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Agent trace

Two-tower retriever sol-high-08

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 152 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · high effort
Recorded conversation
46:43 · 158 steps
Private reward
0.015065 · Failed
Attempt cost
$4.02
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
System instructions

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

Personality

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

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

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

Writing style

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

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

Technical communication

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

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

Working with the user

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

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

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

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

Intermediate commentary

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

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

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

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

Final answer

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

Formatting rules

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

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

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

Good candidates include:

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

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

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

Rules for getting work done

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

File editing constraints

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

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

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

Autonomy and persistence

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

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

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

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

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

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

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

Destructive actions

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

Before taking a destructive action:

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

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

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

Using skills

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

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

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

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

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

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

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

02

Developer instructions

+00:02
Developer instructions

<skills_instructions>

Skills

A skill is a set of instructions provided through a SKILL.md source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. file locators are on the host filesystem, environment resource locators are owned by an execution environment, orchestrator resource locators are opaque non-filesystem resources, and custom resource locators use their provider's access mechanism.

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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)
  • skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) </skills_instructions>

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

<apps_instructions>

Apps (Connectors)

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

03

Developer instructions

+00:02
Developer instructions

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

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

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

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

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

They may be addressed as to=/root

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

All agents share the same directory. In detail:

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

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

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

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

04

Developer instructions

+00:02
Developer instructions

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

05

User context

+00:02
User context

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

  • Airtable (airtable@openai-curated-remote)
  • Alpaca (alpaca@openai-curated-remote)
  • Apollo.io (apollo@openai-curated-remote)
  • Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote)
  • AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote)
  • Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote)
  • Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote)
  • LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote)
  • SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote)
  • Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote)
  • Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote)
  • Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote)
  • Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote)
  • True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote)
  • Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote)
  • Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote)
  • Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote)
  • Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote)
  • Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)
  • Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote)
  • Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote)
  • Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote)
  • Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote)
  • vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote)
  • TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote)
  • Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote)
  • Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote)
  • Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote)
  • Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote)
  • COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote)
  • TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote)
  • CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote)
  • Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote)
  • Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote)
  • freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote)
  • Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote)
  • Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote)
  • Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote)
  • CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote)
  • Asana (asana@openai-curated-remote)
  • Atlassian Rovo (atlassian-rovo@openai-curated-remote)
  • Base44 (base44@openai-curated-remote)
  • Binance (binance@openai-curated-remote)
  • Box (box@openai-curated-remote)
  • Canva (canva@openai-curated-remote)
  • ClickUp (clickup@openai-curated-remote)
  • Cloudflare (cloudflare@openai-curated-remote)
  • Codex Security (codex-security@openai-curated-remote)
  • Figma (figma@openai-curated-remote) </recommended_plugins>

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-08-31</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,
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-08-31",
      "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 instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "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": []
    },
    "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": "01a05911-913d-7072-bf9c-edee554c43a2",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-08-31",
  "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": "high",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "high",
  "summary": "auto"
}
06

Task instructions

+00:02
Task instructions

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

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

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

Data

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

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

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

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

The two public windows have different roles:

  1. Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
  2. After selecting the approach, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

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

Additional files under /task/data/:

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

The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery objective

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

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

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

The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Public tools

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

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

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

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

Deliverable

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

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

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model provider connection managed by Harbor.
  • The environment includes CPU PyTorch 2.8.0. No accelerator is available.
  • You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
07

exec command

+00:06
Thinking unavailable No text recorded

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

Model text

I’ll inspect the data and evaluator first, then establish a valid two-tower baseline, score it, and iterate on sampling/history features while keeping the best valid ranking saved. I’ll write only under /app/solution/ and keep validation strictly cutoff-correct.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({plan:[
  {step:"Inspect data schemas, sizes, cutoffs, eligibility, and evaluator",status:"in_progress"},
  {step:"Implement scalable PyTorch two-tower training and ranking pipeline",status:"pending"},
  {step:"Train validation variants and evaluate public NDCG@10",status:"pending"},
  {step:"Refit selected model on train+validation and create valid test submission",status:"pending"},
  {step:"Verify artifacts and document reproducible commands",status:"pending"}
]}); text(p)

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
08

exec command

+00:14
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,200p' /task/requirements.txt","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000}); text(r.output)

Tool response

Script completed
Wall time 0.1 seconds
Output:

/app
candidate_catalog.parquet 349352 bytes
target_users.parquet 84127 bytes
test_eligible_candidates.npz 7736341 bytes
test_preference_state.parquet 1092329 bytes
train_dislikes.parquet 857213 bytes
train_likes.parquet 5374652 bytes
train_listens.parquet 319183255 bytes
train_undislikes.parquet 155988 bytes
train_unlikes.parquet 1855885 bytes
val_dislikes.parquet 36571 bytes
val_likes.parquet 298484 bytes
val_listens.parquet 9947609 bytes
val_undislikes.parquet 7845 bytes
val_unlikes.parquet 72536 bytes
validation_eligible_candidates.npz 7573821 bytes
validation_preference_state.parquet 1061761 bytes
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

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

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

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

Usage::

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

import json
import sys
from pathlib import Path

import pandas as pd

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


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


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

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

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

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

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


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

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

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

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

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


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

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

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


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

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


if __name__ == "__main__":
    main()
"""Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
# 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
09

exec command

+00:22
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"mkdir -p /app/solution && python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np, os\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n    pf=pq.ParquetFile(p)\n    print(p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'cols',pf.schema.names)\n    if p.name.startswith(('train_','val_')):\n        t=pq.read_table(p,columns=['timestamp']).column(0).to_numpy()\n        print('  time',t.min(),t.max())\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n    with np.load(D/name) as z:\n      print(name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n      bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n      counts=bits.sum(1)\n      print(' eligible min/mean/max',counts.min(),counts.mean(),counts.max(),'uids first',z['uids'][:5],'items first',z['item_ids'][:10])\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),len(pd.read_parquet(D/'target_users.parquet')))\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(20),pd.read_parquet(D/'candidate_catalog.parquet').columns)\nprint('torch threads env',os.cpu_count())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output)

Tool response

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
ModuleNotFoundError: No module named 'pyarrow'
10

exec command

+00:39
Thinking unavailable No text recorded

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Model text

The shell’s default python is not the benchmark environment (it lacks the pinned packages), so I’m locating the task runtime before touching the implementation. Once found, I’ll use Parquet metadata and compact aggregates rather than loading the full 319 MB listen log repeatedly.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"which -a python python3 || true\npython --version || true\npython3 --version || true\nfind / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50\nfind / -maxdepth 4 -type d \\( -name site-packages -o -name dist-packages \\) 2>/dev/null | head -50\nenv | sort | sed -n '1,160p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000}); text(r.output)

Tool response

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
Python 3.12.13
Python 3.12.13
CODEX_CI=1
CODEX_HOME=/tmp/codex-home
CODEX_THREAD_ID=01a05911-9128-75b1-a0c4-9ebeddfa171e
COLORTERM=
GH_PAGER=cat
GIT_PAGER=cat
GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305
HOME=/home/agent
HOSTNAME=b6b3938d4ce2
LANG=C.UTF-8
LC_ALL=C.UTF-8
LC_CTYPE=C.UTF-8
NO_COLOR=1
PAGER=cat
PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games
PWD=/app
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13
SHLVL=1
TERM=dumb
_=/usr/local/bin/codex
11

exec command

+00:49
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"find /task -maxdepth 3 -type f -o -type l | sed -n '1,200p'\nls -la /usr/local/lib/python3.12/site-packages 2>&1 | sed -n '1,100p'\npython -m pip list 2>&1 | sed -n '1,120p'","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000}); text(r.output)

Tool response

Script completed
Wall time 0.7 seconds
Output:

/task/data/target_users.parquet
/task/data/test_preference_state.parquet
/task/data/candidate_catalog.parquet
/task/data/train_unlikes.parquet
/task/data/val_likes.parquet
/task/data/val_listens.parquet
/task/data/val_undislikes.parquet
/task/data/train_undislikes.parquet
/task/data/train_likes.parquet
/task/data/train_listens.parquet
/task/data/validation_eligible_candidates.npz
/task/data/train_dislikes.parquet
/task/data/val_unlikes.parquet
/task/data/val_dislikes.parquet
/task/data/test_eligible_candidates.npz
/task/data/validation_preference_state.parquet
/task/.keep
/task/requirements.txt
/task/ranking_evaluation.py
/task/evaluate_public.py
total 20
drwxr-xr-x  4 root root 4096 Aug  5 01:13 .
drwxr-xr-x 38 root root 4096 Aug  5 01:13 ..
-rw-r--r--  1 root root  119 Aug  5 01:13 README.txt
drwxr-xr-x  5 root root 4096 Aug  5 01:13 pip
drwxr-xr-x  2 root root 4096 Aug  5 01:13 pip-25.0.1.dist-info
Package Version
------- -------
pip     25.0.1
12

exec command

+00:57
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"find / -type f -path '*/bin/python*' -o -type l -path '*/bin/python*' 2>/dev/null | head -100\nfind / -type d -name 'torch' 2>/dev/null | head -30\nfind / -type d -name 'pyarrow' 2>/dev/null | head -30\nls -la /opt /opt/* 2>/dev/null | sed -n '1,160p'","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output)

Tool response

Script completed
Wall time 0.6 seconds
Output:

/opt/task-venv/bin/python
/opt/task-venv/bin/python3
/opt/task-venv/bin/python3.12
/usr/local/bin/python-config
/usr/local/bin/python
/usr/local/bin/python3.12-config
/usr/local/bin/python3
/usr/local/bin/python3-config
/usr/local/bin/python3.12
/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch
/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch
/opt/task-venv/lib/python3.12/site-packages/torch
/opt/task-venv/lib/python3.12/site-packages/torch/include/torch
/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch
/opt/task-venv/lib/python3.12/site-packages/pyarrow
/opt:
total 16
drwxr-xr-x 1 root root 4096 Aug 31 18:22 .
drwxr-xr-x 1 root root 4096 Aug 31 18:25 ..
drwxr-xr-x 4 root root 4096 Aug 31 18:22 codex-home
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 task-venv

/opt/codex-home:
total 16
drwxr-xr-x 4 root root 4096 Aug 31 18:22 .
drwxr-xr-x 1 root root 4096 Aug 31 18:22 ..
drwxr-xr-x 3 root root 4096 Aug 31 18:21 packages
drwxr-xr-x 3 root root 4096 Aug 31 18:22 tmp

