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Agent trace

Two-tower retriever sol-high-09

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 173 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · high effort
Recorded conversation
40:18 · 179 steps
Private reward
0.026616 · Pass
Attempt cost
$4.50
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
System instructions

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

Personality

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

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

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

Writing style

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

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

Technical communication

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

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

Working with the user

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

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

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

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

Intermediate commentary

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

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

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

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

Final answer

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

Formatting rules

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

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

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

Good candidates include:

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

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

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

Rules for getting work done

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

File editing constraints

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

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

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

Autonomy and persistence

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

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

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

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

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

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

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

Destructive actions

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

Before taking a destructive action:

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

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

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

Using skills

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

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

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

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

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

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

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

02

Developer instructions

+00:01
Developer instructions

<skills_instructions>

Skills

A skill is a set of 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

+00:01
Developer instructions

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

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

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

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

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

They may be addressed as to=/root

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

All agents share the same directory. In detail:

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

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

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

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

04

Developer instructions

+00:01
Developer instructions

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

05

User context

+00:01
User context

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

  • 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": "01a05912-4f60-7390-b9b7-47c9098e00d4",
  "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

+00:01
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

+00:04
Thinking unavailable No text recorded

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

Model text

I’ll build and validate this end to end, keeping a valid submission in place early and then iterating on ranking quality. I’ll first inspect the data shapes, eligibility density, and interaction statistics, then implement a scalable event-sampled PyTorch two-tower and tune it only against the public window.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({plan:[
  {step:"Inspect data, schemas, environment, and public evaluator",status:"in_progress"},
  {step:"Implement scalable history-feature two-tower training and ranking pipeline",status:"pending"},
  {step:"Train validation models, evaluate, and tune",status:"pending"},
  {step:"Refit on train+validation and write a valid test submission",status:"pending"},
  {step:"Verify artifacts and document the solution",status:"pending"}
]});
const r = await tools.exec_command({cmd:"pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.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
/task/requirements.txt
/task/evaluate_public.py
/task/ranking_evaluation.py
/task/data/validation_preference_state.parquet
/task/data/test_eligible_candidates.npz
/task/data/val_dislikes.parquet
/task/data/val_unlikes.parquet
/task/data/train_dislikes.parquet
/task/data/validation_eligible_candidates.npz
/task/data/train_listens.parquet
/task/data/train_likes.parquet
/task/data/train_undislikes.parquet
/task/data/val_undislikes.parquet
/task/data/val_listens.parquet
/task/data/val_likes.parquet
/task/data/train_unlikes.parquet
/task/data/candidate_catalog.parquet
/task/data/test_preference_state.parquet
/task/data/target_users.parquet
/task/data/train_undislikes.parquet
/task/data/val_undislikes.parquet
/task/data/val_listens.parquet
/task/data/val_likes.parquet
/task/data/train_unlikes.parquet
/task/data/candidate_catalog.parquet
/task/data/test_preference_state.parquet
/task/data/target_users.parquet
/task/data/train_dislikes.parquet
/task/data/validation_eligible_candidates.npz
/task/data/train_listens.parquet
/task/data/train_likes.parquet
/task/data/val_dislikes.parquet
/task/data/val_unlikes.parquet
/task/data/test_eligible_candidates.npz
/task/data/validation_preference_state.parquet
"""Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

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

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

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

Usage::

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

import json
import sys
from pathlib import Path

import pandas as pd

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


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


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

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

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

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

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


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

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

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

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

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


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

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

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


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

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


if __name__ == "__main__":
    main()
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
08

exec command

+00:20
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, d.shape, list(d.columns), {c:(int(d[c].min()),int(d[c].max())) for c in ['timestamp'] if c in d})\n print(d.head(2).to_dict('records'))\nfor stage in ['validation','test']:\n with np.load(p/f'{stage}_eligible_candidates.npz') as z:\n  print(stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\n  pc=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n  print('eligible min/mean/max percentiles',pc.min(),pc.mean(),pc.max(),np.percentile(pc,[1,25,50,75,99]))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
ModuleNotFoundError: No module named 'pandas'
09

exec command

+00:31
Thinking unavailable No text recorded

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Model text

The default shell’s python is a minimal interpreter despite the task’s pinned environment. I’m locating the task’s actual PyTorch/Pandas interpreter now; this is an environment-path issue, not a data issue.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"which -a python python3 uv 2>/dev/null || true\nls -la /task /app\nfind / -maxdepth 4 -type f -name 'python*' 2>/dev/null | sed -n '1,100p'\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
/app:
total 12
drwxr-xr-x 3 agent agent 4096 Aug 31 18:23 .
drwxr-xr-x 1 root  root  4096 Aug 31 18:26 ..
drwxr-xr-x 2 agent agent 4096 Aug 31 18:23 solution

/task:
total 36
dr-xr-xr-x 1 root root 4096 Aug 31 18:23 .
drwxr-xr-x 1 root root 4096 Aug 31 18:26 ..
-r--r--r-- 1 root root    0 Aug 31 18:09 .keep
dr-xr-xr-x 1 root root 4096 Aug 31 18:10 data
-r--r--r-- 1 root root 5774 Aug 31 18:09 evaluate_public.py
-r--r--r-- 1 root root 8265 Aug 31 18:09 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Aug 31 18:09 requirements.txt
/usr/local/bin/python3.12-config
/usr/local/bin/python3.12
CODEX_CI=1
CODEX_HOME=/tmp/codex-home
CODEX_THREAD_ID=01a05912-4f4c-7c83-8d0f-badb40e6a17c
COLORTERM=
GH_PAGER=cat
GIT_PAGER=cat
GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305
HOME=/home/agent
HOSTNAME=cd3e042a7152
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
10

exec command

+00:39
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Tool callexecTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 0.4 seconds
Output:

/opt:
total 16
drwxr-xr-x 1 root root 4096 Aug 31 18:22 .
drwxr-xr-x 1 root root 4096 Aug 31 18:26 ..
drwxr-xr-x 4 root root 4096 Aug 31 18:22 codex-home
dr-xr-xr-x 1 root root 4096 Aug 31 18:23 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 18:22 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 18:23 .
drwxr-xr-x 1 root root 4096 Aug 31 18:22 ..
-r--r--r-- 1 root root    0 Aug 31 18:23 .lock
dr-xr-xr-x 1 root root 4096 Aug 31 18:23 bin
dr-xr-xr-x 1 root root 4096 Aug 31 18:23 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 18:23 share
/opt/task-venv/lib/python3.12/site-packages
/usr/local/lib/python3.12/site-packages
11

exec command

+00:45
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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 pandas as pd, numpy as np, torch\nprint('versions',pd.__version__,np.__version__,torch.__version__)\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n mm={c:(int(d[c].min()),int(d[c].max())) for c in ['timestamp'] if c in d and len(d)}\n print(f.name, d.shape, list(d.columns), mm)\n print(' ',d.head(2).to_dict('records'))\nfor stage in ['validation','test']:\n with np.load(p/f'{stage}_eligible_candidates.npz') as z:\n  print(stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\n  pc=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n  print(' eligible min/mean/max percentiles',pc.min(),pc.mean(),pc.max(),np.percentile(pc,[1,25,50,75,99]))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000});
text(r.output);

Tool response

Script completed
Wall time 5.6 seconds
Output:

versions 2.3.3 2.2.6 2.8.0+cpu
candidate_catalog.parquet (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] {}
  [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]
target_users.parquet (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens'] {}
  [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]
test_preference_state.parquet (484776, 3) ['uid', 'item_id', 'state'] {}
  [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]
train_dislikes.parquet (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (75, 24786580)}
  [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]
train_likes.parquet (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (80, 24786790)}
  [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]
train_listens.parquet (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] {'timestamp': (0, 24786795)}
  [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]
train_undislikes.parquet (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (15950, 24785395)}
  [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]
train_unlikes.parquet (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (250, 24786570)}
  [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]
val_dislikes.parquet (3563, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24789920, 25393280)}
  [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]
val_likes.parquet (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24788815, 25393390)}
  [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]
val_listens.parquet (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] {'timestamp': (24788600, 25393395)}
  [{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]
val_undislikes.parquet (606, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24810715, 25393035)}
  [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]
val_unlikes.parquet (9346, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] {'timestamp': (24789005, 25393385)}
  [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]
validation_preference_state.parquet (469124, 3) ['uid', 'item_id', 'state'] {}
  [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]
validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
 eligible min/mean/max percentiles 24657 29067.306291390727 30000 [26724.2  28682.75 29242.   29644.   29979.  ]
test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
 eligible min/mean/max percentiles 24552 29041.3178807947 30000 [26672.7  28657.   29220.   29619.25 29975.  ]
12

