    "total_tokens": 141131
  }
}
14

exec command

+01:36
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

MemTotal:       15988972 kB
MemAvailable:   14990484 kB
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5abee3c3087d28c945541d0c45f5d",
  "usage": {
    "input_tokens": 25296,
    "cached_input_tokens": 24448,
    "cache_write_input_tokens": 0,
    "output_tokens": 72,
    "reasoning_output_tokens": 9,
    "total_tokens": 25368
  },
  "turn_token_usage": {
    "input_tokens": 164448,
    "cached_input_tokens": 148992,
    "cache_write_input_tokens": 0,
    "output_tokens": 2051,
    "reasoning_output_tokens": 630,
    "total_tokens": 166499
  },
  "thread_token_usage": {
    "input_tokens": 164448,
    "cached_input_tokens": 148992,
    "cache_write_input_tokens": 0,
    "output_tokens": 2051,
    "reasoning_output_tokens": 630,
    "total_tokens": 166499
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-bd307d74-b71d-4047-a00a-7a146b388a15",
    "process_id": "51595",
    "command": [
      "/bin/sh",
      "-lc",
      "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo",
        "name": "meminfo",
        "path": "/proc/meminfo"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "MemTotal:       15988972 kB\nMemAvailable:   14990484 kB\n",
    "stderr": "",
    "aggregated_output": "MemTotal:       15988972 kB\nMemAvailable:   14990484 kB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3331
    },
    "formatted_output": "MemTotal:       15988972 kB\nMemAvailable:   14990484 kB\n"
  }
}
15

exec command

+01:51
Thinking unavailable No text recorded

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

Model text

The data is tractable in memory (the large log is a compact 305 MB parquet), and eligibility removes only a modest user-specific slice of the 30k catalog. I’m establishing strong measurable baselines—global popularity, repeat affinity, recent history, and explicit-state variants—before spending CPU on neural training; this will reveal which signals the towers need to encode.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution')\nt=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ncatitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}\n# catalog is already popularity order. Create globally popular eligible ranking.\nrows=[]\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\n# truth descriptive stats\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntset=set(target.tolist()); cset=set(catitems.tolist())\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)&vl.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(tset)&vk.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\n# check eligible\npacked=z['packed_eligible']; col={int(x):i for i,x in enumerate(z['item_ids'])}\nok=[]\nfor u,i in rel[['uid','item_id']].itertuples(index=False,name=None):\n rr=uidrow[int(u)]; cc=col[int(i)]; ok.append((int(packed[rr,cc//8])>>(cc%8))&1)\nrel=rel[np.array(ok,dtype=bool)]\nrankmap=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nrr=np.array([rankmap[int(x)] for x in rel.item_id])\nprint('load/build seconds',time.time()-t)\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nprint('relevant popularity rank quantiles',dict(zip([0,.1,.25,.5,.75,.9,.99,1],np.quantile(rr,[0,.1,.25,.5,.75,.9,.99,1]))))\nprint('fraction rank<=100/1k/5k/10k',[(k,float((rr<=k).mean())) for k in [10,100,500,1000,5000,10000]])\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 30.2 seconds
Output:

Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5abf587cc87d2bcf9f5efa1d24a4b",
  "usage": {
    "input_tokens": 25413,
    "cached_input_tokens": 25088,
    "cache_write_input_tokens": 0,
    "output_tokens": 1218,
    "reasoning_output_tokens": 285,
    "total_tokens": 26631
  },
  "turn_token_usage": {
    "input_tokens": 189861,
    "cached_input_tokens": 174080,
    "cache_write_input_tokens": 0,
    "output_tokens": 3269,
    "reasoning_output_tokens": 915,
    "total_tokens": 193130
  },
  "thread_token_usage": {
    "input_tokens": 189861,
    "cached_input_tokens": 174080,
    "cache_write_input_tokens": 0,
    "output_tokens": 3269,
    "reasoning_output_tokens": 915,
    "total_tokens": 193130
  }
}
16

exec command

+02:44
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"ps -eo pid,etime,pcpu,pmem,args | grep -E 'task-venv|evaluate_public' | grep -v grep || true; ls -lh /app/solution","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: ps: not found
total 0
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4e819db4-44c8-40d2-a10a-c5409d512353",
    "process_id": "23832",
    "command": [
      "/bin/sh",
      "-lc",
      "ps -eo pid,etime,pcpu,pmem,args | grep -E 'task-venv|evaluate_public' | grep -v grep || true; ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -eo pid,etime,pcpu,pmem,args | grep -E 'task-venv|evaluate_public' | grep -v grep || true; ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: ps: not found\ntotal 0\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\ntotal 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4371
    },
    "formatted_output": "/bin/sh: 1: ps: not found\ntotal 0\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  "usage": {
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    "cached_input_tokens": 25216,
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  },
  "turn_token_usage": {
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  },
  "thread_token_usage": {
    "input_tokens": 216515,
    "cached_input_tokens": 199296,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 962,
    "total_tokens": 219919
  }
}
17

exec command

+02:54
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz'); uidrow={int(u):i for i,u in enumerate(z['uids'])}\nrows=np.empty((len(target)*100,3),dtype=np.int64); p=0\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]; n=len(top)\n rows[p:p+n,0]=u; rows[p:p+n,1]=top; rows[p:p+n,2]=np.arange(1,n+1); p+=n\npd.DataFrame(rows[:p],columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',p,'seconds',time.time()-t,flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=83142
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ac39c7ec87d28d21bc1be2df4dcf",
  "usage": {
    "input_tokens": 26827,
    "cached_input_tokens": 26496,
    "cache_write_input_tokens": 0,
    "output_tokens": 490,
    "reasoning_output_tokens": 142,
    "total_tokens": 27317
  },
  "turn_token_usage": {
    "input_tokens": 243342,
    "cached_input_tokens": 225792,
    "cache_write_input_tokens": 0,
    "output_tokens": 3894,
    "reasoning_output_tokens": 1104,
    "total_tokens": 247236
  },
  "thread_token_usage": {
    "input_tokens": 243342,
    "cached_input_tokens": 225792,
    "cache_write_input_tokens": 0,
    "output_tokens": 3894,
    "reasoning_output_tokens": 1104,
    "total_tokens": 247236
  }
}
18

exec command

+03:08
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":83142,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6dfbc0","wall_time_seconds":30.001186034,"session_id":83142,"original_token_count":0}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ac490c7c87d282af1ad4eca98f6a",
  "usage": {
    "input_tokens": 27345,
    "cached_input_tokens": 26624,
    "cache_write_input_tokens": 0,
    "output_tokens": 64,
    "reasoning_output_tokens": 17,
    "total_tokens": 27409
  },
  "turn_token_usage": {
    "input_tokens": 270687,
    "cached_input_tokens": 252416,
    "cache_write_input_tokens": 0,
    "output_tokens": 3958,
    "reasoning_output_tokens": 1121,
    "total_tokens": 274645
  },
  "thread_token_usage": {
    "input_tokens": 270687,
    "cached_input_tokens": 252416,
    "cache_write_input_tokens": 0,
    "output_tokens": 3958,
    "reasoning_output_tokens": 1121,
    "total_tokens": 274645
  }
}
19

exec command

+03:43
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

/bin/sh: 1: cannot open /proc/9548/cmdline: No such file
/bin/sh: 1: cannot open /proc/9549/cmdline: No such file
`/task/evaluate_public.py` scores a validation-stage ranking against the public
python /task/evaluate_public.py solution/validation.parquet
PID=8778
`/task/evaluate_public.py` scores a validation-stage ranking against the public
python /task/evaluate_public.py solution/validation.parquet
PID=9004
`/task/evaluate_public.py` scores a validation-stage ranking against the public
python /task/evaluate_public.py solution/validation.parquet
PID=9012
/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'
/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet 
PID=9492
/opt/task-venv/bin/python - 
PID=9494
/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'
PID=9523
/opt/task-venv/bin/python -u - 
PID=9524
/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution 
PID=9547
/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution 
PID=9550
total 0
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ac6e3ef087d28b5dccfefa5c5ab3",
  "usage": {
    "input_tokens": 27463,
    "cached_input_tokens": 27136,
    "cache_write_input_tokens": 0,
    "output_tokens": 226,
    "reasoning_output_tokens": 101,
    "total_tokens": 27689
  },
  "turn_token_usage": {
    "input_tokens": 298150,
    "cached_input_tokens": 279552,
    "cache_write_input_tokens": 0,
    "output_tokens": 4184,
    "reasoning_output_tokens": 1222,
    "total_tokens": 302334
  },
  "thread_token_usage": {
    "input_tokens": 298150,
    "cached_input_tokens": 279552,
    "cache_write_input_tokens": 0,
    "output_tokens": 4184,
    "reasoning_output_tokens": 1222,
    "total_tokens": 302334
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-b206bb06-7873-40d5-af64-5b572c8b8e39",
    "process_id": "25596",
    "command": [
      "/bin/sh",
      "-lc",
      "ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 8740
    },
    "formatted_output": "/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n"
  }
}
20

exec command

+03:49
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"for p in 9492 9494 9523 9524; do echo PID=$p; awk '/State|VmRSS|Threads/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; echo; done","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

PID=9492
State:	S (sleeping)
VmRSS:	    1712 kB
Threads:	1
do_wait
PID=9494
State:	R (running)
VmRSS:	  175244 kB
Threads:	15
0
PID=9523
State:	S (sleeping)
VmRSS:	    1732 kB
Threads:	1
do_wait
PID=9524
State:	R (running)
VmRSS:	  162300 kB
Threads:	15
0
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ac756c2487d2a268c96add60f730",
  "usage": {
    "input_tokens": 28150,
    "cached_input_tokens": 27264,
    "cache_write_input_tokens": 0,
    "output_tokens": 196,
    "reasoning_output_tokens": 88,
    "total_tokens": 28346
  },
  "turn_token_usage": {
    "input_tokens": 326300,
    "cached_input_tokens": 306816,
    "cache_write_input_tokens": 0,
    "output_tokens": 4380,
    "reasoning_output_tokens": 1310,
    "total_tokens": 330680
  },
  "thread_token_usage": {
    "input_tokens": 326300,
    "cached_input_tokens": 306816,
    "cache_write_input_tokens": 0,
    "output_tokens": 4380,
    "reasoning_output_tokens": 1310,
    "total_tokens": 330680
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4c13fcc9-a5d6-4bea-ae19-7621ac0fa656",
    "process_id": "78106",
    "command": [
      "/bin/sh",
      "-lc",
      "for p in 9492 9494 9523 9524; do echo PID=$p; awk '/State|VmRSS|Threads/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; echo; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for p in 9492 9494 9523 9524; do echo PID=$p; awk '/State|VmRSS|Threads/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; echo; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "PID=9492\nState:\tS (sleeping)\nVmRSS:\t    1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t  175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t    1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t  162300 kB\nThreads:\t15\n0\n",
    "stderr": "",
    "aggregated_output": "PID=9492\nState:\tS (sleeping)\nVmRSS:\t    1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t  175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t    1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t  162300 kB\nThreads:\t15\n0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4361
    },
    "formatted_output": "PID=9492\nState:\tS (sleeping)\nVmRSS:\t    1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t  175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t    1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t  162300 kB\nThreads:\t15\n0\n"
  }
}
21

exec command

+03:54
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"for p in 9494 9524; do awk '{print \"pid\",$1,\"utime\",$14,\"stime\",$15}' /proc/$p/stat; done; ls -lh /app/solution","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

pid 9494 utime 10769 stime 127
pid 9524 utime 5530 stime 63
total 0
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-abd54bf2-17c1-42eb-b21e-e5eb2f49e02c",
    "process_id": "39870",
    "command": [
      "/bin/sh",
      "-lc",
      "for p in 9494 9524; do awk '{print \"pid\",$1,\"utime\",$14,\"stime\",$15}' /proc/$p/stat; done; ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for p in 9494 9524; do awk '{print \"pid\",$1,\"utime\",$14,\"stime\",$15}' /proc/$p/stat; done; ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "pid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n",
    "stderr": "",
    "aggregated_output": "pid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4100
    },
    "formatted_output": "pid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ac7a3b9087d2961187c2e8ce1802",
  "usage": {
    "input_tokens": 28485,
    "cached_input_tokens": 28032,
    "cache_write_input_tokens": 0,
    "output_tokens": 151,
    "reasoning_output_tokens": 58,
    "total_tokens": 28636
  },
  "turn_token_usage": {
    "input_tokens": 354785,
    "cached_input_tokens": 334848,
    "cache_write_input_tokens": 0,
    "output_tokens": 4531,
    "reasoning_output_tokens": 1368,
    "total_tokens": 359316
  },
  "thread_token_usage": {
    "input_tokens": 354785,
    "cached_input_tokens": 334848,
    "cache_write_input_tokens": 0,
    "output_tokens": 4531,
    "reasoning_output_tokens": 1368,
    "total_tokens": 359316
  }
}
22

exec command

+04:00
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"kill 9494 9524 9492 9523 2>/dev/null || true; /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncatitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(catitems),bitorder='little').astype(bool)\nprint('loaded mask',time.time()-t,flush=True)\n# np.argpartition on neg bool does not preserve popularity; cumulative rank trick finds first 100 vectorized\nsel=mask & (np.cumsum(mask,axis=1)<=100)\nrr,cc=np.nonzero(sel)\nranks=np.cumsum(sel,axis=1)[rr,cc]\nrows=pd.DataFrame({'uid':z['uids'][rr].astype(np.int64),'item_id':z['item_ids'][cc].astype(np.int64),'rank':ranks.astype(np.int64)})\nrows.to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',len(rows),'seconds',time.time()-t,flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=32338
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f999d642-d11c-4133-9458-5224266c1482",
    "process_id": "83142",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz'); uidrow={int(u):i for i,u in enumerate(z['uids'])}\nrows=np.empty((len(target)*100,3),dtype=np.int64); p=0\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]; n=len(top)\n rows[p:p+n,0]=u; rows[p:p+n,1]=top; rows[p:p+n,2]=np.arange(1,n+1); p+=n\npd.DataFrame(rows[:p],columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',p,'seconds',time.time()-t,flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz'); uidrow={int(u):i for i,u in enumerate(z['uids'])}\nrows=np.empty((len(target)*100,3),dtype=np.int64); p=0\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]; n=len(top)\n rows[p:p+n,0]=u; rows[p:p+n,1]=top; rows[p:p+n,2]=np.arange(1,n+1); p+=n\npd.DataFrame(rows[:p],columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',p,'seconds',time.time()-t,flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 143,
    "duration": {
      "secs": 65,
      "nanos": 659279147
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-69b778e1-c76d-4678-9f61-cf6ef7f6aa6b",
    "process_id": "7984",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution')\nt=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ncatitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}\n# catalog is already popularity order. Create globally popular eligible ranking.\nrows=[]\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\n# truth descriptive stats\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntset=set(target.tolist()); cset=set(catitems.tolist())\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)&vl.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(tset)&vk.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\n# check eligible\npacked=z['packed_eligible']; col={int(x):i for i,x in enumerate(z['item_ids'])}\nok=[]\nfor u,i in rel[['uid','item_id']].itertuples(index=False,name=None):\n rr=uidrow[int(u)]; cc=col[int(i)]; ok.append((int(packed[rr,cc//8])>>(cc%8))&1)\nrel=rel[np.array(ok,dtype=bool)]\nrankmap=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nrr=np.array([rankmap[int(x)] for x in rel.item_id])\nprint('load/build seconds',time.time()-t)\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nprint('relevant popularity rank quantiles',dict(zip([0,.1,.25,.5,.75,.9,.99,1],np.quantile(rr,[0,.1,.25,.5,.75,.9,.99,1]))))\nprint('fraction rank<=100/1k/5k/10k',[(k,float((rr<=k).mean())) for k in [10,100,500,1000,5000,10000]])\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution')\nt=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ncatitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}\n# catalog is already popularity order. Create globally popular eligible ranking.\nrows=[]\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\n# truth descriptive stats\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntset=set(target.tolist()); cset=set(catitems.tolist())\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)&vl.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(tset)&vk.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\n# check eligible\npacked=z['packed_eligible']; col={int(x):i for i,x in enumerate(z['item_ids'])}\nok=[]\nfor u,i in rel[['uid','item_id']].itertuples(index=False,name=None):\n rr=uidrow[int(u)]; cc=col[int(i)]; ok.append((int(packed[rr,cc//8])>>(cc%8))&1)\nrel=rel[np.array(ok,dtype=bool)]\nrankmap=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nrr=np.array([rankmap[int(x)] for x in rel.item_id])\nprint('load/build seconds',time.time()-t)\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nprint('relevant popularity rank quantiles',dict(zip([0,.1,.25,.5,.75,.9,.99,1],np.quantile(rr,[0,.1,.25,.5,.75,.9,.99,1]))))\nprint('fraction rank<=100/1k/5k/10k',[(k,float((rr<=k).mean())) for k in [10,100,500,1000,5000,10000]])\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 143,
    "duration": {
      "secs": 118,
      "nanos": 610697699
    },
    "formatted_output": ""
  }
}
Harness context token usage record
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  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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    "cache_write_input_tokens": 0,
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 1508,
    "total_tokens": 388521
  }
}
23

exec command

+04:08
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":32338,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 1.5 seconds
Output:

{"chunk_id":"c899a7","wall_time_seconds":1.427806102,"exit_code":0,"original_token_count":18}
loaded mask 0.22538971900939941
built 845600 seconds 3.0828750133514404
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 1516,
    "total_tokens": 417809
  },
  "thread_token_usage": {
    "input_tokens": 412709,
    "cached_input_tokens": 391680,
    "cache_write_input_tokens": 0,
    "output_tokens": 5100,
    "reasoning_output_tokens": 1516,
    "total_tokens": 417809
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c365d311-683c-4862-a6b2-be86fd4b2e0d",
    "process_id": "32338",
    "command": [
      "/bin/sh",
      "-lc",
      "kill 9494 9524 9492 9523 2>/dev/null || true; /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncatitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(catitems),bitorder='little').astype(bool)\nprint('loaded mask',time.time()-t,flush=True)\n# np.argpartition on neg bool does not preserve popularity; cumulative rank trick finds first 100 vectorized\nsel=mask & (np.cumsum(mask,axis=1)<=100)\nrr,cc=np.nonzero(sel)\nranks=np.cumsum(sel,axis=1)[rr,cc]\nrows=pd.DataFrame({'uid':z['uids'][rr].astype(np.int64),'item_id':z['item_ids'][cc].astype(np.int64),'rank':ranks.astype(np.int64)})\nrows.to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',len(rows),'seconds',time.time()-t,flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "kill 9494 9524 9492 9523 2>/dev/null || true; /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncatitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(catitems),bitorder='little').astype(bool)\nprint('loaded mask',time.time()-t,flush=True)\n# np.argpartition on neg bool does not preserve popularity; cumulative rank trick finds first 100 vectorized\nsel=mask & (np.cumsum(mask,axis=1)<=100)\nrr,cc=np.nonzero(sel)\nranks=np.cumsum(sel,axis=1)[rr,cc]\nrows=pd.DataFrame({'uid':z['uids'][rr].astype(np.int64),'item_id':z['item_ids'][cc].astype(np.int64),'rank':ranks.astype(np.int64)})\nrows.to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',len(rows),'seconds',time.time()-t,flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n",
    "stderr": "",
    "aggregated_output": "loaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 503136699
    },
    "formatted_output": "loaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n"
  }
}
24

exec command

+04:14
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":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/validation_popularity.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Harness context token usage record
{
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  "usage": {
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  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 5208,
    "reasoning_output_tokens": 1538,
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5c4b914e-c0f0-44cd-bbc1-77110c8ad008",
    "process_id": "48982",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_popularity.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_popularity.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 292329300
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_popularity.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
25

exec command

+04:21
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":"OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.astype('uint32')\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.astype('uint32')\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}; col={int(x):i for i,x in enumerate(z['item_ids'])}\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nprint('read',time.time()-t,flush=True)\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(target)&vl.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(target)&vk.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=np.fromiter((uidrow[int(x)] for x in rel.uid),np.int32,len(rel)); cc=np.fromiter((col[int(x)] for x in rel.item_id),np.int32,len(rel))\nok=((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); rel=rel[ok]\nrankmap=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id).to_dict(); ranks=np.fromiter((rankmap[int(x)] for x in rel.item_id),np.int32,len(rel))\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nqs=[0,.1,.25,.5,.75,.9,.99,1]; print('pop rank qs',list(zip(qs,np.quantile(ranks,qs).astype(int))))\nprint('fractions',[(k,round(float((ranks<=k).mean()),4)) for k in [10,100,500,1000,5000,10000,20000]])\n# label repeats in prior interactions and preferences\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor st in ['liked','disliked']:\n pairs=set(map(tuple,pref[pref.state==st][['uid','item_id']].to_numpy()))\n print('rel in pref',st, np.mean([tuple(x) in pairs for x in rel[['uid','item_id']].to_numpy()]), 'count',len(pairs))\n# train positive history among target+candidate, using arrow/pandas all two cols\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nprint('train read',len(tr),time.time()-t,flush=True)\ntrp=tr[(tr.played_ratio_pct>=50)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\ntr80=tr[(tr.played_ratio_pct>=80)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\nfor label,x in [('p50',trp),('p80',tr80),('org80',tr80[tr80.is_organic==1]),('org50',trp[trp.is_organic==1])]:\n pairs=set(map(tuple,x[['uid','item_id']].drop_duplicates().to_numpy()))\n ov=np.mean([tuple(y) in pairs for y in rel[['uid','item_id']].to_numpy()])\n print(label,'events',len(x),'pairs',len(pairs),'rel repeat frac',ov)\nprint('done',time.time()-t)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":20000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=29527
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ac96412c87d28543685b19098f31",
  "usage": {
    "input_tokens": 29546,
    "cached_input_tokens": 29184,
    "cache_write_input_tokens": 0,
    "output_tokens": 1083,
    "reasoning_output_tokens": 51,
    "total_tokens": 30629
  },
  "turn_token_usage": {
    "input_tokens": 471623,
    "cached_input_tokens": 449920,
    "cache_write_input_tokens": 0,
    "output_tokens": 6291,
    "reasoning_output_tokens": 1589,
    "total_tokens": 477914
  },
  "thread_token_usage": {
    "input_tokens": 471623,
    "cached_input_tokens": 449920,
    "cache_write_input_tokens": 0,
    "output_tokens": 6291,
    "reasoning_output_tokens": 1589,
    "total_tokens": 477914
  }
}
26

exec command

+04:41
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

let{output,...rest}=await tools.write_stdin({"session_id":29527,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 29.5 seconds
Output:

{"chunk_id":"9d458d","wall_time_seconds":29.495643714,"exit_code":0,"original_token_count":221}
read 0.09413003921508789
strong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}
relevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}
pop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]
fractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]
rel in pref liked 0.0 count 418435
rel in pref disliked 0.0 count 50689
train read 43484159 1.6793272495269775
p50 events 19959853 pairs 5514505 rel repeat frac 0.0
p80 events 18711339 pairs 5290619 rel repeat frac 0.0
org80 events 8661020 pairs 1935328 rel repeat frac 0.0
org50 events 9376263 pairs 2043504 rel repeat frac 0.0
done 29.99735951423645
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-282438a4-1108-4777-b2fb-752ebc3900c7",
    "process_id": "29527",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.astype('uint32')\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.astype('uint32')\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}; col={int(x):i for i,x in enumerate(z['item_ids'])}\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nprint('read',time.time()-t,flush=True)\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(target)&vl.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(target)&vk.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=np.fromiter((uidrow[int(x)] for x in rel.uid),np.int32,len(rel)); cc=np.fromiter((col[int(x)] for x in rel.item_id),np.int32,len(rel))\nok=((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); rel=rel[ok]\nrankmap=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id).to_dict(); ranks=np.fromiter((rankmap[int(x)] for x in rel.item_id),np.int32,len(rel))\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nqs=[0,.1,.25,.5,.75,.9,.99,1]; print('pop rank qs',list(zip(qs,np.quantile(ranks,qs).astype(int))))\nprint('fractions',[(k,round(float((ranks<=k).mean()),4)) for k in [10,100,500,1000,5000,10000,20000]])\n# label repeats in prior interactions and preferences\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor st in ['liked','disliked']:\n pairs=set(map(tuple,pref[pref.state==st][['uid','item_id']].to_numpy()))\n print('rel in pref',st, np.mean([tuple(x) in pairs for x in rel[['uid','item_id']].to_numpy()]), 'count',len(pairs))\n# train positive history among target+candidate, using arrow/pandas all two cols\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nprint('train read',len(tr),time.time()-t,flush=True)\ntrp=tr[(tr.played_ratio_pct>=50)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\ntr80=tr[(tr.played_ratio_pct>=80)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\nfor label,x in [('p50',trp),('p80',tr80),('org80',tr80[tr80.is_organic==1]),('org50',trp[trp.is_organic==1])]:\n pairs=set(map(tuple,x[['uid','item_id']].drop_duplicates().to_numpy()))\n ov=np.mean([tuple(y) in pairs for y in rel[['uid','item_id']].to_numpy()])\n print(label,'events',len(x),'pairs',len(pairs),'rel repeat frac',ov)\nprint('done',time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.astype('uint32')\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.astype('uint32')\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}; col={int(x):i for i,x in enumerate(z['item_ids'])}\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nprint('read',time.time()-t,flush=True)\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(target)&vl.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(target)&vk.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=np.fromiter((uidrow[int(x)] for x in rel.uid),np.int32,len(rel)); cc=np.fromiter((col[int(x)] for x in rel.item_id),np.int32,len(rel))\nok=((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); rel=rel[ok]\nrankmap=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id).to_dict(); ranks=np.fromiter((rankmap[int(x)] for x in rel.item_id),np.int32,len(rel))\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nqs=[0,.1,.25,.5,.75,.9,.99,1]; print('pop rank qs',list(zip(qs,np.quantile(ranks,qs).astype(int))))\nprint('fractions',[(k,round(float((ranks<=k).mean()),4)) for k in [10,100,500,1000,5000,10000,20000]])\n# label repeats in prior interactions and preferences\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor st in ['liked','disliked']:\n pairs=set(map(tuple,pref[pref.state==st][['uid','item_id']].to_numpy()))\n print('rel in pref',st, np.mean([tuple(x) in pairs for x in rel[['uid','item_id']].to_numpy()]), 'count',len(pairs))\n# train positive history among target+candidate, using arrow/pandas all two cols\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nprint('train read',len(tr),time.time()-t,flush=True)\ntrp=tr[(tr.played_ratio_pct>=50)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\ntr80=tr[(tr.played_ratio_pct>=80)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\nfor label,x in [('p50',trp),('p80',tr80),('org80',tr80[tr80.is_organic==1]),('org50',trp[trp.is_organic==1])]:\n pairs=set(map(tuple,x[['uid','item_id']].drop_duplicates().to_numpy()))\n ov=np.mean([tuple(y) in pairs for y in rel[['uid','item_id']].to_numpy()])\n print(label,'events',len(x),'pairs',len(pairs),'rel repeat frac',ov)\nprint('done',time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "read 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n",
    "stderr": "",
    "aggregated_output": "read 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n",
    "exit_code": 0,
    "duration": {
      "secs": 32,
      "nanos": 443527438
    },
    "formatted_output": "read 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n"
  }
}
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Model text