/opt/task-venv:
total 32
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 .
drwxr-xr-x 1 root root 4096 Aug 31 18:22 ..
-r--r--r-- 1 root root    0 Aug 31 18:22 .lock
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 bin
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 etc
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 include
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 lib
lrwxrwxrwx 1 root root    3 Aug 31 18:22 lib64 -> lib
-r--r--r-- 1 root root  171 Aug 31 18:22 pyvenv.cfg
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 share
13

exec command

+01:04
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np, os, torch\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n    pf=pq.ParquetFile(p)\n    print(p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'cols',pf.schema.names)\n    if p.name.startswith(('train_','val_')):\n        t=pq.read_table(p,columns=['timestamp']).column(0).to_numpy()\n        print('  time',t.min(),t.max())\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n    with np.load(D/name) as z:\n      print(name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n      bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n      counts=bits.sum(1)\n      print(' eligible min/mean/max',counts.min(),counts.mean(),counts.max(),'uids first',z['uids'][:5],'items first',z['item_ids'][:10])\nT=pd.read_parquet(D/'target_users.parquet'); C=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets',T.head(),len(T),T.dtypes)\nprint('catalog',C.head(20),len(C),C.columns,C.dtypes)\nfor name in ['validation_preference_state.parquet','test_preference_state.parquet']:\n    x=pd.read_parquet(D/name); print(name,x.head(),x.state.value_counts(),x.dtypes)\nprint('torch',torch.__version__,'threads',torch.get_num_threads(),'cpus',os.cpu_count())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output)

Tool response

Script completed
Wall time 5.3 seconds
Output:

candidate_catalog.parquet rows 30000 groups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet rows 8456 groups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet rows 484776 groups 1 cols ['uid', 'item_id', 'state']
train_dislikes.parquet rows 101075 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 75 24786580
train_likes.parquet rows 814831 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 80 24786790
train_listens.parquet rows 43484159 groups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
  time 0 24786795
train_undislikes.parquet rows 19960 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 15950 24785395
train_unlikes.parquet rows 292665 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 250 24786570
val_dislikes.parquet rows 3563 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 24789920 25393280
val_likes.parquet rows 37795 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 24788815 25393390
val_listens.parquet rows 1501697 groups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
  time 24788600 25393395
val_undislikes.parquet rows 606 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 24810715 25393035
val_unlikes.parquet rows 9346 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']
  time 24789005 25393385
validation_preference_state.parquet rows 469124 groups 1 cols ['uid', 'item_id', 'state']
validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
 eligible min/mean/max 24657 29067.306291390727 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749]
test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
 eligible min/mean/max 24552 29041.3178807947 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749]
targets    uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359
3  600                   1985.0                      2423
4  700                   1776.0                      6114 8456 uid                          uint32
recent_positive_listens     float64
history_positive_listens      int64
dtype: object
catalog     item_id  positive_listens  organic_positive_listens  popularity_rank
0   5862961             26615                     20620                1
1   6901374             25536                     17347                2
2   5635052             24691                     17194                3
3   3542184             24075                     17699                4
4   9378983             24036                     18195                5
5   5463340             22746                     17398                6
6   8213481             22505                     17071                7
7    906358             22238                     17183                8
8   2859641             18380                     13704                9
9   3033749             16682                     12267               10
10  2651314             16286                     11801               11
11  6853370             15024                     11692               12
12  5252571             14897                     11331               13
13  5436843             14897                     12127               14
14  3418493             14815                      9039               15
15  5927996             14751                     10424               16
16  6155741             14354                      9251               17
17  8902934             14210                      9160               18
18  8351511             14061                      9739               19
19  5734320             14008                      9467               20 30000 Index(['item_id', 'positive_listens', 'organic_positive_listens',
       'popularity_rank'],
      dtype='object') item_id                     uint32
positive_listens             int64
organic_positive_listens    uint64
popularity_rank              int32
dtype: object
validation_preference_state.parquet    uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked state
liked       418435
disliked     50689
Name: count, dtype: int64 uid        uint32
item_id    uint32
state      object
dtype: object
test_preference_state.parquet    uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked state
liked       432659
disliked     52117
Name: count, dtype: int64 uid        uint32
item_id    uint32
state      object
dtype: object
torch 2.8.0+cpu threads 4 cpus 8
14

Agent message

+01:45
Thinking unavailable No text recorded

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15

exec command

+01:56
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_popularity.py\n+#!/usr/bin/env python3\n+\"\"\"Build a structurally valid popularity ranking for either cutoff.\"\"\"\n+from pathlib import Path\n+import argparse\n+import numpy as np\n+import pandas as pd\n+\n+DATA = Path(\"/task/data\")\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n+    parser.add_argument(\"--output\", type=Path, required=True)\n+    args = parser.parse_args()\n+    artifact = np.load(DATA / f\"{args.stage}_eligible_candidates.npz\")\n+    uids = artifact[\"uids\"].astype(np.int64)\n+    items = artifact[\"item_ids\"].astype(np.int64)\n+    packed = artifact[\"packed_eligible\"]\n+    rows = []\n+    for row, uid in enumerate(uids):\n+        eligible = np.unpackbits(\n+            packed[row], count=len(items), bitorder=\"little\"\n+        ).astype(bool)\n+        chosen = items[eligible][:100]\n+        rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\n+    args.output.parent.mkdir(parents=True, exist_ok=True)\n+    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n+        args.output, index=False\n+    )\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
16

exec command

+02:12
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 6.1 seconds
Output:

total 16
drwxr-xr-x 1 agent agent 4096 Aug 31 18:27 .
drwxr-xr-x 1 agent agent 4096 Aug 31 18:22 ..
-rw-r--r-- 1 agent agent 1148 Aug 31 18:27 build_popularity.py
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
17

exec command

+02:28
Thinking unavailable No text recorded

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

Model text

The valid popularity fallback scores 0.010710 over 4,263 labeled users and is now saved. The main opportunity is personalization: I’m measuring how much target-user history intersects the 30k catalog, how recent histories behave, and whether validation relevance favors repeats, neighbors of prior tracks, or broad popularity. That will determine the most efficient history encoder and negative sampler.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd, pyarrow.parquet as pq, time\nD=Path('/task/data')\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\nT=set(targets.tolist()); C=set(cat.tolist())\nfor fname in ['train_listens.parquet','val_listens.parquet']:\n  pf=pq.ParquetFile(D/fname); n_t=n_tc=n_pos=n_posc=n_strongorg=0; us=set(); st=time.time()\n  for b in pf.iter_batches(batch_size=1_000_000,columns=['uid','item_id','played_ratio_pct','is_organic']):\n    x=b.to_pandas(); mt=x.uid.isin(T); y=x[mt]; n_t+=len(y); n_tc+=y.item_id.isin(C).sum();\n    p=y.played_ratio_pct>=50; n_pos+=p.sum(); n_posc+=(p & y.item_id.isin(C)).sum(); n_strongorg+=((y.played_ratio_pct>=80)&(y.is_organic==1)&y.item_id.isin(C)).sum(); us.update(y.uid.unique())\n  print(fname,'target',n_t,'target-cat',n_tc,'positive',n_pos,'positive-cat',n_posc,'strongorgcat',n_strongorg,'users',len(us),'sec',time.time()-st)\nfor fname in ['train_likes.parquet','val_likes.parquet']:\n x=pd.read_parquet(D/fname); y=x[x.uid.isin(T)]; print(fname,'target',len(y),'cat',y.item_id.isin(C).sum(),'organic-cat',((y.is_organic==1)&y.item_id.isin(C)).sum())\n# validation truth ranks under popularity and whether train seen\nvl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\nrel1=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.item_id.isin(C)][['uid','item_id']].drop_duplicates(); rel1['g']=1\nrel3=vk[(vk.uid.isin(T))&(vk.is_organic==1)&vk.item_id.isin(C)][['uid','item_id']].drop_duplicates(); rel3['g']=3\ntruth=pd.concat([rel1,rel3]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrank={v:i+1 for i,v in enumerate(cat)}\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'grade counts',truth.g.value_counts().to_dict())\nprint('pop ranks q',truth.item_id.map(rank).dropna().quantile([0,.1,.25,.5,.75,.9,1]).to_dict())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output)

Tool response

Script running with cell ID 10
Wall time 11.0 seconds
Output:
18

wait command

+03:09
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "10",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 3.5 seconds
Output:

train_listens.parquet target 43222297 target-cat 31062540 positive 27367343 positive-cat 19959853 strongorgcat 8661020 users 8456 sec 14.3329176902771
val_listens.parquet target 1482553 target-cat 999062 positive 955678 positive-cat 650295 strongorgcat 288828 users 7729 sec 0.5363175868988037
train_likes.parquet target 690968 cat 472889 organic-cat 223046
val_likes.parquet target 33177 cat 18307 organic-cat 10406
raw truth 175729 users 6186 grade counts {1: 167201, 3: 8528}
pop ranks q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 1.0: 30000.0}
19

exec command

+03:22
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data'); z=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; ip={int(x):i for i,x in enumerate(items)}; ur={int(x):i for i,x in enumerate(uids)}\nsel=set(map(int,uids[:20])); seen={u:set() for u in sel}; cnt={u:0 for u in sel}\nfor b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(columns=['uid','item_id','played_ratio_pct'],batch_size=1_000_000):\n x=b.to_pandas(); x=x[x.uid.isin(sel)&(x.played_ratio_pct>=50)&x.item_id.isin(ip)]\n for u,g in x.groupby('uid'): seen[int(u)].update(map(int,g.item_id))\nfor u in list(sel)[:20]:\n bits=np.unpackbits(z['packed_eligible'][ur[u]],count=len(items),bitorder='little')\n ss=seen[u]; elig_seen=sum(bits[ip[i]] for i in ss)\n print(u,'seen',len(ss),'eligible seen',elig_seen,'excluded total',len(items)-bits.sum())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output)

Tool response

Script completed
Wall time 10.2 seconds
Output:

900 seen 26 eligible seen 0 excluded total 334
2200 seen 1043 eligible seen 0 excluded total 1096
800 seen 1910 eligible seen 0 excluded total 2524
1700 seen 1429 eligible seen 0 excluded total 2164
300 seen 45 eligible seen 0 excluded total 69
2100 seen 562 eligible seen 0 excluded total 645
700 seen 1233 eligible seen 0 excluded total 1653
1600 seen 1207 eligible seen 0 excluded total 1524
2500 seen 555 eligible seen 0 excluded total 636
200 seen 163 eligible seen 0 excluded total 168
1100 seen 1337 eligible seen 0 excluded total 1440
2000 seen 265 eligible seen 0 excluded total 374
600 seen 657 eligible seen 0 excluded total 680
1500 seen 503 eligible seen 0 excluded total 524
2400 seen 437 eligible seen 0 excluded total 471
100 seen 600 eligible seen 0 excluded total 682
1000 seen 429 eligible seen 0 excluded total 499
1900 seen 1786 eligible seen 0 excluded total 2041
1400 seen 1139 eligible seen 0 excluded total 1264
2300 seen 1064 eligible seen 0 excluded total 1406
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Agent message