exec command

+01:17
Thinking unavailable No text recorded

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

Model text

The data is substantial enough to reflect the intended scaling regime (43.5M listens), while retrieval is tractable (8,456 targets × 30,000 catalog items, with ~29k eligible per user). I’m establishing popularity and repeat-history baselines before training so every learned change has a measured reference and a structurally valid file remains available.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"mkdir -p /app/solution\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nD=Path('/task/data'); O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank')\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\n pos={int(x):i for i,x in enumerate(items)}\n order=np.array([pos[int(x)] for x in cat.item_id],dtype=np.int32)\n rows=[]\n for r,u in enumerate(uids):\n  m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n  top=items[order[m[order]][:100]]\n  rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); cset=set(cat.item_id.astype(int))\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ur={int(x):i for i,x in enumerate(uids)}; ic={int(x):i for i,x in enumerate(items)}\n def elig(u,i):\n  r=ur.get(int(u)); c=ic.get(int(i)); return r is not None and c is not None and ((int(packed[r,c//8])>>(c%8))&1)\nstrong=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\ng={}\nfor u,i in strong.itertuples(index=False,name=None):\n if elig(u,i):g.setdefault(int(u),{})[int(i)]=1\nfor u,i in likes.itertuples(index=False,name=None):\n if elig(u,i):g.setdefault(int(u),{})[int(i)]=3\nprint('gold users/items',len(g),sum(map(len,g.values())),'per user',np.percentile([len(x) for x in g.values()],[0,25,50,75,90,99,100]))\n# load training seen sets only for gold users/items to measure repeat\nneedu=set(g); needpairs={(u,i) for u,x in g.items() for i in x}\ntrli=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','played_ratio_pct'])\ntrli=trli[trli.uid.isin(needu)&(trli.played_ratio_pct>=50)][['uid','item_id']].drop_duplicates()\nseen=set(map(tuple,trli.itertuples(index=False,name=None)))\ntrla=pd.read_parquet(D/'train_likes.parquet',columns=['uid','item_id']); trla=trla[trla.uid.isin(needu)].drop_duplicates(); liked=set(map(tuple,trla.itertuples(index=False,name=None)))\nprint('gold repeated positive listen',sum(p in seen for p in needpairs)/len(needpairs),'prior like event',sum(p in liked for p in needpairs)/len(needpairs))\nprint('train unique users/items listens',trli.uid.nunique(),trli.item_id.nunique())\n# top gold popularity ranks\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nrr=np.array([rank[i] for u,x in g.items() for i in x]);print('gold catalog rank pct',np.percentile(rr,[0,10,25,50,75,90,99,100]))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script running with cell ID 6
Wall time 11.0 seconds
Output:
13

wait command

+02:12
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": "6",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 6
Wall time 1.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "6",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 6
Wall time 1.0 seconds
Output:
14

wait command

+02:18
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "6",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 0.0 seconds
Output:

{
  "model": "/app/solution/validation_popularity.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
gold users/items 4263 37769 per user [  1.   2.   4.  10.  21.  63. 442.]
gold repeated positive listen 0.0 prior like event 0.0
train unique users/items listens 4263 448434
gold catalog rank pct [1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04
 2.91010e+04 3.00000e+04]
15

Agent message

+02:38
Thinking unavailable No text recorded

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16

exec command

+02:44
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); cat=set(pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id']).item_id.astype(int)); tar=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nt=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),'sec',time.time()-t,'unique',x.uid.nunique(),x.item_id.nunique())\nfor name,m in [('positive',x.played_ratio_pct>=50),('strong',x.played_ratio_pct>=80),('organic strong',(x.is_organic==1)&(x.played_ratio_pct>=80)),('target organic strong',x.uid.isin(tar)&(x.is_organic==1)&(x.played_ratio_pct>=80))]:\n y=x[m]; print(name,len(y),'users',y.uid.nunique(),'items',y.item_id.nunique(),'catalog events',y.item_id.isin(cat).sum(),'catalog unique',y.loc[y.item_id.isin(cat),'item_id'].nunique())\nprint('catalog events by organic/recommended and ratio')\ny=x[x.item_id.isin(cat)];print(pd.crosstab(y.is_organic,pd.cut(y.played_ratio_pct,[-1,49,79,100])).to_string())\n# target history size recent (last 90d if timestamps seconds? cutoff 24m sec ~287d; 90 days seconds)\nz=x[x.uid.isin(tar)&(x.played_ratio_pct>=50)]\nfor days in [7,30,90,999]:\n a=z[z.timestamp>=24786800-days*86400]\n print('target pos days',days,len(a),np.percentile(a.groupby('uid').size().reindex(list(tar),fill_value=0),[0,10,50,90,99,100]))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script running with cell ID 7
Wall time 11.0 seconds
Output:
17

wait command

+03:14
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": "7",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 7
Wall time 1.0 seconds
Output:
18

wait command

+03:17
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "7",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 7
Wall time 1.0 seconds
Output:
19

wait command

+03:20
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": "7",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 7
Wall time 1.0 seconds
Output:
20

wait command

+03:24
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": "7",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 7
Wall time 1.0 seconds
Output:
21

wait command

+03:27
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "7",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script running with cell ID 7
Wall time 1.0 seconds
Output:
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wait command

+03:30
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "7",
  "yield_time_ms": 1000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 0.6 seconds
Output:

23

exec command

+03:45
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Model text

The first diagnostic tried to materialize too much of the 43.5M-row table at once and was terminated before emitting results. I’m switching the implementation to Parquet batch scans and bounded arrays—the same streaming discipline the final training pipeline will use—so memory stays proportional to selected interactions rather than the full log.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\nfrom pathlib import Path\nD=Path('/task/data'); cats=np.sort(pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id']).item_id.to_numpy(np.int64)); tar=np.sort(pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64))\npf=pq.ParquetFile(D/'train_listens.parquet'); counts=np.zeros(6,dtype=np.int64); us=set(); start=time.time()\nfor bi,b in enumerate(pf.iter_batches(batch_size=1_000_000,columns=['uid','item_id','is_organic','played_ratio_pct'])):\n u=b.column(0).to_numpy(); i=b.column(1).to_numpy(); o=b.column(2).to_numpy(); p=b.column(3).to_numpy()\n ic=np.isin(i,cats); strong=p>=80; org=o==1; ut=np.isin(u,tar)\n counts += [len(u),ic.sum(),(ic&strong).sum(),(ic&strong&org).sum(),(ic&strong&org&ut).sum(),(strong&org).sum()]\n us.update(map(int,u[ic&strong&org]))\n if bi%10==0: print('batch',bi,'counts',counts.tolist(),'sec',round(time.time()-start,1),flush=True)\nprint('FINAL total,cat,cat strong,cat orgstrong,target cat orgstrong,all orgstrong',counts.tolist(),'users cat orgstrong',len(us),flush=True)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 5.4 seconds
Output:

batch 0 counts [1000000, 705306, 419072, 205405, 204376, 296188] sec 0.1
batch 10 counts [11000000, 7856999, 4688162, 2178007, 2168245, 3055481] sec 1.0
batch 20 counts [21000000, 15032871, 9070049, 4175767, 4157729, 5871778] sec 1.8
batch 30 counts [31000000, 22196837, 13387087, 6161736, 6130586, 8665545] sec 2.7
batch 40 counts [41000000, 29422799, 17725212, 8180552, 8143551, 11476291] sec 3.5
FINAL total,cat,cat strong,cat orgstrong,target cat orgstrong,all orgstrong [43484159, 31249613, 18801138, 8699160, 8661020, 12177583] users cat orgstrong 8588
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+04:27
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+04:46
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+05:04
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+05:23
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+05:41
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+06:00
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+06:19
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+06:37
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+06:56
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+07:06
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/train_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Train and apply a scalable history-encoded PyTorch two-tower ranker.\n+\n+The script never creates a user/item matrix.  It streams the event parquet,\n+forms rolling temporal next-discovery examples, learns with in-batch sampled\n+softmax, precomputes the catalog tower once, and scores eligible candidates by\n+inner product.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import math\n+import random\n+import time\n+from dataclasses import dataclass\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+from torch import nn\n+from torch.utils.data import DataLoader, Dataset\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_END = 24_786_800\n+VAL_END = 25_393_400\n+DAY = 86_400\n+GAP = 1_800\n+N_CHANNELS = 4\n+HISTORY_LEN = 96\n+WINDOWS = 18\n+\n+\n+def seed_everything(seed: int) -> None:\n+    random.seed(seed)\n+    np.random.seed(seed)\n+    torch.manual_seed(seed)\n+    torch.set_num_threads(max(1, min(12, torch.get_num_threads())))\n+\n+\n+@dataclass\n+class Example:\n+    ids: np.ndarray       # [channel, history]\n+    weights: np.ndarray   # [channel, history]\n+    stats: np.ndarray\n+    targets: np.ndarray\n+    target_weights: np.ndarray\n+\n+\n+class RollingDataset(Dataset):\n+    def __init__(self, examples: list[Example], seed: int):\n+        self.examples = examples\n+        self.seed = seed\n+        self.epoch = 0\n+\n+    def __len__(self) -> int:\n+        return len(self.examples)\n+\n+    def __getitem__(self, index: int):\n+        ex = self.examples[index]\n+        # Epoch-dependent deterministic weighted sampling gives likes (grade 3)\n+        # more training mass without materializing duplicate contexts.\n+        rng = np.random.default_rng(self.seed + 1_000_003 * self.epoch + index)\n+        p = ex.target_weights.astype(np.float64)\n+        p /= p.sum()\n+        j = int(rng.choice(len(ex.targets), p=p))\n+        return (\n+            torch.from_numpy(ex.ids.astype(np.int64, copy=False)),\n+            torch.from_numpy(ex.weights.astype(np.float32, copy=False)),\n+            torch.from_numpy(ex.stats.astype(np.float32, copy=False)),\n+            torch.tensor(int(ex.targets[j]), dtype=torch.long),\n+        )\n+\n+\n+class UserTower(nn.Module):\n+    \"\"\"History-only user encoder: four weighted bags followed by an MLP.\"\"\"\n+\n+    def __init__(self, n_items: int, dim: int):\n+        super().__init__()\n+        self.n_items = n_items\n+        self.dim = dim\n+        self.history_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n+        self.channel_scale = nn.Parameter(torch.ones(N_CHANNELS, dim))\n+        self.mlp = nn.Sequential(\n+            nn.LayerNorm(N_CHANNELS * dim + 6),\n+            nn.Linear(N_CHANNELS * dim + 6, 2 * dim),\n+            nn.GELU(),\n+            nn.Linear(2 * dim, dim),\n+        )\n+        nn.init.normal_(self.history_embedding.weight, std=0.04)\n+        with torch.no_grad():\n+            self.history_embedding.weight[n_items].zero_()\n+\n+    def latent(self, ids: torch.Tensor, weights: torch.Tensor, stats: torch.Tensor):\n+        safe_ids = ids.clamp(min=0)\n+        emb = self.history_embedding(safe_ids)  # B,C,L,D\n+        mask = (ids >= 0).to(weights.dtype)\n+        w = weights * mask\n+        pooled = (emb * w[..., None]).sum(2) / w.sum(2, keepdim=True).clamp_min(1e-6)\n+        pooled = pooled * self.channel_scale[None]\n+        z = self.mlp(torch.cat([pooled.flatten(1), stats], dim=1))\n+        return nn.functional.normalize(z, dim=1)\n+\n+    def forward(self, ids: torch.Tensor, weights: torch.Tensor, stats: torch.Tensor,\n+                popularity_dims: int = 0):\n+        z = self.latent(ids, weights, stats)\n+        if popularity_dims:\n+            return torch.cat([z, torch.ones((len(z), popularity_dims), device=z.device)], 1)\n+        return z\n+\n+\n+class ItemTower(nn.Module):\n+    \"\"\"Independently computable catalog-item encoder.\"\"\"\n+\n+    def __init__(self, n_items: int, dim: int, fixed_features: torch.Tensor):\n+        super().__init__()\n+        self.item_embedding = nn.Embedding(n_items, dim)\n+        self.register_buffer(\"fixed_features\", fixed_features)\n+        self.mlp = nn.Sequential(\n+            nn.LayerNorm(dim + fixed_features.shape[1]),\n+            nn.Linear(dim + fixed_features.shape[1], 2 * dim),\n+            nn.GELU(),\n+            nn.Linear(2 * dim, dim),\n+        )\n+        nn.init.normal_(self.item_embedding.weight, std=0.04)\n+\n+    def latent(self, item_index: torch.Tensor):\n+        x = torch.cat([self.item_embedding(item_index), self.fixed_features[item_index]], 1)\n+        return nn.functional.normalize(self.mlp(x), dim=1)\n+\n+    def forward(self, item_index: torch.Tensor, popularity_coeffs=None):\n+        z = self.latent(item_index)\n+        if popularity_coeffs is not None:\n+            coeff = torch.as_tensor(popularity_coeffs, dtype=z.dtype, device=z.device)\n+            return torch.cat([z, self.fixed_features[item_index, :len(coeff)] * coeff], 1)\n+        return z\n+\n+\n+class TwoTower(nn.Module):\n+    def __init__(self, n_items: int, dim: int, fixed_features: torch.Tensor):\n+        super().__init__()\n+        self.user_tower = UserTower(n_items, dim)\n+        self.item_tower = ItemTower(n_items, dim, fixed_features)\n+        self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\n+\n+    def loss(self, ids, weights, stats, target):\n+        u = self.user_tower.latent(ids, weights, stats)\n+        v = self.item_tower.latent(target)\n+        scale = self.logit_scale.exp().clamp(3.0, 40.0)\n+        logits = scale * (u @ v.T)\n+        labels = torch.arange(len(target), device=target.device)\n+        # Symmetric contrastive loss makes both independently used towers train.\n+        return 0.5 * (nn.functional.cross_entropy(logits, labels) +\n+                      nn.functional.cross_entropy(logits.T, labels))\n+\n+\n+def catalog_info():\n+    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\").sort_values(\"popularity_rank\")\n+    raw_ids = cat.item_id.to_numpy(np.int64)\n+    item_to_index = {int(x): i for i, x in enumerate(raw_ids)}\n+    pop = np.log1p(cat.positive_listens.to_numpy(np.float32))\n+    org = np.log1p(cat.organic_positive_listens.to_numpy(np.float32))\n+    ratio = np.log1p(cat.organic_positive_listens.to_numpy(np.float32)) - pop\n+    feats = np.stack([(pop - pop.mean()) / pop.std(),\n+                      (org - org.mean()) / org.std(),\n+                      (ratio - ratio.mean()) / ratio.std()], 1).astype(np.float32)\n+    sorted_raw = np.sort(raw_ids)\n+    sorted_to_index = np.array([item_to_index[int(x)] for x in sorted_raw], np.int32)\n+    return cat, raw_ids, item_to_index, sorted_raw, sorted_to_index, feats\n+\n+\n+def map_catalog(raw: np.ndarray, sorted_raw: np.ndarray, sorted_to_index: np.ndarray):\n+    pos = np.searchsorted(sorted_raw, raw)\n+    ok = pos < len(sorted_raw)\n+    ok[ok] &= sorted_raw[pos[ok]] == raw[ok]\n+    mapped = np.full(len(raw), -1, np.int32)\n+    mapped[ok] = sorted_to_index[pos[ok]]\n+    return mapped, ok\n+\n+\n+def load_small_events(stage: str, item_to_index: dict[int, int]):\n+    \"\"\"Load explicit events and the small validation listen tail by user.\"\"\"\n+    families = [\"likes\", \"unlikes\", \"dislikes\", \"undislikes\"]\n+    action_code = {\"likes\": 1, \"unlikes\": -1, \"dislikes\": 2, \"undislikes\": -2}\n+    actions: dict[int, list[np.ndarray]] = {}\n+    for family in families:\n+        parts = [pd.read_parquet(DATA / f\"train_{family}.parquet\")]\n+        if stage == \"final\":\n+            parts.append(pd.read_parquet(DATA / f\"val_{family}.parquet\"))\n+        frame = pd.concat(parts, ignore_index=True)\n+        idx = frame.item_id.map(item_to_index)\n+        frame = frame[idx.notna()].copy()\n+        frame[\"idx\"] = idx[idx.notna()].astype(np.int32)\n+        frame[\"code\"] = action_code[family]\n+        for uid, g in frame.groupby(\"uid\", sort=False):\n+            a = np.stack([g.timestamp.to_numpy(np.int64), g.idx.to_numpy(np.int64),\n+                          np.full(len(g), action_code[family], np.int64),\n+                          g.is_organic.to_numpy(np.int64)], 1)\n+            actions.setdefault(int(uid), []).append(a)\n+    actions2 = {u: np.concatenate(v)[np.argsort(np.concatenate(v)[:, 0])]\n+                for u, v in actions.items()}\n+\n+    tails: dict[int, np.ndarray] = {}\n+    if stage == \"final\":\n+        f = pd.read_parquet(DATA / \"val_listens.parquet\",\n+                            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\n+        idx = f.item_id.map(item_to_index)\n+        f = f[idx.notna()].copy(); f[\"idx\"] = idx[idx.notna()].astype(np.int32)\n+        for uid, g in f.groupby(\"uid\", sort=False):\n+            tails[int(uid)] = np.stack([g.timestamp.to_numpy(np.int64),\n+                g.idx.to_numpy(np.int64), g.is_organic.to_numpy(np.int64),\n+                g.played_ratio_pct.to_numpy(np.int64)], 1)\n+    return actions2, tails\n+\n+\n+def active_explicit(actions: np.ndarray | None, cutoff: int):\n+    if actions is None or not len(actions):\n+        return np.empty(0, np.int32), np.empty(0, np.int32)\n+    a = actions[actions[:, 0] < cutoff]\n+    like, dislike = {}, {}\n+    for ts, item, code, _org in a:\n+        if code == 1: like[int(item)] = True\n+        elif code == -1: like[int(item)] = False\n+        elif code == 2: dislike[int(item)] = True\n+        elif code == -2: dislike[int(item)] = False\n+    return (np.fromiter((x for x, on in like.items() if on), np.int32),\n+            np.fromiter((x for x, on in dislike.items() if on), np.int32))\n+\n+\n+def pad_channel(ids: np.ndarray, weights: np.ndarray):\n+    out_i = np.full(HISTORY_LEN, -1, np.int32)\n+    out_w = np.zeros(HISTORY_LEN, np.float16)\n+    n = min(HISTORY_LEN, len(ids))\n+    if n:\n+        out_i[:n] = ids[:n]\n+        out_w[:n] = weights[:n].astype(np.float16)\n+    return out_i, out_w\n+\n+\n+def make_context(events: np.ndarray, actions: np.ndarray | None, cutoff: int,\n+                 supplied_like=None, supplied_dislike=None):\n+    pre = events[events[:, 0] < cutoff]\n+    pos = pre[pre[:, 3] >= 50]\n+    channels_i, channels_w = [], []\n+    if len(pos):\n+        # Most-recent distinct positives with a 45-day recency decay.\n+        rev_items = pos[::-1, 1].astype(np.int32)\n+        _, first = np.unique(rev_items, return_index=True)\n+        take = np.sort(first)[:HISTORY_LEN]\n+        rows = pos[::-1][take]\n+        age = np.maximum(0, cutoff - rows[:, 0]) / DAY\n+        w = np.exp(-age / 45.0) * (0.7 + 0.3 * (rows[:, 3] / 100.0)) * (1 + 0.25 * rows[:, 2])\n+        channels_i.append(rows[:, 1].astype(np.int32)); channels_w.append(w)\n+\n+        # Long-term taste: highest frequency/organic-quality distinct tracks.