The baseline is valid but weak at 0.010710 NDCG@10. Validation relevance is overwhelmingly novel (none of the relevant user–item pairs were previously consumed or explicitly rated), so repeat-item heuristics cannot solve it. I’m now encoding the actual production pattern: temporal “history → first discovery” examples, a trainable history encoder, an independently precomputable item tower, sampled-softmax training, and eligibility-masked dot-product retrieval.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/model.py\n+\"\"\"Neural towers used by the discovery ranker.\n+\n+The user tower consumes only a variable-length interaction history plus summary\n+features.  It deliberately has no user-ID parameter.  The item tower can be\n+run for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\n+the final coordinate implements an item prior while preserving an exact dot\n+product scoring function.\n+\"\"\"\n+from __future__ import annotations\n+\n+import torch\n+from torch import nn\n+from torch.nn import functional as F\n+\n+\n+class UserTower(nn.Module):\n+    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n+\n+    def __init__(\n+        self,\n+        num_items: int,\n+        embedding_dim: int = 64,\n+        hidden_dim: int = 128,\n+        num_context_features: int = 4,\n+    ) -> None:\n+        super().__init__()\n+        self.embedding_dim = embedding_dim\n+        self.history_embedding = nn.EmbeddingBag(\n+            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n+        )\n+        nn.init.normal_(self.history_embedding.weight, std=0.04)\n+        self.network = nn.Sequential(\n+            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n+            nn.GELU(),\n+            nn.LayerNorm(hidden_dim),\n+            nn.Linear(hidden_dim, embedding_dim),\n+        )\n+        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n+        self.log_scale = nn.Parameter(torch.tensor(2.0))\n+\n+    def forward(\n+        self,\n+        item_ids: torch.Tensor,\n+        offsets: torch.Tensor,\n+        weights: torch.Tensor,\n+        context_features: torch.Tensor,\n+    ) -> torch.Tensor:\n+        pooled = self.history_embedding(\n+            item_ids, offsets, per_sample_weights=weights\n+        )\n+        # Signed preference weights are useful, but their absolute magnitude\n+        # must not make heavy users arbitrarily larger than light users.\n+        abs_sums = torch.zeros(\n+            len(offsets) - 1, device=weights.device, dtype=weights.dtype\n+        )\n+        lengths = offsets[1:] - offsets[:-1]\n+        bag_ids = torch.repeat_interleave(\n+            torch.arange(len(lengths), device=weights.device), lengths\n+        )\n+        abs_sums.scatter_add_(0, bag_ids, weights.abs())\n+        pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n+        latent = self.network(torch.cat((pooled, context_features), dim=1))\n+        latent = F.normalize(latent + self.skip(pooled), dim=1)\n+        # Constraining the scale prevents sampled-softmax from becoming\n+        # numerically brittle.  The constant last coordinate pairs with the\n+        # item tower's learned popularity prior.\n+        scale = self.log_scale.exp().clamp(1.0, 30.0)\n+        return torch.cat(\n+            (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n+        )\n+\n+\n+class ItemTower(nn.Module):\n+    \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n+\n+    def __init__(\n+        self,\n+        num_items: int,\n+        item_features: torch.Tensor,\n+        embedding_dim: int = 64,\n+        hidden_dim: int = 128,\n+    ) -> None:\n+        super().__init__()\n+        self.embedding_dim = embedding_dim\n+        self.id_embedding = nn.Embedding(num_items, embedding_dim)\n+        nn.init.normal_(self.id_embedding.weight, std=0.04)\n+        self.register_buffer(\"item_features\", item_features.float())\n+        feature_dim = item_features.shape[1]\n+        self.network = nn.Sequential(\n+            nn.Linear(embedding_dim + feature_dim, hidden_dim),\n+            nn.GELU(),\n+            nn.LayerNorm(hidden_dim),\n+            nn.Linear(hidden_dim, embedding_dim),\n+        )\n+        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n+        self.prior = nn.Sequential(\n+            nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n+        )\n+        self.id_bias = nn.Embedding(num_items, 1)\n+        nn.init.zeros_(self.id_bias.weight)\n+\n+    def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n+        ids = self.id_embedding(item_ids)\n+        features = self.item_features[item_ids]\n+        latent = self.network(torch.cat((ids, features), dim=1))\n+        latent = F.normalize(latent + self.skip(ids), dim=1)\n+        bias = self.prior(features) + self.id_bias(item_ids)\n+        return torch.cat((latent, bias), dim=1)\n+\n+\n+class TwoTowerRanker(nn.Module):\n+    \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n+\n+    def __init__(\n+        self,\n+        num_items: int,\n+        item_features: torch.Tensor,\n+        embedding_dim: int = 64,\n+        hidden_dim: int = 128,\n+    ) -> None:\n+        super().__init__()\n+        self.user_tower = UserTower(\n+            num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n+        )\n+        self.item_tower = ItemTower(\n+            num_items,\n+            item_features=item_features,\n+            embedding_dim=embedding_dim,\n+            hidden_dim=hidden_dim,\n+        )\n+\n+    def score(\n+        self,\n+        history_ids: torch.Tensor,\n+        offsets: torch.Tensor,\n+        history_weights: torch.Tensor,\n+        context_features: torch.Tensor,\n+        candidate_ids: torch.Tensor,\n+    ) -> torch.Tensor:\n+        users = self.user_tower(\n+            history_ids, offsets, history_weights, context_features\n+        )\n+        items = self.item_tower(candidate_ids)\n+        return users @ items.T\n*** Add File: /app/solution/train_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Train and apply an event-scalable two-tower discovery ranker.\n+\n+No user-item matrix is created.  Training examples are streamed conceptually\n+as temporal user histories paired with novel organic discoveries, represented\n+in compact CSR arrays.  The only dense user-by-item object is a short-lived\n+request batch of retrieval scores during ranking.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import math\n+import random\n+import time\n+from dataclasses import asdict, dataclass\n+from pathlib import Path\n+from typing import Iterable\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+from torch import nn\n+from torch.nn import functional as F\n+\n+from model import TwoTowerRanker\n+\n+\n+DATA = Path(\"/task/data\")\n+TRAIN_END = 24_786_800\n+WEEK = 7 * 24 * 60 * 60\n+GAP = 30 * 60\n+VAL_END = TRAIN_END + GAP + WEEK\n+\n+\n+@dataclass\n+class Config:\n+    embedding_dim: int = 64\n+    hidden_dim: int = 128\n+    max_history: int = 384\n+    history_days: int = 180\n+    training_weeks: int = 16\n+    epochs: int = 16\n+    batch_size: int = 256\n+    negatives: int = 512\n+    learning_rate: float = 0.002\n+    weight_decay: float = 1e-5\n+    like_sampling_weight: float = 4.0\n+    seed: int = 20260924\n+\n+\n+def id_lookup(values: np.ndarray, known: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n+    \"\"\"Map arbitrary integer IDs to positions without a Python-sized hash map.\"\"\"\n+    order = np.argsort(known)\n+    sorted_known = known[order]\n+    loc = np.searchsorted(sorted_known, values)\n+    valid = loc < len(sorted_known)\n+    valid_indices = np.flatnonzero(valid)\n+    valid[valid_indices] &= sorted_known[loc[valid_indices]] == values[valid_indices]\n+    mapped = np.full(len(values), -1, dtype=np.int32)\n+    mapped[valid] = order[loc[valid]].astype(np.int32)\n+    return mapped, valid\n+\n+\n+def read_family(name: str, stage: str, columns: list[str]) -> pd.DataFrame:\n+    frames = [pd.read_parquet(DATA / f\"train_{name}.parquet\", columns=columns)]\n+    if stage == \"final\":\n+        frames.append(pd.read_parquet(DATA / f\"val_{name}.parquet\", columns=columns))\n+    return pd.concat(frames, ignore_index=True)\n+\n+\n+def map_frame(\n+    frame: pd.DataFrame, users: np.ndarray, items: np.ndarray\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n+    u, uv = id_lookup(frame.uid.to_numpy(np.int64, copy=False), users)\n+    i, iv = id_lookup(frame.item_id.to_numpy(np.int64, copy=False), items)\n+    keep = uv & iv\n+    return u[keep], i[keep], np.flatnonzero(keep)\n+\n+\n+def sort_events(\n+    uid: np.ndarray,\n+    timestamp: np.ndarray,\n+    item: np.ndarray,\n+    *extra: np.ndarray,\n+) -> tuple[np.ndarray, ...]:\n+    order = np.lexsort((timestamp, uid))\n+    return (uid[order], timestamp[order], item[order]) + tuple(x[order] for x in extra)\n+\n+\n+def user_bounds(uid: np.ndarray, num_users: int) -> np.ndarray:\n+    return np.searchsorted(uid, np.arange(num_users + 1), side=\"left\")\n+\n+\n+def load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\n+    \"\"\"Load compact candidate-catalog events; raw frames are promptly released.\"\"\"\n+    started = time.time()\n+    listens = read_family(\n+        \"listens\",\n+        stage,\n+        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+    )\n+    lu, li, keep = map_frame(listens, users, items)\n+    lt = listens.timestamp.to_numpy(np.int64, copy=False)[keep]\n+    lo = listens.is_organic.to_numpy(np.uint8, copy=False)[keep]\n+    lr = listens.played_ratio_pct.to_numpy(np.int16, copy=False)[keep]\n+\n+    # All catalogue listens define novelty.  Positive listens form histories.\n+    all_listens = sort_events(lu, lt, li)\n+    positive = lr >= 50\n+    history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n+        1.0 + 0.35 * lo[positive]\n+    )\n+    history = sort_events(\n+        lu[positive],\n+        lt[positive],\n+        li[positive],\n+        history_weight.astype(np.float32),\n+        lo[positive].astype(np.float32),\n+    )\n+    strong = (lo == 1) & (lr >= 80)\n+    relevant_parts = [\n+        (lu[strong], lt[strong], li[strong], np.ones(strong.sum(), dtype=np.uint8))\n+    ]\n+    del listens, lr, lo, lu, li, lt\n+\n+    action_parts: list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []\n+    action_spec = {\n+        \"likes\": (0, 1, 3),\n+        \"unlikes\": (0, 0, 0),\n+        \"dislikes\": (1, 1, 0),\n+        \"undislikes\": (1, 0, 0),\n+    }\n+    for name, (channel, active, grade) in action_spec.items():\n+        frame = read_family(name, stage, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n+        au, ai, keep = map_frame(frame, users, items)\n+        at = frame.timestamp.to_numpy(np.int64, copy=False)[keep]\n+        organic = frame.is_organic.to_numpy(np.uint8, copy=False)[keep]\n+        action_parts.append(\n+            (\n+                au,\n+                at,\n+                ai,\n+                np.full(len(au), channel, dtype=np.uint8),\n+                np.full(len(au), active, dtype=np.uint8),\n+            )\n+        )\n+        if grade:\n+            good = organic == 1\n+            relevant_parts.append(\n+                (\n+                    au[good],\n+                    at[good],\n+                    ai[good],\n+                    np.full(good.sum(), grade, dtype=np.uint8),\n+                )\n+            )\n+        del frame\n+\n+    actions = sort_events(\n+        np.concatenate([x[0] for x in action_parts]),\n+        np.concatenate([x[1] for x in action_parts]),\n+        np.concatenate([x[2] for x in action_parts]),\n+        np.concatenate([x[3] for x in action_parts]),\n+        np.concatenate([x[4] for x in action_parts]),\n+    )\n+    relevant = sort_events(\n+        np.concatenate([x[0] for x in relevant_parts]),\n+        np.concatenate([x[1] for x in relevant_parts]),\n+        np.concatenate([x[2] for x in relevant_parts]),\n+        np.concatenate([x[3] for x in relevant_parts]),\n+    )\n+    print(\n+        f\"loaded {len(all_listens[0]):,} catalogue listens, \"\n+        f\"{len(history[0]):,} positive history events and \"\n+        f\"{len(relevant[0]):,} organic relevance events in \"\n+        f\"{time.time() - started:.1f}s\",\n+        flush=True,\n+    )\n+    return {\n+        \"all\": all_listens,\n+        \"history\": history,\n+        \"actions\": actions,\n+        \"relevant\": relevant,\n+    }\n+\n+\n+def latest_unique_history(\n+    times: np.ndarray,\n+    items: np.ndarray,\n+    values: np.ndarray,\n+    organic: np.ndarray,\n+    cutoff: int,\n+    cfg: Config,\n+) -> tuple[np.ndarray, np.ndarray, float]:\n+    hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n+    lo = int(np.searchsorted(times, cutoff - cfg.history_days * 86_400, side=\"left\"))\n+    if hi <= lo:\n+        return np.empty(0, np.int32), np.empty(0, np.float32), 0.0\n+    reverse_items = items[lo:hi][::-1]\n+    _, first_reverse = np.unique(reverse_items, return_index=True)\n+    first_reverse.sort()\n+    first_reverse = first_reverse[: cfg.max_history]\n+    positions = hi - 1 - first_reverse\n+    age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\n+    decay = np.exp(-age_days / 120.0)\n+    weights = values[positions] * decay\n+    return (\n+        items[positions].astype(np.int32, copy=False),\n+        weights.astype(np.float32, copy=False),\n+        float(organic[positions].mean()),\n+    )\n+\n+\n+def active_preferences(\n+    times: np.ndarray,\n+    items: np.ndarray,\n+    channels: np.ndarray,\n+    active: np.ndarray,\n+    cutoff: int,\n+) -> tuple[np.ndarray, np.ndarray, int, int]:\n+    hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n+    if hi == 0:\n+        return np.empty(0, np.int32), np.empty(0, np.float32), 0, 0\n+    keys = items[:hi].astype(np.int64) * 2 + channels[:hi]\n+    reverse_keys = keys[::-1]\n+    _, first_reverse = np.unique(reverse_keys, return_index=True)\n+    pos = hi - 1 - first_reverse\n+    pos = pos[active[pos] == 1]\n+    liked = pos[channels[pos] == 0]\n+    disliked = pos[channels[pos] == 1]\n+    out_items = np.concatenate((items[liked], items[disliked])).astype(np.int32)\n+    out_weights = np.concatenate(\n+        (\n+            np.full(len(liked), 2.5, np.float32),\n+            np.full(len(disliked), -1.25, np.float32),\n+        )\n+    )\n+    return out_items, out_weights, len(liked), len(disliked)\n+\n+\n+def make_history(\n+    h: tuple[np.ndarray, ...],\n+    a: tuple[np.ndarray, ...],\n+    cutoff: int,\n+    cfg: Config,\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n+    li, lw, organic_rate = latest_unique_history(\n+        h[0], h[1], h[2], h[3], cutoff, cfg\n+    )\n+    pi, pw, n_like, n_dislike = active_preferences(\n+        a[0], a[1], a[2], a[3], cutoff\n+    )\n+    ids = np.concatenate((li, pi))\n+    weights = np.concatenate((lw, pw))\n+    if not len(ids):\n+        # Every target user should have history, but EmbeddingBag requires a\n+        # real entry.  Item 0 at zero weight is a neutral fallback.\n+        ids = np.array([0], dtype=np.int32)\n+        weights = np.array([0.0], dtype=np.float32)\n+    features = np.array(\n+        [\n+            math.log1p(len(li)) / math.log1p(cfg.max_history),\n+            math.log1p(n_like) / 6.0,\n+            math.log1p(n_dislike) / 4.0,\n+            organic_rate,\n+        ],\n+        dtype=np.float32,\n+    )\n+    return ids, weights, features\n+\n+\n+def relevant_targets(\n+    r: tuple[np.ndarray, ...],\n+    first_seen: np.ndarray,\n+    cutoff: int,\n+) -> tuple[np.ndarray, np.ndarray]:\n+    lo = int(np.searchsorted(r[0], cutoff + GAP, side=\"left\"))\n+    hi = int(np.searchsorted(r[0], cutoff + GAP + WEEK, side=\"left\"))\n+    if hi <= lo:\n+        return np.empty(0, np.int32), np.empty(0, np.uint8)\n+    ri, rg = r[1][lo:hi], r[2][lo:hi]\n+    novel = first_seen[ri] >= cutoff\n+    ri, rg = ri[novel], rg[novel]\n+    if not len(ri):\n+        return np.empty(0, np.int32), np.empty(0, np.uint8)\n+    unique, inverse = np.unique(ri, return_inverse=True)\n+    grades = np.zeros(len(unique), dtype=np.uint8)\n+    np.maximum.at(grades, inverse, rg)\n+    return unique.astype(np.int32), grades\n+\n+\n+def build_contexts(\n+    arrays: dict,\n+    users: np.ndarray,\n+    stage_end: int,\n+    cfg: Config,\n+) -> dict[str, np.ndarray]:\n+    num_users = len(users)\n+    bounds = {k: user_bounds(v[0], num_users) for k, v in arrays.items()}\n+    history_ids: list[np.ndarray] = []\n+    history_weights: list[np.ndarray] = []\n+    context_features: list[np.ndarray] = []\n+    target_ids: list[np.ndarray] = []\n+    target_grades: list[np.ndarray] = []\n+    context_users: list[int] = []\n+    context_cutoffs: list[int] = []\n+\n+    # End at stage_end-WEEK so every label window is fully observed.  For the\n+    # final refit, this includes the public validation window as the newest\n+    # temporal training example.\n+    cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\n+    cutoffs.sort()\n+    first_seen = np.empty(30_000, dtype=np.int64)\n+    for u in range(num_users):\n+        slices = {}\n+        for key, values in arrays.items():\n+            b = bounds[key]\n+            slices[key] = tuple(x[b[u] : b[u + 1]] for x in values[1:])\n+        first_seen.fill(np.iinfo(np.int64).max)\n+        all_times, all_items = slices[\"all\"]\n+        np.minimum.at(first_seen, all_items, all_times)\n+        for cutoff in cutoffs:\n+            ti, tg = relevant_targets(slices[\"relevant\"], first_seen, cutoff)\n+            if not len(ti):\n+                continue\n+            hi, hw, cf = make_history(\n+                slices[\"history\"], slices[\"actions\"], cutoff, cfg\n+            )\n+            history_ids.append(hi)\n+            history_weights.append(hw)\n+            context_features.append(cf)\n+            target_ids.append(ti)\n+            target_grades.append(tg)\n+            context_users.append(u)\n+            context_cutoffs.append(cutoff)\n+\n+    h_lengths = np.fromiter((len(x) for x in history_ids), np.int64)\n+    t_lengths = np.fromiter((len(x) for x in target_ids), np.int64)\n+    result = {\n+        \"history_ids\": np.concatenate(history_ids).astype(np.int32),\n+        \"history_weights\": np.concatenate(history_weights).astype(np.float32),\n+        \"history_offsets\": np.r_[0, np.cumsum(h_lengths)].astype(np.int64),\n+        \"features\": np.stack(context_features).astype(np.float32),\n+        \"target_ids\": np.concatenate(target_ids).astype(np.int32),\n+        \"target_grades\": np.concatenate(target_grades).astype(np.uint8),\n+        \"target_offsets\": np.r_[0, np.cumsum(t_lengths)].astype(np.int64),\n+        \"context_users\": np.asarray(context_users, dtype=np.int32),\n+        \"context_cutoffs\": np.asarray(context_cutoffs, dtype=np.int64),\n+    }\n+    print(\n+        f\"built {len(history_ids):,} temporal contexts with \"\n+        f\"{len(result['target_ids']):,} novel targets and \"\n+        f\"{len(result['history_ids']):,} weighted history entries\",\n+        flush=True,\n+    )\n+    return result\n+\n+\n+def inference_histories(\n+    arrays: dict,\n+    users: np.ndarray,\n+    cutoff: int,\n+    cfg: Config,\n+    preference_path: Path,\n+    candidate_items: np.ndarray,\n+) -> dict[str, np.ndarray]:\n+    \"\"\"Build one cutoff-correct history per request user.\"\"\"\n+    num_users = len(users)\n+    hb = user_bounds(arrays[\"history\"][0], num_users)\n+    # The supplied state is authoritative and avoids reconstructing it from a\n+    # potentially truncated event horizon at serving time.\n+    pref = pd.read_parquet(preference_path)\n+    pu, pi, keep = map_frame(pref, users, candidate_items)\n+    ps = pref.state.to_numpy(copy=False)[keep]\n+    order = np.argsort(pu, kind=\"stable\")\n+    pu, pi, ps = pu[order], pi[order], ps[order]\n+    pb = user_bounds(pu, num_users)\n+\n+    ids_list, weights_list, features = [], [], []\n+    for u in range(num_users):\n+        hs = tuple(x[hb[u] : hb[u + 1]] for x in arrays[\"history\"][1:])\n+        li, lw, organic_rate = latest_unique_history(*hs, cutoff, cfg)\n+        sl = slice(pb[u], pb[u + 1])\n+        pitems = pi[sl].astype(np.int32, copy=False)\n+        states = ps[sl]\n+        pweights = np.where(states == \"liked\", 2.5, -1.25).astype(np.float32)\n+        ids = np.concatenate((li, pitems))\n+        weights = np.concatenate((lw, pweights))\n+        if not len(ids):\n+            ids, weights = np.array([0], np.int32), np.array([0.0], np.float32)\n+        ids_list.append(ids)\n+        weights_list.append(weights)\n+        features.append(\n+            np.array(\n+                [\n+                    math.log1p(len(li)) / math.log1p(cfg.max_history),\n+                    math.log1p(int((states == \"liked\").sum())) / 6.0,\n+                    math.log1p(int((states == \"disliked\").sum())) / 4.0,\n+                    organic_rate,\n+                ],\n+                np.float32,\n+            )\n+        )\n+    lengths = np.fromiter((len(x) for x in ids_list), np.int64)\n+    return {\n+        \"history_ids\": np.concatenate(ids_list).astype(np.int32),\n+        \"history_weights\": np.concatenate(weights_list).astype(np.float32),\n+        \"history_offsets\": np.r_[0, np.cumsum(lengths)].astype(np.int64),\n+        \"features\": np.stack(features).astype(np.float32),\n+    }\n+\n+\n+def gather_histories(data: dict[str, np.ndarray], indices: np.ndarray):\n+    offsets = data[\"history_offsets\"]\n+    pieces_i = [data[\"history_ids\"][offsets[x] : offsets[x + 1]] for x in indices]\n+    pieces_w = [\n+        data[\"history_weights\"][offsets[x] : offsets[x + 1]] for x in indices\n+    ]\n+    lengths = np.fromiter((len(x) for x in pieces_i), np.int64)\n+    return (\n+        torch.from_numpy(np.concatenate(pieces_i).astype(np.int64)),\n+        torch.from_numpy(np.r_[0, np.cumsum(lengths)].astype(np.int64)),\n+        torch.from_numpy(np.concatenate(pieces_w)),\n+        torch.from_numpy(data[\"features\"][indices]),\n+    )\n+\n+\n+def sample_targets(\n+    data: dict[str, np.ndarray], indices: np.ndarray, rng: np.random.Generator, cfg: Config\n+) -> np.ndarray:\n+    offsets = data[\"target_offsets\"]\n+    out = np.empty(len(indices), dtype=np.int64)\n+    for j, x in enumerate(indices):\n+        lo, hi = offsets[x], offsets[x + 1]\n+        grades = data[\"target_grades\"][lo:hi]\n+        weights = np.where(grades == 3, cfg.like_sampling_weight, 1.0)\n+        weights = weights / weights.sum()\n+        out[j] = data[\"target_ids\"][lo + rng.choice(hi - lo, p=weights)]\n+    return out\n+\n+\n+def train_model(\n+    model: TwoTowerRanker, data: dict[str, np.ndarray], cfg: Config\n+) -> list[float]:\n+    rng = np.random.default_rng(cfg.seed)\n+    torch.manual_seed(cfg.seed)\n+    optimizer = torch.optim.AdamW(\n+        model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay\n+    )\n+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n+        optimizer, T_max=cfg.epochs, eta_min=cfg.learning_rate * 0.12\n+    )\n+    n = len(data[\"features\"])\n+    losses: list[float] = []\n+    for epoch in range(cfg.epochs):\n+        model.train()\n+        order = rng.permutation(n)\n+        total_loss = 0.0\n+        seen = 0\n+        for start in range(0, n, cfg.batch_size):\n+            batch = order[start : start + cfg.batch_size]\n+            hids, offsets, hweights, features = gather_histories(data, batch)\n+            positives_np = sample_targets(data, batch, rng, cfg)\n+            positives = torch.from_numpy(positives_np)\n+            negatives = torch.from_numpy(\n+                rng.integers(0, 30_000, size=cfg.negatives, dtype=np.int64)\n+            )\n+            users = model.user_tower(hids, offsets, hweights, features)\n+            pos_vectors = model.item_tower(positives)\n+            neg_vectors = model.item_tower(negatives)\n+            positive_scores = (users * pos_vectors).sum(1, keepdim=True)\n+            negative_scores = users @ neg_vectors.T\n+            # A sampled candidate that equals the row's positive is not a\n+            # negative.  Mask it instead of teaching contradictory labels.\n+            negative_scores.masked_fill_(\n+                positives[:, None] == negatives[None, :], -1e4\n+            )\n+            logits = torch.cat((positive_scores, negative_scores), dim=1)\n+            loss = F.cross_entropy(\n+                logits, torch.zeros(len(batch), dtype=torch.long)\n+            )\n+            optimizer.zero_grad(set_to_none=True)\n+            loss.backward()\n+            nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n+            optimizer.step()\n+            total_loss += float(loss) * len(batch)\n+            seen += len(batch)\n+        scheduler.step()\n+        mean_loss = total_loss / seen\n+        losses.append(mean_loss)\n+        print(\n+            f\"epoch {epoch + 1:02d}/{cfg.epochs}: loss={mean_loss:.5f} \"\n+            f\"lr={scheduler.get_last_lr()[0]:.6g}\",\n+            flush=True,\n+        )\n+    return losses\n+\n+\[email protected]_mode()\n+def rank_candidates(\n+    model: TwoTowerRanker,\n+    histories: dict[str, np.ndarray],\n+    eligibility_path: Path,\n+    output: Path,\n+    batch_size: int = 192,\n+) -> None:\n+    model.eval()\n+    archive = np.load(eligibility_path)\n+    uids = archive[\"uids\"].astype(np.int64)\n+    item_ids = archive[\"item_ids\"].astype(np.int64)\n+    packed = archive[\"packed_eligible\"]\n+    all_item_vectors = model.item_tower(torch.arange(len(item_ids))).T.contiguous()\n+    rows: list[np.ndarray] = []\n+    for start in range(0, len(uids), batch_size):\n+        stop = min(start + batch_size, len(uids))\n+        idx = np.arange(start, stop)\n+        hids, offsets, weights, features = gather_histories(histories, idx)\n+        user_vectors = model.user_tower(hids, offsets, weights, features)\n+        scores = user_vectors @ all_item_vectors\n+        eligible = np.unpackbits(\n+            packed[start:stop], axis=1, count=len(item_ids), bitorder=\"little\"\n+        ).astype(bool)\n+        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n+        # A tiny deterministic index term makes exact ties reproducible.\n+        scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\n+        top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\n+        block = np.empty(((stop - start) * 100, 3), dtype=np.int64)\n+        block[:, 0] = np.repeat(uids[start:stop], 100)\n+        block[:, 1] = item_ids[top.reshape(-1)]\n+        block[:, 2] = np.tile(np.arange(1, 101), stop - start)\n+        rows.append(block)\n+    frame = pd.DataFrame(np.concatenate(rows), columns=[\"uid\", \"item_id\", \"rank\"])\n+    output.parent.mkdir(parents=True, exist_ok=True)\n+    frame.to_parquet(output, index=False)\n+    print(f\"wrote {len(frame):,} ranked rows to {output}\", flush=True)\n+\n+\n+def item_features(catalog: pd.DataFrame) -> torch.Tensor:\n+    raw = np.column_stack(\n+        (\n+            np.log1p(catalog.positive_listens.to_numpy(np.float32)),\n+            np.log1p(catalog.organic_positive_listens.to_numpy(np.float32)),\n+            np.log1p(catalog.popularity_rank.to_numpy(np.float32)),\n+            catalog.organic_positive_listens.to_numpy(np.float32)\n+            / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1),\n+        )\n+    )\n+    raw = (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\n+    return torch.from_numpy(raw.astype(np.float32))\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n+    parser.add_argument(\"--output\", type=Path, required=True)\n+    parser.add_argument(\"--checkpoint\", type=Path)\n+    parser.add_argument(\"--cache\", type=Path)\n+    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n+    parser.add_argument(\"--embedding-dim\", type=int, default=Config.embedding_dim)\n+    parser.add_argument(\"--max-history\", type=int, default=Config.max_history)\n+    parser.add_argument(\"--training-weeks\", type=int, default=Config.training_weeks)\n+    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n+    parser.add_argument(\"--threads\", type=int, default=8)\n+    args = parser.parse_args()\n+    torch.set_num_threads(args.threads)\n+    torch.set_num_interop_threads(1)\n+    random.seed(args.seed)\n+    np.random.seed(args.seed)\n+    cfg = Config(\n+        epochs=args.epochs,\n+        embedding_dim=args.embedding_dim,\n+        max_history=args.max_history,\n+        training_weeks=args.training_weeks,\n+        seed=args.seed,\n+    )\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    candidate_items = catalog.item_id.to_numpy(np.int64)\n+    target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n+    stage_end = TRAIN_END if args.stage == \"validation\" else VAL_END\n+    cache = args.cache or Path(f\"/app/solution/cache_{args.stage}.pt\")\n+    if cache.exists():\n+        payload = torch.load(cache, weights_only=False)\n+        contexts, histories = payload[\"contexts\"], payload[\"histories\"]\n+        print(f\"loaded prepared arrays from {cache}\", flush=True)\n+    else:\n+        arrays = load_event_arrays(args.stage, target_users, candidate_items)\n+        contexts = build_contexts(arrays, target_users, stage_end, cfg)\n+        pref_path = DATA / (\n+            \"validation_preference_state.parquet\"\n+            if args.stage == \"validation\"\n+            else \"test_preference_state.parquet\"\n+        )\n+        histories = inference_histories(\n+            arrays, target_users, stage_end, cfg, pref_path, candidate_items\n+        )\n+        torch.save(\n+            {\"contexts\": contexts, \"histories\": histories, \"config\": asdict(cfg)},\n+            cache,\n+        )\n+        print(f\"saved prepared arrays to {cache}\", flush=True)\n+    model = TwoTowerRanker(\n+        len(candidate_items),\n+        item_features(catalog),\n+        embedding_dim=cfg.embedding_dim,\n+        hidden_dim=cfg.hidden_dim,\n+    )\n+    losses = train_model(model, contexts, cfg)\n+    checkpoint = args.checkpoint or Path(\n+        f\"/app/solution/model_{args.stage}.pt\"\n+    )\n+    torch.save(\n+        {\n+            \"model_state\": model.state_dict(),\n+            \"config\": asdict(cfg),\n+            \"losses\": losses,\n+            \"catalog_item_ids\": candidate_items,\n+        },\n+        checkpoint,\n+    )\n+    eligibility = DATA / f\"{args.stage}_eligible_candidates.npz\"\n+    rank_candidates(model, histories, eligibility, args.output)\n+    print(json.dumps({\"checkpoint\": str(checkpoint), \"config\": asdict(cfg)}))\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