+04:05
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exec command

+04:24
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor i in range(p.metadata.num_row_groups):\n c=p.metadata.row_group(i).column(p.schema.names.index('timestamp')).statistics\n print(i,c.min,c.max,p.metadata.row_group(i).num_rows)\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000}); text(r.output)

Tool response

Script completed
Wall time 0.5 seconds
Output:

0 15 24786690 1048576
1 185 24786725 1048576
2 15320 24786790 1048576
3 3625 24786795 1048576
4 5735 24786740 1048576
5 120 24786775 1048576
6 140 24786750 1048576
7 220 24786745 1048576
8 1890 24786770 1048576
9 65 24786735 1048576
10 15 24786720 1048576
11 350 24786795 1048576
12 60 24786730 1048576
13 1370 24786790 1048576
14 5 24786770 1048576
15 30 24786785 1048576
16 9145 24786750 1048576
17 95 24786795 1048576
18 20 24786760 1048576
19 0 24786760 1048576
20 40 24786795 1048576
21 300 24786790 1048576
22 45 24786730 1048576
23 530 24786730 1048576
24 8680 24786790 1048576
25 65 24786785 1048576
26 20 24786785 1048576
27 40 24786725 1048576
28 125 24786770 1048576
29 7330 24786785 1048576
30 30 24786775 1048576
31 60 24786670 1048576
32 20 24786795 1048576
33 355 24786795 1048576
34 565 24786735 1048576
35 205 24786785 1048576
36 50 24786740 1048576
37 1190 24786775 1048576
38 190 24786705 1048576
39 10 24786760 1048576
40 230 24786710 1048576
41 20 24786690 492543
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exec command

+04:32
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nx=pq.read_table('/task/data/train_listens.parquet',columns=['uid','timestamp','item_id']).slice(0,30).to_pandas(); print(x.to_string(index=False))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":5000}); text(r.output)

Tool response

Script completed
Wall time 1.9 seconds
Output:

 uid  timestamp  item_id
 100      39420  8326270
 100      39420  1441281
 100      39625   286361
 100      40110   732449
 100      40360  3397170
 100      40380  7849270
 100      40640  1449307
 100      40785  6887232
 100      40885  2996197
 100      41130  6474571
 100      41545  7847600
 100      41580  7509517
 100      41580   470320
 100      42115  6641244
 100      42115  2581903
 100      42115  5485459
 100      42115  5474898
 100      42115  3088475
 100      42115  7521298
 100      42210  8329413
 100      42380  1853659
 100      42520  1302519
 100      43425  7498961
 100      43425  3630949
 100      43425  2729049
 100      43425   732246
 100      43425  5910506
 100      43720  1907213
 100      43720  6451550
 100      43910  2170705
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exec command

+04:41
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"free -h && df -h /app","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000}); text(r.output)

Tool response

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: free: not found
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exec command

+04:45
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"sed -n '1,12p' /proc/meminfo\ndf -h /app","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000}); text(r.output)

Tool response

Script completed
Wall time 0.0 seconds
Output:

MemTotal:       15988980 kB
MemFree:         1914336 kB
MemAvailable:   14934508 kB
Buffers:          227092 kB
Cached:         12500220 kB
SwapCached:            0 kB
Active:           501636 kB
Inactive:       12498080 kB
Active(anon):     298856 kB
Inactive(anon):        0 kB
Active(file):     202780 kB
Inactive(file): 12498080 kB
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   14G   83G  15% /
25

