Agent trace

Two-tower retriever sol-0.153.4-max-03

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

Thinking text is unavailable for this run. The archive contains 126 empty thinking entries; tool calls are shown separately.

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Agent: gpt-5.6-sol · Codex · max effort
Recorded conversation: 46:13 · 132 steps
Private reward: 0.030171 · Pass
Attempt cost: $4.25

Thinking
Model text
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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.

Developer instructions

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Developer instructions

<skills_instructions>

Skills

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

Skill roots

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

Available skills

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

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

<apps_instructions>

Apps (Connectors)

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

Developer instructions

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Developer instructions

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

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

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

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

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

They may be addressed as to=/root

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

All agents share the same directory. In detail:

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

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

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

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

Developer instructions

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Developer instructions

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

User context

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User context

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

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

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

Harness context world state

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

Harness context turn context

{
  "type": "turn_context",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-25",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "max",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "max",
  "summary": "auto"
}

Task instructions

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Task instructions

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:

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

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

The two public windows have different roles:

Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
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.

exec command

+00:04

Thinking unavailable No text recorded

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

Model text

I’ll inspect the data distributions and evaluator first, then build a scalable event-sampled two-tower baseline, score it on the public window, and iterate on history features and training loss. I’ll keep a valid final submission in /app/solution/ as soon as the first model is ready.

Tool callexecTool call Returned

Tool call · Input +00:08

const r = await tools.exec_command({"cmd":"pwd && rg --files /task | sort | sed -n '1,160p' && printf '\\nRequirements:\\n' && sed -n '1,240p' /task/requirements.txt && printf '\\nEvaluator:\\n' && sed -n '1,280p' /task/evaluate_public.py && printf '\\nRanking helpers:\\n' && sed -n '1,320p' /task/ranking_evaluation.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

Tool response +00:08

Script completed
Wall time 0.1 seconds
Output:

/app
/task/data/candidate_catalog.parquet
/task/data/target_users.parquet
/task/data/test_eligible_candidates.npz
/task/data/test_preference_state.parquet
/task/data/train_dislikes.parquet
/task/data/train_likes.parquet
/task/data/train_listens.parquet
/task/data/train_undislikes.parquet
/task/data/train_unlikes.parquet
/task/data/val_dislikes.parquet
/task/data/val_likes.parquet
/task/data/val_listens.parquet
/task/data/val_undislikes.parquet
/task/data/val_unlikes.parquet
/task/data/validation_eligible_candidates.npz
/task/data/validation_preference_state.parquet
/task/evaluate_public.py
/task/ranking_evaluation.py
/task/requirements.txt

Requirements:
# 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

Evaluator:
#!/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()

Ranking helpers:
"""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),
    }

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6fab011d-7f61-49f2-9a5d-245878b226d2",
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    "status": "completed",
    "stdout": "/app\n/task/data/candidate_catalog.parquet\n/task/data/target_users.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_likes.parquet\n/task/data/val_listens.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/validation_preference_state.parquet\n/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/requirements.txt\n\nRequirements:\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\nEvaluator:\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\nRanking helpers:\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",
    "stderr": "",
    "aggregated_output": "/app\n/task/data/candidate_catalog.parquet\n/task/data/target_users.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_likes.parquet\n/task/data/val_listens.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/validation_preference_state.parquet\n/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/requirements.txt\n\nRequirements:\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\nEvaluator:\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\nRanking helpers:\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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2240
    },
    "formatted_output": "/app\n/task/data/candidate_catalog.parquet\n/task/data/target_users.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_likes.parquet\n/task/data/val_listens.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/validation_preference_state.parquet\n/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/requirements.txt\n\nRequirements:\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\nEvaluator:\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\nRanking helpers:\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"
  }
}

exec command

+00:11

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +00:16

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    df=pd.read_parquet(p)\n    print(p.name, df.shape, df.dtypes.to_dict())\n    print(' head',df.head(2).to_dict('records'))\n    if 'timestamp' in df:\n        print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\nfor p in sorted(D.glob('*.npz')):\n    with np.load(p) as z:\n        print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n        counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n        print(' eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'mean',counts.mean())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response +00:16

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

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  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-09eb8867-8029-4579-8377-37e36da8b480",
    "process_id": "69346",
    "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    df=pd.read_parquet(p)\n    print(p.name, df.shape, df.dtypes.to_dict())\n    print(' head',df.head(2).to_dict('records'))\n    if 'timestamp' in df:\n        print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\nfor p in sorted(D.glob('*.npz')):\n    with np.load(p) as z:\n        print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n        counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n        print(' eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'mean',counts.mean())\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    df=pd.read_parquet(p)\n    print(p.name, df.shape, df.dtypes.to_dict())\n    print(' head',df.head(2).to_dict('records'))\n    if 'timestamp' in df:\n        print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\nfor p in sorted(D.glob('*.npz')):\n    with np.load(p) as z:\n        print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n        counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n        print(' eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'mean',counts.mean())\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": 2140
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\nModuleNotFoundError: No module named 'pandas'\n"
  }
}

exec command

+00:18

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 python executable is not the benchmark environment—it lacks pandas despite the supplied inventory. I’m locating the preconfigured interpreter before touching any artifacts.

Tool callexecTool call Returned

Tool call · Input +00:21

const r = await tools.exec_command({"cmd":"which -a python python3 || true\nls -la /app\nls -la /task\nfind / -maxdepth 3 -type f \\( -name python -o -name python3 \\) 2>/dev/null | sed -n '1,120p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response +00:21

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
/usr/bin/python3
/bin/python3
total 12
drwxr-xr-x 3 agent agent 4096 Sep 25 00:11 .
drwxr-xr-x 1 root  root  4096 Sep 25 00:15 ..
drwxr-xr-x 2 agent agent 4096 Sep 25 00:11 solution
total 36
dr-xr-xr-x 1 root root 4096 Sep 25 00:11 .
drwxr-xr-x 1 root root 4096 Sep 25 00:15 ..
-r--r--r-- 1 root root    0 Sep 24 23:58 .keep
dr-xr-xr-x 1 root root 4096 Sep 24 23:58 data
-r--r--r-- 1 root root 5774 Sep 24 23:58 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 23:58 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 24 23:58 requirements.txt

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-28641e24-db4a-4412-a74b-f8b09fd43d42",
    "process_id": "6493",
    "command": [
      "/bin/sh",
      "-lc",
      "which -a python python3 || true\nls -la /app\nls -la /task\nfind / -maxdepth 3 -type f \\( -name python -o -name python3 \\) 2>/dev/null | sed -n '1,120p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "which -a python python3 || true\nls -la /app\nls -la /task\nfind / -maxdepth 3 -type f \\( -name python -o -name python3 \\) 2>/dev/null | sed -n '1,120p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:15 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:11 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:15 ..\n-r--r--r-- 1 root root    0 Sep 24 23:58 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 data\n-r--r--r-- 1 root root 5774 Sep 24 23:58 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:58 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:58 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:15 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:11 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:15 ..\n-r--r--r-- 1 root root    0 Sep 24 23:58 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 data\n-r--r--r-- 1 root root 5774 Sep 24 23:58 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:58 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:58 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2050
    },
    "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:15 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:11 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:15 ..\n-r--r--r-- 1 root root    0 Sep 24 23:58 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 data\n-r--r--r-- 1 root root 5774 Sep 24 23:58 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:58 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:58 requirements.txt\n"
  }
}

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 +00:25

const r = await tools.exec_command({"cmd":"python -V\npython -m pip list --format=columns | sed -n '1,120p'\n/usr/bin/python3 -V\n/usr/bin/python3 - <<'PY'\ntry:\n import pandas,torch,pyarrow,numpy\n print('ok',pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nexcept Exception as e: print(type(e),e)\nPY\nfind / -type d -name 'site-packages' 2>/dev/null | sed -n '1,80p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response +00:27

Script completed
Wall time 1.2 seconds
Output:

Python 3.12.13
Package Version
------- -------
pip     25.0.1
Python 3.11.2
<class 'ModuleNotFoundError'> No module named 'pandas'
/usr/local/lib/python3.12/site-packages
/opt/task-venv/lib/python3.12/site-packages

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
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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-dd570438-280d-4955-a6e0-0ff6b00f4d9b",
    "process_id": "57495",
    "command": [
      "/bin/sh",
      "-lc",
      "python -V\npython -m pip list --format=columns | sed -n '1,120p'\n/usr/bin/python3 -V\n/usr/bin/python3 - <<'PY'\ntry:\n import pandas,torch,pyarrow,numpy\n print('ok',pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nexcept Exception as e: print(type(e),e)\nPY\nfind / -type d -name 'site-packages' 2>/dev/null | sed -n '1,80p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python -V\npython -m pip list --format=columns | sed -n '1,120p'\n/usr/bin/python3 -V\n/usr/bin/python3 - <<'PY'\ntry:\n import pandas,torch,pyarrow,numpy\n print('ok',pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nexcept Exception as e: print(type(e),e)\nPY\nfind / -type d -name 'site-packages' 2>/dev/null | sed -n '1,80p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Package Version\n------- -------\npip     25.0.1\nPython 3.11.2\n<class 'ModuleNotFoundError'> No module named 'pandas'\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n",
    "stderr": "",
    "aggregated_output": "Package Version\n------- -------\npip     25.0.1\nPython 3.11.2\n<class 'ModuleNotFoundError'> No module named 'pandas'\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 28729507
    },
    "formatted_output": "Package Version\n------- -------\npip     25.0.1\nPython 3.11.2\n<class 'ModuleNotFoundError'> No module named 'pandas'\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\n"
  }
}

exec command

+00:28

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +00:30

const r = await tools.exec_command({"cmd":"ls -la /opt/task-venv/bin | sed -n '1,100p'\n/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint('versions',pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response +00:34

Script completed
Wall time 4.0 seconds
Output:

total 124
dr-xr-xr-x 1 root root 4096 Sep 25 00:11 .
dr-xr-xr-x 1 root root 4096 Sep 25 00:11 ..
-r--r--r-- 1 root root 9033 Sep 25 00:11 Activate.ps1
-r--r--r-- 1 root root 2158 Sep 25 00:11 activate
-r--r--r-- 1 root root  920 Sep 25 00:11 activate.csh
-r--r--r-- 1 root root 2195 Sep 25 00:11 activate.fish
-r-xr-xr-x 1 root root  302 Sep 25 00:11 chardetect
-r-xr-xr-x 1 root root  304 Sep 25 00:11 dotenv
-r-xr-xr-x 1 root root  308 Sep 25 00:11 f2py
-r-xr-xr-x 1 root root  299 Sep 25 00:11 idna
-r-xr-xr-x 1 root root  297 Sep 25 00:11 isympy
-r-xr-xr-x 1 root root  305 Sep 25 00:11 jsonschema
-r-xr-xr-x 1 root root  311 Sep 25 00:11 jupyter
-r-xr-xr-x 1 root root  311 Sep 25 00:11 jupyter-migrate
-r-xr-xr-x 1 root root  316 Sep 25 00:11 jupyter-troubleshoot
-r-xr-xr-x 1 root root  344 Sep 25 00:11 jupyter-trust
-r-xr-xr-x 1 root root  311 Sep 25 00:11 jupytext
-r-xr-xr-x 1 root root  315 Sep 25 00:11 jupytext-config
-r-xr-xr-x 1 root root  301 Sep 25 00:11 kaggle
-r-xr-xr-x 1 root root  312 Sep 25 00:11 markdown-it
-r-xr-xr-x 1 root root  325 Sep 25 00:11 normalizer
-r-xr-xr-x 1 root root  308 Sep 25 00:11 numpy-config
-r-xr-xr-x 1 root root  313 Sep 25 00:11 pip
-r-xr-xr-x 1 root root  313 Sep 25 00:11 pip3
-r-xr-xr-x 1 root root  313 Sep 25 00:11 pip3.12
lrwxrwxrwx 1 root root   21 Sep 25 00:11 python -> /usr/local/bin/python
lrwxrwxrwx 1 root root    6 Sep 25 00:11 python3 -> python
lrwxrwxrwx 1 root root    6 Sep 25 00:11 python3.12 -> python
-r-xr-xr-x 1 root root  307 Sep 25 00:11 slugify
-r-xr-xr-x 1 root root  321 Sep 25 00:11 torchfrtrace
-r-xr-xr-x 1 root root  312 Sep 25 00:11 torchrun
-r-xr-xr-x 1 root root  299 Sep 25 00:11 tqdm
versions 2.3.3 2.8.0+cpu 18.1.0 2.2.6

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5bd5873a887d2ab16d2d751d5f18d",
  "usage": {
    "input_tokens": 20076,
    "cached_input_tokens": 19584,
    "cache_write_input_tokens": 0,
    "output_tokens": 132,
    "reasoning_output_tokens": 15,
    "total_tokens": 20208
  },
  "turn_token_usage": {
    "input_tokens": 92657,
    "cached_input_tokens": 75392,
    "cache_write_input_tokens": 0,
    "output_tokens": 1206,
    "reasoning_output_tokens": 275,
    "total_tokens": 93863
  },
  "thread_token_usage": {
    "input_tokens": 92657,
    "cached_input_tokens": 75392,
    "cache_write_input_tokens": 0,
    "output_tokens": 1206,
    "reasoning_output_tokens": 275,
    "total_tokens": 93863
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d314fc36-68a9-42b5-a8ea-9c226b6fdc25",
    "process_id": "17818",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -la /opt/task-venv/bin | sed -n '1,100p'\n/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint('versions',pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -la /opt/task-venv/bin | sed -n '1,100p'\n/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint('versions',pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "versions 2.3.3 2.8.0+cpu 18.1.0 2.2.6\n",
    "stderr": "",
    "aggregated_output": "versions 2.3.3 2.8.0+cpu 18.1.0 2.2.6\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 845662366
    },
    "formatted_output": "versions 2.3.3 2.8.0+cpu 18.1.0 2.2.6\n"
  }
}

exec command

+00:35

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 +00:41

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    df=pd.read_parquet(p)\n    print('\\n',p.name, df.shape, df.dtypes.to_dict())\n    print(' head',df.head(2).to_dict('records'))\n    if 'timestamp' in df:\n        print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\nfor p in sorted(D.glob('*.npz')):\n    with np.load(p) as z:\n        print('\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n        counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n        print(' eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'mean',counts.mean())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response +00:44

Script completed
Wall time 3.7 seconds
Output:


 candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}
 head [{'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}]

 target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}
 head [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]

 test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
 head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]

 train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]
 ts 75 24786580 uids 5732 items 50876 organic {1: 52278, 0: 48797}

 train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]
 ts 80 24786790 uids 8144 items 172303 organic {1: 461839, 0: 352992}

 train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
 head [{'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}]
 ts 0 24786795 uids 9238 items 845395 organic {1: 22477622, 0: 21006537}

 train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]
 ts 15950 24785395 uids 2769 items 14725 organic {1: 17751, 0: 2209}

 train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]
 ts 250 24786570 uids 6197 items 113072 organic {1: 277065, 0: 15600}

 val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]
 ts 24789920 25393280 uids 955 items 3135 organic {1: 1835, 0: 1728}

 val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]
 ts 24788815 25393390 uids 4114 items 22902 organic {1: 25181, 0: 12614}

 val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
 head [{'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}]
 ts 24788600 25393395 uids 8196 items 191747 organic {1: 776486, 0: 725211}

 val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]
 ts 24810715 25393035 uids 205 items 579 organic {1: 527, 0: 79}

 val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
 head [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]
 ts 24789005 25393385 uids 1313 items 8080 organic {1: 8905, 0: 441}

 validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
 head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]

 test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
 eligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947

 validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
 eligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5bd5fba7487d2b699ccbc7710aa73",
  "usage": {
    "input_tokens": 21021,
    "cached_input_tokens": 19840,
    "cache_write_input_tokens": 0,
    "output_tokens": 337,
    "reasoning_output_tokens": 27,
    "total_tokens": 21358
  },
  "turn_token_usage": {
    "input_tokens": 113678,
    "cached_input_tokens": 95232,
    "cache_write_input_tokens": 0,
    "output_tokens": 1543,
    "reasoning_output_tokens": 302,
    "total_tokens": 115221
  },
  "thread_token_usage": {
    "input_tokens": 113678,
    "cached_input_tokens": 95232,
    "cache_write_input_tokens": 0,
    "output_tokens": 1543,
    "reasoning_output_tokens": 302,
    "total_tokens": 115221
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1c4f14f2-65be-46a9-b2bf-ff95ea35e377",
    "process_id": "93561",
    "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    df=pd.read_parquet(p)\n    print('\\n',p.name, df.shape, df.dtypes.to_dict())\n    print(' head',df.head(2).to_dict('records'))\n    if 'timestamp' in df:\n        print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\nfor p in sorted(D.glob('*.npz')):\n    with np.load(p) as z:\n        print('\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n        counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n        print(' eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'mean',counts.mean())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "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    df=pd.read_parquet(p)\n    print('\\n',p.name, df.shape, df.dtypes.to_dict())\n    print(' head',df.head(2).to_dict('records'))\n    if 'timestamp' in df:\n        print(' ts',df.timestamp.min(),df.timestamp.max(),'uids',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.value_counts().to_dict())\nfor p in sorted(D.glob('*.npz')):\n    with np.load(p) as z:\n        print('\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n        counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n        print(' eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'mean',counts.mean())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n head [{'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}]\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n head [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\n ts 75 24786580 uids 5732 items 50876 organic {1: 52278, 0: 48797}\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\n ts 80 24786790 uids 8144 items 172303 organic {1: 461839, 0: 352992}\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n head [{'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}]\n ts 0 24786795 uids 9238 items 845395 organic {1: 22477622, 0: 21006537}\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\n ts 15950 24785395 uids 2769 items 14725 organic {1: 17751, 0: 2209}\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\n ts 250 24786570 uids 6197 items 113072 organic {1: 277065, 0: 15600}\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\n ts 24789920 25393280 uids 955 items 3135 organic {1: 1835, 0: 1728}\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\n ts 24788815 25393390 uids 4114 items 22902 organic {1: 25181, 0: 12614}\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n head [{'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}]\n ts 24788600 25393395 uids 8196 items 191747 organic {1: 776486, 0: 725211}\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\n ts 24810715 25393035 uids 205 items 579 organic {1: 527, 0: 79}\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\n ts 24789005 25393385 uids 1313 items 8080 organic {1: 8905, 0: 441}\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\n eligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\n eligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727\n",
    "stderr": "",
    "aggregated_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n head [{'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}]\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n head [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\n ts 75 24786580 uids 5732 items 50876 organic {1: 52278, 0: 48797}\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\n ts 80 24786790 uids 8144 items 172303 organic {1: 461839, 0: 352992}\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n head [{'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}]\n ts 0 24786795 uids 9238 items 845395 organic {1: 22477622, 0: 21006537}\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\n ts 15950 24785395 uids 2769 items 14725 organic {1: 17751, 0: 2209}\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\n ts 250 24786570 uids 6197 items 113072 organic {1: 277065, 0: 15600}\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\n ts 24789920 25393280 uids 955 items 3135 organic {1: 1835, 0: 1728}\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\n ts 24788815 25393390 uids 4114 items 22902 organic {1: 25181, 0: 12614}\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n head [{'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}]\n ts 24788600 25393395 uids 8196 items 191747 organic {1: 776486, 0: 725211}\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\n ts 24810715 25393035 uids 205 items 579 organic {1: 527, 0: 79}\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\n ts 24789005 25393385 uids 1313 items 8080 organic {1: 8905, 0: 441}\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\n eligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\n eligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 546335047
    },
    "formatted_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n head [{'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}]\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n head [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\n ts 75 24786580 uids 5732 items 50876 organic {1: 52278, 0: 48797}\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\n ts 80 24786790 uids 8144 items 172303 organic {1: 461839, 0: 352992}\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n head [{'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}]\n ts 0 24786795 uids 9238 items 845395 organic {1: 22477622, 0: 21006537}\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\n ts 15950 24785395 uids 2769 items 14725 organic {1: 17751, 0: 2209}\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\n ts 250 24786570 uids 6197 items 113072 organic {1: 277065, 0: 15600}\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\n ts 24789920 25393280 uids 955 items 3135 organic {1: 1835, 0: 1728}\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\n ts 24788815 25393390 uids 4114 items 22902 organic {1: 25181, 0: 12614}\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n head [{'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}]\n ts 24788600 25393395 uids 8196 items 191747 organic {1: 776486, 0: 725211}\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\n ts 24810715 25393035 uids 205 items 579 organic {1: 527, 0: 79}\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n head [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\n ts 24789005 25393385 uids 1313 items 8080 organic {1: 8905, 0: 441}\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n head [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\n eligible counts [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\n eligible counts [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727\n"
  }
}

exec command

+00:51

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 +00:52

const r = await tools.exec_command({"cmd":"du -h /task/data/* | sort -h\nfree -h\nnproc\nlscpu | sed -n '1,36p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response +00:52

Script completed
Wall time 0.1 seconds
Output:

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
/bin/sh: 2: free: not found
8
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7R13 Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                1
BogoMIPS:                                5300.00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               128 KiB (4 instances)
L1i cache:                               128 KiB (4 instances)
L2 cache:                                2 MiB (4 instances)
L3 cache:                                16 MiB (1 instance)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-7
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Old microcode:             Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-8d178468-b1d1-4c00-9a6e-2dbb8bf9c07e",
    "process_id": "41465",
    "command": [
      "/bin/sh",
      "-lc",
      "du -h /task/data/* | sort -h\nfree -h\nnproc\nlscpu | sed -n '1,36p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "du -h /task/data/* | sort -h\nfree -h\nnproc\nlscpu | sed -n '1,36p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "8.0K\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/bin/sh: 2: free: not found\n8\nArchitecture:                            x86_64\nCPU op-mode(s):                          32-bit, 64-bit\nAddress sizes:                           48 bits physical, 48 bits virtual\nByte Order:                              Little Endian\nCPU(s):                                  8\nOn-line CPU(s) list:                     0-7\nVendor ID:                               AuthenticAMD\nModel name:                              AMD EPYC 7R13 Processor\nCPU family:                              25\nModel:                                   1\nThread(s) per core:                      2\nCore(s) per socket:                      4\nSocket(s):                               1\nStepping:                                1\nBogoMIPS:                                5300.00\nFlags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor:                       KVM\nVirtualization type:                     full\nL1d cache:                               128 KiB (4 instances)\nL1i cache:                               128 KiB (4 instances)\nL2 cache:                                2 MiB (4 instances)\nL3 cache:                                16 MiB (1 instance)\nNUMA node(s):                            1\nNUMA node0 CPU(s):                       0-7\nVulnerability Gather data sampling:      Not affected\nVulnerability Ghostwrite:                Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit:             Not affected\nVulnerability L1tf:                      Not affected\nVulnerability Mds:                       Not affected\nVulnerability Meltdown:                  Not affected\nVulnerability Mmio stale data:           Not affected\nVulnerability Old microcode:             Not affected\nVulnerability Reg file data sampling:    Not affected\nVulnerability Retbleed:                  Not affected\nVulnerability Spec rstack overflow:      Mitigation; Safe RET\n",
    "stderr": "",
    "aggregated_output": "8.0K\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/bin/sh: 2: free: not found\n8\nArchitecture:                            x86_64\nCPU op-mode(s):                          32-bit, 64-bit\nAddress sizes:                           48 bits physical, 48 bits virtual\nByte Order:                              Little Endian\nCPU(s):                                  8\nOn-line CPU(s) list:                     0-7\nVendor ID:                               AuthenticAMD\nModel name:                              AMD EPYC 7R13 Processor\nCPU family:                              25\nModel:                                   1\nThread(s) per core:                      2\nCore(s) per socket:                      4\nSocket(s):                               1\nStepping:                                1\nBogoMIPS:                                5300.00\nFlags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor:                       KVM\nVirtualization type:                     full\nL1d cache:                               128 KiB (4 instances)\nL1i cache:                               128 KiB (4 instances)\nL2 cache:                                2 MiB (4 instances)\nL3 cache:                                16 MiB (1 instance)\nNUMA node(s):                            1\nNUMA node0 CPU(s):                       0-7\nVulnerability Gather data sampling:      Not affected\nVulnerability Ghostwrite:                Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit:             Not affected\nVulnerability L1tf:                      Not affected\nVulnerability Mds:                       Not affected\nVulnerability Meltdown:                  Not affected\nVulnerability Mmio stale data:           Not affected\nVulnerability Old microcode:             Not affected\nVulnerability Reg file data sampling:    Not affected\nVulnerability Retbleed:                  Not affected\nVulnerability Spec rstack overflow:      Mitigation; Safe RET\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3500
    },
    "formatted_output": "8.0K\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/bin/sh: 2: free: not found\n8\nArchitecture:                            x86_64\nCPU op-mode(s):                          32-bit, 64-bit\nAddress sizes:                           48 bits physical, 48 bits virtual\nByte Order:                              Little Endian\nCPU(s):                                  8\nOn-line CPU(s) list:                     0-7\nVendor ID:                               AuthenticAMD\nModel name:                              AMD EPYC 7R13 Processor\nCPU family:                              25\nModel:                                   1\nThread(s) per core:                      2\nCore(s) per socket:                      4\nSocket(s):                               1\nStepping:                                1\nBogoMIPS:                                5300.00\nFlags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor:                       KVM\nVirtualization type:                     full\nL1d cache:                               128 KiB (4 instances)\nL1i cache:                               128 KiB (4 instances)\nL2 cache:                                2 MiB (4 instances)\nL3 cache:                                16 MiB (1 instance)\nNUMA node(s):                            1\nNUMA node0 CPU(s):                       0-7\nVulnerability Gather data sampling:      Not affected\nVulnerability Ghostwrite:                Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit:             Not affected\nVulnerability L1tf:                      Not affected\nVulnerability Mds:                       Not affected\nVulnerability Meltdown:                  Not affected\nVulnerability Mmio stale data:           Not affected\nVulnerability Old microcode:             Not affected\nVulnerability Reg file data sampling:    Not affected\nVulnerability Retbleed:                  Not affected\nVulnerability Spec rstack overflow:      Mitigation; Safe RET\n"
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}

Harness context token usage record

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exec command

+01:01

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 benchmark is interaction-dense (43.5M listens for 9.2K users) while the retrieval catalog is only 30K tracks. I’m measuring what eligibility removes and how much future relevance is explained by prior listens, active likes, recency, and popularity; that determines whether the history encoder should emphasize repetition, sequence context, or broader taste.

Tool callexecTool call Returned

Tool call · Input +01:32

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\n uidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\n def elig(u,i):\n  r=uidrow.get(int(u)); c=itcol.get(int(i)); return r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1)\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',truth.eligible.sum(),'elig users',truth.loc[truth.eligible,'uid'].nunique(),'grades',truth.loc[truth.eligible,'grade'].value_counts().to_dict())\nprint('rel/user eligible',truth[truth.eligible].groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# train catalog events target only, positives\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\n# Aggregate pair features\ntr['pos50']=tr.played_ratio_pct>=50\ntr['strong80']=tr.played_ratio_pct>=80\ntr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),last_ratio=('played_ratio_pct','last')).reset_index()\nrel=truth[truth.eligible].merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'means',rel[c].fillna(0).mean(),'bygrade',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\n# preference overlap\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(**{state:1})\n rr=truth[truth.eligible].merge(p,on=['uid','item_id'],how='left')\n print(state,'relevant overlap',rr[state].notna().mean(), 'prefs total',len(p),'catalog',p.item_id.isin(catset).sum(),'eligible_of_catalog',sum(elig(u,i) for u,i in p[p.item_id.isin(catset)][['uid','item_id']].itertuples(index=False,name=None)))\n# eligibility exclusions intersection with histories, several examples\nrng=np.random.default_rng(1)\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(set(items[mask])).sum())\n# Global popular scores exact evaluator with custom simple rankers\n# Compute all val gold nested\nfrom math import log2\nG={}\nfor u,i,g in truth[truth.eligible][['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(x,0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\npopitems=cat.item_id.to_numpy()\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# Personalized pair heuristic over elig items: norgstrong etc plus pop tie\nA=agg.set_index(['uid','item_id'])\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); its=items[mask]\n  try: au=A.loc[u]\n  except KeyError: au=None\n  vals=np.zeros(len(its),dtype=float)\n  if au is not None:\n   mp=au[col]\n   common=np.intersect1d(its,mp.index.to_numpy(),assume_unique=False)\n   pos=np.searchsorted(its,common) if np.all(its[:-1]<=its[1:]) else None\n   d=dict(zip(mp.index,mp.values)); vals=np.array([d.get(int(i),0) for i in its])\n  # rank history primarily, slight popular tie (catalog ordering matches items likely)\n  order=np.lexsort((np.arange(len(its)), -vals))[:100]\n  ss.append(ndcg(its[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response +01:35

Script completed
Wall time 2.3 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 19, in <module>
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py", line 4119, in __getitem__
raw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 2 grades {3: 266887}
    indexer = self.columns._get_indexer_strict(key, "columns")[1]
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 6212, in _get_indexer_strict
    self._raise_if_missing(keyarr, indexer, axis_name)
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 6261, in _raise_if_missing
    raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index([False, False, False, False, False, False, False,     0,     0, False,\n       ...\n       False, False, False,     0, False,     0, False,     0, False, False],\n      dtype='object', length=266887)] are in the [columns]"

Harness context token usage record

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Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-2a66b965-1c13-4e49-9db5-bdb67af7fb0c",
    "process_id": "38078",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\n uidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\n def elig(u,i):\n  r=uidrow.get(int(u)); c=itcol.get(int(i)); return r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1)\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',truth.eligible.sum(),'elig users',truth.loc[truth.eligible,'uid'].nunique(),'grades',truth.loc[truth.eligible,'grade'].value_counts().to_dict())\nprint('rel/user eligible',truth[truth.eligible].groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# train catalog events target only, positives\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\n# Aggregate pair features\ntr['pos50']=tr.played_ratio_pct>=50\ntr['strong80']=tr.played_ratio_pct>=80\ntr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),last_ratio=('played_ratio_pct','last')).reset_index()\nrel=truth[truth.eligible].merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'means',rel[c].fillna(0).mean(),'bygrade',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\n# preference overlap\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(**{state:1})\n rr=truth[truth.eligible].merge(p,on=['uid','item_id'],how='left')\n print(state,'relevant overlap',rr[state].notna().mean(), 'prefs total',len(p),'catalog',p.item_id.isin(catset).sum(),'eligible_of_catalog',sum(elig(u,i) for u,i in p[p.item_id.isin(catset)][['uid','item_id']].itertuples(index=False,name=None)))\n# eligibility exclusions intersection with histories, several examples\nrng=np.random.default_rng(1)\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(set(items[mask])).sum())\n# Global popular scores exact evaluator with custom simple rankers\n# Compute all val gold nested\nfrom math import log2\nG={}\nfor u,i,g in truth[truth.eligible][['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(x,0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\npopitems=cat.item_id.to_numpy()\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# Personalized pair heuristic over elig items: norgstrong etc plus pop tie\nA=agg.set_index(['uid','item_id'])\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); its=items[mask]\n  try: au=A.loc[u]\n  except KeyError: au=None\n  vals=np.zeros(len(its),dtype=float)\n  if au is not None:\n   mp=au[col]\n   common=np.intersect1d(its,mp.index.to_numpy(),assume_unique=False)\n   pos=np.searchsorted(its,common) if np.all(its[:-1]<=its[1:]) else None\n   d=dict(zip(mp.index,mp.values)); vals=np.array([d.get(int(i),0) for i in its])\n  # rank history primarily, slight popular tie (catalog ordering matches items likely)\n  order=np.lexsort((np.arange(len(its)), -vals))[:100]\n  ss.append(ndcg(its[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\n uidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\n def elig(u,i):\n  r=uidrow.get(int(u)); c=itcol.get(int(i)); return r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1)\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',truth.eligible.sum(),'elig users',truth.loc[truth.eligible,'uid'].nunique(),'grades',truth.loc[truth.eligible,'grade'].value_counts().to_dict())\nprint('rel/user eligible',truth[truth.eligible].groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# train catalog events target only, positives\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\n# Aggregate pair features\ntr['pos50']=tr.played_ratio_pct>=50\ntr['strong80']=tr.played_ratio_pct>=80\ntr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),last_ratio=('played_ratio_pct','last')).reset_index()\nrel=truth[truth.eligible].merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'means',rel[c].fillna(0).mean(),'bygrade',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\n# preference overlap\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(**{state:1})\n rr=truth[truth.eligible].merge(p,on=['uid','item_id'],how='left')\n print(state,'relevant overlap',rr[state].notna().mean(), 'prefs total',len(p),'catalog',p.item_id.isin(catset).sum(),'eligible_of_catalog',sum(elig(u,i) for u,i in p[p.item_id.isin(catset)][['uid','item_id']].itertuples(index=False,name=None)))\n# eligibility exclusions intersection with histories, several examples\nrng=np.random.default_rng(1)\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(set(items[mask])).sum())\n# Global popular scores exact evaluator with custom simple rankers\n# Compute all val gold nested\nfrom math import log2\nG={}\nfor u,i,g in truth[truth.eligible][['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(x,0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\npopitems=cat.item_id.to_numpy()\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# Personalized pair heuristic over elig items: norgstrong etc plus pop tie\nA=agg.set_index(['uid','item_id'])\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); its=items[mask]\n  try: au=A.loc[u]\n  except KeyError: au=None\n  vals=np.zeros(len(its),dtype=float)\n  if au is not None:\n   mp=au[col]\n   common=np.intersect1d(its,mp.index.to_numpy(),assume_unique=False)\n   pos=np.searchsorted(its,common) if np.all(its[:-1]<=its[1:]) else None\n   d=dict(zip(mp.index,mp.values)); vals=np.array([d.get(int(i),0) for i in its])\n  # rank history primarily, slight popular tie (catalog ordering matches items likely)\n  order=np.lexsort((np.arange(len(its)), -vals))[:100]\n  ss.append(ndcg(its[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 19, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 4119, in __getitem__\nraw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 2 grades {3: 266887}\n    indexer = self.columns._get_indexer_strict(key, \"columns\")[1]\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n    raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([False, False, False, False, False, False, False,     0,     0, False,\\n       ...\\n       False, False, False,     0, False,     0, False,     0, False, False],\\n      dtype='object', length=266887)] are in the [columns]\"\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 19, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 4119, in __getitem__\nraw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 2 grades {3: 266887}\n    indexer = self.columns._get_indexer_strict(key, \"columns\")[1]\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n    raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([False, False, False, False, False, False, False,     0,     0, False,\\n       ...\\n       False, False, False,     0, False,     0, False,     0, False, False],\\n      dtype='object', length=266887)] are in the [columns]\"\n",
    "exit_code": 1,
    "duration": {
      "secs": 2,
      "nanos": 160209229
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 19, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 4119, in __getitem__\nraw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 2 grades {3: 266887}\n    indexer = self.columns._get_indexer_strict(key, \"columns\")[1]\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n    raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([False, False, False, False, False, False, False,     0,     0, False,\\n       ...\\n       False, False, False,     0, False,     0, False,     0, False, False],\\n      dtype='object', length=266887)] are in the [columns]\"\n"
  }
}

exec command

+01:40

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 +02:07

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())\nprint('rel/user eligible\\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left')\n pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# map item ids to catalog positions for vector fast per user\nitempos={int(x):i for i,x in enumerate(items)}\nagg['ipos']=agg.item_id.map(itempos)\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  vals=np.zeros(len(items),dtype=np.float64)\n  au=agg[agg.uid==u]\n  vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30\n  order=np.lexsort((np.arange(len(items)), -vals))[:100]\n  ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response +02:37

Script completed
Wall time 30.2 seconds
Output:

Harness context token usage record

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  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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  "usage": {
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  "turn_token_usage": {
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 1241,
    "total_tokens": 194850
  }
}

exec command

+02:40

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 +02:42

const r = await tools.exec_command({"cmd":"ps -eo pid,etimes,rss,cmd | rg 'opt/task-venv|python' | sed -n '1,100p'","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000}); text(JSON.stringify(r));

Tool response +02:42

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"2ae02e","wall_time_seconds":0.00000314,"exit_code":0,"original_token_count":7,"output":"/bin/sh: 1: ps: not found\n"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-cbebe31d-34a1-45da-9f36-b50d37ffa127",
    "process_id": "76571",
    "command": [
      "/bin/sh",
      "-lc",
      "ps -eo pid,etimes,rss,cmd | rg 'opt/task-venv|python' | sed -n '1,100p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -eo pid,etimes,rss,cmd | rg 'opt/task-venv|python' | sed -n '1,100p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: ps: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3140
    },
    "formatted_output": "/bin/sh: 1: ps: not found\n"
  }
}

Harness context token usage record

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    "total_tokens": 224001
  }
}

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 +02:45

const r = await tools.exec_command({"cmd":"pgrep -a python || true","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000}); text(JSON.stringify(r));

Tool response +02:45

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"d52845","wall_time_seconds":0.00000415,"exit_code":0,"original_token_count":8,"output":"/bin/sh: 1: pgrep: not found\n"}

Harness context codex item completed

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    "type": "CommandExecution",
    "id": "exec-96805bf2-ab54-414a-84fa-f1616929b45b",
    "process_id": "39568",
    "command": [
      "/bin/sh",
      "-lc",
      "pgrep -a python || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "pgrep -a python || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: pgrep: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: pgrep: not found\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4150
    },
    "formatted_output": "/bin/sh: 1: pgrep: not found\n"
  }
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Harness context token usage record

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  "turn_token_usage": {
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  }
}

exec command

+02:47

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 +02:49

const r = await tools.exec_command({"cmd":"for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | grep -q python && { printf '%s ' \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; printf '\\n'; }; fi; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000}); text(r.output);

Tool response +02:49

Script completed
Wall time 0.1 seconds
Output:

/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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:

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

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

The two public windows have different roles:

1. 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:

```sh
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.
' 2>&1 </dev/null | tee /logs/agent/codex.txt 
/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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:

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

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

The two public windows have different roles:

1. 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:

```sh
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.
 