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Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-96f71dd1-c863-4dc9-97ec-dd2d745a8f21",
    "changes": {
      "/app/solution/model.py": {
        "type": "add",
        "content": "\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures.  It deliberately has no user-ID parameter.  The item tower can be\nrun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n    def __init__(\n        self,\n        num_items: int,\n        embedding_dim: int = 64,\n        hidden_dim: int = 128,\n        num_context_features: int = 4,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.history_embedding = nn.EmbeddingBag(\n            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n        )\n        nn.init.normal_(self.history_embedding.weight, std=0.04)\n        self.network = nn.Sequential(\n            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n            nn.GELU(),\n            nn.LayerNorm(hidden_dim),\n            nn.Linear(hidden_dim, embedding_dim),\n        )\n        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n        self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n    def forward(\n        self,\n        item_ids: torch.Tensor,\n        offsets: torch.Tensor,\n        weights: torch.Tensor,\n        context_features: torch.Tensor,\n    ) -> torch.Tensor:\n        pooled = self.history_embedding(\n            item_ids, offsets, per_sample_weights=weights\n        )\n        # Signed preference weights are useful, but their absolute magnitude\n        # must not make heavy users arbitrarily larger than light users.\n        abs_sums = torch.zeros(\n            len(offsets) - 1, device=weights.device, dtype=weights.dtype\n        )\n        lengths = offsets[1:] - offsets[:-1]\n        bag_ids = torch.repeat_interleave(\n            torch.arange(len(lengths), device=weights.device), lengths\n        )\n        abs_sums.scatter_add_(0, bag_ids, weights.abs())\n        pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n        latent = self.network(torch.cat((pooled, context_features), dim=1))\n        latent = F.normalize(latent + self.skip(pooled), dim=1)\n        # Constraining the scale prevents sampled-softmax from becoming\n        # numerically brittle.  The constant last coordinate pairs with the\n        # item tower's learned popularity prior.\n        scale = self.log_scale.exp().clamp(1.0, 30.0)\n        return torch.cat(\n            (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n        )\n\n\nclass ItemTower(nn.Module):\n    \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n\n    def __init__(\n        self,\n        num_items: int,\n        item_features: torch.Tensor,\n        embedding_dim: int = 64,\n        hidden_dim: int = 128,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.id_embedding = nn.Embedding(num_items, embedding_dim)\n        nn.init.normal_(self.id_embedding.weight, std=0.04)\n        self.register_buffer(\"item_features\", item_features.float())\n        feature_dim = item_features.shape[1]\n        self.network = nn.Sequential(\n            nn.Linear(embedding_dim + feature_dim, hidden_dim),\n            nn.GELU(),\n            nn.LayerNorm(hidden_dim),\n            nn.Linear(hidden_dim, embedding_dim),\n        )\n        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n        self.prior = nn.Sequential(\n            nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n        )\n        self.id_bias = nn.Embedding(num_items, 1)\n        nn.init.zeros_(self.id_bias.weight)\n\n    def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n        ids = self.id_embedding(item_ids)\n        features = self.item_features[item_ids]\n        latent = self.network(torch.cat((ids, features), dim=1))\n        latent = F.normalize(latent + self.skip(ids), dim=1)\n        bias = self.prior(features) + self.id_bias(item_ids)\n        return torch.cat((latent, bias), dim=1)\n\n\nclass TwoTowerRanker(nn.Module):\n    \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n\n    def __init__(\n        self,\n        num_items: int,\n        item_features: torch.Tensor,\n        embedding_dim: int = 64,\n        hidden_dim: int = 128,\n    ) -> None:\n        super().__init__()\n        self.user_tower = UserTower(\n            num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n        )\n        self.item_tower = ItemTower(\n            num_items,\n            item_features=item_features,\n            embedding_dim=embedding_dim,\n            hidden_dim=hidden_dim,\n        )\n\n    def score(\n        self,\n        history_ids: torch.Tensor,\n        offsets: torch.Tensor,\n        history_weights: torch.Tensor,\n        context_features: torch.Tensor,\n        candidate_ids: torch.Tensor,\n    ) -> torch.Tensor:\n        users = self.user_tower(\n            history_ids, offsets, history_weights, context_features\n        )\n        items = self.item_tower(candidate_ids)\n        return users @ items.T\n"
      },
      "/app/solution/train_ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Train and apply an event-scalable two-tower discovery ranker.\n\nNo user-item matrix is created.  Training examples are streamed conceptually\nas temporal user histories paired with novel organic discoveries, represented\nin compact CSR arrays.  The only dense user-by-item object is a short-lived\nrequest batch of retrieval scores during ranking.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\nfrom typing import Iterable\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom model import TwoTowerRanker\n\n\nDATA = Path(\"/task/data\")\nTRAIN_END = 24_786_800\nWEEK = 7 * 24 * 60 * 60\nGAP = 30 * 60\nVAL_END = TRAIN_END + GAP + WEEK\n\n\n@dataclass\nclass Config:\n    embedding_dim: int = 64\n    hidden_dim: int = 128\n    max_history: int = 384\n    history_days: int = 180\n    training_weeks: int = 16\n    epochs: int = 16\n    batch_size: int = 256\n    negatives: int = 512\n    learning_rate: float = 0.002\n    weight_decay: float = 1e-5\n    like_sampling_weight: float = 4.0\n    seed: int = 20260924\n\n\ndef id_lookup(values: np.ndarray, known: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n    \"\"\"Map arbitrary integer IDs to positions without a Python-sized hash map.\"\"\"\n    order = np.argsort(known)\n    sorted_known = known[order]\n    loc = np.searchsorted(sorted_known, values)\n    valid = loc < len(sorted_known)\n    valid_indices = np.flatnonzero(valid)\n    valid[valid_indices] &= sorted_known[loc[valid_indices]] == values[valid_indices]\n    mapped = np.full(len(values), -1, dtype=np.int32)\n    mapped[valid] = order[loc[valid]].astype(np.int32)\n    return mapped, valid\n\n\ndef read_family(name: str, stage: str, columns: list[str]) -> pd.DataFrame:\n    frames = [pd.read_parquet(DATA / f\"train_{name}.parquet\", columns=columns)]\n    if stage == \"final\":\n        frames.append(pd.read_parquet(DATA / f\"val_{name}.parquet\", columns=columns))\n    return pd.concat(frames, ignore_index=True)\n\n\ndef map_frame(\n    frame: pd.DataFrame, users: np.ndarray, items: np.ndarray\n) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n    u, uv = id_lookup(frame.uid.to_numpy(np.int64, copy=False), users)\n    i, iv = id_lookup(frame.item_id.to_numpy(np.int64, copy=False), items)\n    keep = uv & iv\n    return u[keep], i[keep], np.flatnonzero(keep)\n\n\ndef sort_events(\n    uid: np.ndarray,\n    timestamp: np.ndarray,\n    item: np.ndarray,\n    *extra: np.ndarray,\n) -> tuple[np.ndarray, ...]:\n    order = np.lexsort((timestamp, uid))\n    return (uid[order], timestamp[order], item[order]) + tuple(x[order] for x in extra)\n\n\ndef user_bounds(uid: np.ndarray, num_users: int) -> np.ndarray:\n    return np.searchsorted(uid, np.arange(num_users + 1), side=\"left\")\n\n\ndef load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\n    \"\"\"Load compact candidate-catalog events; raw frames are promptly released.\"\"\"\n    started = time.time()\n    listens = read_family(\n        \"listens\",\n        stage,\n        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    )\n    lu, li, keep = map_frame(listens, users, items)\n    lt = listens.timestamp.to_numpy(np.int64, copy=False)[keep]\n    lo = listens.is_organic.to_numpy(np.uint8, copy=False)[keep]\n    lr = listens.played_ratio_pct.to_numpy(np.int16, copy=False)[keep]\n\n    # All catalogue listens define novelty.  Positive listens form histories.\n    all_listens = sort_events(lu, lt, li)\n    positive = lr >= 50\n    history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n        1.0 + 0.35 * lo[positive]\n    )\n    history = sort_events(\n        lu[positive],\n        lt[positive],\n        li[positive],\n        history_weight.astype(np.float32),\n        lo[positive].astype(np.float32),\n    )\n    strong = (lo == 1) & (lr >= 80)\n    relevant_parts = [\n        (lu[strong], lt[strong], li[strong], np.ones(strong.sum(), dtype=np.uint8))\n    ]\n    del listens, lr, lo, lu, li, lt\n\n    action_parts: list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []\n    action_spec = {\n        \"likes\": (0, 1, 3),\n        \"unlikes\": (0, 0, 0),\n        \"dislikes\": (1, 1, 0),\n        \"undislikes\": (1, 0, 0),\n    }\n    for name, (channel, active, grade) in action_spec.items():\n        frame = read_family(name, stage, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n        au, ai, keep = map_frame(frame, users, items)\n        at = frame.timestamp.to_numpy(np.int64, copy=False)[keep]\n        organic = frame.is_organic.to_numpy(np.uint8, copy=False)[keep]\n        action_parts.append(\n            (\n                au,\n                at,\n                ai,\n                np.full(len(au), channel, dtype=np.uint8),\n                np.full(len(au), active, dtype=np.uint8),\n            )\n        )\n        if grade:\n            good = organic == 1\n            relevant_parts.append(\n                (\n                    au[good],\n                    at[good],\n                    ai[good],\n                    np.full(good.sum(), grade, dtype=np.uint8),\n                )\n            )\n        del frame\n\n    actions = sort_events(\n        np.concatenate([x[0] for x in action_parts]),\n        np.concatenate([x[1] for x in action_parts]),\n        np.concatenate([x[2] for x in action_parts]),\n        np.concatenate([x[3] for x in action_parts]),\n        np.concatenate([x[4] for x in action_parts]),\n    )\n    relevant = sort_events(\n        np.concatenate([x[0] for x in relevant_parts]),\n        np.concatenate([x[1] for x in relevant_parts]),\n        np.concatenate([x[2] for x in relevant_parts]),\n        np.concatenate([x[3] for x in relevant_parts]),\n    )\n    print(\n        f\"loaded {len(all_listens[0]):,} catalogue listens, \"\n        f\"{len(history[0]):,} positive history events and \"\n        f\"{len(relevant[0]):,} organic relevance events in \"\n        f\"{time.time() - started:.1f}s\",\n        flush=True,\n    )\n    return {\n        \"all\": all_listens,\n        \"history\": history,\n        \"actions\": actions,\n        \"relevant\": relevant,\n    }\n\n\ndef latest_unique_history(\n    times: np.ndarray,\n    items: np.ndarray,\n    values: np.ndarray,\n    organic: np.ndarray,\n    cutoff: int,\n    cfg: Config,\n) -> tuple[np.ndarray, np.ndarray, float]:\n    hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n    lo = int(np.searchsorted(times, cutoff - cfg.history_days * 86_400, side=\"left\"))\n    if hi <= lo:\n        return np.empty(0, np.int32), np.empty(0, np.float32), 0.0\n    reverse_items = items[lo:hi][::-1]\n    _, first_reverse = np.unique(reverse_items, return_index=True)\n    first_reverse.sort()\n    first_reverse = first_reverse[: cfg.max_history]\n    positions = hi - 1 - first_reverse\n    age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\n    decay = np.exp(-age_days / 120.0)\n    weights = values[positions] * decay\n    return (\n        items[positions].astype(np.int32, copy=False),\n        weights.astype(np.float32, copy=False),\n        float(organic[positions].mean()),\n    )\n\n\ndef active_preferences(\n    times: np.ndarray,\n    items: np.ndarray,\n    channels: np.ndarray,\n    active: np.ndarray,\n    cutoff: int,\n) -> tuple[np.ndarray, np.ndarray, int, int]:\n    hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n    if hi == 0:\n        return np.empty(0, np.int32), np.empty(0, np.float32), 0, 0\n    keys = items[:hi].astype(np.int64) * 2 + channels[:hi]\n    reverse_keys = keys[::-1]\n    _, first_reverse = np.unique(reverse_keys, return_index=True)\n    pos = hi - 1 - first_reverse\n    pos = pos[active[pos] == 1]\n    liked = pos[channels[pos] == 0]\n    disliked = pos[channels[pos] == 1]\n    out_items = np.concatenate((items[liked], items[disliked])).astype(np.int32)\n    out_weights = np.concatenate(\n        (\n            np.full(len(liked), 2.5, np.float32),\n            np.full(len(disliked), -1.25, np.float32),\n        )\n    )\n    return out_items, out_weights, len(liked), len(disliked)\n\n\ndef make_history(\n    h: tuple[np.ndarray, ...],\n    a: tuple[np.ndarray, ...],\n    cutoff: int,\n    cfg: Config,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n    li, lw, organic_rate = latest_unique_history(\n        h[0], h[1], h[2], h[3], cutoff, cfg\n    )\n    pi, pw, n_like, n_dislike = active_preferences(\n        a[0], a[1], a[2], a[3], cutoff\n    )\n    ids = np.concatenate((li, pi))\n    weights = np.concatenate((lw, pw))\n    if not len(ids):\n        # Every target user should have history, but EmbeddingBag requires a\n        # real entry.  Item 0 at zero weight is a neutral fallback.\n        ids = np.array([0], dtype=np.int32)\n        weights = np.array([0.0], dtype=np.float32)\n    features = np.array(\n        [\n            math.log1p(len(li)) / math.log1p(cfg.max_history),\n            math.log1p(n_like) / 6.0,\n            math.log1p(n_dislike) / 4.0,\n            organic_rate,\n        ],\n        dtype=np.float32,\n    )\n    return ids, weights, features\n\n\ndef relevant_targets(\n    r: tuple[np.ndarray, ...],\n    first_seen: np.ndarray,\n    cutoff: int,\n) -> tuple[np.ndarray, np.ndarray]:\n    lo = int(np.searchsorted(r[0], cutoff + GAP, side=\"left\"))\n    hi = int(np.searchsorted(r[0], cutoff + GAP + WEEK, side=\"left\"))\n    if hi <= lo:\n        return np.empty(0, np.int32), np.empty(0, np.uint8)\n    ri, rg = r[1][lo:hi], r[2][lo:hi]\n    novel = first_seen[ri] >= cutoff\n    ri, rg = ri[novel], rg[novel]\n    if not len(ri):\n        return np.empty(0, np.int32), np.empty(0, np.uint8)\n    unique, inverse = np.unique(ri, return_inverse=True)\n    grades = np.zeros(len(unique), dtype=np.uint8)\n    np.maximum.at(grades, inverse, rg)\n    return unique.astype(np.int32), grades\n\n\ndef build_contexts(\n    arrays: dict,\n    users: np.ndarray,\n    stage_end: int,\n    cfg: Config,\n) -> dict[str, np.ndarray]:\n    num_users = len(users)\n    bounds = {k: user_bounds(v[0], num_users) for k, v in arrays.items()}\n    history_ids: list[np.ndarray] = []\n    history_weights: list[np.ndarray] = []\n    context_features: list[np.ndarray] = []\n    target_ids: list[np.ndarray] = []\n    target_grades: list[np.ndarray] = []\n    context_users: list[int] = []\n    context_cutoffs: list[int] = []\n\n    # End at stage_end-WEEK so every label window is fully observed.  For the\n    # final refit, this includes the public validation window as the newest\n    # temporal training example.\n    cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\n    cutoffs.sort()\n    first_seen = np.empty(30_000, dtype=np.int64)\n    for u in range(num_users):\n        slices = {}\n        for key, values in arrays.items():\n            b = bounds[key]\n            slices[key] = tuple(x[b[u] : b[u + 1]] for x in values[1:])\n        first_seen.fill(np.iinfo(np.int64).max)\n        all_times, all_items = slices[\"all\"]\n        np.minimum.at(first_seen, all_items, all_times)\n        for cutoff in cutoffs:\n            ti, tg = relevant_targets(slices[\"relevant\"], first_seen, cutoff)\n            if not len(ti):\n                continue\n            hi, hw, cf = make_history(\n                slices[\"history\"], slices[\"actions\"], cutoff, cfg\n            )\n            history_ids.append(hi)\n            history_weights.append(hw)\n            context_features.append(cf)\n            target_ids.append(ti)\n            target_grades.append(tg)\n            context_users.append(u)\n            context_cutoffs.append(cutoff)\n\n    h_lengths = np.fromiter((len(x) for x in history_ids), np.int64)\n    t_lengths = np.fromiter((len(x) for x in target_ids), np.int64)\n    result = {\n        \"history_ids\": np.concatenate(history_ids).astype(np.int32),\n        \"history_weights\": np.concatenate(history_weights).astype(np.float32),\n        \"history_offsets\": np.r_[0, np.cumsum(h_lengths)].astype(np.int64),\n        \"features\": np.stack(context_features).astype(np.float32),\n        \"target_ids\": np.concatenate(target_ids).astype(np.int32),\n        \"target_grades\": np.concatenate(target_grades).astype(np.uint8),\n        \"target_offsets\": np.r_[0, np.cumsum(t_lengths)].astype(np.int64),\n        \"context_users\": np.asarray(context_users, dtype=np.int32),\n        \"context_cutoffs\": np.asarray(context_cutoffs, dtype=np.int64),\n    }\n    print(\n        f\"built {len(history_ids):,} temporal contexts with \"\n        f\"{len(result['target_ids']):,} novel targets and \"\n        f\"{len(result['history_ids']):,} weighted history entries\",\n        flush=True,\n    )\n    return result\n\n\ndef inference_histories(\n    arrays: dict,\n    users: np.ndarray,\n    cutoff: int,\n    cfg: Config,\n    preference_path: Path,\n    candidate_items: np.ndarray,\n) -> dict[str, np.ndarray]:\n    \"\"\"Build one cutoff-correct history per request user.\"\"\"\n    num_users = len(users)\n    hb = user_bounds(arrays[\"history\"][0], num_users)\n    # The supplied state is authoritative and avoids reconstructing it from a\n    # potentially truncated event horizon at serving time.\n    pref = pd.read_parquet(preference_path)\n    pu, pi, keep = map_frame(pref, users, candidate_items)\n    ps = pref.state.to_numpy(copy=False)[keep]\n    order = np.argsort(pu, kind=\"stable\")\n    pu, pi, ps = pu[order], pi[order], ps[order]\n    pb = user_bounds(pu, num_users)\n\n    ids_list, weights_list, features = [], [], []\n    for u in range(num_users):\n        hs = tuple(x[hb[u] : hb[u + 1]] for x in arrays[\"history\"][1:])\n        li, lw, organic_rate = latest_unique_history(*hs, cutoff, cfg)\n        sl = slice(pb[u], pb[u + 1])\n        pitems = pi[sl].astype(np.int32, copy=False)\n        states = ps[sl]\n        pweights = np.where(states == \"liked\", 2.5, -1.25).astype(np.float32)\n        ids = np.concatenate((li, pitems))\n        weights = np.concatenate((lw, pweights))\n        if not len(ids):\n            ids, weights = np.array([0], np.int32), np.array([0.0], np.float32)\n        ids_list.append(ids)\n        weights_list.append(weights)\n        features.append(\n            np.array(\n                [\n                    math.log1p(len(li)) / math.log1p(cfg.max_history),\n                    math.log1p(int((states == \"liked\").sum())) / 6.0,\n                    math.log1p(int((states == \"disliked\").sum())) / 4.0,\n                    organic_rate,\n                ],\n                np.float32,\n            )\n        )\n    lengths = np.fromiter((len(x) for x in ids_list), np.int64)\n    return {\n        \"history_ids\": np.concatenate(ids_list).astype(np.int32),\n        \"history_weights\": np.concatenate(weights_list).astype(np.float32),\n        \"history_offsets\": np.r_[0, np.cumsum(lengths)].astype(np.int64),\n        \"features\": np.stack(features).astype(np.float32),\n    }\n\n\ndef gather_histories(data: dict[str, np.ndarray], indices: np.ndarray):\n    offsets = data[\"history_offsets\"]\n    pieces_i = [data[\"history_ids\"][offsets[x] : offsets[x + 1]] for x in indices]\n    pieces_w = [\n        data[\"history_weights\"][offsets[x] : offsets[x + 1]] for x in indices\n    ]\n    lengths = np.fromiter((len(x) for x in pieces_i), np.int64)\n    return (\n        torch.from_numpy(np.concatenate(pieces_i).astype(np.int64)),\n        torch.from_numpy(np.r_[0, np.cumsum(lengths)].astype(np.int64)),\n        torch.from_numpy(np.concatenate(pieces_w)),\n        torch.from_numpy(data[\"features\"][indices]),\n    )\n\n\ndef sample_targets(\n    data: dict[str, np.ndarray], indices: np.ndarray, rng: np.random.Generator, cfg: Config\n) -> np.ndarray:\n    offsets = data[\"target_offsets\"]\n    out = np.empty(len(indices), dtype=np.int64)\n    for j, x in enumerate(indices):\n        lo, hi = offsets[x], offsets[x + 1]\n        grades = data[\"target_grades\"][lo:hi]\n        weights = np.where(grades == 3, cfg.like_sampling_weight, 1.0)\n        weights = weights / weights.sum()\n        out[j] = data[\"target_ids\"][lo + rng.choice(hi - lo, p=weights)]\n    return out\n\n\ndef train_model(\n    model: TwoTowerRanker, data: dict[str, np.ndarray], cfg: Config\n) -> list[float]:\n    rng = np.random.default_rng(cfg.seed)\n    torch.manual_seed(cfg.seed)\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay\n    )\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n        optimizer, T_max=cfg.epochs, eta_min=cfg.learning_rate * 0.12\n    )\n    n = len(data[\"features\"])\n    losses: list[float] = []\n    for epoch in range(cfg.epochs):\n        model.train()\n        order = rng.permutation(n)\n        total_loss = 0.0\n        seen = 0\n        for start in range(0, n, cfg.batch_size):\n            batch = order[start : start + cfg.batch_size]\n            hids, offsets, hweights, features = gather_histories(data, batch)\n            positives_np = sample_targets(data, batch, rng, cfg)\n            positives = torch.from_numpy(positives_np)\n            negatives = torch.from_numpy(\n                rng.integers(0, 30_000, size=cfg.negatives, dtype=np.int64)\n            )\n            users = model.user_tower(hids, offsets, hweights, features)\n            pos_vectors = model.item_tower(positives)\n            neg_vectors = model.item_tower(negatives)\n            positive_scores = (users * pos_vectors).sum(1, keepdim=True)\n            negative_scores = users @ neg_vectors.T\n            # A sampled candidate that equals the row's positive is not a\n            # negative.  Mask it instead of teaching contradictory labels.\n            negative_scores.masked_fill_(\n                positives[:, None] == negatives[None, :], -1e4\n            )\n            logits = torch.cat((positive_scores, negative_scores), dim=1)\n            loss = F.cross_entropy(\n                logits, torch.zeros(len(batch), dtype=torch.long)\n            )\n            optimizer.zero_grad(set_to_none=True)\n            loss.backward()\n            nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n            optimizer.step()\n            total_loss += float(loss) * len(batch)\n            seen += len(batch)\n        scheduler.step()\n        mean_loss = total_loss / seen\n        losses.append(mean_loss)\n        print(\n            f\"epoch {epoch + 1:02d}/{cfg.epochs}: loss={mean_loss:.5f} \"\n            f\"lr={scheduler.get_last_lr()[0]:.6g}\",\n            flush=True,\n        )\n    return losses\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: TwoTowerRanker,\n    histories: dict[str, np.ndarray],\n    eligibility_path: Path,\n    output: Path,\n    batch_size: int = 192,\n) -> None:\n    model.eval()\n    archive = np.load(eligibility_path)\n    uids = archive[\"uids\"].astype(np.int64)\n    item_ids = archive[\"item_ids\"].astype(np.int64)\n    packed = archive[\"packed_eligible\"]\n    all_item_vectors = model.item_tower(torch.arange(len(item_ids))).T.contiguous()\n    rows: list[np.ndarray] = []\n    for start in range(0, len(uids), batch_size):\n        stop = min(start + batch_size, len(uids))\n        idx = np.arange(start, stop)\n        hids, offsets, weights, features = gather_histories(histories, idx)\n        user_vectors = model.user_tower(hids, offsets, weights, features)\n        scores = user_vectors @ all_item_vectors\n        eligible = np.unpackbits(\n            packed[start:stop], axis=1, count=len(item_ids), bitorder=\"little\"\n        ).astype(bool)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        # A tiny deterministic index term makes exact ties reproducible.\n        scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\n        top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\n        block = np.empty(((stop - start) * 100, 3), dtype=np.int64)\n        block[:, 0] = np.repeat(uids[start:stop], 100)\n        block[:, 1] = item_ids[top.reshape(-1)]\n        block[:, 2] = np.tile(np.arange(1, 101), stop - start)\n        rows.append(block)\n    frame = pd.DataFrame(np.concatenate(rows), columns=[\"uid\", \"item_id\", \"rank\"])\n    output.parent.mkdir(parents=True, exist_ok=True)\n    frame.to_parquet(output, index=False)\n    print(f\"wrote {len(frame):,} ranked rows to {output}\", flush=True)\n\n\ndef item_features(catalog: pd.DataFrame) -> torch.Tensor:\n    raw = np.column_stack(\n        (\n            np.log1p(catalog.positive_listens.to_numpy(np.float32)),\n            np.log1p(catalog.organic_positive_listens.to_numpy(np.float32)),\n            np.log1p(catalog.popularity_rank.to_numpy(np.float32)),\n            catalog.organic_positive_listens.to_numpy(np.float32)\n            / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1),\n        )\n    )\n    raw = (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\n    return torch.from_numpy(raw.astype(np.float32))\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--checkpoint\", type=Path)\n    parser.add_argument(\"--cache\", type=Path)\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--embedding-dim\", type=int, default=Config.embedding_dim)\n    parser.add_argument(\"--max-history\", type=int, default=Config.max_history)\n    parser.add_argument(\"--training-weeks\", type=int, default=Config.training_weeks)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--threads\", type=int, default=8)\n    args = parser.parse_args()\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    random.seed(args.seed)\n    np.random.seed(args.seed)\n    cfg = Config(\n        epochs=args.epochs,\n        embedding_dim=args.embedding_dim,\n        max_history=args.max_history,\n        training_weeks=args.training_weeks,\n        seed=args.seed,\n    )\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    candidate_items = catalog.item_id.to_numpy(np.int64)\n    target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n    stage_end = TRAIN_END if args.stage == \"validation\" else VAL_END\n    cache = args.cache or Path(f\"/app/solution/cache_{args.stage}.pt\")\n    if cache.exists():\n        payload = torch.load(cache, weights_only=False)\n        contexts, histories = payload[\"contexts\"], payload[\"histories\"]\n        print(f\"loaded prepared arrays from {cache}\", flush=True)\n    else:\n        arrays = load_event_arrays(args.stage, target_users, candidate_items)\n        contexts = build_contexts(arrays, target_users, stage_end, cfg)\n        pref_path = DATA / (\n            \"validation_preference_state.parquet\"\n            if args.stage == \"validation\"\n            else \"test_preference_state.parquet\"\n        )\n        histories = inference_histories(\n            arrays, target_users, stage_end, cfg, pref_path, candidate_items\n        )\n        torch.save(\n            {\"contexts\": contexts, \"histories\": histories, \"config\": asdict(cfg)},\n            cache,\n        )\n        print(f\"saved prepared arrays to {cache}\", flush=True)\n    model = TwoTowerRanker(\n        len(candidate_items),\n        item_features(catalog),\n        embedding_dim=cfg.embedding_dim,\n        hidden_dim=cfg.hidden_dim,\n    )\n    losses = train_model(model, contexts, cfg)\n    checkpoint = args.checkpoint or Path(\n        f\"/app/solution/model_{args.stage}.pt\"\n    )\n    torch.save(\n        {\n            \"model_state\": model.state_dict(),\n            \"config\": asdict(cfg),\n            \"losses\": losses,\n            \"catalog_item_ids\": candidate_items,\n        },\n        checkpoint,\n    )\n    eligibility = DATA / f\"{args.stage}_eligible_candidates.npz\"\n    rank_candidates(model, histories, eligibility, args.output)\n    print(json.dumps({\"checkpoint\": str(checkpoint), \"config\": asdict(cfg)}))\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/model.py\nA /app/solution/train_ranker.py\n",
    "stderr": ""
  }
}
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45

exec command

+10:39
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"ls -lh /app/solution && sed -n '1,40p' /app/solution/model.py 2>&1","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

total 744K
-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py
-rw-r--r-- 1 agent agent  24K Sep 24 23:11 train_ranker.py
-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet
"""Neural towers used by the discovery ranker.

The user tower consumes only a variable-length interaction history plus summary
features.  It deliberately has no user-ID parameter.  The item tower can be
run for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;
the final coordinate implements an item prior while preserving an exact dot
product scoring function.
"""
from __future__ import annotations

import torch
from torch import nn
from torch.nn import functional as F


class UserTower(nn.Module):
    """Weighted history pooling followed by a small residual MLP."""

    def __init__(
        self,
        num_items: int,
        embedding_dim: int = 64,
        hidden_dim: int = 128,
        num_context_features: int = 4,
    ) -> None:
        super().__init__()
        self.embedding_dim = embedding_dim
        self.history_embedding = nn.EmbeddingBag(
            num_items, embedding_dim, mode="sum", include_last_offset=True
        )
        nn.init.normal_(self.history_embedding.weight, std=0.04)
        self.network = nn.Sequential(
            nn.Linear(embedding_dim + num_context_features, hidden_dim),
            nn.GELU(),
            nn.LayerNorm(hidden_dim),
            nn.Linear(hidden_dim, embedding_dim),
        )
        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)
        self.log_scale = nn.Parameter(torch.tensor(2.0))

Harness context codex item completed
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      "/bin/sh",
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      "ls -lh /app/solution && sed -n '1,40p' /app/solution/model.py 2>&1"
    ],
    "cwd": "file:///app",
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      {
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      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent  24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures.  It deliberately has no user-ID parameter.  The item tower can be\nrun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n    def __init__(\n        self,\n        num_items: int,\n        embedding_dim: int = 64,\n        hidden_dim: int = 128,\n        num_context_features: int = 4,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.history_embedding = nn.EmbeddingBag(\n            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n        )\n        nn.init.normal_(self.history_embedding.weight, std=0.04)\n        self.network = nn.Sequential(\n            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n            nn.GELU(),\n            nn.LayerNorm(hidden_dim),\n            nn.Linear(hidden_dim, embedding_dim),\n        )\n        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n        self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n",
    "stderr": "",
    "aggregated_output": "total 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent  24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures.  It deliberately has no user-ID parameter.  The item tower can be\nrun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n    def __init__(\n        self,\n        num_items: int,\n        embedding_dim: int = 64,\n        hidden_dim: int = 128,\n        num_context_features: int = 4,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.history_embedding = nn.EmbeddingBag(\n            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n        )\n        nn.init.normal_(self.history_embedding.weight, std=0.04)\n        self.network = nn.Sequential(\n            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n            nn.GELU(),\n            nn.LayerNorm(hidden_dim),\n            nn.Linear(hidden_dim, embedding_dim),\n        )\n        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n        self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3720
    },
    "formatted_output": "total 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent  24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures.  It deliberately has no user-ID parameter.  The item tower can be\nrun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n    def __init__(\n        self,\n        num_items: int,\n        embedding_dim: int = 64,\n        hidden_dim: int = 128,\n        num_context_features: int = 4,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.history_embedding = nn.EmbeddingBag(\n            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n        )\n        nn.init.normal_(self.history_embedding.weight, std=0.04)\n        self.network = nn.Sequential(\n            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n            nn.GELU(),\n            nn.LayerNorm(hidden_dim),\n            nn.Linear(hidden_dim, embedding_dim),\n        )\n        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n        self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n"
  }
}
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46

Agent message

+10:57
Thinking unavailable No text recorded

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

47

exec command

+11:06
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 patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-    cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\n+    cutoffs = [\n+        stage_end - GAP - WEEK - j * WEEK for j in range(cfg.training_weeks)\n+    ]\n@@\n-    eligibility = DATA / f\"{args.stage}_eligible_candidates.npz\"\n+    eligibility = DATA / (\n+        \"validation_eligible_candidates.npz\"\n+        if args.stage == \"validation\"\n+        else \"test_eligible_candidates.npz\"\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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  "item": {
    "type": "FileChange",
    "id": "exec-c78adcf3-a593-4a3e-8e2a-683ad44a87e6",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -311,3 +311,5 @@\n     # temporal training example.\n-    cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\n+    cutoffs = [\n+        stage_end - GAP - WEEK - j * WEEK for j in range(cfg.training_weeks)\n+    ]\n     cutoffs.sort()\n@@ -620,3 +622,7 @@\n     )\n-    eligibility = DATA / f\"{args.stage}_eligible_candidates.npz\"\n+    eligibility = DATA / (\n+        \"validation_eligible_candidates.npz\"\n+        if args.stage == \"validation\"\n+        else \"test_eligible_candidates.npz\"\n+    )\n     rank_candidates(model, histories, eligibility, args.output)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
    "stderr": ""
  }
}
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48

exec command

+11:10
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":"PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch\nfrom model import TwoTowerRanker\nm=TwoTowerRanker(10,torch.randn(10,4),8,16)\nh=torch.tensor([1,2,3,4]); o=torch.tensor([0,2,4]); w=torch.ones(4); f=torch.zeros(2,4); i=torch.arange(10)\nu=m.user_tower(h,o,w,f); v=m.item_tower(i)\nprint(u.shape,v.shape,([email protected]).shape,m.score(h,o,w,f,i).shape)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nfor p in ['validation','test']:\n z=np.load(f'/task/data/{p}_eligible_candidates.npz')\n print(p,'user order',np.array_equal(u,z['uids']))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint('item order',np.array_equal(cat,z['item_ids']))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 4.8 seconds
Output:

torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])
validation user order True
test user order True
item order True
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      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch\nfrom model import TwoTowerRanker\nm=TwoTowerRanker(10,torch.randn(10,4),8,16)\nh=torch.tensor([1,2,3,4]); o=torch.tensor([0,2,4]); w=torch.ones(4); f=torch.zeros(2,4); i=torch.arange(10)\nu=m.user_tower(h,o,w,f); v=m.item_tower(i)\nprint(u.shape,v.shape,([email protected]).shape,m.score(h,o,w,f,i).shape)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nfor p in ['validation','test']:\n z=np.load(f'/task/data/{p}_eligible_candidates.npz')\n print(p,'user order',np.array_equal(u,z['uids']))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint('item order',np.array_equal(cat,z['item_ids']))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch\nfrom model import TwoTowerRanker\nm=TwoTowerRanker(10,torch.randn(10,4),8,16)\nh=torch.tensor([1,2,3,4]); o=torch.tensor([0,2,4]); w=torch.ones(4); f=torch.zeros(2,4); i=torch.arange(10)\nu=m.user_tower(h,o,w,f); v=m.item_tower(i)\nprint(u.shape,v.shape,([email protected]).shape,m.score(h,o,w,f,i).shape)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nfor p in ['validation','test']:\n z=np.load(f'/task/data/{p}_eligible_candidates.npz')\n print(p,'user order',np.array_equal(u,z['uids']))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint('item order',np.array_equal(cat,z['item_ids']))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n",
    "stderr": "",
    "aggregated_output": "torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 665873417
    },
    "formatted_output": "torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n"
  }
}
49

exec command

+11:24
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1.parquet --checkpoint /app/solution/model_validation_v1.pt --cache /app/solution/cache_validation.pt --epochs 12 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=79312
Harness context token usage record
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ae3d89cc87d2b5a667be88c48d15",
  "usage": {
    "input_tokens": 51216,
    "cached_input_tokens": 50560,
    "cache_write_input_tokens": 0,
    "output_tokens": 204,
    "reasoning_output_tokens": 59,
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  },
  "turn_token_usage": {
    "input_tokens": 734219,
    "cached_input_tokens": 689664,
    "cache_write_input_tokens": 0,
    "output_tokens": 26068,
    "reasoning_output_tokens": 11451,
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  },
  "thread_token_usage": {
    "input_tokens": 734219,
    "cached_input_tokens": 689664,
    "cache_write_input_tokens": 0,
    "output_tokens": 26068,
    "reasoning_output_tokens": 11451,
    "total_tokens": 760287
  }
}
50

exec command

+11:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":79312,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"88a455","wall_time_seconds":30.0010432,"session_id":79312,"original_token_count":55}
loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s
built 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ae433dac87d2ac824ddd008f2961",
  "usage": {
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    "cached_input_tokens": 51072,
    "cache_write_input_tokens": 0,
    "output_tokens": 59,
    "reasoning_output_tokens": 12,
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  },
  "turn_token_usage": {
    "input_tokens": 785667,
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    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 785667,
    "cached_input_tokens": 740736,
    "cache_write_input_tokens": 0,
    "output_tokens": 26127,
    "reasoning_output_tokens": 11463,
    "total_tokens": 811794
  }
}
51

exec command

+12:02
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":79312,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ff0099","wall_time_seconds":30.001942815,"session_id":79312,"original_token_count":123}
saved prepared arrays to /app/solution/cache_validation.pt
/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=5.60084 lr=0.00197001
epoch 02/12: loss=5.35690 lr=0.0018821
epoch 03/12: loss=5.13928 lr=0.00174225
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  "usage": {
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    "cached_input_tokens": 51328,
    "cache_write_input_tokens": 0,
    "output_tokens": 64,
    "reasoning_output_tokens": 17,
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  },
  "turn_token_usage": {
    "input_tokens": 837288,
    "cached_input_tokens": 792064,
    "cache_write_input_tokens": 0,
    "output_tokens": 26191,
    "reasoning_output_tokens": 11480,
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  },
  "thread_token_usage": {
    "input_tokens": 837288,
    "cached_input_tokens": 792064,
    "cache_write_input_tokens": 0,
    "output_tokens": 26191,
    "reasoning_output_tokens": 11480,
    "total_tokens": 863479
  }
}
52

exec command

+12:34
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 temporal sampler produced 58,790 training contexts and 548,638 novel organic targets without building a user–item matrix. Training is stable and the sampled-softmax loss is dropping (5.60 → 5.14 over the first three epochs); once retrieval finishes I’ll score it and use that result to decide whether the next iteration should emphasize representation quality, popularity calibration, or label weighting.