\n+        uniq, inv, cnt = np.unique(pos[:, 1].astype(np.int32), return_inverse=True, return_counts=True)\n+        org_count = np.bincount(inv, weights=pos[:, 2], minlength=len(uniq))\n+        qual = np.bincount(inv, weights=pos[:, 3] / 100.0, minlength=len(uniq))\n+        taste_w = np.log1p(cnt) * (1.0 + org_count / cnt) * (0.5 + qual / cnt)\n+        order = np.argsort(-taste_w)[:HISTORY_LEN]\n+        channels_i.append(uniq[order]); channels_w.append(taste_w[order])\n+    else:\n+        channels_i.extend([np.empty(0, np.int32)] * 2)\n+        channels_w.extend([np.empty(0, np.float32)] * 2)\n+\n+    if supplied_like is None:\n+        liked, disliked = active_explicit(actions, cutoff)\n+    else:\n+        liked, disliked = supplied_like, supplied_dislike\n+    channels_i.extend([liked[-HISTORY_LEN:][::-1], disliked[-HISTORY_LEN:][::-1]])\n+    channels_w.extend([np.ones(min(HISTORY_LEN, len(liked)), np.float32) * 1.5,\n+                       np.ones(min(HISTORY_LEN, len(disliked)), np.float32) * -1.0])\n+    padded = [pad_channel(i, w) for i, w in zip(channels_i, channels_w)]\n+    ids = np.stack([x[0] for x in padded])\n+    weights = np.stack([x[1] for x in padded])\n+    stats = np.array([math.log1p(len(pos)), math.log1p(len(np.unique(pos[:,1])) if len(pos) else 0),\n+                      math.log1p(len(liked)), math.log1p(len(disliked)),\n+                      float(pos[:,2].mean()) if len(pos) else 0.0,\n+                      float(pos[:,3].mean()/100) if len(pos) else 0.0], np.float32)\n+    return ids, weights, stats\n+\n+\n+def targets_for_window(events: np.ndarray, actions: np.ndarray | None, cutoff: int):\n+    before = events[events[:, 0] < cutoff]\n+    seen = set(map(int, before[:, 1]))\n+    future = events[(events[:, 0] >= cutoff + GAP) & (events[:, 0] < cutoff + GAP + 7 * DAY)]\n+    strong = future[(future[:, 2] == 1) & (future[:, 3] >= 80), 1].astype(np.int32)\n+    grade = {int(x): 1.0 for x in strong if int(x) not in seen}\n+    if actions is not None:\n+        la = actions[(actions[:, 0] >= cutoff + GAP) &\n+                     (actions[:, 0] < cutoff + GAP + 7 * DAY) &\n+                     (actions[:, 2] == 1) & (actions[:, 3] == 1)]\n+        for x in la[:, 1]:\n+            if int(x) not in seen:\n+                grade[int(x)] = 4.0\n+    if not grade:\n+        return np.empty(0, np.int32), np.empty(0, np.float32)\n+    return np.fromiter(grade.keys(), np.int32), np.fromiter(grade.values(), np.float32)\n+\n+\n+def build_data(stage: str, sorted_raw, sorted_to_index, item_to_index):\n+    end = TRAIN_END if stage == \"validation\" else VAL_END\n+    actions, tails = load_small_events(stage, item_to_index)\n+    targets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\n+    examples: list[Example] = []\n+    inference: dict[int, tuple[np.ndarray, np.ndarray, np.ndarray]] = {}\n+\n+    pref_name = \"validation_preference_state.parquet\" if stage == \"validation\" else \"test_preference_state.parquet\"\n+    pref = pd.read_parquet(DATA / pref_name)\n+    pref[\"idx\"] = pref.item_id.map(item_to_index)\n+    pref = pref[pref.idx.notna()]\n+    pref_by_uid = {}\n+    for uid, g in pref.groupby(\"uid\", sort=False):\n+        pref_by_uid[int(uid)] = (g.loc[g.state == \"liked\", \"idx\"].to_numpy(np.int32),\n+                                 g.loc[g.state == \"disliked\", \"idx\"].to_numpy(np.int32))\n+\n+    cutoffs = [end - GAP - 7 * DAY * k for k in range(WINDOWS, 0, -1)]\n+    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n+    current_uid = None\n+    chunks: list[np.ndarray] = []\n+    processed = 0\n+\n+    def finish(uid, pieces):\n+        nonlocal processed\n+        if uid is None:\n+            return\n+        ev = np.concatenate(pieces) if len(pieces) > 1 else pieces[0]\n+        if stage == \"final\" and uid in tails:\n+            ev = np.concatenate([ev, tails[uid]])\n+        ev = ev[np.argsort(ev[:, 0], kind=\"stable\")]\n+        act = actions.get(uid)\n+        for cutoff in cutoffs:\n+            ids, weights, stats = make_context(ev, act, cutoff)\n+            tg, tw = targets_for_window(ev, act, cutoff)\n+            if len(tg) and (ids[:2] >= 0).sum() >= 3:\n+                examples.append(Example(ids, weights, stats, tg, tw))\n+        if uid in targets:\n+            supplied = pref_by_uid.get(uid, (np.empty(0,np.int32), np.empty(0,np.int32)))\n+            inference[uid] = make_context(ev, act, end, *supplied)\n+        processed += 1\n+        if processed % 1000 == 0:\n+            print(f\"  histories {processed}, examples {len(examples)}\", flush=True)\n+\n+    columns = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+    for batch in pf.iter_batches(batch_size=750_000, columns=columns):\n+        uid = batch.column(0).to_numpy().astype(np.int64, copy=False)\n+        ts = batch.column(1).to_numpy().astype(np.int64, copy=False)\n+        raw = batch.column(2).to_numpy().astype(np.int64, copy=False)\n+        org = batch.column(3).to_numpy().astype(np.int64, copy=False)\n+        ratio = batch.column(4).to_numpy().astype(np.int64, copy=False)\n+        idx, ok = map_catalog(raw, sorted_raw, sorted_to_index)\n+        uid, ts, idx, org, ratio = uid[ok], ts[ok], idx[ok], org[ok], ratio[ok]\n+        if not len(uid):\n+            continue\n+        boundaries = np.r_[0, np.flatnonzero(uid[1:] != uid[:-1]) + 1, len(uid)]\n+        for a, b in zip(boundaries[:-1], boundaries[1:]):\n+            u = int(uid[a])\n+            seg = np.stack([ts[a:b], idx[a:b], org[a:b], ratio[a:b]], 1)\n+            if current_uid is None:\n+                current_uid = u\n+            if u != current_uid:\n+                finish(current_uid, chunks)\n+                current_uid, chunks = u, []\n+            chunks.append(seg)\n+    finish(current_uid, chunks)\n+    missing = targets - set(inference)\n+    if missing:\n+        raise RuntimeError(f\"missing inference histories for {len(missing)} targets\")\n+    print(f\"built {len(examples)} rolling examples and {len(inference)} inference contexts\")\n+    return examples, inference\n+\n+\n+def train_model(model: TwoTower, examples: list[Example], epochs: int, seed: int):\n+    ds = RollingDataset(examples, seed)\n+    loader = DataLoader(ds, batch_size=256, shuffle=True, num_workers=0, drop_last=True)\n+    opt = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\n+    for epoch in range(epochs):\n+        ds.epoch = epoch\n+        model.train(); losses = []; start = time.time()\n+        for ids, weights, stats, target in loader:\n+            opt.zero_grad(set_to_none=True)\n+            loss = model.loss(ids, weights, stats, target)\n+            loss.backward()\n+            nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n+            opt.step(); losses.append(float(loss))\n+        print(f\"epoch {epoch+1}/{epochs}: loss={np.mean(losses):.4f} \"\n+              f\"scale={model.logit_scale.exp().item():.2f} sec={time.time()-start:.1f}\", flush=True)\n+\n+\[email protected]_mode()\n+def encode(model: TwoTower, inference, uid_order):\n+    model.eval()\n+    ids = torch.from_numpy(np.stack([inference[int(u)][0] for u in uid_order]).astype(np.int64))\n+    w = torch.from_numpy(np.stack([inference[int(u)][1] for u in uid_order]).astype(np.float32))\n+    st = torch.from_numpy(np.stack([inference[int(u)][2] for u in uid_order]).astype(np.float32))\n+    users = []\n+    for a in range(0, len(uid_order), 256):\n+        users.append(model.user_tower.latent(ids[a:a+256], w[a:a+256], st[a:a+256]).numpy())\n+    item_idx = torch.arange(model.item_tower.item_embedding.num_embeddings)\n+    items = []\n+    for a in range(0, len(item_idx), 2048):\n+        items.append(model.item_tower.latent(item_idx[a:a+2048]).numpy())\n+    return np.concatenate(users), np.concatenate(items)\n+\n+\n+def truth_for_validation(uid_order, raw_ids):\n+    with np.load(DATA / \"validation_eligible_candidates.npz\") as z:\n+        packed = z[\"packed_eligible\"].copy(); euids = z[\"uids\"].astype(np.int64)\n+    uidrow = {int(u): r for r, u in enumerate(euids)}\n+    itemcol = {int(x): c for c, x in enumerate(raw_ids)}\n+    gold = {}\n+    li = pd.read_parquet(DATA / \"val_listens.parquet\")\n+    li = li[(li.is_organic == 1) & (li.played_ratio_pct >= 80)]\n+    la = pd.read_parquet(DATA / \"val_likes.parquet\"); la = la[la.is_organic == 1]\n+    for frame, grade in [(li, 1), (la, 3)]:\n+        for u, x in frame[[\"uid\",\"item_id\"]].drop_duplicates().itertuples(index=False, name=None):\n+            r, c = uidrow.get(int(u)), itemcol.get(int(x))\n+            if r is not None and c is not None and ((int(packed[r,c//8]) >> (c%8)) & 1):\n+                gold.setdefault(int(u), {})[int(x)] = grade\n+    return gold\n+\n+\n+def rank_and_write(stage: str, users: np.ndarray, items: np.ndarray, feats: np.ndarray,\n+                   uid_order: np.ndarray, raw_ids: np.ndarray, output: Path,\n+                   coefficients=(0.0, 0.0), tune=False):\n+    elig_path = DATA / (\"validation_eligible_candidates.npz\" if stage == \"validation\" else \"test_eligible_candidates.npz\")\n+    with np.load(elig_path) as z:\n+        euids = z[\"uids\"].astype(np.int64); packed = z[\"packed_eligible\"].copy()\n+    if not np.array_equal(euids, uid_order):\n+        order = {int(u): i for i, u in enumerate(uid_order)}\n+        users = users[[order[int(u)] for u in euids]]; uid_order = euids\n+\n+    gold = truth_for_validation(uid_order, raw_ids) if tune else None\n+    discounts = 1.0 / np.log2(np.arange(2, 12))\n+\n+    def one(coeff, collect=False):\n+        ndcgs=[]; rows=[]\n+        bias = coeff[0] * feats[:, 0] + coeff[1] * feats[:, 1]\n+        for a in range(0, len(uid_order), 64):\n+            score = users[a:a+64] @ items.T\n+            score += bias[None]\n+            mask = np.unpackbits(packed[a:a+len(score)], axis=1, count=len(raw_ids), bitorder=\"little\").astype(bool)\n+            score[~mask] = -np.inf\n+            ix = np.argpartition(score, -100, axis=1)[:, -100:]\n+            val = np.take_along_axis(score, ix, axis=1)\n+            ix = np.take_along_axis(ix, np.argsort(-val, axis=1), axis=1)\n+            for j, chosen in enumerate(ix):\n+                uid = int(uid_order[a+j])\n+                if collect:\n+                    rows.extend((uid, int(raw_ids[k]), rank+1) for rank,k in enumerate(chosen))\n+                if gold is not None and uid in gold:\n+                    grades=np.array([gold[uid].get(int(raw_ids[k]),0) for k in chosen[:10]])\n+                    dcg=((2.0**grades-1)*discounts[:10]).sum()\n+                    ideal=np.array(sorted(gold[uid].values(),reverse=True)[:10])\n+                    idcg=((2.0**ideal-1)*discounts[:len(ideal)]).sum()\n+                    ndcgs.append(dcg/idcg)\n+        return float(np.mean(ndcgs)) if ndcgs else 0.0, rows\n+\n+    best = tuple(coefficients); best_score = -1.0\n+    if tune:\n+        grid = [(a,b) for a in [0.0,0.01,0.02,0.04,0.07,0.10,0.15]\n+                       for b in [0.0,0.01,0.03,0.06]]\n+        for coeff in grid:\n+            score,_=one(coeff)\n+            print(f\"bias {coeff}: ndcg={score:.6f}\",flush=True)\n+            if score>best_score: best_score,best=score,coeff\n+    else:\n+        best_score = 0.0\n+    score, rows = one(best, collect=True)\n+    pd.DataFrame(rows, columns=[\"uid\",\"item_id\",\"rank\"]).to_parquet(output,index=False)\n+    print(f\"wrote {output}; coefficients={best}; validation_ndcg={score:.6f}\")\n+    return best, score\n+\n+\n+def main():\n+    ap=argparse.ArgumentParser()\n+    ap.add_argument(\"--stage\",choices=[\"validation\",\"final\"],required=True)\n+    ap.add_argument(\"--output\",type=Path,required=True)\n+    ap.add_argument(\"--epochs\",type=int,default=6)\n+    ap.add_argument(\"--dim\",type=int,default=64)\n+    ap.add_argument(\"--seed\",type=int,default=20260831)\n+    ap.add_argument(\"--coefficients\",default=\"0,0\")\n+    ap.add_argument(\"--tune\",action=\"store_true\")\n+    args=ap.parse_args(); OUT.mkdir(parents=True,exist_ok=True); seed_everything(args.seed)\n+    cat,raw,item_to_index,sorted_raw,sorted_to_index,feats=catalog_info()\n+    examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)\n+    model=TwoTower(len(raw),args.dim,torch.from_numpy(feats))\n+    train_model(model,examples,args.epochs,args.seed)\n+    uid_order=np.load(DATA/(\"validation_eligible_candidates.npz\" if args.stage==\"validation\" else \"test_eligible_candidates.npz\"))[\"uids\"].astype(np.int64)\n+    users,items=encode(model,inference,uid_order)\n+    coeff=tuple(map(float,args.coefficients.split(\",\")))\n+    best,score=rank_and_write(args.stage,users,items,feats,uid_order,raw,args.output,coeff,args.tune)\n+    ckpt=OUT/f\"model_{args.stage}.pt\"\n+    torch.save({\"state_dict\":model.state_dict(),\"dim\":args.dim,\"coefficients\":best,\n+                \"stage\":args.stage,\"validation_ndcg\":score},ckpt)\n+    (OUT/f\"metrics_{args.stage}.json\").write_text(json.dumps({\"coefficients\":best,\"validation_ndcg\":score,\n+        \"examples\":len(examples),\"epochs\":args.epochs,\"dim\":args.dim},indent=2)+\"\\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:

{}
34

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+11:34
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -l /app/solution && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation.parquet --epochs 5 --dim 64 --tune","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:

total 736
-rw-r--r-- 1 agent agent  23166 Aug 31 18:37 train_ranker.py
-rw-r--r-- 1 agent agent 728599 Aug 31 18:28 validation_popularity.parquet

SESSION_ID=30602
35

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+11:41
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Model text

The first learned system is now running. Its user tower pools four history channels—recent positives, long-term taste, active likes, and active dislikes—while its item tower embeds catalog IDs plus offline popularity features. Training examples are rolling seven-day “new organic discovery” windows, with in-batch sampled-softmax negatives; neither preprocessing nor training constructs a global interaction matrix.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

36

exec command

+11:52
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

37

exec command

+11:59
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 1000, examples 6766
38

exec command

+12:07
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

39

exec command

+12:15
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 2000, examples 13768
40

exec command

+12:23
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

41

exec command

+12:31
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 3000, examples 20630
42

exec command

+12:39
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

43

exec command

+12:47
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 4000, examples 27686
44

exec command

+12:55
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

45

exec command

+13:03
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 5000, examples 34699
46

exec command

+13:11
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

47

exec command

+13:19
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 6000, examples 41314
48

exec command

+13:27
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

49

exec command

+13:35
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 7000, examples 48162
50

exec command

+13:43
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

51

exec command

+13:51
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 8000, examples 55110
52

exec command

+13:58
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

53

exec command

+14:06
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 5.0 seconds
Output:

  histories 9000, examples 62054
54

exec command

+14:15
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30602,chars:"",yield_time_ms:1000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

Traceback (most recent call last):
  File "/app/solution/train_ranker.py", line 505, in <module>
    main()
  File "/app/solution/train_ranker.py", line 490, in main
    examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/train_ranker.py", line 372, in build_data
    raise RuntimeError(f"missing inference histories for {len(missing)} targets")
RuntimeError: missing inference histories for 4 targets
55

exec command

+14:21
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Model text

Preprocessing found 62,054 usable rolling examples. Four target users have no catalog-mapped listen history even though they satisfy the broader target criterion; I’m handling them with their explicit preference channels (or a cold-start zero history) rather than dropping them, preserving exact target coverage.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n     finish(current_uid, chunks)\n     missing = targets - set(inference)\n-    if missing:\n-        raise RuntimeError(f\"missing inference histories for {len(missing)} targets\")\n+    # A few qualified targets have positive history entirely outside the 30k\n+    # retrieval catalog.  Their explicit-state bags still provide a valid\n+    # history-derived encoding; if those too are empty this is a true cold start.\n+    for uid in missing:\n+        supplied = pref_by_uid.get(uid, (np.empty(0,np.int32), np.empty(0,np.int32)))\n+        inference[uid] = make_context(np.empty((0,4), np.int64), actions.get(uid),\n+                                      end, *supplied)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
56

exec command

+14:33
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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 --stage validation --output /app/solution/validation.parquet --epochs 5 --dim 64 --tune","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=66415
57

exec command

+14:40
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 33
Wall time 11.0 seconds
Output:
58

wait command

+14:54
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "33",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 33
Wall time 11.0 seconds
Output:
59

wait command

+15:08
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "33",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.3 seconds
Output:

  histories 1000, examples 6766
60

exec command

+15:13
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 34
Wall time 11.0 seconds
Output:
61

wait command

+15:27
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "34",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 34
Wall time 11.0 seconds
Output:
62

wait command

+15:41
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "34",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.5 seconds
Output:

  histories 2000, examples 13768
  histories 3000, examples 20630
63

exec command

+15:47
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 35
Wall time 11.0 seconds
Output:
64

wait command

+16:01
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "35",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