/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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:

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

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

The two public windows have different roles:

1. 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:

```sh
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.
 
/proc/9507 /bin/sh -c /opt/task-venv/bin/python - <<'PY'
import pandas as pd, numpy as np, time
from pathlib import Path
D=Path('/task/data'); t=time.time()
target=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))
cat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))
with np.load(D/'validation_eligible_candidates.npz') as z:
 uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()
uidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}
def elig(u,i):
 r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))
vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])
vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])
strong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()
likes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()
truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')
truth['in_cat']=truth.item_id.isin(catset)
truth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]
relt=truth[truth.is_eligible]
print('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())
print('rel/user eligible\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))
tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])
tr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]
print('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)
tr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)
agg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()
rel=relt.merge(agg,on=['uid','item_id'],how='left')
for c in ['n','npos','nstrong','norg','norgstrong','last']:
 print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())
pref=pd.read_parquet(D/'validation_preference_state.parquet')
for state in ['liked','disliked']:
 p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)
 rr=relt.merge(p,on=['uid','item_id'],how='left')
 pc=p[p.item_id.isin(catset)]
 print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))
for uid in list(uids[:5]):
 r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)
 a=agg[agg.uid==uid]
 eligset=set(map(int,items[mask]))
 print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())
from math import log2
G={}
for u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)
def ndcg(rank,g):
 dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))
 ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))
 return dcg/ideal
scores=[]
for u,g in G.items():
 r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]
 scores.append(ndcg(top,g))
print('pop ndcg',np.mean(scores))
# map item ids to catalog positions for vector fast per user
itempos={int(x):i for i,x in enumerate(items)}
agg['ipos']=agg.item_id.map(itempos)
for col in ['n','npos','nstrong','norg','norgstrong','last']:
 ss=[]
 for u,g in G.items():
  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)
  vals=np.zeros(len(items),dtype=np.float64)
  au=agg[agg.uid==u]
  vals[au.ipos.to_numpy()]=au[col].to_numpy()
  vals[~mask]=-1e30
  order=np.lexsort((np.arange(len(items)), -vals))[:100]
  ss.append(ndcg(items[order],g))
 print(col,'heuristic ndcg',np.mean(ss))
print('done sec',time.time()-t)
PY 
/proc/9509 /opt/task-venv/bin/python - 
/proc/9547 /bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" 2>/dev/null | grep -q python && { printf '%s ' "$d"; tr '\0' ' ' < "$d/cmdline"; printf '\n'; }; fi; done

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1c89e4c7-024b-4743-8e48-08b2b4ae6526",
    "process_id": "69882",
    "command": [
      "/bin/sh",
      "-lc",
      "for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | grep -q python && { printf '%s ' \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; printf '\\n'; }; fi; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | grep -q python && { printf '%s ' \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; printf '\\n'; }; fi; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user'\"'\"'s\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9507 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())\nprint('rel/user eligible\\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left')\n pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# map item ids to catalog positions for vector fast per user\nitempos={int(x):i for i,x in enumerate(items)}\nagg['ipos']=agg.item_id.map(itempos)\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  vals=np.zeros(len(items),dtype=np.float64)\n  au=agg[agg.uid==u]\n  vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30\n  order=np.lexsort((np.arange(len(items)), -vals))[:100]\n  ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY \n/proc/9509 /opt/task-venv/bin/python - \n/proc/9547 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | grep -q python && { printf '%s ' \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; printf '\\n'; }; fi; done \n",
    "stderr": "",
    "aggregated_output": "/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user'\"'\"'s\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9507 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())\nprint('rel/user eligible\\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left')\n pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# map item ids to catalog positions for vector fast per user\nitempos={int(x):i for i,x in enumerate(items)}\nagg['ipos']=agg.item_id.map(itempos)\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  vals=np.zeros(len(items),dtype=np.float64)\n  au=agg[agg.uid==u]\n  vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30\n  order=np.lexsort((np.arange(len(items)), -vals))[:100]\n  ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY \n/proc/9509 /opt/task-venv/bin/python - \n/proc/9547 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | grep -q python && { printf '%s ' \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; printf '\\n'; }; fi; done \n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3250
    },
    "formatted_output": "/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user'\"'\"'s\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9507 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())\nprint('rel/user eligible\\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left')\n pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# map item ids to catalog positions for vector fast per user\nitempos={int(x):i for i,x in enumerate(items)}\nagg['ipos']=agg.item_id.map(itempos)\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  vals=np.zeros(len(items),dtype=np.float64)\n  au=agg[agg.uid==u]\n  vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30\n  order=np.lexsort((np.arange(len(items)), -vals))[:100]\n  ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY \n/proc/9509 /opt/task-venv/bin/python - \n/proc/9547 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null | grep -q python && { printf '%s ' \"$d\"; tr '\\0' ' ' < \"$d/cmdline\"; printf '\\n'; }; fi; done \n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5bde349b487d281aff56e54974560",
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  "thread_token_usage": {
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    "reasoning_output_tokens": 1455,
    "total_tokens": 282927
  }
}

exec command

+02:52

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 +02:52

const r = await tools.exec_command({"cmd":"kill 9507 9509 2>/dev/null || true","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000}); text(r.output);

Tool response +02:53

Script completed
Wall time 0.0 seconds
Output:

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-3791cde9-3a69-4b00-a4ac-b9311fc10e48",
    "process_id": "55065",
    "command": [
      "/bin/sh",
      "-lc",
      "kill 9507 9509 2>/dev/null || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "kill 9507 9509 2>/dev/null || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 1820
    },
    "formatted_output": ""
  }
}

Harness context token usage record

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  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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  "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-6c93370e-d0d3-4045-a307-2626edfe1f2a",
    "process_id": "92055",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())\nprint('rel/user eligible\\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left')\n pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# map item ids to catalog positions for vector fast per user\nitempos={int(x):i for i,x in enumerate(items)}\nagg['ipos']=agg.item_id.map(itempos)\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  vals=np.zeros(len(items),dtype=np.float64)\n  au=agg[agg.uid==u]\n  vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30\n  order=np.lexsort((np.arange(len(items)), -vals))[:100]\n  ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict())\nprint('rel/user eligible\\n',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict())\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left')\n pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)))\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]\n eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum())\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10]))\n ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10]))\n return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); top=items[mask][:100]\n scores.append(ndcg(top,g))\nprint('pop ndcg',np.mean(scores))\n# map item ids to catalog positions for vector fast per user\nitempos={int(x):i for i,x in enumerate(items)}\nagg['ipos']=agg.item_id.map(itempos)\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  vals=np.zeros(len(items),dtype=np.float64)\n  au=agg[agg.uid==u]\n  vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30\n  order=np.lexsort((np.arange(len(items)), -vals))[:100]\n  ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss))\nprint('done sec',time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 143,
    "duration": {
      "secs": 45,
      "nanos": 154833179
    },
    "formatted_output": ""
  }
}

exec command

+02:57

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 +03:24

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict(),flush=True)\nprint('rel/user eligible',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]).to_dict(),flush=True)\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t,flush=True)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict(),flush=True)\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left'); pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)),flush=True)\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]; eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum(),flush=True)\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10])); ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10])); return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); scores.append(ndcg(items[mask][:100],g))\nprint('pop ndcg',np.mean(scores),flush=True)\nitempos={int(x):i for i,x in enumerate(items)}; agg['ipos']=agg.item_id.map(itempos)\ngroups={int(u):x for u,x in agg.groupby('uid',sort=False)}\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); vals=np.zeros(len(items),dtype=np.float64); au=groups.get(u)\n  if au is not None: vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30; order=np.lexsort((np.arange(len(items)), -vals))[:100]; ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss),flush=True)\nprint('done sec',time.time()-t,flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +03:51

Script completed
Wall time 27.2 seconds
Output:

{"chunk_id":"ec02a2","wall_time_seconds":26.985743997,"exit_code":0,"original_token_count":379,"output":"raw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 4263 grades {1: 33352, 3: 4417}\nrel/user eligible {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\ntrain target catalog rows 31062540 pairs 7868664 loaded 9.275876760482788\nn present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnpos present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorg present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorgstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nlast present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nliked relevant overlap 0.0 prefs total 418435 catalog 418435 eligible_of_catalog 0\ndisliked relevant overlap 0.0 prefs total 50689 catalog 50689 eligible_of_catalog 0\nuid 100 eligible 29318 histcat 682 hist eligible 0\nuid 200 eligible 29832 histcat 168 hist eligible 0\nuid 300 eligible 29931 histcat 69 hist eligible 0\nuid 600 eligible 29320 histcat 680 hist eligible 0\nuid 700 eligible 28347 histcat 1653 hist eligible 0\npop ndcg 0.010709951701903768\nn heuristic ndcg 0.010709951701903768\nnpos heuristic ndcg 0.010709951701903768\nnstrong heuristic ndcg 0.010709951701903768\nnorg heuristic ndcg 0.010709951701903768\nnorgstrong heuristic ndcg 0.010709951701903768\nlast heuristic ndcg 0.010709951701903768\ndone sec 25.10585618019104\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5bdea614087d2a359796fe29618e8",
  "usage": {
    "input_tokens": 36260,
    "cached_input_tokens": 35968,
    "cache_write_input_tokens": 0,
    "output_tokens": 1723,
    "reasoning_output_tokens": 182,
    "total_tokens": 37983
  },
  "turn_token_usage": {
    "input_tokens": 348973,
    "cached_input_tokens": 303488,
    "cache_write_input_tokens": 0,
    "output_tokens": 8174,
    "reasoning_output_tokens": 1719,
    "total_tokens": 357147
  },
  "thread_token_usage": {
    "input_tokens": 348973,
    "cached_input_tokens": 303488,
    "cache_write_input_tokens": 0,
    "output_tokens": 8174,
    "reasoning_output_tokens": 1719,
    "total_tokens": 357147
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fc1cf00e-ba27-49ff-8d15-fb88842b7f5d",
    "process_id": "35357",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict(),flush=True)\nprint('rel/user eligible',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]).to_dict(),flush=True)\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t,flush=True)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict(),flush=True)\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left'); pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)),flush=True)\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]; eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum(),flush=True)\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10])); ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10])); return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); scores.append(ndcg(items[mask][:100],g))\nprint('pop ndcg',np.mean(scores),flush=True)\nitempos={int(x):i for i,x in enumerate(items)}; agg['ipos']=agg.item_id.map(itempos)\ngroups={int(u):x for u,x in agg.groupby('uid',sort=False)}\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); vals=np.zeros(len(items),dtype=np.float64); au=groups.get(u)\n  if au is not None: vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30; order=np.lexsort((np.arange(len(items)), -vals))[:100]; ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss),flush=True)\nprint('done sec',time.time()-t,flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype('uint32'))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catset=set(cat.item_id.astype('uint32'))\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(u):i for i,u in enumerate(uids)}; itcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itcol.get(int(i)); return bool(r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1))\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'])\nstrong=vl[(vl.uid.isin(target))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.uid.isin(target))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth['in_cat']=truth.item_id.isin(catset)\ntruth['is_eligible']=[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]\nrelt=truth[truth.is_eligible]\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'in catalog',truth.in_cat.sum(),'eligible',len(relt),'elig users',relt.uid.nunique(),'grades',relt.grade.value_counts().to_dict(),flush=True)\nprint('rel/user eligible',relt.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]).to_dict(),flush=True)\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(target)&tr.item_id.isin(catset)]\nprint('train target catalog rows',len(tr),'pairs',tr[['uid','item_id']].drop_duplicates().shape[0],'loaded',time.time()-t,flush=True)\ntr['pos50']=tr.played_ratio_pct>=50; tr['strong80']=tr.played_ratio_pct>=80; tr['orgstrong']=tr.strong80&(tr.is_organic==1)\nagg=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),npos=('pos50','sum'),nstrong=('strong80','sum'),norg=('is_organic','sum'),norgstrong=('orgstrong','sum'),last=('timestamp','max'),first=('timestamp','min')).reset_index()\nrel=relt.merge(agg,on=['uid','item_id'],how='left')\nfor c in ['n','npos','nstrong','norg','norgstrong','last']:\n print(c,'present',rel[c].notna().mean(),'pos',((rel[c].fillna(0)>0)).mean(),'mean',rel[c].fillna(0).mean(),'bygrade presence',rel.groupby('grade')[c].agg(lambda x:x.notna().mean()).to_dict(),flush=True)\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor state in ['liked','disliked']:\n p=pref[pref.state==state][['uid','item_id']].drop_duplicates().assign(flag=1)\n rr=relt.merge(p,on=['uid','item_id'],how='left'); pc=p[p.item_id.isin(catset)]\n print(state,'relevant overlap',rr.flag.notna().mean(), 'prefs total',len(p),'catalog',len(pc),'eligible_of_catalog',sum(elig(u,i) for u,i in pc[['uid','item_id']].itertuples(index=False,name=None)),flush=True)\nfor uid in list(uids[:5]):\n r=uidrow[int(uid)]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n a=agg[agg.uid==uid]; eligset=set(map(int,items[mask]))\n print('uid',uid,'eligible',mask.sum(),'histcat',len(a),'hist eligible',a.item_id.isin(eligset).sum(),flush=True)\nfrom math import log2\nG={}\nfor u,i,g in relt[['uid','item_id','grade']].itertuples(index=False,name=None):G.setdefault(int(u),{})[int(i)]=int(g)\ndef ndcg(rank,g):\n dcg=sum((2**g.get(int(x),0)-1)/log2(k+2) for k,x in enumerate(rank[:10])); ideal=sum((2**x-1)/log2(k+2) for k,x in enumerate(sorted(g.values(),reverse=True)[:10])); return dcg/ideal\nscores=[]\nfor u,g in G.items():\n r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); scores.append(ndcg(items[mask][:100],g))\nprint('pop ndcg',np.mean(scores),flush=True)\nitempos={int(x):i for i,x in enumerate(items)}; agg['ipos']=agg.item_id.map(itempos)\ngroups={int(u):x for u,x in agg.groupby('uid',sort=False)}\nfor col in ['n','npos','nstrong','norg','norgstrong','last']:\n ss=[]\n for u,g in G.items():\n  r=uidrow[u]; mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); vals=np.zeros(len(items),dtype=np.float64); au=groups.get(u)\n  if au is not None: vals[au.ipos.to_numpy()]=au[col].to_numpy()\n  vals[~mask]=-1e30; order=np.lexsort((np.arange(len(items)), -vals))[:100]; ss.append(ndcg(items[order],g))\n print(col,'heuristic ndcg',np.mean(ss),flush=True)\nprint('done sec',time.time()-t,flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "raw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 4263 grades {1: 33352, 3: 4417}\nrel/user eligible {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\ntrain target catalog rows 31062540 pairs 7868664 loaded 9.275876760482788\nn present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnpos present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorg present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorgstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nlast present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nliked relevant overlap 0.0 prefs total 418435 catalog 418435 eligible_of_catalog 0\ndisliked relevant overlap 0.0 prefs total 50689 catalog 50689 eligible_of_catalog 0\nuid 100 eligible 29318 histcat 682 hist eligible 0\nuid 200 eligible 29832 histcat 168 hist eligible 0\nuid 300 eligible 29931 histcat 69 hist eligible 0\nuid 600 eligible 29320 histcat 680 hist eligible 0\nuid 700 eligible 28347 histcat 1653 hist eligible 0\npop ndcg 0.010709951701903768\nn heuristic ndcg 0.010709951701903768\nnpos heuristic ndcg 0.010709951701903768\nnstrong heuristic ndcg 0.010709951701903768\nnorg heuristic ndcg 0.010709951701903768\nnorgstrong heuristic ndcg 0.010709951701903768\nlast heuristic ndcg 0.010709951701903768\ndone sec 25.10585618019104\n",
    "stderr": "",
    "aggregated_output": "raw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 4263 grades {1: 33352, 3: 4417}\nrel/user eligible {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\ntrain target catalog rows 31062540 pairs 7868664 loaded 9.275876760482788\nn present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnpos present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorg present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorgstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nlast present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nliked relevant overlap 0.0 prefs total 418435 catalog 418435 eligible_of_catalog 0\ndisliked relevant overlap 0.0 prefs total 50689 catalog 50689 eligible_of_catalog 0\nuid 100 eligible 29318 histcat 682 hist eligible 0\nuid 200 eligible 29832 histcat 168 hist eligible 0\nuid 300 eligible 29931 histcat 69 hist eligible 0\nuid 600 eligible 29320 histcat 680 hist eligible 0\nuid 700 eligible 28347 histcat 1653 hist eligible 0\npop ndcg 0.010709951701903768\nn heuristic ndcg 0.010709951701903768\nnpos heuristic ndcg 0.010709951701903768\nnstrong heuristic ndcg 0.010709951701903768\nnorg heuristic ndcg 0.010709951701903768\nnorgstrong heuristic ndcg 0.010709951701903768\nlast heuristic ndcg 0.010709951701903768\ndone sec 25.10585618019104\n",
    "exit_code": 0,
    "duration": {
      "secs": 26,
      "nanos": 985733787
    },
    "formatted_output": "raw truth 266887 users 6511 in catalog 175729 eligible 37769 elig users 4263 grades {1: 33352, 3: 4417}\nrel/user eligible {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\ntrain target catalog rows 31062540 pairs 7868664 loaded 9.275876760482788\nn present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnpos present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorg present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nnorgstrong present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nlast present 0.0 pos 0.0 mean 0.0 bygrade presence {1: 0.0, 3: 0.0}\nliked relevant overlap 0.0 prefs total 418435 catalog 418435 eligible_of_catalog 0\ndisliked relevant overlap 0.0 prefs total 50689 catalog 50689 eligible_of_catalog 0\nuid 100 eligible 29318 histcat 682 hist eligible 0\nuid 200 eligible 29832 histcat 168 hist eligible 0\nuid 300 eligible 29931 histcat 69 hist eligible 0\nuid 600 eligible 29320 histcat 680 hist eligible 0\nuid 700 eligible 28347 histcat 1653 hist eligible 0\npop ndcg 0.010709951701903768\nn heuristic ndcg 0.010709951701903768\nnpos heuristic ndcg 0.010709951701903768\nnstrong heuristic ndcg 0.010709951701903768\nnorg heuristic ndcg 0.010709951701903768\nnorgstrong heuristic ndcg 0.010709951701903768\nlast heuristic ndcg 0.010709951701903768\ndone sec 25.10585618019104\n"
  }
}

Agent message

+04:02

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+04:12

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+04:30

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+04:40

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exec command

+04:44

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Model text

Eligibility is strict novelty: every previously listened or explicitly rated catalog track is removed. That rules out repeat heuristics completely. The first valid fallback will therefore be an eligibility-filtered popularity ranking; I’ll preserve it while training the history-to-novel-item neural retrieval model.

Tool callexecTool call Returned

Tool call · Input +04:55

const patch = "*** Begin Patch\n*** Add File: /app/solution/make_fallback.py\n+#!/usr/bin/env python3\n+\"\"\"Create structurally valid popularity fallbacks for both cutoffs.\"\"\"\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+\n+\n+def write_ranking(eligibility: str, output: str, order: np.ndarray) -> None:\n+    with np.load(DATA / eligibility, allow_pickle=False) as artifact:\n+        uids = artifact[\"uids\"].astype(np.int64)\n+        item_ids = artifact[\"item_ids\"].astype(np.int64)\n+        packed = artifact[\"packed_eligible\"]\n+    position = {int(item): col for col, item in enumerate(item_ids)}\n+    ordered_columns = np.asarray([position[int(item)] for item in order])\n+    rows = []\n+    for row, uid in enumerate(uids):\n+        mask = np.unpackbits(\n+            packed[row], count=len(item_ids), bitorder=\"little\"\n+        ).astype(bool)\n+        selected = ordered_columns[mask[ordered_columns]][:100]\n+        rows.extend(\n+            (int(uid), int(item_ids[col]), rank)\n+            for rank, col in enumerate(selected, 1)\n+        )\n+    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n+        OUT / output, index=False\n+    )\n+\n+\n+def main() -> None:\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    # Organic popularity is closer to the evaluation target than all-surface\n+    # popularity. Stable sorting retains the supplied total-popularity order.\n+    order = catalog.sort_values(\n+        [\"organic_positive_listens\", \"positive_listens\"], ascending=False\n+    ).item_id.to_numpy()\n+    write_ranking(\"validation_eligible_candidates.npz\", \"validation_fallback.parquet\", order)\n+    write_ranking(\"test_eligible_candidates.npz\", \"submission.parquet\", order)\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +04:55

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-29fb1ee6-dd8a-4b56-baaa-47a63e127b0a",
    "changes": {
      "/app/solution/make_fallback.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Create structurally valid popularity fallbacks for both cutoffs.\"\"\"\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\n\n\ndef write_ranking(eligibility: str, output: str, order: np.ndarray) -> None:\n    with np.load(DATA / eligibility, allow_pickle=False) as artifact:\n        uids = artifact[\"uids\"].astype(np.int64)\n        item_ids = artifact[\"item_ids\"].astype(np.int64)\n        packed = artifact[\"packed_eligible\"]\n    position = {int(item): col for col, item in enumerate(item_ids)}\n    ordered_columns = np.asarray([position[int(item)] for item in order])\n    rows = []\n    for row, uid in enumerate(uids):\n        mask = np.unpackbits(\n            packed[row], count=len(item_ids), bitorder=\"little\"\n        ).astype(bool)\n        selected = ordered_columns[mask[ordered_columns]][:100]\n        rows.extend(\n            (int(uid), int(item_ids[col]), rank)\n            for rank, col in enumerate(selected, 1)\n        )\n    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n        OUT / output, index=False\n    )\n\n\ndef main() -> None:\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    # Organic popularity is closer to the evaluation target than all-surface\n    # popularity. Stable sorting retains the supplied total-popularity order.\n    order = catalog.sort_values(\n        [\"organic_positive_listens\", \"positive_listens\"], ascending=False\n    ).item_id.to_numpy()\n    write_ranking(\"validation_eligible_candidates.npz\", \"validation_fallback.parquet\", order)\n    write_ranking(\"test_eligible_candidates.npz\", \"submission.parquet\", order)\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/make_fallback.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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exec command

+04:57

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Tool callexecTool call Returned

Tool call · Input +04:59

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

Tool response +05:06

Script completed
Wall time 7.8 seconds
Output:

{
  "model": "/app/solution/validation_fallback.parquet",
  "scores": {
    "quality_ndcg@10": 0.010577,
    "quality_users": 4263
  }
}
total 1.4M
-rw-r--r-- 1 agent agent 1.7K Sep 25 00:20 make_fallback.py
-rw-r--r-- 1 agent agent 688K Sep 25 00:21 submission.parquet
-rw-r--r-- 1 agent agent 682K Sep 25 00:20 validation_fallback.parquet

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-88a30323-ea96-46e3-9737-323af5266730",
    "process_id": "56724",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/make_fallback.py\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fallback.parquet\nls -lh /app/solution"
    ],
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      {
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_fallback.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.010577,\n    \"quality_users\": 4263\n  }\n}\ntotal 1.4M\n-rw-r--r-- 1 agent agent 1.7K Sep 25 00:20 make_fallback.py\n-rw-r--r-- 1 agent agent 688K Sep 25 00:21 submission.parquet\n-rw-r--r-- 1 agent agent 682K Sep 25 00:20 validation_fallback.parquet\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 7,
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  }
}

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exec command

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Model text

The training formulation is now fixed: rolling seven-day temporal retrieval tasks inside the historical log. Each example encodes only tracks known before its cutoff and predicts a newly discovered organic strong-listen/like track after the gap, using sampled negatives. This mirrors both novelty eligibility and the hidden backtest without materializing a user–item matrix.

Tool callexecTool call Returned

Tool call · Input +10:05

const patch = "*** Begin Patch\n*** Add File: /app/solution/ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Train and apply a history-only PyTorch two-tower retrieval ranker.\n+\n+The implementation deliberately operates on event/pair tables and sampled\n+training examples.  It never constructs or factorizes a global user-item\n+matrix.  Candidate vectors are produced once by :class:`ItemTower`; a request\n+requires one :class:`UserTower` call followed by inner-product retrieval.\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 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+from torch.utils.data import DataLoader, Dataset\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_END = 24_786_800\n+FINAL_END = 25_393_400\n+GAP = 1_800\n+WEEK = 7 * 24 * 60 * 60\n+INF_TIME = np.iinfo(np.uint32).max\n+\n+\n+def seed_everything(seed: int) -> None:\n+    random.seed(seed)\n+    np.random.seed(seed)\n+    torch.manual_seed(seed)\n+\n+\n+def read_events(stem: str, final: bool, columns: list[str]) -> pd.DataFrame:\n+    frames = [pd.read_parquet(DATA / f\"train_{stem}.parquet\", columns=columns)]\n+    if final:\n+        frames.append(pd.read_parquet(DATA / f\"val_{stem}.parquet\", columns=columns))\n+    return pd.concat(frames, ignore_index=True) if len(frames) > 1 else frames[0]\n+\n+\n+@dataclass\n+class UserHistory:\n+    \"\"\"Sorted, unique positive catalog interactions for one user.\"\"\"\n+\n+    items: np.ndarray\n+    times: np.ndarray\n+    strong_times: np.ndarray\n+\n+\n+@dataclass\n+class Profile:\n+    \"\"\"One cutoff-specific user profile and its future relevant items.\"\"\"\n+\n+    uid: int\n+    cutoff: int\n+    labels: np.ndarray\n+    grades: np.ndarray\n+\n+\n+class TemporalDataset(Dataset):\n+    \"\"\"Rolling temporal examples generated from per-user event streams.\"\"\"\n+\n+    def __init__(\n+        self,\n+        profiles: list[Profile],\n+        histories: dict[int, UserHistory],\n+        max_history: int,\n+        seed: int,\n+    ) -> None:\n+        self.profiles = profiles\n+        self.histories = histories\n+        self.max_history = max_history\n+        self.rng = np.random.default_rng(seed)\n+\n+    def __len__(self) -> int:\n+        return len(self.profiles)\n+\n+    def encode_history(self, uid: int, cutoff: int) -> tuple[np.ndarray, np.ndarray, np.float32]:\n+        history = self.histories[uid]\n+        end = int(np.searchsorted(history.times, cutoff, side=\"left\"))\n+        start = max(0, end - self.max_history)\n+        items = history.items[start:end]\n+        times = history.times[start:end]\n+        strong = history.strong_times[start:end] < cutoff\n+\n+        padded = np.full(self.max_history, -1, dtype=np.int64)\n+        weights = np.zeros((self.max_history, 3), dtype=np.float32)\n+        n = len(items)\n+        if n:\n+            padded[-n:] = items\n+            age = np.maximum(0.0, (float(cutoff) - times.astype(np.float64)) / 86_400.0)\n+            weights[-n:, 0] = 1.0\n+            weights[-n:, 1] = np.exp(-age / 30.0).astype(np.float32)\n+            weights[-n:, 2] = strong.astype(np.float32)\n+        activity = np.float32(math.log1p(end) / 8.0)\n+        return padded, weights, activity\n+\n+    def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, np.float32, np.int64]:\n+        profile = self.profiles[index]\n+        # Likes have seven times the DCG gain, but using the full 7x weight made\n+        # the much rarer label too dominant.  A sqrt(gain)-like 3x sampler is a\n+        # stable compromise for retrieval training.\n+        probabilities = np.where(profile.grades == 3, 3.0, 1.0)\n+        probabilities /= probabilities.sum()\n+        selected = int(self.rng.choice(len(profile.labels), p=probabilities))\n+        items, weights, activity = self.encode_history(profile.uid, profile.cutoff)\n+        return items, weights, activity, np.int64(profile.labels[selected])\n+\n+\n+class UserTower(nn.Module):\n+    \"\"\"DeepSets-style history encoder; it contains no user-ID parameters.\"\"\"\n+\n+    def __init__(self, item_count: int, token_dim: int, output_dim: int) -> None:\n+        super().__init__()\n+        self.item_count = item_count\n+        self.history_embedding = nn.Embedding(\n+            item_count + 1, token_dim, padding_idx=item_count\n+        )\n+        self.network = nn.Sequential(\n+            nn.Linear(3 * token_dim + 1, 192),\n+            nn.GELU(),\n+            nn.LayerNorm(192),\n+            nn.Linear(192, output_dim),\n+        )\n+\n+    def forward(\n+        self, history_items: torch.Tensor, history_weights: torch.Tensor, activity: torch.Tensor\n+    ) -> torch.Tensor:\n+        safe_items = history_items.masked_fill(history_items < 0, self.item_count)\n+        tokens = self.history_embedding(safe_items)\n+        # Independently normalized long-term, recent, and strong-organic pools.\n+        numerator = torch.einsum(\"blh,blc->bch\", tokens, history_weights)\n+        denominator = history_weights.sum(dim=1).clamp_min(1.0).unsqueeze(-1)\n+        pooled = (numerator / denominator).flatten(1)\n+        vector = self.network(torch.cat([pooled, activity[:, None]], dim=1))\n+        return F.normalize(vector, dim=1)\n+\n+\n+class ItemTower(nn.Module):\n+    \"\"\"Offline candidate encoder using item identity and popularity features.\"\"\"\n+\n+    def __init__(\n+        self, item_features: torch.Tensor, token_dim: int, output_dim: int\n+    ) -> None:\n+        super().__init__()\n+        self.item_embedding = nn.Embedding(len(item_features), token_dim)\n+        self.register_buffer(\"item_features\", item_features)\n+        self.network = nn.Sequential(\n+            nn.Linear(token_dim + item_features.shape[1], 160),\n+            nn.GELU(),\n+            nn.LayerNorm(160),\n+            nn.Linear(160, output_dim),\n+        )\n+\n+    def forward(self, item_indices: torch.Tensor) -> torch.Tensor:\n+        identity = self.item_embedding(item_indices)\n+        features = self.item_features[item_indices]\n+        vector = self.network(torch.cat([identity, features], dim=-1))\n+        return F.normalize(vector, dim=-1)\n+\n+\n+class TwoTower(nn.Module):\n+    \"\"\"Exact dot-product retrieval model with independently callable towers.\"\"\"\n+\n+    def __init__(\n+        self, item_features: torch.Tensor, token_dim: int = 64, output_dim: int = 96\n+    ) -> None:\n+        super().__init__()\n+        self.user_tower = UserTower(len(item_features), token_dim, output_dim)\n+        self.item_tower = ItemTower(item_features, token_dim, output_dim)\n+\n+    def score(\n+        self,\n+        history_items: torch.Tensor,\n+        history_weights: torch.Tensor,\n+        activity: torch.Tensor,\n+        item_indices: torch.Tensor,\n+    ) -> torch.Tensor:\n+        return (\n+            self.user_tower(history_items, history_weights, activity)\n+            * self.item_tower(item_indices)\n+        ).sum(dim=-1)\n+\n+\n+def build_item_features(catalog: pd.DataFrame) -> torch.Tensor:\n+    total = np.log1p(catalog.positive_listens.to_numpy(dtype=np.float32))\n+    organic = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n+    share = catalog.organic_positive_listens.to_numpy(dtype=np.float32) / np.maximum(\n+        1.0, catalog.positive_listens.to_numpy(dtype=np.float32)\n+    )\n+    rank = -np.log1p(catalog.popularity_rank.to_numpy(dtype=np.float32))\n+    features = np.column_stack([total, organic, share, rank]).astype(np.float32)\n+    features = (features - features.mean(axis=0)) / (features.std(axis=0) + 1e-6)\n+    return torch.from_numpy(features)\n+\n+\n+def _aggregate_pair_events(final: bool, catalog: pd.DataFrame) -> pd.DataFrame:\n+    \"\"\"Aggregate event streams into an event-scalable pair table.\"\"\"\n+    started = time.time()\n+    catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n+    dense_item_map = np.full(int(catalog_ids.max()) + 1, -1, dtype=np.int32)\n+    dense_item_map[catalog_ids] = np.arange(len(catalog_ids), dtype=np.int32)\n+\n+    listens = read_events(\n+        \"listens\", final, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+    )\n+    raw_ids = listens.item_id.to_numpy(dtype=np.int64)\n+    in_range = raw_ids < len(dense_item_map)\n+    mapped = np.full(len(raw_ids), -1, dtype=np.int32)\n+    mapped[in_range] = dense_item_map[raw_ids[in_range]]\n+    keep = mapped >= 0\n+    listens = listens.loc[keep, [\"uid\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]].copy()\n+    listens[\"item\"] = mapped[keep]\n+    timestamp = listens.timestamp.to_numpy(dtype=np.uint32)\n+    positive = listens.played_ratio_pct.to_numpy() >= 50\n+    organic_strong = (listens.is_organic.to_numpy() == 1) & (\n+        listens.played_ratio_pct.to_numpy() >= 80\n+    )\n+    listens[\"positive_time\"] = np.where(positive, timestamp, INF_TIME).astype(np.uint32)\n+    listens[\"strong_time\"] = np.where(organic_strong, timestamp, INF_TIME).astype(np.uint32)\n+    pairs = (\n+        listens.groupby([\"uid\", \"item\"], sort=False, observed=True)\n+        .agg(\n+            first_any=(\"timestamp\", \"min\"),\n+            first_positive=(\"positive_time\", \"min\"),\n+            first_strong=(\"strong_time\", \"min\"),\n+        )\n+        .reset_index()\n+    )\n+    del listens, mapped, raw_ids\n+\n+    likes = read_events(\"likes\", final, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n+    like_ids = likes.item_id.to_numpy(dtype=np.int64)\n+    in_range = like_ids < len(dense_item_map)\n+    like_item = np.full(len(like_ids), -1, dtype=np.int32)\n+    like_item[in_range] = dense_item_map[like_ids[in_range]]\n+    keep = like_item >= 0\n+    likes = likes.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n+    likes[\"item\"] = like_item[keep]\n+    like_ts = likes.timestamp.to_numpy(dtype=np.uint32)\n+    likes[\"organic_like_time\"] = np.where(\n+        likes.is_organic.to_numpy() == 1, like_ts, INF_TIME\n+    ).astype(np.uint32)\n+    like_pairs = (\n+        likes.groupby([\"uid\", \"item\"], sort=False, observed=True)\n+        .agg(first_like=(\"timestamp\", \"min\"), first_organic_like=(\"organic_like_time\", \"min\"))\n+        .reset_index()\n+    )\n+    del likes, like_item, like_ids\n+    pairs = pairs.merge(like_pairs, on=[\"uid\", \"item\"], how=\"outer\", sort=False)\n+    for column in [\"first_any\", \"first_positive\", \"first_strong\", \"first_like\", \"first_organic_like\"]:\n+        pairs[column] = pairs[column].fillna(INF_TIME).astype(np.uint32)\n+    pairs[\"first_any\"] = np.minimum(pairs.first_any, pairs.first_like).astype(np.uint32)\n+    pairs[\"first_positive\"] = np.minimum(pairs.first_positive, pairs.first_like).astype(np.uint32)\n+    # A like is a strong positive input even when it came from a recommendation.\n+    pairs[\"strong_context\"] = np.minimum(pairs.first_strong, pairs.first_like).astype(np.uint32)\n+    print(\n+        f\"aggregated {len(pairs):,} catalog user-item pairs in {time.time() - started:.1f}s\",\n+        flush=True,\n+    )\n+    return pairs\n+\n+\n+def build_histories_and_profiles(\n+    pairs: pd.DataFrame, end_time: int, weeks: int\n+) -> tuple[dict[int, UserHistory], list[Profile]]:\n+    histories: dict[int, UserHistory] = {}\n+    context = pairs[pairs.first_positive < INF_TIME].sort_values(\n+        [\"uid\", \"first_positive\"], kind=\"stable\"\n+    )\n+    for raw_uid, frame in context.groupby(\"uid\", sort=False):\n+        histories[int(raw_uid)] = UserHistory(\n+            frame.item.to_numpy(dtype=np.int32),\n+            frame.first_positive.to_numpy(dtype=np.int64),\n+            frame.strong_context.to_numpy(dtype=np.int64),\n+        )\n+\n+    relevant = pairs[\n+        (pairs.first_strong < INF_TIME) | (pairs.first_organic_like < INF_TIME)\n+    ][[\"uid\", \"item\", \"first_any\", \"first_strong\", \"first_organic_like\"]]\n+    relevant_groups = {int(uid): frame for uid, frame in relevant.groupby(\"uid\", sort=False)}\n+    profiles: list[Profile] = []\n+    latest_cutoff = end_time - GAP - WEEK\n+    cutoffs = [latest_cutoff - offset * WEEK for offset in range(weeks)]\n+    for uid, history in histories.items():\n+        future = relevant_groups.get(uid)\n+        if future is None:\n+            continue\n+        discovery = future.first_any.to_numpy(dtype=np.int64)\n+        strong = future.first_strong.to_numpy(dtype=np.int64)\n+        liked = future.first_organic_like.to_numpy(dtype=np.int64)\n+        future_items = future.item.to_numpy(dtype=np.int32)\n+        for cutoff in cutoffs:\n+            history_count = int(np.searchsorted(history.times, cutoff, side=\"left\"))\n+            if history_count < 20:\n+                continue\n+            window_start = cutoff + GAP\n+            window_end = window_start + WEEK\n+            novel = discovery >= cutoff\n+            strong_hit = (strong >= window_start) & (strong < window_end)\n+            like_hit = (liked >= window_start) & (liked < window_end)\n+            hit = novel & (strong_hit | like_hit)\n+            if not np.any(hit):\n+                continue\n+            labels = future_items[hit]\n+            grades = np.where(like_hit[hit], 3, 1).astype(np.int8)\n+            # Pair aggregation already guarantees one row per candidate item.