Agent message

+05:07
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+05:25
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+05:44
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+06:02
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+06:21
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+06:40
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+06:46
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/model.py\n+\"\"\"Strictly decomposable PyTorch two-tower retrieval model.\"\"\"\n+from __future__ import annotations\n+\n+import torch\n+from torch import nn\n+import torch.nn.functional as F\n+\n+\n+class UserTower(nn.Module):\n+    \"\"\"Encode positive/negative interaction history without a user-ID table.\"\"\"\n+\n+    def __init__(self, num_items: int, hidden_dim: int = 96, output_dim: int = 64):\n+        super().__init__()\n+        # Index zero is padding. This table represents tracks in histories, not users.\n+        self.history_embedding = nn.Embedding(num_items + 1, hidden_dim, padding_idx=0)\n+        self.network = nn.Sequential(\n+            nn.Linear(hidden_dim * 3 + 4, 192),\n+            nn.LayerNorm(192),\n+            nn.SiLU(),\n+            nn.Linear(192, 128),\n+            nn.SiLU(),\n+            nn.Linear(128, output_dim),\n+        )\n+\n+    @staticmethod\n+    def _pool(emb: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:\n+        denom = weights.sum(dim=1, keepdim=True).clamp_min(1e-6)\n+        return (emb * weights.unsqueeze(-1)).sum(dim=1) / denom\n+\n+    def forward(\n+        self,\n+        history_ids: torch.Tensor,\n+        long_weights: torch.Tensor,\n+        short_weights: torch.Tensor,\n+        negative_ids: torch.Tensor,\n+        negative_weights: torch.Tensor,\n+        numeric: torch.Tensor,\n+    ) -> torch.Tensor:\n+        positive = self.history_embedding(history_ids)\n+        long_pool = self._pool(positive, long_weights)\n+        short_pool = self._pool(positive, short_weights)\n+        negative = self._pool(self.history_embedding(negative_ids), negative_weights)\n+        vector = self.network(torch.cat([long_pool, short_pool, negative, numeric], dim=1))\n+        return F.normalize(vector, dim=1)\n+\n+\n+class ItemTower(nn.Module):\n+    \"\"\"Independently encode a catalog item for offline ANN indexing.\"\"\"\n+\n+    def __init__(self, num_items: int, feature_dim: int = 4, hidden_dim: int = 96, output_dim: int = 64):\n+        super().__init__()\n+        self.item_embedding = nn.Embedding(num_items + 1, hidden_dim, padding_idx=0)\n+        self.network = nn.Sequential(\n+            nn.Linear(hidden_dim + feature_dim, 160),\n+            nn.LayerNorm(160),\n+            nn.SiLU(),\n+            nn.Linear(160, 128),\n+            nn.SiLU(),\n+            nn.Linear(128, output_dim),\n+        )\n+\n+    def forward(self, item_ids: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\n+        vector = self.network(torch.cat([self.item_embedding(item_ids), features], dim=-1))\n+        return F.normalize(vector, dim=-1)\n+\n+\n+class TwoTower(nn.Module):\n+    def __init__(self, num_items: int, output_dim: int = 64):\n+        super().__init__()\n+        self.user_tower = UserTower(num_items, output_dim=output_dim)\n+        self.item_tower = ItemTower(num_items, output_dim=output_dim)\n+\n*** Add File: /app/solution/train_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Preprocess events, train the two towers, and emit an eligible ranking.\n+\n+The data path is event-stream based: it never constructs a user-item matrix.\n+Catalog item IDs are mapped to dense embedding indices only.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import math\n+import random\n+from collections import defaultdict\n+from pathlib import Path\n+from typing import Iterator\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+import torch.nn.functional as F\n+\n+from model import TwoTower\n+\n+DATA = Path(\"/task/data\")\n+SHELF_GAP = 1_800\n+WINDOW = 7 * 24 * 60 * 60\n+DAY = 24 * 60 * 60\n+MAX_HISTORY = 256\n+MAX_NEGATIVE = 64\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 iter_user_groups(path: Path) -> Iterator[tuple[int, dict[str, np.ndarray]]]:\n+    \"\"\"Stream a uid/time-sorted listen parquet, retaining at most one user carry.\"\"\"\n+    columns = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+    carry: dict[str, np.ndarray] | None = None\n+    for batch in pq.ParquetFile(path).iter_batches(batch_size=750_000, columns=columns):\n+        current = {name: batch.column(i).to_numpy(zero_copy_only=False) for i, name in enumerate(columns)}\n+        if carry is not None:\n+            current = {name: np.concatenate([carry[name], current[name]]) for name in columns}\n+        uids = current[\"uid\"]\n+        boundaries = np.flatnonzero(uids[1:] != uids[:-1]) + 1\n+        starts = np.r_[0, boundaries]\n+        ends = np.r_[boundaries, len(uids)]\n+        for start, end in zip(starts[:-1], ends[:-1]):\n+            yield int(uids[start]), {name: values[start:end] for name, values in current.items()}\n+        start = int(starts[-1])\n+        carry = {name: values[start:] for name, values in current.items()}\n+    if carry is not None and len(carry[\"uid\"]):\n+        yield int(carry[\"uid\"][0]), carry\n+\n+\n+def event_groups(paths: list[Path], columns: list[str]) -> dict[int, pd.DataFrame]:\n+    frames = [pd.read_parquet(path, columns=columns) for path in paths]\n+    frame = pd.concat(frames, ignore_index=True) if len(frames) > 1 else frames[0]\n+    frame = frame.sort_values([\"uid\", \"timestamp\"])\n+    return {int(uid): group.drop(columns=\"uid\") for uid, group in frame.groupby(\"uid\", sort=False)}\n+\n+\n+def map_catalog(raw_items: np.ndarray, sorted_ids: np.ndarray, sorted_to_pos: np.ndarray) -> np.ndarray:\n+    at = np.searchsorted(sorted_ids, raw_items)\n+    good = at < len(sorted_ids)\n+    result = np.full(len(raw_items), -1, dtype=np.int32)\n+    matched = good & (sorted_ids[np.minimum(at, len(sorted_ids) - 1)] == raw_items)\n+    result[matched] = sorted_to_pos[at[matched]]\n+    return result\n+\n+\n+def active_items(events: pd.DataFrame | None, cutoff: int, item_map) -> np.ndarray:\n+    if events is None or events.empty:\n+        return np.empty(0, dtype=np.int32)\n+    rows = events[events.timestamp < cutoff]\n+    if rows.empty:\n+        return np.empty(0, dtype=np.int32)\n+    # Last action independently controls each explicit channel.\n+    last = rows.sort_values(\"timestamp\").drop_duplicates(\"item_id\", keep=\"last\")\n+    last = last[last.active == 1]\n+    return np.asarray([item_map.get(int(x), -1) for x in last.item_id if int(x) in item_map], dtype=np.int32)\n+\n+\n+def state_channel(on_paths: list[Path], off_paths: list[Path]) -> dict[int, pd.DataFrame]:\n+    frames = []\n+    for path in on_paths:\n+        x = pd.read_parquet(path, columns=[\"uid\", \"timestamp\", \"item_id\"])\n+        x[\"active\"] = np.int8(1)\n+        frames.append(x)\n+    for path in off_paths:\n+        x = pd.read_parquet(path, columns=[\"uid\", \"timestamp\", \"item_id\"])\n+        x[\"active\"] = np.int8(0)\n+        frames.append(x)\n+    frame = pd.concat(frames, ignore_index=True).sort_values([\"uid\", \"timestamp\"])\n+    return {int(uid): group.drop(columns=\"uid\") for uid, group in frame.groupby(\"uid\", sort=False)}\n+\n+\n+def select_history(\n+    positions: np.ndarray,\n+    timestamps: np.ndarray,\n+    ratios: np.ndarray,\n+    organic: np.ndarray,\n+    cutoff: int,\n+    liked: np.ndarray,\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n+    mask = (positions >= 0) & (timestamps < cutoff) & (ratios >= 50)\n+    p, t, r, o = positions[mask], timestamps[mask], ratios[mask], organic[mask]\n+    if len(p):\n+        # Aggregate event strength and retain last-event recency per unique track.\n+        order = np.argsort(p, kind=\"stable\")\n+        p, t, r, o = p[order], t[order], r[order], o[order]\n+        unique, starts = np.unique(p, return_index=True)\n+        strength = np.add.reduceat(np.clip(r / 100.0, 0.5, 1.25) * (1.0 + 0.45 * o), starts)\n+        last_time = np.maximum.reduceat(t, starts)\n+        base = np.log1p(strength)\n+        long_w = base * np.exp(-(cutoff - last_time) / (120 * DAY))\n+        short_w = base * np.exp(-(cutoff - last_time) / (18 * DAY))\n+    else:\n+        unique = np.empty(0, dtype=np.int32)\n+        long_w = short_w = np.empty(0, dtype=np.float32)\n+    if len(liked):\n+        all_ids = np.unique(np.concatenate([unique, liked]))\n+        lw = np.zeros(len(all_ids), np.float32)\n+        sw = np.zeros(len(all_ids), np.float32)\n+        if len(unique):\n+            ix = np.searchsorted(all_ids, unique)\n+            lw[ix], sw[ix] = long_w, short_w\n+        ix = np.searchsorted(all_ids, liked)\n+        lw[ix] += 2.5\n+        sw[ix] += 1.5\n+        unique, long_w, short_w = all_ids, lw, sw\n+    if len(unique) > MAX_HISTORY:\n+        keep = np.argpartition(long_w + short_w, -MAX_HISTORY)[-MAX_HISTORY:]\n+        unique, long_w, short_w = unique[keep], long_w[keep], short_w[keep]\n+    return unique.astype(np.int32), long_w.astype(np.float32), short_w.astype(np.float32), mask\n+\n+\n+def pad(values: np.ndarray, size: int, dtype, offset: int = 0) -> np.ndarray:\n+    result = np.zeros(size, dtype=dtype)\n+    n = min(size, len(values))\n+    if n:\n+        result[:n] = values[:n] + offset\n+    return result\n+\n+\n+def build_cache(stage: str, output: Path, weeks: int) -> None:\n+    test = stage == \"test\"\n+    listen_paths = [DATA / \"train_listens.parquet\"]\n+    suffixes = [\"train\"]\n+    if test:\n+        listen_paths.append(DATA / \"val_listens.parquet\")\n+        suffixes.append(\"val\")\n+    end = 25_393_400 if test else 24_786_800\n+    cutoffs = [end - j * (WINDOW + SHELF_GAP) for j in range(weeks, 0, -1)]\n+\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    raw_catalog = catalog.item_id.to_numpy(np.int64)\n+    item_map = {int(item): i for i, item in enumerate(raw_catalog)}\n+    order = np.argsort(raw_catalog)\n+    sorted_ids, sorted_to_pos = raw_catalog[order], order.astype(np.int32)\n+\n+    like_on = [DATA / f\"{s}_likes.parquet\" for s in suffixes]\n+    like_off = [DATA / f\"{s}_unlikes.parquet\" for s in suffixes]\n+    dislike_on = [DATA / f\"{s}_dislikes.parquet\" for s in suffixes]\n+    dislike_off = [DATA / f\"{s}_undislikes.parquet\" for s in suffixes]\n+    likes = state_channel(like_on, like_off)\n+    dislikes = state_channel(dislike_on, dislike_off)\n+    organic_likes = event_groups(like_on, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n+\n+    # Validation has one stream. Test appends the small validation stream by uid.\n+    val_listens = {}\n+    if test:\n+        vf = pd.read_parquet(listen_paths[1], columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\n+        val_listens = {int(uid): g for uid, g in vf.groupby(\"uid\", sort=False)}\n+\n+    histories, longs, shorts, negatives, neg_weights, numerics = [], [], [], [], [], []\n+    target_lists, target_gains = [], []\n+    inference = None\n+    targets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\n+    for number, (uid, data) in enumerate(iter_user_groups(listen_paths[0])):\n+        if uid not in targets:\n+            continue\n+        if test and uid in val_listens:\n+            extra = val_listens[uid]\n+            for name in [\"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]:\n+                data[name] = np.concatenate([data[name], extra[name].to_numpy()])\n+            chronology = np.argsort(data[\"timestamp\"], kind=\"stable\")\n+            data = {name: value[chronology] for name, value in data.items()}\n+        pos = map_catalog(data[\"item_id\"].astype(np.int64), sorted_ids, sorted_to_pos)\n+        user_likes = likes.get(uid)\n+        user_dislikes = dislikes.get(uid)\n+        user_organic_likes = organic_likes.get(uid)\n+\n+        for cutoff in cutoffs + [end]:\n+            liked = active_items(user_likes, cutoff, item_map)\n+            disliked = active_items(user_dislikes, cutoff, item_map)\n+            hist, lw, sw, _ = select_history(\n+                pos, data[\"timestamp\"], data[\"played_ratio_pct\"], data[\"is_organic\"], cutoff, liked\n+            )\n+            if not len(hist):\n+                continue\n+            histories.append(pad(hist, MAX_HISTORY, np.int32, 1))\n+            longs.append(pad(lw, MAX_HISTORY, np.float32))\n+            shorts.append(pad(sw, MAX_HISTORY, np.float32))\n+            negatives.append(pad(disliked[:MAX_NEGATIVE], MAX_NEGATIVE, np.int32, 1))\n+            neg_weights.append(pad(np.ones(min(len(disliked), MAX_NEGATIVE)), MAX_NEGATIVE, np.float32))\n+            numerics.append(np.asarray([\n+                math.log1p(len(hist)) / 6.0,\n+                math.log1p(np.count_nonzero(data[\"timestamp\"] < cutoff)) / 10.0,\n+                math.log1p(len(liked)) / 5.0,\n+                math.log1p(len(disliked)) / 4.0,\n+            ], np.float32))\n+            if cutoff == end:\n+                target_lists.append(np.empty(0, np.int32))\n+                target_gains.append(np.empty(0, np.float32))\n+                continue\n+\n+            prior_seen = set(pos[(pos >= 0) & (data[\"timestamp\"] < cutoff)].tolist())\n+            future = (\n+                (data[\"timestamp\"] >= cutoff + SHELF_GAP)\n+                & (data[\"timestamp\"] < cutoff + SHELF_GAP + WINDOW)\n+                & (data[\"is_organic\"] == 1)\n+                & (data[\"played_ratio_pct\"] >= 80)\n+                & (pos >= 0)\n+            )\n+            grades: dict[int, int] = {int(x): 1 for x in np.unique(pos[future]) if int(x) not in prior_seen}\n+            if user_organic_likes is not None:\n+                q = user_organic_likes\n+                q = q[(q.timestamp >= cutoff + SHELF_GAP) & (q.timestamp < cutoff + SHELF_GAP + WINDOW) & (q.is_organic == 1)]\n+                for item in q.item_id:\n+                    mapped = item_map.get(int(item))\n+                    if mapped is not None and mapped not in prior_seen:\n+                        grades[mapped] = 3\n+            item_targets = np.fromiter(grades.keys(), dtype=np.int32)\n+            gains = np.fromiter((7.0 if g == 3 else 1.0 for g in grades.values()), dtype=np.float32)\n+            target_lists.append(item_targets)\n+            target_gains.append(gains)\n+\n+        if number and number % 1500 == 0:\n+            print(f\"preprocessed {number:,} users\", flush=True)\n+\n+    # The last context for every processed user is inference; train contexts are nonempty target rows.\n+    lengths = np.asarray([len(x) for x in target_lists], np.int32)\n+    is_train = lengths > 0\n+    is_infer = lengths == 0\n+    offsets = np.r_[0, np.cumsum(lengths[is_train], dtype=np.int64)]\n+    flat_targets = np.concatenate([x for x, ok in zip(target_lists, is_train) if ok]).astype(np.int32)\n+    flat_gains = np.concatenate([x for x, ok in zip(target_gains, is_train) if ok]).astype(np.float32)\n+    # recover context uids: construction produces weeks+1 contexts/user unless empty history\n+    # Re-run order cheaply from target uids; all target users have sufficient history here.\n+    user_order = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n+    infer_count = int(is_infer.sum())\n+    if infer_count != len(user_order):\n+        raise RuntimeError(f\"expected {len(user_order)} inference contexts, got {infer_count}\")\n+    np.savez_compressed(\n+        output,\n+        history=np.stack(histories), long=np.stack(longs), short=np.stack(shorts),\n+        negative=np.stack(negatives), negative_weight=np.stack(neg_weights), numeric=np.stack(numerics),\n+        train_rows=np.flatnonzero(is_train).astype(np.int32), infer_rows=np.flatnonzero(is_infer).astype(np.int32),\n+        target_offsets=offsets, targets=flat_targets, target_gains=flat_gains, infer_uids=user_order,\n+    )\n+    print(json.dumps({\"cache\": str(output), \"contexts\": len(histories), \"train_contexts\": int(is_train.sum()), \"targets\": len(flat_targets)}))\n+\n+\n+def item_features() -> np.ndarray:\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    positive = np.log1p(catalog.positive_listens.to_numpy(np.float32))\n+    organic = np.log1p(catalog.organic_positive_listens.to_numpy(np.float32))\n+    share = catalog.organic_positive_listens.to_numpy(np.float32) / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1)\n+    rank = np.log1p(catalog.popularity_rank.to_numpy(np.float32))\n+    raw = np.c_[positive, organic, share, rank].astype(np.float32)\n+    return (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\n+\n+\n+def tensor_rows(cache, name: str, rows: np.ndarray, device=\"cpu\") -> torch.Tensor:\n+    return torch.from_numpy(cache[name][rows]).to(device)\n+\n+\n+def train_model(cache_path: Path, model_path: Path, epochs: int, seed: int, dim: int) -> None:\n+    seed_everything(seed)\n+    torch.set_num_threads(min(8, torch.get_num_threads()))\n+    cache = np.load(cache_path)\n+    rows = cache[\"train_rows\"]\n+    offsets, targets, gains = cache[\"target_offsets\"], cache[\"targets\"], cache[\"target_gains\"]\n+    features = torch.from_numpy(item_features())\n+    num_items = len(features)\n+    model = TwoTower(num_items, output_dim=dim)\n+    optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n+    generator = np.random.default_rng(seed)\n+    batch_size, negatives_per = 192, 40\n+    pop_prob = np.power(np.arange(1, num_items + 1, dtype=np.float64), -0.35)\n+    pop_prob /= pop_prob.sum()\n+\n+    model.train()\n+    for epoch in range(epochs):\n+        sequence = generator.permutation(len(rows))\n+        total = batches = 0.0\n+        for start in range(0, len(sequence), batch_size):\n+            cx = sequence[start:start + batch_size]\n+            data_rows = rows[cx]\n+            chosen = []\n+            for index in cx:\n+                lo, hi = offsets[index], offsets[index + 1]\n+                probability = gains[lo:hi].astype(np.float64)\n+                probability /= probability.sum()\n+                chosen.append(targets[lo + generator.choice(hi - lo, p=probability)])\n+            positive = np.asarray(chosen, np.int64)\n+            # Half popularity-shaped and half uniform negatives improves both head and tail separation.\n+            n = len(cx)\n+            hard = generator.choice(num_items, size=(n, negatives_per // 2), p=pop_prob)\n+            uniform = generator.integers(0, num_items, size=(n, negatives_per - negatives_per // 2))\n+            negative = np.concatenate([hard, uniform], axis=1).astype(np.int64)\n+            collision = negative == positive[:, None]\n+            negative[collision] = (negative[collision] + 1) % num_items\n+\n+            user = model.user_tower(\n+                tensor_rows(cache, \"history\", data_rows), tensor_rows(cache, \"long\", data_rows),\n+                tensor_rows(cache, \"short\", data_rows), tensor_rows(cache, \"negative\", data_rows),\n+                tensor_rows(cache, \"negative_weight\", data_rows), tensor_rows(cache, \"numeric\", data_rows),\n+            )\n+            pos_ids = torch.from_numpy(positive + 1)\n+            pos_vec = model.item_tower(pos_ids, features[positive])\n+            neg_ids = torch.from_numpy(negative + 1)\n+            neg_vec = model.item_tower(neg_ids, features[negative])\n+            pos_score = (user * pos_vec).sum(1, keepdim=True)\n+            neg_score = (user[:, None, :] * neg_vec).sum(2)\n+            loss = F.softplus((neg_score - pos_score) * 9.0).mean()\n+            optimizer.zero_grad(set_to_none=True)\n+            loss.backward()\n+            torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n+            optimizer.step()\n+            total += float(loss); batches += 1\n+        print(f\"epoch {epoch + 1:02d} loss={total / batches:.5f}\", flush=True)\n+    torch.save({\"state_dict\": model.state_dict(), \"num_items\": num_items, \"dim\": dim, \"seed\": seed}, model_path)\n+\n+\[email protected]_mode()\n+def rank(cache_path: Path, model_path: Path, stage: str, output: Path, pop_blend: float) -> None:\n+    cache = np.load(cache_path)\n+    checkpoint = torch.load(model_path, map_location=\"cpu\", weights_only=True)\n+    model = TwoTower(checkpoint[\"num_items\"], output_dim=checkpoint[\"dim\"])\n+    model.load_state_dict(checkpoint[\"state_dict\"])\n+    model.eval()\n+    features = torch.from_numpy(item_features())\n+    all_ids = torch.arange(1, checkpoint[\"num_items\"] + 1)\n+    item_vectors = []\n+    for start in range(0, len(all_ids), 2048):\n+        ids = all_ids[start:start + 2048]\n+        item_vectors.append(model.item_tower(ids, features[start:start + len(ids)]))\n+    item_vectors = torch.cat(item_vectors)\n+    rows = cache[\"infer_rows\"]\n+    user_vectors = []\n+    for start in range(0, len(rows), 256):\n+        ix = rows[start:start + 256]\n+        user_vectors.append(model.user_tower(\n+            tensor_rows(cache, \"history\", ix), tensor_rows(cache, \"long\", ix), tensor_rows(cache, \"short\", ix),\n+            tensor_rows(cache, \"negative\", ix), tensor_rows(cache, \"negative_weight\", ix), tensor_rows(cache, \"numeric\", ix),\n+        ))\n+    user_vectors = torch.cat(user_vectors)\n+    eligibility = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n+    uids = eligibility[\"uids\"].astype(np.int64)\n+    candidate_items = eligibility[\"item_ids\"].astype(np.int64)\n+    if not np.array_equal(uids, cache[\"infer_uids\"]):\n+        raise RuntimeError(\"cache user order does not align with eligibility\")\n+    pop = -np.log1p(np.arange(len(candidate_items), dtype=np.float32))\n+    pop = (pop - pop.mean()) / pop.std()\n+    result = []\n+    for start in range(0, len(uids), 128):\n+        stop = min(start + 128, len(uids))\n+        score = (user_vectors[start:stop] @ item_vectors.T).numpy()\n+        score += pop_blend * pop[None, :]\n+        mask = np.unpackbits(eligibility[\"packed_eligible\"][start:stop], axis=1, count=len(candidate_items), bitorder=\"little\").astype(bool)\n+        score[~mask] = -np.inf\n+        top = np.argpartition(score, -100, axis=1)[:, -100:]\n+        top_scores = np.take_along_axis(score, top, axis=1)\n+        top = np.take_along_axis(top, np.argsort(-top_scores, axis=1), axis=1)\n+        for row, uid in enumerate(uids[start:stop]):\n+            result.extend((int(uid), int(candidate_items[col]), rank_) for rank_, col in enumerate(top[row], 1))\n+    pd.DataFrame(result, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(output, index=False)\n+\n+\n+def parse_args() -> argparse.Namespace:\n+    p = argparse.ArgumentParser()\n+    sub = p.add_subparsers(dest=\"command\", required=True)\n+    q = sub.add_parser(\"preprocess\")\n+    q.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n+    q.add_argument(\"--output\", type=Path, required=True)\n+    q.add_argument(\"--weeks\", type=int, default=6)\n+    q = sub.add_parser(\"train\")\n+    q.add_argument(\"--cache\", type=Path, required=True)\n+    q.add_argument(\"--model\", type=Path, required=True)\n+    q.add_argument(\"--epochs\", type=int, default=16)\n+    q.add_argument(\"--seed\", type=int, default=2026)\n+    q.add_argument(\"--dim\", type=int, default=64)\n+    q = sub.add_parser(\"rank\")\n+    q.add_argument(\"--cache\", type=Path, required=True)\n+    q.add_argument(\"--model\", type=Path, required=True)\n+    q.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n+    q.add_argument(\"--output\", type=Path, required=True)\n+    q.add_argument(\"--pop-blend\", type=float, default=0.0)\n+    return p.parse_args()\n+\n+\n+if __name__ == \"__main__\":\n+    args = parse_args()\n+    if args.command == \"preprocess\":\n+        build_cache(args.stage, args.output, args.weeks)\n+    elif args.command == \"train\":\n+        train_model(args.cache, args.model, args.epochs, args.seed, args.dim)\n+    else:\n+        rank(args.cache, args.model, args.stage, args.output, args.pop_blend)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
32