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

wait command

+16:15
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "35",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.8 seconds
Output:

  histories 4000, examples 27686
  histories 5000, examples 34699
66

exec command

+16:20
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

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

wait command

+16:34
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "36",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 36
Wall time 11.0 seconds
Output:
68

wait command

+16:48
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "36",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.4 seconds
Output:

  histories 6000, examples 41314
  histories 7000, examples 48162
69

exec command

+16:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 37
Wall time 11.0 seconds
Output:
70

wait command

+17:07
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": "37",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 37
Wall time 11.0 seconds
Output:
71

wait command

+17:20
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": "37",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.5 seconds
Output:

  histories 8000, examples 55110
  histories 9000, examples 62054
/app/solution/train_ranker.py:394: 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.)
  opt.step(); losses.append(float(loss))
72

exec command

+17:26
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 38
Wall time 11.0 seconds
Output:
73

wait command

+17:40
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": "38",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 38
Wall time 11.0 seconds
Output:
74

wait command

+17:53
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "38",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.4 seconds
Output:

built 63587 rolling examples and 8456 inference contexts
epoch 1/5: loss=5.5704 scale=10.79 sec=13.2
epoch 2/5: loss=5.4493 scale=10.37 sec=13.1
epoch 3/5: loss=5.2270 scale=10.26 sec=13.1
75

exec command

+17:59
Thinking unavailable No text recorded

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Model text

Preprocessing is complete with full target coverage, and optimization is converging (contrastive loss fell from 5.57 to 5.23 by epoch 3). Once encoding finishes, I’ll tune only the two allowed tower-output popularity coordinates on public NDCG and independently verify the resulting file with the supplied evaluator.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 39
Wall time 11.0 seconds
Output:
76

wait command

+18:16
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "39",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 39
Wall time 11.0 seconds
Output:
77

wait command

+18:30
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "39",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.4 seconds
Output:

epoch 4/5: loss=4.9430 scale=11.05 sec=13.1
epoch 5/5: loss=4.6822 scale=12.27 sec=13.0
bias (0.0, 0.0): ndcg=0.001191
bias (0.0, 0.01): ndcg=0.001542
bias (0.0, 0.03): ndcg=0.003831
bias (0.0, 0.06): ndcg=0.007696
bias (0.01, 0.0): ndcg=0.001478
bias (0.01, 0.01): ndcg=0.002701
bias (0.01, 0.03): ndcg=0.005176
bias (0.01, 0.06): ndcg=0.009149
78

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:66415,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 40
Wall time 11.0 seconds
Output:
79

wait command

+18:49
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "40",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 40
Wall time 11.0 seconds
Output:
80

wait command

+19:03
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "40",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 0.0 seconds
Output:

bias (0.02, 0.0): ndcg=0.002767
bias (0.02, 0.01): ndcg=0.003770
bias (0.02, 0.03): ndcg=0.006236
bias (0.02, 0.06): ndcg=0.009738
bias (0.04, 0.0): ndcg=0.005032
bias (0.04, 0.01): ndcg=0.006311
bias (0.04, 0.03): ndcg=0.009199
bias (0.04, 0.06): ndcg=0.010516
bias (0.07, 0.0): ndcg=0.008185
bias (0.07, 0.01): ndcg=0.009308
bias (0.07, 0.03): ndcg=0.010484
bias (0.07, 0.06): ndcg=0.011221
bias (0.1, 0.0): ndcg=0.009955
bias (0.1, 0.01): ndcg=0.010099
bias (0.1, 0.03): ndcg=0.010847
bias (0.1, 0.06): ndcg=0.011702
bias (0.15, 0.0): ndcg=0.010760
bias (0.15, 0.01): ndcg=0.010948
bias (0.15, 0.03): ndcg=0.011194
bias (0.15, 0.06): ndcg=0.011302
wrote /app/solution/validation.parquet; coefficients=(0.1, 0.06); validation_ndcg=0.011702
81

Agent message

+19:24
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82

Agent message

+19:42
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83

Agent message

+20:01
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84

exec command

+20:03
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Model text

The first learned ranker scores 0.011702 internally, modestly above the 0.010710 popularity baseline. I found one concrete encoder defect before accepting that result: the disliked-history channel used signed weights in its normalization denominator, which could amplify that channel dramatically. I’m correcting the denominator to use absolute mass and removing duplicate-positive false negatives from the sampled-softmax batch, then retraining; both changes preserve the exact two-tower decomposition.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n         mask = (ids >= 0).to(weights.dtype)\n         w = weights * mask\n-        pooled = (emb * w[..., None]).sum(2) / w.sum(2, keepdim=True).clamp_min(1e-6)\n+        pooled = (emb * w[..., None]).sum(2) / w.abs().sum(2, keepdim=True).clamp_min(1e-6)\n@@\n         logits = scale * (u @ v.T)\n         labels = torch.arange(len(target), device=target.device)\n-        # Symmetric contrastive loss makes both independently used towers train.\n-        return 0.5 * (nn.functional.cross_entropy(logits, labels) +\n-                      nn.functional.cross_entropy(logits.T, labels))\n+        # Equal target IDs in a batch are the same positive vector, not\n+        # legitimate negatives. Mask those duplicate columns for each row.\n+        duplicate = target[:, None].eq(target[None, :])\n+        duplicate.fill_diagonal_(False)\n+        logits = logits.masked_fill(duplicate, -torch.inf)\n+        return nn.functional.cross_entropy(logits, labels)\n@@\n     ap.add_argument(\"--tune\",action=\"store_true\")\n+    ap.add_argument(\"--cache\",action=\"store_true\",help=\"reuse preprocessed stage data\")\n@@\n-    examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)\n+    cache_path=OUT/f\"cache_{args.stage}.pt\"\n+    if args.cache and cache_path.exists():\n+        cached=torch.load(cache_path,weights_only=False)\n+        examples,inference=cached[\"examples\"],cached[\"inference\"]\n+        print(f\"loaded {len(examples)} examples from {cache_path}\")\n+    else:\n+        examples,inference=build_data(args.stage,sorted_raw,sorted_to_index,item_to_index)\n+        if args.cache:\n+            torch.save({\"examples\":examples,\"inference\":inference},cache_path)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
85

exec command

+20:27
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation_v2.parquet --epochs 8 --dim 64 --tune --cache","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=23054
86

exec command

+20:34
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 43
Wall time 11.0 seconds
Output:
87

wait command

+20:48
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "43",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 43
Wall time 11.0 seconds
Output:
88

wait command

+21:02
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": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.3 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011702,
    "quality_users": 4263
  }
}
  histories 1000, examples 6766
89

exec command

+21:07
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 44
Wall time 11.0 seconds
Output:
90

wait command

+21:20
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "44",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 44
Wall time 11.0 seconds
Output:
91

wait command

+21:34
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": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.4 seconds
Output:

  histories 2000, examples 13768
  histories 3000, examples 20630
92

exec command

+21:39
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:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 45
Wall time 11.0 seconds
Output:
93

wait command

+21:53
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "45",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 45
Wall time 11.0 seconds
Output:
94

wait command

+22:07
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": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

  histories 4000, examples 27686
  histories 5000, examples 34699
95

exec command

+22:12
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 46
Wall time 11.0 seconds
Output:
96

wait command

+22:26
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "46",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 46
Wall time 11.0 seconds
Output:
97

wait command

+22:39
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": "46",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.6 seconds
Output:

  histories 6000, examples 41314
  histories 7000, examples 48162
98

exec command

+22:45
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:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 47
Wall time 11.0 seconds
Output:
99

wait command

+22:59
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": "47",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 47
Wall time 11.0 seconds
Output:
100

wait command

+23:13
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "47",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.1 seconds
Output:

  histories 8000, examples 55110
  histories 9000, examples 62054
101

exec command

+23:18
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:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 48
Wall time 11.0 seconds
Output:
102

wait command

+23:32
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": "48",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 48
Wall time 11.0 seconds
Output:
103

wait command

+23:46
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": "48",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.0 seconds
Output:

/app/solution/train_ranker.py:397: 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.)
  opt.step(); losses.append(float(loss))
built 63587 rolling examples and 8456 inference contexts
epoch 1/8: loss=5.5282 scale=11.33 sec=11.3
epoch 2/8: loss=5.2892 scale=10.95 sec=11.2
104

exec command

+23:51
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:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 49
Wall time 11.0 seconds
Output:
105

wait command

+24:05
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": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 49
Wall time 11.0 seconds
Output:
106

wait command

+24:18
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": "49",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

epoch 3/8: loss=5.0591 scale=10.87 sec=11.4
epoch 4/8: loss=4.8768 scale=11.05 sec=11.3
epoch 5/8: loss=4.7087 scale=11.47 sec=11.2
107

exec command

+24:23
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 50
Wall time 11.0 seconds
Output:
108

wait command

+24:39
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": "50",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 50
Wall time 11.0 seconds
Output:
109

wait command

+24:52
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": "50",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 1.8 seconds
Output:

epoch 6/8: loss=4.5389 scale=12.30 sec=11.2
epoch 7/8: loss=4.3898 scale=13.22 sec=11.1
epoch 8/8: loss=4.2427 scale=14.25 sec=11.2
bias (0.0, 0.0): ndcg=0.003578
bias (0.0, 0.01): ndcg=0.004725
bias (0.0, 0.03): ndcg=0.009224
bias (0.0, 0.06): ndcg=0.014832
110

exec command

+24:56
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:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 51
Wall time 11.0 seconds
Output:
111

wait command

+25:10
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": "51",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 51
Wall time 11.0 seconds
Output:
112