\n+            profiles.append(Profile(uid, int(cutoff), labels, grades))\n+    label_count = sum(len(profile.labels) for profile in profiles)\n+    like_count = sum(int(np.sum(profile.grades == 3)) for profile in profiles)\n+    print(\n+        f\"built {len(profiles):,} rolling profiles with {label_count:,} labels \"\n+        f\"({like_count:,} grade-3)\",\n+        flush=True,\n+    )\n+    return histories, profiles\n+\n+\n+def train_model(\n+    model: TwoTower,\n+    dataset: TemporalDataset,\n+    catalog: pd.DataFrame,\n+    epochs: int,\n+    batch_size: int,\n+    seed: int,\n+) -> None:\n+    torch.set_num_threads(min(8, torch.get_num_threads()))\n+    loader = DataLoader(\n+        dataset,\n+        batch_size=batch_size,\n+        shuffle=True,\n+        num_workers=0,\n+        drop_last=True,\n+    )\n+    optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n+    # Half uniform negatives and half popularity-distributed negatives balance\n+    # tail discrimination with hard, frequently eligible candidates.\n+    popularity = np.sqrt(catalog.positive_listens.to_numpy(dtype=np.float64) + 1.0)\n+    popularity /= popularity.sum()\n+    rng = np.random.default_rng(seed + 11)\n+    temperature = 0.075\n+    negative_count = 36\n+    model.train()\n+    for epoch in range(epochs):\n+        began = time.time()\n+        running = 0.0\n+        batches = 0\n+        for history_items, history_weights, activity, positive in loader:\n+            history_items = history_items.long()\n+            history_weights = history_weights.float()\n+            activity = activity.float()\n+            positive = positive.long()\n+            batch = len(positive)\n+            uniform = rng.integers(\n+                0, len(catalog), size=(batch, negative_count // 2), dtype=np.int64\n+            )\n+            popular = rng.choice(\n+                len(catalog),\n+                size=(batch, negative_count - negative_count // 2),\n+                replace=True,\n+                p=popularity,\n+            )\n+            negatives = torch.from_numpy(np.concatenate([uniform, popular], axis=1)).long()\n+\n+            user_vector = model.user_tower(history_items, history_weights, activity)\n+            positive_vector = model.item_tower(positive)\n+            negative_vector = model.item_tower(negatives)\n+            positive_score = (user_vector * positive_vector).sum(dim=1, keepdim=True)\n+            negative_score = torch.einsum(\"bd,bkd->bk\", user_vector, negative_vector)\n+            pairwise = F.softplus((negative_score - positive_score) / temperature).mean()\n+            # In-batch targets add many inexpensive, behaviorally sampled hard negatives.\n+            in_batch = F.cross_entropy((user_vector @ positive_vector.T) / temperature, torch.arange(batch))\n+            loss = pairwise + 0.15 * in_batch\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+            running += float(loss.detach())\n+            batches += 1\n+        print(\n+            f\"epoch {epoch + 1}/{epochs}: loss={running / max(1, batches):.4f} \"\n+            f\"batches={batches} elapsed={time.time() - began:.1f}s\",\n+            flush=True,\n+        )\n+\n+\n+def encode_inference_users(\n+    model: TwoTower,\n+    histories: dict[int, UserHistory],\n+    target_uids: np.ndarray,\n+    cutoff: int,\n+    max_history: int,\n+) -> torch.Tensor:\n+    # Reuse exactly the request-time featurizer used by training examples.\n+    helper = TemporalDataset([], histories, max_history, seed=0)\n+    all_vectors = []\n+    model.eval()\n+    with torch.inference_mode():\n+        for start in range(0, len(target_uids), 512):\n+            batch_uids = target_uids[start : start + 512]\n+            item_rows, weight_rows, activity_rows = [], [], []\n+            for raw_uid in batch_uids:\n+                uid = int(raw_uid)\n+                if uid not in histories:\n+                    item_rows.append(np.full(max_history, -1, dtype=np.int64))\n+                    weight_rows.append(np.zeros((max_history, 3), dtype=np.float32))\n+                    activity_rows.append(np.float32(0.0))\n+                else:\n+                    items, weights, activity = helper.encode_history(uid, cutoff)\n+                    item_rows.append(items)\n+                    weight_rows.append(weights)\n+                    activity_rows.append(activity)\n+            vectors = model.user_tower(\n+                torch.from_numpy(np.stack(item_rows)).long(),\n+                torch.from_numpy(np.stack(weight_rows)).float(),\n+                torch.as_tensor(activity_rows, dtype=torch.float32),\n+            )\n+            all_vectors.append(vectors.cpu())\n+    return torch.cat(all_vectors)\n+\n+\n+def write_rankings(\n+    model: TwoTower,\n+    histories: dict[int, UserHistory],\n+    eligibility_name: str,\n+    cutoff: int,\n+    max_history: int,\n+    variants: dict[str, float],\n+    catalog: pd.DataFrame,\n+) -> None:\n+    with np.load(DATA / eligibility_name, allow_pickle=False) as artifact:\n+        uids = artifact[\"uids\"].astype(np.int64, copy=True)\n+        eligibility_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n+        packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n+    model_items = catalog.item_id.to_numpy(dtype=np.int64)\n+    if not np.array_equal(model_items, eligibility_items):\n+        raise ValueError(\"candidate catalog and eligibility item order differ\")\n+\n+    user_vectors = encode_inference_users(model, histories, uids, cutoff, max_history)\n+    model.eval()\n+    with torch.inference_mode():\n+        item_vectors = []\n+        for start in range(0, len(catalog), 2048):\n+            indices = torch.arange(start, min(start + 2048, len(catalog)))\n+            item_vectors.append(model.item_tower(indices).cpu())\n+        item_vectors = torch.cat(item_vectors)\n+\n+    # Bounded zero-mean organic popularity.  Concatenating this scalar to each\n+    # tower makes every blended score an exact augmented-vector dot product.\n+    pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n+    pop = (pop - pop.mean()) / (pop.std() + 1e-6)\n+    output_rows: dict[str, list[tuple[int, int, int]]] = {name: [] for name in variants}\n+    for start in range(0, len(uids), 96):\n+        stop = min(start + 96, len(uids))\n+        cosine = (user_vectors[start:stop] @ item_vectors.T).numpy()\n+        masks = np.unpackbits(\n+            packed[start:stop], axis=1, count=len(catalog), bitorder=\"little\"\n+        ).astype(bool)\n+        for name, alpha in variants.items():\n+            score = cosine + float(alpha) * pop[None, :]\n+            score[~masks] = -np.inf\n+            # argpartition followed by a stable local sort avoids sorting 30K\n+            # candidates for every request while preserving exact top-100 order.\n+            top = np.argpartition(score, -100, axis=1)[:, -100:]\n+            top_scores = np.take_along_axis(score, top, axis=1)\n+            order = np.argsort(-top_scores, axis=1, kind=\"stable\")\n+            top = np.take_along_axis(top, order, axis=1)\n+            for local_row, columns in enumerate(top):\n+                uid = int(uids[start + local_row])\n+                output_rows[name].extend(\n+                    (uid, int(model_items[column]), rank)\n+                    for rank, column in enumerate(columns, 1)\n+                )\n+    for name, rows in output_rows.items():\n+        pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n+            OUT / name, index=False\n+        )\n+        print(f\"wrote {OUT / name} ({len(rows):,} rows)\", flush=True)\n+\n+\n+def parse_variants(stage: str, raw: str) -> dict[str, float]:\n+    alphas = [float(value) for value in raw.split(\",\")]\n+    if stage == \"final\":\n+        if len(alphas) != 1:\n+            raise ValueError(\"final stage accepts exactly one selected blend\")\n+        return {\"submission.parquet\": alphas[0]}\n+    return {\n+        f\"validation_a{alpha:+.3f}.parquet\": alpha\n+        for alpha in alphas\n+    }\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n+    parser.add_argument(\"--epochs\", type=int, default=5)\n+    parser.add_argument(\"--batch-size\", type=int, default=256)\n+    parser.add_argument(\"--weeks\", type=int, default=18)\n+    parser.add_argument(\"--max-history\", type=int, default=256)\n+    parser.add_argument(\"--seed\", type=int, default=20260925)\n+    parser.add_argument(\n+        \"--pop-blends\",\n+        default=\"0.0,0.01,0.02,0.04,0.07,0.1\" ,\n+        help=\"comma-separated popularity coordinates (one value for final)\",\n+    )\n+    args = parser.parse_args()\n+    seed_everything(args.seed)\n+    final = args.stage == \"final\"\n+    end_time = FINAL_END if final else TRAIN_END\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    pairs = _aggregate_pair_events(final, catalog)\n+    histories, profiles = build_histories_and_profiles(pairs, end_time, args.weeks)\n+    del pairs\n+    if not profiles:\n+        raise RuntimeError(\"no temporal training profiles were generated\")\n+    dataset = TemporalDataset(profiles, histories, args.max_history, args.seed)\n+    model = TwoTower(build_item_features(catalog))\n+    train_model(model, dataset, catalog, args.epochs, args.batch_size, args.seed)\n+    checkpoint = OUT / f\"two_tower_{args.stage}.pt\"\n+    torch.save(\n+        {\n+            \"model\": model.state_dict(),\n+            \"stage\": args.stage,\n+            \"max_history\": args.max_history,\n+            \"epochs\": args.epochs,\n+            \"seed\": args.seed,\n+        },\n+        checkpoint,\n+    )\n+    metadata = {\n+        \"stage\": args.stage,\n+        \"profiles\": len(profiles),\n+        \"epochs\": args.epochs,\n+        \"max_history\": args.max_history,\n+        \"checkpoint\": str(checkpoint),\n+    }\n+    (OUT / f\"run_{args.stage}.json\").write_text(json.dumps(metadata, indent=2) + \"\\n\")\n+    eligibility = (\n+        \"test_eligible_candidates.npz\" if final else \"validation_eligible_candidates.npz\"\n+    )\n+    write_rankings(\n+        model,\n+        histories,\n+        eligibility,\n+        end_time,\n+        args.max_history,\n+        parse_variants(args.stage, args.pop_blends),\n+        catalog,\n+    )\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +10:05

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-99f66c6a-f1d3-4fbd-8e34-b387a4756aa9",
    "changes": {
      "/app/solution/ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a history-only PyTorch two-tower retrieval ranker.\n\nThe implementation deliberately operates on event/pair tables and sampled\ntraining examples.  It never constructs or factorizes a global user-item\nmatrix.  Candidate vectors are produced once by :class:`ItemTower`; a request\nrequires one :class:`UserTower` call followed by inner-product retrieval.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom dataclasses import 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\nfrom torch.utils.data import DataLoader, Dataset\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_END = 24_786_800\nFINAL_END = 25_393_400\nGAP = 1_800\nWEEK = 7 * 24 * 60 * 60\nINF_TIME = np.iinfo(np.uint32).max\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef read_events(stem: str, final: bool, columns: list[str]) -> pd.DataFrame:\n    frames = [pd.read_parquet(DATA / f\"train_{stem}.parquet\", columns=columns)]\n    if final:\n        frames.append(pd.read_parquet(DATA / f\"val_{stem}.parquet\", columns=columns))\n    return pd.concat(frames, ignore_index=True) if len(frames) > 1 else frames[0]\n\n\n@dataclass\nclass UserHistory:\n    \"\"\"Sorted, unique positive catalog interactions for one user.\"\"\"\n\n    items: np.ndarray\n    times: np.ndarray\n    strong_times: np.ndarray\n\n\n@dataclass\nclass Profile:\n    \"\"\"One cutoff-specific user profile and its future relevant items.\"\"\"\n\n    uid: int\n    cutoff: int\n    labels: np.ndarray\n    grades: np.ndarray\n\n\nclass TemporalDataset(Dataset):\n    \"\"\"Rolling temporal examples generated from per-user event streams.\"\"\"\n\n    def __init__(\n        self,\n        profiles: list[Profile],\n        histories: dict[int, UserHistory],\n        max_history: int,\n        seed: int,\n    ) -> None:\n        self.profiles = profiles\n        self.histories = histories\n        self.max_history = max_history\n        self.rng = np.random.default_rng(seed)\n\n    def __len__(self) -> int:\n        return len(self.profiles)\n\n    def encode_history(self, uid: int, cutoff: int) -> tuple[np.ndarray, np.ndarray, np.float32]:\n        history = self.histories[uid]\n        end = int(np.searchsorted(history.times, cutoff, side=\"left\"))\n        start = max(0, end - self.max_history)\n        items = history.items[start:end]\n        times = history.times[start:end]\n        strong = history.strong_times[start:end] < cutoff\n\n        padded = np.full(self.max_history, -1, dtype=np.int64)\n        weights = np.zeros((self.max_history, 3), dtype=np.float32)\n        n = len(items)\n        if n:\n            padded[-n:] = items\n            age = np.maximum(0.0, (float(cutoff) - times.astype(np.float64)) / 86_400.0)\n            weights[-n:, 0] = 1.0\n            weights[-n:, 1] = np.exp(-age / 30.0).astype(np.float32)\n            weights[-n:, 2] = strong.astype(np.float32)\n        activity = np.float32(math.log1p(end) / 8.0)\n        return padded, weights, activity\n\n    def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, np.float32, np.int64]:\n        profile = self.profiles[index]\n        # Likes have seven times the DCG gain, but using the full 7x weight made\n        # the much rarer label too dominant.  A sqrt(gain)-like 3x sampler is a\n        # stable compromise for retrieval training.\n        probabilities = np.where(profile.grades == 3, 3.0, 1.0)\n        probabilities /= probabilities.sum()\n        selected = int(self.rng.choice(len(profile.labels), p=probabilities))\n        items, weights, activity = self.encode_history(profile.uid, profile.cutoff)\n        return items, weights, activity, np.int64(profile.labels[selected])\n\n\nclass UserTower(nn.Module):\n    \"\"\"DeepSets-style history encoder; it contains no user-ID parameters.\"\"\"\n\n    def __init__(self, item_count: int, token_dim: int, output_dim: int) -> None:\n        super().__init__()\n        self.item_count = item_count\n        self.history_embedding = nn.Embedding(\n            item_count + 1, token_dim, padding_idx=item_count\n        )\n        self.network = nn.Sequential(\n            nn.Linear(3 * token_dim + 1, 192),\n            nn.GELU(),\n            nn.LayerNorm(192),\n            nn.Linear(192, output_dim),\n        )\n\n    def forward(\n        self, history_items: torch.Tensor, history_weights: torch.Tensor, activity: torch.Tensor\n    ) -> torch.Tensor:\n        safe_items = history_items.masked_fill(history_items < 0, self.item_count)\n        tokens = self.history_embedding(safe_items)\n        # Independently normalized long-term, recent, and strong-organic pools.\n        numerator = torch.einsum(\"blh,blc->bch\", tokens, history_weights)\n        denominator = history_weights.sum(dim=1).clamp_min(1.0).unsqueeze(-1)\n        pooled = (numerator / denominator).flatten(1)\n        vector = self.network(torch.cat([pooled, activity[:, None]], dim=1))\n        return F.normalize(vector, dim=1)\n\n\nclass ItemTower(nn.Module):\n    \"\"\"Offline candidate encoder using item identity and popularity features.\"\"\"\n\n    def __init__(\n        self, item_features: torch.Tensor, token_dim: int, output_dim: int\n    ) -> None:\n        super().__init__()\n        self.item_embedding = nn.Embedding(len(item_features), token_dim)\n        self.register_buffer(\"item_features\", item_features)\n        self.network = nn.Sequential(\n            nn.Linear(token_dim + item_features.shape[1], 160),\n            nn.GELU(),\n            nn.LayerNorm(160),\n            nn.Linear(160, output_dim),\n        )\n\n    def forward(self, item_indices: torch.Tensor) -> torch.Tensor:\n        identity = self.item_embedding(item_indices)\n        features = self.item_features[item_indices]\n        vector = self.network(torch.cat([identity, features], dim=-1))\n        return F.normalize(vector, dim=-1)\n\n\nclass TwoTower(nn.Module):\n    \"\"\"Exact dot-product retrieval model with independently callable towers.\"\"\"\n\n    def __init__(\n        self, item_features: torch.Tensor, token_dim: int = 64, output_dim: int = 96\n    ) -> None:\n        super().__init__()\n        self.user_tower = UserTower(len(item_features), token_dim, output_dim)\n        self.item_tower = ItemTower(item_features, token_dim, output_dim)\n\n    def score(\n        self,\n        history_items: torch.Tensor,\n        history_weights: torch.Tensor,\n        activity: torch.Tensor,\n        item_indices: torch.Tensor,\n    ) -> torch.Tensor:\n        return (\n            self.user_tower(history_items, history_weights, activity)\n            * self.item_tower(item_indices)\n        ).sum(dim=-1)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> torch.Tensor:\n    total = np.log1p(catalog.positive_listens.to_numpy(dtype=np.float32))\n    organic = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n    share = catalog.organic_positive_listens.to_numpy(dtype=np.float32) / np.maximum(\n        1.0, catalog.positive_listens.to_numpy(dtype=np.float32)\n    )\n    rank = -np.log1p(catalog.popularity_rank.to_numpy(dtype=np.float32))\n    features = np.column_stack([total, organic, share, rank]).astype(np.float32)\n    features = (features - features.mean(axis=0)) / (features.std(axis=0) + 1e-6)\n    return torch.from_numpy(features)\n\n\ndef _aggregate_pair_events(final: bool, catalog: pd.DataFrame) -> pd.DataFrame:\n    \"\"\"Aggregate event streams into an event-scalable pair table.\"\"\"\n    started = time.time()\n    catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n    dense_item_map = np.full(int(catalog_ids.max()) + 1, -1, dtype=np.int32)\n    dense_item_map[catalog_ids] = np.arange(len(catalog_ids), dtype=np.int32)\n\n    listens = read_events(\n        \"listens\", final, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n    )\n    raw_ids = listens.item_id.to_numpy(dtype=np.int64)\n    in_range = raw_ids < len(dense_item_map)\n    mapped = np.full(len(raw_ids), -1, dtype=np.int32)\n    mapped[in_range] = dense_item_map[raw_ids[in_range]]\n    keep = mapped >= 0\n    listens = listens.loc[keep, [\"uid\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]].copy()\n    listens[\"item\"] = mapped[keep]\n    timestamp = listens.timestamp.to_numpy(dtype=np.uint32)\n    positive = listens.played_ratio_pct.to_numpy() >= 50\n    organic_strong = (listens.is_organic.to_numpy() == 1) & (\n        listens.played_ratio_pct.to_numpy() >= 80\n    )\n    listens[\"positive_time\"] = np.where(positive, timestamp, INF_TIME).astype(np.uint32)\n    listens[\"strong_time\"] = np.where(organic_strong, timestamp, INF_TIME).astype(np.uint32)\n    pairs = (\n        listens.groupby([\"uid\", \"item\"], sort=False, observed=True)\n        .agg(\n            first_any=(\"timestamp\", \"min\"),\n            first_positive=(\"positive_time\", \"min\"),\n            first_strong=(\"strong_time\", \"min\"),\n        )\n        .reset_index()\n    )\n    del listens, mapped, raw_ids\n\n    likes = read_events(\"likes\", final, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n    like_ids = likes.item_id.to_numpy(dtype=np.int64)\n    in_range = like_ids < len(dense_item_map)\n    like_item = np.full(len(like_ids), -1, dtype=np.int32)\n    like_item[in_range] = dense_item_map[like_ids[in_range]]\n    keep = like_item >= 0\n    likes = likes.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n    likes[\"item\"] = like_item[keep]\n    like_ts = likes.timestamp.to_numpy(dtype=np.uint32)\n    likes[\"organic_like_time\"] = np.where(\n        likes.is_organic.to_numpy() == 1, like_ts, INF_TIME\n    ).astype(np.uint32)\n    like_pairs = (\n        likes.groupby([\"uid\", \"item\"], sort=False, observed=True)\n        .agg(first_like=(\"timestamp\", \"min\"), first_organic_like=(\"organic_like_time\", \"min\"))\n        .reset_index()\n    )\n    del likes, like_item, like_ids\n    pairs = pairs.merge(like_pairs, on=[\"uid\", \"item\"], how=\"outer\", sort=False)\n    for column in [\"first_any\", \"first_positive\", \"first_strong\", \"first_like\", \"first_organic_like\"]:\n        pairs[column] = pairs[column].fillna(INF_TIME).astype(np.uint32)\n    pairs[\"first_any\"] = np.minimum(pairs.first_any, pairs.first_like).astype(np.uint32)\n    pairs[\"first_positive\"] = np.minimum(pairs.first_positive, pairs.first_like).astype(np.uint32)\n    # A like is a strong positive input even when it came from a recommendation.\n    pairs[\"strong_context\"] = np.minimum(pairs.first_strong, pairs.first_like).astype(np.uint32)\n    print(\n        f\"aggregated {len(pairs):,} catalog user-item pairs in {time.time() - started:.1f}s\",\n        flush=True,\n    )\n    return pairs\n\n\ndef build_histories_and_profiles(\n    pairs: pd.DataFrame, end_time: int, weeks: int\n) -> tuple[dict[int, UserHistory], list[Profile]]:\n    histories: dict[int, UserHistory] = {}\n    context = pairs[pairs.first_positive < INF_TIME].sort_values(\n        [\"uid\", \"first_positive\"], kind=\"stable\"\n    )\n    for raw_uid, frame in context.groupby(\"uid\", sort=False):\n        histories[int(raw_uid)] = UserHistory(\n            frame.item.to_numpy(dtype=np.int32),\n            frame.first_positive.to_numpy(dtype=np.int64),\n            frame.strong_context.to_numpy(dtype=np.int64),\n        )\n\n    relevant = pairs[\n        (pairs.first_strong < INF_TIME) | (pairs.first_organic_like < INF_TIME)\n    ][[\"uid\", \"item\", \"first_any\", \"first_strong\", \"first_organic_like\"]]\n    relevant_groups = {int(uid): frame for uid, frame in relevant.groupby(\"uid\", sort=False)}\n    profiles: list[Profile] = []\n    latest_cutoff = end_time - GAP - WEEK\n    cutoffs = [latest_cutoff - offset * WEEK for offset in range(weeks)]\n    for uid, history in histories.items():\n        future = relevant_groups.get(uid)\n        if future is None:\n            continue\n        discovery = future.first_any.to_numpy(dtype=np.int64)\n        strong = future.first_strong.to_numpy(dtype=np.int64)\n        liked = future.first_organic_like.to_numpy(dtype=np.int64)\n        future_items = future.item.to_numpy(dtype=np.int32)\n        for cutoff in cutoffs:\n            history_count = int(np.searchsorted(history.times, cutoff, side=\"left\"))\n            if history_count < 20:\n                continue\n            window_start = cutoff + GAP\n            window_end = window_start + WEEK\n            novel = discovery >= cutoff\n            strong_hit = (strong >= window_start) & (strong < window_end)\n            like_hit = (liked >= window_start) & (liked < window_end)\n            hit = novel & (strong_hit | like_hit)\n            if not np.any(hit):\n                continue\n            labels = future_items[hit]\n            grades = np.where(like_hit[hit], 3, 1).astype(np.int8)\n            # Pair aggregation already guarantees one row per candidate item.\n            profiles.append(Profile(uid, int(cutoff), labels, grades))\n    label_count = sum(len(profile.labels) for profile in profiles)\n    like_count = sum(int(np.sum(profile.grades == 3)) for profile in profiles)\n    print(\n        f\"built {len(profiles):,} rolling profiles with {label_count:,} labels \"\n        f\"({like_count:,} grade-3)\",\n        flush=True,\n    )\n    return histories, profiles\n\n\ndef train_model(\n    model: TwoTower,\n    dataset: TemporalDataset,\n    catalog: pd.DataFrame,\n    epochs: int,\n    batch_size: int,\n    seed: int,\n) -> None:\n    torch.set_num_threads(min(8, torch.get_num_threads()))\n    loader = DataLoader(\n        dataset,\n        batch_size=batch_size,\n        shuffle=True,\n        num_workers=0,\n        drop_last=True,\n    )\n    optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n    # Half uniform negatives and half popularity-distributed negatives balance\n    # tail discrimination with hard, frequently eligible candidates.\n    popularity = np.sqrt(catalog.positive_listens.to_numpy(dtype=np.float64) + 1.0)\n    popularity /= popularity.sum()\n    rng = np.random.default_rng(seed + 11)\n    temperature = 0.075\n    negative_count = 36\n    model.train()\n    for epoch in range(epochs):\n        began = time.time()\n        running = 0.0\n        batches = 0\n        for history_items, history_weights, activity, positive in loader:\n            history_items = history_items.long()\n            history_weights = history_weights.float()\n            activity = activity.float()\n            positive = positive.long()\n            batch = len(positive)\n            uniform = rng.integers(\n                0, len(catalog), size=(batch, negative_count // 2), dtype=np.int64\n            )\n            popular = rng.choice(\n                len(catalog),\n                size=(batch, negative_count - negative_count // 2),\n                replace=True,\n                p=popularity,\n            )\n            negatives = torch.from_numpy(np.concatenate([uniform, popular], axis=1)).long()\n\n            user_vector = model.user_tower(history_items, history_weights, activity)\n            positive_vector = model.item_tower(positive)\n            negative_vector = model.item_tower(negatives)\n            positive_score = (user_vector * positive_vector).sum(dim=1, keepdim=True)\n            negative_score = torch.einsum(\"bd,bkd->bk\", user_vector, negative_vector)\n            pairwise = F.softplus((negative_score - positive_score) / temperature).mean()\n            # In-batch targets add many inexpensive, behaviorally sampled hard negatives.\n            in_batch = F.cross_entropy((user_vector @ positive_vector.T) / temperature, torch.arange(batch))\n            loss = pairwise + 0.15 * in_batch\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            running += float(loss.detach())\n            batches += 1\n        print(\n            f\"epoch {epoch + 1}/{epochs}: loss={running / max(1, batches):.4f} \"\n            f\"batches={batches} elapsed={time.time() - began:.1f}s\",\n            flush=True,\n        )\n\n\ndef encode_inference_users(\n    model: TwoTower,\n    histories: dict[int, UserHistory],\n    target_uids: np.ndarray,\n    cutoff: int,\n    max_history: int,\n) -> torch.Tensor:\n    # Reuse exactly the request-time featurizer used by training examples.\n    helper = TemporalDataset([], histories, max_history, seed=0)\n    all_vectors = []\n    model.eval()\n    with torch.inference_mode():\n        for start in range(0, len(target_uids), 512):\n            batch_uids = target_uids[start : start + 512]\n            item_rows, weight_rows, activity_rows = [], [], []\n            for raw_uid in batch_uids:\n                uid = int(raw_uid)\n                if uid not in histories:\n                    item_rows.append(np.full(max_history, -1, dtype=np.int64))\n                    weight_rows.append(np.zeros((max_history, 3), dtype=np.float32))\n                    activity_rows.append(np.float32(0.0))\n                else:\n                    items, weights, activity = helper.encode_history(uid, cutoff)\n                    item_rows.append(items)\n                    weight_rows.append(weights)\n                    activity_rows.append(activity)\n            vectors = model.user_tower(\n                torch.from_numpy(np.stack(item_rows)).long(),\n                torch.from_numpy(np.stack(weight_rows)).float(),\n                torch.as_tensor(activity_rows, dtype=torch.float32),\n            )\n            all_vectors.append(vectors.cpu())\n    return torch.cat(all_vectors)\n\n\ndef write_rankings(\n    model: TwoTower,\n    histories: dict[int, UserHistory],\n    eligibility_name: str,\n    cutoff: int,\n    max_history: int,\n    variants: dict[str, float],\n    catalog: pd.DataFrame,\n) -> None:\n    with np.load(DATA / eligibility_name, allow_pickle=False) as artifact:\n        uids = artifact[\"uids\"].astype(np.int64, copy=True)\n        eligibility_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n        packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n    model_items = catalog.item_id.to_numpy(dtype=np.int64)\n    if not np.array_equal(model_items, eligibility_items):\n        raise ValueError(\"candidate catalog and eligibility item order differ\")\n\n    user_vectors = encode_inference_users(model, histories, uids, cutoff, max_history)\n    model.eval()\n    with torch.inference_mode():\n        item_vectors = []\n        for start in range(0, len(catalog), 2048):\n            indices = torch.arange(start, min(start + 2048, len(catalog)))\n            item_vectors.append(model.item_tower(indices).cpu())\n        item_vectors = torch.cat(item_vectors)\n\n    # Bounded zero-mean organic popularity.  Concatenating this scalar to each\n    # tower makes every blended score an exact augmented-vector dot product.\n    pop = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float32))\n    pop = (pop - pop.mean()) / (pop.std() + 1e-6)\n    output_rows: dict[str, list[tuple[int, int, int]]] = {name: [] for name in variants}\n    for start in range(0, len(uids), 96):\n        stop = min(start + 96, len(uids))\n        cosine = (user_vectors[start:stop] @ item_vectors.T).numpy()\n        masks = np.unpackbits(\n            packed[start:stop], axis=1, count=len(catalog), bitorder=\"little\"\n        ).astype(bool)\n        for name, alpha in variants.items():\n            score = cosine + float(alpha) * pop[None, :]\n            score[~masks] = -np.inf\n            # argpartition followed by a stable local sort avoids sorting 30K\n            # candidates for every request while preserving exact top-100 order.\n            top = np.argpartition(score, -100, axis=1)[:, -100:]\n            top_scores = np.take_along_axis(score, top, axis=1)\n            order = np.argsort(-top_scores, axis=1, kind=\"stable\")\n            top = np.take_along_axis(top, order, axis=1)\n            for local_row, columns in enumerate(top):\n                uid = int(uids[start + local_row])\n                output_rows[name].extend(\n                    (uid, int(model_items[column]), rank)\n                    for rank, column in enumerate(columns, 1)\n                )\n    for name, rows in output_rows.items():\n        pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n            OUT / name, index=False\n        )\n        print(f\"wrote {OUT / name} ({len(rows):,} rows)\", flush=True)\n\n\ndef parse_variants(stage: str, raw: str) -> dict[str, float]:\n    alphas = [float(value) for value in raw.split(\",\")]\n    if stage == \"final\":\n        if len(alphas) != 1:\n            raise ValueError(\"final stage accepts exactly one selected blend\")\n        return {\"submission.parquet\": alphas[0]}\n    return {\n        f\"validation_a{alpha:+.3f}.parquet\": alpha\n        for alpha in alphas\n    }\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n    parser.add_argument(\"--epochs\", type=int, default=5)\n    parser.add_argument(\"--batch-size\", type=int, default=256)\n    parser.add_argument(\"--weeks\", type=int, default=18)\n    parser.add_argument(\"--max-history\", type=int, default=256)\n    parser.add_argument(\"--seed\", type=int, default=20260925)\n    parser.add_argument(\n        \"--pop-blends\",\n        default=\"0.0,0.01,0.02,0.04,0.07,0.1\" ,\n        help=\"comma-separated popularity coordinates (one value for final)\",\n    )\n    args = parser.parse_args()\n    seed_everything(args.seed)\n    final = args.stage == \"final\"\n    end_time = FINAL_END if final else TRAIN_END\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    pairs = _aggregate_pair_events(final, catalog)\n    histories, profiles = build_histories_and_profiles(pairs, end_time, args.weeks)\n    del pairs\n    if not profiles:\n        raise RuntimeError(\"no temporal training profiles were generated\")\n    dataset = TemporalDataset(profiles, histories, args.max_history, args.seed)\n    model = TwoTower(build_item_features(catalog))\n    train_model(model, dataset, catalog, args.epochs, args.batch_size, args.seed)\n    checkpoint = OUT / f\"two_tower_{args.stage}.pt\"\n    torch.save(\n        {\n            \"model\": model.state_dict(),\n            \"stage\": args.stage,\n            \"max_history\": args.max_history,\n            \"epochs\": args.epochs,\n            \"seed\": args.seed,\n        },\n        checkpoint,\n    )\n    metadata = {\n        \"stage\": args.stage,\n        \"profiles\": len(profiles),\n        \"epochs\": args.epochs,\n        \"max_history\": args.max_history,\n        \"checkpoint\": str(checkpoint),\n    }\n    (OUT / f\"run_{args.stage}.json\").write_text(json.dumps(metadata, indent=2) + \"\\n\")\n    eligibility = (\n        \"test_eligible_candidates.npz\" if final else \"validation_eligible_candidates.npz\"\n    )\n    write_rankings(\n        model,\n        histories,\n        eligibility,\n        end_time,\n        args.max_history,\n        parse_variants(args.stage, args.pop_blends),\n        catalog,\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/ranker.py\n",
    "stderr": ""
  }
}

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    "cached_input_tokens": 420096,
    "cache_write_input_tokens": 0,
    "output_tokens": 28275,
    "reasoning_output_tokens": 14274,
    "total_tokens": 500456
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}

exec command

+10:09

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +10:12

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/ranker.py\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 4 --weeks 18 --max-history 256 --pop-blends=0,0.01,0.02,0.04,0.07,0.1","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +10:42

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"42d541","wall_time_seconds":30.001649533,"session_id":60342,"original_token_count":30,"output":"aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 67,158 rolling profiles with 566,206 labels (93,904 grade-3)\n"}

Harness context token usage record

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}

exec command

+10:43

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 run produced 67,158 cutoff-specific profiles and 566,206 novel organic labels, including 93,904 like-grade labels. Training is underway; the fallback submission remains intact until a complete test-cutoff ranker replaces it.

Tool callexecTool call Returned

Tool call · Input +10:45

const r = await tools.write_stdin({"session_id":60342,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +11:15

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e91c22","wall_time_seconds":30.00156273,"session_id":60342,"original_token_count":25,"output":"epoch 1/4: loss=1.4550 batches=262 elapsed=14.1s\nepoch 2/4: loss=1.3671 batches=262 elapsed=14.0s\n"}

Harness context token usage record

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exec command

+11:16

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +11:17

const r = await tools.write_stdin({"session_id":60342,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +11:41

Script completed
Wall time 24.1 seconds
Output:

{"chunk_id":"4a6473","wall_time_seconds":24.053645091,"exit_code":0,"original_token_count":118,"output":"epoch 3/4: loss=1.3171 batches=262 elapsed=14.1s\nepoch 4/4: loss=1.2791 batches=262 elapsed=13.8s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.010.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.070.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\n"}

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ed8e9278-bd42-4ead-9108-7969f24a7353",
    "process_id": "60342",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/ranker.py\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 4 --weeks 18 --max-history 256 --pop-blends=0,0.01,0.02,0.04,0.07,0.1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/ranker.py\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 4 --weeks 18 --max-history 256 --pop-blends=0,0.01,0.02,0.04,0.07,0.1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 67,158 rolling profiles with 566,206 labels (93,904 grade-3)\nepoch 1/4: loss=1.4550 batches=262 elapsed=14.1s\nepoch 2/4: loss=1.3671 batches=262 elapsed=14.0s\nepoch 3/4: loss=1.3171 batches=262 elapsed=14.1s\nepoch 4/4: loss=1.2791 batches=262 elapsed=13.8s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.010.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.070.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 67,158 rolling profiles with 566,206 labels (93,904 grade-3)\nepoch 1/4: loss=1.4550 batches=262 elapsed=14.1s\nepoch 2/4: loss=1.3671 batches=262 elapsed=14.0s\nepoch 3/4: loss=1.3171 batches=262 elapsed=14.1s\nepoch 4/4: loss=1.2791 batches=262 elapsed=13.8s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.010.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.070.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 88,
      "nanos": 921670275
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 67,158 rolling profiles with 566,206 labels (93,904 grade-3)\nepoch 1/4: loss=1.4550 batches=262 elapsed=14.1s\nepoch 2/4: loss=1.3671 batches=262 elapsed=14.0s\nepoch 3/4: loss=1.3171 batches=262 elapsed=14.1s\nepoch 4/4: loss=1.2791 batches=262 elapsed=13.8s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.010.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.070.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\n"
  }
}

exec command

+11:43

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 +11:44

const r = await tools.exec_command({"cmd":"for f in /app/solution/validation_a*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(JSON.stringify(r));

Tool response +12:04

Script completed
Wall time 20.2 seconds
Output:

{"chunk_id":"8caf37","wall_time_seconds":20.041767676,"exit_code":0,"original_token_count":204,"output":"{\n  \"model\": \"/app/solution/validation_a+0.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.008571,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.010.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012708,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014931,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016135,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.070.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01417,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012991,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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Harness context codex item completed

{
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    ],
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      }
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    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_a+0.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.008571,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.010.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012708,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014931,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016135,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.070.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01417,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012991,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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      "secs": 20,
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  }
}

Agent message

+12:14

Thinking unavailable No text recorded

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

Agent message

+12:24

Thinking unavailable No text recorded

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

exec command

+12:29

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 first model lifts NDCG@10 from 0.0106 (popularity) to 0.0161, with the optimum combining personalized cosine retrieval and a small popularity coordinate. The personalized component is still undertrained—the loss was falling sharply at epoch four—so I’m extending training capacity/history and will keep the 0.0161 ranking as the incumbent.

Tool callexecTool call Returned

Tool call · Input +12:34

const r = await tools.exec_command({"cmd":"cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_v1.pt\ncp /app/solution/validation_a+0.040.parquet /app/solution/validation_best.parquet\ncp /app/solution/run_validation.json /app/solution/run_validation_v1.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 10 --weeks 22 --max-history 512 --seed 20260926 --pop-blends=0,0.02,0.03,0.04,0.05,0.06,0.08","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +13:04

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"ea068f","wall_time_seconds":30.001003607,"session_id":34835,"original_token_count":31,"output":"aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\n"}

Harness context token usage record

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exec command

+13:05

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 +13:06

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +13:36

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"05edbe","wall_time_seconds":30.001015946,"session_id":34835,"original_token_count":25,"output":"epoch 1/10: loss=1.4423 batches=312 elapsed=19.5s\nepoch 2/10: loss=1.3618 batches=312 elapsed=19.4s\n"}

Harness context token usage record

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}

exec command

+13:38

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 +13:38

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +14:08

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"49b8fc","wall_time_seconds":30.001377912,"session_id":34835,"original_token_count":13,"output":"epoch 3/10: loss=1.3131 batches=312 elapsed=19.2s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
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  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c06e1d2487d29e0c3e654f270f7d",
  "usage": {
    "input_tokens": 62264,
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    "cache_write_input_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": {
    "input_tokens": 894744,
    "cached_input_tokens": 821376,
    "cache_write_input_tokens": 0,
    "output_tokens": 30608,
    "reasoning_output_tokens": 15873,
    "total_tokens": 925352
  }
}

exec command

+14:14

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 longer-history run uses 79,962 rolling profiles over 22 weeks. Loss continues to improve normally; I’ll score the full blend sweep after epoch ten and only promote it if it beats the saved 0.016135 incumbent.

Tool callexecTool call Returned

Tool call · Input +14:14

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +14:44

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c48ddb","wall_time_seconds":30.001908132,"session_id":34835,"original_token_count":25,"output":"epoch 4/10: loss=1.2769 batches=312 elapsed=19.3s\nepoch 5/10: loss=1.2303 batches=312 elapsed=19.4s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c08e2ea087d29f33099f285e9aa0",
  "usage": {
    "input_tokens": 62402,
    "cached_input_tokens": 62080,
    "cache_write_input_tokens": 0,
    "output_tokens": 109,
    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
    "input_tokens": 957146,
    "cached_input_tokens": 883456,
    "cache_write_input_tokens": 0,
    "output_tokens": 30717,
    "reasoning_output_tokens": 15879,
    "total_tokens": 987863
  },
  "thread_token_usage": {
    "input_tokens": 957146,
    "cached_input_tokens": 883456,
    "cache_write_input_tokens": 0,
    "output_tokens": 30717,
    "reasoning_output_tokens": 15879,
    "total_tokens": 987863
  }
}

exec command

+14:46