exec command

+11:08
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-    target_lists, target_gains = [], []\n+    target_lists, target_gains, infer_flags, infer_uids = [], [], [], []\n@@\n             histories.append(pad(hist, MAX_HISTORY, np.int32, 1))\n@@\n             numerics.append(np.asarray([\n@@\n             ], np.float32))\n+            infer_flags.append(cutoff == end)\n             if cutoff == end:\n                 target_lists.append(np.empty(0, np.int32))\n                 target_gains.append(np.empty(0, np.float32))\n+                infer_uids.append(uid)\n                 continue\n@@\n-    is_infer = lengths == 0\n+    is_infer = np.asarray(infer_flags, dtype=bool)\n@@\n-    # recover context uids: construction produces weeks+1 contexts/user unless empty history\n-    # Re-run order cheaply from target uids; all target users have sufficient history here.\n-    user_order = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n+    user_order = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n@@\n-        target_offsets=offsets, targets=flat_targets, target_gains=flat_gains, infer_uids=user_order,\n+        target_offsets=offsets, targets=flat_targets, target_gains=flat_gains, infer_uids=np.asarray(infer_uids, np.int64),\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
33

exec command

+11:28
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py /app/solution/build_popularity.py && /usr/bin/time -f 'elapsed=%E maxrss=%MKB' /opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 0.2 seconds
Output:

/bin/sh: 1: /usr/bin/time: not found
34

exec command

+11:35
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 19
Wall time 11.0 seconds
Output:
35

wait command

+11:51
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "19",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

/app/solution/train_ranker.py:120: RuntimeWarning: overflow encountered in exp
  short_w = base * np.exp(-(cutoff - last_time) / (18 * DAY))
/app/solution/train_ranker.py:130: RuntimeWarning: overflow encountered in cast
  lw[ix], sw[ix] = long_w, short_w
/app/solution/train_ranker.py:138: RuntimeWarning: overflow encountered in cast
  return unique.astype(np.int32), long_w.astype(np.float32), short_w.astype(np.float32), mask
preprocessed 1,500 users

SESSION_ID=62573
36

Agent message

+12:14
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37

exec command

+12:16
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Model text

The first streaming pass exposed an unsigned-timestamp arithmetic issue in recency decay; I’m correcting that before training so old events cannot overflow their weights. The event scan itself is progressing normally and remains memory-bounded by one user plus compact cached contexts.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:62573,chars:"\u0003",yield_time_ms:1000,max_output_tokens:5000}); text(r.output)

Tool response

Script completed
Wall time 0.7 seconds
Output:

Traceback (most recent call last):
  File "/app/solution/train_ranker.py", line 410, in <module>
    build_cache(args.stage, args.output, args.weeks)
  File "/app/solution/train_ranker.py", line 199, in build_cache
    disliked = active_items(user_dislikes, cutoff, item_map)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/train_ranker.py", line 78, in active_items
    rows = events[events.timestamp < cutoff]
                  ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 62, in new_method
    @wraps(method)