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": "51",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

bias (0.01, 0.0): ndcg=0.004982
bias (0.01, 0.01): ndcg=0.007230
bias (0.01, 0.03): ndcg=0.011423
bias (0.01, 0.06): ndcg=0.015878
bias (0.02, 0.0): ndcg=0.007246
bias (0.02, 0.01): ndcg=0.009582
bias (0.02, 0.03): ndcg=0.013610
bias (0.02, 0.06): ndcg=0.016754
bias (0.04, 0.0): ndcg=0.011249
bias (0.04, 0.01): ndcg=0.013616
bias (0.04, 0.03): ndcg=0.015797
bias (0.04, 0.06): ndcg=0.016895
bias (0.07, 0.0): ndcg=0.015038
bias (0.07, 0.01): ndcg=0.015999
bias (0.07, 0.03): ndcg=0.016344
bias (0.07, 0.06): ndcg=0.015374
bias (0.1, 0.0): ndcg=0.014908
bias (0.1, 0.01): ndcg=0.015174
bias (0.1, 0.03): ndcg=0.014464
bias (0.1, 0.06): ndcg=0.014235
bias (0.15, 0.0): ndcg=0.013171
bias (0.15, 0.01): ndcg=0.013020
bias (0.15, 0.03): ndcg=0.012914
113

exec command

+25:30
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:23054,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 0.6 seconds
Output:

bias (0.15, 0.06): ndcg=0.012770
wrote /app/solution/validation_v2.parquet; coefficients=(0.04, 0.06); validation_ndcg=0.016895
114

exec command

+25:41
Thinking unavailable No text recorded

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Model text

The corrected model improves public-style NDCG to 0.016895, a 58% relative gain over popularity and 44% over the first neural run. The personalized latent score is now contributing meaningfully. I’m checking whether a few continuation epochs improve or overfit, while retaining validation_v2.parquet as the best valid ranking.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n     ap.add_argument(\"--cache\",action=\"store_true\",help=\"reuse preprocessed stage data\")\n+    ap.add_argument(\"--resume\",type=Path,help=\"checkpoint whose state_dict is continued\")\n@@\n     model=TwoTower(len(raw),args.dim,torch.from_numpy(feats))\n+    if args.resume:\n+        model.load_state_dict(torch.load(args.resume,weights_only=False)[\"state_dict\"])\n+        print(f\"resumed weights from {args.resume}\")\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
115

exec command

+25:51
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_v2.parquet\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation_v3.parquet --epochs 5 --dim 64 --tune --cache --resume /app/solution/model_validation.pt","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=48086
116

exec command

+25:59
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48086,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 55
Wall time 11.0 seconds
Output:
117

wait command

+26:14
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "55",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 55
Wall time 11.0 seconds
Output:
118

wait command

+26:28
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "55",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 1.6 seconds
Output:

{
  "model": "/app/solution/validation_v2.parquet",
  "scores": {
    "quality_ndcg@10": 0.016895,
    "quality_users": 4263
  }
}
/app/solution/train_ranker.py:397: 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.)
  opt.step(); losses.append(float(loss))
loaded 63587 examples from /app/solution/cache_validation.pt
resumed weights from /app/solution/model_validation.pt
epoch 1/5: loss=4.0056 scale=15.43 sec=12.7
119

exec command

+26:32
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48086,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 56
Wall time 11.0 seconds
Output:
120

wait command

+26:46
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "56",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 56
Wall time 11.0 seconds
Output:
121

wait command

+26:59
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "56",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.3 seconds
Output:

epoch 2/5: loss=3.8502 scale=16.59 sec=12.5
epoch 3/5: loss=3.7115 scale=17.59 sec=12.1
epoch 4/5: loss=3.5775 scale=18.78 sec=11.2
122

exec command

+27:05
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48086,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 57
Wall time 11.0 seconds
Output:
123

wait command

+27:19
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "57",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 57
Wall time 11.0 seconds
Output:
124

wait command

+27:33
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "57",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.7 seconds
Output:

epoch 5/5: loss=3.4537 scale=19.86 sec=11.6
bias (0.0, 0.0): ndcg=0.004542
bias (0.0, 0.01): ndcg=0.006073
bias (0.0, 0.03): ndcg=0.009917
bias (0.0, 0.06): ndcg=0.013442
bias (0.01, 0.0): ndcg=0.006166
bias (0.01, 0.01): ndcg=0.007927
bias (0.01, 0.03): ndcg=0.011645
bias (0.01, 0.06): ndcg=0.013743
bias (0.02, 0.0): ndcg=0.007880
bias (0.02, 0.01): ndcg=0.010147
bias (0.02, 0.03): ndcg=0.012709
bias (0.02, 0.06): ndcg=0.013925
bias (0.04, 0.0): ndcg=0.010892
bias (0.04, 0.01): ndcg=0.012560
bias (0.04, 0.03): ndcg=0.013488
bias (0.04, 0.06): ndcg=0.013309
bias (0.07, 0.0): ndcg=0.012164
bias (0.07, 0.01): ndcg=0.012335
bias (0.07, 0.03): ndcg=0.012639
125

Agent message

+27:56
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126

exec command

+28:11
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Model text

Continuation training overfit (the latent fit improved but validation NDCG fell), so eight epochs remains selected. I’m now testing an orthogonal, production-safe feature: recent catalog discovery momentum computed from event counts. It belongs entirely in the offline item tower and can improve temporal freshness without any request-time work or user–item matrix.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48086,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

bias (0.07, 0.06): ndcg=0.012804
bias (0.1, 0.0): ndcg=0.011666
bias (0.1, 0.01): ndcg=0.011992
bias (0.1, 0.03): ndcg=0.012538
bias (0.1, 0.06): ndcg=0.012092
bias (0.15, 0.0): ndcg=0.011728
bias (0.15, 0.01): ndcg=0.011775
bias (0.15, 0.03): ndcg=0.012099
bias (0.15, 0.06): ndcg=0.011700
wrote /app/solution/validation_v3.parquet; coefficients=(0.02, 0.06); validation_ndcg=0.013925
127

exec command

+28:18
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,subprocess\nD=Path('/task/data');O=Path('/app/solution'); end=24786800;day=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank'); raw=cat.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(raw)}\nsr=np.sort(raw); sti=np.array([mp[int(x)] for x in sr]); periods=[3,7,14,30,90]; counts={d:np.zeros(len(raw),np.float64) for d in periods}; allc={d:np.zeros(len(raw),np.float64) for d in periods}\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\n ts=b.column(0).to_numpy();x=b.column(1).to_numpy();org=b.column(2).to_numpy();ratio=b.column(3).to_numpy(); pos=np.searchsorted(sr,x);ok=pos<len(sr);ok[ok]&=sr[pos[ok]]==x[ok];idx=sti[pos[ok]];t=ts[ok];o=org[ok];q=ratio[ok]\n for d in periods:\n  m=(t>=end-d*day)&(q>=80);allc[d]+=np.bincount(idx[m],minlength=len(raw));m&=(o==1);counts[d]+=np.bincount(idx[m],minlength=len(raw))\n# likes recent\nlikes=pd.read_parquet(D/'train_likes.parquet');likes=likes[likes.item_id.isin(mp) & (likes.is_organic==1)]; like30=np.zeros(len(raw)); ii=likes.item_id.map(mp).to_numpy();m=likes.timestamp.to_numpy()>=end-30*day;like30+=np.bincount(ii[m],minlength=len(raw))\nfeatures={'org3':np.log1p(counts[3]),'org7':np.log1p(counts[7]),'org14':np.log1p(counts[14]),'org30':np.log1p(counts[30]),'all7':np.log1p(allc[7]),'like30':np.log1p(like30),'mix':np.log1p(counts[14])+0.5*np.log1p(like30)+0.2*np.log1p(allc[7])}\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'];items=z['item_ids'];packed=z['packed_eligible']\nassert np.array_equal(items,raw)\nfor name,s in features.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(uids):\n  mask=np.unpackbits(packed[r],count=len(raw),bitorder='little').astype(bool);chosen=order[mask[order]][:100];rows.extend((int(u),int(raw[k]),j+1) for j,k in enumerate(chosen))\n path=O/f'validation_{name}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n out=subprocess.check_output(['/opt/task-venv/bin/python','/task/evaluate_public.py',str(path)],text=True)\n print(name,out.replace('\\n',' '),flush=True)\nnp.savez_compressed(O/'recent_features_validation.npz',**features)\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response

Script running with cell ID 59
Wall time 11.0 seconds
Output:
128

wait command

+29:01
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "59",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 59
Wall time 11.0 seconds
Output:
129

wait command

+29:15
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "59",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.1 seconds
Output:

org3 {   "model": "/app/solution/validation_org3.parquet",   "scores": {     "quality_ndcg@10": 0.025721,     "quality_users": 4263   } } 
org7 {   "model": "/app/solution/validation_org7.parquet",   "scores": {     "quality_ndcg@10": 0.028131,     "quality_users": 4263   } } 
org14 {   "model": "/app/solution/validation_org14.parquet",   "scores": {     "quality_ndcg@10": 0.022878,     "quality_users": 4263   } } 
org30 {   "model": "/app/solution/validation_org30.parquet",   "scores": {     "quality_ndcg@10": 0.013314,     "quality_users": 4263   } } 
all7 {   "model": "/app/solution/validation_all7.parquet",   "scores": {     "quality_ndcg@10": 0.030107,     "quality_users": 4263   } } 
130