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +14:46

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +15:16

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"48bda8","wall_time_seconds":30.001521001,"session_id":34835,"original_token_count":25,"output":"epoch 6/10: loss=1.1867 batches=312 elapsed=19.3s\nepoch 7/10: loss=1.1456 batches=312 elapsed=19.2s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c0b1bfd087d28456e37f9488793a",
  "usage": {
    "input_tokens": 62612,
    "cached_input_tokens": 62208,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 62666
  },
  "turn_token_usage": {
    "input_tokens": 1019758,
    "cached_input_tokens": 945664,
    "cache_write_input_tokens": 0,
    "output_tokens": 30771,
    "reasoning_output_tokens": 15885,
    "total_tokens": 1050529
  },
  "thread_token_usage": {
    "input_tokens": 1019758,
    "cached_input_tokens": 945664,
    "cache_write_input_tokens": 0,
    "output_tokens": 30771,
    "reasoning_output_tokens": 15885,
    "total_tokens": 1050529
  }
}

exec command

+15:18

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 +15:18

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +15:48

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"1f5a34","wall_time_seconds":30.000913694,"session_id":34835,"original_token_count":13,"output":"epoch 8/10: loss=1.1082 batches=312 elapsed=19.2s\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c0d2092c87d2ac9d5ed33eafeaeb",
  "usage": {
    "input_tokens": 62768,
    "cached_input_tokens": 62464,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 62822
  },
  "turn_token_usage": {
    "input_tokens": 1082526,
    "cached_input_tokens": 1008128,
    "cache_write_input_tokens": 0,
    "output_tokens": 30825,
    "reasoning_output_tokens": 15891,
    "total_tokens": 1113351
  },
  "thread_token_usage": {
    "input_tokens": 1082526,
    "cached_input_tokens": 1008128,
    "cache_write_input_tokens": 0,
    "output_tokens": 30825,
    "reasoning_output_tokens": 15891,
    "total_tokens": 1113351
  }
}

exec command

+15:51

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 +15:51

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +16:21

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"14fe7d","wall_time_seconds":30.001214855,"session_id":34835,"original_token_count":134,"output":"epoch 9/10: loss=1.0766 batches=312 elapsed=19.2s\nepoch 10/10: loss=1.0419 batches=312 elapsed=19.2s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.030.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.050.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.080.parquet (845,600 rows)\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c0f24c5887d281750c597c9fdac7",
  "usage": {
    "input_tokens": 62902,
    "cached_input_tokens": 62592,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 62956
  },
  "turn_token_usage": {
    "input_tokens": 1145428,
    "cached_input_tokens": 1070720,
    "cache_write_input_tokens": 0,
    "output_tokens": 30879,
    "reasoning_output_tokens": 15897,
    "total_tokens": 1176307
  },
  "thread_token_usage": {
    "input_tokens": 1145428,
    "cached_input_tokens": 1070720,
    "cache_write_input_tokens": 0,
    "output_tokens": 30879,
    "reasoning_output_tokens": 15897,
    "total_tokens": 1176307
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-8c4efd69-1a71-4cae-86c9-525cedaaf8da",
    "process_id": "34835",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_v1.pt\ncp /app/solution/validation_a+0.040.parquet /app/solution/validation_best.parquet\ncp /app/solution/run_validation.json /app/solution/run_validation_v1.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 10 --weeks 22 --max-history 512 --seed 20260926 --pop-blends=0,0.02,0.03,0.04,0.05,0.06,0.08"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_v1.pt\ncp /app/solution/validation_a+0.040.parquet /app/solution/validation_best.parquet\ncp /app/solution/run_validation.json /app/solution/run_validation_v1.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 10 --weeks 22 --max-history 512 --seed 20260926 --pop-blends=0,0.02,0.03,0.04,0.05,0.06,0.08"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nepoch 1/10: loss=1.4423 batches=312 elapsed=19.5s\nepoch 2/10: loss=1.3618 batches=312 elapsed=19.4s\nepoch 3/10: loss=1.3131 batches=312 elapsed=19.2s\nepoch 4/10: loss=1.2769 batches=312 elapsed=19.3s\nepoch 5/10: loss=1.2303 batches=312 elapsed=19.4s\nepoch 6/10: loss=1.1867 batches=312 elapsed=19.3s\nepoch 7/10: loss=1.1456 batches=312 elapsed=19.2s\nepoch 8/10: loss=1.1082 batches=312 elapsed=19.2s\nepoch 9/10: loss=1.0766 batches=312 elapsed=19.2s\nepoch 10/10: loss=1.0419 batches=312 elapsed=19.2s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.030.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.050.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.080.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nepoch 1/10: loss=1.4423 batches=312 elapsed=19.5s\nepoch 2/10: loss=1.3618 batches=312 elapsed=19.4s\nepoch 3/10: loss=1.3131 batches=312 elapsed=19.2s\nepoch 4/10: loss=1.2769 batches=312 elapsed=19.3s\nepoch 5/10: loss=1.2303 batches=312 elapsed=19.4s\nepoch 6/10: loss=1.1867 batches=312 elapsed=19.3s\nepoch 7/10: loss=1.1456 batches=312 elapsed=19.2s\nepoch 8/10: loss=1.1082 batches=312 elapsed=19.2s\nepoch 9/10: loss=1.0766 batches=312 elapsed=19.2s\nepoch 10/10: loss=1.0419 batches=312 elapsed=19.2s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.030.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.050.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.080.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 227,
      "nanos": 662317174
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nepoch 1/10: loss=1.4423 batches=312 elapsed=19.5s\nepoch 2/10: loss=1.3618 batches=312 elapsed=19.4s\nepoch 3/10: loss=1.3131 batches=312 elapsed=19.2s\nepoch 4/10: loss=1.2769 batches=312 elapsed=19.3s\nepoch 5/10: loss=1.2303 batches=312 elapsed=19.4s\nepoch 6/10: loss=1.1867 batches=312 elapsed=19.3s\nepoch 7/10: loss=1.1456 batches=312 elapsed=19.2s\nepoch 8/10: loss=1.1082 batches=312 elapsed=19.2s\nepoch 9/10: loss=1.0766 batches=312 elapsed=19.2s\nepoch 10/10: loss=1.0419 batches=312 elapsed=19.2s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.020.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.030.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.050.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.080.parquet (845,600 rows)\n"
  }
}

exec command

+16:24

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 +16:25

const r = await tools.write_stdin({"session_id":34835,"chars":"","yield_time_ms":5000,"max_output_tokens":10000}); text(JSON.stringify(r));

Tool response +16:25

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"400e68","wall_time_seconds":0.00000152,"exit_code":0,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c11329a487d2958fa0da5efa9923",
  "usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 33,
    "total_tokens": 63287
  },
  "turn_token_usage": {
    "input_tokens": 1208634,
    "cached_input_tokens": 1133440,
    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 1208634,
    "cached_input_tokens": 1133440,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 15930,
    "total_tokens": 1239594
  }
}

exec command

+16:26

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +16:29

const r = await tools.exec_command({"cmd":"for f in /app/solution/validation_a+0.000.parquet /app/solution/validation_a+0.020.parquet /app/solution/validation_a+0.030.parquet /app/solution/validation_a+0.040.parquet /app/solution/validation_a+0.050.parquet /app/solution/validation_a+0.060.parquet /app/solution/validation_a+0.080.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +16:53

Script completed
Wall time 23.9 seconds
Output:

{"chunk_id":"5f2acc","wall_time_seconds":23.697087397,"exit_code":0,"original_token_count":238,"output":"{\n  \"model\": \"/app/solution/validation_a+0.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.009385,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012805,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.030.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015059,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016436,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.050.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017951,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018596,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.080.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019162,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

{
  "type": "token_usage_record",
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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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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-4cfd3327-c7e0-4004-ac48-652e8c698b9a",
    "process_id": "83227",
    "command": [
      "/bin/sh",
      "-lc",
      "for f in /app/solution/validation_a+0.000.parquet /app/solution/validation_a+0.020.parquet /app/solution/validation_a+0.030.parquet /app/solution/validation_a+0.040.parquet /app/solution/validation_a+0.050.parquet /app/solution/validation_a+0.060.parquet /app/solution/validation_a+0.080.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for f in /app/solution/validation_a+0.000.parquet /app/solution/validation_a+0.020.parquet /app/solution/validation_a+0.030.parquet /app/solution/validation_a+0.040.parquet /app/solution/validation_a+0.050.parquet /app/solution/validation_a+0.060.parquet /app/solution/validation_a+0.080.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_a+0.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.009385,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012805,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.030.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015059,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016436,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.050.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017951,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018596,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.080.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019162,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_a+0.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.009385,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012805,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.030.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015059,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016436,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.050.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017951,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018596,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.080.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019162,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 23,
      "nanos": 697083777
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_a+0.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.009385,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012805,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.030.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015059,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016436,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.050.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017951,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018596,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.080.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019162,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+16:58

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 +17:02

const patch = "*** Begin Patch\n*** Update File: /app/solution/ranker.py\n@@\n     parser.add_argument(\"--seed\", type=int, default=20260925)\n+    parser.add_argument(\n+        \"--load-checkpoint\",\n+        type=Path,\n+        help=\"load tower weights before optional additional training\",\n+    )\n@@\n     dataset = TemporalDataset(profiles, histories, args.max_history, args.seed)\n     model = TwoTower(build_item_features(catalog))\n-    train_model(model, dataset, catalog, args.epochs, args.batch_size, args.seed)\n+    if args.load_checkpoint is not None:\n+        saved = torch.load(args.load_checkpoint, map_location=\"cpu\", weights_only=True)\n+        model.load_state_dict(saved[\"model\"])\n+        print(f\"loaded {args.load_checkpoint}\", flush=True)\n+    if args.epochs:\n+        train_model(model, dataset, catalog, args.epochs, args.batch_size, args.seed)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +17:03

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-41f4d2cd-3763-434d-9008-d3f01a516d42",
    "changes": {
      "/app/solution/ranker.py": {
        "type": "update",
        "unified_diff": "@@ -510,2 +510,7 @@\n     parser.add_argument(\n+        \"--load-checkpoint\",\n+        type=Path,\n+        help=\"load tower weights before optional additional training\",\n+    )\n+    parser.add_argument(\n         \"--pop-blends\",\n@@ -526,3 +531,8 @@\n     model = TwoTower(build_item_features(catalog))\n-    train_model(model, dataset, catalog, args.epochs, args.batch_size, args.seed)\n+    if args.load_checkpoint is not None:\n+        saved = torch.load(args.load_checkpoint, map_location=\"cpu\", weights_only=True)\n+        model.load_state_dict(saved[\"model\"])\n+        print(f\"loaded {args.load_checkpoint}\", flush=True)\n+    if args.epochs:\n+        train_model(model, dataset, catalog, args.epochs, args.batch_size, args.seed)\n     checkpoint = OUT / f\"two_tower_{args.stage}.pt\"\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/ranker.py\n",
    "stderr": ""
  }
}

Harness context token usage record

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}

exec command

+17:04

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 extended model improves the incumbent to 0.019162. Its optimum has shifted toward more popularity weight, so I’m doing an inference-only fine sweep around that boundary before changing the training formulation again.

Tool callexecTool call Returned

Tool call · Input +17:08

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 0 --weeks 22 --max-history 512 --seed 20260926 --load-checkpoint /app/solution/two_tower_validation.pt --pop-blends=0.075,0.085,0.09,0.1,0.12,0.15,0.2","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +17:38

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"366014","wall_time_seconds":30.001546602,"session_id":54792,"original_token_count":88,"output":"aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation.pt\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.090.parquet (845,600 rows)\n"}

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",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 0 --weeks 22 --max-history 512 --seed 20260926 --load-checkpoint /app/solution/two_tower_validation.pt --pop-blends=0.075,0.085,0.09,0.1,0.12,0.15,0.2"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 0 --weeks 22 --max-history 512 --seed 20260926 --load-checkpoint /app/solution/two_tower_validation.pt --pop-blends=0.075,0.085,0.09,0.1,0.12,0.15,0.2"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation.pt\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.090.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.120.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.150.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.200.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation.pt\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.090.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.120.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.150.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.200.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 32,
      "nanos": 100917320
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation.pt\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.090.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.100.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.120.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.150.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.200.parquet (845,600 rows)\n"
  }
}

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 +17:42

const r = await tools.write_stdin({"session_id":54792,"chars":"","yield_time_ms":30000,"max_output_tokens":10000}); text(JSON.stringify(r));

Tool response +17:42

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"0d6ef0","wall_time_seconds":0.00000258,"exit_code":0,"original_token_count":62,"output":"wrote /app/solution/validation_a+0.100.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.120.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.150.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.200.parquet (845,600 rows)\n"}

Harness context token usage record

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exec command

+17:44

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 +17:47

const r = await tools.exec_command({"cmd":"for f in /app/solution/validation_a+0.075.parquet /app/solution/validation_a+0.085.parquet /app/solution/validation_a+0.090.parquet /app/solution/validation_a+0.100.parquet /app/solution/validation_a+0.120.parquet /app/solution/validation_a+0.150.parquet /app/solution/validation_a+0.200.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +18:10

Script completed
Wall time 23.5 seconds
Output:

{"chunk_id":"2b6e9b","wall_time_seconds":23.292725783,"exit_code":0,"original_token_count":238,"output":"{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019085,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019444,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.090.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019149,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018403,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.120.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017428,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.150.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015371,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.013076,\n    \"quality_users\": 4263\n  }\n}\n"}

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",
      "for f in /app/solution/validation_a+0.075.parquet /app/solution/validation_a+0.085.parquet /app/solution/validation_a+0.090.parquet /app/solution/validation_a+0.100.parquet /app/solution/validation_a+0.120.parquet /app/solution/validation_a+0.150.parquet /app/solution/validation_a+0.200.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019085,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019444,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.090.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019149,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018403,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.120.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017428,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.150.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015371,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.013076,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 23,
      "nanos": 292719713
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019085,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019444,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.090.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019149,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018403,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.120.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017428,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.150.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015371,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.013076,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+18:16

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 +18:19

const r = await tools.exec_command({"cmd":"cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_e10.pt\ncp /app/solution/validation_a+0.085.parquet /app/solution/validation_best.parquet\ncp /app/solution/run_validation.json /app/solution/run_validation_e10.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 5 --weeks 22 --max-history 512 --seed 20260927 --load-checkpoint /app/solution/two_tower_validation_e10.pt --pop-blends=0.04,0.06,0.075,0.085,0.095,0.11","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +18:49

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"7bc1e6","wall_time_seconds":30.001146007,"session_id":30455,"original_token_count":43,"output":"aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation_e10.pt\n"}

Harness context token usage record

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}

exec command

+18:52

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 +18:52

const r = await tools.write_stdin({"session_id":30455,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +19:22

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"339334","wall_time_seconds":30.000932745,"session_id":30455,"original_token_count":25,"output":"epoch 1/5: loss=1.0212 batches=312 elapsed=19.3s\nepoch 2/5: loss=1.0004 batches=312 elapsed=19.1s\n"}

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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    "input_tokens": 1661807,
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    "cache_write_input_tokens": 0,
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}

exec command

+19:23

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 +19:23

const r = await tools.write_stdin({"session_id":30455,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +19:53

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2e0ffa","wall_time_seconds":30.001016045,"session_id":30455,"original_token_count":13,"output":"epoch 3/5: loss=0.9786 batches=312 elapsed=19.2s\n"}

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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  "usage": {
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}

exec command

+19:55

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 +19:55

const r = await tools.write_stdin({"session_id":30455,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +20:25

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"4692b0","wall_time_seconds":30.000590547,"session_id":30455,"original_token_count":25,"output":"epoch 4/5: loss=0.9567 batches=312 elapsed=19.1s\nepoch 5/5: loss=0.9381 batches=312 elapsed=19.0s\n"}

Harness context token usage record

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}

exec command

+20:26

Thinking unavailable No text recorded

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

Model text

At fifteen total epochs the retrieval loss is still decreasing, from 1.46 initially to 0.94. I’m checking whether that translates to held-out ranking rather than assuming lower training loss is better; the previous best remains safely copied.

Tool callexecTool call Returned

Tool call · Input +20:28

const r = await tools.write_stdin({"session_id":30455,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +20:29

Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"6a8378","wall_time_seconds":0.249449455,"exit_code":0,"original_token_count":93,"output":"wrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.095.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.110.parquet (845,600 rows)\n"}

Harness context token usage record

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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4ad9a016-598b-49a7-8e52-aabdcfa48eb6",
    "process_id": "30455",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_e10.pt\ncp /app/solution/validation_a+0.085.parquet /app/solution/validation_best.parquet\ncp /app/solution/run_validation.json /app/solution/run_validation_e10.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 5 --weeks 22 --max-history 512 --seed 20260927 --load-checkpoint /app/solution/two_tower_validation_e10.pt --pop-blends=0.04,0.06,0.075,0.085,0.095,0.11"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_e10.pt\ncp /app/solution/validation_a+0.085.parquet /app/solution/validation_best.parquet\ncp /app/solution/run_validation.json /app/solution/run_validation_e10.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 5 --weeks 22 --max-history 512 --seed 20260927 --load-checkpoint /app/solution/two_tower_validation_e10.pt --pop-blends=0.04,0.06,0.075,0.085,0.095,0.11"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation_e10.pt\nepoch 1/5: loss=1.0212 batches=312 elapsed=19.3s\nepoch 2/5: loss=1.0004 batches=312 elapsed=19.1s\nepoch 3/5: loss=0.9786 batches=312 elapsed=19.2s\nepoch 4/5: loss=0.9567 batches=312 elapsed=19.1s\nepoch 5/5: loss=0.9381 batches=312 elapsed=19.0s\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.095.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.110.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation_e10.pt\nepoch 1/5: loss=1.0212 batches=312 elapsed=19.3s\nepoch 2/5: loss=1.0004 batches=312 elapsed=19.1s\nepoch 3/5: loss=0.9786 batches=312 elapsed=19.2s\nepoch 4/5: loss=0.9567 batches=312 elapsed=19.1s\nepoch 5/5: loss=0.9381 batches=312 elapsed=19.0s\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.095.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.110.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 129,
      "nanos": 67703929
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 79,962 rolling profiles with 675,077 labels (110,182 grade-3)\nloaded /app/solution/two_tower_validation_e10.pt\nepoch 1/5: loss=1.0212 batches=312 elapsed=19.3s\nepoch 2/5: loss=1.0004 batches=312 elapsed=19.1s\nepoch 3/5: loss=0.9786 batches=312 elapsed=19.2s\nepoch 4/5: loss=0.9567 batches=312 elapsed=19.1s\nepoch 5/5: loss=0.9381 batches=312 elapsed=19.0s\nwrote /app/solution/validation_a+0.040.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.060.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.075.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.085.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.095.parquet (845,600 rows)\nwrote /app/solution/validation_a+0.110.parquet (845,600 rows)\n"
  }
}

exec command

+20:30

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +20:32

const r = await tools.exec_command({"cmd":"for f in /app/solution/validation_a+0.040.parquet /app/solution/validation_a+0.060.parquet /app/solution/validation_a+0.075.parquet /app/solution/validation_a+0.085.parquet /app/solution/validation_a+0.095.parquet /app/solution/validation_a+0.110.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +20:53

Script completed
Wall time 20.5 seconds
Output:

{"chunk_id":"d3ff32","wall_time_seconds":20.323594536,"exit_code":0,"original_token_count":204,"output":"{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015543,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017195,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018858,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019115,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.095.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018743,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.110.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018756,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c01f5539-c611-41a4-8ca8-0c6eccceabc4",
    "process_id": "67474",
    "command": [
      "/bin/sh",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015543,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017195,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018858,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019115,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.095.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018743,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.110.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018756,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015543,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017195,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018858,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019115,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.095.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018743,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.110.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018756,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 20,
      "nanos": 323587745
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_a+0.040.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.015543,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.060.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.017195,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.075.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018858,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.085.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019115,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.095.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018743,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_a+0.110.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018756,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+21:03

Thinking unavailable No text recorded

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

Model text

The extra five epochs slightly overfit (best 0.019115 versus 0.019444 at epoch ten), so epoch ten stays selected. Next I’m testing cutoff-local catalog momentum—weekly organic discovery often has strong trend effects that all-history catalog popularity misses, and such a signal remains an offline item-tower feature with zero request-time cost.

Tool callexecTool call Returned

Tool call · Input +21:22

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time\nD=Path('/task/data'); O=Path('/app/solution'); end=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids)\ndense=np.full(ids.max()+1,-1,np.int32); dense[ids]=np.arange(n,dtype=np.int32)\nl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nrid=l.item_id.to_numpy(np.int64); ok=rid<len(dense); ix=np.full(len(l),-1,np.int32); ix[ok]=dense[rid[ok]]; keep=ix>=0\nl=l.loc[keep].copy(); l['ix']=ix[keep]; print('catalog rows',len(l),flush=True)\nk=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\nrid=k.item_id.to_numpy(np.int64); ok=rid<len(dense); ix=np.full(len(k),-1,np.int32); ix[ok]=dense[rid[ok]]; keep=ix>=0;k=k.loc[keep].copy();k['ix']=ix[keep]\nfeatures={}\nfor days in [3,7,14,30,60,90,180]:\n start=end-days*86400\n f=l[l.timestamp>=start]\n features[f'pos{days}']=np.bincount(f.loc[f.played_ratio_pct>=50,'ix'],minlength=n)\n features[f'org80_{days}']=np.bincount(f.loc[(f.is_organic==1)&(f.played_ratio_pct>=80),'ix'],minlength=n)\n features[f'users{days}']=f.loc[(f.is_organic==1)&(f.played_ratio_pct>=80),['uid','ix']].drop_duplicates().groupby('ix').size().reindex(range(n),fill_value=0).to_numpy()\n q=k[(k.timestamp>=start)&(k.is_organic==1)]\n features[f'likes{days}']=np.bincount(q.ix,minlength=n)\n# exponential organic counts with 7/30d half life; bincount weighted\nf=l[(l.is_organic==1)&(l.played_ratio_pct>=80)]\nage=(end-f.timestamp.to_numpy())/86400\nfor half in [3,7,14,30,60]: features[f'exp{half}']=np.bincount(f.ix,weights=np.exp2(-age/half),minlength=n)\n# combos intuitive\nfeatures['combo7']=np.log1p(features['org80_7'])+.7*np.log1p(features['likes7'])\nfeatures['combo30']=np.log1p(features['org80_30'])+.7*np.log1p(features['likes30'])\nfeatures['velocity']=np.log1p(features['org80_14'])-.3*np.log1p(features['org80_90'])\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(items,ids)\ndef write(name,score):\n order=np.argsort(-score,kind='stable'); rows=[]\n for r,u in enumerate(uids):\n  mask=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool); sel=order[mask[order]][:100]\n  rows.extend((int(u),int(ids[j]),rank) for rank,j in enumerate(sel,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'pop_{name}.parquet',index=False)\nfor name,score in features.items(): write(name,np.asarray(score))\nprint('wrote',len(features),'rankings',flush=True)\nPY\nfor f in /app/solution/pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; printf '\\n'; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +21:52

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"8a7b59","wall_time_seconds":30.000859004,"session_id":16341,"original_token_count":6,"output":"catalog rows 31249613\n"}

Harness context token usage record

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}

exec command

+21:54

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 +21:54

const r = await tools.write_stdin({"session_id":16341,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +22:24

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b5f05d","wall_time_seconds":30.000692761,"session_id":16341,"original_token_count":70,"output":"wrote 36 rankings\n{   \"model\": \"/app/solution/pop_combo30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020231,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_combo7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030767,     \"quality_users\": 4263   } } \n"}

Harness context token usage record

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exec command

+22:26

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +22:27

const r = await tools.write_stdin({"session_id":16341,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +22:57

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"4988c7","wall_time_seconds":30.000899935,"session_id":16341,"original_token_count":322,"output":"{   \"model\": \"/app/solution/pop_exp14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017692,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.027438,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016117,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015044,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022218,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.026726,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030503,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018429,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016741,     \"quality_users\": 4263   } } \n"}

Harness context token usage record

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exec command

+22:58

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 +22:59

const r = await tools.write_stdin({"session_id":16341,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +23:29

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"1cac33","wall_time_seconds":30.001581429,"session_id":16341,"original_token_count":294,"output":"{   \"model\": \"/app/solution/pop_likes7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.029297,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016217,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022878,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011908,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025721,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013314,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015802,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028131,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015822,     \"quality_users\": 4263   } } \n"}

Harness context token usage record

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exec command

+23:30

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +23:31

const r = await tools.write_stdin({"session_id":16341,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +24:01

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"df4591","wall_time_seconds":30.000883495,"session_id":16341,"original_token_count":322,"output":"{   \"model\": \"/app/solution/pop_pos14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025124,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012116,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028592,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017182,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016892,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030933,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015972,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025481,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012821,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.031654,     \"quality_users\": 4263   } } \n"}

Harness context token usage record

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exec command

+24:02

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 +24:03

const r = await tools.write_stdin({"session_id":16341,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +24:16

Script completed
Wall time 13.3 seconds
Output:

{"chunk_id":"807365","wall_time_seconds":13.295012768,"exit_code":0,"original_token_count":163,"output":"{   \"model\": \"/app/solution/pop_users30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018928,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017371,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030286,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015848,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_velocity.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024588,     \"quality_users\": 4263   } } \n"}

Harness context token usage record

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    "cache_write_input_tokens": 0,
    "output_tokens": 34578,
    "reasoning_output_tokens": 16839,
    "total_tokens": 2378839
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-dd1645c0-8ca0-4c3a-9853-4335a898c852",
    "process_id": "16341",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time\nD=Path('/task/data'); O=Path('/app/solution'); end=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids)\ndense=np.full(ids.max()+1,-1,np.int32); dense[ids]=np.arange(n,dtype=np.int32)\nl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nrid=l.item_id.to_numpy(np.int64); ok=rid<len(dense); ix=np.full(len(l),-1,np.int32); ix[ok]=dense[rid[ok]]; keep=ix>=0\nl=l.loc[keep].copy(); l['ix']=ix[keep]; print('catalog rows',len(l),flush=True)\nk=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\nrid=k.item_id.to_numpy(np.int64); ok=rid<len(dense); ix=np.full(len(k),-1,np.int32); ix[ok]=dense[rid[ok]]; keep=ix>=0;k=k.loc[keep].copy();k['ix']=ix[keep]\nfeatures={}\nfor days in [3,7,14,30,60,90,180]:\n start=end-days*86400\n f=l[l.timestamp>=start]\n features[f'pos{days}']=np.bincount(f.loc[f.played_ratio_pct>=50,'ix'],minlength=n)\n features[f'org80_{days}']=np.bincount(f.loc[(f.is_organic==1)&(f.played_ratio_pct>=80),'ix'],minlength=n)\n features[f'users{days}']=f.loc[(f.is_organic==1)&(f.played_ratio_pct>=80),['uid','ix']].drop_duplicates().groupby('ix').size().reindex(range(n),fill_value=0).to_numpy()\n q=k[(k.timestamp>=start)&(k.is_organic==1)]\n features[f'likes{days}']=np.bincount(q.ix,minlength=n)\n# exponential organic counts with 7/30d half life; bincount weighted\nf=l[(l.is_organic==1)&(l.played_ratio_pct>=80)]\nage=(end-f.timestamp.to_numpy())/86400\nfor half in [3,7,14,30,60]: features[f'exp{half}']=np.bincount(f.ix,weights=np.exp2(-age/half),minlength=n)\n# combos intuitive\nfeatures['combo7']=np.log1p(features['org80_7'])+.7*np.log1p(features['likes7'])\nfeatures['combo30']=np.log1p(features['org80_30'])+.7*np.log1p(features['likes30'])\nfeatures['velocity']=np.log1p(features['org80_14'])-.3*np.log1p(features['org80_90'])\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(items,ids)\ndef write(name,score):\n order=np.argsort(-score,kind='stable'); rows=[]\n for r,u in enumerate(uids):\n  mask=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool); sel=order[mask[order]][:100]\n  rows.extend((int(u),int(ids[j]),rank) for rank,j in enumerate(sel,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'pop_{name}.parquet',index=False)\nfor name,score in features.items(): write(name,np.asarray(score))\nprint('wrote',len(features),'rankings',flush=True)\nPY\nfor f in /app/solution/pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; printf '\\n'; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time\nD=Path('/task/data'); O=Path('/app/solution'); end=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids)\ndense=np.full(ids.max()+1,-1,np.int32); dense[ids]=np.arange(n,dtype=np.int32)\nl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nrid=l.item_id.to_numpy(np.int64); ok=rid<len(dense); ix=np.full(len(l),-1,np.int32); ix[ok]=dense[rid[ok]]; keep=ix>=0\nl=l.loc[keep].copy(); l['ix']=ix[keep]; print('catalog rows',len(l),flush=True)\nk=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\nrid=k.item_id.to_numpy(np.int64); ok=rid<len(dense); ix=np.full(len(k),-1,np.int32); ix[ok]=dense[rid[ok]]; keep=ix>=0;k=k.loc[keep].copy();k['ix']=ix[keep]\nfeatures={}\nfor days in [3,7,14,30,60,90,180]:\n start=end-days*86400\n f=l[l.timestamp>=start]\n features[f'pos{days}']=np.bincount(f.loc[f.played_ratio_pct>=50,'ix'],minlength=n)\n features[f'org80_{days}']=np.bincount(f.loc[(f.is_organic==1)&(f.played_ratio_pct>=80),'ix'],minlength=n)\n features[f'users{days}']=f.loc[(f.is_organic==1)&(f.played_ratio_pct>=80),['uid','ix']].drop_duplicates().groupby('ix').size().reindex(range(n),fill_value=0).to_numpy()\n q=k[(k.timestamp>=start)&(k.is_organic==1)]\n features[f'likes{days}']=np.bincount(q.ix,minlength=n)\n# exponential organic counts with 7/30d half life; bincount weighted\nf=l[(l.is_organic==1)&(l.played_ratio_pct>=80)]\nage=(end-f.timestamp.to_numpy())/86400\nfor half in [3,7,14,30,60]: features[f'exp{half}']=np.bincount(f.ix,weights=np.exp2(-age/half),minlength=n)\n# combos intuitive\nfeatures['combo7']=np.log1p(features['org80_7'])+.7*np.log1p(features['likes7'])\nfeatures['combo30']=np.log1p(features['org80_30'])+.7*np.log1p(features['likes30'])\nfeatures['velocity']=np.log1p(features['org80_14'])-.3*np.log1p(features['org80_90'])\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(items,ids)\ndef write(name,score):\n order=np.argsort(-score,kind='stable'); rows=[]\n for r,u in enumerate(uids):\n  mask=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool); sel=order[mask[order]][:100]\n  rows.extend((int(u),int(ids[j]),rank) for rank,j in enumerate(sel,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'pop_{name}.parquet',index=False)\nfor name,score in features.items(): write(name,np.asarray(score))\nprint('wrote',len(features),'rankings',flush=True)\nPY\nfor f in /app/solution/pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; printf '\\n'; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "catalog rows 31249613\nwrote 36 rankings\n{   \"model\": \"/app/solution/pop_combo30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020231,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_combo7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030767,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017692,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.027438,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016117,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015044,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022218,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.026726,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030503,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018429,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016741,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.029297,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016217,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022878,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011908,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025721,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013314,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015802,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028131,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015822,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025124,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012116,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028592,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017182,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016892,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030933,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015972,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025481,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012821,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.031654,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018928,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017371,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030286,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015848,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_velocity.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024588,     \"quality_users\": 4263   } } \n",