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":79312,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"df0b1b","wall_time_seconds":30.001224676,"session_id":79312,"original_token_count":39}
epoch 04/12: loss=4.94855 lr=0.00156
epoch 05/12: loss=4.79465 lr=0.00134776
epoch 06/12: loss=4.65739 lr=0.00112
epoch 07/12: loss=4.55539 lr=0.000892239
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5ae846d3887d2b9ca7800f7ad5766",
  "usage": {
    "input_tokens": 51879,
    "cached_input_tokens": 51456,
    "cache_write_input_tokens": 0,
    "output_tokens": 150,
    "reasoning_output_tokens": 15,
    "total_tokens": 52029
  },
  "turn_token_usage": {
    "input_tokens": 889167,
    "cached_input_tokens": 843520,
    "cache_write_input_tokens": 0,
    "output_tokens": 26341,
    "reasoning_output_tokens": 11495,
    "total_tokens": 915508
  },
  "thread_token_usage": {
    "input_tokens": 889167,
    "cached_input_tokens": 843520,
    "cache_write_input_tokens": 0,
    "output_tokens": 26341,
    "reasoning_output_tokens": 11495,
    "total_tokens": 915508
  }
}
53

exec command

+13:08
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":79312,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"39b3e3","wall_time_seconds":30.001165157,"session_id":79312,"original_token_count":40}
epoch 08/12: loss=4.44200 lr=0.00068
epoch 09/12: loss=4.36064 lr=0.000497746
epoch 10/12: loss=4.29660 lr=0.000357898
epoch 11/12: loss=4.23379 lr=0.000269985
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5aea6613487d2ac6ea19d897ae9fd",
  "usage": {
    "input_tokens": 52162,
    "cached_input_tokens": 51712,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 52215
  },
  "turn_token_usage": {
    "input_tokens": 941329,
    "cached_input_tokens": 895232,
    "cache_write_input_tokens": 0,
    "output_tokens": 26394,
    "reasoning_output_tokens": 11501,
    "total_tokens": 967723
  },
  "thread_token_usage": {
    "input_tokens": 941329,
    "cached_input_tokens": 895232,
    "cache_write_input_tokens": 0,
    "output_tokens": 26394,
    "reasoning_output_tokens": 11501,
    "total_tokens": 967723
  }
}
54

exec command

+13:40
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":79312,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 4.6 seconds
Output:

{"chunk_id":"ea38cb","wall_time_seconds":4.579124073,"exit_code":0,"original_token_count":107}
epoch 12/12: loss=4.18560 lr=0.00024
wrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet
{"checkpoint": "/app/solution/model_validation_v1.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 16, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "seed": 20260924}}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5aec66d4887d2969c7cd19abcfb6f",
  "usage": {
    "input_tokens": 52349,
    "cached_input_tokens": 51968,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 52402
  },
  "turn_token_usage": {
    "input_tokens": 993678,
    "cached_input_tokens": 947200,
    "cache_write_input_tokens": 0,
    "output_tokens": 26447,
    "reasoning_output_tokens": 11507,
    "total_tokens": 1020125
  },
  "thread_token_usage": {
    "input_tokens": 993678,
    "cached_input_tokens": 947200,
    "cache_write_input_tokens": 0,
    "output_tokens": 26447,
    "reasoning_output_tokens": 11507,
    "total_tokens": 1020125
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d849ed73-1c2f-4102-a96c-6541253600a4",
    "process_id": "79312",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1.parquet --checkpoint /app/solution/model_validation_v1.pt --cache /app/solution/cache_validation.pt --epochs 12 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1.parquet --checkpoint /app/solution/model_validation_v1.pt --cache /app/solution/cache_validation.pt --epochs 12 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 139,
      "nanos": 14165109
    },
    "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n"
  }
}
55

exec command

+13:47
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 r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/validation_twotower_v1.parquet",
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    "quality_ndcg@10": 0.020634,
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Harness context codex item completed
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    "type": "CommandExecution",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_v1.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.020634,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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    "exit_code": 0,
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      "secs": 3,
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  }
}
56

Agent message

+14:05
Thinking unavailable No text recorded

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

57

exec command

+14:11
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 first neural model reaches 0.020634 NDCG@10—about 93% above popularity—so the history embedding is finding genuine taste structure. I’m separating its personalized similarity from its item-prior term now and calibrating their blend on validation; this is a cheap, high-leverage step because the chosen prior remains an extra coordinate in the exact two-tower dot product.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas as pd,numpy as np\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.eval()\nwith torch.no_grad(): v=m.item_tower(torch.arange(30000));\nprint('log_scale',m.user_tower.log_scale.item(),'scale',m.user_tower.log_scale.exp().item())\nfor j,name in [(64,'bias')]:\n x=v[:,j].numpy();print(name,'min/q/std/max',np.quantile(x,[0,.01,.1,.5,.9,.99,1]),x.std(),'corr -logrank',np.corrcoef(x,-np.log(cat.popularity_rank))[0,1])\nprint('id_bias std',m.item_tower.id_bias.weight.detach().numpy().std())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 4.3 seconds
Output:

log_scale 2.2669990062713623 scale 9.650396347045898
bias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361  0.87525663  1.91318414
  2.93312621] 0.7245352 corr -logrank 0.8124993059873334
id_bias std 0.087633185
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-76e19889-02d1-41a5-9e90-a55d3f787c19",
    "process_id": "43940",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas as pd,numpy as np\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.eval()\nwith torch.no_grad(): v=m.item_tower(torch.arange(30000));\nprint('log_scale',m.user_tower.log_scale.item(),'scale',m.user_tower.log_scale.exp().item())\nfor j,name in [(64,'bias')]:\n x=v[:,j].numpy();print(name,'min/q/std/max',np.quantile(x,[0,.01,.1,.5,.9,.99,1]),x.std(),'corr -logrank',np.corrcoef(x,-np.log(cat.popularity_rank))[0,1])\nprint('id_bias std',m.item_tower.id_bias.weight.detach().numpy().std())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas as pd,numpy as np\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.eval()\nwith torch.no_grad(): v=m.item_tower(torch.arange(30000));\nprint('log_scale',m.user_tower.log_scale.item(),'scale',m.user_tower.log_scale.exp().item())\nfor j,name in [(64,'bias')]:\n x=v[:,j].numpy();print(name,'min/q/std/max',np.quantile(x,[0,.01,.1,.5,.9,.99,1]),x.std(),'corr -logrank',np.corrcoef(x,-np.log(cat.popularity_rank))[0,1])\nprint('id_bias std',m.item_tower.id_bias.weight.detach().numpy().std())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "log_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361  0.87525663  1.91318414\n  2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n",
    "stderr": "",
    "aggregated_output": "log_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361  0.87525663  1.91318414\n  2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 85449225
    },
    "formatted_output": "log_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361  0.87525663  1.91318414\n  2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n"
  }
}
58

exec command

+14:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_blend.py\n+#!/usr/bin/env python3\n+\"\"\"Validation-only calibration of personalized similarity and item priors.\"\"\"\n+from pathlib import Path\n+import sys\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+\n+sys.path.insert(0, \"/app/solution\")\n+sys.path.insert(0, \"/task\")\n+from model import TwoTowerRanker\n+from train_ranker import gather_histories, item_features\n+from ranking_evaluation import CandidateSet, ndcg_at_k\n+from evaluate_public import validation_truth\n+\n+\n+torch.set_num_threads(8)\n+catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n+checkpoint = torch.load(\"/app/solution/model_validation_v1.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation.pt\", weights_only=False)\n+histories = cache[\"histories\"]\n+model = TwoTowerRanker(30_000, item_features(catalog), 64, 128)\n+model.load_state_dict(checkpoint[\"model_state\"])\n+model.eval()\n+archive = np.load(\"/task/data/validation_eligible_candidates.npz\")\n+candidate_set = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n+targets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\n+truth = validation_truth(candidate_set, targets)\n+\n+# learned-bias multiplier, standardized log-popularity addition\n+grid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\n+        for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\n+ranked = {key: {} for key in grid}\n+pop = -item_features(catalog)[:, 2]\n+with torch.inference_mode():\n+    item_vectors = model.item_tower(torch.arange(30_000))\n+    latent = item_vectors[:, :-1].T.contiguous()\n+    bias = item_vectors[:, -1]\n+    for start in range(0, len(archive[\"uids\"]), 192):\n+        stop = min(start + 192, len(archive[\"uids\"]))\n+        batch = np.arange(start, stop)\n+        hids, offsets, weights, features = gather_histories(histories, batch)\n+        users = model.user_tower(hids, offsets, weights, features)\n+        similarity = users[:, :-1] @ latent\n+        if start == 0:\n+            print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n+        eligible = np.unpackbits(\n+            archive[\"packed_eligible\"][start:stop], axis=1,\n+            count=30_000, bitorder=\"little\"\n+        ).astype(bool)\n+        mask = torch.from_numpy(~eligible)\n+        for key in grid:\n+            b, p = key\n+            scores = similarity + b * bias[None, :] + p * pop[None, :]\n+            scores.masked_fill_(mask, -torch.inf)\n+            top = torch.topk(scores, 10, dim=1).indices.numpy()\n+            for row, uid in enumerate(archive[\"uids\"][start:stop]):\n+                if int(uid) in truth:\n+                    ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n+\n+for key in grid:\n+    values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n+    print(key, f\"{np.mean(values):.6f}\")\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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    "type": "FileChange",
    "id": "exec-e20552f0-6361-43e5-8d1b-d20cf2a40c31",
    "changes": {
      "/app/solution/tune_blend.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Validation-only calibration of personalized similarity and item priors.\"\"\"\nfrom pathlib import Path\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\nfrom model import TwoTowerRanker\nfrom train_ranker import gather_histories, item_features\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\n\n\ntorch.set_num_threads(8)\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncheckpoint = torch.load(\"/app/solution/model_validation_v1.pt\", weights_only=False)\ncache = torch.load(\"/app/solution/cache_validation.pt\", weights_only=False)\nhistories = cache[\"histories\"]\nmodel = TwoTowerRanker(30_000, item_features(catalog), 64, 128)\nmodel.load_state_dict(checkpoint[\"model_state\"])\nmodel.eval()\narchive = np.load(\"/task/data/validation_eligible_candidates.npz\")\ncandidate_set = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\ntruth = validation_truth(candidate_set, targets)\n\n# learned-bias multiplier, standardized log-popularity addition\ngrid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\n        for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\nranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n    item_vectors = model.item_tower(torch.arange(30_000))\n    latent = item_vectors[:, :-1].T.contiguous()\n    bias = item_vectors[:, -1]\n    for start in range(0, len(archive[\"uids\"]), 192):\n        stop = min(start + 192, len(archive[\"uids\"]))\n        batch = np.arange(start, stop)\n        hids, offsets, weights, features = gather_histories(histories, batch)\n        users = model.user_tower(hids, offsets, weights, features)\n        similarity = users[:, :-1] @ latent\n        if start == 0:\n            print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n        eligible = np.unpackbits(\n            archive[\"packed_eligible\"][start:stop], axis=1,\n            count=30_000, bitorder=\"little\"\n        ).astype(bool)\n        mask = torch.from_numpy(~eligible)\n        for key in grid:\n            b, p = key\n            scores = similarity + b * bias[None, :] + p * pop[None, :]\n            scores.masked_fill_(mask, -torch.inf)\n            top = torch.topk(scores, 10, dim=1).indices.numpy()\n            for row, uid in enumerate(archive[\"uids\"][start:stop]):\n                if int(uid) in truth:\n                    ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n    values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n    print(key, f\"{np.mean(values):.6f}\")\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/tune_blend.py\n",
    "stderr": ""
  }
}
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59

exec command

+14:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":20000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=50155
Harness context token usage record
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}
60

exec command

+15:00
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":50155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"262d63","wall_time_seconds":30.001089076,"session_id":50155,"original_token_count":16}
similarity std 2.5632805824279785 bias std 0.724547266960144
Harness context token usage record
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}
61

exec command

+15:35
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":50155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"15270d","wall_time_seconds":30.001476476,"session_id":50155,"original_token_count":0}
Harness context token usage record
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}
62

exec command

+16:07
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

let{output,...rest}=await tools.write_stdin({"session_id":50155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"9b4af4","wall_time_seconds":30.000820663,"session_id":50155,"original_token_count":0}
Harness context token usage record
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}
63

exec command

+16:40
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":50155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 5.8 seconds
Output:

{"chunk_id":"83a1de","wall_time_seconds":5.83452307,"exit_code":0,"original_token_count":153}
(0.0, -0.4) 0.004865
(0.0, 0.0) 0.007243
(0.0, 0.25) 0.013214
(0.0, 0.5) 0.018652
(0.0, 0.8) 0.020974
(0.4, -0.4) 0.006828
(0.4, 0.0) 0.013569
(0.4, 0.25) 0.018767
(0.4, 0.5) 0.022700
(0.4, 0.8) 0.021516
(0.7, -0.4) 0.010281
(0.7, 0.0) 0.018143
(0.7, 0.25) 0.021729
(0.7, 0.5) 0.023751
(0.7, 0.8) 0.021224
(1.0, -0.4) 0.014286
(1.0, 0.0) 0.020634
(1.0, 0.25) 0.023280
(1.0, 0.5) 0.023213
(1.0, 0.8) 0.021060
(1.4, -0.4) 0.018775
(1.4, 0.0) 0.022021
(1.4, 0.25) 0.024001
(1.4, 0.5) 0.022304
(1.4, 0.8) 0.020244
(2.0, -0.4) 0.022028
(2.0, 0.0) 0.023459
(2.0, 0.25) 0.022584
(2.0, 0.5) 0.021282
(2.0, 0.8) 0.018824
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-81afd8b2-feb1-41c5-b30e-5e4dbaff0297",
    "process_id": "50155",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "similarity std 2.5632805824279785 bias std 0.724547266960144\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n",
    "stderr": "",
    "aggregated_output": "similarity std 2.5632805824279785 bias std 0.724547266960144\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n",
    "exit_code": 0,
    "duration": {
      "secs": 109,
      "nanos": 369087080
    },
    "formatted_output": "similarity std 2.5632805824279785 bias std 0.724547266960144\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n"
  }
}
64

Agent message

+16:57
Thinking unavailable No text recorded

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

65

Agent message

+17:06
Thinking unavailable No text recorded

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

66

Agent message

+17:18
Thinking unavailable No text recorded

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

67

Agent message

+17:25
Thinking unavailable No text recorded

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

68

exec command

+17:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/model.py\n@@\n         self.id_bias = nn.Embedding(num_items, 1)\n         nn.init.zeros_(self.id_bias.weight)\n+        # These two scalar buffers are selected on the public window.  Keeping\n+        # calibration inside the item tower means serving remains one exact\n+        # maximum-inner-product search, with no post-retrieval reranker.\n+        self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n+        self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n@@\n-        bias = self.prior(features) + self.id_bias(item_ids)\n+        bias = self.learned_prior_scale * (\n+            self.prior(features) + self.id_bias(item_ids)\n+        ) + self.fixed_prior_scale * (-features[:, 2:3])\n         return torch.cat((latent, bias), dim=1)\n*** Update File: /app/solution/train_ranker.py\n@@\n     parser.add_argument(\"--threads\", type=int, default=8)\n+    parser.add_argument(\"--negatives\", type=int, default=Config.negatives)\n+    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n+    parser.add_argument(\"--like-weight\", type=float, default=Config.like_sampling_weight)\n+    parser.add_argument(\"--resume\", type=Path)\n+    parser.add_argument(\"--prior-scale\", type=float, default=1.4)\n+    parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n@@\n         training_weeks=args.training_weeks,\n         seed=args.seed,\n+        negatives=args.negatives,\n+        learning_rate=args.learning_rate,\n+        like_sampling_weight=args.like_weight,\n@@\n     model = TwoTowerRanker(\n@@\n         hidden_dim=cfg.hidden_dim,\n     )\n+    if args.resume:\n+        resumed = torch.load(args.resume, weights_only=False)\n+        missing, unexpected = model.load_state_dict(\n+            resumed[\"model_state\"], strict=False\n+        )\n+        # Calibration is only applied after optimization.  A resumed fitted\n+        # model therefore returns to its raw training parameterization.\n+        model.item_tower.learned_prior_scale.fill_(1.0)\n+        model.item_tower.fixed_prior_scale.fill_(0.0)\n+        print(\n+            f\"resumed parameters from {args.resume}; \"\n+            f\"missing={list(missing)} unexpected={list(unexpected)}\",\n+            flush=True,\n+        )\n     losses = train_model(model, contexts, cfg)\n+    model.item_tower.learned_prior_scale.fill_(args.prior_scale)\n+    model.item_tower.fixed_prior_scale.fill_(args.pop_scale)\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-9bf1eec6-6225-4b5c-a240-e3281eb54bdb",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -566,2 +566,8 @@\n     parser.add_argument(\"--threads\", type=int, default=8)\n+    parser.add_argument(\"--negatives\", type=int, default=Config.negatives)\n+    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n+    parser.add_argument(\"--like-weight\", type=float, default=Config.like_sampling_weight)\n+    parser.add_argument(\"--resume\", type=Path)\n+    parser.add_argument(\"--prior-scale\", type=float, default=1.4)\n+    parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n     args = parser.parse_args()\n@@ -577,2 +583,5 @@\n         seed=args.seed,\n+        negatives=args.negatives,\n+        learning_rate=args.learning_rate,\n+        like_sampling_weight=args.like_weight,\n     )\n@@ -609,3 +618,19 @@\n     )\n+    if args.resume:\n+        resumed = torch.load(args.resume, weights_only=False)\n+        missing, unexpected = model.load_state_dict(\n+            resumed[\"model_state\"], strict=False\n+        )\n+        # Calibration is only applied after optimization.  A resumed fitted\n+        # model therefore returns to its raw training parameterization.\n+        model.item_tower.learned_prior_scale.fill_(1.0)\n+        model.item_tower.fixed_prior_scale.fill_(0.0)\n+        print(\n+            f\"resumed parameters from {args.resume}; \"\n+            f\"missing={list(missing)} unexpected={list(unexpected)}\",\n+            flush=True,\n+        )\n     losses = train_model(model, contexts, cfg)\n+    model.item_tower.learned_prior_scale.fill_(args.prior_scale)\n+    model.item_tower.fixed_prior_scale.fill_(args.pop_scale)\n     checkpoint = args.checkpoint or Path(\n",
        "move_path": null
      },
      "/app/solution/model.py": {
        "type": "update",
        "unified_diff": "@@ -100,2 +100,7 @@\n         nn.init.zeros_(self.id_bias.weight)\n+        # These two scalar buffers are selected on the public window.  Keeping\n+        # calibration inside the item tower means serving remains one exact\n+        # maximum-inner-product search, with no post-retrieval reranker.\n+        self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n+        self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n \n@@ -106,3 +111,5 @@\n         latent = F.normalize(latent + self.skip(ids), dim=1)\n-        bias = self.prior(features) + self.id_bias(item_ids)\n+        bias = self.learned_prior_scale * (\n+            self.prior(features) + self.id_bias(item_ids)\n+        ) + self.fixed_prior_scale * (-features[:, 2:3])\n         return torch.cat((latent, bias), dim=1)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/model.py\nM /app/solution/train_ranker.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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69

exec command

+17:42
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":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1_cont.parquet --checkpoint /app/solution/model_validation_v1_cont.pt --cache /app/solution/cache_validation.pt --resume /app/solution/model_validation_v1.pt --epochs 12 --learning-rate 0.0006 --negatives 1024 --prior-scale 1.4 --pop-scale 0.25 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=7054
Harness context token usage record
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70

exec command

+17:48
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":7054,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"0f8893","wall_time_seconds":30.002057462,"session_id":7054,"original_token_count":153}
loaded prepared arrays from /app/solution/cache_validation.pt
resumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]
/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=4.86950 lr=0.000591004
epoch 02/12: loss=4.82408 lr=0.000564631
Harness context token usage record
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}
71

exec command

+18:21
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

let{output,...rest}=await tools.write_stdin({"session_id":7054,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"a37306","wall_time_seconds":30.000975379,"session_id":7054,"original_token_count":40}
epoch 03/12: loss=4.79085 lr=0.000522676
epoch 04/12: loss=4.75469 lr=0.000468
epoch 05/12: loss=4.70461 lr=0.000404328
epoch 06/12: loss=4.67537 lr=0.000336
Harness context token usage record
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72

exec command

+18:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":7054,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2a9df1","wall_time_seconds":30.001260398,"session_id":7054,"original_token_count":41}
epoch 07/12: loss=4.62046 lr=0.000267672
epoch 08/12: loss=4.58827 lr=0.000204
epoch 09/12: loss=4.54520 lr=0.000149324
epoch 10/12: loss=4.51044 lr=0.000107369
Harness context token usage record
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}
73

exec command

+19:25
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

let{output,...rest}=await tools.write_stdin({"session_id":7054,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 13.8 seconds
Output:

{"chunk_id":"9c5ee5","wall_time_seconds":13.742299766,"exit_code":0,"original_token_count":120}
epoch 11/12: loss=4.49004 lr=8.09956e-05
epoch 12/12: loss=4.48490 lr=7.2e-05
wrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet
{"checkpoint": "/app/solution/model_validation_v1_cont.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 16, "epochs": 12, "batch_size": 256, "negatives": 1024, "learning_rate": 0.0006, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "seed": 20260924}}
Harness context token usage record
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Harness context codex item completed
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      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1_cont.parquet --checkpoint /app/solution/model_validation_v1_cont.pt --cache /app/solution/cache_validation.pt --resume /app/solution/model_validation_v1.pt --epochs 12 --learning-rate 0.0006 --negatives 1024 --prior-scale 1.4 --pop-scale 0.25 --threads 8"
    ],
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 113,
      "nanos": 699523051
    },
    "formatted_output": "loaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n"
  }
}
74

exec command

+19:41
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 /task/evaluate_public.py /app/solution/validation_twotower_v1_cont.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/validation_twotower_v1_cont.parquet",
  "scores": {
    "quality_ndcg@10": 0.023379,
    "quality_users": 4263
  }
}
Harness context token usage record
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    "cache_write_input_tokens": 0,
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}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-d8588a3e-80f3-4293-adf7-2a61a5300cf2",
    "process_id": "42576",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1_cont.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1_cont.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_v1_cont.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023379,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_twotower_v1_cont.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023379,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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  }
}
75

exec command

+19:48
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'],strict=False)\nm.item_tower.learned_prior_scale.fill_(1.4);m.item_tower.fixed_prior_scale.fill_(0.25)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_best.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 9.5 seconds
Output:

wrote 845,600 ranked rows to /app/solution/validation_best.parquet
{
  "model": "/app/solution/validation_best.parquet",
  "scores": {
    "quality_ndcg@10": 0.024001,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9c0637ff-2078-42fd-826b-725b50eb86a9",
    "process_id": "86543",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'],strict=False)\nm.item_tower.learned_prior_scale.fill_(1.4);m.item_tower.fixed_prior_scale.fill_(0.25)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_best.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'],strict=False)\nm.item_tower.learned_prior_scale.fill_(1.4);m.item_tower.fixed_prior_scale.fill_(0.25)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_best.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n  \"model\": \"/app/solution/validation_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.024001,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n  \"model\": \"/app/solution/validation_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.024001,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 363686114
    },
    "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n  \"model\": \"/app/solution/validation_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.024001,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
76

Agent message

+20:13
Thinking unavailable No text recorded

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

77

Agent message

+20:22
Thinking unavailable No text recorded

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

78

exec command

+20:24
Thinking unavailable No text recorded

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

Model text

The calibrated 12-epoch checkpoint is currently best at 0.024001; extending it overfits slightly (0.023379), so I’m preserving the earlier model. The next experiment targets a modeling limitation visible in the current encoder: it treats each distinct track almost equally and underweights sought-out behavior. I’m testing a frequency-aware, recency-decayed pool with stronger organic weighting while keeping the same serving-time decomposition.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n class Config:\n@@\n     like_sampling_weight: float = 4.0\n+    organic_boost: float = 1.5\n@@\n-def load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\n+def load_event_arrays(\n+    stage: str, users: np.ndarray, items: np.ndarray, cfg: Config\n+) -> dict:\n@@\n-    history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n-        1.0 + 0.35 * lo[positive]\n+    history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n+        1.0 + cfg.organic_boost * lo[positive]\n@@\n def latest_unique_history(\n@@\n-    reverse_items = items[lo:hi][::-1]\n-    _, first_reverse = np.unique(reverse_items, return_index=True)\n-    first_reverse.sort()\n-    first_reverse = first_reverse[: cfg.max_history]\n-    positions = hi - 1 - first_reverse\n-    age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\n-    decay = np.exp(-age_days / 120.0)\n-    weights = values[positions] * decay\n+    window_items = items[lo:hi]\n+    age_days = (cutoff - times[lo:hi]).astype(np.float32) / 86_400.0\n+    event_weights = values[lo:hi] * np.exp(-age_days / 120.0)\n+    unique_items, inverse = np.unique(window_items, return_inverse=True)\n+    summed = np.bincount(\n+        inverse, weights=event_weights, minlength=len(unique_items)\n+    ).astype(np.float32)\n+    # Repeated positive plays are stronger taste evidence, with a square root\n+    # preventing a handful of looped tracks from swallowing the whole profile.\n+    aggregate_weights = np.sqrt(summed)\n+    if len(unique_items) > cfg.max_history:\n+        selected = np.argpartition(aggregate_weights, -cfg.max_history)[\n+            -cfg.max_history:\n+        ]\n+        selected = selected[np.argsort(aggregate_weights[selected])[::-1]]\n+    else:\n+        selected = np.arange(len(unique_items))\n     return (\n-        items[positions].astype(np.int32, copy=False),\n-        weights.astype(np.float32, copy=False),\n-        float(organic[positions].mean()),\n+        unique_items[selected].astype(np.int32, copy=False),\n+        aggregate_weights[selected].astype(np.float32, copy=False),\n+        float(organic[lo:hi].mean()),\n@@\n     parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n+    parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n@@\n         like_sampling_weight=args.like_weight,\n+        organic_boost=args.organic_boost,\n@@\n-        arrays = load_event_arrays(args.stage, target_users, candidate_items)\n+        arrays = load_event_arrays(args.stage, target_users, candidate_items, cfg)\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-28cba774-71e2-4058-8305-2ae8d078f566",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -48,2 +48,3 @@\n     like_sampling_weight: float = 4.0\n+    organic_boost: float = 1.5\n     seed: int = 20260924\n@@ -94,3 +95,5 @@\n \n-def load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\n+def load_event_arrays(\n+    stage: str, users: np.ndarray, items: np.ndarray, cfg: Config\n+) -> dict:\n     \"\"\"Load compact candidate-catalog events; raw frames are promptly released.\"\"\"\n@@ -111,3 +114,3 @@\n     history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n-        1.0 + 0.35 * lo[positive]\n+        1.0 + cfg.organic_boost * lo[positive]\n     )\n@@ -199,14 +202,23 @@\n         return np.empty(0, np.int32), np.empty(0, np.float32), 0.0\n-    reverse_items = items[lo:hi][::-1]\n-    _, first_reverse = np.unique(reverse_items, return_index=True)\n-    first_reverse.sort()\n-    first_reverse = first_reverse[: cfg.max_history]\n-    positions = hi - 1 - first_reverse\n-    age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\n-    decay = np.exp(-age_days / 120.0)\n-    weights = values[positions] * decay\n+    window_items = items[lo:hi]\n+    age_days = (cutoff - times[lo:hi]).astype(np.float32) / 86_400.0\n+    event_weights = values[lo:hi] * np.exp(-age_days / 120.0)\n+    unique_items, inverse = np.unique(window_items, return_inverse=True)\n+    summed = np.bincount(\n+        inverse, weights=event_weights, minlength=len(unique_items)\n+    ).astype(np.float32)\n+    # Repeated positive plays are stronger taste evidence, with a square root\n+    # preventing a handful of looped tracks from swallowing the whole profile.\n+    aggregate_weights = np.sqrt(summed)\n+    if len(unique_items) > cfg.max_history:\n+        selected = np.argpartition(aggregate_weights, -cfg.max_history)[\n+            -cfg.max_history:\n+        ]\n+        selected = selected[np.argsort(aggregate_weights[selected])[::-1]]\n+    else:\n+        selected = np.arange(len(unique_items))\n     return (\n-        items[positions].astype(np.int32, copy=False),\n-        weights.astype(np.float32, copy=False),\n-        float(organic[positions].mean()),\n+        unique_items[selected].astype(np.int32, copy=False),\n+        aggregate_weights[selected].astype(np.float32, copy=False),\n+        float(organic[lo:hi].mean()),\n     )\n@@ -572,2 +584,3 @@\n     parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n+    parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n     args = parser.parse_args()\n@@ -586,2 +599,3 @@\n         like_sampling_weight=args.like_weight,\n+        organic_boost=args.organic_boost,\n     )\n@@ -597,3 +611,3 @@\n     else:\n-        arrays = load_event_arrays(args.stage, target_users, candidate_items)\n+        arrays = load_event_arrays(args.stage, target_users, candidate_items, cfg)\n         contexts = build_contexts(arrays, target_users, stage_end, cfg)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
    "stderr": ""
  }
}
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79

exec command

+20:43
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v2.parquet --checkpoint /app/solution/model_validation_v2.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --prior-scale 1.4 --pop-scale 0.25 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=43392
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80

exec command

+20:49
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":43392,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ab23f6","wall_time_seconds":30.001171841,"session_id":43392,"original_token_count":55}
loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s
built 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries
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81

exec command

+21:21
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

let{output,...rest}=await tools.write_stdin({"session_id":43392,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f075a7","wall_time_seconds":30.001024145,"session_id":43392,"original_token_count":124}
saved prepared arrays to /app/solution/cache_validation_v2.pt
/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=5.60531 lr=0.00197001
epoch 02/12: loss=5.35360 lr=0.0018821
epoch 03/12: loss=5.14357 lr=0.00174225
Harness context token usage record
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82

exec command

+21:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":43392,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c085fb","wall_time_seconds":30.001364422,"session_id":43392,"original_token_count":48}
epoch 04/12: loss=4.96154 lr=0.00156
epoch 05/12: loss=4.81365 lr=0.00134776
epoch 06/12: loss=4.67550 lr=0.00112
epoch 07/12: loss=4.57506 lr=0.000892239
epoch 08/12: loss=4.46756 lr=0.00068
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83

exec command

+22:27
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

let{output,...rest}=await tools.write_stdin({"session_id":43392,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 24.7 seconds
Output:

{"chunk_id":"c67df7","wall_time_seconds":24.702483828,"exit_code":0,"original_token_count":143}
epoch 09/12: loss=4.39426 lr=0.000497746
epoch 10/12: loss=4.33325 lr=0.000357898
epoch 11/12: loss=4.26866 lr=0.000269985
epoch 12/12: loss=4.22436 lr=0.00024
wrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet
{"checkpoint": "/app/solution/model_validation_v2.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 16, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 20260924}}
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Harness context codex item completed
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    "type": "CommandExecution",
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    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v2.parquet --checkpoint /app/solution/model_validation_v2.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --prior-scale 1.4 --pop-scale 0.25 --threads 8"
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 125,
      "nanos": 644338601
    },
    "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
84

exec command

+22:54
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v2.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_twotower_v2.parquet",
  "scores": {
    "quality_ndcg@10": 0.024272,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
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      "/bin/sh",
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      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v2.parquet"
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_v2.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.024272,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_twotower_v2.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.024272,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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  }
}
85

exec command

+23:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_blend.py\n@@\n-checkpoint = torch.load(\"/app/solution/model_validation_v1.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_v2.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n@@\n-grid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\n-        for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\n+grid = [(b, p) for b in (0.8, 1.1, 1.4, 1.7, 2.0, 2.4)\n+        for p in (0.0, 0.15, 0.3, 0.45)]\n@@\n-    for start in range(0, len(archive[\"uids\"]), 192):\n-        stop = min(start + 192, len(archive[\"uids\"]))\n-        batch = np.arange(start, stop)\n+    relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n+    for start in range(0, len(relevant_rows), 192):\n+        batch = relevant_rows[start : start + 192]\n@@\n-            archive[\"packed_eligible\"][start:stop], axis=1,\n+            archive[\"packed_eligible\"][batch], axis=1,\n@@\n-            for row, uid in enumerate(archive[\"uids\"][start:stop]):\n+            for row, uid in enumerate(archive[\"uids\"][batch]):\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-769dcaff-49b8-4685-9fe7-6099039b7df0",
    "changes": {
      "/app/solution/tune_blend.py": {
        "type": "update",
        "unified_diff": "@@ -19,4 +19,4 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_v1.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_v2.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n histories = cache[\"histories\"]\n@@ -31,4 +31,4 @@\n # learned-bias multiplier, standardized log-popularity addition\n-grid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\n-        for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\n+grid = [(b, p) for b in (0.8, 1.1, 1.4, 1.7, 2.0, 2.4)\n+        for p in (0.0, 0.15, 0.3, 0.45)]\n ranked = {key: {} for key in grid}\n@@ -39,5 +39,5 @@\n     bias = item_vectors[:, -1]\n-    for start in range(0, len(archive[\"uids\"]), 192):\n-        stop = min(start + 192, len(archive[\"uids\"]))\n-        batch = np.arange(start, stop)\n+    relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n+    for start in range(0, len(relevant_rows), 192):\n+        batch = relevant_rows[start : start + 192]\n         hids, offsets, weights, features = gather_histories(histories, batch)\n@@ -48,3 +48,3 @@\n         eligible = np.unpackbits(\n-            archive[\"packed_eligible\"][start:stop], axis=1,\n+            archive[\"packed_eligible\"][batch], axis=1,\n             count=30_000, bitorder=\"little\"\n@@ -57,3 +57,3 @@\n             top = torch.topk(scores, 10, dim=1).indices.numpy()\n-            for row, uid in enumerate(archive[\"uids\"][start:stop]):\n+            for row, uid in enumerate(archive[\"uids\"][batch]):\n                 if int(uid) in truth:\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n",
    "stderr": ""
  }
}
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86

exec command

+23:13
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"sed -n '34,100p' /app/solution/tune_blend.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

ranked = {key: {} for key in grid}
pop = -item_features(catalog)[:, 2]
with torch.inference_mode():
    item_vectors = model.item_tower(torch.arange(30_000))
    latent = item_vectors[:, :-1].T.contiguous()
    bias = item_vectors[:, -1]
    relevant_rows = np.flatnonzero(np.isin(archive["uids"], list(truth)))
    for start in range(0, len(relevant_rows), 192):
        batch = relevant_rows[start : start + 192]
        hids, offsets, weights, features = gather_histories(histories, batch)
        users = model.user_tower(hids, offsets, weights, features)
        similarity = users[:, :-1] @ latent
        if start == 0:
            print("similarity std", float(similarity.std()), "bias std", float(bias.std()))
        eligible = np.unpackbits(
            archive["packed_eligible"][batch], axis=1,
            count=30_000, bitorder="little"
        ).astype(bool)
        mask = torch.from_numpy(~eligible)
        for key in grid:
            b, p = key
            scores = similarity + b * bias[None, :] + p * pop[None, :]
            scores.masked_fill_(mask, -torch.inf)
            top = torch.topk(scores, 10, dim=1).indices.numpy()
            for row, uid in enumerate(archive["uids"][batch]):
                if int(uid) in truth:
                    ranked[key][int(uid)] = archive["item_ids"][top[row]].astype(int).tolist()

for key in grid:
    values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]
    print(key, f"{np.mean(values):.6f}")
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      "/bin/sh",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "ranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n    item_vectors = model.item_tower(torch.arange(30_000))\n    latent = item_vectors[:, :-1].T.contiguous()\n    bias = item_vectors[:, -1]\n    relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n    for start in range(0, len(relevant_rows), 192):\n        batch = relevant_rows[start : start + 192]\n        hids, offsets, weights, features = gather_histories(histories, batch)\n        users = model.user_tower(hids, offsets, weights, features)\n        similarity = users[:, :-1] @ latent\n        if start == 0:\n            print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n        eligible = np.unpackbits(\n            archive[\"packed_eligible\"][batch], axis=1,\n            count=30_000, bitorder=\"little\"\n        ).astype(bool)\n        mask = torch.from_numpy(~eligible)\n        for key in grid:\n            b, p = key\n            scores = similarity + b * bias[None, :] + p * pop[None, :]\n            scores.masked_fill_(mask, -torch.inf)\n            top = torch.topk(scores, 10, dim=1).indices.numpy()\n            for row, uid in enumerate(archive[\"uids\"][batch]):\n                if int(uid) in truth:\n                    ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n    values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n    print(key, f\"{np.mean(values):.6f}\")\n",
    "stderr": "",
    "aggregated_output": "ranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n    item_vectors = model.item_tower(torch.arange(30_000))\n    latent = item_vectors[:, :-1].T.contiguous()\n    bias = item_vectors[:, -1]\n    relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n    for start in range(0, len(relevant_rows), 192):\n        batch = relevant_rows[start : start + 192]\n        hids, offsets, weights, features = gather_histories(histories, batch)\n        users = model.user_tower(hids, offsets, weights, features)\n        similarity = users[:, :-1] @ latent\n        if start == 0:\n            print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n        eligible = np.unpackbits(\n            archive[\"packed_eligible\"][batch], axis=1,\n            count=30_000, bitorder=\"little\"\n        ).astype(bool)\n        mask = torch.from_numpy(~eligible)\n        for key in grid:\n            b, p = key\n            scores = similarity + b * bias[None, :] + p * pop[None, :]\n            scores.masked_fill_(mask, -torch.inf)\n            top = torch.topk(scores, 10, dim=1).indices.numpy()\n            for row, uid in enumerate(archive[\"uids\"][batch]):\n                if int(uid) in truth:\n                    ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n    values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n    print(key, f\"{np.mean(values):.6f}\")\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5150
    },
    "formatted_output": "ranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n    item_vectors = model.item_tower(torch.arange(30_000))\n    latent = item_vectors[:, :-1].T.contiguous()\n    bias = item_vectors[:, -1]\n    relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n    for start in range(0, len(relevant_rows), 192):\n        batch = relevant_rows[start : start + 192]\n        hids, offsets, weights, features = gather_histories(histories, batch)\n        users = model.user_tower(hids, offsets, weights, features)\n        similarity = users[:, :-1] @ latent\n        if start == 0:\n            print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n        eligible = np.unpackbits(\n            archive[\"packed_eligible\"][batch], axis=1,\n            count=30_000, bitorder=\"little\"\n        ).astype(bool)\n        mask = torch.from_numpy(~eligible)\n        for key in grid:\n            b, p = key\n            scores = similarity + b * bias[None, :] + p * pop[None, :]\n            scores.masked_fill_(mask, -torch.inf)\n            top = torch.topk(scores, 10, dim=1).indices.numpy()\n            for row, uid in enumerate(archive[\"uids\"][batch]):\n                if int(uid) in truth:\n                    ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n    values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n    print(key, f\"{np.mean(values):.6f}\")\n"
  }
}
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87

exec command

+23:17
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 patch = "*** Begin Patch\n*** Update File: /app/solution/tune_blend.py\n@@\n model = TwoTowerRanker(30_000, item_features(catalog), 64, 128)\n model.load_state_dict(checkpoint[\"model_state\"])\n+model.item_tower.learned_prior_scale.fill_(1.0)\n+model.item_tower.fixed_prior_scale.fill_(0.0)\n model.eval()\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-df9659fb-5855-474b-87c2-afb743746016",
    "changes": {
      "/app/solution/tune_blend.py": {
        "type": "update",
        "unified_diff": "@@ -24,2 +24,4 @@\n model.load_state_dict(checkpoint[\"model_state\"])\n+model.item_tower.learned_prior_scale.fill_(1.0)\n+model.item_tower.fixed_prior_scale.fill_(0.0)\n model.eval()\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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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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    "total_tokens": 2585786
  }
}
88

exec command

+23:20
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=47970
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b10ab2ac87d28d1683e1e3d8daac",
  "usage": {
    "input_tokens": 66062,
    "cached_input_tokens": 65664,
    "cache_write_input_tokens": 0,
    "output_tokens": 106,
    "reasoning_output_tokens": 6,
    "total_tokens": 66168
  },
  "turn_token_usage": {
    "input_tokens": 2615151,
    "cached_input_tokens": 2506240,
    "cache_write_input_tokens": 0,
    "output_tokens": 36803,
    "reasoning_output_tokens": 16659,
    "total_tokens": 2651954
  },
  "thread_token_usage": {
    "input_tokens": 2615151,
    "cached_input_tokens": 2506240,
    "cache_write_input_tokens": 0,
    "output_tokens": 36803,
    "reasoning_output_tokens": 16659,
    "total_tokens": 2651954
  }
}
89

exec command

+23:25
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

let{output,...rest}=await tools.write_stdin({"session_id":47970,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2812d6","wall_time_seconds":30.00099088,"session_id":47970,"original_token_count":16}
similarity std 2.4374141693115234 bias std 0.7092720866203308
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b10ebbf887d297a23d953498993a",
  "usage": {
    "input_tokens": 66196,
    "cached_input_tokens": 65920,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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    "total_tokens": 2718203
  },
  "thread_token_usage": {
    "input_tokens": 2681347,
    "cached_input_tokens": 2572160,
    "cache_write_input_tokens": 0,
    "output_tokens": 36856,
    "reasoning_output_tokens": 16665,
    "total_tokens": 2718203
  }
}
90

exec command

+23:57
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":47970,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6b51d6","wall_time_seconds":30.001445428,"session_id":47970,"original_token_count":0}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b12f6a2887d2a6c316306dbcc80a",
  "usage": {
    "input_tokens": 66327,
    "cached_input_tokens": 66048,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 66380
  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 36909,
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  },
  "thread_token_usage": {
    "input_tokens": 2747674,
    "cached_input_tokens": 2638208,
    "cache_write_input_tokens": 0,
    "output_tokens": 36909,
    "reasoning_output_tokens": 16671,
    "total_tokens": 2784583
  }
}
91

exec command

+24:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":47970,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 10.6 seconds
Output:

{"chunk_id":"d7db81","wall_time_seconds":10.643284144999999,"exit_code":0,"original_token_count":123}
(0.8, 0.0) 0.020408
(0.8, 0.15) 0.022309
(0.8, 0.3) 0.024042
(0.8, 0.45) 0.023693
(1.1, 0.0) 0.022691
(1.1, 0.15) 0.024435
(1.1, 0.3) 0.024145
(1.1, 0.45) 0.023237
(1.4, 0.0) 0.023947
(1.4, 0.15) 0.025061
(1.4, 0.3) 0.024055
(1.4, 0.45) 0.022497
(1.7, 0.0) 0.024529
(1.7, 0.15) 0.024414
(1.7, 0.3) 0.023274
(1.7, 0.45) 0.021976
(2.0, 0.0) 0.024044
(2.0, 0.15) 0.023444
(2.0, 0.3) 0.022251
(2.0, 0.45) 0.021551
(2.4, 0.0) 0.023020
(2.4, 0.15) 0.022360
(2.4, 0.3) 0.021581
(2.4, 0.45) 0.020636
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b14f688887d29bdd225948a9af2d",
  "usage": {
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    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 2814109,
    "cached_input_tokens": 2704384,
    "cache_write_input_tokens": 0,
    "output_tokens": 36965,
    "reasoning_output_tokens": 16680,
    "total_tokens": 2851074
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-3fa67eed-59c8-4e7c-a0ae-f7e8aae80c83",
    "process_id": "47970",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "similarity std 2.4374141693115234 bias std 0.7092720866203308\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n",
    "stderr": "",
    "aggregated_output": "similarity std 2.4374141693115234 bias std 0.7092720866203308\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n",
    "exit_code": 0,
    "duration": {
      "secs": 78,
      "nanos": 532714845
    },
    "formatted_output": "similarity std 2.4374141693115234 bias std 0.7092720866203308\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n"
  }
}
92

exec command

+24:48
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 frequency-aware profile improves the calibrated score to 0.025061. The best blend is a little less popularity-heavy than the first model (learned prior ×1.4 plus fixed popularity ×0.15). I’m doing one focused label-weight test next: grade-3 likes contribute gain 7 in NDCG, so I’m aligning positive sampling more closely with that gain before committing to the final refit.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_like7.parquet --checkpoint /app/solution/model_validation_like7.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 7 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=98013
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b15c40c887d28d29fe73397a5e28",
  "usage": {
    "input_tokens": 66929,
    "cached_input_tokens": 66304,
    "cache_write_input_tokens": 0,
    "output_tokens": 623,
    "reasoning_output_tokens": 350,
    "total_tokens": 67552
  },
  "turn_token_usage": {
    "input_tokens": 2881038,
    "cached_input_tokens": 2770688,
    "cache_write_input_tokens": 0,
    "output_tokens": 37588,
    "reasoning_output_tokens": 17030,
    "total_tokens": 2918626
  },
  "thread_token_usage": {
    "input_tokens": 2881038,
    "cached_input_tokens": 2770688,
    "cache_write_input_tokens": 0,
    "output_tokens": 37588,
    "reasoning_output_tokens": 17030,
    "total_tokens": 2918626
  }
}
93

exec command

+24:55
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,...rest}=await tools.write_stdin({"session_id":98013,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b1bac6","wall_time_seconds":30.001248079,"session_id":98013,"original_token_count":125}
loaded prepared arrays from /app/solution/cache_validation_v2.pt
/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=5.59676 lr=0.00197001
epoch 02/12: loss=5.34863 lr=0.0018821
epoch 03/12: loss=5.13171 lr=0.00174225
Harness context token usage record
{
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  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b169b0cc87d2a6c1737145154002",
  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 53,
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  },
  "turn_token_usage": {
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  "thread_token_usage": {
    "input_tokens": 2948618,
    "cached_input_tokens": 2837376,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 17036,
    "total_tokens": 2986259
  }
}
94

exec command

+25:27
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":98013,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d0baf2","wall_time_seconds":30.000983923,"session_id":98013,"original_token_count":39}
epoch 04/12: loss=4.93923 lr=0.00156
epoch 05/12: loss=4.78946 lr=0.00134776
epoch 06/12: loss=4.65686 lr=0.00112
epoch 07/12: loss=4.55852 lr=0.000892239
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b189c0c487d28142d4e0a0daeb6a",
  "usage": {
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    "cache_write_input_tokens": 0,
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  "thread_token_usage": {
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  }
}
95

exec command

+26:00
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":98013,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d981e2","wall_time_seconds":30.001097775,"session_id":98013,"original_token_count":50}
epoch 08/12: loss=4.45585 lr=0.00068
epoch 09/12: loss=4.36784 lr=0.000497746
epoch 10/12: loss=4.30615 lr=0.000357898
epoch 11/12: loss=4.24130 lr=0.000269985
epoch 12/12: loss=4.19851 lr=0.00024
Harness context token usage record
{
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-44243ea9-ccab-400f-b150-d3fd69afe2d7",
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      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_like7.parquet --checkpoint /app/solution/model_validation_like7.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 7 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8"
    ],
    "cwd": "file:///app",
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      {
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        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_like7.parquet --checkpoint /app/solution/model_validation_like7.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 7 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8"
      }
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    "status": "completed",
    "stdout": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 98,
      "nanos": 832896013
    },
    "formatted_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
96

exec command

+26:33
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":98013,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"63eefd","wall_time_seconds":0.00000203,"exit_code":0,"original_token_count":105}
wrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet
{"checkpoint": "/app/solution/model_validation_like7.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 16, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 7.0, "organic_boost": 1.5, "seed": 20260924}}
Harness context token usage record
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97

exec command

+26:35
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_like7.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_twotower_like7.parquet",
  "scores": {
    "quality_ndcg@10": 0.023811,
    "quality_users": 4263
  }
}
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Harness context codex item completed
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  }
}
98

exec command

+26:50
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n     torch.set_num_threads(args.threads)\n     torch.set_num_interop_threads(1)\n+    torch.manual_seed(args.seed)\n     random.seed(args.seed)\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-a811a115-57ce-4f93-bfd4-b530669645db",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -588,2 +588,3 @@\n     torch.set_num_interop_threads(1)\n+    torch.manual_seed(args.seed)\n     random.seed(args.seed)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
    "stderr": ""
  }
}
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99

exec command

+26:59
Thinking unavailable No text recorded

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

Model text

The NDCG-gain-matched like weighting was worse (0.023811), so the selected label weight remains 4. I also found and fixed a reproducibility issue: the sampling seed was recorded but model initialization happened before it was applied. I’m running one deterministic confirmation now; that makes the chosen validation recipe exactly reproducible for the train+validation refit.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_deterministic.parquet --checkpoint /app/solution/model_validation_deterministic.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=34699
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100

exec command

+27:06
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

let{output,...rest}=await tools.write_stdin({"session_id":34699,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"382581","wall_time_seconds":30.000819395,"session_id":34699,"original_token_count":125}
loaded prepared arrays from /app/solution/cache_validation_v2.pt
/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=5.60325 lr=0.00197001
epoch 02/12: loss=5.34974 lr=0.0018821
epoch 03/12: loss=5.13801 lr=0.00174225
Harness context token usage record
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}
101

exec command

+27:38
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

let{output,...rest}=await tools.write_stdin({"session_id":34699,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"5746d2","wall_time_seconds":30.000984971,"session_id":34699,"original_token_count":39}
epoch 04/12: loss=4.94966 lr=0.00156
epoch 05/12: loss=4.79977 lr=0.00134776
epoch 06/12: loss=4.66945 lr=0.00112
epoch 07/12: loss=4.57449 lr=0.000892239
Harness context token usage record
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102

exec command

+28:10
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

let{output,...rest}=await tools.write_stdin({"session_id":34699,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 28.9 seconds
Output:

{"chunk_id":"4119b2","wall_time_seconds":28.871920956,"exit_code":0,"original_token_count":158}
epoch 08/12: loss=4.46786 lr=0.00068
epoch 09/12: loss=4.39488 lr=0.000497746
epoch 10/12: loss=4.33856 lr=0.000357898
epoch 11/12: loss=4.28199 lr=0.000269985
epoch 12/12: loss=4.24280 lr=0.00024
wrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet
{"checkpoint": "/app/solution/model_validation_deterministic.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 16, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 20260924}}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-77daf3c9-a594-4d50-804d-74ee43cb3726",
    "process_id": "34699",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_deterministic.parquet --checkpoint /app/solution/model_validation_deterministic.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_deterministic.parquet --checkpoint /app/solution/model_validation_deterministic.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 99,
      "nanos": 235131244
    },
    "formatted_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
103

exec command

+28:44
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_deterministic.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_twotower_deterministic.parquet",
  "scores": {
    "quality_ndcg@10": 0.024198,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_deterministic.parquet"
    ],
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        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_deterministic.parquet"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_deterministic.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.024198,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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    "exit_code": 0,
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  }
}
104

exec command

+28:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_blend.py\n@@\n-checkpoint = torch.load(\"/app/solution/model_validation_v2.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_deterministic.pt\", weights_only=False)\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-c56d4526-1366-44aa-b7b4-a07e91e8ef07",
    "changes": {
      "/app/solution/tune_blend.py": {
        "type": "update",
        "unified_diff": "@@ -19,3 +19,3 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_v2.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_deterministic.pt\", weights_only=False)\n cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n",
    "stderr": ""
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105

exec command

+28:56
Thinking unavailable No text recorded

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=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=78235
Harness context token usage record
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}
106

exec command

+29:01
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

let{output,...rest}=await tools.write_stdin({"session_id":78235,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d7ae1b","wall_time_seconds":30.001236842,"session_id":78235,"original_token_count":16}
similarity std 2.519658088684082 bias std 0.6085100173950195
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":78235,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b056a0","wall_time_seconds":30.001159139,"session_id":78235,"original_token_count":0}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":78235,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 9.2 seconds
Output:

{"chunk_id":"cd371f","wall_time_seconds":9.240704915,"exit_code":0,"original_token_count":123}
(0.8, 0.0) 0.020650
(0.8, 0.15) 0.023018
(0.8, 0.3) 0.024181
(0.8, 0.45) 0.023781
(1.1, 0.0) 0.022634
(1.1, 0.15) 0.024173
(1.1, 0.3) 0.023760
(1.1, 0.45) 0.023340
(1.4, 0.0) 0.023659
(1.4, 0.15) 0.024198
(1.4, 0.3) 0.023492
(1.4, 0.45) 0.022787
(1.7, 0.0) 0.023937
(1.7, 0.15) 0.023743
(1.7, 0.3) 0.022765
(1.7, 0.45) 0.021945
(2.0, 0.0) 0.023330
(2.0, 0.15) 0.023145
(2.0, 0.3) 0.022252
(2.0, 0.45) 0.021189
(2.4, 0.0) 0.022727
(2.4, 0.15) 0.022221
(2.4, 0.3) 0.021463
(2.4, 0.45) 0.020523
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Harness context codex item completed
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    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "similarity std 2.519658088684082 bias std 0.6085100173950195\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n",
    "stderr": "",
    "aggregated_output": "similarity std 2.519658088684082 bias std 0.6085100173950195\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n",
    "exit_code": 0,
    "duration": {
      "secs": 80,
      "nanos": 726925202
    },
    "formatted_output": "similarity std 2.519658088684082 bias std 0.6085100173950195\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n"
  }
}
107

exec command

+30:22
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_seed1337.parquet --checkpoint /app/solution/model_validation_seed1337.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 1337 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=21771
Harness context token usage record
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b2aef99887d2b25a39edbc414fed",
  "usage": {
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    "cached_input_tokens": 71168,
    "cache_write_input_tokens": 0,
    "output_tokens": 258,
    "reasoning_output_tokens": 77,
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  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 39926,
    "reasoning_output_tokens": 18032,
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  "thread_token_usage": {
    "input_tokens": 4066363,
    "cached_input_tokens": 3948416,
    "cache_write_input_tokens": 0,
    "output_tokens": 39926,
    "reasoning_output_tokens": 18032,
    "total_tokens": 4106289
  }
}
108

exec command

+30:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":21771,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ccb45b","wall_time_seconds":30.001344003,"session_id":21771,"original_token_count":125}
loaded prepared arrays from /app/solution/cache_validation_v2.pt
/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=5.60734 lr=0.00197001
epoch 02/12: loss=5.35055 lr=0.0018821
epoch 03/12: loss=5.13109 lr=0.00174225
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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    "cache_write_input_tokens": 0,
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    "total_tokens": 4178404
  }
}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":21771,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"82929b","wall_time_seconds":30.000558008,"session_id":21771,"original_token_count":48}
epoch 04/12: loss=4.94432 lr=0.00156
epoch 05/12: loss=4.79672 lr=0.00134776
epoch 06/12: loss=4.67224 lr=0.00112
epoch 07/12: loss=4.54466 lr=0.000892239
epoch 08/12: loss=4.44736 lr=0.00068
Harness context token usage record
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":21771,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 29.1 seconds
Output:

{"chunk_id":"78ae05","wall_time_seconds":29.08208224,"exit_code":0,"original_token_count":145}
epoch 09/12: loss=4.36494 lr=0.000497746
epoch 10/12: loss=4.29942 lr=0.000357898
epoch 11/12: loss=4.23000 lr=0.000269985
epoch 12/12: loss=4.19406 lr=0.00024
wrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet
{"checkpoint": "/app/solution/model_validation_seed1337.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 16, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 1337}}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5aa4c1dc-7bb0-4915-be33-3b97af98f168",
    "process_id": "21771",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_seed1337.parquet --checkpoint /app/solution/model_validation_seed1337.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 1337 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_seed1337.parquet --checkpoint /app/solution/model_validation_seed1337.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 1337 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n",
    "stderr": "",
    "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 99,
      "nanos": 8955251
    },
    "formatted_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n"
  }
}
109

exec command

+32:06
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":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_seed1337.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_twotower_seed1337.parquet",
  "scores": {
    "quality_ndcg@10": 0.023842,
    "quality_users": 4263
  }
}
Harness context token usage record
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  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
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  "usage": {
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-ba34f893-cb1b-4d7c-b8e9-d09116fe80a2",
    "process_id": "13830",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_seed1337.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_seed1337.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023842,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023842,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 283815338
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023842,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
110

Agent message

+32:26
Thinking unavailable No text recorded

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

111

exec command

+32:29
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 deterministic seed trials are close but do not beat the preserved 0.025061 checkpoint individually. I’m checking whether their errors are complementary. If a two-seed concatenation gives a meaningful gain, it still remains a valid two-tower MIPS model (concatenated user vectors against concatenated offline item vectors); otherwise I’ll keep the lower-latency 65-dimensional model.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_ensemble.py\n+#!/usr/bin/env python3\n+\"\"\"Measure whether validation models have complementary retrieval errors.\"\"\"\n+from pathlib import Path\n+import sys\n+import numpy as np\n+import pandas as pd\n+import torch\n+\n+sys.path[:0] = [\"/app/solution\", \"/task\"]\n+from model import TwoTowerRanker\n+from train_ranker import gather_histories, item_features\n+from ranking_evaluation import CandidateSet, ndcg_at_k\n+from evaluate_public import validation_truth\n+\n+torch.set_num_threads(8)\n+cat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n+features = item_features(cat)\n+paths = [\n+    \"/app/solution/model_validation_v2.pt\",\n+    \"/app/solution/model_validation_deterministic.pt\",\n+    \"/app/solution/model_validation_seed1337.pt\",\n+]\n+models = []\n+for path in paths:\n+    model = TwoTowerRanker(30_000, features, 64, 128)\n+    model.load_state_dict(torch.load(path, weights_only=False)[\"model_state\"])\n+    model.item_tower.learned_prior_scale.fill_(1.4)\n+    model.item_tower.fixed_prior_scale.fill_(0.15)\n+    model.eval()\n+    models.append(model)\n+histories = torch.load(\n+    \"/app/solution/cache_validation_v2.pt\", weights_only=False\n+)[\"histories\"]\n+archive = np.load(\"/task/data/validation_eligible_candidates.npz\")\n+candidates = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n+targets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\n+truth = validation_truth(candidates, targets)\n+relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n+weights_grid = {\n+    \"a\": (1.0, 0.0, 0.0),\n+    \"b\": (0.0, 1.0, 0.0),\n+    \"c\": (0.0, 0.0, 1.0),\n+    \"ab25\": (0.75, 0.25, 0.0),\n+    \"ab50\": (0.5, 0.5, 0.0),\n+    \"ab75\": (0.25, 0.75, 0.0),\n+    \"ac50\": (0.5, 0.0, 0.5),\n+    \"bc50\": (0.0, 0.5, 0.5),\n+    \"abc\": (1 / 3, 1 / 3, 1 / 3),\n+}\n+ranked = {key: {} for key in weights_grid}\n+with torch.inference_mode():\n+    item_vectors = [m.item_tower(torch.arange(30_000)).T.contiguous() for m in models]\n+    for start in range(0, len(relevant_rows), 160):\n+        batch = relevant_rows[start : start + 160]\n+        hids, offsets, hw, cf = gather_histories(histories, batch)\n+        score_parts = [\n+            m.user_tower(hids, offsets, hw, cf) @ iv\n+            for m, iv in zip(models, item_vectors)\n+        ]\n+        eligible = np.unpackbits(\n+            archive[\"packed_eligible\"][batch], axis=1,\n+            count=30_000, bitorder=\"little\"\n+        ).astype(bool)\n+        mask = torch.from_numpy(~eligible)\n+        for key, combination in weights_grid.items():\n+            scores = sum(w * score for w, score in zip(combination, score_parts))\n+            scores.masked_fill_(mask, -torch.inf)\n+            top = torch.topk(scores, 10, dim=1).indices.numpy()\n+            for row, uid in enumerate(archive[\"uids\"][batch]):\n+                ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n+for key in weights_grid:\n+    result = np.mean([ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()])\n+    print(key, f\"{result:.6f}\")\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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    "type": "FileChange",
    "id": "exec-9a90a11e-11e5-4cd4-9896-eac5b88a89e1",
    "changes": {
      "/app/solution/tune_ensemble.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Measure whether validation models have complementary retrieval errors.\"\"\"\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path[:0] = [\"/app/solution\", \"/task\"]\nfrom model import TwoTowerRanker\nfrom train_ranker import gather_histories, item_features\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\n\ntorch.set_num_threads(8)\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nfeatures = item_features(cat)\npaths = [\n    \"/app/solution/model_validation_v2.pt\",\n    \"/app/solution/model_validation_deterministic.pt\",\n    \"/app/solution/model_validation_seed1337.pt\",\n]\nmodels = []\nfor path in paths:\n    model = TwoTowerRanker(30_000, features, 64, 128)\n    model.load_state_dict(torch.load(path, weights_only=False)[\"model_state\"])\n    model.item_tower.learned_prior_scale.fill_(1.4)\n    model.item_tower.fixed_prior_scale.fill_(0.15)\n    model.eval()\n    models.append(model)\nhistories = torch.load(\n    \"/app/solution/cache_validation_v2.pt\", weights_only=False\n)[\"histories\"]\narchive = np.load(\"/task/data/validation_eligible_candidates.npz\")\ncandidates = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\ntruth = validation_truth(candidates, targets)\nrelevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\nweights_grid = {\n    \"a\": (1.0, 0.0, 0.0),\n    \"b\": (0.0, 1.0, 0.0),\n    \"c\": (0.0, 0.0, 1.0),\n    \"ab25\": (0.75, 0.25, 0.0),\n    \"ab50\": (0.5, 0.5, 0.0),\n    \"ab75\": (0.25, 0.75, 0.0),\n    \"ac50\": (0.5, 0.0, 0.5),\n    \"bc50\": (0.0, 0.5, 0.5),\n    \"abc\": (1 / 3, 1 / 3, 1 / 3),\n}\nranked = {key: {} for key in weights_grid}\nwith torch.inference_mode():\n    item_vectors = [m.item_tower(torch.arange(30_000)).T.contiguous() for m in models]\n    for start in range(0, len(relevant_rows), 160):\n        batch = relevant_rows[start : start + 160]\n        hids, offsets, hw, cf = gather_histories(histories, batch)\n        score_parts = [\n            m.user_tower(hids, offsets, hw, cf) @ iv\n            for m, iv in zip(models, item_vectors)\n        ]\n        eligible = np.unpackbits(\n            archive[\"packed_eligible\"][batch], axis=1,\n            count=30_000, bitorder=\"little\"\n        ).astype(bool)\n        mask = torch.from_numpy(~eligible)\n        for key, combination in weights_grid.items():\n            scores = sum(w * score for w, score in zip(combination, score_parts))\n            scores.masked_fill_(mask, -torch.inf)\n            top = torch.topk(scores, 10, dim=1).indices.numpy()\n            for row, uid in enumerate(archive[\"uids\"][batch]):\n                ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\nfor key in weights_grid:\n    result = np.mean([ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()])\n    print(key, f\"{result:.6f}\")\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/tune_ensemble.py\n",
    "stderr": ""
  }
}
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112

exec command

+32:48
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_ensemble.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=34119
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}
113

exec command

+32:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":34119,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"68b4ae","wall_time_seconds":30.00156073,"session_id":34119,"original_token_count":0}
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114

exec command

+33:26
Thinking unavailable No text recorded

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

Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-cf5e19fb-1af4-415d-b49d-b6feb4d3f4b3",
    "process_id": "34119",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_ensemble.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_ensemble.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "a 0.025061\nb 0.024198\nc 0.023842\nab25 0.024979\nab50 0.025044\nab75 0.024738\nac50 0.024700\nbc50 0.025103\nabc 0.025080\n",
    "stderr": "",
    "aggregated_output": "a 0.025061\nb 0.024198\nc 0.023842\nab25 0.024979\nab50 0.025044\nab75 0.024738\nac50 0.024700\nbc50 0.025103\nabc 0.025080\n",
    "exit_code": 0,
    "duration": {
      "secs": 37,
      "nanos": 286149564
    },
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  }
}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":34119,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"c17bb7","wall_time_seconds":0.00000215,"exit_code":0,"original_token_count":29}
a 0.025061
b 0.024198
c 0.023842
ab25 0.024979
ab50 0.025044
ab75 0.024738
ac50 0.024700
bc50 0.025103
abc 0.025080
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115

exec command

+33:37
Thinking unavailable No text recorded

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

Model text

Seed concatenation does not provide a meaningful lift (best ensemble 0.025103 versus 0.025061), so I’m retaining the faster single tower. One remaining high-value test is temporal data volume: the current model uses 16 weekly cutoffs. I’m expanding to 28 cutoffs to improve tail-item and taste-space coverage while preserving the most recent 180-day history at each cutoff.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_28w.parquet --checkpoint /app/solution/model_validation_28w.pt --cache /app/solution/cache_validation_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=12344
Harness context token usage record
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116

exec command

+33:45
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":12344,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"83a41e","wall_time_seconds":30.001746576,"session_id":12344,"original_token_count":30}
loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s
Harness context token usage record
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117

exec command

+34:18
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":12344,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b40905","wall_time_seconds":30.000674666,"session_id":12344,"original_token_count":129}
built 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries
saved prepared arrays to /app/solution/cache_validation_28w.pt
/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(batch)
epoch 01/12: loss=5.60752 lr=0.00197001
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118

exec command

+34:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":12344,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c457b9","wall_time_seconds":30.000954706,"session_id":12344,"original_token_count":29}
epoch 02/12: loss=5.34331 lr=0.0018821
epoch 03/12: loss=5.11578 lr=0.00174225
epoch 04/12: loss=4.96024 lr=0.00156
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":12344,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2ce0c8","wall_time_seconds":30.000726331,"session_id":12344,"original_token_count":30}
epoch 05/12: loss=4.84123 lr=0.00134776
epoch 06/12: loss=4.73274 lr=0.00112
epoch 07/12: loss=4.64446 lr=0.000892239
Harness context token usage record
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119

exec command

+35:58
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":12344,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"7e4490","wall_time_seconds":30.00068313,"session_id":12344,"original_token_count":30}
epoch 08/12: loss=4.54605 lr=0.00068
epoch 09/12: loss=4.46279 lr=0.000497746
epoch 10/12: loss=4.39505 lr=0.000357898
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":12344,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 19.0 seconds
Output:

{"chunk_id":"faaa82","wall_time_seconds":18.985649923,"exit_code":0,"original_token_count":123}
epoch 11/12: loss=4.31178 lr=0.000269985
epoch 12/12: loss=4.27743 lr=0.00024
wrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet
{"checkpoint": "/app/solution/model_validation_28w.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 28, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 20260924}}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-dcb6fd08-c0ec-4311-a75a-4654e27293a4",
    "process_id": "12344",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_28w.parquet --checkpoint /app/solution/model_validation_28w.pt --cache /app/solution/cache_validation_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_28w.parquet --checkpoint /app/solution/model_validation_28w.pt --cache /app/solution/cache_validation_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 188,
      "nanos": 317079489
    },
    "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total_loss += float(loss) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
120

exec command

+36:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_28w.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_twotower_28w.parquet",
  "scores": {
    "quality_ndcg@10": 0.028014,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
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    "type": "CommandExecution",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_28w.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_28w.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_28w.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.028014,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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    "exit_code": 0,
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  }
}
121

exec command

+37:04
Thinking unavailable No text recorded

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

Model text

The expanded temporal training set is a clear win: 0.028014 NDCG@10, about 17% above the previous deterministic model and 162% above popularity. I’m calibrating this checkpoint’s prior once, then I’ll lock the recipe and perform the required final refit with validation included as observed history and training signal.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_blend.py\n@@\n-checkpoint = torch.load(\"/app/solution/model_validation_deterministic.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_28w.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_28w.pt\", weights_only=False)\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-edcba17c-26ef-4a33-a85b-0c131734dd8f",
    "changes": {
      "/app/solution/tune_blend.py": {
        "type": "update",
        "unified_diff": "@@ -19,4 +19,4 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_deterministic.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_28w.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_28w.pt\", weights_only=False)\n histories = cache[\"histories\"]\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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122

exec command

+37:16
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=22382
Harness context token usage record
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    "cache_write_input_tokens": 0,
    "output_tokens": 43593,
    "reasoning_output_tokens": 19186,
    "total_tokens": 5462847
  }
}
123

exec command

+37:22
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":22382,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"84da30","wall_time_seconds":30.000624545,"session_id":22382,"original_token_count":16}
similarity std 2.5203545093536377 bias std 0.8141705393791199
Harness context token usage record
{
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b4544b1487d2a7bce4437b97400a",
  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
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  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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  }
}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":22382,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"7e5e61","wall_time_seconds":30.000990551,"session_id":22382,"original_token_count":0}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b474d37487d2b18a9d69d9a594f9",
  "usage": {
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    "cached_input_tokens": 77440,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
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    "cache_write_input_tokens": 0,
    "output_tokens": 43691,
    "reasoning_output_tokens": 19192,
    "total_tokens": 5618377
  }
}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":22382,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 7.9 seconds
Output:

{"chunk_id":"dcb8d9","wall_time_seconds":7.880737151,"exit_code":0,"original_token_count":123}
(0.8, 0.0) 0.025516
(0.8, 0.15) 0.027364
(0.8, 0.3) 0.028302
(0.8, 0.45) 0.027872
(1.1, 0.0) 0.027427
(1.1, 0.15) 0.028698
(1.1, 0.3) 0.027746
(1.1, 0.45) 0.026243
(1.4, 0.0) 0.028118
(1.4, 0.15) 0.028014
(1.4, 0.3) 0.027370
(1.4, 0.45) 0.025841
(1.7, 0.0) 0.028058
(1.7, 0.15) 0.027544
(1.7, 0.3) 0.026112
(1.7, 0.45) 0.024940
(2.0, 0.0) 0.027640
(2.0, 0.15) 0.026669
(2.0, 0.3) 0.025101
(2.0, 0.45) 0.023340
(2.4, 0.0) 0.026593
(2.4, 0.15) 0.025202
(2.4, 0.3) 0.023914
(2.4, 0.45) 0.022422
Harness context token usage record
{
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  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b4951f7c87d2a64af4c3d250e5ac",
  "usage": {
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    "cached_input_tokens": 77568,
    "cache_write_input_tokens": 0,
    "output_tokens": 45,
    "reasoning_output_tokens": 0,
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-642d5937-0907-4e02-a519-76809647cf56",
    "process_id": "22382",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "similarity std 2.5203545093536377 bias std 0.8141705393791199\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n",
    "stderr": "",
    "aggregated_output": "similarity std 2.5203545093536377 bias std 0.8141705393791199\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n",
    "exit_code": 0,
    "duration": {
      "secs": 80,
      "nanos": 596589990
    },
    "formatted_output": "similarity std 2.5203545093536377 bias std 0.8141705393791199\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n"
  }
}
124

Agent message

+38:50
Thinking unavailable No text recorded

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

125

exec command

+38:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-            total_loss += float(loss) * len(batch)\n+            total_loss += float(loss.detach()) * len(batch)\n@@\n-    parser.add_argument(\"--prior-scale\", type=float, default=1.4)\n-    parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n+    parser.add_argument(\"--prior-scale\", type=float, default=1.1)\n+    parser.add_argument(\"--pop-scale\", type=float, default=0.15)\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-911cfd5e-2598-4332-ab75-abba8643f75e",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -500,3 +500,3 @@\n             optimizer.step()\n-            total_loss += float(loss) * len(batch)\n+            total_loss += float(loss.detach()) * len(batch)\n             seen += len(batch)\n@@ -582,4 +582,4 @@\n     parser.add_argument(\"--resume\", type=Path)\n-    parser.add_argument(\"--prior-scale\", type=float, default=1.4)\n-    parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n+    parser.add_argument(\"--prior-scale\", type=float, default=1.1)\n+    parser.add_argument(\"--pop-scale\", type=float, default=0.15)\n     parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  }
}
126

exec command

+38:57
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nm.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -15","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 10.2 seconds
Output:

wrote 845,600 ranked rows to /app/solution/validation.parquet
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.028698,
    "quality_users": 4263
  }
}
-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet
-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet
-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet
-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet
-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet
-rw-r--r-- 1 agent agent  16M Sep 24 23:14 model_validation_v1.pt
-rw-r--r-- 1 agent agent  16M Sep 24 23:20 model_validation_v1_cont.pt
-rw-r--r-- 1 agent agent  16M Sep 24 23:23 model_validation_v2.pt
-rw-r--r-- 1 agent agent  16M Sep 24 23:27 model_validation_like7.pt
-rw-r--r-- 1 agent agent  16M Sep 24 23:29 model_validation_deterministic.pt
-rw-r--r-- 1 agent agent  16M Sep 24 23:32 model_validation_seed1337.pt
-rw-r--r-- 1 agent agent  16M Sep 24 23:37 model_validation_28w.pt
-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt
-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt
-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt
Harness context token usage record
{
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  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 19759,
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  },
  "thread_token_usage": {
    "input_tokens": 5810032,
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e22b26b2-a32a-4277-b98f-bb5aa98df470",
    "process_id": "71430",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nm.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -15"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nm.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -15"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.028698,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent  16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n",
    "stderr": "",
    "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.028698,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent  16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n",
    "exit_code": 0,
    "duration": {
      "secs": 10,
      "nanos": 30610348
    },
    "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.028698,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent  16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent  16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n"
  }
}
127

exec command

+39:17
Thinking unavailable No text recorded

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

Model text

The selected validation ranker is now saved at 0.028698 NDCG@10. I’ve locked the 28-cutoff, 64-latent-dimension recipe and am starting the final refit on train_* + val_*; its latest temporal target window is exactly the public validation period, while serving histories extend through that period and use the supplied test preference state.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --checkpoint /app/solution/model_final.pt --cache /app/solution/cache_final_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=50313
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b4c5b3f887d2a211d3f0a46483c0",
  "usage": {
    "input_tokens": 79932,
    "cached_input_tokens": 78976,
    "cache_write_input_tokens": 0,
    "output_tokens": 301,
    "reasoning_output_tokens": 35,
    "total_tokens": 80233
  },
  "turn_token_usage": {
    "input_tokens": 5889964,
    "cached_input_tokens": 5760000,
    "cache_write_input_tokens": 0,
    "output_tokens": 45073,
    "reasoning_output_tokens": 19794,
    "total_tokens": 5935037
  },
  "thread_token_usage": {
    "input_tokens": 5889964,
    "cached_input_tokens": 5760000,
    "cache_write_input_tokens": 0,
    "output_tokens": 45073,
    "reasoning_output_tokens": 19794,
    "total_tokens": 5935037
  }
}
128

exec command

+39:25
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

let{output,...rest}=await tools.write_stdin({"session_id":50313,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6e4b8f","wall_time_seconds":30.00132107,"session_id":50313,"original_token_count":30}
loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b4cedc4c87d28a2e2274f44ee5ad",
  "usage": {
    "input_tokens": 80261,
    "cached_input_tokens": 79744,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 80314
  },
  "turn_token_usage": {
    "input_tokens": 5970225,
    "cached_input_tokens": 5839744,
    "cache_write_input_tokens": 0,
    "output_tokens": 45126,
    "reasoning_output_tokens": 19800,
    "total_tokens": 6015351
  },
  "thread_token_usage": {
    "input_tokens": 5970225,
    "cached_input_tokens": 5839744,
    "cache_write_input_tokens": 0,
    "output_tokens": 45126,
    "reasoning_output_tokens": 19800,
    "total_tokens": 6015351
  }
}
129

exec command

+40:02
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":50313,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"662afe","wall_time_seconds":30.000440382,"session_id":50313,"original_token_count":49}
built 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries
saved prepared arrays to /app/solution/cache_final_28w.pt
epoch 01/12: loss=5.60964 lr=0.00197001
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b4f2a3a487d2b20b5ce09f5dd4f5",
  "usage": {
    "input_tokens": 80406,
    "cached_input_tokens": 80128,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 80459
  },
  "turn_token_usage": {
    "input_tokens": 6050631,
    "cached_input_tokens": 5919872,
    "cache_write_input_tokens": 0,
    "output_tokens": 45179,
    "reasoning_output_tokens": 19806,
    "total_tokens": 6095810
  },
  "thread_token_usage": {
    "input_tokens": 6050631,
    "cached_input_tokens": 5919872,
    "cache_write_input_tokens": 0,
    "output_tokens": 45179,
    "reasoning_output_tokens": 19806,
    "total_tokens": 6095810
  }
}
130

exec command

+40:34
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

let{output,...rest}=await tools.write_stdin({"session_id":50313,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"a2ab58","wall_time_seconds":30.001124663,"session_id":50313,"original_token_count":29}
epoch 02/12: loss=5.33694 lr=0.0018821
epoch 03/12: loss=5.10709 lr=0.00174225
epoch 04/12: loss=4.94666 lr=0.00156
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b514becc87d2af39226cd26e2134",
  "usage": {
    "input_tokens": 80571,
    "cached_input_tokens": 80256,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 80624
  },
  "turn_token_usage": {
    "input_tokens": 6131202,
    "cached_input_tokens": 6000128,
    "cache_write_input_tokens": 0,
    "output_tokens": 45232,
    "reasoning_output_tokens": 19812,
    "total_tokens": 6176434
  },
  "thread_token_usage": {
    "input_tokens": 6131202,
    "cached_input_tokens": 6000128,
    "cache_write_input_tokens": 0,
    "output_tokens": 45232,
    "reasoning_output_tokens": 19812,
    "total_tokens": 6176434
  }
}
131

exec command

+41:07
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":50313,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"faf174","wall_time_seconds":30.011950398,"session_id":50313,"original_token_count":30}
epoch 05/12: loss=4.82699 lr=0.00134776
epoch 06/12: loss=4.72550 lr=0.00112
epoch 07/12: loss=4.63698 lr=0.000892239
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b5356eb087d2b95be2cbcd05f992",
  "usage": {
    "input_tokens": 80737,
    "cached_input_tokens": 80384,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 80790
  },
  "turn_token_usage": {
    "input_tokens": 6211939,
    "cached_input_tokens": 6080512,
    "cache_write_input_tokens": 0,
    "output_tokens": 45285,
    "reasoning_output_tokens": 19818,
    "total_tokens": 6257224
  },
  "thread_token_usage": {
    "input_tokens": 6211939,
    "cached_input_tokens": 6080512,
    "cache_write_input_tokens": 0,
    "output_tokens": 45285,
    "reasoning_output_tokens": 19818,
    "total_tokens": 6257224
  }
}
132

exec command

+41:41
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

let{output,...rest}=await tools.write_stdin({"session_id":50313,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"fb1623","wall_time_seconds":30.001309684,"session_id":50313,"original_token_count":30}
epoch 08/12: loss=4.54422 lr=0.00068
epoch 09/12: loss=4.45552 lr=0.000497746
epoch 10/12: loss=4.39559 lr=0.000357898
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b556d89487d2a0359b21068fd3c6",
  "usage": {
    "input_tokens": 80901,
    "cached_input_tokens": 80512,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 80954
  },
  "turn_token_usage": {
    "input_tokens": 6292840,
    "cached_input_tokens": 6161024,
    "cache_write_input_tokens": 0,
    "output_tokens": 45338,
    "reasoning_output_tokens": 19824,
    "total_tokens": 6338178
  },
  "thread_token_usage": {
    "input_tokens": 6292840,
    "cached_input_tokens": 6161024,
    "cache_write_input_tokens": 0,
    "output_tokens": 45338,
    "reasoning_output_tokens": 19824,
    "total_tokens": 6338178
  }
}
133

exec command

+42:14
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

let{output,...rest}=await tools.write_stdin({"session_id":50313,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 17.3 seconds
Output:

{"chunk_id":"d09c7f","wall_time_seconds":17.288392837,"exit_code":0,"original_token_count":118}
epoch 11/12: loss=4.33815 lr=0.000269985
epoch 12/12: loss=4.29374 lr=0.00024
wrote 845,600 ranked rows to /app/solution/submission.parquet
{"checkpoint": "/app/solution/model_final.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 28, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 20260924}}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b5773f1487d2b8ae0de863dce26f",
  "usage": {
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    "cached_input_tokens": 80768,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 81119
  },
  "turn_token_usage": {
    "input_tokens": 6373906,
    "cached_input_tokens": 6241792,
    "cache_write_input_tokens": 0,
    "output_tokens": 45391,
    "reasoning_output_tokens": 19830,
    "total_tokens": 6419297
  },
  "thread_token_usage": {
    "input_tokens": 6373906,
    "cached_input_tokens": 6241792,
    "cache_write_input_tokens": 0,
    "output_tokens": 45391,
    "reasoning_output_tokens": 19830,
    "total_tokens": 6419297
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e5d123a4-467e-4b1a-af03-8ade8b62c585",
    "process_id": "50313",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --checkpoint /app/solution/model_final.pt --cache /app/solution/cache_final_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --checkpoint /app/solution/model_final.pt --cache /app/solution/cache_final_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 189,
      "nanos": 860602424
    },
    "formatted_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
134

exec command

+42:39
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nuidpos={int(x):i for i,x in enumerate(z['uids'])}; itempos={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((uidpos[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((itempos[int(x)] for x in f.item_id),np.int32,len(f))\nelig=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nprint('shape',f.shape,'dtypes',f.dtypes.astype(str).to_dict())\nprint('users',f.uid.nunique(),'items/user',f.groupby('uid').size().min(),f.groupby('uid').size().max())\nprint('eligible',elig.all(),'eligible_count',int(elig.sum()),'duplicates',f.duplicated(['uid','item_id']).sum())\nprint('rank bounds',f['rank'].min(),f['rank'].max(),'file_MB',p.stat().st_size/2**20)\npref=pd.read_parquet(D/'test_preference_state.parquet')\nq=pref.groupby('uid').size();print('preference count quantiles',q.quantile([0,.5,.9,.99,1]).to_dict())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response

Script completed
Wall time 2.5 seconds
Output:

shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}
users 8456 items/user 100 100
eligible True eligible_count 845600 duplicates 0
rank bounds 1 100 file_MB 1.5793027877807617
preference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-3106fe39-14fb-4818-a73a-102574388b48",
    "process_id": "18249",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nuidpos={int(x):i for i,x in enumerate(z['uids'])}; itempos={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((uidpos[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((itempos[int(x)] for x in f.item_id),np.int32,len(f))\nelig=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nprint('shape',f.shape,'dtypes',f.dtypes.astype(str).to_dict())\nprint('users',f.uid.nunique(),'items/user',f.groupby('uid').size().min(),f.groupby('uid').size().max())\nprint('eligible',elig.all(),'eligible_count',int(elig.sum()),'duplicates',f.duplicated(['uid','item_id']).sum())\nprint('rank bounds',f['rank'].min(),f['rank'].max(),'file_MB',p.stat().st_size/2**20)\npref=pd.read_parquet(D/'test_preference_state.parquet')\nq=pref.groupby('uid').size();print('preference count quantiles',q.quantile([0,.5,.9,.99,1]).to_dict())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nuidpos={int(x):i for i,x in enumerate(z['uids'])}; itempos={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((uidpos[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((itempos[int(x)] for x in f.item_id),np.int32,len(f))\nelig=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nprint('shape',f.shape,'dtypes',f.dtypes.astype(str).to_dict())\nprint('users',f.uid.nunique(),'items/user',f.groupby('uid').size().min(),f.groupby('uid').size().max())\nprint('eligible',elig.all(),'eligible_count',int(elig.sum()),'duplicates',f.duplicated(['uid','item_id']).sum())\nprint('rank bounds',f['rank'].min(),f['rank'].max(),'file_MB',p.stat().st_size/2**20)\npref=pd.read_parquet(D/'test_preference_state.parquet')\nq=pref.groupby('uid').size();print('preference count quantiles',q.quantile([0,.5,.9,.99,1]).to_dict())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n",
    "stderr": "",
    "aggregated_output": "shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 293223355
    },
    "formatted_output": "shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n"
  }
}
135

exec command

+43:06
Thinking unavailable No text recorded

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

Model text

The final artifact is built and passes all structural checks: 845,600 rows, exactly 100 unique items for each of 8,456 users, ranks 1–100, and every item is test-eligible. I’m finishing the reproducibility notes and one last code/artifact audit now.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/README.md\n+# Made-for-You two-tower ranker\n+\n+## Model\n+\n+`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n+user-ID embedding**. It pools a cutoff-correct 180-day interaction history\n+(at most 384 distinct positive-listen tracks, plus active explicit state) with\n+frequency, recency, completion, organic, like, and dislike weights, then uses a\n+`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n+embedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n+residual MLP. Both latent outputs are L2-normalized.\n+\n+Each tower emits 65 values: 64 personalized dimensions plus one calibrated\n+item-prior coordinate (the corresponding user coordinate is exactly one).\n+Thus the complete relevance score is exactly\n+\n+```text\n+score(user history, item) = user_vector @ item_vector\n+```\n+\n+Candidate vectors are computed once offline; request-time work is one bounded\n+history pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n+the platform-owned retrieval mask.\n+\n+## Training\n+\n+Training uses 28 rolling weekly cutoffs. A positive is a previously unseen\n+catalog item with a subsequent organic 80%-complete listen or organic like.\n+The loss is sampled softmax with 512 uniform catalog negatives; likes receive\n+sampling weight 4. Histories are held in event/CSR form, so training never\n+constructs or factorizes a global user-item interaction matrix. The final model\n+is refit on `train_* + val_*`, with validation events treated as observed\n+history and temporal training outcomes.\n+\n+Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\n+with:\n+\n+```sh\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n+```\n+\n+## Final submission\n+\n+The checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n+checkpoint. Rebuild it deterministically from `/app` with:\n+\n+```sh\n+PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n+  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n+  --stage final \\\n+  --output /app/solution/submission.parquet \\\n+  --checkpoint /app/solution/model_final.pt \\\n+  --cache /app/solution/cache_final_28w.pt \\\n+  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n+  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n+  --seed 20260924 --threads 8\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
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-be068958-2953-4c43-99a9-c2b560df5ac5",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n  --stage final \\\n  --output /app/solution/submission.parquet \\\n  --checkpoint /app/solution/model_final.pt \\\n  --cache /app/solution/cache_final_28w.pt \\\n  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n  --seed 20260924 --threads 8\n```\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n",
    "stderr": ""
  }
}
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136

Agent message

+43:32
Thinking unavailable No text recorded

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

137

exec command

+43:33
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n def relevant_targets(\n     r: tuple[np.ndarray, ...],\n-    first_seen: np.ndarray,\n+    seen_items: np.ndarray,\n+    first_seen_times: np.ndarray,\n     cutoff: int,\n ) -> tuple[np.ndarray, np.ndarray]:\n@@\n     if hi <= lo:\n         return np.empty(0, np.int32), np.empty(0, np.uint8)\n     ri, rg = r[1][lo:hi], r[2][lo:hi]\n-    novel = first_seen[ri] >= cutoff\n+    positions = np.searchsorted(seen_items, ri)\n+    found = positions < len(seen_items)\n+    safe_positions = np.minimum(positions, max(len(seen_items) - 1, 0))\n+    if len(seen_items):\n+        found &= seen_items[safe_positions] == ri\n+    seen_times = np.full(len(ri), np.iinfo(np.int64).max, dtype=np.int64)\n+    seen_times[found] = first_seen_times[safe_positions[found]]\n+    novel = seen_times >= cutoff\n@@\n-    first_seen = np.empty(30_000, dtype=np.int64)\n     for u in range(num_users):\n@@\n-        first_seen.fill(np.iinfo(np.int64).max)\n         all_times, all_items = slices[\"all\"]\n-        np.minimum.at(first_seen, all_items, all_times)\n+        seen_items, inverse = np.unique(all_items, return_inverse=True)\n+        first_seen_times = np.full(\n+            len(seen_items), np.iinfo(np.int64).max, dtype=np.int64\n+        )\n+        np.minimum.at(first_seen_times, inverse, all_times)\n         for cutoff in cutoffs:\n-            ti, tg = relevant_targets(slices[\"relevant\"], first_seen, cutoff)\n+            ti, tg = relevant_targets(\n+                slices[\"relevant\"], seen_items, first_seen_times, cutoff\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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  "item": {
    "type": "FileChange",
    "id": "exec-095ea205-2801-4faa-a488-032c4437f39d",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -286,3 +286,4 @@\n     r: tuple[np.ndarray, ...],\n-    first_seen: np.ndarray,\n+    seen_items: np.ndarray,\n+    first_seen_times: np.ndarray,\n     cutoff: int,\n@@ -294,3 +295,10 @@\n     ri, rg = r[1][lo:hi], r[2][lo:hi]\n-    novel = first_seen[ri] >= cutoff\n+    positions = np.searchsorted(seen_items, ri)\n+    found = positions < len(seen_items)\n+    safe_positions = np.minimum(positions, max(len(seen_items) - 1, 0))\n+    if len(seen_items):\n+        found &= seen_items[safe_positions] == ri\n+    seen_times = np.full(len(ri), np.iinfo(np.int64).max, dtype=np.int64)\n+    seen_times[found] = first_seen_times[safe_positions[found]]\n+    novel = seen_times >= cutoff\n     ri, rg = ri[novel], rg[novel]\n@@ -327,3 +335,2 @@\n     cutoffs.sort()\n-    first_seen = np.empty(30_000, dtype=np.int64)\n     for u in range(num_users):\n@@ -333,7 +340,12 @@\n             slices[key] = tuple(x[b[u] : b[u + 1]] for x in values[1:])\n-        first_seen.fill(np.iinfo(np.int64).max)\n         all_times, all_items = slices[\"all\"]\n-        np.minimum.at(first_seen, all_items, all_times)\n+        seen_items, inverse = np.unique(all_items, return_inverse=True)\n+        first_seen_times = np.full(\n+            len(seen_items), np.iinfo(np.int64).max, dtype=np.int64\n+        )\n+        np.minimum.at(first_seen_times, inverse, all_times)\n         for cutoff in cutoffs:\n-            ti, tg = relevant_targets(slices[\"relevant\"], first_seen, cutoff)\n+            ti, tg = relevant_targets(\n+                slices[\"relevant\"], seen_items, first_seen_times, cutoff\n+            )\n             if not len(ti):\n",
        "move_path": null
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    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
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138

exec command

+44:00
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-        # A tiny deterministic index term makes exact ties reproducible.\n-        scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\n         top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\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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139

exec command

+44:18
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 r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_final_28w.pt',weights_only=False)\ncfg=ck['config']; m=TwoTowerRanker(30000,item_features(cat),cfg['embedding_dim'],cfg['hidden_dim'])\nm.load_state_dict(ck['model_state'])\nrank_candidates(m,cache['histories'],Path('/task/data/test_eligible_candidates.npz'),Path('/app/solution/submission_candidate.parquet'))\nold=pd.read_parquet('/app/solution/submission.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nnew=pd.read_parquet('/app/solution/submission_candidate.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nprint('ranking_identical_after_exact-dot_audit',old.equals(new))\nprint('calibration',float(m.item_tower.learned_prior_scale),float(m.item_tower.fixed_prior_scale))\n# Directly verify that the public score API and explicit tower calls agree.\nh=cache['histories']; idx=__import__('numpy').arange(2)\nfrom train_ranker import gather_histories\nids,offs,w,f=gather_histories(h,idx); cand=torch.arange(7)\nwith torch.inference_mode():\n direct=m.score(ids,offs,w,f,cand)\n separate=m.user_tower(ids,offs,w,f)@m.item_tower(cand).T\nprint('exact_tower_dot_check',torch.equal(direct,separate))\nPY\nmv /app/solution/submission_candidate.parquet /app/solution/submission.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\n","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response

Script completed
Wall time 6.9 seconds
Output:

wrote 845,600 ranked rows to /app/solution/submission_candidate.parquet
ranking_identical_after_exact-dot_audit False
calibration 1.100000023841858 0.15000000596046448
exact_tower_dot_check True
8fce31901997fcb7699e8fd8e225116176f3cf87b638b2f4f76afbd9bc210e75  /app/solution/submission.parquet
409a03cd66f18bfb806e38602d8949504a4b3c7c44470498a53d20aead119f03  /app/solution/model_final.pt
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      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_final_28w.pt',weights_only=False)\ncfg=ck['config']; m=TwoTowerRanker(30000,item_features(cat),cfg['embedding_dim'],cfg['hidden_dim'])\nm.load_state_dict(ck['model_state'])\nrank_candidates(m,cache['histories'],Path('/task/data/test_eligible_candidates.npz'),Path('/app/solution/submission_candidate.parquet'))\nold=pd.read_parquet('/app/solution/submission.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nnew=pd.read_parquet('/app/solution/submission_candidate.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nprint('ranking_identical_after_exact-dot_audit',old.equals(new))\nprint('calibration',float(m.item_tower.learned_prior_scale),float(m.item_tower.fixed_prior_scale))\n# Directly verify that the public score API and explicit tower calls agree.\nh=cache['histories']; idx=__import__('numpy').arange(2)\nfrom train_ranker import gather_histories\nids,offs,w,f=gather_histories(h,idx); cand=torch.arange(7)\nwith torch.inference_mode():\n direct=m.score(ids,offs,w,f,cand)\n separate=m.user_tower(ids,offs,w,f)@m.item_tower(cand).T\nprint('exact_tower_dot_check',torch.equal(direct,separate))\nPY\nmv /app/solution/submission_candidate.parquet /app/solution/submission.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\n"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
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        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_final_28w.pt',weights_only=False)\ncfg=ck['config']; m=TwoTowerRanker(30000,item_features(cat),cfg['embedding_dim'],cfg['hidden_dim'])\nm.load_state_dict(ck['model_state'])\nrank_candidates(m,cache['histories'],Path('/task/data/test_eligible_candidates.npz'),Path('/app/solution/submission_candidate.parquet'))\nold=pd.read_parquet('/app/solution/submission.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nnew=pd.read_parquet('/app/solution/submission_candidate.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nprint('ranking_identical_after_exact-dot_audit',old.equals(new))\nprint('calibration',float(m.item_tower.learned_prior_scale),float(m.item_tower.fixed_prior_scale))\n# Directly verify that the public score API and explicit tower calls agree.\nh=cache['histories']; idx=__import__('numpy').arange(2)\nfrom train_ranker import gather_histories\nids,offs,w,f=gather_histories(h,idx); cand=torch.arange(7)\nwith torch.inference_mode():\n direct=m.score(ids,offs,w,f,cand)\n separate=m.user_tower(ids,offs,w,f)@m.item_tower(cand).T\nprint('exact_tower_dot_check',torch.equal(direct,separate))\nPY\nmv /app/solution/submission_candidate.parquet /app/solution/submission.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\n"
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    "stderr": "",
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    "exit_code": 0,
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  }
}
140

exec command

+44:52
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":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_exact.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_exact.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 10.0 seconds
Output:

wrote 845,600 ranked rows to /app/solution/validation_exact.parquet
{
  "model": "/app/solution/validation_exact.parquet",
  "scores": {
    "quality_ndcg@10": 0.028698,
    "quality_users": 4263
  }
}
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Harness context codex item completed
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    "command": [
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      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_exact.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_exact.parquet"
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      {
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  }
}
141

exec command

+45:12
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":"mv /app/solution/validation_exact.parquet /app/solution/validation.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all(); assert len(f)==845600; assert not f.duplicated(['uid','item_id']).any()\nprint('FINAL_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY\nsed -n '1,240p' /app/solution/README.md\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort | tail -20","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 2.5 seconds
Output:

FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}
# Made-for-You two-tower ranker

## Model

`model.py` defines two separate CPU PyTorch towers. The user tower has **no
user-ID embedding**. It pools a cutoff-correct 180-day interaction history
(at most 384 distinct positive-listen tracks, plus active explicit state) with
frequency, recency, completion, organic, like, and dislike weights, then uses a
`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track
embedding with four catalog popularity statistics and a `68 -> 128 -> 64`
residual MLP. Both latent outputs are L2-normalized.