KeyboardInterrupt
38

exec command

+12:31
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n def select_history(\n@@\n ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n+    timestamps = timestamps.astype(np.int64, copy=False)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
39

exec command

+12:36
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 22
Wall time 11.0 seconds
Output:
40

wait command

+12:52
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "22",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.3 seconds
Output:

preprocessed 1,500 users

SESSION_ID=54126
41

exec command

+13:11
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

const r = await tools.write_stdin({session_id:54126,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 23
Wall time 11.0 seconds
Output:
42

wait command

+13:26
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "23",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.0 seconds
Output:

preprocessed 3,000 users

SESSION_ID=54126
43

exec command

+13:45
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:54126,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 24
Wall time 11.0 seconds
Output:
44

wait command

+14:00
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "24",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.8 seconds
Output:

preprocessed 4,500 users
preprocessed 6,000 users

SESSION_ID=54126
45

exec command

+14:19
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:54126,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 25
Wall time 11.0 seconds
Output:
46

wait command

+14:33
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "25",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 11.8 seconds
Output:

preprocessed 7,500 users
preprocessed 9,000 users
Traceback (most recent call last):
  File "/app/solution/train_ranker.py", line 411, in <module>
    build_cache(args.stage, args.output, args.weeks)
  File "/app/solution/train_ranker.py", line 258, in build_cache
    raise RuntimeError(f"expected {len(user_order)} inference contexts, got {infer_count}")
RuntimeError: expected 8456 inference contexts, got 8449
47

exec command

+14:52
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-            if not len(hist):\n-                continue\n             histories.append(pad(hist, MAX_HISTORY, np.int32, 1))\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
48

exec command

+14:57
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 27
Wall time 11.0 seconds
Output:
49

wait command

+15:12
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "27",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

preprocessed 1,500 users

SESSION_ID=14477
50

exec command

+15:33
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:14477,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 28
Wall time 11.0 seconds
Output:
51

wait command

+15:49
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "28",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.3 seconds
Output:

preprocessed 3,000 users

SESSION_ID=14477
52

exec command

+16:06
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:14477,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 29
Wall time 11.0 seconds
Output:
53

wait command

+16:22
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "29",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.4 seconds
Output:

preprocessed 4,500 users
preprocessed 6,000 users

SESSION_ID=14477
54

exec command

+16:42
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:14477,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 30
Wall time 11.0 seconds
Output:
55

wait command

+16:57
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "30",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.9 seconds
Output:

preprocessed 7,500 users
preprocessed 9,000 users
{"cache": "/app/solution/validation_cache.npz", "contexts": 59192, "train_contexts": 24594, "targets": 234017}
56

exec command

+17:16
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -lh /app/solution/validation_cache.npz && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nz=np.load('/app/solution/validation_cache.npz')\nfor k in z.files:\n x=z[k]; print(k,x.shape,x.dtype,'finite' if x.dtype.kind=='f' and np.isfinite(x).all() else '')\nprint('uid aligned',np.array_equal(z['infer_uids'],np.load('/task/data/validation_eligible_candidates.npz')['uids']))\nprint('target context label quantiles',np.quantile(np.diff(z['target_offsets']),[0,.25,.5,.75,.9,.99,1]))\nPY\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache.npz --model /app/solution/validation_model.pt --epochs 14 --seed 2026 --dim 64","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 31
Wall time 11.0 seconds
Output:
57

wait command

+17:39
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "31",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

-rw-r--r-- 1 agent agent 98M Aug 31 18:42 /app/solution/validation_cache.npz
history (59192, 256) int32 
long (59192, 256) float32 finite
short (59192, 256) float32 finite
negative (59192, 64) int32 
negative_weight (59192, 64) float32 finite
numeric (59192, 4) float32 finite
train_rows (24594,) int32 
infer_rows (8456,) int32 
target_offsets (24595,) int64 
targets (234017,) int32 
target_gains (234017,) float32 finite
infer_uids (8456,) int64 
uid aligned True
target context label quantiles [  1.   2.   4.  11.  22.  76. 349.]
/app/solution/train_ranker.py:334: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total += float(loss); batches += 1

SESSION_ID=81188
58

exec command

+17:58
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Model text

Preprocessing produced 24,594 nonempty temporal training contexts and 234,017 unseen organic positives across six rolling weeks; all 8,456 inference users align exactly with eligibility. The model is now training with mixed popularity-shaped and uniform sampled negatives, which preserves the production-friendly event/sampling path and avoids any global interaction matrix.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81188,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 32
Wall time 11.0 seconds
Output:
59

wait command

+18:15
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "32",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:


SESSION_ID=81188
60

exec command

+18:35
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81188,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 33
Wall time 11.0 seconds
Output:
61

wait command

+18:50
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "33",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.7 seconds
Output:

epoch 01 loss=0.58880

SESSION_ID=81188
62

exec command

+19:09
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81188,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 34
Wall time 11.0 seconds
Output:
63

wait command

+19:24
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "34",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:


SESSION_ID=81188
64

exec command

+19:45
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81188,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 35
Wall time 11.0 seconds
Output:
65

wait command

+20:00
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "35",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.2 seconds
Output:


SESSION_ID=81188
66

exec command

+20:19
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81188,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 36
Wall time 11.0 seconds
Output:
67

wait command

+20:34
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "36",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.4 seconds
Output:

epoch 02 loss=0.51000

SESSION_ID=81188
68

exec command

+21:02
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81188,chars:"\u0003",yield_time_ms:1000,max_output_tokens:5000}); text(r.output)

Tool response

Script completed
Wall time 0.4 seconds
Output:

Traceback (most recent call last):
  File "/app/solution/train_ranker.py", line 411, in <module>
    train_model(args.cache, args.model, args.epochs, args.seed, args.dim)
  File "/app/solution/train_ranker.py", line 320, in train_model
    tensor_rows(cache, "short", data_rows), tensor_rows(cache, "negative", data_rows),
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/train_ranker.py", line 278, in tensor_rows
    return torch.from_numpy(cache[name][rows]).to(device)
                            ~~~~~^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__
    return format.read_array(bytes,
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array
    data = _read_bytes(fp, read_size, "array data")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes
    r = fp.read(size - len(data))
        ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read
    data = self._read1(n)
           ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1098, in _read1
    self._update_crc(data)
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1023, in _update_crc
    self._running_crc = crc32(newdata, self._running_crc)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
69

exec command

+21:10
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n def train_model(cache_path: Path, model_path: Path, epochs: int, seed: int, dim: int) -> None:\n     seed_everything(seed)\n-    torch.set_num_threads(min(8, torch.get_num_threads()))\n-    cache = np.load(cache_path)\n+    torch.set_num_threads(8)\n+    with np.load(cache_path) as archive:\n+        cache = {name: archive[name] for name in archive.files}\n@@\n-    batch_size, negatives_per = 192, 40\n+    batch_size, negatives_per = 256, 32\n@@\n-            total += float(loss); batches += 1\n+            total += float(loss.detach()); batches += 1\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
70

exec command

+21:21
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache.npz --model /app/solution/validation_model.pt --epochs 10 --seed 2026 --dim 64","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 39
Wall time 11.0 seconds
Output:
71

wait command

+21:38
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "39",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

epoch 01 loss=0.59207
epoch 02 loss=0.52318
epoch 03 loss=0.46337

SESSION_ID=43340
72

exec command

+21:57
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:43340,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 40
Wall time 11.0 seconds
Output:
73

wait command

+22:12
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "40",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

epoch 04 loss=0.41948
epoch 05 loss=0.38892
epoch 06 loss=0.36797
epoch 07 loss=0.35050
epoch 08 loss=0.33681
epoch 09 loss=0.32130

SESSION_ID=43340
74

exec command

+22:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:43340,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 0.0 seconds
Output:

epoch 10 loss=0.31241
75

exec command

+22:38
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n def rank(cache_path: Path, model_path: Path, stage: str, output: Path, pop_blend: float) -> None:\n-    cache = np.load(cache_path)\n+    with np.load(cache_path) as archive:\n+        cache = {name: archive[name] for name in archive.files}\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
76

exec command

+22:48
Thinking unavailable No text recorded

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Model text

Training is now efficient after caching the compressed arrays once in memory; the ten-epoch loss fell from 0.592 to 0.312. I’m generating cutoff-valid rankings over all 30,000 candidates and testing only a small popularity-blend grid—the tower score remains the user/item dot product, while the blend check tells me whether the learned item tower already calibrated head-item prevalence sufficiently.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for b in 0 0.02 0.05 0.08 0.12 0.18; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache.npz --model /app/solution/validation_model.pt --stage validation --pop-blend \"$b\" --output \"/app/solution/validation_b${b}.parquet\"; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_b${b}.parquet\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 43
Wall time 11.0 seconds
Output:
77

wait command

+23:10
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "43",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.8 seconds
Output:

{
  "model": "/app/solution/validation_b0.parquet",
  "scores": {
    "quality_ndcg@10": 0.014898,
    "quality_users": 4263
  }
}

SESSION_ID=7674
78

exec command

+23:32
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:7674,chars:"",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 44
Wall time 11.0 seconds
Output:
79

wait command

+23:47
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "44",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

{
  "model": "/app/solution/validation_b0.02.parquet",
  "scores": {
    "quality_ndcg@10": 0.016418,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_b0.05.parquet",
  "scores": {
    "quality_ndcg@10": 0.018334,
    "quality_users": 4263
  }
}

SESSION_ID=7674
80

exec command

+24:06
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:7674,chars:"",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 45
Wall time 11.0 seconds
Output:
81

wait command

+24:21
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "45",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{
  "model": "/app/solution/validation_b0.08.parquet",
  "scores": {
    "quality_ndcg@10": 0.017738,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_b0.12.parquet",
  "scores": {
    "quality_ndcg@10": 0.015999,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_b0.18.parquet",
  "scores": {
    "quality_ndcg@10": 0.013797,
    "quality_users": 4263
  }
}
82

Agent message

+24:58
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83

exec command

+25:03
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Model text

The personalized tower reaches 0.018334 at a 0.05 popularity blend, versus 0.010710 for popularity alone (about a 71% lift). I’m expanding the rolling temporal supervision from six to fourteen weeks; this should improve collaborative track geometry without changing inference cost or violating the temporal boundary. The current 0.018334 ranking remains saved as the fallback.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 14 --output /app/solution/validation_cache_w14.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 46
Wall time 11.0 seconds
Output:
84

wait command

+25:24
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "46",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 14.6 seconds
Output:


SESSION_ID=40623
85

exec command

+25:41
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 47
Wall time 11.0 seconds
Output:
86

wait 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 callwaitTool call Returned