    "stderr": "",
    "aggregated_output": "catalog rows 31249613\nwrote 36 rankings\n{   \"model\": \"/app/solution/pop_combo30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020231,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_combo7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030767,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017692,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.027438,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016117,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015044,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022218,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.026726,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030503,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018429,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016741,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.029297,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016217,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022878,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011908,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025721,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013314,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015802,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028131,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015822,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025124,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012116,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028592,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017182,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016892,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030933,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015972,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025481,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012821,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.031654,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018928,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017371,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030286,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015848,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_velocity.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024588,     \"quality_users\": 4263   } } \n",
    "exit_code": 0,
    "duration": {
      "secs": 173,
      "nanos": 633250264
    },
    "formatted_output": "catalog rows 31249613\nwrote 36 rankings\n{   \"model\": \"/app/solution/pop_combo30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.020231,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_combo7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030767,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017692,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.027438,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016117,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015044,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_exp7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022218,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.026726,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030503,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018429,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016741,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.029297,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_likes90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016217,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.022878,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.011908,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025721,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013314,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015802,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028131,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_org80_90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015822,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025124,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012116,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.028592,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017182,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016892,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030933,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_pos90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015972,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users14.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.025481,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users180.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.012821,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users3.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.031654,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users30.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018928,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users60.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.017371,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users7.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.030286,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_users90.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.015848,     \"quality_users\": 4263   } } \n{   \"model\": \"/app/solution/pop_velocity.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.024588,     \"quality_users\": 4263   } } \n"
  }
}

Agent message

+24:26

Thinking unavailable No text recorded

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

Agent message

+24:36

Thinking unavailable No text recorded

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

Agent message

+24:45

Thinking unavailable No text recorded

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

exec command

+24:50

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 +25:18

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time,json\nD=Path('/task/data'); O=Path('/app/solution'); end=24786800;t=time.time()\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); dense=np.full(ids.max()+1,-1,np.int32);dense[ids]=np.arange(n,dtype=np.int32)\nl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); rid=l.item_id.to_numpy(np.int64);ok=rid<len(dense);ix=np.full(len(l),-1,np.int32);ix[ok]=dense[rid[ok]];keep=ix>=0;l=l.loc[keep].copy();l['ix']=ix[keep]\nk=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']);rid=k.item_id.to_numpy(np.int64);ok=rid<len(dense);ix=np.full(len(k),-1,np.int32);ix[ok]=dense[rid[ok]];keep=ix>=0;k=k.loc[keep].copy();k['ix']=ix[keep]\nfeatures=[];names=[]\ndef add(name,x):names.append(name);features.append(np.asarray(x,dtype=np.float32))\nfor days in [1,2,3,5,7,10,14,21,30,60]:\n start=end-days*86400; f=l[l.timestamp>=start]\n pos=f[f.played_ratio_pct>=50]; org=f[(f.is_organic==1)&(f.played_ratio_pct>=80)]\n add(f'pos{days}',np.bincount(pos.ix,minlength=n));add(f'org{days}',np.bincount(org.ix,minlength=n))\n add(f'users{days}',org[['uid','ix']].drop_duplicates().groupby('ix').size().reindex(range(n),fill_value=0))\n q=k[(k.timestamp>=start)&(k.is_organic==1)];add(f'likes{days}',np.bincount(q.ix,minlength=n))\n# alltime catalog columns\nadd('allpos',cat.positive_listens);add('allorg',cat.organic_positive_listens)\nX=np.stack(features,axis=1);np.savez_compressed(O/'trend_features_validation.npz',features=X,names=np.array(names),item_ids=ids)\n# public truth\nwith np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].astype(np.int64);packed=z['packed_eligible'];ei=z['item_ids']\nuidrow={int(u):r for r,u in enumerate(eu)}\nvl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet'); targets=set(eu.tolist())\ns=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(grade=1)\nq=vk[(vk.uid.isin(targets))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(grade=3)\ntruth=pd.concat([s,q],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nrows=[];cols=[];grades=[]\nfor u,i,g in truth.itertuples(index=False,name=None):\n c=dense[int(i)] if int(i)<len(dense) else -1;r=uidrow.get(int(u),-1)\n if r>=0 and c>=0 and ((int(packed[r,c//8])>>(c%8))&1):rows.append(r);cols.append(c);grades.append(g)\nscore_rows=np.unique(rows); rowmap=np.full(len(eu),-1,np.int32);rowmap[score_rows]=np.arange(len(score_rows))\nG=np.zeros((len(score_rows),n),np.uint8)\nG[rowmap[np.array(rows)],np.array(cols)]=np.array(grades,np.uint8)\nE=np.unpackbits(packed[score_rows],axis=1,count=n,bitorder='little').astype(bool)\ndisc=1/np.log2(np.arange(2,12)); ideal=np.sort(G,axis=1)[:,-10:][:,::-1];idcg=((2.**ideal-1)*disc).sum(1)\ndef metric(score):\n order=np.argsort(-score,kind='stable')[:80]; e=E[:,order]; ranks=np.cumsum(e,axis=1); take=e&(ranks<=10); gain=2.**G[:,order]-1; weights=np.zeros_like(gain,dtype=np.float64); rr,cc=np.where(take);weights[rr,cc]=disc[ranks[rr,cc]-1];return float(np.mean((gain*weights).sum(1)/idcg))\n# log-z transform\nZ=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6)\nindividual=sorted([(metric(Z[:,j]),names[j]) for j in range(len(names))],reverse=True)\nprint('individual top',individual[:15],flush=True)\nsel_names=['users2','users3','users5','pos5','pos7','likes2','likes3','likes5','org3','allorg']; sel=np.array([names.index(x) for x in sel_names]); A=Z[:,sel]\nrng=np.random.default_rng(42);best=[]\n# Dirichlet mixtures at multiple concentrations; allow a small negative all-time coefficient separately\nfor concentration in [.15,.3,.7,1.5]:\n for _ in range(600):\n  w=rng.dirichlet(np.full(len(sel_names),concentration)); sc=metric(A@w);best.append((sc,w.copy()))\n# local perturb around winners\nbest.sort(key=lambda x:x[0],reverse=True)\nfor _ in range(600):\n w=np.maximum(0,best[rng.integers(min(30,len(best)))][1]+rng.normal(0,.04,len(sel_names)));w/=w.sum();best.append((metric(A@w),w))\nbest.sort(key=lambda x:x[0],reverse=True)\nfor sc,w in best[:12]:print('mix',sc,{nm:round(float(v),3) for nm,v in zip(sel_names,w) if v>.015},flush=True)\nnp.savez_compressed(O/'trend_blend_validation.npz',features=X,names=np.array(names),item_ids=ids,best_weights=best[0][1],selected_names=np.array(sel_names))\nprint('elapsed',time.time()-t,flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +25:48

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"dd85d6","wall_time_seconds":30.001318407,"session_id":44322,"original_token_count":128,"output":"individual top [(0.032866839431410624, 'users1'), (0.0327479219292982, 'users2'), (0.03157738444504427, 'users3'), (0.030932762235216175, 'pos7'), (0.03052195989214847, 'users5'), (0.030503291719404538, 'likes3'), (0.03016943788640836, 'users7'), (0.02948500619558697, 'pos5'), (0.029424922908395296, 'likes10'), (0.029296797300008143, 'likes7'), (0.02881789175667384, 'pos10'), (0.028591963268355895, 'pos3'), (0.028192378641262072, 'users10'), (0.02813072548782609, 'org7'), (0.02805124640123192, 'likes5')]\n"}

Harness context token usage record

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  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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  "usage": {
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    "cached_input_tokens": 70784,
    "cache_write_input_tokens": 0,
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  "turn_token_usage": {
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  "thread_token_usage": {
    "input_tokens": 2415546,
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  }
}

exec command

+25:51

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 +25:52

const r = await tools.write_stdin({"session_id":44322,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +25:54

Script completed
Wall time 2.1 seconds
Output:

{"chunk_id":"29f350","wall_time_seconds":2.080668041,"exit_code":0,"original_token_count":378,"output":"mix 0.03521787307514779 {'users3': 0.159, 'pos5': 0.579, 'likes3': 0.182, 'org3': 0.02, 'allorg': 0.06}\nmix 0.03517307468257728 {'users3': 0.104, 'pos5': 0.377, 'pos7': 0.289, 'likes3': 0.13, 'likes5': 0.049, 'allorg': 0.04}\nmix 0.03515858191096935 {'users5': 0.149, 'pos5': 0.531, 'pos7': 0.056, 'likes2': 0.037, 'likes3': 0.133, 'org3': 0.094}\nmix 0.0351569077958871 {'users3': 0.039, 'users5': 0.141, 'pos5': 0.534, 'pos7': 0.07, 'likes3': 0.171, 'likes5': 0.019, 'allorg': 0.025}\nmix 0.035136888602734645 {'users3': 0.46, 'users5': 0.025, 'pos5': 0.374, 'pos7': 0.028, 'likes3': 0.095}\nmix 0.03510953492389795 {'users3': 0.13, 'pos5': 0.696, 'likes2': 0.051, 'likes3': 0.09, 'org3': 0.023}\nmix 0.03510584238066583 {'users3': 0.171, 'pos5': 0.616, 'likes3': 0.163, 'allorg': 0.047}\nmix 0.03509378302223337 {'users2': 0.186, 'users3': 0.081, 'pos5': 0.467, 'pos7': 0.057, 'likes3': 0.063, 'likes5': 0.085, 'org3': 0.017, 'allorg': 0.043}\nmix 0.03509240776780178 {'users2': 0.073, 'users3': 0.043, 'users5': 0.261, 'pos5': 0.427, 'likes3': 0.072, 'likes5': 0.04, 'org3': 0.085}\nmix 0.03508629936942931 {'users2': 0.148, 'users3': 0.018, 'pos5': 0.483, 'pos7': 0.189, 'likes2': 0.08, 'likes5': 0.026, 'allorg': 0.023}\nmix 0.03508501051142448 {'users2': 0.041, 'users3': 0.049, 'users5': 0.319, 'pos5': 0.348, 'likes3': 0.193, 'allorg': 0.033}\nmix 0.03508410051462522 {'users2': 0.066, 'users3': 0.05, 'pos5': 0.384, 'pos7': 0.231, 'likes3': 0.155, 'likes5': 0.022, 'allorg': 0.089}\nelapsed 34.051100730895996\n"}

Harness context token usage record

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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5a3ed80e-f1a0-4178-8381-d94d7f099a36",
    "process_id": "44322",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time,json\nD=Path('/task/data'); O=Path('/app/solution'); end=24786800;t=time.time()\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); dense=np.full(ids.max()+1,-1,np.int32);dense[ids]=np.arange(n,dtype=np.int32)\nl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); rid=l.item_id.to_numpy(np.int64);ok=rid<len(dense);ix=np.full(len(l),-1,np.int32);ix[ok]=dense[rid[ok]];keep=ix>=0;l=l.loc[keep].copy();l['ix']=ix[keep]\nk=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']);rid=k.item_id.to_numpy(np.int64);ok=rid<len(dense);ix=np.full(len(k),-1,np.int32);ix[ok]=dense[rid[ok]];keep=ix>=0;k=k.loc[keep].copy();k['ix']=ix[keep]\nfeatures=[];names=[]\ndef add(name,x):names.append(name);features.append(np.asarray(x,dtype=np.float32))\nfor days in [1,2,3,5,7,10,14,21,30,60]:\n start=end-days*86400; f=l[l.timestamp>=start]\n pos=f[f.played_ratio_pct>=50]; org=f[(f.is_organic==1)&(f.played_ratio_pct>=80)]\n add(f'pos{days}',np.bincount(pos.ix,minlength=n));add(f'org{days}',np.bincount(org.ix,minlength=n))\n add(f'users{days}',org[['uid','ix']].drop_duplicates().groupby('ix').size().reindex(range(n),fill_value=0))\n q=k[(k.timestamp>=start)&(k.is_organic==1)];add(f'likes{days}',np.bincount(q.ix,minlength=n))\n# alltime catalog columns\nadd('allpos',cat.positive_listens);add('allorg',cat.organic_positive_listens)\nX=np.stack(features,axis=1);np.savez_compressed(O/'trend_features_validation.npz',features=X,names=np.array(names),item_ids=ids)\n# public truth\nwith np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].astype(np.int64);packed=z['packed_eligible'];ei=z['item_ids']\nuidrow={int(u):r for r,u in enumerate(eu)}\nvl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet'); targets=set(eu.tolist())\ns=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(grade=1)\nq=vk[(vk.uid.isin(targets))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(grade=3)\ntruth=pd.concat([s,q],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nrows=[];cols=[];grades=[]\nfor u,i,g in truth.itertuples(index=False,name=None):\n c=dense[int(i)] if int(i)<len(dense) else -1;r=uidrow.get(int(u),-1)\n if r>=0 and c>=0 and ((int(packed[r,c//8])>>(c%8))&1):rows.append(r);cols.append(c);grades.append(g)\nscore_rows=np.unique(rows); rowmap=np.full(len(eu),-1,np.int32);rowmap[score_rows]=np.arange(len(score_rows))\nG=np.zeros((len(score_rows),n),np.uint8)\nG[rowmap[np.array(rows)],np.array(cols)]=np.array(grades,np.uint8)\nE=np.unpackbits(packed[score_rows],axis=1,count=n,bitorder='little').astype(bool)\ndisc=1/np.log2(np.arange(2,12)); ideal=np.sort(G,axis=1)[:,-10:][:,::-1];idcg=((2.**ideal-1)*disc).sum(1)\ndef metric(score):\n order=np.argsort(-score,kind='stable')[:80]; e=E[:,order]; ranks=np.cumsum(e,axis=1); take=e&(ranks<=10); gain=2.**G[:,order]-1; weights=np.zeros_like(gain,dtype=np.float64); rr,cc=np.where(take);weights[rr,cc]=disc[ranks[rr,cc]-1];return float(np.mean((gain*weights).sum(1)/idcg))\n# log-z transform\nZ=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6)\nindividual=sorted([(metric(Z[:,j]),names[j]) for j in range(len(names))],reverse=True)\nprint('individual top',individual[:15],flush=True)\nsel_names=['users2','users3','users5','pos5','pos7','likes2','likes3','likes5','org3','allorg']; sel=np.array([names.index(x) for x in sel_names]); A=Z[:,sel]\nrng=np.random.default_rng(42);best=[]\n# Dirichlet mixtures at multiple concentrations; allow a small negative all-time coefficient separately\nfor concentration in [.15,.3,.7,1.5]:\n for _ in range(600):\n  w=rng.dirichlet(np.full(len(sel_names),concentration)); sc=metric(A@w);best.append((sc,w.copy()))\n# local perturb around winners\nbest.sort(key=lambda x:x[0],reverse=True)\nfor _ in range(600):\n w=np.maximum(0,best[rng.integers(min(30,len(best)))][1]+rng.normal(0,.04,len(sel_names)));w/=w.sum();best.append((metric(A@w),w))\nbest.sort(key=lambda x:x[0],reverse=True)\nfor sc,w in best[:12]:print('mix',sc,{nm:round(float(v),3) for nm,v in zip(sel_names,w) if v>.015},flush=True)\nnp.savez_compressed(O/'trend_blend_validation.npz',features=X,names=np.array(names),item_ids=ids,best_weights=best[0][1],selected_names=np.array(sel_names))\nprint('elapsed',time.time()-t,flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time,json\nD=Path('/task/data'); O=Path('/app/solution'); end=24786800;t=time.time()\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); dense=np.full(ids.max()+1,-1,np.int32);dense[ids]=np.arange(n,dtype=np.int32)\nl=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); rid=l.item_id.to_numpy(np.int64);ok=rid<len(dense);ix=np.full(len(l),-1,np.int32);ix[ok]=dense[rid[ok]];keep=ix>=0;l=l.loc[keep].copy();l['ix']=ix[keep]\nk=pd.read_parquet(D/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']);rid=k.item_id.to_numpy(np.int64);ok=rid<len(dense);ix=np.full(len(k),-1,np.int32);ix[ok]=dense[rid[ok]];keep=ix>=0;k=k.loc[keep].copy();k['ix']=ix[keep]\nfeatures=[];names=[]\ndef add(name,x):names.append(name);features.append(np.asarray(x,dtype=np.float32))\nfor days in [1,2,3,5,7,10,14,21,30,60]:\n start=end-days*86400; f=l[l.timestamp>=start]\n pos=f[f.played_ratio_pct>=50]; org=f[(f.is_organic==1)&(f.played_ratio_pct>=80)]\n add(f'pos{days}',np.bincount(pos.ix,minlength=n));add(f'org{days}',np.bincount(org.ix,minlength=n))\n add(f'users{days}',org[['uid','ix']].drop_duplicates().groupby('ix').size().reindex(range(n),fill_value=0))\n q=k[(k.timestamp>=start)&(k.is_organic==1)];add(f'likes{days}',np.bincount(q.ix,minlength=n))\n# alltime catalog columns\nadd('allpos',cat.positive_listens);add('allorg',cat.organic_positive_listens)\nX=np.stack(features,axis=1);np.savez_compressed(O/'trend_features_validation.npz',features=X,names=np.array(names),item_ids=ids)\n# public truth\nwith np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].astype(np.int64);packed=z['packed_eligible'];ei=z['item_ids']\nuidrow={int(u):r for r,u in enumerate(eu)}\nvl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet'); targets=set(eu.tolist())\ns=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(grade=1)\nq=vk[(vk.uid.isin(targets))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(grade=3)\ntruth=pd.concat([s,q],ignore_index=True).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nrows=[];cols=[];grades=[]\nfor u,i,g in truth.itertuples(index=False,name=None):\n c=dense[int(i)] if int(i)<len(dense) else -1;r=uidrow.get(int(u),-1)\n if r>=0 and c>=0 and ((int(packed[r,c//8])>>(c%8))&1):rows.append(r);cols.append(c);grades.append(g)\nscore_rows=np.unique(rows); rowmap=np.full(len(eu),-1,np.int32);rowmap[score_rows]=np.arange(len(score_rows))\nG=np.zeros((len(score_rows),n),np.uint8)\nG[rowmap[np.array(rows)],np.array(cols)]=np.array(grades,np.uint8)\nE=np.unpackbits(packed[score_rows],axis=1,count=n,bitorder='little').astype(bool)\ndisc=1/np.log2(np.arange(2,12)); ideal=np.sort(G,axis=1)[:,-10:][:,::-1];idcg=((2.**ideal-1)*disc).sum(1)\ndef metric(score):\n order=np.argsort(-score,kind='stable')[:80]; e=E[:,order]; ranks=np.cumsum(e,axis=1); take=e&(ranks<=10); gain=2.**G[:,order]-1; weights=np.zeros_like(gain,dtype=np.float64); rr,cc=np.where(take);weights[rr,cc]=disc[ranks[rr,cc]-1];return float(np.mean((gain*weights).sum(1)/idcg))\n# log-z transform\nZ=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6)\nindividual=sorted([(metric(Z[:,j]),names[j]) for j in range(len(names))],reverse=True)\nprint('individual top',individual[:15],flush=True)\nsel_names=['users2','users3','users5','pos5','pos7','likes2','likes3','likes5','org3','allorg']; sel=np.array([names.index(x) for x in sel_names]); A=Z[:,sel]\nrng=np.random.default_rng(42);best=[]\n# Dirichlet mixtures at multiple concentrations; allow a small negative all-time coefficient separately\nfor concentration in [.15,.3,.7,1.5]:\n for _ in range(600):\n  w=rng.dirichlet(np.full(len(sel_names),concentration)); sc=metric(A@w);best.append((sc,w.copy()))\n# local perturb around winners\nbest.sort(key=lambda x:x[0],reverse=True)\nfor _ in range(600):\n w=np.maximum(0,best[rng.integers(min(30,len(best)))][1]+rng.normal(0,.04,len(sel_names)));w/=w.sum();best.append((metric(A@w),w))\nbest.sort(key=lambda x:x[0],reverse=True)\nfor sc,w in best[:12]:print('mix',sc,{nm:round(float(v),3) for nm,v in zip(sel_names,w) if v>.015},flush=True)\nnp.savez_compressed(O/'trend_blend_validation.npz',features=X,names=np.array(names),item_ids=ids,best_weights=best[0][1],selected_names=np.array(sel_names))\nprint('elapsed',time.time()-t,flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "individual top [(0.032866839431410624, 'users1'), (0.0327479219292982, 'users2'), (0.03157738444504427, 'users3'), (0.030932762235216175, 'pos7'), (0.03052195989214847, 'users5'), (0.030503291719404538, 'likes3'), (0.03016943788640836, 'users7'), (0.02948500619558697, 'pos5'), (0.029424922908395296, 'likes10'), (0.029296797300008143, 'likes7'), (0.02881789175667384, 'pos10'), (0.028591963268355895, 'pos3'), (0.028192378641262072, 'users10'), (0.02813072548782609, 'org7'), (0.02805124640123192, 'likes5')]\nmix 0.03521787307514779 {'users3': 0.159, 'pos5': 0.579, 'likes3': 0.182, 'org3': 0.02, 'allorg': 0.06}\nmix 0.03517307468257728 {'users3': 0.104, 'pos5': 0.377, 'pos7': 0.289, 'likes3': 0.13, 'likes5': 0.049, 'allorg': 0.04}\nmix 0.03515858191096935 {'users5': 0.149, 'pos5': 0.531, 'pos7': 0.056, 'likes2': 0.037, 'likes3': 0.133, 'org3': 0.094}\nmix 0.0351569077958871 {'users3': 0.039, 'users5': 0.141, 'pos5': 0.534, 'pos7': 0.07, 'likes3': 0.171, 'likes5': 0.019, 'allorg': 0.025}\nmix 0.035136888602734645 {'users3': 0.46, 'users5': 0.025, 'pos5': 0.374, 'pos7': 0.028, 'likes3': 0.095}\nmix 0.03510953492389795 {'users3': 0.13, 'pos5': 0.696, 'likes2': 0.051, 'likes3': 0.09, 'org3': 0.023}\nmix 0.03510584238066583 {'users3': 0.171, 'pos5': 0.616, 'likes3': 0.163, 'allorg': 0.047}\nmix 0.03509378302223337 {'users2': 0.186, 'users3': 0.081, 'pos5': 0.467, 'pos7': 0.057, 'likes3': 0.063, 'likes5': 0.085, 'org3': 0.017, 'allorg': 0.043}\nmix 0.03509240776780178 {'users2': 0.073, 'users3': 0.043, 'users5': 0.261, 'pos5': 0.427, 'likes3': 0.072, 'likes5': 0.04, 'org3': 0.085}\nmix 0.03508629936942931 {'users2': 0.148, 'users3': 0.018, 'pos5': 0.483, 'pos7': 0.189, 'likes2': 0.08, 'likes5': 0.026, 'allorg': 0.023}\nmix 0.03508501051142448 {'users2': 0.041, 'users3': 0.049, 'users5': 0.319, 'pos5': 0.348, 'likes3': 0.193, 'allorg': 0.033}\nmix 0.03508410051462522 {'users2': 0.066, 'users3': 0.05, 'pos5': 0.384, 'pos7': 0.231, 'likes3': 0.155, 'likes5': 0.022, 'allorg': 0.089}\nelapsed 34.051100730895996\n",
    "stderr": "",
    "aggregated_output": "individual top [(0.032866839431410624, 'users1'), (0.0327479219292982, 'users2'), (0.03157738444504427, 'users3'), (0.030932762235216175, 'pos7'), (0.03052195989214847, 'users5'), (0.030503291719404538, 'likes3'), (0.03016943788640836, 'users7'), (0.02948500619558697, 'pos5'), (0.029424922908395296, 'likes10'), (0.029296797300008143, 'likes7'), (0.02881789175667384, 'pos10'), (0.028591963268355895, 'pos3'), (0.028192378641262072, 'users10'), (0.02813072548782609, 'org7'), (0.02805124640123192, 'likes5')]\nmix 0.03521787307514779 {'users3': 0.159, 'pos5': 0.579, 'likes3': 0.182, 'org3': 0.02, 'allorg': 0.06}\nmix 0.03517307468257728 {'users3': 0.104, 'pos5': 0.377, 'pos7': 0.289, 'likes3': 0.13, 'likes5': 0.049, 'allorg': 0.04}\nmix 0.03515858191096935 {'users5': 0.149, 'pos5': 0.531, 'pos7': 0.056, 'likes2': 0.037, 'likes3': 0.133, 'org3': 0.094}\nmix 0.0351569077958871 {'users3': 0.039, 'users5': 0.141, 'pos5': 0.534, 'pos7': 0.07, 'likes3': 0.171, 'likes5': 0.019, 'allorg': 0.025}\nmix 0.035136888602734645 {'users3': 0.46, 'users5': 0.025, 'pos5': 0.374, 'pos7': 0.028, 'likes3': 0.095}\nmix 0.03510953492389795 {'users3': 0.13, 'pos5': 0.696, 'likes2': 0.051, 'likes3': 0.09, 'org3': 0.023}\nmix 0.03510584238066583 {'users3': 0.171, 'pos5': 0.616, 'likes3': 0.163, 'allorg': 0.047}\nmix 0.03509378302223337 {'users2': 0.186, 'users3': 0.081, 'pos5': 0.467, 'pos7': 0.057, 'likes3': 0.063, 'likes5': 0.085, 'org3': 0.017, 'allorg': 0.043}\nmix 0.03509240776780178 {'users2': 0.073, 'users3': 0.043, 'users5': 0.261, 'pos5': 0.427, 'likes3': 0.072, 'likes5': 0.04, 'org3': 0.085}\nmix 0.03508629936942931 {'users2': 0.148, 'users3': 0.018, 'pos5': 0.483, 'pos7': 0.189, 'likes2': 0.08, 'likes5': 0.026, 'allorg': 0.023}\nmix 0.03508501051142448 {'users2': 0.041, 'users3': 0.049, 'users5': 0.319, 'pos5': 0.348, 'likes3': 0.193, 'allorg': 0.033}\nmix 0.03508410051462522 {'users2': 0.066, 'users3': 0.05, 'pos5': 0.384, 'pos7': 0.231, 'likes3': 0.155, 'likes5': 0.022, 'allorg': 0.089}\nelapsed 34.051100730895996\n",
    "exit_code": 0,
    "duration": {
      "secs": 35,
      "nanos": 482918789
    },
    "formatted_output": "individual top [(0.032866839431410624, 'users1'), (0.0327479219292982, 'users2'), (0.03157738444504427, 'users3'), (0.030932762235216175, 'pos7'), (0.03052195989214847, 'users5'), (0.030503291719404538, 'likes3'), (0.03016943788640836, 'users7'), (0.02948500619558697, 'pos5'), (0.029424922908395296, 'likes10'), (0.029296797300008143, 'likes7'), (0.02881789175667384, 'pos10'), (0.028591963268355895, 'pos3'), (0.028192378641262072, 'users10'), (0.02813072548782609, 'org7'), (0.02805124640123192, 'likes5')]\nmix 0.03521787307514779 {'users3': 0.159, 'pos5': 0.579, 'likes3': 0.182, 'org3': 0.02, 'allorg': 0.06}\nmix 0.03517307468257728 {'users3': 0.104, 'pos5': 0.377, 'pos7': 0.289, 'likes3': 0.13, 'likes5': 0.049, 'allorg': 0.04}\nmix 0.03515858191096935 {'users5': 0.149, 'pos5': 0.531, 'pos7': 0.056, 'likes2': 0.037, 'likes3': 0.133, 'org3': 0.094}\nmix 0.0351569077958871 {'users3': 0.039, 'users5': 0.141, 'pos5': 0.534, 'pos7': 0.07, 'likes3': 0.171, 'likes5': 0.019, 'allorg': 0.025}\nmix 0.035136888602734645 {'users3': 0.46, 'users5': 0.025, 'pos5': 0.374, 'pos7': 0.028, 'likes3': 0.095}\nmix 0.03510953492389795 {'users3': 0.13, 'pos5': 0.696, 'likes2': 0.051, 'likes3': 0.09, 'org3': 0.023}\nmix 0.03510584238066583 {'users3': 0.171, 'pos5': 0.616, 'likes3': 0.163, 'allorg': 0.047}\nmix 0.03509378302223337 {'users2': 0.186, 'users3': 0.081, 'pos5': 0.467, 'pos7': 0.057, 'likes3': 0.063, 'likes5': 0.085, 'org3': 0.017, 'allorg': 0.043}\nmix 0.03509240776780178 {'users2': 0.073, 'users3': 0.043, 'users5': 0.261, 'pos5': 0.427, 'likes3': 0.072, 'likes5': 0.04, 'org3': 0.085}\nmix 0.03508629936942931 {'users2': 0.148, 'users3': 0.018, 'pos5': 0.483, 'pos7': 0.189, 'likes2': 0.08, 'likes5': 0.026, 'allorg': 0.023}\nmix 0.03508501051142448 {'users2': 0.041, 'users3': 0.049, 'users5': 0.319, 'pos5': 0.348, 'likes3': 0.193, 'allorg': 0.033}\nmix 0.03508410051462522 {'users2': 0.066, 'users3': 0.05, 'pos5': 0.384, 'pos7': 0.231, 'likes3': 0.155, 'likes5': 0.022, 'allorg': 0.089}\nelapsed 34.051100730895996\n"
  }
}

exec command

+25:56

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 +26:02

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution')\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);score=Z[:,[names.index(x) for x in sel]]@w\nwith np.load(D/'validation_eligible_candidates.npz') as q:uids=q['uids'];items=q['item_ids'];packed=q['packed_eligible']\norder=np.argsort(-score,kind='stable');rows=[]\nfor r,u in enumerate(uids):\n mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);top=order[mask[order]][:100];rows.extend((int(u),int(items[i]),j) for j,i in enumerate(top,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_trend_best.parquet',index=False)\nprint(dict(zip(sel,w)))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend_best.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response +26:08

Script completed
Wall time 6.5 seconds
Output:

{np.str_('users2'): np.float64(0.0), np.str_('users3'): np.float64(0.158737274347962), np.str_('users5'): np.float64(0.0002423638345245049), np.str_('pos5'): np.float64(0.5787504658690691), np.str_('pos7'): np.float64(0.0), np.str_('likes2'): np.float64(0.0), np.str_('likes3'): np.float64(0.1821678782002581), np.str_('likes5'): np.float64(0.0), np.str_('org3'): np.float64(0.020257015766419646), np.str_('allorg'): np.float64(0.059845001981766614)}
{
  "model": "/app/solution/validation_trend_best.parquet",
  "scores": {
    "quality_ndcg@10": 0.035218,
    "quality_users": 4263
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c34f8b3c87d2a36b7d9c1f4d7500",
  "usage": {
    "input_tokens": 75941,
    "cached_input_tokens": 74752,
    "cache_write_input_tokens": 0,
    "output_tokens": 399,
    "reasoning_output_tokens": 51,
    "total_tokens": 76340
  },
  "turn_token_usage": {
    "input_tokens": 2566442,
    "cached_input_tokens": 2474880,
    "cache_write_input_tokens": 0,
    "output_tokens": 38508,
    "reasoning_output_tokens": 18806,
    "total_tokens": 2604950
  },
  "thread_token_usage": {
    "input_tokens": 2566442,
    "cached_input_tokens": 2474880,
    "cache_write_input_tokens": 0,
    "output_tokens": 38508,
    "reasoning_output_tokens": 18806,
    "total_tokens": 2604950
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c46469e6-2af9-4d4c-9f05-4c7f40698184",
    "process_id": "43889",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution')\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);score=Z[:,[names.index(x) for x in sel]]@w\nwith np.load(D/'validation_eligible_candidates.npz') as q:uids=q['uids'];items=q['item_ids'];packed=q['packed_eligible']\norder=np.argsort(-score,kind='stable');rows=[]\nfor r,u in enumerate(uids):\n mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);top=order[mask[order]][:100];rows.extend((int(u),int(items[i]),j) for j,i in enumerate(top,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_trend_best.parquet',index=False)\nprint(dict(zip(sel,w)))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend_best.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution')\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);score=Z[:,[names.index(x) for x in sel]]@w\nwith np.load(D/'validation_eligible_candidates.npz') as q:uids=q['uids'];items=q['item_ids'];packed=q['packed_eligible']\norder=np.argsort(-score,kind='stable');rows=[]\nfor r,u in enumerate(uids):\n mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);top=order[mask[order]][:100];rows.extend((int(u),int(items[i]),j) for j,i in enumerate(top,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_trend_best.parquet',index=False)\nprint(dict(zip(sel,w)))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend_best.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{np.str_('users2'): np.float64(0.0), np.str_('users3'): np.float64(0.158737274347962), np.str_('users5'): np.float64(0.0002423638345245049), np.str_('pos5'): np.float64(0.5787504658690691), np.str_('pos7'): np.float64(0.0), np.str_('likes2'): np.float64(0.0), np.str_('likes3'): np.float64(0.1821678782002581), np.str_('likes5'): np.float64(0.0), np.str_('org3'): np.float64(0.020257015766419646), np.str_('allorg'): np.float64(0.059845001981766614)}\n{\n  \"model\": \"/app/solution/validation_trend_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035218,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{np.str_('users2'): np.float64(0.0), np.str_('users3'): np.float64(0.158737274347962), np.str_('users5'): np.float64(0.0002423638345245049), np.str_('pos5'): np.float64(0.5787504658690691), np.str_('pos7'): np.float64(0.0), np.str_('likes2'): np.float64(0.0), np.str_('likes3'): np.float64(0.1821678782002581), np.str_('likes5'): np.float64(0.0), np.str_('org3'): np.float64(0.020257015766419646), np.str_('allorg'): np.float64(0.059845001981766614)}\n{\n  \"model\": \"/app/solution/validation_trend_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035218,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 6,
      "nanos": 307831642
    },
    "formatted_output": "{np.str_('users2'): np.float64(0.0), np.str_('users3'): np.float64(0.158737274347962), np.str_('users5'): np.float64(0.0002423638345245049), np.str_('pos5'): np.float64(0.5787504658690691), np.str_('pos7'): np.float64(0.0), np.str_('likes2'): np.float64(0.0), np.str_('likes3'): np.float64(0.1821678782002581), np.str_('likes5'): np.float64(0.0), np.str_('org3'): np.float64(0.020257015766419646), np.str_('allorg'): np.float64(0.059845001981766614)}\n{\n  \"model\": \"/app/solution/validation_trend_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035218,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+26:17

Thinking unavailable No text recorded

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

Model text

Cutoff-local momentum is the dominant signal: a blend of recent unique organic listeners, five-day positive plays, three-day organic likes, and a small long-run prior scores 0.035218. I’m now adding the neural history score as an orthogonal dot-product block; this remains exact MIPS by concatenating the trend coordinate with the learned tower vectors.

Tool callexecTool call Returned

Tool call · Input +26:31

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution')\nimport ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));saved=torch.load(O/'two_tower_validation_e10.pt',map_location='cpu',weights_only=True);model.load_state_dict(saved['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode(): iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32)\n# Standardizing the global item coordinate is an item-tower-only affine transform.\ntrend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[.02,.05,.08,.12,.18,.25,.35,.5,.75]\nrows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False);print('wrote',b,flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +27:01

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"670807","wall_time_seconds":30.001035003,"session_id":40186,"original_token_count":32,"output":"aggregated 8,044,619 catalog user-item pairs in 8.6s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote 0.02\n"}

Harness context token usage record

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exec command

+27:02

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 +27:03

const r = await tools.write_stdin({"session_id":40186,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +27:05

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"6963b2","wall_time_seconds":2.309588298,"exit_code":0,"original_token_count":22,"output":"wrote 0.05\nwrote 0.08\nwrote 0.12\nwrote 0.18\nwrote 0.25\nwrote 0.35\nwrote 0.5\nwrote 0.75\n"}

Harness context token usage record

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Harness context codex item completed

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    "type": "CommandExecution",
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    "process_id": "40186",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution')\nimport ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));saved=torch.load(O/'two_tower_validation_e10.pt',map_location='cpu',weights_only=True);model.load_state_dict(saved['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode(): iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32)\n# Standardizing the global item coordinate is an item-tower-only affine transform.\ntrend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[.02,.05,.08,.12,.18,.25,.35,.5,.75]\nrows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False);print('wrote',b,flush=True)\nPY"
    ],
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    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution')\nimport ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));saved=torch.load(O/'two_tower_validation_e10.pt',map_location='cpu',weights_only=True);model.load_state_dict(saved['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode(): iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32)\n# Standardizing the global item coordinate is an item-tower-only affine transform.\ntrend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[.02,.05,.08,.12,.18,.25,.35,.5,.75]\nrows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False);print('wrote',b,flush=True)\nPY"
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    "stderr": "",
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  }
}

exec command

+27:07

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 +27:08

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

Tool response +27:38

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"dc3ad5","wall_time_seconds":30.001527919,"session_id":88192,"original_token_count":294,"output":"{\n  \"model\": \"/app/solution/validation_trend_neural_0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035232,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.050.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035282,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.080.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035293,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.120.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035314,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.180.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035316,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.250.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035393,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.350.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035477,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.500.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035617,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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Harness context codex item completed

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    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 30,
      "nanos": 109878137
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_trend_neural_0.020.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035232,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.050.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035282,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.080.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035293,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.120.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035314,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.180.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035316,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.250.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035393,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.350.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035477,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.500.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035617,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_0.750.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.03615,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+27:39

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +27:40

const r = await tools.write_stdin({"session_id":88192,"chars":"","yield_time_ms":10000,"max_output_tokens":10000}); text(JSON.stringify(r));

Tool response +27:40

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"662197","wall_time_seconds":0.00000205,"exit_code":0,"original_token_count":37,"output":"{\n  \"model\": \"/app/solution/validation_trend_neural_0.750.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.03615,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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  "thread_token_usage": {
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Agent message

+27:52

Thinking unavailable No text recorded

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

exec command

+27:54

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 +28:07

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution');import ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));model.load_state_dict(torch.load(O/'two_tower_validation_e10.pt',map_location='cpu',weights_only=True)['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode():iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nnp.save(O/'validation_user_vectors.npy',uv.numpy());np.save(O/'validation_item_vectors.npy',iv.numpy())\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[.9,1.1,1.4,1.8,2.4,3.2]\nrows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False);print('wrote',b,flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +28:36

Script completed
Wall time 29.1 seconds
Output:

{"chunk_id":"6fd593","wall_time_seconds":28.906898265,"exit_code":0,"original_token_count":45,"output":"aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote 0.9\nwrote 1.1\nwrote 1.4\nwrote 1.8\nwrote 2.4\nwrote 3.2\n"}

Harness context token usage record

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  "usage": {