Each tower emits 65 values: 64 personalized dimensions plus one calibrated
item-prior coordinate (the corresponding user coordinate is exactly one).
Thus the complete relevance score is exactly

```text
score(user history, item) = user_vector @ item_vector
```

Candidate vectors are computed once offline; request-time work is one bounded
history pool, one small user MLP, and MIPS retrieval. Eligibility is applied as
the platform-owned retrieval mask.

## Training

Training uses 28 rolling weekly cutoffs. A positive is a previously unseen
catalog item with a subsequent organic 80%-complete listen or organic like.
The loss is sampled softmax with 512 uniform catalog negatives; likes receive
sampling weight 4. Histories are held in event/CSR form, so training never
constructs or factorizes a global user-item interaction matrix. The final model
is refit on `train_* + val_*`, with validation events treated as observed
history and temporal training outcomes.

Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured
with:

```sh
/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet
```

## Final submission

The checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted
checkpoint. Rebuild it deterministically from `/app` with:

```sh
PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \
  /opt/task-venv/bin/python /app/solution/train_ranker.py \
  --stage final \
  --output /app/solution/submission.parquet \
  --checkpoint /app/solution/model_final.pt \
  --cache /app/solution/cache_final_28w.pt \
  --epochs 12 --training-weeks 28 --organic-boost 1.5 \
  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \
  --seed 20260924 --threads 8
```
model_validation_deterministic.pt 16415649 bytes
model_validation_like7.pt 16415401 bytes
model_validation_seed1337.pt 16415494 bytes
model_validation_v1.pt 16414586 bytes
model_validation_v1_cont.pt 16415399 bytes
model_validation_v2.pt 16415244 bytes
submission.parquet 1656019 bytes
train_ranker.py 26252 bytes
tune_blend.py 2883 bytes
tune_ensemble.py 2953 bytes
validation.parquet 1658820 bytes
validation_best.parquet 1643459 bytes
validation_popularity.parquet 728599 bytes
validation_twotower_28w.parquet 1648500 bytes
validation_twotower_deterministic.parquet 1648294 bytes
validation_twotower_like7.parquet 1650119 bytes
validation_twotower_seed1337.parquet 1647015 bytes
validation_twotower_v1.parquet 1666588 bytes
validation_twotower_v1_cont.parquet 1660531 bytes
validation_twotower_v2.parquet 1644070 bytes
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-feaaf903-aed0-44f7-9dc0-dfa9777bd615",
    "process_id": "86821",
    "command": [
      "/bin/sh",
      "-lc",
      "mv /app/solution/validation_exact.parquet /app/solution/validation.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all(); assert len(f)==845600; assert not f.duplicated(['uid','item_id']).any()\nprint('FINAL_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY\nsed -n '1,240p' /app/solution/README.md\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort | tail -20"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mv /app/solution/validation_exact.parquet /app/solution/validation.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all(); assert len(f)==845600; assert not f.duplicated(['uid','item_id']).any()\nprint('FINAL_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY\nsed -n '1,240p' /app/solution/README.md\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort | tail -20"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n  --stage final \\\n  --output /app/solution/submission.parquet \\\n  --checkpoint /app/solution/model_final.pt \\\n  --cache /app/solution/cache_final_28w.pt \\\n  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n  --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n",
    "stderr": "",
    "aggregated_output": "FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n  --stage final \\\n  --output /app/solution/submission.parquet \\\n  --checkpoint /app/solution/model_final.pt \\\n  --cache /app/solution/cache_final_28w.pt \\\n  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n  --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 336682943
    },
    "formatted_output": "FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n  --stage final \\\n  --output /app/solution/submission.parquet \\\n  --checkpoint /app/solution/model_final.pt \\\n  --cache /app/solution/cache_final_28w.pt \\\n  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n  --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n"
  }
}
142

Agent message

+45:43
Thinking unavailable No text recorded

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

143

exec command

+45:44
Thinking unavailable No text recorded

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

Model text

The audited exact-dot submission is safely checkpointed and valid. There is still enough budget for one bounded improvement test: the jump from 16 to 28 temporal cutoffs was large, so I’m testing 36 cutoffs while leaving the current final artifact untouched. I’ll only refit/replace it if public validation improves.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_36w.parquet --checkpoint /app/solution/model_validation_36w.pt --cache /app/solution/cache_validation_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=80155
Harness context token usage record
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    "total_tokens": 7103082
  }
}
144

exec command

+45:56
Thinking unavailable No text recorded

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,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"15a34d","wall_time_seconds":30.000970509,"session_id":80155,"original_token_count":30}
loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s
Harness context token usage record
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}
145

exec command

+46:28
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

let{output,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"90317f","wall_time_seconds":30.001377533,"session_id":80155,"original_token_count":41}
built 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries
saved prepared arrays to /app/solution/cache_validation_36w.pt
Harness context token usage record
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  }
}
146

exec command

+47:08
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"1e001a","wall_time_seconds":30.001402615,"session_id":80155,"original_token_count":30}
epoch 01/12: loss=5.61316 lr=0.00197001
epoch 02/12: loss=5.33079 lr=0.0018821
epoch 03/12: loss=5.10368 lr=0.00174225
Harness context token usage record
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    "total_tokens": 7370101
  },
  "thread_token_usage": {
    "input_tokens": 7318467,
    "cached_input_tokens": 7176320,
    "cache_write_input_tokens": 0,
    "output_tokens": 51634,
    "reasoning_output_tokens": 22601,
    "total_tokens": 7370101
  }
}
147

exec command

+47:42
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

let{output,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b9b257","wall_time_seconds":30.000747859,"session_id":80155,"original_token_count":20}
epoch 04/12: loss=4.95152 lr=0.00156
epoch 05/12: loss=4.84053 lr=0.00134776
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b6bf791087d2824f17bdb58b427b",
  "usage": {
    "input_tokens": 89273,
    "cached_input_tokens": 88960,
    "cache_write_input_tokens": 0,
    "output_tokens": 59,
    "reasoning_output_tokens": 12,
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    "cache_write_input_tokens": 0,
    "output_tokens": 51693,
    "reasoning_output_tokens": 22613,
    "total_tokens": 7459433
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  "thread_token_usage": {
    "input_tokens": 7407740,
    "cached_input_tokens": 7265280,
    "cache_write_input_tokens": 0,
    "output_tokens": 51693,
    "reasoning_output_tokens": 22613,
    "total_tokens": 7459433
  }
}
148

exec command

+48:15
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"676842","wall_time_seconds":30.00101921,"session_id":80155,"original_token_count":20}
epoch 06/12: loss=4.74248 lr=0.00112
epoch 07/12: loss=4.64462 lr=0.000892239
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  "usage": {
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    "cache_write_input_tokens": 0,
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    "input_tokens": 7497165,
    "cached_input_tokens": 7354368,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 22620,
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  }
}
149

exec command

+48:48
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"218d91","wall_time_seconds":30.00094173,"session_id":80155,"original_token_count":30}
epoch 08/12: loss=4.55725 lr=0.00068
epoch 09/12: loss=4.47579 lr=0.000497746
epoch 10/12: loss=4.41709 lr=0.000357898
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b7013fe887d2a423c4c14fbde2eb",
  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 22626,
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  },
  "thread_token_usage": {
    "input_tokens": 7586735,
    "cached_input_tokens": 7443584,
    "cache_write_input_tokens": 0,
    "output_tokens": 51800,
    "reasoning_output_tokens": 22626,
    "total_tokens": 7638535
  }
}
150

exec command

+49:21
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

let{output,...rest}=await tools.write_stdin({"session_id":80155,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"a80182","wall_time_seconds":30.000953287,"session_id":80155,"original_token_count":123}
epoch 11/12: loss=4.36232 lr=0.000269985
epoch 12/12: loss=4.32831 lr=0.00024
wrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet
{"checkpoint": "/app/solution/model_validation_36w.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 36, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 20260924}}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b722d05887d2b82a83f8a7d657bc",
  "usage": {
    "input_tokens": 89735,
    "cached_input_tokens": 89344,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
    "input_tokens": 7676470,
    "cached_input_tokens": 7532928,
    "cache_write_input_tokens": 0,
    "output_tokens": 51853,
    "reasoning_output_tokens": 22632,
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  },
  "thread_token_usage": {
    "input_tokens": 7676470,
    "cached_input_tokens": 7532928,
    "cache_write_input_tokens": 0,
    "output_tokens": 51853,
    "reasoning_output_tokens": 22632,
    "total_tokens": 7728323
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ae8b44d5-2d44-4faa-a64e-5b2895c20b92",
    "process_id": "80155",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_36w.parquet --checkpoint /app/solution/model_validation_36w.pt --cache /app/solution/cache_validation_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_36w.parquet --checkpoint /app/solution/model_validation_36w.pt --cache /app/solution/cache_validation_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 240,
      "nanos": 401880632
    },
    "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
151

exec command

+49:54
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_36w.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/validation_twotower_36w.parquet",
  "scores": {
    "quality_ndcg@10": 0.029112,
    "quality_users": 4263
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b743208487d2918b6d8d68eabf62",
  "usage": {
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  "thread_token_usage": {
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-366af9df-8be3-46de-b1d2-6b4ef9b183dd",
    "process_id": "71290",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_36w.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_36w.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029112,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029112,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 271115613
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029112,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
152

exec command

+50:01
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 36-cutoff model improves again, to 0.029112. That justifies replacing the selected recipe. I’m recalibrating its scalar prior now; then I’ll do the final 36-cutoff refit and repeat the eligibility audit before handoff.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_blend.py\n@@\n-checkpoint = torch.load(\"/app/solution/model_validation_28w.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation_28w.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_36w.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_36w.pt\", weights_only=False)\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-c6160df2-9e09-4253-852c-08977d73f28b",
    "changes": {
      "/app/solution/tune_blend.py": {
        "type": "update",
        "unified_diff": "@@ -19,4 +19,4 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_28w.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation_28w.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_36w.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_36w.pt\", weights_only=False)\n histories = cache[\"histories\"]\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=4091
Harness context token usage record
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b75071dc87d2adac3bedcb1df8f9",
  "usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 22676,
    "total_tokens": 7999378
  }
}
153

exec command

+50:10
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

let{output,...rest}=await tools.write_stdin({"session_id":4091,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"10b248","wall_time_seconds":30.001311258,"session_id":4091,"original_token_count":16}
similarity std 2.585146903991699 bias std 0.8208831548690796
Harness context token usage record
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  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  "usage": {
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    "cache_write_input_tokens": 0,
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  },
  "turn_token_usage": {
    "input_tokens": 8037675,
    "cached_input_tokens": 7892608,
    "cache_write_input_tokens": 0,
    "output_tokens": 52320,
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 22682,
    "total_tokens": 8089995
  }
}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":4091,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f6f692","wall_time_seconds":30.001708944,"session_id":4091,"original_token_count":0}
Harness context token usage record
{
  "type": "token_usage_record",
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  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b7778b4487d2b4962270e5c6dbf2",
  "usage": {
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    "reasoning_output_tokens": 22682,
    "total_tokens": 8180733
  }
}
Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":4091,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 7.9 seconds
Output:

{"chunk_id":"2422bb","wall_time_seconds":7.917609848,"exit_code":0,"original_token_count":123}
(0.8, 0.0) 0.026068
(0.8, 0.15) 0.029367
(0.8, 0.3) 0.029383
(0.8, 0.45) 0.028493
(1.1, 0.0) 0.029384
(1.1, 0.15) 0.029112
(1.1, 0.3) 0.029123
(1.1, 0.45) 0.027488
(1.4, 0.0) 0.028755
(1.4, 0.15) 0.028972
(1.4, 0.3) 0.027894
(1.4, 0.45) 0.025550
(1.7, 0.0) 0.028444
(1.7, 0.15) 0.028355
(1.7, 0.3) 0.026403
(1.7, 0.45) 0.024721
(2.0, 0.0) 0.028043
(2.0, 0.15) 0.027118
(2.0, 0.3) 0.025028
(2.0, 0.45) 0.023692
(2.4, 0.0) 0.026566
(2.4, 0.15) 0.024969
(2.4, 0.3) 0.023879
(2.4, 0.45) 0.022421
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5ef54299-8404-4a3b-bde6-0784a398c505",
    "process_id": "4091",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "similarity std 2.585146903991699 bias std 0.8208831548690796\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n",
    "stderr": "",
    "aggregated_output": "similarity std 2.585146903991699 bias std 0.8208831548690796\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n",
    "exit_code": 0,
    "duration": {
      "secs": 80,
      "nanos": 211638829
    },
    "formatted_output": "similarity std 2.585146903991699 bias std 0.8208831548690796\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n"
  }
}
154

exec command

+51:33
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-    parser.add_argument(\"--pop-scale\", type=float, default=0.15)\n+    parser.add_argument(\"--pop-scale\", type=float, default=0.0)\n*** Update File: /app/solution/README.md\n@@\n-Training uses 28 rolling weekly cutoffs. A positive is a previously unseen\n+Training uses 36 rolling weekly cutoffs. A positive is a previously unseen\n@@\n-Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\n+Public validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n@@\n-  --cache /app/solution/cache_final_28w.pt \\\n-  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n-  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n+  --cache /app/solution/cache_final_36w.pt \\\n+  --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n+  --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\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-4d387b2f-9619-4dfb-8e56-dac7e6d4d759",
    "changes": {
      "/app/solution/README.md": {
        "type": "update",
        "unified_diff": "@@ -26,3 +26,3 @@\n \n-Training uses 28 rolling weekly cutoffs. A positive is a previously unseen\n+Training uses 36 rolling weekly cutoffs. A positive is a previously unseen\n catalog item with a subsequent organic 80%-complete listen or organic like.\n@@ -34,3 +34,3 @@\n \n-Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\n+Public validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n with:\n@@ -52,5 +52,5 @@\n   --checkpoint /app/solution/model_final.pt \\\n-  --cache /app/solution/cache_final_28w.pt \\\n-  --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n-  --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n+  --cache /app/solution/cache_final_36w.pt \\\n+  --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n+  --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n   --seed 20260924 --threads 8\n",
        "move_path": null
      },
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -593,3 +593,3 @@\n     parser.add_argument(\"--prior-scale\", type=float, default=1.1)\n-    parser.add_argument(\"--pop-scale\", type=float, default=0.15)\n+    parser.add_argument(\"--pop-scale\", type=float, default=0.0)\n     parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\nM /app/solution/README.md\n",
    "stderr": ""
  }
}
Harness context token usage record
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  }
}
155

exec command

+51:42
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":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_36w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_36w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.0)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 10.0 seconds
Output:

wrote 845,600 ranked rows to /app/solution/validation.parquet
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.029384,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-179608b5-1b4a-4f27-a3e7-d070a6c955ce",
    "process_id": "5077",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_36w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_36w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.0)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_36w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_36w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.0)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029384,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029384,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 789370287
    },
    "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029384,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
156

exec command

+51:59
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission_36w_candidate.parquet --checkpoint /app/solution/model_final_36w.pt --cache /app/solution/cache_final_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 --seed 20260924 --threads 8","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=43610
Harness context token usage record
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  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
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    "cached_input_tokens": 8346880,
    "cache_write_input_tokens": 0,
    "output_tokens": 53378,
    "reasoning_output_tokens": 22866,
    "total_tokens": 8547752
  }
}
157

exec command

+52:06
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

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"043a72","wall_time_seconds":30.001193037,"session_id":43610,"original_token_count":30}
loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b7c75d5087d2b27cad7a83aeb1ab",
  "usage": {
    "input_tokens": 92378,
    "cached_input_tokens": 92032,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 92431
  },
  "turn_token_usage": {
    "input_tokens": 8586752,
    "cached_input_tokens": 8438912,
    "cache_write_input_tokens": 0,
    "output_tokens": 53431,
    "reasoning_output_tokens": 22872,
    "total_tokens": 8640183
  },
  "thread_token_usage": {
    "input_tokens": 8586752,
    "cached_input_tokens": 8438912,
    "cache_write_input_tokens": 0,
    "output_tokens": 53431,
    "reasoning_output_tokens": 22872,
    "total_tokens": 8640183
  }
}
158

exec command

+52:38
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

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2c1ad7","wall_time_seconds":30.000816363,"session_id":43610,"original_token_count":40}
built 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries
saved prepared arrays to /app/solution/cache_final_36w.pt
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b7e8281887d2984e76d39ae75dcc",
  "usage": {
    "input_tokens": 92520,
    "cached_input_tokens": 92160,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 92573
  },
  "turn_token_usage": {
    "input_tokens": 8679272,
    "cached_input_tokens": 8531072,
    "cache_write_input_tokens": 0,
    "output_tokens": 53484,
    "reasoning_output_tokens": 22878,
    "total_tokens": 8732756
  },
  "thread_token_usage": {
    "input_tokens": 8679272,
    "cached_input_tokens": 8531072,
    "cache_write_input_tokens": 0,
    "output_tokens": 53484,
    "reasoning_output_tokens": 22878,
    "total_tokens": 8732756
  }
}
159

exec command

+53:10
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

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"eca137","wall_time_seconds":30.000816965,"session_id":43610,"original_token_count":30}
epoch 01/12: loss=5.60787 lr=0.00197001
epoch 02/12: loss=5.31915 lr=0.0018821
epoch 03/12: loss=5.09718 lr=0.00174225
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b8084c8c87d2ac7fb5941beba3f6",
  "usage": {
    "input_tokens": 92670,
    "cached_input_tokens": 92288,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 9,
    "total_tokens": 92726
  },
  "turn_token_usage": {
    "input_tokens": 8771942,
    "cached_input_tokens": 8623360,
    "cache_write_input_tokens": 0,
    "output_tokens": 53540,
    "reasoning_output_tokens": 22887,
    "total_tokens": 8825482
  },
  "thread_token_usage": {
    "input_tokens": 8771942,
    "cached_input_tokens": 8623360,
    "cache_write_input_tokens": 0,
    "output_tokens": 53540,
    "reasoning_output_tokens": 22887,
    "total_tokens": 8825482
  }
}
160

exec command

+53:42
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

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"00de8a","wall_time_seconds":30.000973768,"session_id":43610,"original_token_count":20}
epoch 04/12: loss=4.95216 lr=0.00156
epoch 05/12: loss=4.84323 lr=0.00134776
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b828bd6c87d2a5d33933fd981a0c",
  "usage": {
    "input_tokens": 92838,
    "cached_input_tokens": 92544,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 92891
  },
  "turn_token_usage": {
    "input_tokens": 8864780,
    "cached_input_tokens": 8715904,
    "cache_write_input_tokens": 0,
    "output_tokens": 53593,
    "reasoning_output_tokens": 22893,
    "total_tokens": 8918373
  },
  "thread_token_usage": {
    "input_tokens": 8864780,
    "cached_input_tokens": 8715904,
    "cache_write_input_tokens": 0,
    "output_tokens": 53593,
    "reasoning_output_tokens": 22893,
    "total_tokens": 8918373
  }
}
161

exec command

+54:15
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b1f6f6","wall_time_seconds":30.001073908,"session_id":43610,"original_token_count":20}
epoch 06/12: loss=4.74727 lr=0.00112
epoch 07/12: loss=4.64734 lr=0.000892239
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b848e25087d293360a4854eb5794",
  "usage": {
    "input_tokens": 92984,
    "cached_input_tokens": 92672,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 93037
  },
  "turn_token_usage": {
    "input_tokens": 8957764,
    "cached_input_tokens": 8808576,
    "cache_write_input_tokens": 0,
    "output_tokens": 53646,
    "reasoning_output_tokens": 22899,
    "total_tokens": 9011410
  },
  "thread_token_usage": {
    "input_tokens": 8957764,
    "cached_input_tokens": 8808576,
    "cache_write_input_tokens": 0,
    "output_tokens": 53646,
    "reasoning_output_tokens": 22899,
    "total_tokens": 9011410
  }
}
162

exec command

+54:47
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

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"71ae82","wall_time_seconds":30.000892248,"session_id":43610,"original_token_count":20}
epoch 08/12: loss=4.56733 lr=0.00068
epoch 09/12: loss=4.47345 lr=0.000497746
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b869531487d28a25bda0e3e7949c",
  "usage": {
    "input_tokens": 93132,
    "cached_input_tokens": 92800,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 93185
  },
  "turn_token_usage": {
    "input_tokens": 9050896,
    "cached_input_tokens": 8901376,
    "cache_write_input_tokens": 0,
    "output_tokens": 53699,
    "reasoning_output_tokens": 22905,
    "total_tokens": 9104595
  },
  "thread_token_usage": {
    "input_tokens": 9050896,
    "cached_input_tokens": 8901376,
    "cache_write_input_tokens": 0,
    "output_tokens": 53699,
    "reasoning_output_tokens": 22905,
    "total_tokens": 9104595
  }
}
163

exec command

+55:20
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"515bac","wall_time_seconds":30.000916003,"session_id":43610,"original_token_count":21}
epoch 10/12: loss=4.41508 lr=0.000357898
epoch 11/12: loss=4.35731 lr=0.000269985
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b889f8ac87d29f44dc2f38d4b222",
  "usage": {
    "input_tokens": 93277,
    "cached_input_tokens": 92928,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 93330
  },
  "turn_token_usage": {
    "input_tokens": 9144173,
    "cached_input_tokens": 8994304,
    "cache_write_input_tokens": 0,
    "output_tokens": 53752,
    "reasoning_output_tokens": 22911,
    "total_tokens": 9197925
  },
  "thread_token_usage": {
    "input_tokens": 9144173,
    "cached_input_tokens": 8994304,
    "cache_write_input_tokens": 0,
    "output_tokens": 53752,
    "reasoning_output_tokens": 22911,
    "total_tokens": 9197925
  }
}
164

exec command

+55:52
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

let{output,...rest}=await tools.write_stdin({"session_id":43610,"yield_time_ms":30000,"chars":""});
text(rest);
text(output);

Tool response

Script completed
Wall time 7.7 seconds
Output:

{"chunk_id":"c57c42","wall_time_seconds":7.696088155,"exit_code":0,"original_token_count":112}
epoch 12/12: loss=4.31833 lr=0.00024
wrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet
{"checkpoint": "/app/solution/model_final_36w.pt", "config": {"embedding_dim": 64, "hidden_dim": 128, "max_history": 384, "history_days": 180, "training_weeks": 36, "epochs": 12, "batch_size": 256, "negatives": 512, "learning_rate": 0.002, "weight_decay": 1e-05, "like_sampling_weight": 4.0, "organic_boost": 1.5, "seed": 20260924}}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141",
  "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75",
  "response_id": "resp_009ddf540f98c275016ab5b8aa64dc87d2b7a53fcd6b40b48e",
  "usage": {
    "input_tokens": 93422,
    "cached_input_tokens": 93056,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 93475
  },
  "turn_token_usage": {
    "input_tokens": 9237595,
    "cached_input_tokens": 9087360,
    "cache_write_input_tokens": 0,
    "output_tokens": 53805,
    "reasoning_output_tokens": 22917,
    "total_tokens": 9291400
  },
  "thread_token_usage": {
    "input_tokens": 9237595,
    "cached_input_tokens": 9087360,
    "cache_write_input_tokens": 0,
    "output_tokens": 53805,
    "reasoning_output_tokens": 22917,
    "total_tokens": 9291400
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-64dbdd5b-0dc4-4af4-96e0-3f85ff01d2cd",
    "process_id": "43610",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission_36w_candidate.parquet --checkpoint /app/solution/model_final_36w.pt --cache /app/solution/cache_final_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 --seed 20260924 --threads 8"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission_36w_candidate.parquet --checkpoint /app/solution/model_final_36w.pt --cache /app/solution/cache_final_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 --seed 20260924 --threads 8"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "stderr": "",
    "aggregated_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n",
    "exit_code": 0,
    "duration": {
      "secs": 238,
      "nanos": 366224167
    },
    "formatted_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n"
  }
}
165

exec command

+56:04
Thinking unavailable No text recorded

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

Model text

The 36-cutoff final refit completed cleanly. I’m validating the new candidate against every test eligibility bit before promoting it over the already-valid 28-cutoff submission; the older artifact remains in place until this check passes.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys,torch\nsys.path[:0]=['/task','/app/solution']\nfrom ranking_evaluation import validate_ranking,read_ranking\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\nD=Path('/task/data'); p=Path('/app/solution/submission_36w_candidate.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all() and len(f)==845600 and not f.duplicated(['uid','item_id']).any()\nck=torch.load('/app/solution/model_final_36w.pt',weights_only=False);cat=pd.read_parquet(D/'candidate_catalog.parquet');m=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nassert float(m.item_tower.fixed_prior_scale)==0.0\nprint('PROMOTION_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'prior_scale':float(m.item_tower.learned_prior_scale),'fixed_pop_scale':float(m.item_tower.fixed_prior_scale)})\nPY\nmv /app/solution/submission.parquet /app/solution/submission_28w_archived.parquet\nmv /app/solution/submission_36w_candidate.parquet /app/solution/submission.parquet\nmv /app/solution/model_final.pt /app/solution/model_final_28w_archived.pt\nmv /app/solution/model_final_36w.pt /app/solution/model_final.pt\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

PROMOTION_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100, 'prior_scale': 1.100000023841858, 'fixed_pop_scale': 0.0}
d904b65b482a956c89be2fbb9c846bd5c4ab12b1639f36f58249c8963a37ff60  /app/solution/submission.parquet
271272ff3d99652199cef46008423156045f0e13ef0275e20d1ff6a0d10ca423  /app/solution/model_final.pt
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      "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys,torch\nsys.path[:0]=['/task','/app/solution']\nfrom ranking_evaluation import validate_ranking,read_ranking\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\nD=Path('/task/data'); p=Path('/app/solution/submission_36w_candidate.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all() and len(f)==845600 and not f.duplicated(['uid','item_id']).any()\nck=torch.load('/app/solution/model_final_36w.pt',weights_only=False);cat=pd.read_parquet(D/'candidate_catalog.parquet');m=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nassert float(m.item_tower.fixed_prior_scale)==0.0\nprint('PROMOTION_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'prior_scale':float(m.item_tower.learned_prior_scale),'fixed_pop_scale':float(m.item_tower.fixed_prior_scale)})\nPY\nmv /app/solution/submission.parquet /app/solution/submission_28w_archived.parquet\nmv /app/solution/submission_36w_candidate.parquet /app/solution/submission.parquet\nmv /app/solution/model_final.pt /app/solution/model_final_28w_archived.pt\nmv /app/solution/model_final_36w.pt /app/solution/model_final.pt\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt"
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    "stdout": "PROMOTION_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100, 'prior_scale': 1.100000023841858, 'fixed_pop_scale': 0.0}\nd904b65b482a956c89be2fbb9c846bd5c4ab12b1639f36f58249c8963a37ff60  /app/solution/submission.parquet\n271272ff3d99652199cef46008423156045f0e13ef0275e20d1ff6a0d10ca423  /app/solution/model_final.pt\n",
    "stderr": "",
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166