Tool call · Input

{
  "cell_id": "47",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.3 seconds
Output:

preprocessed 1,500 users

SESSION_ID=40623
87

exec command

+26:16
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 48
Wall time 11.0 seconds
Output:
88

wait command

+26:31
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "48",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

preprocessed 3,000 users

SESSION_ID=40623
89

exec command

+26:49
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 49
Wall time 11.0 seconds
Output:
90

wait command

+27:04
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "49",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

preprocessed 4,500 users

SESSION_ID=40623
91

exec command

+27:23
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 50
Wall time 11.0 seconds
Output:
92

wait command

+27:37
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "50",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

preprocessed 6,000 users

SESSION_ID=40623
93

exec command

+27:56
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 51
Wall time 11.0 seconds
Output:
94

wait command

+28:11
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "51",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

preprocessed 7,500 users

SESSION_ID=40623
95

exec command

+28:30
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

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 52
Wall time 11.0 seconds
Output:
96

wait command

+28:44
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "52",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:


SESSION_ID=40623
97

exec command

+29:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:40623,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 53
Wall time 11.0 seconds
Output:
98

wait command

+29:18
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "53",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 6.8 seconds
Output:

preprocessed 9,000 users
{"cache": "/app/solution/validation_cache_w14.npz", "contexts": 126840, "train_contexts": 52461, "targets": 492274}
99

exec command

+29:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14.pt --epochs 10 --seed 2026 --dim 64","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 54
Wall time 11.0 seconds
Output:
100

wait command

+29:46
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "54",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.3 seconds
Output:

epoch 01 loss=0.56491

SESSION_ID=83725
101

exec command

+30:05
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:83725,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 55
Wall time 11.0 seconds
Output:
102

wait command

+30:20
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "55",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

epoch 02 loss=0.49672
epoch 03 loss=0.44811
epoch 04 loss=0.41821

SESSION_ID=83725
103

exec command

+30:39
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:83725,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 56
Wall time 11.0 seconds
Output:
104

wait command

+30:54
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "56",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.2 seconds
Output:

epoch 05 loss=0.39901
epoch 06 loss=0.38602
epoch 07 loss=0.36958

SESSION_ID=83725
105

exec command

+31:14
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:83725,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 57
Wall time 11.0 seconds
Output:
106

wait command

+31:28
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "57",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

epoch 08 loss=0.35282
epoch 09 loss=0.33951

SESSION_ID=83725
107

exec command

+31:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:83725,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 0.0 seconds
Output:

epoch 10 loss=0.32587
108

exec command

+31:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for b in 0.03 0.045 0.06 0.075; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14.pt --stage validation --pop-blend \"$b\" --output \"/app/solution/validation_w14_b${b}.parquet\"; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_w14_b${b}.parquet\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 59
Wall time 11.0 seconds
Output:
109

wait command

+32:10
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "59",
  "yield_time_ms": 30000,
  "max_tokens": 16000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{
  "model": "/app/solution/validation_w14_b0.03.parquet",
  "scores": {
    "quality_ndcg@10": 0.019052,
    "quality_users": 4263
  }
}

SESSION_ID=54812
110

exec command

+32:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:54812,chars:"",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 60
Wall time 11.0 seconds
Output:
111

wait command

+32:44
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "60",
  "yield_time_ms": 30000,
  "max_tokens": 16000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{
  "model": "/app/solution/validation_w14_b0.045.parquet",
  "scores": {
    "quality_ndcg@10": 0.019178,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_w14_b0.06.parquet",
  "scores": {
    "quality_ndcg@10": 0.019164,
    "quality_users": 4263
  }
}

SESSION_ID=54812
112

exec command

+33:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:54812,chars:"",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 2.7 seconds
Output:

{
  "model": "/app/solution/validation_w14_b0.075.parquet",
  "scores": {
    "quality_ndcg@10": 0.018046,
    "quality_users": 4263
  }
}
113

Agent message

+33:27
Thinking unavailable No text recorded

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114

exec command

+33:35
Thinking unavailable No text recorded

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

Model text

Fourteen weeks improves the public score again to 0.019178, with the optimum centered tightly around a 0.045 calibration weight. I’m checking a second initialization for variance and possible ensemble gain; if it is not complementary, I’ll retain the stronger single model and proceed directly to the required train+validation refit.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14_s314.pt --epochs 10 --seed 314 --dim 64 && /opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14_s314.pt --stage validation --pop-blend 0.045 --output /app/solution/validation_w14_s314.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_w14_s314.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 62
Wall time 11.0 seconds
Output:
115

wait command

+33:59
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "62",
  "yield_time_ms": 30000,
  "max_tokens": 16000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

epoch 01 loss=0.56264

SESSION_ID=87734
116

exec command

+34:18
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:87734,chars:"",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 63
Wall time 11.0 seconds
Output:
117

wait command

+34:32
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "63",
  "yield_time_ms": 30000,
  "max_tokens": 16000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

epoch 02 loss=0.49339
epoch 03 loss=0.44482
epoch 04 loss=0.41867

SESSION_ID=87734
118

exec command

+34:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:87734,chars:"",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 64
Wall time 11.0 seconds
Output:
119

wait command

+35:06
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "64",
  "yield_time_ms": 30000,
  "max_tokens": 16000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

epoch 05 loss=0.40006
epoch 06 loss=0.38560
epoch 07 loss=0.37323

SESSION_ID=87734
120

exec command

+35:26
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

const r = await tools.write_stdin({session_id:87734,chars:"",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 65
Wall time 11.0 seconds
Output:
121

wait command

+35:40
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "65",
  "yield_time_ms": 30000,
  "max_tokens": 16000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

epoch 08 loss=0.35850
epoch 09 loss=0.34477
epoch 10 loss=0.33173

SESSION_ID=87734
122

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.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:87734,chars:"",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 8.8 seconds
Output:

{
  "model": "/app/solution/validation_w14_s314.parquet",
  "scores": {
    "quality_ndcg@10": 0.018042,
    "quality_users": 4263
  }
}
123

Agent message

+36:33
Thinking unavailable No text recorded

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124

exec command

+36:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/model.py\n@@\n class TwoTower(nn.Module):\n@@\n         self.item_tower = ItemTower(num_items, output_dim=output_dim)\n+\n+\n+class CalibratedUserTower(nn.Module):\n+    \"\"\"Deployment user tower with a constant coordinate for popularity calibration.\"\"\"\n+\n+    def __init__(self, base: UserTower):\n+        super().__init__()\n+        self.base = base\n+\n+    def forward(self, *args: torch.Tensor) -> torch.Tensor:\n+        vector = self.base(*args)\n+        return torch.cat([vector, torch.ones_like(vector[:, :1])], dim=1)\n+\n+\n+class CalibratedItemTower(nn.Module):\n+    \"\"\"Deployment item tower; its output remains independently precomputable.\"\"\"\n+\n+    def __init__(self, base: ItemTower, popularity: torch.Tensor, weight: float):\n+        super().__init__()\n+        self.base = base\n+        self.register_buffer(\"popularity\", popularity)\n+        self.weight = float(weight)\n+\n+    def forward(self, item_ids: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\n+        vector = self.base(item_ids, features)\n+        coordinate = self.popularity[item_ids - 1].unsqueeze(-1) * self.weight\n+        return torch.cat([vector, coordinate], dim=-1)\n*** Update File: /app/solution/train_ranker.py\n@@\n-from model import TwoTower\n+from model import CalibratedItemTower, CalibratedUserTower, TwoTower\n@@\n     model = TwoTower(checkpoint[\"num_items\"], output_dim=checkpoint[\"dim\"])\n     model.load_state_dict(checkpoint[\"state_dict\"])\n     model.eval()\n     features = torch.from_numpy(item_features())\n+    pop = -np.log1p(np.arange(checkpoint[\"num_items\"], dtype=np.float32))\n+    pop = (pop - pop.mean()) / pop.std()\n+    user_tower = CalibratedUserTower(model.user_tower)\n+    item_tower = CalibratedItemTower(model.item_tower, torch.from_numpy(pop), pop_blend)\n     all_ids = torch.arange(1, checkpoint[\"num_items\"] + 1)\n     item_vectors = []\n     for start in range(0, len(all_ids), 2048):\n         ids = all_ids[start:start + 2048]\n-        item_vectors.append(model.item_tower(ids, features[start:start + len(ids)]))\n+        item_vectors.append(item_tower(ids, features[start:start + len(ids)]))\n@@\n-        user_vectors.append(model.user_tower(\n+        user_vectors.append(user_tower(\n@@\n-    pop = -np.log1p(np.arange(len(candidate_items), dtype=np.float32))\n-    pop = (pop - pop.mean()) / pop.std()\n     result = []\n@@\n         stop = min(start + 128, len(uids))\n         score = (user_vectors[start:stop] @ item_vectors.T).numpy()\n-        score += pop_blend * pop[None, :]\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
125

exec command

+37:17
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"cp /app/solution/validation_w14_b0.045.parquet /app/solution/validation.parquet\n/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\n/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage test --weeks 14 --output /app/solution/test_cache_w14.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 10.6 seconds
Output:

Traceback (most recent call last):
  File "/app/solution/train_ranker.py", line 412, in <module>
    build_cache(args.stage, args.output, args.weeks)
  File "/app/solution/train_ranker.py", line 192, in build_cache
    data = {name: value[chronology] for name, value in data.items()}
                  ~~~~~^^^^^^^^^^^^
IndexError: index 3266 is out of bounds for axis 0 with size 3266
126

exec command

+37:37
Thinking unavailable No text recorded

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

Model text

The first refit pass hit a merge-only bug: the validation events were appended to the four event fields, while the unused uid array retained its old length. I’ve isolated it to test-stage preprocessing; validation artifacts and the chosen score are unaffected. I’m dropping that redundant per-group field and restarting the refit scan.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n     targets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\n     for number, (uid, data) in enumerate(iter_user_groups(listen_paths[0])):\n         if uid not in targets:\n             continue\n+        data.pop(\"uid\", None)\n         if test and uid in val_listens:\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
127

exec command

+37:46
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage test --weeks 14 --output /app/solution/test_cache_w14.npz","workdir":"/app","yield_time_ms":30000,"max_output_tokens":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 70
Wall time 11.0 seconds
Output:
128

wait command

+38:01
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "70",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.8 seconds
Output:


SESSION_ID=90796
129

exec command

+38:21
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 71
Wall time 11.0 seconds
Output:
130

wait command

+38:35
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "71",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

preprocessed 1,500 users

SESSION_ID=90796
131

exec command

+38:56
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 72
Wall time 11.0 seconds
Output:
132

wait command

+39:11
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "72",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

preprocessed 3,000 users

SESSION_ID=90796
133

exec command

+39:30
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

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 73
Wall time 11.0 seconds
Output:
134

wait command

+39:44
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "73",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.3 seconds
Output:

preprocessed 4,500 users

SESSION_ID=90796
135

exec command

+40:04
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

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 74
Wall time 11.0 seconds
Output:
136

wait command

+40:18
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "74",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

preprocessed 6,000 users

SESSION_ID=90796
137

exec command

+40:38
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 75
Wall time 11.0 seconds
Output:
138

wait command

+40:53
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "75",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:


SESSION_ID=90796
139

exec command

+41:12
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 76
Wall time 11.0 seconds
Output:
140

wait command

+41:26
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "76",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

preprocessed 7,500 users

SESSION_ID=90796
141

exec command

+41:46
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90796,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 77
Wall time 11.0 seconds
Output:
142

wait command

+42:04
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "77",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 8.0 seconds
Output:

preprocessed 9,000 users
{"cache": "/app/solution/test_cache_w14.npz", "contexts": 126840, "train_contexts": 53540, "targets": 500389}
143

exec command

+42:16
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/test_cache_w14.npz --model /app/solution/final_model.pt --epochs 10 --seed 2026 --dim 64","workdir":"/app","yield_time_ms":30000,"max_output_tokens":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 78
Wall time 11.0 seconds
Output:
144

wait command

+42:32
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "78",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

epoch 01 loss=0.56720

SESSION_ID=67881
145

exec command

+42:51
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:67881,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 79
Wall time 11.0 seconds
Output:
146

wait command

+43:07
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "79",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 15.5 seconds
Output:

epoch 02 loss=0.49599
epoch 03 loss=0.44594
epoch 04 loss=0.42056

SESSION_ID=67881
147

exec command

+43:25
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:67881,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 80
Wall time 11.0 seconds
Output:
148

wait command

+43:40
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "80",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 16.1 seconds
Output:

epoch 05 loss=0.40152
epoch 06 loss=0.38533

SESSION_ID=67881
149

exec command

+43:58
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:67881,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 81
Wall time 11.0 seconds
Output:
150

wait command

+44:15
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "81",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 14.8 seconds
Output:

epoch 07 loss=0.37165
epoch 08 loss=0.35662
epoch 09 loss=0.34501

SESSION_ID=67881
151

exec command

+44:34
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:67881,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 1.1 seconds
Output:

epoch 10 loss=0.32992
152

exec command

+44:40
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Model text

The required refit is complete: it uses train plus validation history, including the public window as observed temporal supervision, with 53,540 contexts and 500,389 unseen organic positives. I’m now writing the test ranking through the exact 65-dimensional deployed dot product (64 learned coordinates plus one calibrated popularity coordinate), then independently checking every schema, rank, duplicate, user, and eligibility invariant.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/test_cache_w14.npz --model /app/solution/final_model.pt --stage test --pop-blend 0.045 --output /app/solution/submission.parquet\n/opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14.pt --stage validation --pop-blend 0.045 --output /app/solution/validation.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script running with cell ID 83
Wall time 11.0 seconds
Output:
153

wait command

+45:05
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "83",
  "yield_time_ms": 30000,
  "max_tokens": 14000
}

Tool response

Script completed
Wall time 15.2 seconds
Output:


SESSION_ID=62874
154

exec command

+45:23
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:62874,chars:"",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)

Tool response

Script completed
Wall time 0.0 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.019178,
    "quality_users": 4263
  }
}
155

exec command

+45:43
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/README.md\n+# CPU PyTorch two-tower ranker\n+\n+## Model\n+\n+`model.py` defines separate trainable `UserTower` and `ItemTower` PyTorch\n+networks. The user tower contains no user-ID embedding: it pools up to 256\n+catalog tracks from positive listen/like history into long- and short-recency\n+views, separately pools active dislikes, appends four history-size features,\n+and applies a 192/128-unit MLP. The item tower combines a trainable track\n+embedding with four catalog popularity features and applies a 160/128-unit\n+MLP. Both return 64-dimensional normalized vectors.\n+\n+Deployment wraps these towers with one calibrated popularity coordinate. The\n+resulting independently computable user and item vectors are both 65-D and the\n+complete relevance score is exactly their inner product. Item vectors can be\n+materialized offline in one pass; request-time work is one history-based user\n+tower evaluation followed by maximum-inner-product retrieval.\n+\n+## Training\n+\n+`train_ranker.py` streams uid/time-sorted event Parquets one user at a time and\n+never constructs or factorizes a global user-item matrix. Sixteen-byte catalog\n+indices are not used as user IDs. Supervision comes from fourteen rolling,\n+cutoff-correct temporal backtests: a context uses only earlier interactions and\n+targets previously unseen catalog tracks with an organic 80% listen (gain 1)\n+or organic like (gain 7). The pairwise softplus/BPR objective uses 32 sampled\n+negatives per positive, half popularity-shaped and half uniform. Validation\n+selection used train events only; the final model was refit from scratch with\n+train plus validation events as history/supervision.\n+\n+## Validation\n+\n+Public graded NDCG@10: **0.019178** over 4,263 scored users. The popularity-only\n+fallback scored 0.010710. The retained validation ranking is\n+`validation.parquet`.\n+\n+## Final submission command\n+\n+Run from `/app/solution` with the task's read-only virtual environment:\n+\n+```sh\n+/opt/task-venv/bin/python train_ranker.py preprocess --stage test --weeks 14 --output test_cache_w14.npz && \\\n+/opt/task-venv/bin/python train_ranker.py train --cache test_cache_w14.npz --model final_model.pt --epochs 10 --seed 2026 --dim 64 && \\\n+/opt/task-venv/bin/python train_ranker.py rank --cache test_cache_w14.npz --model final_model.pt --stage test --pop-blend 0.045 --output submission.parquet\n+```\n+\n+The checked-in `submission.parquet` was produced by that command.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
156

exec command

+46:12
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nframe=validate_ranking(pd.read_parquet(S),targets)\ncandidates=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(int(u),int(i)) for u,i in frame[['uid','item_id']].itertuples(index=False,name=None) if not candidates.is_eligible(int(u),int(i))]\nprint({'rows':len(frame),'users':frame.uid.nunique(),'items_per_user_min':int(frame.groupby('uid').size().min()),'items_per_user_max':int(frame.groupby('uid').size().max()),'rank_min':int(frame['rank'].min()),'rank_max':int(frame['rank'].max()),'duplicate_pairs':int(frame.duplicated(['uid','item_id']).sum()),'ineligible':len(bad),'dtypes':{k:str(v) for k,v in frame.dtypes.items()}})\nPY\nsha256sum /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/final_model.pt\nls -lh /app/solution | sed -n '1,120p'\n/opt/task-venv/bin/python -m py_compile /app/solution/*.py","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000}); text(r.output)

Tool response

Script completed
Wall time 3.1 seconds
Output:

{'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'duplicate_pairs': 0, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}
99eca83d4bdfe351db6f8a34e912f223d239624d96c010698b8859ab60dc6be9  /app/solution/submission.parquet
9e2e79936c0e5080e3ab390d0d873eb52a6c157eb1974372a2e0dbeffcc979f2  /app/solution/validation.parquet
4c235367aed81ae34f579f31d2f4f81bdfb20377f892439a96655ca7dc963bf9  /app/solution/final_model.pt
total 588M
-rw-r--r-- 1 agent agent 2.4K Aug 31 19:11 README.md
drwxr-xr-x 2 agent agent 4.0K Aug 31 19:02 __pycache__
-rw-r--r-- 1 agent agent 1.2K Aug 31 18:27 build_popularity.py
-rw-r--r-- 1 agent agent  23M Aug 31 19:09 final_model.pt
-rw-r--r-- 1 agent agent 3.7K Aug 31 19:02 model.py
-rw-r--r-- 1 agent agent 1.4M Aug 31 19:10 submission.parquet
-rw-r--r-- 1 agent agent 194M Aug 31 19:07 test_cache_w14.npz
-rw-r--r-- 1 agent agent  20K Aug 31 19:03 train_ranker.py
-rw-r--r-- 1 agent agent 1.4M Aug 31 19:10 validation.parquet
-rw-r--r-- 1 agent agent 1.5M Aug 31 18:48 validation_b0.02.parquet
-rw-r--r-- 1 agent agent 1.4M Aug 31 18:49 validation_b0.05.parquet
-rw-r--r-- 1 agent agent 1.4M Aug 31 18:49 validation_b0.08.parquet
-rw-r--r-- 1 agent agent 1.3M Aug 31 18:49 validation_b0.12.parquet
-rw-r--r-- 1 agent agent 1.2M Aug 31 18:49 validation_b0.18.parquet
-rw-r--r-- 1 agent agent 1.5M Aug 31 18:48 validation_b0.parquet
-rw-r--r-- 1 agent agent  98M Aug 31 18:42 validation_cache.npz
-rw-r--r-- 1 agent agent 190M Aug 31 18:54 validation_cache_w14.npz
-rw-r--r-- 1 agent agent  23M Aug 31 18:47 validation_model.pt
-rw-r--r-- 1 agent agent  23M Aug 31 18:57 validation_model_w14.pt
-rw-r--r-- 1 agent agent  23M Aug 31 19:01 validation_model_w14_s314.pt
-rw-r--r-- 1 agent agent 1.4M Aug 31 18:57 validation_w14_b0.03.parquet
-rw-r--r-- 1 agent agent 1.4M Aug 31 18:57 validation_w14_b0.045.parquet
-rw-r--r-- 1 agent agent 1.4M Aug 31 18:58 validation_w14_b0.06.parquet
-rw-r--r-- 1 agent agent 1.4M Aug 31 18:58 validation_w14_b0.075.parquet
-rw-r--r-- 1 agent agent 1.4M Aug 31 19:01 validation_w14_s314.parquet
157

exec command

+46:33
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"Implemented, tuned, refit, ranked, and independently validated the final artifact.",plan:[
  {step:"Inspect data schemas, sizes, cutoffs, eligibility, and evaluator",status:"completed"},
  {step:"Implement scalable PyTorch two-tower training and ranking pipeline",status:"completed"},
  {step:"Train validation variants and evaluate public NDCG@10",status:"completed"},
  {step:"Refit selected model on train+validation and create valid test submission",status:"completed"},
  {step:"Verify artifacts and document reproducible commands",status:"completed"}
]}); text(p)

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
158

Final response

+46:43
Thinking unavailable No text recorded

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

Model text

Implemented and verified the CPU PyTorch two-tower ranker.

Public NDCG@10: 0.019178 over 4,263 users.

Final submission validation:

  • 845,600 rows
  • 8,456 users
  • Exactly 100 unique items and ranks 1–100 per user
  • Zero ineligible items
  • Final model refitted on train + validation history
  • Exact 65-dimensional user/item dot-product scoring
  • No user-ID embeddings or global user-item matrix construction
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
run-008-gpt-5-6-sol-high-2026-08-31T11-04-49
Recorded model
gpt-5.6-sol
Harness version
0.147.0
Session
01a05911-9128-75b1-a0c4-9ebeddfa171e
Source SHA-256
160bd28229393948b72b4a359190b44713aae769fd0ff41cbd2c80001a733d2f

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