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  "turn_token_usage": {
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  "thread_token_usage": {
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-241341c3-f3bf-4b5e-b495-a120d5b7c21f",
    "process_id": "66469",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution');import ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));model.load_state_dict(torch.load(O/'two_tower_validation_e10.pt',map_location='cpu',weights_only=True)['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode():iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nnp.save(O/'validation_user_vectors.npy',uv.numpy());np.save(O/'validation_item_vectors.npy',iv.numpy())\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[.9,1.1,1.4,1.8,2.4,3.2]\nrows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False);print('wrote',b,flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution');import ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));model.load_state_dict(torch.load(O/'two_tower_validation_e10.pt',map_location='cpu',weights_only=True)['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode():iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nnp.save(O/'validation_user_vectors.npy',uv.numpy());np.save(O/'validation_item_vectors.npy',iv.numpy())\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[.9,1.1,1.4,1.8,2.4,3.2]\nrows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False);print('wrote',b,flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote 0.9\nwrote 1.1\nwrote 1.4\nwrote 1.8\nwrote 2.4\nwrote 3.2\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote 0.9\nwrote 1.1\nwrote 1.4\nwrote 1.8\nwrote 2.4\nwrote 3.2\n",
    "exit_code": 0,
    "duration": {
      "secs": 28,
      "nanos": 906888935
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote 0.9\nwrote 1.1\nwrote 1.4\nwrote 1.8\nwrote 2.4\nwrote 3.2\n"
  }
}

exec command

+28:37

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +28:40

const r = await tools.exec_command({"cmd":"for f in /app/solution/validation_trend_neural_0.900.parquet /app/solution/validation_trend_neural_1.100.parquet /app/solution/validation_trend_neural_1.400.parquet /app/solution/validation_trend_neural_1.800.parquet /app/solution/validation_trend_neural_2.400.parquet /app/solution/validation_trend_neural_3.200.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +29:00

Script completed
Wall time 20.1 seconds
Output:

{"chunk_id":"58581d","wall_time_seconds":19.970611014,"exit_code":0,"original_token_count":221,"output":"{\n  \"model\": \"/app/solution/validation_trend_neural_0.900.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036565,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036597,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036866,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.800.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.037343,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_2.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038131,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_3.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038857,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

{
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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c3f2008487d2bd0b971648ac5976",
  "usage": {
    "input_tokens": 80388,
    "cached_input_tokens": 78720,
    "cache_write_input_tokens": 0,
    "output_tokens": 174,
    "reasoning_output_tokens": 6,
    "total_tokens": 80562
  },
  "turn_token_usage": {
    "input_tokens": 3036924,
    "cached_input_tokens": 2939776,
    "cache_write_input_tokens": 0,
    "output_tokens": 41476,
    "reasoning_output_tokens": 19921,
    "total_tokens": 3078400
  },
  "thread_token_usage": {
    "input_tokens": 3036924,
    "cached_input_tokens": 2939776,
    "cache_write_input_tokens": 0,
    "output_tokens": 41476,
    "reasoning_output_tokens": 19921,
    "total_tokens": 3078400
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-168bddcd-7740-4444-b96c-400e73dd673f",
    "process_id": "77928",
    "command": [
      "/bin/sh",
      "-lc",
      "for f in /app/solution/validation_trend_neural_0.900.parquet /app/solution/validation_trend_neural_1.100.parquet /app/solution/validation_trend_neural_1.400.parquet /app/solution/validation_trend_neural_1.800.parquet /app/solution/validation_trend_neural_2.400.parquet /app/solution/validation_trend_neural_3.200.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for f in /app/solution/validation_trend_neural_0.900.parquet /app/solution/validation_trend_neural_1.100.parquet /app/solution/validation_trend_neural_1.400.parquet /app/solution/validation_trend_neural_1.800.parquet /app/solution/validation_trend_neural_2.400.parquet /app/solution/validation_trend_neural_3.200.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_trend_neural_0.900.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036565,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036597,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036866,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.800.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.037343,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_2.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038131,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_3.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038857,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_trend_neural_0.900.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036565,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036597,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036866,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.800.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.037343,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_2.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038131,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_3.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038857,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 19,
      "nanos": 970603544
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_trend_neural_0.900.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036565,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.100.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036597,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036866,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_1.800.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.037343,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_2.400.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038131,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_3.200.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038857,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+29:04

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +29:15

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');uv=np.load(O/'validation_user_vectors.npy');iv=np.load(O/'validation_item_vectors.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[4,5,6.5,8,10,14];rows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=uv[start:stop]@iv.T;mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False)\nPY\nfor f in /app/solution/validation_trend_neural_4.000.parquet /app/solution/validation_trend_neural_5.000.parquet /app/solution/validation_trend_neural_6.500.parquet /app/solution/validation_trend_neural_8.000.parquet /app/solution/validation_trend_neural_10.000.parquet /app/solution/validation_trend_neural_14.000.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +29:45

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"ab9cf3","wall_time_seconds":30.00090445,"session_id":56563,"original_token_count":147,"output":"{\n  \"model\": \"/app/solution/validation_trend_neural_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039463,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_5.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039833,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_6.500.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040002,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040061,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c40a080087d2a4d073c3aa629c3a",
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-50ab5474-3280-4492-bb08-724347a2c693",
    "process_id": "56563",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');uv=np.load(O/'validation_user_vectors.npy');iv=np.load(O/'validation_item_vectors.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[4,5,6.5,8,10,14];rows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=uv[start:stop]@iv.T;mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False)\nPY\nfor f in /app/solution/validation_trend_neural_4.000.parquet /app/solution/validation_trend_neural_5.000.parquet /app/solution/validation_trend_neural_6.500.parquet /app/solution/validation_trend_neural_8.000.parquet /app/solution/validation_trend_neural_10.000.parquet /app/solution/validation_trend_neural_14.000.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');uv=np.load(O/'validation_user_vectors.npy');iv=np.load(O/'validation_item_vectors.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[4,5,6.5,8,10,14];rows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=uv[start:stop]@iv.T;mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_neural_{b:.3f}.parquet',index=False)\nPY\nfor f in /app/solution/validation_trend_neural_4.000.parquet /app/solution/validation_trend_neural_5.000.parquet /app/solution/validation_trend_neural_6.500.parquet /app/solution/validation_trend_neural_8.000.parquet /app/solution/validation_trend_neural_10.000.parquet /app/solution/validation_trend_neural_14.000.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_trend_neural_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039463,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_5.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039833,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_6.500.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040002,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040061,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_10.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039884,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_14.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038783,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_trend_neural_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039463,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_5.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039833,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_6.500.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040002,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040061,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_10.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039884,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_14.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038783,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 35,
      "nanos": 23204554
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_trend_neural_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039463,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_5.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039833,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_6.500.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040002,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.040061,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_10.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039884,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_14.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038783,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

exec command

+29:51

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 neural block is materially useful once calibrated against the stronger trend prior: the combined score has reached 0.040061, more than double the neural-only tuned result. The optimum is flattening near a neural coefficient of 6–8, and I’m checking the last two points before locking calibration.

Tool callexecTool call Returned

Tool call · Input +29:51

const r = await tools.write_stdin({"session_id":56563,"chars":"","yield_time_ms":10000,"max_output_tokens":10000}); text(JSON.stringify(r));

Tool response +29:51

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"877335","wall_time_seconds":0.00000195,"exit_code":0,"original_token_count":74,"output":"{\n  \"model\": \"/app/solution/validation_trend_neural_10.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039884,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_neural_14.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038783,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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  }
}

exec command

+29:53

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 +29:55

const r = await tools.exec_command({"cmd":"cp /app/solution/validation_trend_neural_8.000.parquet /app/solution/validation_best.parquet\ncp /app/solution/two_tower_validation_e10.pt /app/solution/two_tower_validation_selected.pt\nls -lh /app/solution/validation_best.parquet /app/solution/two_tower_validation_selected.pt","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(r.output);

Tool response +29:55

Script completed
Wall time 0.0 seconds
Output:

-rw-r--r-- 1 agent agent  16M Sep 25 00:45 /app/solution/two_tower_validation_selected.pt
-rw-r--r-- 1 agent agent 1.3M Sep 25 00:45 /app/solution/validation_best.parquet

Harness context codex item completed

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    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/validation_trend_neural_8.000.parquet /app/solution/validation_best.parquet\ncp /app/solution/two_tower_validation_e10.pt /app/solution/two_tower_validation_selected.pt\nls -lh /app/solution/validation_best.parquet /app/solution/two_tower_validation_selected.pt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/validation_trend_neural_8.000.parquet /app/solution/validation_best.parquet\ncp /app/solution/two_tower_validation_e10.pt /app/solution/two_tower_validation_selected.pt\nls -lh /app/solution/validation_best.parquet /app/solution/two_tower_validation_selected.pt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "-rw-r--r-- 1 agent agent  16M Sep 25 00:45 /app/solution/two_tower_validation_selected.pt\n-rw-r--r-- 1 agent agent 1.3M Sep 25 00:45 /app/solution/validation_best.parquet\n",
    "stderr": "",
    "aggregated_output": "-rw-r--r-- 1 agent agent  16M Sep 25 00:45 /app/solution/two_tower_validation_selected.pt\n-rw-r--r-- 1 agent agent 1.3M Sep 25 00:45 /app/solution/validation_best.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6071
    },
    "formatted_output": "-rw-r--r-- 1 agent agent  16M Sep 25 00:45 /app/solution/two_tower_validation_selected.pt\n-rw-r--r-- 1 agent agent 1.3M Sep 25 00:45 /app/solution/validation_best.parquet\n"
  }
}

Harness context token usage record

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}

exec command

+30:04

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +30:06

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 12 --weeks 8 --max-history 512 --seed 20260928 --pop-blends=0","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +30:36

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"2e1aad","wall_time_seconds":30.001142177,"session_id":94687,"original_token_count":43,"output":"aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 33,119 rolling profiles with 282,434 labels (47,682 grade-3)\nepoch 1/12: loss=1.4894 batches=129 elapsed=8.0s\n"}

Harness context token usage record

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}

exec command

+30:37

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +30:38

const r = await tools.write_stdin({"session_id":94687,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +31:08

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"406324","wall_time_seconds":30.001842371,"session_id":94687,"original_token_count":49,"output":"epoch 2/12: loss=1.3884 batches=129 elapsed=8.0s\nepoch 3/12: loss=1.3308 batches=129 elapsed=8.0s\nepoch 4/12: loss=1.2796 batches=129 elapsed=8.0s\nepoch 5/12: loss=1.2382 batches=129 elapsed=7.9s\n"}

Harness context token usage record

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}

exec command

+31:09

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +31:09

const r = await tools.write_stdin({"session_id":94687,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +31:39

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"870a19","wall_time_seconds":30.000867594,"session_id":94687,"original_token_count":49,"output":"epoch 6/12: loss=1.2075 batches=129 elapsed=7.9s\nepoch 7/12: loss=1.1700 batches=129 elapsed=7.9s\nepoch 8/12: loss=1.1440 batches=129 elapsed=7.9s\nepoch 9/12: loss=1.1093 batches=129 elapsed=8.0s\n"}

Harness context token usage record

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exec command

+31:42

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 +31:43

const r = await tools.write_stdin({"session_id":94687,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +32:05

Script completed
Wall time 22.1 seconds
Output:

{"chunk_id":"20687e","wall_time_seconds":22.073117224,"exit_code":0,"original_token_count":53,"output":"epoch 10/12: loss=1.0807 batches=129 elapsed=8.0s\nepoch 11/12: loss=1.0495 batches=129 elapsed=7.9s\nepoch 12/12: loss=1.0172 batches=129 elapsed=8.0s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\n"}

Harness context token usage record

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Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 12 --weeks 8 --max-history 512 --seed 20260928 --pop-blends=0"
    ],
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      {
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        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage validation --epochs 12 --weeks 8 --max-history 512 --seed 20260928 --pop-blends=0"
      }
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    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 33,119 rolling profiles with 282,434 labels (47,682 grade-3)\nepoch 1/12: loss=1.4894 batches=129 elapsed=8.0s\nepoch 2/12: loss=1.3884 batches=129 elapsed=8.0s\nepoch 3/12: loss=1.3308 batches=129 elapsed=8.0s\nepoch 4/12: loss=1.2796 batches=129 elapsed=8.0s\nepoch 5/12: loss=1.2382 batches=129 elapsed=7.9s\nepoch 6/12: loss=1.2075 batches=129 elapsed=7.9s\nepoch 7/12: loss=1.1700 batches=129 elapsed=7.9s\nepoch 8/12: loss=1.1440 batches=129 elapsed=7.9s\nepoch 9/12: loss=1.1093 batches=129 elapsed=8.0s\nepoch 10/12: loss=1.0807 batches=129 elapsed=8.0s\nepoch 11/12: loss=1.0495 batches=129 elapsed=7.9s\nepoch 12/12: loss=1.0172 batches=129 elapsed=8.0s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 33,119 rolling profiles with 282,434 labels (47,682 grade-3)\nepoch 1/12: loss=1.4894 batches=129 elapsed=8.0s\nepoch 2/12: loss=1.3884 batches=129 elapsed=8.0s\nepoch 3/12: loss=1.3308 batches=129 elapsed=8.0s\nepoch 4/12: loss=1.2796 batches=129 elapsed=8.0s\nepoch 5/12: loss=1.2382 batches=129 elapsed=7.9s\nepoch 6/12: loss=1.2075 batches=129 elapsed=7.9s\nepoch 7/12: loss=1.1700 batches=129 elapsed=7.9s\nepoch 8/12: loss=1.1440 batches=129 elapsed=7.9s\nepoch 9/12: loss=1.1093 batches=129 elapsed=8.0s\nepoch 10/12: loss=1.0807 batches=129 elapsed=8.0s\nepoch 11/12: loss=1.0495 batches=129 elapsed=7.9s\nepoch 12/12: loss=1.0172 batches=129 elapsed=8.0s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 119,
      "nanos": 36070425
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.7s\nbuilt 33,119 rolling profiles with 282,434 labels (47,682 grade-3)\nepoch 1/12: loss=1.4894 batches=129 elapsed=8.0s\nepoch 2/12: loss=1.3884 batches=129 elapsed=8.0s\nepoch 3/12: loss=1.3308 batches=129 elapsed=8.0s\nepoch 4/12: loss=1.2796 batches=129 elapsed=8.0s\nepoch 5/12: loss=1.2382 batches=129 elapsed=7.9s\nepoch 6/12: loss=1.2075 batches=129 elapsed=7.9s\nepoch 7/12: loss=1.1700 batches=129 elapsed=7.9s\nepoch 8/12: loss=1.1440 batches=129 elapsed=7.9s\nepoch 9/12: loss=1.1093 batches=129 elapsed=8.0s\nepoch 10/12: loss=1.0807 batches=129 elapsed=8.0s\nepoch 11/12: loss=1.0495 batches=129 elapsed=7.9s\nepoch 12/12: loss=1.0172 batches=129 elapsed=8.0s\nwrote /app/solution/validation_a+0.000.parquet (845,600 rows)\n"
  }
}

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 +32:08

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_a+0.000.parquet\ncp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_recent.pt","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000}); text(r.output);

Tool response +32:11

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_a+0.000.parquet",
  "scores": {
    "quality_ndcg@10": 0.004708,
    "quality_users": 4263
  }
}

Harness context token usage record

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Harness context codex item completed

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      {
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        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_a+0.000.parquet\ncp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_recent.pt"
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  }
}

exec command

+32:14

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 short-horizon tower is substantially worse on its own (0.004708), confirming that broad collaborative history—not just recent identities—is needed. I’ll do one inexpensive orthogonality check against the trend prior, then discard it unless it adds held-out value.

Tool callexecTool call Returned

Tool call · Input +32:28

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution');import ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));model.load_state_dict(torch.load(O/'two_tower_validation_recent.pt',map_location='cpu',weights_only=True)['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode():iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nnp.save(O/'validation_user_vectors_recent.npy',uv.numpy());np.save(O/'validation_item_vectors_recent.npy',iv.numpy())\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[2,4,6,8];rows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_recent_{b:.3f}.parquet',index=False)\nPY\nfor f in /app/solution/validation_trend_recent_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +32:58

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"a95ca1","wall_time_seconds":30.001704131,"session_id":61164,"original_token_count":66,"output":"aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\n{\n  \"model\": \"/app/solution/validation_trend_recent_2.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036643,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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100

exec command

+33:00

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 +33:00

const r = await tools.write_stdin({"session_id":61164,"chars":"","yield_time_ms":30000,"max_output_tokens":20000}); text(JSON.stringify(r));

Tool response +33:07

Script completed
Wall time 6.9 seconds
Output:

{"chunk_id":"685dd1","wall_time_seconds":6.860944739,"exit_code":0,"original_token_count":110,"output":"{\n  \"model\": \"/app/solution/validation_trend_recent_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038316,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_6.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039493,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.03989,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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Harness context codex item completed

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      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution');import ranker as R\nimport numpy as np,pandas as pd,torch\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');pairs=R._aggregate_pair_events(False,cat);hist,_=R.build_histories_and_profiles(pairs,R.TRAIN_END,1);del pairs\nmodel=R.TwoTower(R.build_item_features(cat));model.load_state_dict(torch.load(O/'two_tower_validation_recent.pt',map_location='cpu',weights_only=True)['model']);model.eval()\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nuv=R.encode_inference_users(model,hist,uids,R.TRAIN_END,512)\nwith torch.inference_mode():iv=torch.cat([model.item_tower(torch.arange(s,min(s+2048,len(cat)))) for s in range(0,len(cat),2048)])\nnp.save(O/'validation_user_vectors_recent.npy',uv.numpy());np.save(O/'validation_item_vectors_recent.npy',iv.numpy())\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6)\nbetas=[2,4,6,8];rows={b:[] for b in betas}\nfor start in range(0,len(uids),96):\n stop=min(start+96,len(uids));cos=(uv[start:stop]@iv.T).numpy();mask=np.unpackbits(packed[start:stop],axis=1,count=len(items),bitorder='little').astype(bool)\n for b in betas:\n  score=trend[None,:]+b*cos;score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n  for rr,cols in enumerate(top):rows[b].extend((int(uids[start+rr]),int(items[c]),rank) for rank,c in enumerate(cols,1))\nfor b,x in rows.items():pd.DataFrame(x,columns=['uid','item_id','rank']).to_parquet(O/f'validation_trend_recent_{b:.3f}.parquet',index=False)\nPY\nfor f in /app/solution/validation_trend_recent_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\n{\n  \"model\": \"/app/solution/validation_trend_recent_2.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036643,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038316,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_6.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039493,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.03989,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\n{\n  \"model\": \"/app/solution/validation_trend_recent_2.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036643,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038316,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_6.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039493,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.03989,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 38,
      "nanos": 882867175
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.8s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\n{\n  \"model\": \"/app/solution/validation_trend_recent_2.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036643,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_4.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038316,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_6.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039493,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"/app/solution/validation_trend_recent_8.000.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.03989,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

101

exec command

+33:15

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 +33:21

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nO=Path('/app/solution');z=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);s=Z[:,[names.index(x) for x in sel]]@w;order=np.argsort(-s);rank=np.empty(len(s),int);rank[order]=np.arange(len(s))+1\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');mp=dict(zip(cat.item_id,rank));df=pd.read_parquet(O/'validation_best.parquet');r=df.item_id.map(mp).to_numpy();print(np.quantile(r,[0,.5,.9,.95,.99,1]),'top10',np.quantile(df[df['rank']<=10].item_id.map(mp),[0,.5,.9,.95,.99,1]))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000}); text(r.output);

Tool response +33:23

Script completed
Wall time 1.7 seconds
Output:

[1.0000e+00 8.8000e+01 3.6600e+02 5.5600e+02 1.4970e+03 2.5577e+04] top10 [1.000e+00 1.400e+01 5.600e+01 7.300e+01 2.770e+02 1.428e+04]

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c500f56887d2950739a78ebf51d6",
  "usage": {
    "input_tokens": 85307,
    "cached_input_tokens": 84864,
    "cache_write_input_tokens": 0,
    "output_tokens": 644,
    "reasoning_output_tokens": 358,
    "total_tokens": 85951
  },
  "turn_token_usage": {
    "input_tokens": 3953049,
    "cached_input_tokens": 3849216,
    "cache_write_input_tokens": 0,
    "output_tokens": 44871,
    "reasoning_output_tokens": 20978,
    "total_tokens": 3997920
  },
  "thread_token_usage": {
    "input_tokens": 3953049,
    "cached_input_tokens": 3849216,
    "cache_write_input_tokens": 0,
    "output_tokens": 44871,
    "reasoning_output_tokens": 20978,
    "total_tokens": 3997920
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fe87e50a-3edf-4f20-95cc-c60fd3b7061f",
    "process_id": "53679",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nO=Path('/app/solution');z=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);s=Z[:,[names.index(x) for x in sel]]@w;order=np.argsort(-s);rank=np.empty(len(s),int);rank[order]=np.arange(len(s))+1\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');mp=dict(zip(cat.item_id,rank));df=pd.read_parquet(O/'validation_best.parquet');r=df.item_id.map(mp).to_numpy();print(np.quantile(r,[0,.5,.9,.95,.99,1]),'top10',np.quantile(df[df['rank']<=10].item_id.map(mp),[0,.5,.9,.95,.99,1]))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nO=Path('/app/solution');z=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);s=Z[:,[names.index(x) for x in sel]]@w;order=np.argsort(-s);rank=np.empty(len(s),int);rank[order]=np.arange(len(s))+1\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');mp=dict(zip(cat.item_id,rank));df=pd.read_parquet(O/'validation_best.parquet');r=df.item_id.map(mp).to_numpy();print(np.quantile(r,[0,.5,.9,.95,.99,1]),'top10',np.quantile(df[df['rank']<=10].item_id.map(mp),[0,.5,.9,.95,.99,1]))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "[1.0000e+00 8.8000e+01 3.6600e+02 5.5600e+02 1.4970e+03 2.5577e+04] top10 [1.000e+00 1.400e+01 5.600e+01 7.300e+01 2.770e+02 1.428e+04]\n",
    "stderr": "",
    "aggregated_output": "[1.0000e+00 8.8000e+01 3.6600e+02 5.5600e+02 1.4970e+03 2.5577e+04] top10 [1.000e+00 1.400e+01 5.600e+01 7.300e+01 2.770e+02 1.428e+04]\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 527793531
    },
    "formatted_output": "[1.0000e+00 8.8000e+01 3.6600e+02 5.5600e+02 1.4970e+03 2.5577e+04] top10 [1.000e+00 1.400e+01 5.600e+01 7.300e+01 2.770e+02 1.428e+04]\n"
  }
}

102

exec command

+33:30

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +33:48

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time,itertools\nD=Path('/task/data');O=Path('/app/solution');t=time.time()\nu1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);cols=np.argsort(-trend)[:5000]\n# truth\nuidrow={int(u):r for r,u in enumerate(uids)};itemcol={int(x):c for c,x in enumerate(items)};vl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet');T=set(uids.tolist());s=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1);q=vk[(vk.uid.isin(T))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3);truth=pd.concat([s,q]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,it,g in truth.itertuples(index=False,name=None):\n r=uidrow.get(int(u));c=itemcol.get(int(it));\n if r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1):gold.setdefault(r,{})[c]=int(g)\nrows=np.array(sorted(gold));rr={r:j for j,r in enumerate(rows)};submap={c:j for j,c in enumerate(cols)};G=np.zeros((len(rows),len(cols)),np.uint8)\nfor r,d in gold.items():\n for c,g in d.items():\n  if c in submap:G[rr[r],submap[c]]=g\nE=np.unpackbits(packed[rows],axis=1,count=len(items),bitorder='little')[:,cols].astype(bool)\nC1=u1[rows]@i1[cols].T;C2=u2[rows]@i2[cols].T; base=trend[cols][None,:]\nideal=np.array([sorted(d.values(),reverse=True)[:10] for d in gold.values()],dtype=object);disc=1/np.log2(np.arange(2,12));idcg=np.array([sum((2**g-1)*disc[j] for j,g in enumerate(x)) for x in ideal])\ndef metric(a,b):\n S=base+a*C1+b*C2;S[~E]=-np.inf;top=np.argpartition(S,-10,axis=1)[:,-10:];ss=np.take_along_axis(S,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1);gain=2.**np.take_along_axis(G,top,axis=1)-1;return np.mean((gain*disc).sum(1)/idcg)\nres=[]\nfor a in np.arange(0,12.01,1):\n for b in np.arange(0,12.01,1):res.append((metric(a,b),a,b))\nres.sort(reverse=True);print('top coarse',res[:20],flush=True)\nbest=res[0]\nres2=[]\nfor a in np.arange(max(0,best[1]-1),best[1]+1.01,.25):\n for b in np.arange(max(0,best[2]-1),best[2]+1.01,.25):res2.append((metric(a,b),a,b))\nres2.sort(reverse=True);print('top fine',res2[:15],flush=True);print('elapsed',time.time()-t)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +34:18

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"cd1c40","wall_time_seconds":30.001646741,"session_id":45603,"original_token_count":0,"output":""}

Harness context token usage record

{
  "type": "token_usage_record",
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  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c510e9f887d2b5c9e8a4bb14dfc8",
  "usage": {
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    "cached_input_tokens": 85120,
    "cache_write_input_tokens": 0,
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  },
  "turn_token_usage": {
    "input_tokens": 4039117,
    "cached_input_tokens": 3934336,
    "cache_write_input_tokens": 0,
    "output_tokens": 46220,
    "reasoning_output_tokens": 21276,
    "total_tokens": 4085337
  },
  "thread_token_usage": {
    "input_tokens": 4039117,
    "cached_input_tokens": 3934336,
    "cache_write_input_tokens": 0,
    "output_tokens": 46220,
    "reasoning_output_tokens": 21276,
    "total_tokens": 4085337
  }
}

103

exec command

+34:20

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 +34:21

const r = await tools.write_stdin({"session_id":45603,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +34:42

Script completed
Wall time 21.3 seconds
Output:

{"chunk_id":"641d34","wall_time_seconds":21.261793288,"exit_code":0,"original_token_count":620,"output":"top coarse [(np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10582722751885286), np.float64(7.0), np.float64(9.0)), (np.float64(0.10546841893846913), np.float64(7.0), np.float64(7.0)), (np.float64(0.10514630917346275), np.float64(9.0), np.float64(7.0)), (np.float64(0.10501803075975533), np.float64(8.0), np.float64(8.0)), (np.float64(0.10497017735888099), np.float64(2.0), np.float64(8.0)), (np.float64(0.10482834960967578), np.float64(4.0), np.float64(8.0)), (np.float64(0.10477441915019176), np.float64(4.0), np.float64(9.0)), (np.float64(0.10472041020168282), np.float64(6.0), np.float64(10.0)), (np.float64(0.10459470508100854), np.float64(6.0), np.float64(7.0)), (np.float64(0.10457142265451044), np.float64(7.0), np.float64(6.0)), (np.float64(0.10456335066620683), np.float64(8.0), np.float64(6.0)), (np.float64(0.10454609661212973), np.float64(8.0), np.float64(5.0)), (np.float64(0.10454186363539748), np.float64(7.0), np.float64(10.0)), (np.float64(0.10453396517316396), np.float64(6.0), np.float64(8.0)), (np.float64(0.10453347781102681), np.float64(8.0), np.float64(7.0)), (np.float64(0.10452380954217029), np.float64(8.0), np.float64(4.0)), (np.float64(0.10449597306765633), np.float64(3.0), np.float64(8.0)), (np.float64(0.10442556287705458), np.float64(7.0), np.float64(5.0)), (np.float64(0.10441182759845576), np.float64(10.0), np.float64(6.0))]\ntop fine [(np.float64(0.1064801461610459), np.float64(2.75), np.float64(9.0)), (np.float64(0.1062392406998508), np.float64(3.0), np.float64(8.75)), (np.float64(0.1062307913621427), np.float64(3.25), np.float64(8.75)), (np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10606609113778744), np.float64(3.0), np.float64(9.25)), (np.float64(0.10594267867056455), np.float64(3.25), np.float64(8.5)), (np.float64(0.10589863616463568), np.float64(3.25), np.float64(9.0)), (np.float64(0.10588133800701707), np.float64(3.0), np.float64(8.5)), (np.float64(0.10567777121862555), np.float64(2.75), np.float64(9.25)), (np.float64(0.10565913007752996), np.float64(2.75), np.float64(8.75)), (np.float64(0.1055418911350627), np.float64(3.75), np.float64(8.75)), (np.float64(0.10552745531801228), np.float64(3.0), np.float64(8.25)), (np.float64(0.1055098865675039), np.float64(3.5), np.float64(8.25)), (np.float64(0.10548012740407688), np.float64(3.5), np.float64(8.75)), (np.float64(0.10539333329387275), np.float64(3.5), np.float64(8.5))]\nelapsed 52.07986760139465\n"}

Harness context token usage record

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  },
  "turn_token_usage": {
    "input_tokens": 4126592,
    "cached_input_tokens": 4020224,
    "cache_write_input_tokens": 0,
    "output_tokens": 46281,
    "reasoning_output_tokens": 21289,
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  },
  "thread_token_usage": {
    "input_tokens": 4126592,
    "cached_input_tokens": 4020224,
    "cache_write_input_tokens": 0,
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  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-684d9c82-35f2-4a1c-aa46-d5a0d7ac5e6a",
    "process_id": "45603",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time,itertools\nD=Path('/task/data');O=Path('/app/solution');t=time.time()\nu1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);cols=np.argsort(-trend)[:5000]\n# truth\nuidrow={int(u):r for r,u in enumerate(uids)};itemcol={int(x):c for c,x in enumerate(items)};vl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet');T=set(uids.tolist());s=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1);q=vk[(vk.uid.isin(T))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3);truth=pd.concat([s,q]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,it,g in truth.itertuples(index=False,name=None):\n r=uidrow.get(int(u));c=itemcol.get(int(it));\n if r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1):gold.setdefault(r,{})[c]=int(g)\nrows=np.array(sorted(gold));rr={r:j for j,r in enumerate(rows)};submap={c:j for j,c in enumerate(cols)};G=np.zeros((len(rows),len(cols)),np.uint8)\nfor r,d in gold.items():\n for c,g in d.items():\n  if c in submap:G[rr[r],submap[c]]=g\nE=np.unpackbits(packed[rows],axis=1,count=len(items),bitorder='little')[:,cols].astype(bool)\nC1=u1[rows]@i1[cols].T;C2=u2[rows]@i2[cols].T; base=trend[cols][None,:]\nideal=np.array([sorted(d.values(),reverse=True)[:10] for d in gold.values()],dtype=object);disc=1/np.log2(np.arange(2,12));idcg=np.array([sum((2**g-1)*disc[j] for j,g in enumerate(x)) for x in ideal])\ndef metric(a,b):\n S=base+a*C1+b*C2;S[~E]=-np.inf;top=np.argpartition(S,-10,axis=1)[:,-10:];ss=np.take_along_axis(S,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1);gain=2.**np.take_along_axis(G,top,axis=1)-1;return np.mean((gain*disc).sum(1)/idcg)\nres=[]\nfor a in np.arange(0,12.01,1):\n for b in np.arange(0,12.01,1):res.append((metric(a,b),a,b))\nres.sort(reverse=True);print('top coarse',res[:20],flush=True)\nbest=res[0]\nres2=[]\nfor a in np.arange(max(0,best[1]-1),best[1]+1.01,.25):\n for b in np.arange(max(0,best[2]-1),best[2]+1.01,.25):res2.append((metric(a,b),a,b))\nres2.sort(reverse=True);print('top fine',res2[:15],flush=True);print('elapsed',time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time,itertools\nD=Path('/task/data');O=Path('/app/solution');t=time.time()\nu1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);cols=np.argsort(-trend)[:5000]\n# truth\nuidrow={int(u):r for r,u in enumerate(uids)};itemcol={int(x):c for c,x in enumerate(items)};vl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet');T=set(uids.tolist());s=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1);q=vk[(vk.uid.isin(T))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3);truth=pd.concat([s,q]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,it,g in truth.itertuples(index=False,name=None):\n r=uidrow.get(int(u));c=itemcol.get(int(it));\n if r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1):gold.setdefault(r,{})[c]=int(g)\nrows=np.array(sorted(gold));rr={r:j for j,r in enumerate(rows)};submap={c:j for j,c in enumerate(cols)};G=np.zeros((len(rows),len(cols)),np.uint8)\nfor r,d in gold.items():\n for c,g in d.items():\n  if c in submap:G[rr[r],submap[c]]=g\nE=np.unpackbits(packed[rows],axis=1,count=len(items),bitorder='little')[:,cols].astype(bool)\nC1=u1[rows]@i1[cols].T;C2=u2[rows]@i2[cols].T; base=trend[cols][None,:]\nideal=np.array([sorted(d.values(),reverse=True)[:10] for d in gold.values()],dtype=object);disc=1/np.log2(np.arange(2,12));idcg=np.array([sum((2**g-1)*disc[j] for j,g in enumerate(x)) for x in ideal])\ndef metric(a,b):\n S=base+a*C1+b*C2;S[~E]=-np.inf;top=np.argpartition(S,-10,axis=1)[:,-10:];ss=np.take_along_axis(S,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1);gain=2.**np.take_along_axis(G,top,axis=1)-1;return np.mean((gain*disc).sum(1)/idcg)\nres=[]\nfor a in np.arange(0,12.01,1):\n for b in np.arange(0,12.01,1):res.append((metric(a,b),a,b))\nres.sort(reverse=True);print('top coarse',res[:20],flush=True)\nbest=res[0]\nres2=[]\nfor a in np.arange(max(0,best[1]-1),best[1]+1.01,.25):\n for b in np.arange(max(0,best[2]-1),best[2]+1.01,.25):res2.append((metric(a,b),a,b))\nres2.sort(reverse=True);print('top fine',res2[:15],flush=True);print('elapsed',time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "top coarse [(np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10582722751885286), np.float64(7.0), np.float64(9.0)), (np.float64(0.10546841893846913), np.float64(7.0), np.float64(7.0)), (np.float64(0.10514630917346275), np.float64(9.0), np.float64(7.0)), (np.float64(0.10501803075975533), np.float64(8.0), np.float64(8.0)), (np.float64(0.10497017735888099), np.float64(2.0), np.float64(8.0)), (np.float64(0.10482834960967578), np.float64(4.0), np.float64(8.0)), (np.float64(0.10477441915019176), np.float64(4.0), np.float64(9.0)), (np.float64(0.10472041020168282), np.float64(6.0), np.float64(10.0)), (np.float64(0.10459470508100854), np.float64(6.0), np.float64(7.0)), (np.float64(0.10457142265451044), np.float64(7.0), np.float64(6.0)), (np.float64(0.10456335066620683), np.float64(8.0), np.float64(6.0)), (np.float64(0.10454609661212973), np.float64(8.0), np.float64(5.0)), (np.float64(0.10454186363539748), np.float64(7.0), np.float64(10.0)), (np.float64(0.10453396517316396), np.float64(6.0), np.float64(8.0)), (np.float64(0.10453347781102681), np.float64(8.0), np.float64(7.0)), (np.float64(0.10452380954217029), np.float64(8.0), np.float64(4.0)), (np.float64(0.10449597306765633), np.float64(3.0), np.float64(8.0)), (np.float64(0.10442556287705458), np.float64(7.0), np.float64(5.0)), (np.float64(0.10441182759845576), np.float64(10.0), np.float64(6.0))]\ntop fine [(np.float64(0.1064801461610459), np.float64(2.75), np.float64(9.0)), (np.float64(0.1062392406998508), np.float64(3.0), np.float64(8.75)), (np.float64(0.1062307913621427), np.float64(3.25), np.float64(8.75)), (np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10606609113778744), np.float64(3.0), np.float64(9.25)), (np.float64(0.10594267867056455), np.float64(3.25), np.float64(8.5)), (np.float64(0.10589863616463568), np.float64(3.25), np.float64(9.0)), (np.float64(0.10588133800701707), np.float64(3.0), np.float64(8.5)), (np.float64(0.10567777121862555), np.float64(2.75), np.float64(9.25)), (np.float64(0.10565913007752996), np.float64(2.75), np.float64(8.75)), (np.float64(0.1055418911350627), np.float64(3.75), np.float64(8.75)), (np.float64(0.10552745531801228), np.float64(3.0), np.float64(8.25)), (np.float64(0.1055098865675039), np.float64(3.5), np.float64(8.25)), (np.float64(0.10548012740407688), np.float64(3.5), np.float64(8.75)), (np.float64(0.10539333329387275), np.float64(3.5), np.float64(8.5))]\nelapsed 52.07986760139465\n",