Agent message

+56:35
Thinking unavailable No text recorded

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

167

exec command

+56:44
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/README.md\n@@\n-frequency, recency, completion, organic, like, and dislike weights, then uses a\n-`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n+frequency, recency, completion, organic, like, and dislike weights, then uses a\n+`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\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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    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/README.md\n",
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168

exec command

+56:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({"cmd":"PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python - <<'PY'\nimport torch\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\nprint('final_config',ck['config'])\nprint('final_epochs',len(ck['losses']),'final_loss',ck['losses'][-1])\nPY\nnl -ba /app/solution/model.py | sed -n '1,180p'\nnl -ba /app/solution/README.md | sed -n '1,180p'","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 6.5 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.029384,
    "quality_users": 4263
  }
}
final_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}
final_epochs 12 final_loss 4.318330001489008
     1	"""Neural towers used by the discovery ranker.
     2	
     3	The user tower consumes only a variable-length interaction history plus summary
     4	features.  It deliberately has no user-ID parameter.  The item tower can be
     5	run for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;
     6	the final coordinate implements an item prior while preserving an exact dot
     7	product scoring function.
     8	"""
     9	from __future__ import annotations
    10	
    11	import torch
    12	from torch import nn
    13	from torch.nn import functional as F
    14	
    15	
    16	class UserTower(nn.Module):
    17	    """Weighted history pooling followed by a small residual MLP."""
    18	
    19	    def __init__(
    20	        self,
    21	        num_items: int,
    22	        embedding_dim: int = 64,
    23	        hidden_dim: int = 128,
    24	        num_context_features: int = 4,
    25	    ) -> None:
    26	        super().__init__()
    27	        self.embedding_dim = embedding_dim
    28	        self.history_embedding = nn.EmbeddingBag(
    29	            num_items, embedding_dim, mode="sum", include_last_offset=True
    30	        )
    31	        nn.init.normal_(self.history_embedding.weight, std=0.04)
    32	        self.network = nn.Sequential(
    33	            nn.Linear(embedding_dim + num_context_features, hidden_dim),
    34	            nn.GELU(),
    35	            nn.LayerNorm(hidden_dim),
    36	            nn.Linear(hidden_dim, embedding_dim),
    37	        )
    38	        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)
    39	        self.log_scale = nn.Parameter(torch.tensor(2.0))
    40	
    41	    def forward(
    42	        self,
    43	        item_ids: torch.Tensor,
    44	        offsets: torch.Tensor,
    45	        weights: torch.Tensor,
    46	        context_features: torch.Tensor,
    47	    ) -> torch.Tensor:
    48	        pooled = self.history_embedding(
    49	            item_ids, offsets, per_sample_weights=weights
    50	        )
    51	        # Signed preference weights are useful, but their absolute magnitude
    52	        # must not make heavy users arbitrarily larger than light users.
    53	        abs_sums = torch.zeros(
    54	            len(offsets) - 1, device=weights.device, dtype=weights.dtype
    55	        )
    56	        lengths = offsets[1:] - offsets[:-1]
    57	        bag_ids = torch.repeat_interleave(
    58	            torch.arange(len(lengths), device=weights.device), lengths
    59	        )
    60	        abs_sums.scatter_add_(0, bag_ids, weights.abs())
    61	        pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)
    62	        latent = self.network(torch.cat((pooled, context_features), dim=1))
    63	        latent = F.normalize(latent + self.skip(pooled), dim=1)
    64	        # Constraining the scale prevents sampled-softmax from becoming
    65	        # numerically brittle.  The constant last coordinate pairs with the
    66	        # item tower's learned popularity prior.
    67	        scale = self.log_scale.exp().clamp(1.0, 30.0)
    68	        return torch.cat(
    69	            (latent * scale, torch.ones_like(latent[:, :1])), dim=1
    70	        )
    71	
    72	
    73	class ItemTower(nn.Module):
    74	    """ID plus catalogue-statistic item encoder, suitable for offline export."""
    75	
    76	    def __init__(
    77	        self,
    78	        num_items: int,
    79	        item_features: torch.Tensor,
    80	        embedding_dim: int = 64,
    81	        hidden_dim: int = 128,
    82	    ) -> None:
    83	        super().__init__()
    84	        self.embedding_dim = embedding_dim
    85	        self.id_embedding = nn.Embedding(num_items, embedding_dim)
    86	        nn.init.normal_(self.id_embedding.weight, std=0.04)
    87	        self.register_buffer("item_features", item_features.float())
    88	        feature_dim = item_features.shape[1]
    89	        self.network = nn.Sequential(
    90	            nn.Linear(embedding_dim + feature_dim, hidden_dim),
    91	            nn.GELU(),
    92	            nn.LayerNorm(hidden_dim),
    93	            nn.Linear(hidden_dim, embedding_dim),
    94	        )
    95	        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)
    96	        self.prior = nn.Sequential(
    97	            nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)
    98	        )
    99	        self.id_bias = nn.Embedding(num_items, 1)
   100	        nn.init.zeros_(self.id_bias.weight)
   101	        # These two scalar buffers are selected on the public window.  Keeping
   102	        # calibration inside the item tower means serving remains one exact
   103	        # maximum-inner-product search, with no post-retrieval reranker.
   104	        self.register_buffer("learned_prior_scale", torch.tensor(1.0))
   105	        self.register_buffer("fixed_prior_scale", torch.tensor(0.0))
   106	
   107	    def forward(self, item_ids: torch.Tensor) -> torch.Tensor:
   108	        ids = self.id_embedding(item_ids)
   109	        features = self.item_features[item_ids]
   110	        latent = self.network(torch.cat((ids, features), dim=1))
   111	        latent = F.normalize(latent + self.skip(ids), dim=1)
   112	        bias = self.learned_prior_scale * (
   113	            self.prior(features) + self.id_bias(item_ids)
   114	        ) + self.fixed_prior_scale * (-features[:, 2:3])
   115	        return torch.cat((latent, bias), dim=1)
   116	
   117	
   118	class TwoTowerRanker(nn.Module):
   119	    """Container whose score is exactly ``user_vector @ item_vector.T``."""
   120	
   121	    def __init__(
   122	        self,
   123	        num_items: int,
   124	        item_features: torch.Tensor,
   125	        embedding_dim: int = 64,
   126	        hidden_dim: int = 128,
   127	    ) -> None:
   128	        super().__init__()
   129	        self.user_tower = UserTower(
   130	            num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim
   131	        )
   132	        self.item_tower = ItemTower(
   133	            num_items,
   134	            item_features=item_features,
   135	            embedding_dim=embedding_dim,
   136	            hidden_dim=hidden_dim,
   137	        )
   138	
   139	    def score(
   140	        self,
   141	        history_ids: torch.Tensor,
   142	        offsets: torch.Tensor,
   143	        history_weights: torch.Tensor,
   144	        context_features: torch.Tensor,
   145	        candidate_ids: torch.Tensor,
   146	    ) -> torch.Tensor:
   147	        users = self.user_tower(
   148	            history_ids, offsets, history_weights, context_features
   149	        )
   150	        items = self.item_tower(candidate_ids)
   151	        return users @ items.T
     1	# Made-for-You two-tower ranker
     2	
     3	## Model
     4	
     5	`model.py` defines two separate CPU PyTorch towers. The user tower has **no
     6	user-ID embedding**. It pools a cutoff-correct 180-day interaction history
     7	(at most 384 distinct positive-listen tracks, plus active explicit state) with
     8	frequency, recency, completion, organic, like, and dislike weights, then uses a
     9	`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track
    10	embedding with four catalog popularity statistics and a `68 -> 128 -> 64`
    11	residual MLP. Both latent outputs are L2-normalized.
    12	
    13	Each tower emits 65 values: 64 personalized dimensions plus one calibrated
    14	item-prior coordinate (the corresponding user coordinate is exactly one).
    15	Thus the complete relevance score is exactly
    16	
    17	```text
    18	score(user history, item) = user_vector @ item_vector
    19	```
    20	
    21	Candidate vectors are computed once offline; request-time work is one bounded
    22	history pool, one small user MLP, and MIPS retrieval. Eligibility is applied as
    23	the platform-owned retrieval mask.
    24	
    25	## Training
    26	
    27	Training uses 36 rolling weekly cutoffs. A positive is a previously unseen
    28	catalog item with a subsequent organic 80%-complete listen or organic like.
    29	The loss is sampled softmax with 512 uniform catalog negatives; likes receive
    30	sampling weight 4. Histories are held in event/CSR form, so training never
    31	constructs or factorizes a global user-item interaction matrix. The final model
    32	is refit on `train_* + val_*`, with validation events treated as observed
    33	history and temporal training outcomes.
    34	
    35	Public validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured
    36	with:
    37	
    38	```sh
    39	/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet
    40	```
    41	
    42	## Final submission
    43	
    44	The checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted
    45	checkpoint. Rebuild it deterministically from `/app` with:
    46	
    47	```sh
    48	PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \
    49	  /opt/task-venv/bin/python /app/solution/train_ranker.py \
    50	  --stage final \
    51	  --output /app/solution/submission.parquet \
    52	  --checkpoint /app/solution/model_final.pt \
    53	  --cache /app/solution/cache_final_36w.pt \
    54	  --epochs 12 --training-weeks 36 --organic-boost 1.5 \
    55	  --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \
    56	  --seed 20260924 --threads 8
    57	```
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e0519f03-87b3-4c5c-9dca-430c27d77669",
    "process_id": "17924",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python - <<'PY'\nimport torch\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\nprint('final_config',ck['config'])\nprint('final_epochs',len(ck['losses']),'final_loss',ck['losses'][-1])\nPY\nnl -ba /app/solution/model.py | sed -n '1,180p'\nnl -ba /app/solution/README.md | sed -n '1,180p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python - <<'PY'\nimport torch\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\nprint('final_config',ck['config'])\nprint('final_epochs',len(ck['losses']),'final_loss',ck['losses'][-1])\nPY\nnl -ba /app/solution/model.py | sed -n '1,180p'\nnl -ba /app/solution/README.md | sed -n '1,180p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029384,\n    \"quality_users\": 4263\n  }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n     1\t\"\"\"Neural towers used by the discovery ranker.\n     2\t\n     3\tThe user tower consumes only a variable-length interaction history plus summary\n     4\tfeatures.  It deliberately has no user-ID parameter.  The item tower can be\n     5\trun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\n     6\tthe final coordinate implements an item prior while preserving an exact dot\n     7\tproduct scoring function.\n     8\t\"\"\"\n     9\tfrom __future__ import annotations\n    10\t\n    11\timport torch\n    12\tfrom torch import nn\n    13\tfrom torch.nn import functional as F\n    14\t\n    15\t\n    16\tclass UserTower(nn.Module):\n    17\t    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n    18\t\n    19\t    def __init__(\n    20\t        self,\n    21\t        num_items: int,\n    22\t        embedding_dim: int = 64,\n    23\t        hidden_dim: int = 128,\n    24\t        num_context_features: int = 4,\n    25\t    ) -> None:\n    26\t        super().__init__()\n    27\t        self.embedding_dim = embedding_dim\n    28\t        self.history_embedding = nn.EmbeddingBag(\n    29\t            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n    30\t        )\n    31\t        nn.init.normal_(self.history_embedding.weight, std=0.04)\n    32\t        self.network = nn.Sequential(\n    33\t            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n    34\t            nn.GELU(),\n    35\t            nn.LayerNorm(hidden_dim),\n    36\t            nn.Linear(hidden_dim, embedding_dim),\n    37\t        )\n    38\t        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n    39\t        self.log_scale = nn.Parameter(torch.tensor(2.0))\n    40\t\n    41\t    def forward(\n    42\t        self,\n    43\t        item_ids: torch.Tensor,\n    44\t        offsets: torch.Tensor,\n    45\t        weights: torch.Tensor,\n    46\t        context_features: torch.Tensor,\n    47\t    ) -> torch.Tensor:\n    48\t        pooled = self.history_embedding(\n    49\t            item_ids, offsets, per_sample_weights=weights\n    50\t        )\n    51\t        # Signed preference weights are useful, but their absolute magnitude\n    52\t        # must not make heavy users arbitrarily larger than light users.\n    53\t        abs_sums = torch.zeros(\n    54\t            len(offsets) - 1, device=weights.device, dtype=weights.dtype\n    55\t        )\n    56\t        lengths = offsets[1:] - offsets[:-1]\n    57\t        bag_ids = torch.repeat_interleave(\n    58\t            torch.arange(len(lengths), device=weights.device), lengths\n    59\t        )\n    60\t        abs_sums.scatter_add_(0, bag_ids, weights.abs())\n    61\t        pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n    62\t        latent = self.network(torch.cat((pooled, context_features), dim=1))\n    63\t        latent = F.normalize(latent + self.skip(pooled), dim=1)\n    64\t        # Constraining the scale prevents sampled-softmax from becoming\n    65\t        # numerically brittle.  The constant last coordinate pairs with the\n    66\t        # item tower's learned popularity prior.\n    67\t        scale = self.log_scale.exp().clamp(1.0, 30.0)\n    68\t        return torch.cat(\n    69\t            (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n    70\t        )\n    71\t\n    72\t\n    73\tclass ItemTower(nn.Module):\n    74\t    \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n    75\t\n    76\t    def __init__(\n    77\t        self,\n    78\t        num_items: int,\n    79\t        item_features: torch.Tensor,\n    80\t        embedding_dim: int = 64,\n    81\t        hidden_dim: int = 128,\n    82\t    ) -> None:\n    83\t        super().__init__()\n    84\t        self.embedding_dim = embedding_dim\n    85\t        self.id_embedding = nn.Embedding(num_items, embedding_dim)\n    86\t        nn.init.normal_(self.id_embedding.weight, std=0.04)\n    87\t        self.register_buffer(\"item_features\", item_features.float())\n    88\t        feature_dim = item_features.shape[1]\n    89\t        self.network = nn.Sequential(\n    90\t            nn.Linear(embedding_dim + feature_dim, hidden_dim),\n    91\t            nn.GELU(),\n    92\t            nn.LayerNorm(hidden_dim),\n    93\t            nn.Linear(hidden_dim, embedding_dim),\n    94\t        )\n    95\t        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n    96\t        self.prior = nn.Sequential(\n    97\t            nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n    98\t        )\n    99\t        self.id_bias = nn.Embedding(num_items, 1)\n   100\t        nn.init.zeros_(self.id_bias.weight)\n   101\t        # These two scalar buffers are selected on the public window.  Keeping\n   102\t        # calibration inside the item tower means serving remains one exact\n   103\t        # maximum-inner-product search, with no post-retrieval reranker.\n   104\t        self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n   105\t        self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n   106\t\n   107\t    def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n   108\t        ids = self.id_embedding(item_ids)\n   109\t        features = self.item_features[item_ids]\n   110\t        latent = self.network(torch.cat((ids, features), dim=1))\n   111\t        latent = F.normalize(latent + self.skip(ids), dim=1)\n   112\t        bias = self.learned_prior_scale * (\n   113\t            self.prior(features) + self.id_bias(item_ids)\n   114\t        ) + self.fixed_prior_scale * (-features[:, 2:3])\n   115\t        return torch.cat((latent, bias), dim=1)\n   116\t\n   117\t\n   118\tclass TwoTowerRanker(nn.Module):\n   119\t    \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n   120\t\n   121\t    def __init__(\n   122\t        self,\n   123\t        num_items: int,\n   124\t        item_features: torch.Tensor,\n   125\t        embedding_dim: int = 64,\n   126\t        hidden_dim: int = 128,\n   127\t    ) -> None:\n   128\t        super().__init__()\n   129\t        self.user_tower = UserTower(\n   130\t            num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n   131\t        )\n   132\t        self.item_tower = ItemTower(\n   133\t            num_items,\n   134\t            item_features=item_features,\n   135\t            embedding_dim=embedding_dim,\n   136\t            hidden_dim=hidden_dim,\n   137\t        )\n   138\t\n   139\t    def score(\n   140\t        self,\n   141\t        history_ids: torch.Tensor,\n   142\t        offsets: torch.Tensor,\n   143\t        history_weights: torch.Tensor,\n   144\t        context_features: torch.Tensor,\n   145\t        candidate_ids: torch.Tensor,\n   146\t    ) -> torch.Tensor:\n   147\t        users = self.user_tower(\n   148\t            history_ids, offsets, history_weights, context_features\n   149\t        )\n   150\t        items = self.item_tower(candidate_ids)\n   151\t        return users @ items.T\n     1\t# Made-for-You two-tower ranker\n     2\t\n     3\t## Model\n     4\t\n     5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n     6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n     7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n     8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n     9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n    10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n    11\tresidual MLP. Both latent outputs are L2-normalized.\n    12\t\n    13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n    14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n    15\tThus the complete relevance score is exactly\n    16\t\n    17\t```text\n    18\tscore(user history, item) = user_vector @ item_vector\n    19\t```\n    20\t\n    21\tCandidate vectors are computed once offline; request-time work is one bounded\n    22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n    23\tthe platform-owned retrieval mask.\n    24\t\n    25\t## Training\n    26\t\n    27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n    28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n    29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n    30\tsampling weight 4. Histories are held in event/CSR form, so training never\n    31\tconstructs or factorizes a global user-item interaction matrix. The final model\n    32\tis refit on `train_* + val_*`, with validation events treated as observed\n    33\thistory and temporal training outcomes.\n    34\t\n    35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n    36\twith:\n    37\t\n    38\t```sh\n    39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n    40\t```\n    41\t\n    42\t## Final submission\n    43\t\n    44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n    45\tcheckpoint. Rebuild it deterministically from `/app` with:\n    46\t\n    47\t```sh\n    48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n    49\t  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n    50\t  --stage final \\\n    51\t  --output /app/solution/submission.parquet \\\n    52\t  --checkpoint /app/solution/model_final.pt \\\n    53\t  --cache /app/solution/cache_final_36w.pt \\\n    54\t  --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n    55\t  --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n    56\t  --seed 20260924 --threads 8\n    57\t```\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029384,\n    \"quality_users\": 4263\n  }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n     1\t\"\"\"Neural towers used by the discovery ranker.\n     2\t\n     3\tThe user tower consumes only a variable-length interaction history plus summary\n     4\tfeatures.  It deliberately has no user-ID parameter.  The item tower can be\n     5\trun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\n     6\tthe final coordinate implements an item prior while preserving an exact dot\n     7\tproduct scoring function.\n     8\t\"\"\"\n     9\tfrom __future__ import annotations\n    10\t\n    11\timport torch\n    12\tfrom torch import nn\n    13\tfrom torch.nn import functional as F\n    14\t\n    15\t\n    16\tclass UserTower(nn.Module):\n    17\t    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n    18\t\n    19\t    def __init__(\n    20\t        self,\n    21\t        num_items: int,\n    22\t        embedding_dim: int = 64,\n    23\t        hidden_dim: int = 128,\n    24\t        num_context_features: int = 4,\n    25\t    ) -> None:\n    26\t        super().__init__()\n    27\t        self.embedding_dim = embedding_dim\n    28\t        self.history_embedding = nn.EmbeddingBag(\n    29\t            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n    30\t        )\n    31\t        nn.init.normal_(self.history_embedding.weight, std=0.04)\n    32\t        self.network = nn.Sequential(\n    33\t            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n    34\t            nn.GELU(),\n    35\t            nn.LayerNorm(hidden_dim),\n    36\t            nn.Linear(hidden_dim, embedding_dim),\n    37\t        )\n    38\t        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n    39\t        self.log_scale = nn.Parameter(torch.tensor(2.0))\n    40\t\n    41\t    def forward(\n    42\t        self,\n    43\t        item_ids: torch.Tensor,\n    44\t        offsets: torch.Tensor,\n    45\t        weights: torch.Tensor,\n    46\t        context_features: torch.Tensor,\n    47\t    ) -> torch.Tensor:\n    48\t        pooled = self.history_embedding(\n    49\t            item_ids, offsets, per_sample_weights=weights\n    50\t        )\n    51\t        # Signed preference weights are useful, but their absolute magnitude\n    52\t        # must not make heavy users arbitrarily larger than light users.\n    53\t        abs_sums = torch.zeros(\n    54\t            len(offsets) - 1, device=weights.device, dtype=weights.dtype\n    55\t        )\n    56\t        lengths = offsets[1:] - offsets[:-1]\n    57\t        bag_ids = torch.repeat_interleave(\n    58\t            torch.arange(len(lengths), device=weights.device), lengths\n    59\t        )\n    60\t        abs_sums.scatter_add_(0, bag_ids, weights.abs())\n    61\t        pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n    62\t        latent = self.network(torch.cat((pooled, context_features), dim=1))\n    63\t        latent = F.normalize(latent + self.skip(pooled), dim=1)\n    64\t        # Constraining the scale prevents sampled-softmax from becoming\n    65\t        # numerically brittle.  The constant last coordinate pairs with the\n    66\t        # item tower's learned popularity prior.\n    67\t        scale = self.log_scale.exp().clamp(1.0, 30.0)\n    68\t        return torch.cat(\n    69\t            (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n    70\t        )\n    71\t\n    72\t\n    73\tclass ItemTower(nn.Module):\n    74\t    \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n    75\t\n    76\t    def __init__(\n    77\t        self,\n    78\t        num_items: int,\n    79\t        item_features: torch.Tensor,\n    80\t        embedding_dim: int = 64,\n    81\t        hidden_dim: int = 128,\n    82\t    ) -> None:\n    83\t        super().__init__()\n    84\t        self.embedding_dim = embedding_dim\n    85\t        self.id_embedding = nn.Embedding(num_items, embedding_dim)\n    86\t        nn.init.normal_(self.id_embedding.weight, std=0.04)\n    87\t        self.register_buffer(\"item_features\", item_features.float())\n    88\t        feature_dim = item_features.shape[1]\n    89\t        self.network = nn.Sequential(\n    90\t            nn.Linear(embedding_dim + feature_dim, hidden_dim),\n    91\t            nn.GELU(),\n    92\t            nn.LayerNorm(hidden_dim),\n    93\t            nn.Linear(hidden_dim, embedding_dim),\n    94\t        )\n    95\t        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n    96\t        self.prior = nn.Sequential(\n    97\t            nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n    98\t        )\n    99\t        self.id_bias = nn.Embedding(num_items, 1)\n   100\t        nn.init.zeros_(self.id_bias.weight)\n   101\t        # These two scalar buffers are selected on the public window.  Keeping\n   102\t        # calibration inside the item tower means serving remains one exact\n   103\t        # maximum-inner-product search, with no post-retrieval reranker.\n   104\t        self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n   105\t        self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n   106\t\n   107\t    def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n   108\t        ids = self.id_embedding(item_ids)\n   109\t        features = self.item_features[item_ids]\n   110\t        latent = self.network(torch.cat((ids, features), dim=1))\n   111\t        latent = F.normalize(latent + self.skip(ids), dim=1)\n   112\t        bias = self.learned_prior_scale * (\n   113\t            self.prior(features) + self.id_bias(item_ids)\n   114\t        ) + self.fixed_prior_scale * (-features[:, 2:3])\n   115\t        return torch.cat((latent, bias), dim=1)\n   116\t\n   117\t\n   118\tclass TwoTowerRanker(nn.Module):\n   119\t    \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n   120\t\n   121\t    def __init__(\n   122\t        self,\n   123\t        num_items: int,\n   124\t        item_features: torch.Tensor,\n   125\t        embedding_dim: int = 64,\n   126\t        hidden_dim: int = 128,\n   127\t    ) -> None:\n   128\t        super().__init__()\n   129\t        self.user_tower = UserTower(\n   130\t            num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n   131\t        )\n   132\t        self.item_tower = ItemTower(\n   133\t            num_items,\n   134\t            item_features=item_features,\n   135\t            embedding_dim=embedding_dim,\n   136\t            hidden_dim=hidden_dim,\n   137\t        )\n   138\t\n   139\t    def score(\n   140\t        self,\n   141\t        history_ids: torch.Tensor,\n   142\t        offsets: torch.Tensor,\n   143\t        history_weights: torch.Tensor,\n   144\t        context_features: torch.Tensor,\n   145\t        candidate_ids: torch.Tensor,\n   146\t    ) -> torch.Tensor:\n   147\t        users = self.user_tower(\n   148\t            history_ids, offsets, history_weights, context_features\n   149\t        )\n   150\t        items = self.item_tower(candidate_ids)\n   151\t        return users @ items.T\n     1\t# Made-for-You two-tower ranker\n     2\t\n     3\t## Model\n     4\t\n     5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n     6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n     7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n     8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n     9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n    10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n    11\tresidual MLP. Both latent outputs are L2-normalized.\n    12\t\n    13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n    14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n    15\tThus the complete relevance score is exactly\n    16\t\n    17\t```text\n    18\tscore(user history, item) = user_vector @ item_vector\n    19\t```\n    20\t\n    21\tCandidate vectors are computed once offline; request-time work is one bounded\n    22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n    23\tthe platform-owned retrieval mask.\n    24\t\n    25\t## Training\n    26\t\n    27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n    28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n    29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n    30\tsampling weight 4. Histories are held in event/CSR form, so training never\n    31\tconstructs or factorizes a global user-item interaction matrix. The final model\n    32\tis refit on `train_* + val_*`, with validation events treated as observed\n    33\thistory and temporal training outcomes.\n    34\t\n    35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n    36\twith:\n    37\t\n    38\t```sh\n    39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n    40\t```\n    41\t\n    42\t## Final submission\n    43\t\n    44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n    45\tcheckpoint. Rebuild it deterministically from `/app` with:\n    46\t\n    47\t```sh\n    48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n    49\t  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n    50\t  --stage final \\\n    51\t  --output /app/solution/submission.parquet \\\n    52\t  --checkpoint /app/solution/model_final.pt \\\n    53\t  --cache /app/solution/cache_final_36w.pt \\\n    54\t  --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n    55\t  --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n    56\t  --seed 20260924 --threads 8\n    57\t```\n",
    "exit_code": 0,
    "duration": {
      "secs": 6,
      "nanos": 377710457
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.029384,\n    \"quality_users\": 4263\n  }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n     1\t\"\"\"Neural towers used by the discovery ranker.\n     2\t\n     3\tThe user tower consumes only a variable-length interaction history plus summary\n     4\tfeatures.  It deliberately has no user-ID parameter.  The item tower can be\n     5\trun for the whole catalogue offline.  Both return ``embedding_dim + 1`` values;\n     6\tthe final coordinate implements an item prior while preserving an exact dot\n     7\tproduct scoring function.\n     8\t\"\"\"\n     9\tfrom __future__ import annotations\n    10\t\n    11\timport torch\n    12\tfrom torch import nn\n    13\tfrom torch.nn import functional as F\n    14\t\n    15\t\n    16\tclass UserTower(nn.Module):\n    17\t    \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n    18\t\n    19\t    def __init__(\n    20\t        self,\n    21\t        num_items: int,\n    22\t        embedding_dim: int = 64,\n    23\t        hidden_dim: int = 128,\n    24\t        num_context_features: int = 4,\n    25\t    ) -> None:\n    26\t        super().__init__()\n    27\t        self.embedding_dim = embedding_dim\n    28\t        self.history_embedding = nn.EmbeddingBag(\n    29\t            num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n    30\t        )\n    31\t        nn.init.normal_(self.history_embedding.weight, std=0.04)\n    32\t        self.network = nn.Sequential(\n    33\t            nn.Linear(embedding_dim + num_context_features, hidden_dim),\n    34\t            nn.GELU(),\n    35\t            nn.LayerNorm(hidden_dim),\n    36\t            nn.Linear(hidden_dim, embedding_dim),\n    37\t        )\n    38\t        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n    39\t        self.log_scale = nn.Parameter(torch.tensor(2.0))\n    40\t\n    41\t    def forward(\n    42\t        self,\n    43\t        item_ids: torch.Tensor,\n    44\t        offsets: torch.Tensor,\n    45\t        weights: torch.Tensor,\n    46\t        context_features: torch.Tensor,\n    47\t    ) -> torch.Tensor:\n    48\t        pooled = self.history_embedding(\n    49\t            item_ids, offsets, per_sample_weights=weights\n    50\t        )\n    51\t        # Signed preference weights are useful, but their absolute magnitude\n    52\t        # must not make heavy users arbitrarily larger than light users.\n    53\t        abs_sums = torch.zeros(\n    54\t            len(offsets) - 1, device=weights.device, dtype=weights.dtype\n    55\t        )\n    56\t        lengths = offsets[1:] - offsets[:-1]\n    57\t        bag_ids = torch.repeat_interleave(\n    58\t            torch.arange(len(lengths), device=weights.device), lengths\n    59\t        )\n    60\t        abs_sums.scatter_add_(0, bag_ids, weights.abs())\n    61\t        pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n    62\t        latent = self.network(torch.cat((pooled, context_features), dim=1))\n    63\t        latent = F.normalize(latent + self.skip(pooled), dim=1)\n    64\t        # Constraining the scale prevents sampled-softmax from becoming\n    65\t        # numerically brittle.  The constant last coordinate pairs with the\n    66\t        # item tower's learned popularity prior.\n    67\t        scale = self.log_scale.exp().clamp(1.0, 30.0)\n    68\t        return torch.cat(\n    69\t            (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n    70\t        )\n    71\t\n    72\t\n    73\tclass ItemTower(nn.Module):\n    74\t    \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n    75\t\n    76\t    def __init__(\n    77\t        self,\n    78\t        num_items: int,\n    79\t        item_features: torch.Tensor,\n    80\t        embedding_dim: int = 64,\n    81\t        hidden_dim: int = 128,\n    82\t    ) -> None:\n    83\t        super().__init__()\n    84\t        self.embedding_dim = embedding_dim\n    85\t        self.id_embedding = nn.Embedding(num_items, embedding_dim)\n    86\t        nn.init.normal_(self.id_embedding.weight, std=0.04)\n    87\t        self.register_buffer(\"item_features\", item_features.float())\n    88\t        feature_dim = item_features.shape[1]\n    89\t        self.network = nn.Sequential(\n    90\t            nn.Linear(embedding_dim + feature_dim, hidden_dim),\n    91\t            nn.GELU(),\n    92\t            nn.LayerNorm(hidden_dim),\n    93\t            nn.Linear(hidden_dim, embedding_dim),\n    94\t        )\n    95\t        self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n    96\t        self.prior = nn.Sequential(\n    97\t            nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n    98\t        )\n    99\t        self.id_bias = nn.Embedding(num_items, 1)\n   100\t        nn.init.zeros_(self.id_bias.weight)\n   101\t        # These two scalar buffers are selected on the public window.  Keeping\n   102\t        # calibration inside the item tower means serving remains one exact\n   103\t        # maximum-inner-product search, with no post-retrieval reranker.\n   104\t        self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n   105\t        self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n   106\t\n   107\t    def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n   108\t        ids = self.id_embedding(item_ids)\n   109\t        features = self.item_features[item_ids]\n   110\t        latent = self.network(torch.cat((ids, features), dim=1))\n   111\t        latent = F.normalize(latent + self.skip(ids), dim=1)\n   112\t        bias = self.learned_prior_scale * (\n   113\t            self.prior(features) + self.id_bias(item_ids)\n   114\t        ) + self.fixed_prior_scale * (-features[:, 2:3])\n   115\t        return torch.cat((latent, bias), dim=1)\n   116\t\n   117\t\n   118\tclass TwoTowerRanker(nn.Module):\n   119\t    \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n   120\t\n   121\t    def __init__(\n   122\t        self,\n   123\t        num_items: int,\n   124\t        item_features: torch.Tensor,\n   125\t        embedding_dim: int = 64,\n   126\t        hidden_dim: int = 128,\n   127\t    ) -> None:\n   128\t        super().__init__()\n   129\t        self.user_tower = UserTower(\n   130\t            num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n   131\t        )\n   132\t        self.item_tower = ItemTower(\n   133\t            num_items,\n   134\t            item_features=item_features,\n   135\t            embedding_dim=embedding_dim,\n   136\t            hidden_dim=hidden_dim,\n   137\t        )\n   138\t\n   139\t    def score(\n   140\t        self,\n   141\t        history_ids: torch.Tensor,\n   142\t        offsets: torch.Tensor,\n   143\t        history_weights: torch.Tensor,\n   144\t        context_features: torch.Tensor,\n   145\t        candidate_ids: torch.Tensor,\n   146\t    ) -> torch.Tensor:\n   147\t        users = self.user_tower(\n   148\t            history_ids, offsets, history_weights, context_features\n   149\t        )\n   150\t        items = self.item_tower(candidate_ids)\n   151\t        return users @ items.T\n     1\t# Made-for-You two-tower ranker\n     2\t\n     3\t## Model\n     4\t\n     5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n     6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n     7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n     8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n     9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n    10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n    11\tresidual MLP. Both latent outputs are L2-normalized.\n    12\t\n    13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n    14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n    15\tThus the complete relevance score is exactly\n    16\t\n    17\t```text\n    18\tscore(user history, item) = user_vector @ item_vector\n    19\t```\n    20\t\n    21\tCandidate vectors are computed once offline; request-time work is one bounded\n    22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n    23\tthe platform-owned retrieval mask.\n    24\t\n    25\t## Training\n    26\t\n    27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n    28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n    29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n    30\tsampling weight 4. Histories are held in event/CSR form, so training never\n    31\tconstructs or factorizes a global user-item interaction matrix. The final model\n    32\tis refit on `train_* + val_*`, with validation events treated as observed\n    33\thistory and temporal training outcomes.\n    34\t\n    35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n    36\twith:\n    37\t\n    38\t```sh\n    39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n    40\t```\n    41\t\n    42\t## Final submission\n    43\t\n    44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n    45\tcheckpoint. Rebuild it deterministically from `/app` with:\n    46\t\n    47\t```sh\n    48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n    49\t  /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n    50\t  --stage final \\\n    51\t  --output /app/solution/submission.parquet \\\n    52\t  --checkpoint /app/solution/model_final.pt \\\n    53\t  --cache /app/solution/cache_final_36w.pt \\\n    54\t  --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n    55\t  --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n    56\t  --seed 20260924 --threads 8\n    57\t```\n"
  }
}
169

Final response

+57:06
Thinking unavailable No text recorded

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

Model text

Completed the latency-efficient PyTorch two-tower ranker.

  • Final artifact: submission.parquet
  • Model architecture: model.py
  • Training and retrieval pipeline: train_ranker.py
  • Documentation and rebuild command: README.md
  • Final checkpoint: model_final.pt
  • Public validation NDCG@10: 0.029384
  • Submission validated: 8,456 users × 100 unique items, ranks 1–100, all test-eligible.
  • Serving score is exactly a 65-dimensional user/item dot product; no global interaction matrix is constructed.
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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.

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Attempt
sol-max-0-153-4-retriever-r01-20260924T221413Z
Recorded model
gpt-5.6-sol
Harness version
0.153.4
Session
01a0d5a6-369b-76d1-953a-8c850355d141
Source SHA-256
86301d06eb1b8ebf38c392af5a4e8bb49bc125637faea6f175a243e9945fb7fe

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. System instructions present in this archive are included above.