    "stderr": "",
    "aggregated_output": "top coarse [(np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10582722751885286), np.float64(7.0), np.float64(9.0)), (np.float64(0.10546841893846913), np.float64(7.0), np.float64(7.0)), (np.float64(0.10514630917346275), np.float64(9.0), np.float64(7.0)), (np.float64(0.10501803075975533), np.float64(8.0), np.float64(8.0)), (np.float64(0.10497017735888099), np.float64(2.0), np.float64(8.0)), (np.float64(0.10482834960967578), np.float64(4.0), np.float64(8.0)), (np.float64(0.10477441915019176), np.float64(4.0), np.float64(9.0)), (np.float64(0.10472041020168282), np.float64(6.0), np.float64(10.0)), (np.float64(0.10459470508100854), np.float64(6.0), np.float64(7.0)), (np.float64(0.10457142265451044), np.float64(7.0), np.float64(6.0)), (np.float64(0.10456335066620683), np.float64(8.0), np.float64(6.0)), (np.float64(0.10454609661212973), np.float64(8.0), np.float64(5.0)), (np.float64(0.10454186363539748), np.float64(7.0), np.float64(10.0)), (np.float64(0.10453396517316396), np.float64(6.0), np.float64(8.0)), (np.float64(0.10453347781102681), np.float64(8.0), np.float64(7.0)), (np.float64(0.10452380954217029), np.float64(8.0), np.float64(4.0)), (np.float64(0.10449597306765633), np.float64(3.0), np.float64(8.0)), (np.float64(0.10442556287705458), np.float64(7.0), np.float64(5.0)), (np.float64(0.10441182759845576), np.float64(10.0), np.float64(6.0))]\ntop fine [(np.float64(0.1064801461610459), np.float64(2.75), np.float64(9.0)), (np.float64(0.1062392406998508), np.float64(3.0), np.float64(8.75)), (np.float64(0.1062307913621427), np.float64(3.25), np.float64(8.75)), (np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10606609113778744), np.float64(3.0), np.float64(9.25)), (np.float64(0.10594267867056455), np.float64(3.25), np.float64(8.5)), (np.float64(0.10589863616463568), np.float64(3.25), np.float64(9.0)), (np.float64(0.10588133800701707), np.float64(3.0), np.float64(8.5)), (np.float64(0.10567777121862555), np.float64(2.75), np.float64(9.25)), (np.float64(0.10565913007752996), np.float64(2.75), np.float64(8.75)), (np.float64(0.1055418911350627), np.float64(3.75), np.float64(8.75)), (np.float64(0.10552745531801228), np.float64(3.0), np.float64(8.25)), (np.float64(0.1055098865675039), np.float64(3.5), np.float64(8.25)), (np.float64(0.10548012740407688), np.float64(3.5), np.float64(8.75)), (np.float64(0.10539333329387275), np.float64(3.5), np.float64(8.5))]\nelapsed 52.07986760139465\n",
    "exit_code": 0,
    "duration": {
      "secs": 53,
      "nanos": 442200243
    },
    "formatted_output": "top coarse [(np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10582722751885286), np.float64(7.0), np.float64(9.0)), (np.float64(0.10546841893846913), np.float64(7.0), np.float64(7.0)), (np.float64(0.10514630917346275), np.float64(9.0), np.float64(7.0)), (np.float64(0.10501803075975533), np.float64(8.0), np.float64(8.0)), (np.float64(0.10497017735888099), np.float64(2.0), np.float64(8.0)), (np.float64(0.10482834960967578), np.float64(4.0), np.float64(8.0)), (np.float64(0.10477441915019176), np.float64(4.0), np.float64(9.0)), (np.float64(0.10472041020168282), np.float64(6.0), np.float64(10.0)), (np.float64(0.10459470508100854), np.float64(6.0), np.float64(7.0)), (np.float64(0.10457142265451044), np.float64(7.0), np.float64(6.0)), (np.float64(0.10456335066620683), np.float64(8.0), np.float64(6.0)), (np.float64(0.10454609661212973), np.float64(8.0), np.float64(5.0)), (np.float64(0.10454186363539748), np.float64(7.0), np.float64(10.0)), (np.float64(0.10453396517316396), np.float64(6.0), np.float64(8.0)), (np.float64(0.10453347781102681), np.float64(8.0), np.float64(7.0)), (np.float64(0.10452380954217029), np.float64(8.0), np.float64(4.0)), (np.float64(0.10449597306765633), np.float64(3.0), np.float64(8.0)), (np.float64(0.10442556287705458), np.float64(7.0), np.float64(5.0)), (np.float64(0.10441182759845576), np.float64(10.0), np.float64(6.0))]\ntop fine [(np.float64(0.1064801461610459), np.float64(2.75), np.float64(9.0)), (np.float64(0.1062392406998508), np.float64(3.0), np.float64(8.75)), (np.float64(0.1062307913621427), np.float64(3.25), np.float64(8.75)), (np.float64(0.10614257386224447), np.float64(3.0), np.float64(9.0)), (np.float64(0.10606609113778744), np.float64(3.0), np.float64(9.25)), (np.float64(0.10594267867056455), np.float64(3.25), np.float64(8.5)), (np.float64(0.10589863616463568), np.float64(3.25), np.float64(9.0)), (np.float64(0.10588133800701707), np.float64(3.0), np.float64(8.5)), (np.float64(0.10567777121862555), np.float64(2.75), np.float64(9.25)), (np.float64(0.10565913007752996), np.float64(2.75), np.float64(8.75)), (np.float64(0.1055418911350627), np.float64(3.75), np.float64(8.75)), (np.float64(0.10552745531801228), np.float64(3.0), np.float64(8.25)), (np.float64(0.1055098865675039), np.float64(3.5), np.float64(8.25)), (np.float64(0.10548012740407688), np.float64(3.5), np.float64(8.75)), (np.float64(0.10539333329387275), np.float64(3.5), np.float64(8.5))]\nelapsed 52.07986760139465\n"
  }
}

104

exec command

+34:47

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 +35:06

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time\nD=Path('/task/data');O=Path('/app/solution');t=time.time();u1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);cols=np.argsort(-trend)[:5000]\nuidrow={int(u):r for r,u in enumerate(uids)};itemcol={int(x):c for c,x in enumerate(items)};vl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet');T=set(uids.tolist());s=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1);q=vk[(vk.uid.isin(T))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3);truth=pd.concat([s,q]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,it,g in truth.itertuples(index=False,name=None):\n r=uidrow.get(int(u));c=itemcol.get(int(it));\n if r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1):gold.setdefault(r,{})[c]=int(g)\nrows=np.array(sorted(gold));rr={r:j for j,r in enumerate(rows)};submap={c:j for j,c in enumerate(cols)};G=np.zeros((len(rows),len(cols)),np.uint8)\nfor r,d in gold.items():\n for c,g in d.items():\n  j=submap.get(c)\n  if j is not None:G[rr[r],j]=g\nE=np.unpackbits(packed[rows],axis=1,count=len(items),bitorder='little')[:,cols].astype(bool);C1=u1[rows]@i1[cols].T;C2=u2[rows]@i2[cols].T;base=trend[cols][None,:];disc=1/np.log2(np.arange(2,12));idcg=np.array([sum((2**g-1)*disc[j] for j,g in enumerate(sorted(gold[r].values(),reverse=True)[:10])) for r in rows])\ndef metric(a,b):\n S=base+a*C1+b*C2;S[~E]=-np.inf;top=np.argpartition(S,-10,axis=1)[:,-10:];ss=np.take_along_axis(S,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1);gain=2.**np.take_along_axis(G,top,axis=1)-1;return float(np.mean((gain*disc).sum(1)/idcg))\ncoarse=[]\nfor a in [0,2,4,6,8,10,12]:\n for b in [0,2,4,6,8,10,12]:coarse.append((metric(a,b),a,b))\ncoarse.sort(reverse=True);print('coarse',coarse[:15],flush=True);_,aa,bb=coarse[0];fine=[]\nfor a in np.arange(max(0,aa-2),aa+2.01,.5):\n for b in np.arange(max(0,bb-2),bb+2.01,.5):fine.append((metric(a,b),a,b))\nfine.sort(reverse=True);print('fine',fine[:15],flush=True);print('sanity',metric(0,0),metric(8,0),metric(0,8),'elapsed',time.time()-t)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +35:33

Script completed
Wall time 27.2 seconds
Output:

{"chunk_id":"b0a928","wall_time_seconds":27.03896084,"exit_code":0,"original_token_count":351,"output":"coarse [(0.041402682631678525, 8, 4), (0.04134399456687542, 2, 8), (0.04111839119060655, 6, 6), (0.04107271859223405, 4, 4), (0.04102725879555241, 4, 6), (0.04101356659571699, 6, 4), (0.040852632543974345, 6, 2), (0.04078270218295889, 8, 6), (0.040716521422971164, 4, 8), (0.04065804458939383, 6, 8), (0.04060732767585899, 10, 4), (0.040580349714790895, 6, 10), (0.04054534249033261, 8, 8), (0.04053380419543212, 8, 2), (0.04051176155889668, 4, 10)]\nfine [(0.04140635216104516, np.float64(6.5), np.float64(5.0)), (0.041402682631678525, np.float64(8.0), np.float64(4.0)), (0.04136697524605427, np.float64(6.5), np.float64(4.5)), (0.04133571773788341, np.float64(8.0), np.float64(3.5)), (0.04128172724565291, np.float64(8.0), np.float64(4.5)), (0.0412243769044777, np.float64(7.5), np.float64(4.0)), (0.04122412947906196, np.float64(6.0), np.float64(5.0)), (0.04121106838199536, np.float64(7.5), np.float64(4.5)), (0.04118020108706507, np.float64(7.0), np.float64(4.0)), (0.04117523950686598, np.float64(6.0), np.float64(5.5)), (0.04116328098550221, np.float64(6.5), np.float64(4.0)), (0.04116229765858235, np.float64(6.0), np.float64(4.5)), (0.04115959692394152, np.float64(7.0), np.float64(5.0)), (0.0411588590104633, np.float64(6.5), np.float64(5.5)), (0.041134930995477555, np.float64(7.5), np.float64(3.5))]\nsanity 0.03521787307514778 0.04006130559470743 0.03988990978014266 elapsed 25.66008973121643\n"}

Harness context token usage record

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  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
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  "usage": {
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  "turn_token_usage": {
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-b7cde8a0-6e3d-4dde-995d-0cf6cc2b90e0",
    "process_id": "71994",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time\nD=Path('/task/data');O=Path('/app/solution');t=time.time();u1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);cols=np.argsort(-trend)[:5000]\nuidrow={int(u):r for r,u in enumerate(uids)};itemcol={int(x):c for c,x in enumerate(items)};vl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet');T=set(uids.tolist());s=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1);q=vk[(vk.uid.isin(T))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3);truth=pd.concat([s,q]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,it,g in truth.itertuples(index=False,name=None):\n r=uidrow.get(int(u));c=itemcol.get(int(it));\n if r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1):gold.setdefault(r,{})[c]=int(g)\nrows=np.array(sorted(gold));rr={r:j for j,r in enumerate(rows)};submap={c:j for j,c in enumerate(cols)};G=np.zeros((len(rows),len(cols)),np.uint8)\nfor r,d in gold.items():\n for c,g in d.items():\n  j=submap.get(c)\n  if j is not None:G[rr[r],j]=g\nE=np.unpackbits(packed[rows],axis=1,count=len(items),bitorder='little')[:,cols].astype(bool);C1=u1[rows]@i1[cols].T;C2=u2[rows]@i2[cols].T;base=trend[cols][None,:];disc=1/np.log2(np.arange(2,12));idcg=np.array([sum((2**g-1)*disc[j] for j,g in enumerate(sorted(gold[r].values(),reverse=True)[:10])) for r in rows])\ndef metric(a,b):\n S=base+a*C1+b*C2;S[~E]=-np.inf;top=np.argpartition(S,-10,axis=1)[:,-10:];ss=np.take_along_axis(S,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1);gain=2.**np.take_along_axis(G,top,axis=1)-1;return float(np.mean((gain*disc).sum(1)/idcg))\ncoarse=[]\nfor a in [0,2,4,6,8,10,12]:\n for b in [0,2,4,6,8,10,12]:coarse.append((metric(a,b),a,b))\ncoarse.sort(reverse=True);print('coarse',coarse[:15],flush=True);_,aa,bb=coarse[0];fine=[]\nfor a in np.arange(max(0,aa-2),aa+2.01,.5):\n for b in np.arange(max(0,bb-2),bb+2.01,.5):fine.append((metric(a,b),a,b))\nfine.sort(reverse=True);print('fine',fine[:15],flush=True);print('sanity',metric(0,0),metric(8,0),metric(0,8),'elapsed',time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,time\nD=Path('/task/data');O=Path('/app/solution');t=time.time();u1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as a:uids=a['uids'].astype(np.int64);items=a['item_ids'].astype(np.int64);packed=a['packed_eligible'].copy()\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);cols=np.argsort(-trend)[:5000]\nuidrow={int(u):r for r,u in enumerate(uids)};itemcol={int(x):c for c,x in enumerate(items)};vl=pd.read_parquet(D/'val_listens.parquet');vk=pd.read_parquet(D/'val_likes.parquet');T=set(uids.tolist());s=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1);q=vk[(vk.uid.isin(T))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3);truth=pd.concat([s,q]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,it,g in truth.itertuples(index=False,name=None):\n r=uidrow.get(int(u));c=itemcol.get(int(it));\n if r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1):gold.setdefault(r,{})[c]=int(g)\nrows=np.array(sorted(gold));rr={r:j for j,r in enumerate(rows)};submap={c:j for j,c in enumerate(cols)};G=np.zeros((len(rows),len(cols)),np.uint8)\nfor r,d in gold.items():\n for c,g in d.items():\n  j=submap.get(c)\n  if j is not None:G[rr[r],j]=g\nE=np.unpackbits(packed[rows],axis=1,count=len(items),bitorder='little')[:,cols].astype(bool);C1=u1[rows]@i1[cols].T;C2=u2[rows]@i2[cols].T;base=trend[cols][None,:];disc=1/np.log2(np.arange(2,12));idcg=np.array([sum((2**g-1)*disc[j] for j,g in enumerate(sorted(gold[r].values(),reverse=True)[:10])) for r in rows])\ndef metric(a,b):\n S=base+a*C1+b*C2;S[~E]=-np.inf;top=np.argpartition(S,-10,axis=1)[:,-10:];ss=np.take_along_axis(S,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1),axis=1);gain=2.**np.take_along_axis(G,top,axis=1)-1;return float(np.mean((gain*disc).sum(1)/idcg))\ncoarse=[]\nfor a in [0,2,4,6,8,10,12]:\n for b in [0,2,4,6,8,10,12]:coarse.append((metric(a,b),a,b))\ncoarse.sort(reverse=True);print('coarse',coarse[:15],flush=True);_,aa,bb=coarse[0];fine=[]\nfor a in np.arange(max(0,aa-2),aa+2.01,.5):\n for b in np.arange(max(0,bb-2),bb+2.01,.5):fine.append((metric(a,b),a,b))\nfine.sort(reverse=True);print('fine',fine[:15],flush=True);print('sanity',metric(0,0),metric(8,0),metric(0,8),'elapsed',time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "coarse [(0.041402682631678525, 8, 4), (0.04134399456687542, 2, 8), (0.04111839119060655, 6, 6), (0.04107271859223405, 4, 4), (0.04102725879555241, 4, 6), (0.04101356659571699, 6, 4), (0.040852632543974345, 6, 2), (0.04078270218295889, 8, 6), (0.040716521422971164, 4, 8), (0.04065804458939383, 6, 8), (0.04060732767585899, 10, 4), (0.040580349714790895, 6, 10), (0.04054534249033261, 8, 8), (0.04053380419543212, 8, 2), (0.04051176155889668, 4, 10)]\nfine [(0.04140635216104516, np.float64(6.5), np.float64(5.0)), (0.041402682631678525, np.float64(8.0), np.float64(4.0)), (0.04136697524605427, np.float64(6.5), np.float64(4.5)), (0.04133571773788341, np.float64(8.0), np.float64(3.5)), (0.04128172724565291, np.float64(8.0), np.float64(4.5)), (0.0412243769044777, np.float64(7.5), np.float64(4.0)), (0.04122412947906196, np.float64(6.0), np.float64(5.0)), (0.04121106838199536, np.float64(7.5), np.float64(4.5)), (0.04118020108706507, np.float64(7.0), np.float64(4.0)), (0.04117523950686598, np.float64(6.0), np.float64(5.5)), (0.04116328098550221, np.float64(6.5), np.float64(4.0)), (0.04116229765858235, np.float64(6.0), np.float64(4.5)), (0.04115959692394152, np.float64(7.0), np.float64(5.0)), (0.0411588590104633, np.float64(6.5), np.float64(5.5)), (0.041134930995477555, np.float64(7.5), np.float64(3.5))]\nsanity 0.03521787307514778 0.04006130559470743 0.03988990978014266 elapsed 25.66008973121643\n",
    "stderr": "",
    "aggregated_output": "coarse [(0.041402682631678525, 8, 4), (0.04134399456687542, 2, 8), (0.04111839119060655, 6, 6), (0.04107271859223405, 4, 4), (0.04102725879555241, 4, 6), (0.04101356659571699, 6, 4), (0.040852632543974345, 6, 2), (0.04078270218295889, 8, 6), (0.040716521422971164, 4, 8), (0.04065804458939383, 6, 8), (0.04060732767585899, 10, 4), (0.040580349714790895, 6, 10), (0.04054534249033261, 8, 8), (0.04053380419543212, 8, 2), (0.04051176155889668, 4, 10)]\nfine [(0.04140635216104516, np.float64(6.5), np.float64(5.0)), (0.041402682631678525, np.float64(8.0), np.float64(4.0)), (0.04136697524605427, np.float64(6.5), np.float64(4.5)), (0.04133571773788341, np.float64(8.0), np.float64(3.5)), (0.04128172724565291, np.float64(8.0), np.float64(4.5)), (0.0412243769044777, np.float64(7.5), np.float64(4.0)), (0.04122412947906196, np.float64(6.0), np.float64(5.0)), (0.04121106838199536, np.float64(7.5), np.float64(4.5)), (0.04118020108706507, np.float64(7.0), np.float64(4.0)), (0.04117523950686598, np.float64(6.0), np.float64(5.5)), (0.04116328098550221, np.float64(6.5), np.float64(4.0)), (0.04116229765858235, np.float64(6.0), np.float64(4.5)), (0.04115959692394152, np.float64(7.0), np.float64(5.0)), (0.0411588590104633, np.float64(6.5), np.float64(5.5)), (0.041134930995477555, np.float64(7.5), np.float64(3.5))]\nsanity 0.03521787307514778 0.04006130559470743 0.03988990978014266 elapsed 25.66008973121643\n",
    "exit_code": 0,
    "duration": {
      "secs": 27,
      "nanos": 38930059
    },
    "formatted_output": "coarse [(0.041402682631678525, 8, 4), (0.04134399456687542, 2, 8), (0.04111839119060655, 6, 6), (0.04107271859223405, 4, 4), (0.04102725879555241, 4, 6), (0.04101356659571699, 6, 4), (0.040852632543974345, 6, 2), (0.04078270218295889, 8, 6), (0.040716521422971164, 4, 8), (0.04065804458939383, 6, 8), (0.04060732767585899, 10, 4), (0.040580349714790895, 6, 10), (0.04054534249033261, 8, 8), (0.04053380419543212, 8, 2), (0.04051176155889668, 4, 10)]\nfine [(0.04140635216104516, np.float64(6.5), np.float64(5.0)), (0.041402682631678525, np.float64(8.0), np.float64(4.0)), (0.04136697524605427, np.float64(6.5), np.float64(4.5)), (0.04133571773788341, np.float64(8.0), np.float64(3.5)), (0.04128172724565291, np.float64(8.0), np.float64(4.5)), (0.0412243769044777, np.float64(7.5), np.float64(4.0)), (0.04122412947906196, np.float64(6.0), np.float64(5.0)), (0.04121106838199536, np.float64(7.5), np.float64(4.5)), (0.04118020108706507, np.float64(7.0), np.float64(4.0)), (0.04117523950686598, np.float64(6.0), np.float64(5.5)), (0.04116328098550221, np.float64(6.5), np.float64(4.0)), (0.04116229765858235, np.float64(6.0), np.float64(4.5)), (0.04115959692394152, np.float64(7.0), np.float64(5.0)), (0.0411588590104633, np.float64(6.5), np.float64(5.5)), (0.041134930995477555, np.float64(7.5), np.float64(3.5))]\nsanity 0.03521787307514778 0.04006130559470743 0.03988990978014266 elapsed 25.66008973121643\n"
  }
}

105

exec command

+35:35

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 +35:44

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');u1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as q:uids=q['uids'];items=q['item_ids'];packed=q['packed_eligible']\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);rows=[]\nfor s in range(0,len(uids),96):\n e=min(s+96,len(uids));score=trend[None,:]+6.5*(u1[s:e]@i1.T)+5*(u2[s:e]@i2.T);mask=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder='little').astype(bool);score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n for r,cols in enumerate(top):rows.extend((int(uids[s+r]),int(items[c]),rank) for rank,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_ensemble_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_best.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response +35:52

Script completed
Wall time 8.0 seconds
Output:

{
  "model": "/app/solution/validation_ensemble_best.parquet",
  "scores": {
    "quality_ndcg@10": 0.041406,
    "quality_users": 4263
  }
}

Harness context token usage record

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  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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  "turn_token_usage": {
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  "thread_token_usage": {
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    "reasoning_output_tokens": 21561,
    "total_tokens": 4354062
  }
}

Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-5477c536-fb9f-4170-95d3-e77eddcdf278",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');u1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as q:uids=q['uids'];items=q['item_ids'];packed=q['packed_eligible']\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);rows=[]\nfor s in range(0,len(uids),96):\n e=min(s+96,len(uids));score=trend[None,:]+6.5*(u1[s:e]@i1.T)+5*(u2[s:e]@i2.T);mask=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder='little').astype(bool);score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n for r,cols in enumerate(top):rows.extend((int(uids[s+r]),int(items[c]),rank) for rank,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_ensemble_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_best.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');u1=np.load(O/'validation_user_vectors.npy');i1=np.load(O/'validation_item_vectors.npy');u2=np.load(O/'validation_user_vectors_recent.npy');i2=np.load(O/'validation_item_vectors_recent.npy')\nwith np.load(D/'validation_eligible_candidates.npz') as q:uids=q['uids'];items=q['item_ids'];packed=q['packed_eligible']\nz=np.load(O/'trend_blend_validation.npz');X=z['features'];names=list(z['names']);sel=list(z['selected_names']);w=z['best_weights'];Z=np.log1p(X);Z=(Z-Z.mean(0))/(Z.std(0)+1e-6);trend=(Z[:,[names.index(x) for x in sel]]@w).astype(np.float32);trend=(trend-trend.mean())/(trend.std()+1e-6);rows=[]\nfor s in range(0,len(uids),96):\n e=min(s+96,len(uids));score=trend[None,:]+6.5*(u1[s:e]@i1.T)+5*(u2[s:e]@i2.T);mask=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder='little').astype(bool);score[~mask]=-np.inf;top=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ss,axis=1,kind='stable'),axis=1)\n for r,cols in enumerate(top):rows.extend((int(uids[s+r]),int(items[c]),rank) for rank,c in enumerate(cols,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_ensemble_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_best.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation_ensemble_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation_ensemble_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 7,
      "nanos": 852790942
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation_ensemble_best.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

106

exec command

+36:02

Thinking unavailable No text recorded

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

Model text

The complementary short-horizon tower does add value in ensemble. The selected exact-dot score is now trend + 6.5 × broad-history cosine + 5.0 × recent-history cosine, scoring 0.041406 publicly. I’m locking that formulation and refitting both towers on train + validation; the current valid fallback remains in place until the final ensemble passes structural checks.

Tool callexecTool call Returned

Tool call · Input +36:09

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/';c=pd.read_parquet(D+'candidate_catalog.parquet');ids=c.item_id.to_numpy(np.int64);m=np.full(ids.max()+1,-1,np.int32);m[ids]=np.arange(len(ids));l=pd.read_parquet(D+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']);rid=l.item_id.to_numpy(np.int64);ok=rid<len(m);ix=np.full(len(rid),-1,np.int32);ix[ok]=m[rid[ok]]\nfor th in [50,80]:\n q=(ix>=0)&(l.is_organic.to_numpy()==1)&(l.played_ratio_pct.to_numpy()>=th);v=np.bincount(ix[q],minlength=len(ids));print(th,'exact',np.array_equal(v,c.organic_positive_listens),'corr',np.corrcoef(v,c.organic_positive_listens)[0,1],'sum',v.sum(),c.organic_positive_listens.sum())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":2000}); text(r.output);

Tool response +36:11

Script completed
Wall time 2.5 seconds
Output:

50 exact True corr 1.0 sum 9417793 9417793
80 exact False corr 0.9995814320238703 sum 8699160 9417793

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7e71affe-1462-4a3b-9420-fe668cbc4ba7",
    "process_id": "93846",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/';c=pd.read_parquet(D+'candidate_catalog.parquet');ids=c.item_id.to_numpy(np.int64);m=np.full(ids.max()+1,-1,np.int32);m[ids]=np.arange(len(ids));l=pd.read_parquet(D+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']);rid=l.item_id.to_numpy(np.int64);ok=rid<len(m);ix=np.full(len(rid),-1,np.int32);ix[ok]=m[rid[ok]]\nfor th in [50,80]:\n q=(ix>=0)&(l.is_organic.to_numpy()==1)&(l.played_ratio_pct.to_numpy()>=th);v=np.bincount(ix[q],minlength=len(ids));print(th,'exact',np.array_equal(v,c.organic_positive_listens),'corr',np.corrcoef(v,c.organic_positive_listens)[0,1],'sum',v.sum(),c.organic_positive_listens.sum())\nPY"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/';c=pd.read_parquet(D+'candidate_catalog.parquet');ids=c.item_id.to_numpy(np.int64);m=np.full(ids.max()+1,-1,np.int32);m[ids]=np.arange(len(ids));l=pd.read_parquet(D+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']);rid=l.item_id.to_numpy(np.int64);ok=rid<len(m);ix=np.full(len(rid),-1,np.int32);ix[ok]=m[rid[ok]]\nfor th in [50,80]:\n q=(ix>=0)&(l.is_organic.to_numpy()==1)&(l.played_ratio_pct.to_numpy()>=th);v=np.bincount(ix[q],minlength=len(ids));print(th,'exact',np.array_equal(v,c.organic_positive_listens),'corr',np.corrcoef(v,c.organic_positive_listens)[0,1],'sum',v.sum(),c.organic_positive_listens.sum())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "50 exact True corr 1.0 sum 9417793 9417793\n80 exact False corr 0.9995814320238703 sum 8699160 9417793\n",
    "stderr": "",
    "aggregated_output": "50 exact True corr 1.0 sum 9417793 9417793\n80 exact False corr 0.9995814320238703 sum 8699160 9417793\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 307974465
    },
    "formatted_output": "50 exact True corr 1.0 sum 9417793 9417793\n80 exact False corr 0.9995814320238703 sum 8699160 9417793\n"
  }
}

107

exec command

+36:17

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +36:27

const patch = "*** Begin Patch\n*** Update File: /app/solution/ranker.py\n@@\n def build_item_features(catalog: pd.DataFrame) -> torch.Tensor:\n@@\n     return torch.from_numpy(features)\n+\n+\n+def refresh_catalog_popularity(catalog: pd.DataFrame, final: bool) -> pd.DataFrame:\n+    \"\"\"Add newly observed validation-window popularity for final refitting.\"\"\"\n+    if not final:\n+        return catalog\n+    catalog = catalog.copy()\n+    item_ids = catalog.item_id.to_numpy(dtype=np.int64)\n+    dense = np.full(int(item_ids.max()) + 1, -1, dtype=np.int32)\n+    dense[item_ids] = np.arange(len(item_ids), dtype=np.int32)\n+    events = pd.read_parquet(\n+        DATA / \"val_listens.parquet\",\n+        columns=[\"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+    )\n+    raw = events.item_id.to_numpy(dtype=np.int64)\n+    valid = raw < len(dense)\n+    mapped = np.full(len(raw), -1, dtype=np.int32)\n+    mapped[valid] = dense[raw[valid]]\n+    positive = (mapped >= 0) & (events.played_ratio_pct.to_numpy() >= 50)\n+    organic = positive & (events.is_organic.to_numpy() == 1)\n+    catalog[\"positive_listens\"] = (\n+        catalog.positive_listens.to_numpy(dtype=np.int64)\n+        + np.bincount(mapped[positive], minlength=len(catalog))\n+    )\n+    catalog[\"organic_positive_listens\"] = (\n+        catalog.organic_positive_listens.to_numpy(dtype=np.int64)\n+        + np.bincount(mapped[organic], minlength=len(catalog))\n+    )\n+    order = np.argsort(-catalog.positive_listens.to_numpy(), kind=\"stable\")\n+    rank = np.empty(len(catalog), dtype=np.int32)\n+    rank[order] = np.arange(1, len(catalog) + 1, dtype=np.int32)\n+    catalog[\"popularity_rank\"] = rank\n+    return catalog\n@@\n-    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    catalog = refresh_catalog_popularity(\n+        pd.read_parquet(DATA / \"candidate_catalog.parquet\"), final\n+    )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +36:28

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-811e3ea2-298a-40fb-b89e-7305f12830d3",
    "changes": {
      "/app/solution/ranker.py": {
        "type": "update",
        "unified_diff": "@@ -205,2 +205,35 @@\n \n+def refresh_catalog_popularity(catalog: pd.DataFrame, final: bool) -> pd.DataFrame:\n+    \"\"\"Add newly observed validation-window popularity for final refitting.\"\"\"\n+    if not final:\n+        return catalog\n+    catalog = catalog.copy()\n+    item_ids = catalog.item_id.to_numpy(dtype=np.int64)\n+    dense = np.full(int(item_ids.max()) + 1, -1, dtype=np.int32)\n+    dense[item_ids] = np.arange(len(item_ids), dtype=np.int32)\n+    events = pd.read_parquet(\n+        DATA / \"val_listens.parquet\",\n+        columns=[\"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+    )\n+    raw = events.item_id.to_numpy(dtype=np.int64)\n+    valid = raw < len(dense)\n+    mapped = np.full(len(raw), -1, dtype=np.int32)\n+    mapped[valid] = dense[raw[valid]]\n+    positive = (mapped >= 0) & (events.played_ratio_pct.to_numpy() >= 50)\n+    organic = positive & (events.is_organic.to_numpy() == 1)\n+    catalog[\"positive_listens\"] = (\n+        catalog.positive_listens.to_numpy(dtype=np.int64)\n+        + np.bincount(mapped[positive], minlength=len(catalog))\n+    )\n+    catalog[\"organic_positive_listens\"] = (\n+        catalog.organic_positive_listens.to_numpy(dtype=np.int64)\n+        + np.bincount(mapped[organic], minlength=len(catalog))\n+    )\n+    order = np.argsort(-catalog.positive_listens.to_numpy(), kind=\"stable\")\n+    rank = np.empty(len(catalog), dtype=np.int32)\n+    rank[order] = np.arange(1, len(catalog) + 1, dtype=np.int32)\n+    catalog[\"popularity_rank\"] = rank\n+    return catalog\n+\n+\n def _aggregate_pair_events(final: bool, catalog: pd.DataFrame) -> pd.DataFrame:\n@@ -523,3 +556,5 @@\n     end_time = FINAL_END if final else TRAIN_END\n-    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    catalog = refresh_catalog_popularity(\n+        pd.read_parquet(DATA / \"candidate_catalog.parquet\"), final\n+    )\n     pairs = _aggregate_pair_events(final, catalog)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/ranker.py\n",
    "stderr": ""
  }
}

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108

exec command

+36:29

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 +36:31

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage final --epochs 10 --weeks 22 --max-history 512 --seed 20260926 --pop-blends=0","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +37:01

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"aebb72","wall_time_seconds":30.000497417,"session_id":98218,"original_token_count":31,"output":"aggregated 8,274,638 catalog user-item pairs in 8.8s\nbuilt 81,374 rolling profiles with 687,026 labels (112,552 grade-3)\n"}

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109

exec command

+37:03

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 +37:03

const r = await tools.write_stdin({"session_id":98218,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +37:33

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b0a8ed","wall_time_seconds":30.001457998,"session_id":98218,"original_token_count":25,"output":"epoch 1/10: loss=1.4413 batches=317 elapsed=19.5s\nepoch 2/10: loss=1.3585 batches=317 elapsed=19.3s\n"}

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110

exec command

+37:35

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 +37:35

const r = await tools.write_stdin({"session_id":98218,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +38:05

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"19284a","wall_time_seconds":30.001854506,"session_id":98218,"original_token_count":13,"output":"epoch 3/10: loss=1.3103 batches=317 elapsed=19.3s\n"}

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111

exec command

+38:07

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 +38:07

const r = await tools.write_stdin({"session_id":98218,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +38:37

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"3bd319","wall_time_seconds":30.00138653,"session_id":98218,"original_token_count":25,"output":"epoch 4/10: loss=1.2724 batches=317 elapsed=19.5s\nepoch 5/10: loss=1.2265 batches=317 elapsed=19.5s\n"}

Harness context token usage record

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}

112

exec command

+38:39

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 +38:39

const r = await tools.write_stdin({"session_id":98218,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +39:09

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"26fc58","wall_time_seconds":30.001333563,"session_id":98218,"original_token_count":13,"output":"epoch 6/10: loss=1.1801 batches=317 elapsed=19.5s\n"}

Harness context token usage record

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  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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    "cache_write_input_tokens": 0,
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}

113

exec command

+39:12

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 +39:12

const r = await tools.write_stdin({"session_id":98218,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +39:42

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f12b21","wall_time_seconds":30.001141978,"session_id":98218,"original_token_count":25,"output":"epoch 7/10: loss=1.1381 batches=317 elapsed=19.5s\nepoch 8/10: loss=1.1014 batches=317 elapsed=19.4s\n"}

Harness context token usage record

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}

114

exec command

+39:48

Thinking unavailable No text recorded

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

Model text

The broad final refit has absorbed the validation history and labels cleanly; its learning curve closely matches the selected validation run. Once it finishes I’ll train the smaller recent tower, compose the calibrated vectors, and validate every submitted item against the test eligibility bits.

Tool callexecTool call Returned

Tool call · Input +39:48

const r = await tools.write_stdin({"session_id":98218,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +40:11

Script completed
Wall time 22.8 seconds
Output:

{"chunk_id":"570ab5","wall_time_seconds":22.787144121,"exit_code":0,"original_token_count":39,"output":"epoch 9/10: loss=1.0656 batches=317 elapsed=19.5s\nepoch 10/10: loss=1.0391 batches=317 elapsed=19.6s\nwrote /app/solution/submission.parquet (845,600 rows)\n"}

Harness context token usage record

{
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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-d4ff095c-b574-47e8-9966-c1cdd1abc736",
    "process_id": "98218",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage final --epochs 10 --weeks 22 --max-history 512 --seed 20260926 --pop-blends=0"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage final --epochs 10 --weeks 22 --max-history 512 --seed 20260926 --pop-blends=0"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,274,638 catalog user-item pairs in 8.8s\nbuilt 81,374 rolling profiles with 687,026 labels (112,552 grade-3)\nepoch 1/10: loss=1.4413 batches=317 elapsed=19.5s\nepoch 2/10: loss=1.3585 batches=317 elapsed=19.3s\nepoch 3/10: loss=1.3103 batches=317 elapsed=19.3s\nepoch 4/10: loss=1.2724 batches=317 elapsed=19.5s\nepoch 5/10: loss=1.2265 batches=317 elapsed=19.5s\nepoch 6/10: loss=1.1801 batches=317 elapsed=19.5s\nepoch 7/10: loss=1.1381 batches=317 elapsed=19.5s\nepoch 8/10: loss=1.1014 batches=317 elapsed=19.4s\nepoch 9/10: loss=1.0656 batches=317 elapsed=19.5s\nepoch 10/10: loss=1.0391 batches=317 elapsed=19.6s\nwrote /app/solution/submission.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,274,638 catalog user-item pairs in 8.8s\nbuilt 81,374 rolling profiles with 687,026 labels (112,552 grade-3)\nepoch 1/10: loss=1.4413 batches=317 elapsed=19.5s\nepoch 2/10: loss=1.3585 batches=317 elapsed=19.3s\nepoch 3/10: loss=1.3103 batches=317 elapsed=19.3s\nepoch 4/10: loss=1.2724 batches=317 elapsed=19.5s\nepoch 5/10: loss=1.2265 batches=317 elapsed=19.5s\nepoch 6/10: loss=1.1801 batches=317 elapsed=19.5s\nepoch 7/10: loss=1.1381 batches=317 elapsed=19.5s\nepoch 8/10: loss=1.1014 batches=317 elapsed=19.4s\nepoch 9/10: loss=1.0656 batches=317 elapsed=19.5s\nepoch 10/10: loss=1.0391 batches=317 elapsed=19.6s\nwrote /app/solution/submission.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 220,
      "nanos": 88559987
    },
    "formatted_output": "aggregated 8,274,638 catalog user-item pairs in 8.8s\nbuilt 81,374 rolling profiles with 687,026 labels (112,552 grade-3)\nepoch 1/10: loss=1.4413 batches=317 elapsed=19.5s\nepoch 2/10: loss=1.3585 batches=317 elapsed=19.3s\nepoch 3/10: loss=1.3103 batches=317 elapsed=19.3s\nepoch 4/10: loss=1.2724 batches=317 elapsed=19.5s\nepoch 5/10: loss=1.2265 batches=317 elapsed=19.5s\nepoch 6/10: loss=1.1801 batches=317 elapsed=19.5s\nepoch 7/10: loss=1.1381 batches=317 elapsed=19.5s\nepoch 8/10: loss=1.1014 batches=317 elapsed=19.4s\nepoch 9/10: loss=1.0656 batches=317 elapsed=19.5s\nepoch 10/10: loss=1.0391 batches=317 elapsed=19.6s\nwrote /app/solution/submission.parquet (845,600 rows)\n"
  }
}

115

exec command

+40:12

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 +40:15

const r = await tools.exec_command({"cmd":"cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_broad.pt\ncp /app/solution/run_final.json /app/solution/run_final_broad.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage final --epochs 12 --weeks 8 --max-history 512 --seed 20260928 --pop-blends=0","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +40:45

Script completed
Wall time 30.2 seconds
Output:

{"chunk_id":"4e8193","wall_time_seconds":30.001764156,"session_id":63839,"original_token_count":43,"output":"aggregated 8,274,638 catalog user-item pairs in 8.9s\nbuilt 33,938 rolling profiles with 290,366 labels (48,734 grade-3)\nepoch 1/12: loss=1.4854 batches=132 elapsed=8.2s\n"}

Harness context token usage record

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}

116

exec command

+40:46

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +40:47

const r = await tools.write_stdin({"session_id":63839,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +41:17

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"bfd42f","wall_time_seconds":30.001129815,"session_id":63839,"original_token_count":49,"output":"epoch 2/12: loss=1.3868 batches=132 elapsed=8.2s\nepoch 3/12: loss=1.3302 batches=132 elapsed=8.2s\nepoch 4/12: loss=1.2802 batches=132 elapsed=8.2s\nepoch 5/12: loss=1.2393 batches=132 elapsed=8.2s\n"}

Harness context token usage record

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}

117

exec command

+41:18

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 +41:19

const r = await tools.write_stdin({"session_id":63839,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +41:49

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6bf189","wall_time_seconds":30.000867468,"session_id":63839,"original_token_count":37,"output":"epoch 6/12: loss=1.2097 batches=132 elapsed=8.2s\nepoch 7/12: loss=1.1737 batches=132 elapsed=8.2s\nepoch 8/12: loss=1.1405 batches=132 elapsed=8.2s\n"}

Harness context token usage record

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  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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}

118

exec command

+41:51

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 +41:52

const r = await tools.write_stdin({"session_id":63839,"chars":"","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +42:17

Script completed
Wall time 25.4 seconds
Output:

{"chunk_id":"6cf18c","wall_time_seconds":25.351991703,"exit_code":0,"original_token_count":64,"output":"epoch 9/12: loss=1.1118 batches=132 elapsed=8.2s\nepoch 10/12: loss=1.0816 batches=132 elapsed=8.2s\nepoch 11/12: loss=1.0517 batches=132 elapsed=8.1s\nepoch 12/12: loss=1.0205 batches=132 elapsed=8.1s\nwrote /app/solution/submission.parquet (845,600 rows)\n"}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d5837679-d5af-4588-8837-10ed16d86051",
    "process_id": "63839",
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      "/bin/sh",
      "-lc",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_broad.pt\ncp /app/solution/run_final.json /app/solution/run_final_broad.json\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/ranker.py --stage final --epochs 12 --weeks 8 --max-history 512 --seed 20260928 --pop-blends=0"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,274,638 catalog user-item pairs in 8.9s\nbuilt 33,938 rolling profiles with 290,366 labels (48,734 grade-3)\nepoch 1/12: loss=1.4854 batches=132 elapsed=8.2s\nepoch 2/12: loss=1.3868 batches=132 elapsed=8.2s\nepoch 3/12: loss=1.3302 batches=132 elapsed=8.2s\nepoch 4/12: loss=1.2802 batches=132 elapsed=8.2s\nepoch 5/12: loss=1.2393 batches=132 elapsed=8.2s\nepoch 6/12: loss=1.2097 batches=132 elapsed=8.2s\nepoch 7/12: loss=1.1737 batches=132 elapsed=8.2s\nepoch 8/12: loss=1.1405 batches=132 elapsed=8.2s\nepoch 9/12: loss=1.1118 batches=132 elapsed=8.2s\nepoch 10/12: loss=1.0816 batches=132 elapsed=8.2s\nepoch 11/12: loss=1.0517 batches=132 elapsed=8.1s\nepoch 12/12: loss=1.0205 batches=132 elapsed=8.1s\nwrote /app/solution/submission.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,274,638 catalog user-item pairs in 8.9s\nbuilt 33,938 rolling profiles with 290,366 labels (48,734 grade-3)\nepoch 1/12: loss=1.4854 batches=132 elapsed=8.2s\nepoch 2/12: loss=1.3868 batches=132 elapsed=8.2s\nepoch 3/12: loss=1.3302 batches=132 elapsed=8.2s\nepoch 4/12: loss=1.2802 batches=132 elapsed=8.2s\nepoch 5/12: loss=1.2393 batches=132 elapsed=8.2s\nepoch 6/12: loss=1.2097 batches=132 elapsed=8.2s\nepoch 7/12: loss=1.1737 batches=132 elapsed=8.2s\nepoch 8/12: loss=1.1405 batches=132 elapsed=8.2s\nepoch 9/12: loss=1.1118 batches=132 elapsed=8.2s\nepoch 10/12: loss=1.0816 batches=132 elapsed=8.2s\nepoch 11/12: loss=1.0517 batches=132 elapsed=8.1s\nepoch 12/12: loss=1.0205 batches=132 elapsed=8.1s\nwrote /app/solution/submission.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 122,
      "nanos": 340950622
    },
    "formatted_output": "aggregated 8,274,638 catalog user-item pairs in 8.9s\nbuilt 33,938 rolling profiles with 290,366 labels (48,734 grade-3)\nepoch 1/12: loss=1.4854 batches=132 elapsed=8.2s\nepoch 2/12: loss=1.3868 batches=132 elapsed=8.2s\nepoch 3/12: loss=1.3302 batches=132 elapsed=8.2s\nepoch 4/12: loss=1.2802 batches=132 elapsed=8.2s\nepoch 5/12: loss=1.2393 batches=132 elapsed=8.2s\nepoch 6/12: loss=1.2097 batches=132 elapsed=8.2s\nepoch 7/12: loss=1.1737 batches=132 elapsed=8.2s\nepoch 8/12: loss=1.1405 batches=132 elapsed=8.2s\nepoch 9/12: loss=1.1118 batches=132 elapsed=8.2s\nepoch 10/12: loss=1.0816 batches=132 elapsed=8.2s\nepoch 11/12: loss=1.0517 batches=132 elapsed=8.1s\nepoch 12/12: loss=1.0205 batches=132 elapsed=8.1s\nwrote /app/solution/submission.parquet (845,600 rows)\n"
  }
}

119

Agent message

+42:28

Thinking unavailable No text recorded

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

120

exec command

+42:33

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 +42:35

const r = await tools.exec_command({"cmd":"cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_recent.pt\ncp /app/solution/run_final.json /app/solution/run_final_recent.json\nls -lh /app/solution/two_tower_final_*.pt","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(r.output);

Tool response +42:35

Script completed
Wall time 0.0 seconds
Output:

-rw-r--r-- 1 agent agent 16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt
-rw-r--r-- 1 agent agent 16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt

Harness context codex item completed

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    "process_id": "31202",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_recent.pt\ncp /app/solution/run_final.json /app/solution/run_final_recent.json\nls -lh /app/solution/two_tower_final_*.pt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_recent.pt\ncp /app/solution/run_final.json /app/solution/run_final_recent.json\nls -lh /app/solution/two_tower_final_*.pt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "-rw-r--r-- 1 agent agent 16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt\n-rw-r--r-- 1 agent agent 16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt\n",
    "stderr": "",
    "aggregated_output": "-rw-r--r-- 1 agent agent 16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt\n-rw-r--r-- 1 agent agent 16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6560
    },
    "formatted_output": "-rw-r--r-- 1 agent agent 16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt\n-rw-r--r-- 1 agent agent 16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt\n"
  }
}

Harness context token usage record

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  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
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121

exec command

+42:41

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 +43:35

const patch = "*** Begin Patch\n*** Add File: /app/solution/make_ensemble.py\n+#!/usr/bin/env python3\n+\"\"\"Compose the selected towers and cutoff-local item trend into exact MIPS.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+from torch import nn\n+\n+import ranker\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+\n+# Selected on the public temporal backtest.  Every feature is available before\n+# the prediction window and is independently precomputable per candidate.\n+TREND_WEIGHTS = {\n+    \"users3\": 0.158737274347962,\n+    \"users5\": 0.0002423638345245049,\n+    \"pos5\": 0.5787504658690691,\n+    \"likes3\": 0.1821678782002581,\n+    \"org3\": 0.020257015766419646,\n+    \"allorg\": 0.059845001981766614,\n+}\n+BROAD_WEIGHT = 6.5\n+RECENT_WEIGHT = 5.0\n+\n+\n+def catalog_trend(final: bool, catalog: pd.DataFrame, cutoff: int) -> np.ndarray:\n+    \"\"\"Build the cutoff-local scalar appended to every offline item vector.\"\"\"\n+    ids = catalog.item_id.to_numpy(dtype=np.int64)\n+    dense = np.full(int(ids.max()) + 1, -1, dtype=np.int32)\n+    dense[ids] = np.arange(len(ids), dtype=np.int32)\n+    listens = ranker.read_events(\n+        \"listens\",\n+        final,\n+        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+    )\n+    raw = listens.item_id.to_numpy(dtype=np.int64)\n+    in_range = raw < len(dense)\n+    mapped = np.full(len(raw), -1, dtype=np.int32)\n+    mapped[in_range] = dense[raw[in_range]]\n+    listens = listens.loc[mapped >= 0].copy()\n+    listens[\"item\"] = mapped[mapped >= 0]\n+    likes = ranker.read_events(\n+        \"likes\", final, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n+    )\n+    raw = likes.item_id.to_numpy(dtype=np.int64)\n+    in_range = raw < len(dense)\n+    mapped = np.full(len(raw), -1, dtype=np.int32)\n+    mapped[in_range] = dense[raw[in_range]]\n+    likes = likes.loc[mapped >= 0].copy()\n+    likes[\"item\"] = mapped[mapped >= 0]\n+\n+    def recent_listens(days: int) -> pd.DataFrame:\n+        return listens[listens.timestamp >= cutoff - days * 86_400]\n+\n+    three = recent_listens(3)\n+    five = recent_listens(5)\n+    organic_three = three[\n+        (three.is_organic == 1) & (three.played_ratio_pct >= 80)\n+    ]\n+    organic_five = five[\n+        (five.is_organic == 1) & (five.played_ratio_pct >= 80)\n+    ]\n+    features: dict[str, np.ndarray] = {\n+        \"users3\": organic_three[[\"uid\", \"item\"]]\n+        .drop_duplicates()\n+        .groupby(\"item\")\n+        .size()\n+        .reindex(range(len(ids)), fill_value=0)\n+        .to_numpy(),\n+        \"users5\": organic_five[[\"uid\", \"item\"]]\n+        .drop_duplicates()\n+        .groupby(\"item\")\n+        .size()\n+        .reindex(range(len(ids)), fill_value=0)\n+        .to_numpy(),\n+        \"pos5\": np.bincount(\n+            five.loc[five.played_ratio_pct >= 50, \"item\"], minlength=len(ids)\n+        ),\n+        \"likes3\": np.bincount(\n+            likes.loc[\n+                (likes.timestamp >= cutoff - 3 * 86_400) & (likes.is_organic == 1),\n+                \"item\",\n+            ],\n+            minlength=len(ids),\n+        ),\n+        \"org3\": np.bincount(organic_three.item, minlength=len(ids)),\n+        \"allorg\": catalog.organic_positive_listens.to_numpy(dtype=np.float64),\n+    }\n+    trend = np.zeros(len(ids), dtype=np.float64)\n+    for name, weight in TREND_WEIGHTS.items():\n+        value = np.log1p(features[name].astype(np.float64))\n+        value = (value - value.mean()) / (value.std() + 1e-6)\n+        trend += weight * value\n+    trend = (trend - trend.mean()) / (trend.std() + 1e-6)\n+    return trend.astype(np.float32)\n+\n+\n+class CompositeUserTower(nn.Module):\n+    \"\"\"One request-time tower formed by concatenating two learned encoders.\"\"\"\n+\n+    def __init__(self, broad: ranker.UserTower, recent: ranker.UserTower) -> None:\n+        super().__init__()\n+        self.broad = broad\n+        self.recent = recent\n+\n+    def forward(\n+        self, items: torch.Tensor, weights: torch.Tensor, activity: torch.Tensor\n+    ) -> torch.Tensor:\n+        broad = BROAD_WEIGHT * self.broad(items, weights, activity)\n+        recent = RECENT_WEIGHT * self.recent(items, weights, activity)\n+        constant = torch.ones((len(items), 1), dtype=broad.dtype, device=broad.device)\n+        return torch.cat([broad, recent, constant], dim=1)\n+\n+\n+class CompositeItemTower(nn.Module):\n+    \"\"\"Offline tower whose last coordinate is the selected trend prior.\"\"\"\n+\n+    def __init__(\n+        self,\n+        broad: ranker.ItemTower,\n+        recent: ranker.ItemTower,\n+        trend: torch.Tensor,\n+    ) -> None:\n+        super().__init__()\n+        self.broad = broad\n+        self.recent = recent\n+        self.register_buffer(\"trend\", trend)\n+\n+    def forward(self, item_indices: torch.Tensor) -> torch.Tensor:\n+        return torch.cat(\n+            [\n+                self.broad(item_indices),\n+                self.recent(item_indices),\n+                self.trend[item_indices, None],\n+            ],\n+            dim=1,\n+        )\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"final\")\n+    parser.add_argument(\"--broad-checkpoint\", type=Path)\n+    parser.add_argument(\"--recent-checkpoint\", type=Path)\n+    parser.add_argument(\"--output\", type=Path)\n+    args = parser.parse_args()\n+    final = args.stage == \"final\"\n+    cutoff = ranker.FINAL_END if final else ranker.TRAIN_END\n+    broad_path = args.broad_checkpoint or OUT / f\"two_tower_{args.stage}_broad.pt\"\n+    recent_path = args.recent_checkpoint or OUT / f\"two_tower_{args.stage}_recent.pt\"\n+    output = args.output or OUT / (\n+        \"submission.parquet\" if final else \"validation_ensemble.parquet\"\n+    )\n+\n+    catalog = ranker.refresh_catalog_popularity(\n+        pd.read_parquet(DATA / \"candidate_catalog.parquet\"), final\n+    )\n+    item_features = ranker.build_item_features(catalog)\n+    broad_model = ranker.TwoTower(item_features)\n+    recent_model = ranker.TwoTower(item_features)\n+    broad_model.load_state_dict(\n+        torch.load(broad_path, map_location=\"cpu\", weights_only=True)[\"model\"]\n+    )\n+    recent_model.load_state_dict(\n+        torch.load(recent_path, map_location=\"cpu\", weights_only=True)[\"model\"]\n+    )\n+    trend = torch.from_numpy(catalog_trend(final, catalog, cutoff))\n+    user_tower = CompositeUserTower(\n+        broad_model.user_tower, recent_model.user_tower\n+    ).eval()\n+    item_tower = CompositeItemTower(\n+        broad_model.item_tower, recent_model.item_tower, trend\n+    ).eval()\n+\n+    pairs = ranker._aggregate_pair_events(final, catalog)\n+    histories, _ = ranker.build_histories_and_profiles(pairs, cutoff, 1)\n+    del pairs\n+    eligibility_name = (\n+        \"test_eligible_candidates.npz\"\n+        if final\n+        else \"validation_eligible_candidates.npz\"\n+    )\n+    with np.load(DATA / eligibility_name, allow_pickle=False) as artifact:\n+        uids = artifact[\"uids\"].astype(np.int64, copy=True)\n+        item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n+        packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n+    if not np.array_equal(item_ids, catalog.item_id.to_numpy(dtype=np.int64)):\n+        raise ValueError(\"candidate order mismatch\")\n+\n+    helper = ranker.TemporalDataset([], histories, 512, seed=0)\n+    user_vectors = []\n+    with torch.inference_mode():\n+        for start in range(0, len(uids), 512):\n+            item_rows, weight_rows, activities = [], [], []\n+            for raw_uid in uids[start : start + 512]:\n+                items, weights, activity = helper.encode_history(int(raw_uid), cutoff)\n+                item_rows.append(items)\n+                weight_rows.append(weights)\n+                activities.append(activity)\n+            user_vectors.append(\n+                user_tower(\n+                    torch.from_numpy(np.stack(item_rows)).long(),\n+                    torch.from_numpy(np.stack(weight_rows)).float(),\n+                    torch.as_tensor(activities, dtype=torch.float32),\n+                ).cpu()\n+            )\n+        user_vectors = torch.cat(user_vectors)\n+        item_vectors = torch.cat(\n+            [\n+                item_tower(torch.arange(start, min(start + 2048, len(catalog)))).cpu()\n+                for start in range(0, len(catalog), 2048)\n+            ]\n+        )\n+\n+    rows: list[tuple[int, int, int]] = []\n+    for start in range(0, len(uids), 96):\n+        stop = min(start + 96, len(uids))\n+        scores = (user_vectors[start:stop] @ item_vectors.T).numpy()\n+        eligible = np.unpackbits(\n+            packed[start:stop], axis=1, count=len(item_ids), bitorder=\"little\"\n+        ).astype(bool)\n+        scores[~eligible] = -np.inf\n+        top = np.argpartition(scores, -100, axis=1)[:, -100:]\n+        top_scores = np.take_along_axis(scores, top, axis=1)\n+        top = np.take_along_axis(\n+            top, np.argsort(-top_scores, axis=1, kind=\"stable\"), axis=1\n+        )\n+        for local_row, columns in enumerate(top):\n+            uid = int(uids[start + local_row])\n+            rows.extend(\n+                (uid, int(item_ids[column]), rank)\n+                for rank, column in enumerate(columns, 1)\n+            )\n+    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n+        output, index=False\n+    )\n+    (OUT / f\"ensemble_{args.stage}.json\").write_text(\n+        json.dumps(\n+            {\n+                \"broad_checkpoint\": str(broad_path),\n+                \"recent_checkpoint\": str(recent_path),\n+                \"broad_weight\": BROAD_WEIGHT,\n+                \"recent_weight\": RECENT_WEIGHT,\n+                \"trend_weights\": TREND_WEIGHTS,\n+                \"output\": str(output),\n+            },\n+            indent=2,\n+        )\n+        + \"\\n\"\n+    )\n+    print(f\"wrote exact-dot ensemble to {output} ({len(rows):,} rows)\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +43:36

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-82428465-98f6-4c74-8e92-5a6ea1355ac0",
    "changes": {
      "/app/solution/make_ensemble.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Compose the selected towers and cutoff-local item trend into exact MIPS.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\n\nimport ranker\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\n\n# Selected on the public temporal backtest.  Every feature is available before\n# the prediction window and is independently precomputable per candidate.\nTREND_WEIGHTS = {\n    \"users3\": 0.158737274347962,\n    \"users5\": 0.0002423638345245049,\n    \"pos5\": 0.5787504658690691,\n    \"likes3\": 0.1821678782002581,\n    \"org3\": 0.020257015766419646,\n    \"allorg\": 0.059845001981766614,\n}\nBROAD_WEIGHT = 6.5\nRECENT_WEIGHT = 5.0\n\n\ndef catalog_trend(final: bool, catalog: pd.DataFrame, cutoff: int) -> np.ndarray:\n    \"\"\"Build the cutoff-local scalar appended to every offline item vector.\"\"\"\n    ids = catalog.item_id.to_numpy(dtype=np.int64)\n    dense = np.full(int(ids.max()) + 1, -1, dtype=np.int32)\n    dense[ids] = np.arange(len(ids), dtype=np.int32)\n    listens = ranker.read_events(\n        \"listens\",\n        final,\n        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    )\n    raw = listens.item_id.to_numpy(dtype=np.int64)\n    in_range = raw < len(dense)\n    mapped = np.full(len(raw), -1, dtype=np.int32)\n    mapped[in_range] = dense[raw[in_range]]\n    listens = listens.loc[mapped >= 0].copy()\n    listens[\"item\"] = mapped[mapped >= 0]\n    likes = ranker.read_events(\n        \"likes\", final, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n    )\n    raw = likes.item_id.to_numpy(dtype=np.int64)\n    in_range = raw < len(dense)\n    mapped = np.full(len(raw), -1, dtype=np.int32)\n    mapped[in_range] = dense[raw[in_range]]\n    likes = likes.loc[mapped >= 0].copy()\n    likes[\"item\"] = mapped[mapped >= 0]\n\n    def recent_listens(days: int) -> pd.DataFrame:\n        return listens[listens.timestamp >= cutoff - days * 86_400]\n\n    three = recent_listens(3)\n    five = recent_listens(5)\n    organic_three = three[\n        (three.is_organic == 1) & (three.played_ratio_pct >= 80)\n    ]\n    organic_five = five[\n        (five.is_organic == 1) & (five.played_ratio_pct >= 80)\n    ]\n    features: dict[str, np.ndarray] = {\n        \"users3\": organic_three[[\"uid\", \"item\"]]\n        .drop_duplicates()\n        .groupby(\"item\")\n        .size()\n        .reindex(range(len(ids)), fill_value=0)\n        .to_numpy(),\n        \"users5\": organic_five[[\"uid\", \"item\"]]\n        .drop_duplicates()\n        .groupby(\"item\")\n        .size()\n        .reindex(range(len(ids)), fill_value=0)\n        .to_numpy(),\n        \"pos5\": np.bincount(\n            five.loc[five.played_ratio_pct >= 50, \"item\"], minlength=len(ids)\n        ),\n        \"likes3\": np.bincount(\n            likes.loc[\n                (likes.timestamp >= cutoff - 3 * 86_400) & (likes.is_organic == 1),\n                \"item\",\n            ],\n            minlength=len(ids),\n        ),\n        \"org3\": np.bincount(organic_three.item, minlength=len(ids)),\n        \"allorg\": catalog.organic_positive_listens.to_numpy(dtype=np.float64),\n    }\n    trend = np.zeros(len(ids), dtype=np.float64)\n    for name, weight in TREND_WEIGHTS.items():\n        value = np.log1p(features[name].astype(np.float64))\n        value = (value - value.mean()) / (value.std() + 1e-6)\n        trend += weight * value\n    trend = (trend - trend.mean()) / (trend.std() + 1e-6)\n    return trend.astype(np.float32)\n\n\nclass CompositeUserTower(nn.Module):\n    \"\"\"One request-time tower formed by concatenating two learned encoders.\"\"\"\n\n    def __init__(self, broad: ranker.UserTower, recent: ranker.UserTower) -> None:\n        super().__init__()\n        self.broad = broad\n        self.recent = recent\n\n    def forward(\n        self, items: torch.Tensor, weights: torch.Tensor, activity: torch.Tensor\n    ) -> torch.Tensor:\n        broad = BROAD_WEIGHT * self.broad(items, weights, activity)\n        recent = RECENT_WEIGHT * self.recent(items, weights, activity)\n        constant = torch.ones((len(items), 1), dtype=broad.dtype, device=broad.device)\n        return torch.cat([broad, recent, constant], dim=1)\n\n\nclass CompositeItemTower(nn.Module):\n    \"\"\"Offline tower whose last coordinate is the selected trend prior.\"\"\"\n\n    def __init__(\n        self,\n        broad: ranker.ItemTower,\n        recent: ranker.ItemTower,\n        trend: torch.Tensor,\n    ) -> None:\n        super().__init__()\n        self.broad = broad\n        self.recent = recent\n        self.register_buffer(\"trend\", trend)\n\n    def forward(self, item_indices: torch.Tensor) -> torch.Tensor:\n        return torch.cat(\n            [\n                self.broad(item_indices),\n                self.recent(item_indices),\n                self.trend[item_indices, None],\n            ],\n            dim=1,\n        )\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], default=\"final\")\n    parser.add_argument(\"--broad-checkpoint\", type=Path)\n    parser.add_argument(\"--recent-checkpoint\", type=Path)\n    parser.add_argument(\"--output\", type=Path)\n    args = parser.parse_args()\n    final = args.stage == \"final\"\n    cutoff = ranker.FINAL_END if final else ranker.TRAIN_END\n    broad_path = args.broad_checkpoint or OUT / f\"two_tower_{args.stage}_broad.pt\"\n    recent_path = args.recent_checkpoint or OUT / f\"two_tower_{args.stage}_recent.pt\"\n    output = args.output or OUT / (\n        \"submission.parquet\" if final else \"validation_ensemble.parquet\"\n    )\n\n    catalog = ranker.refresh_catalog_popularity(\n        pd.read_parquet(DATA / \"candidate_catalog.parquet\"), final\n    )\n    item_features = ranker.build_item_features(catalog)\n    broad_model = ranker.TwoTower(item_features)\n    recent_model = ranker.TwoTower(item_features)\n    broad_model.load_state_dict(\n        torch.load(broad_path, map_location=\"cpu\", weights_only=True)[\"model\"]\n    )\n    recent_model.load_state_dict(\n        torch.load(recent_path, map_location=\"cpu\", weights_only=True)[\"model\"]\n    )\n    trend = torch.from_numpy(catalog_trend(final, catalog, cutoff))\n    user_tower = CompositeUserTower(\n        broad_model.user_tower, recent_model.user_tower\n    ).eval()\n    item_tower = CompositeItemTower(\n        broad_model.item_tower, recent_model.item_tower, trend\n    ).eval()\n\n    pairs = ranker._aggregate_pair_events(final, catalog)\n    histories, _ = ranker.build_histories_and_profiles(pairs, cutoff, 1)\n    del pairs\n    eligibility_name = (\n        \"test_eligible_candidates.npz\"\n        if final\n        else \"validation_eligible_candidates.npz\"\n    )\n    with np.load(DATA / eligibility_name, allow_pickle=False) as artifact:\n        uids = artifact[\"uids\"].astype(np.int64, copy=True)\n        item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n        packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n    if not np.array_equal(item_ids, catalog.item_id.to_numpy(dtype=np.int64)):\n        raise ValueError(\"candidate order mismatch\")\n\n    helper = ranker.TemporalDataset([], histories, 512, seed=0)\n    user_vectors = []\n    with torch.inference_mode():\n        for start in range(0, len(uids), 512):\n            item_rows, weight_rows, activities = [], [], []\n            for raw_uid in uids[start : start + 512]:\n                items, weights, activity = helper.encode_history(int(raw_uid), cutoff)\n                item_rows.append(items)\n                weight_rows.append(weights)\n                activities.append(activity)\n            user_vectors.append(\n                user_tower(\n                    torch.from_numpy(np.stack(item_rows)).long(),\n                    torch.from_numpy(np.stack(weight_rows)).float(),\n                    torch.as_tensor(activities, dtype=torch.float32),\n                ).cpu()\n            )\n        user_vectors = torch.cat(user_vectors)\n        item_vectors = torch.cat(\n            [\n                item_tower(torch.arange(start, min(start + 2048, len(catalog)))).cpu()\n                for start in range(0, len(catalog), 2048)\n            ]\n        )\n\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(uids), 96):\n        stop = min(start + 96, len(uids))\n        scores = (user_vectors[start:stop] @ item_vectors.T).numpy()\n        eligible = np.unpackbits(\n            packed[start:stop], axis=1, count=len(item_ids), bitorder=\"little\"\n        ).astype(bool)\n        scores[~eligible] = -np.inf\n        top = np.argpartition(scores, -100, axis=1)[:, -100:]\n        top_scores = np.take_along_axis(scores, top, axis=1)\n        top = np.take_along_axis(\n            top, np.argsort(-top_scores, axis=1, kind=\"stable\"), axis=1\n        )\n        for local_row, columns in enumerate(top):\n            uid = int(uids[start + local_row])\n            rows.extend(\n                (uid, int(item_ids[column]), rank)\n                for rank, column in enumerate(columns, 1)\n            )\n    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n        output, index=False\n    )\n    (OUT / f\"ensemble_{args.stage}.json\").write_text(\n        json.dumps(\n            {\n                \"broad_checkpoint\": str(broad_path),\n                \"recent_checkpoint\": str(recent_path),\n                \"broad_weight\": BROAD_WEIGHT,\n                \"recent_weight\": RECENT_WEIGHT,\n                \"trend_weights\": TREND_WEIGHTS,\n                \"output\": str(output),\n            },\n            indent=2,\n        )\n        + \"\\n\"\n    )\n    print(f\"wrote exact-dot ensemble to {output} ({len(rows):,} rows)\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/make_ensemble.py\n",
    "stderr": ""
  }
}

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122

exec command

+43:38

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 +43:39

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -m py_compile /app/solution/make_ensemble.py\nPYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/make_ensemble.py --stage final","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +43:59

Script completed
Wall time 19.3 seconds
Output:

{"chunk_id":"45422a","wall_time_seconds":19.151760778,"exit_code":1,"original_token_count":146,"output":"aggregated 8,274,638 catalog user-item pairs in 8.6s\nbuilt 4,655 rolling profiles with 39,817 labels (6,286 grade-3)\nTraceback (most recent call last):\n  File \"/app/solution/make_ensemble.py\", line 263, in <module>\n    main()\n  File \"/app/solution/make_ensemble.py\", line 204, in main\n    items, weights, activity = helper.encode_history(int(raw_uid), cutoff)\n                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/ranker.py\", line 88, in encode_history\n    history = self.histories[uid]\n              ~~~~~~~~~~~~~~^^^^^\nKeyError: 121300\n"}

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Harness context codex item completed

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      }
    ],
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    "stderr": "",
    "aggregated_output": "aggregated 8,274,638 catalog user-item pairs in 8.6s\nbuilt 4,655 rolling profiles with 39,817 labels (6,286 grade-3)\nTraceback (most recent call last):\n  File \"/app/solution/make_ensemble.py\", line 263, in <module>\n    main()\n  File \"/app/solution/make_ensemble.py\", line 204, in main\n    items, weights, activity = helper.encode_history(int(raw_uid), cutoff)\n                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/ranker.py\", line 88, in encode_history\n    history = self.histories[uid]\n              ~~~~~~~~~~~~~~^^^^^\nKeyError: 121300\n",
    "exit_code": 1,
    "duration": {
      "secs": 19,
      "nanos": 151753888
    },
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  }
}

123

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.

Model text

The ensemble build hit a legitimate cold-profile edge case: one target user has no positive catalog history after aggregation. The component ranker already handles this with a padded empty history; I’m applying the same fallback to the composite tower, then rerunning and validating the artifact.

Tool callexecTool call Returned

Tool call · Input +44:05

const patch = "*** Begin Patch\n*** Update File: /app/solution/make_ensemble.py\n@@\n             item_rows, weight_rows, activities = [], [], []\n             for raw_uid in uids[start : start + 512]:\n-                items, weights, activity = helper.encode_history(int(raw_uid), cutoff)\n+                uid = int(raw_uid)\n+                if uid in histories:\n+                    items, weights, activity = helper.encode_history(uid, cutoff)\n+                else:\n+                    items = np.full(512, -1, dtype=np.int64)\n+                    weights = np.zeros((512, 3), dtype=np.float32)\n+                    activity = np.float32(0.0)\n                 item_rows.append(items)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +44:05

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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    "type": "FileChange",
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        "unified_diff": "@@ -203,3 +203,9 @@\n             for raw_uid in uids[start : start + 512]:\n-                items, weights, activity = helper.encode_history(int(raw_uid), cutoff)\n+                uid = int(raw_uid)\n+                if uid in histories:\n+                    items, weights, activity = helper.encode_history(uid, cutoff)\n+                else:\n+                    items = np.full(512, -1, dtype=np.int64)\n+                    weights = np.zeros((512, 3), dtype=np.float32)\n+                    activity = np.float32(0.0)\n                 item_rows.append(items)\n",
        "move_path": null
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    "stdout": "Success. Updated the following files:\nM /app/solution/make_ensemble.py\n",
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  }
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124

exec command

+44:07

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 +44:08

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/make_ensemble.py --stage final","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +44:30

Script completed
Wall time 22.1 seconds
Output:

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  }
}

125

exec command

+44:32

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +44:38

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data');p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(p),targets);cs=CandidateSet(D/'test_eligible_candidates.npz');bad=sum(not cs.is_eligible(u,i) for u,i in df[['uid','item_id']].itertuples(index=False,name=None));print({'rows':len(df),'users':df.uid.nunique(),'bad_eligibility':bad,'duplicate_pairs':int(df.duplicated(['uid','item_id']).sum()),'rank_min':int(df['rank'].min()),'rank_max':int(df['rank'].max())})\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet /app/solution/*.py /app/solution/*final*.pt","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response +44:40

Script completed
Wall time 2.7 seconds
Output:

{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100}
afe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet
-rw-r--r-- 1 agent agent 9.7K Sep 25 01:00 /app/solution/make_ensemble.py
-rw-r--r-- 1 agent agent 1.7K Sep 25 00:20 /app/solution/make_fallback.py
-rw-r--r-- 1 agent agent  25K Sep 25 00:52 /app/solution/ranker.py
-rw-r--r-- 1 agent agent 1.4M Sep 25 01:00 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final.pt
-rw-r--r-- 1 agent agent  16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt
-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-595868ef-b2e6-4fae-882a-95f60d244940",
    "process_id": "50828",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data');p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(p),targets);cs=CandidateSet(D/'test_eligible_candidates.npz');bad=sum(not cs.is_eligible(u,i) for u,i in df[['uid','item_id']].itertuples(index=False,name=None));print({'rows':len(df),'users':df.uid.nunique(),'bad_eligibility':bad,'duplicate_pairs':int(df.duplicated(['uid','item_id']).sum()),'rank_min':int(df['rank'].min()),'rank_max':int(df['rank'].max())})\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet /app/solution/*.py /app/solution/*final*.pt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data');p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(p),targets);cs=CandidateSet(D/'test_eligible_candidates.npz');bad=sum(not cs.is_eligible(u,i) for u,i in df[['uid','item_id']].itertuples(index=False,name=None));print({'rows':len(df),'users':df.uid.nunique(),'bad_eligibility':bad,'duplicate_pairs':int(df.duplicated(['uid','item_id']).sum()),'rank_min':int(df['rank'].min()),'rank_max':int(df['rank'].max())})\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet /app/solution/*.py /app/solution/*final*.pt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100}\nafe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 9.7K Sep 25 01:00 /app/solution/make_ensemble.py\n-rw-r--r-- 1 agent agent 1.7K Sep 25 00:20 /app/solution/make_fallback.py\n-rw-r--r-- 1 agent agent  25K Sep 25 00:52 /app/solution/ranker.py\n-rw-r--r-- 1 agent agent 1.4M Sep 25 01:00 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final.pt\n-rw-r--r-- 1 agent agent  16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt\n-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100}\nafe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 9.7K Sep 25 01:00 /app/solution/make_ensemble.py\n-rw-r--r-- 1 agent agent 1.7K Sep 25 00:20 /app/solution/make_fallback.py\n-rw-r--r-- 1 agent agent  25K Sep 25 00:52 /app/solution/ranker.py\n-rw-r--r-- 1 agent agent 1.4M Sep 25 01:00 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final.pt\n-rw-r--r-- 1 agent agent  16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt\n-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 561390202
    },
    "formatted_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100}\nafe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 9.7K Sep 25 01:00 /app/solution/make_ensemble.py\n-rw-r--r-- 1 agent agent 1.7K Sep 25 00:20 /app/solution/make_fallback.py\n-rw-r--r-- 1 agent agent  25K Sep 25 00:52 /app/solution/ranker.py\n-rw-r--r-- 1 agent agent 1.4M Sep 25 01:00 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final.pt\n-rw-r--r-- 1 agent agent  16M Sep 25 00:56 /app/solution/two_tower_final_broad.pt\n-rw-r--r-- 1 agent agent  16M Sep 25 00:58 /app/solution/two_tower_final_recent.pt\n"
  }
}

126

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Thinking unavailable No text recorded

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127

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+45:00

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128

exec command

+45:01

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Tool callexecTool call Returned

Tool call · Input +45:14

const patch = "*** Begin Patch\n*** Add File: /app/solution/run_final.sh\n+#!/bin/sh\n+set -eu\n+\n+PYTHON_BIN=/opt/task-venv/bin/python\n+SOLUTION_DIR=/app/solution\n+\n+\"$PYTHON_BIN\" \"$SOLUTION_DIR/ranker.py\" \\\n+  --stage final --epochs 10 --weeks 22 --max-history 512 \\\n+  --seed 20260926 --pop-blends=0\n+cp \"$SOLUTION_DIR/two_tower_final.pt\" \"$SOLUTION_DIR/two_tower_final_broad.pt\"\n+\n+\"$PYTHON_BIN\" \"$SOLUTION_DIR/ranker.py\" \\\n+  --stage final --epochs 12 --weeks 8 --max-history 512 \\\n+  --seed 20260928 --pop-blends=0\n+cp \"$SOLUTION_DIR/two_tower_final.pt\" \"$SOLUTION_DIR/two_tower_final_recent.pt\"\n+\n+\"$PYTHON_BIN\" \"$SOLUTION_DIR/make_ensemble.py\" --stage final\n*** Add File: /app/solution/README.md\n+# CPU PyTorch two-tower discovery ranker\n+\n+The final ranker is an exact maximum-inner-product model. Its 193-dimensional\n+request vector is the concatenation of two normalized 96-dimensional neural\n+user encodings and one constant coordinate. The matching offline item vector\n+contains the two neural item encodings plus one cutoff-local trend coordinate.\n+The final score is exactly their dot product:\n+\n+`6.5 * broad_cosine + 5.0 * recent_cosine + trend`.\n+\n+Neither user encoder has a user-ID table. Each is a DeepSets-style PyTorch\n+network over at most 512 distinct positive history tracks, with separate\n+long-term, 30-day-decayed, and strong-positive pools. Each item tower uses a\n+trainable item embedding and four catalog-popularity features followed by an\n+MLP. Candidate vectors can therefore be computed offline; request work is one\n+history encoding and MIPS. The scalar trend block uses only pre-cutoff item\n+statistics (recent unique organic listeners, positive plays, and organic\n+likes), so it too is precomputed offline. Eligibility is applied after scoring.\n+\n+Training uses event/pair streams rather than a global user-item matrix. Rolling\n+seven-day temporal examples require the target item to be unseen at the\n+profile cutoff. The loss combines sampled pairwise logistic ranking (uniform\n+and popularity negatives) with in-batch softmax negatives. Organic likes are\n+upweighted to reflect their larger graded gain. A broad 22-week tower and a\n+recent 8-week tower are refit on `train_* + val_*` for the hidden cutoff.\n+\n+Public validation NDCG@10: **0.041406** over 4,263 scored users. The selected\n+validation artifact is `validation_ensemble_best.parquet`.\n+\n+Rebuild the final submission with:\n+\n+```sh\n+sh /app/solution/run_final.sh\n+```\n+\n+The checked-in final artifact is `/app/solution/submission.parquet`.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response +45:14

Script completed
Wall time 0.0 seconds
Output:

{}

Harness context codex item completed

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  "item": {
    "type": "FileChange",
    "id": "exec-337a6b55-28a4-4855-8132-14a08634378d",
    "changes": {
      "/app/solution/run_final.sh": {
        "type": "add",
        "content": "#!/bin/sh\nset -eu\n\nPYTHON_BIN=/opt/task-venv/bin/python\nSOLUTION_DIR=/app/solution\n\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/ranker.py\" \\\n  --stage final --epochs 10 --weeks 22 --max-history 512 \\\n  --seed 20260926 --pop-blends=0\ncp \"$SOLUTION_DIR/two_tower_final.pt\" \"$SOLUTION_DIR/two_tower_final_broad.pt\"\n\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/ranker.py\" \\\n  --stage final --epochs 12 --weeks 8 --max-history 512 \\\n  --seed 20260928 --pop-blends=0\ncp \"$SOLUTION_DIR/two_tower_final.pt\" \"$SOLUTION_DIR/two_tower_final_recent.pt\"\n\n\"$PYTHON_BIN\" \"$SOLUTION_DIR/make_ensemble.py\" --stage final\n"
      },
      "/app/solution/README.md": {
        "type": "add",
        "content": "# CPU PyTorch two-tower discovery ranker\n\nThe final ranker is an exact maximum-inner-product model. Its 193-dimensional\nrequest vector is the concatenation of two normalized 96-dimensional neural\nuser encodings and one constant coordinate. The matching offline item vector\ncontains the two neural item encodings plus one cutoff-local trend coordinate.\nThe final score is exactly their dot product:\n\n`6.5 * broad_cosine + 5.0 * recent_cosine + trend`.\n\nNeither user encoder has a user-ID table. Each is a DeepSets-style PyTorch\nnetwork over at most 512 distinct positive history tracks, with separate\nlong-term, 30-day-decayed, and strong-positive pools. Each item tower uses a\ntrainable item embedding and four catalog-popularity features followed by an\nMLP. Candidate vectors can therefore be computed offline; request work is one\nhistory encoding and MIPS. The scalar trend block uses only pre-cutoff item\nstatistics (recent unique organic listeners, positive plays, and organic\nlikes), so it too is precomputed offline. Eligibility is applied after scoring.\n\nTraining uses event/pair streams rather than a global user-item matrix. Rolling\nseven-day temporal examples require the target item to be unseen at the\nprofile cutoff. The loss combines sampled pairwise logistic ranking (uniform\nand popularity negatives) with in-batch softmax negatives. Organic likes are\nupweighted to reflect their larger graded gain. A broad 22-week tower and a\nrecent 8-week tower are refit on `train_* + val_*` for the hidden cutoff.\n\nPublic validation NDCG@10: **0.041406** over 4,263 scored users. The selected\nvalidation artifact is `validation_ensemble_best.parquet`.\n\nRebuild the final submission with:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThe checked-in final artifact is `/app/solution/submission.parquet`.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/run_final.sh\nA /app/solution/README.md\n",
    "stderr": ""
  }
}

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129

exec command

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Model text

The final artifact is structurally valid: 845,600 rows, exactly 100 unique eligible items for each of 8,456 users, ranks 1–100, and zero eligibility violations. I’ve also added the one-command rebuild and README; I’m doing a final reproducibility check that the checked-in ensemble code recreates the reported public score.

Tool callexecTool call Returned

Tool call · Input +45:23

const r = await tools.exec_command({"cmd":"PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/make_ensemble.py --stage validation --broad-checkpoint /app/solution/two_tower_validation_selected.pt --recent-checkpoint /app/solution/two_tower_validation_recent.pt --output /app/solution/validation_reproduced.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_reproduced.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(JSON.stringify(r));

Tool response +45:48

Script completed
Wall time 25.1 seconds
Output:

{"chunk_id":"1452e4","wall_time_seconds":24.949114971,"exit_code":0,"original_token_count":86,"output":"aggregated 8,044,619 catalog user-item pairs in 8.5s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote exact-dot ensemble to /app/solution/validation_reproduced.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation_reproduced.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n"}

Harness context token usage record

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Harness context codex item completed

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    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/make_ensemble.py --stage validation --broad-checkpoint /app/solution/two_tower_validation_selected.pt --recent-checkpoint /app/solution/two_tower_validation_recent.pt --output /app/solution/validation_reproduced.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_reproduced.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONUNBUFFERED=1 /opt/task-venv/bin/python /app/solution/make_ensemble.py --stage validation --broad-checkpoint /app/solution/two_tower_validation_selected.pt --recent-checkpoint /app/solution/two_tower_validation_recent.pt --output /app/solution/validation_reproduced.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_reproduced.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "aggregated 8,044,619 catalog user-item pairs in 8.5s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote exact-dot ensemble to /app/solution/validation_reproduced.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation_reproduced.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "aggregated 8,044,619 catalog user-item pairs in 8.5s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote exact-dot ensemble to /app/solution/validation_reproduced.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation_reproduced.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 24,
      "nanos": 949108911
    },
    "formatted_output": "aggregated 8,044,619 catalog user-item pairs in 8.5s\nbuilt 4,482 rolling profiles with 40,030 labels (6,906 grade-3)\nwrote exact-dot ensemble to /app/solution/validation_reproduced.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation_reproduced.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041406,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

130

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+46:00

Thinking unavailable No text recorded

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

131

exec command

+46:01

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 +46:06

const r = await tools.exec_command({"cmd":"sh -n /app/solution/run_final.sh\n/opt/task-venv/bin/python -m py_compile /app/solution/ranker.py /app/solution/make_ensemble.py /app/solution/make_fallback.py\n/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 CandidateSet,validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(S),targets);eligible=CandidateSet(D/'test_eligible_candidates.npz');assert all(eligible.is_eligible(u,i) for u,i in df[['uid','item_id']].itertuples(index=False,name=None));print(f'PASS rows={len(df)} users={df.uid.nunique()} eligible=all')\nPY\nsha256sum /app/solution/submission.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response +46:09

Script completed
Wall time 2.8 seconds
Output:

PASS rows=845600 users=8456 eligible=all
afe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet

Harness context token usage record

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Harness context codex item completed

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    "type": "CommandExecution",
    "id": "exec-a360b180-4f59-4c70-b692-2f93a4b5f0dc",
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    "command": [
      "/bin/sh",
      "-lc",
      "sh -n /app/solution/run_final.sh\n/opt/task-venv/bin/python -m py_compile /app/solution/ranker.py /app/solution/make_ensemble.py /app/solution/make_fallback.py\n/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 CandidateSet,validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(S),targets);eligible=CandidateSet(D/'test_eligible_candidates.npz');assert all(eligible.is_eligible(u,i) for u,i in df[['uid','item_id']].itertuples(index=False,name=None));print(f'PASS rows={len(df)} users={df.uid.nunique()} eligible=all')\nPY\nsha256sum /app/solution/submission.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "PASS rows=845600 users=8456 eligible=all\nafe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "PASS rows=845600 users=8456 eligible=all\nafe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 676841655
    },
    "formatted_output": "PASS rows=845600 users=8456 eligible=all\nafe5758b190f805a1a65710754296afcc2dd094adf46d36362d38a8a33e7d854  /app/solution/submission.parquet\n"
  }
}

132

Final response

+46:13

Thinking unavailable No text recorded

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

Model text

Built and validated the CPU PyTorch two-tower ranker.

Final submission: submission.parquet
Training code: ranker.py
Ensemble/MIPS composition: make_ensemble.py
Rebuild command: run_final.sh
Documentation: README.md

Public NDCG@10: 0.041406

Final validation passed: 845,600 rows, 8,456 users, exactly 100 unique eligible items per user, and ranks 1–100.

Harness context token usage record

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  "type": "token_usage_record",
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  "turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "session_id": "01a0d5eb-369f-7f02-965c-a99b90be9e9b",
  "root_turn_id": "01a0d5eb-36ad-7c22-bcdb-503cd988164e",
  "response_id": "resp_0459cfbd5d68d3be016ab5c80e9abc87d291c98c02dbc0ef36",
  "usage": {
    "input_tokens": 104318,
    "cached_input_tokens": 103168,
    "cache_write_input_tokens": 0,
    "output_tokens": 211,
    "reasoning_output_tokens": 57,
    "total_tokens": 104529
  },
  "turn_token_usage": {
    "input_tokens": 6525933,
    "cached_input_tokens": 6398208,
    "cache_write_input_tokens": 0,
    "output_tokens": 58940,
    "reasoning_output_tokens": 25443,
    "total_tokens": 6584873
  },
  "thread_token_usage": {
    "input_tokens": 6525933,
    "cached_input_tokens": 6398208,
    "cache_write_input_tokens": 0,
    "output_tokens": 58940,
    "reasoning_output_tokens": 25443,
    "total_tokens": 6584873
  }
}

About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt: sol-max-0-153-4-retriever-r03-20260924T221413Z
Recorded model: gpt-5.6-sol
Harness version: 0.153.4
Session: 01a0d5eb-369f-7f02-965c-a99b90be9e9b
Source SHA-256: 3a309d007f89050a15da5c26171182c4caefb2bcf6358541e35383e79ac7ae00

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

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