Agent message

+29:40
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131

exec command

+29:53
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Model text

Recent item momentum is much stronger than lifetime popularity: a seven-day strong-listen count alone reaches 0.030107 public NDCG, nearly 3× the original baseline. I’m folding cutoff-correct seven-day organic and all-surface momentum into the item tower, then training the personalized model around those offline features. This remains latency-neutral: the features are precomputed once per item, and request-time scoring is still one dot product.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-def catalog_info():\n+def recent_item_features(stage: str, raw_ids: np.ndarray, item_to_index: dict[int,int]):\n+    \"\"\"Cutoff-correct seven-day item momentum; computed entirely offline.\"\"\"\n+    end = TRAIN_END if stage == \"validation\" else VAL_END\n+    files = [DATA / \"train_listens.parquet\"] if stage == \"validation\" else [DATA / \"val_listens.parquet\"]\n+    all_count = np.zeros(len(raw_ids), np.float64)\n+    org_count = np.zeros(len(raw_ids), np.float64)\n+    sorted_raw = np.sort(raw_ids)\n+    sorted_to_index = np.array([item_to_index[int(x)] for x in sorted_raw], np.int32)\n+    for path in files:\n+        pf = pq.ParquetFile(path)\n+        for b in pf.iter_batches(batch_size=1_000_000,\n+                columns=[\"timestamp\",\"item_id\",\"is_organic\",\"played_ratio_pct\"]):\n+            ts=b.column(0).to_numpy(); raw=b.column(1).to_numpy()\n+            org=b.column(2).to_numpy(); ratio=b.column(3).to_numpy()\n+            idx,ok=map_catalog(raw,sorted_raw,sorted_to_index)\n+            m=ok & (ts >= end-7*DAY) & (ts < end) & (ratio >= 80)\n+            all_count += np.bincount(idx[m], minlength=len(raw_ids))\n+            mo=m & (org == 1)\n+            org_count += np.bincount(idx[mo], minlength=len(raw_ids))\n+    return np.log1p(all_count).astype(np.float32), np.log1p(org_count).astype(np.float32)\n+\n+\n+def catalog_info(stage: str):\n@@\n-    feats = np.stack([(pop - pop.mean()) / pop.std(),\n+    recent_all,recent_org=recent_item_features(stage,raw_ids,item_to_index)\n+    feats = np.stack([(recent_all-recent_all.mean())/recent_all.std(),\n+                      (recent_org-recent_org.mean())/recent_org.std(),\n+                      (pop - pop.mean()) / pop.std(),\n                       (org - org.mean()) / org.std(),\n                       (ratio - ratio.mean()) / ratio.std()], 1).astype(np.float32)\n@@\n-        bias = coeff[0] * feats[:, 0] + coeff[1] * feats[:, 1]\n+        bias = feats[:, :len(coeff)] @ np.asarray(coeff, np.float32)\n@@\n-        grid = [(a,b) for a in [0.0,0.01,0.02,0.04,0.07,0.10,0.15]\n-                       for b in [0.0,0.01,0.03,0.06]]\n+        # [recent all-surface strong, recent organic strong, lifetime positive]\n+        grid = [(a,b,c) for a in [0.02,0.04,0.06,0.10,0.15,0.25]\n+                          for b in [0.0,0.02,0.04,0.06]\n+                          for c in [0.0,0.03]]\n@@\n-    cat,raw,item_to_index,sorted_raw,sorted_to_index,feats=catalog_info()\n+    cat,raw,item_to_index,sorted_raw,sorted_to_index,feats=catalog_info(args.stage)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
132

exec command

+30:31
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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/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --output /app/solution/validation_v4.parquet --epochs 8 --dim 64 --tune --cache","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=53441
133

exec command

+30:38
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:53441,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 62
Wall time 11.0 seconds
Output:
134

wait command

+30:52
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "62",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 62
Wall time 11.0 seconds
Output:
135

wait command

+31:06
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "62",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.8 seconds
Output:

/app/solution/train_ranker.py:422: 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.)
  opt.step(); losses.append(float(loss))
loaded 63587 examples from /app/solution/cache_validation.pt
epoch 1/8: loss=5.5225 scale=11.25 sec=12.2
136

exec command

+31:11
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:53441,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 63
Wall time 11.0 seconds
Output:
137

wait command

+31:25
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "63",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 63
Wall time 11.0 seconds
Output:
138

wait command

+31:39
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "63",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.4 seconds
Output:

epoch 2/8: loss=5.2636 scale=10.76 sec=11.6
epoch 3/8: loss=5.0220 scale=10.71 sec=11.1
epoch 4/8: loss=4.8108 scale=11.14 sec=11.2
139

exec command

+31:44
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:53441,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 64
Wall time 11.0 seconds
Output:
140

wait command

+31:58
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": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 64
Wall time 11.0 seconds
Output:
141

wait command

+32:12
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "64",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

epoch 5/8: loss=4.6241 scale=11.88 sec=11.3
epoch 6/8: loss=4.4507 scale=12.74 sec=11.1
epoch 7/8: loss=4.2903 scale=13.77 sec=11.1
142

exec command

+32:17
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:53441,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 65
Wall time 11.0 seconds
Output:
143

wait command

+32:31
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": "65",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 65
Wall time 11.0 seconds
Output:
144

wait command

+32:45
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "65",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.9 seconds
Output:

epoch 8/8: loss=4.1477 scale=14.76 sec=11.0
bias (0.02, 0.0, 0.0): ndcg=0.012449
bias (0.02, 0.0, 0.03): ndcg=0.018579
bias (0.02, 0.02, 0.0): ndcg=0.019595
bias (0.02, 0.02, 0.03): ndcg=0.023754
bias (0.02, 0.04, 0.0): ndcg=0.025810
bias (0.02, 0.04, 0.03): ndcg=0.027449
bias (0.02, 0.06, 0.0): ndcg=0.028744
bias (0.02, 0.06, 0.03): ndcg=0.029683
bias (0.04, 0.0, 0.0): ndcg=0.019006
bias (0.04, 0.0, 0.03): ndcg=0.022419
bias (0.04, 0.02, 0.0): ndcg=0.025235
bias (0.04, 0.02, 0.03): ndcg=0.026779
bias (0.04, 0.04, 0.0): ndcg=0.028912
bias (0.04, 0.04, 0.03): ndcg=0.029780
bias (0.04, 0.06, 0.0): ndcg=0.030835
bias (0.04, 0.06, 0.03): ndcg=0.030733
bias (0.06, 0.0, 0.0): ndcg=0.024298
bias (0.06, 0.0, 0.03): ndcg=0.026187
bias (0.06, 0.02, 0.0): ndcg=0.028527
bias (0.06, 0.02, 0.03): ndcg=0.029188
145

exec command

+32:52
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:53441,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 66
Wall time 11.0 seconds
Output:
146

wait command

+33:06
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "66",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 66
Wall time 11.0 seconds
Output:
147

wait command

+33:19
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "66",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 2.9 seconds
Output:

bias (0.06, 0.04, 0.0): ndcg=0.031096
bias (0.06, 0.04, 0.03): ndcg=0.030841
bias (0.06, 0.06, 0.0): ndcg=0.032421
bias (0.06, 0.06, 0.03): ndcg=0.031884
bias (0.1, 0.0, 0.0): ndcg=0.030256
bias (0.1, 0.0, 0.03): ndcg=0.030034
bias (0.1, 0.02, 0.0): ndcg=0.032876
bias (0.1, 0.02, 0.03): ndcg=0.031677
bias (0.1, 0.04, 0.0): ndcg=0.033616
bias (0.1, 0.04, 0.03): ndcg=0.032646
bias (0.1, 0.06, 0.0): ndcg=0.034451
bias (0.1, 0.06, 0.03): ndcg=0.033447
bias (0.15, 0.0, 0.0): ndcg=0.034157
bias (0.15, 0.0, 0.03): ndcg=0.032445
bias (0.15, 0.02, 0.0): ndcg=0.034944
bias (0.15, 0.02, 0.03): ndcg=0.033349
bias (0.15, 0.04, 0.0): ndcg=0.034905
bias (0.15, 0.04, 0.03): ndcg=0.033986
bias (0.15, 0.06, 0.0): ndcg=0.035645
bias (0.15, 0.06, 0.03): ndcg=0.034372
bias (0.25, 0.0, 0.0): ndcg=0.036656
bias (0.25, 0.0, 0.03): ndcg=0.034691
bias (0.25, 0.02, 0.0): ndcg=0.036834
bias (0.25, 0.02, 0.03): ndcg=0.035042
bias (0.25, 0.04, 0.0): ndcg=0.036748
bias (0.25, 0.04, 0.03): ndcg=0.035130
148

exec command

+33:25
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:53441,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 2.0 seconds
Output:

bias (0.25, 0.06, 0.0): ndcg=0.036501
bias (0.25, 0.06, 0.03): ndcg=0.035404
wrote /app/solution/validation_v4.parquet; coefficients=(0.25, 0.02, 0.0); validation_ndcg=0.036834
149

exec command

+33:45
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Model text

The freshness-aware two-tower reaches 0.036834 on the supplied evaluator—over 3.4× lifetime popularity and 22% above the recent-momentum-only baseline. I’ve selected the eight-epoch model with retrieval coefficients (0.25, 0.02, 0.0). I’m writing a valid test-stage momentum fallback first, then refitting from scratch on train + validation and replacing it with the final personalized ranking.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_v4.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution')\nimport numpy as np,pandas as pd\nfrom train_ranker import catalog_info,DATA\ncat,raw,mp,sr,sti,feats=catalog_info('final')\norder=np.argsort(-feats[:,0],kind='stable')\nwith np.load(DATA/'test_eligible_candidates.npz') as z:\n uids=z['uids']; packed=z['packed_eligible']; items=z['item_ids']\nrows=[]\nfor r,u in enumerate(uids):\n m=np.unpackbits(packed[r],count=len(raw),bitorder='little').astype(bool)\n top=order[m[order]][:100]\n rows.extend((int(u),int(raw[k]),j+1) for j,k in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/submission.parquet',index=False)\nprint('fallback rows',len(rows))\nPY\n/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');f=pd.read_parquet('/app/solution/submission.parquet');t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));n=validate_ranking(f,t);c=CandidateSet(D/'test_eligible_candidates.npz');print('valid',len(n),all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None)))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":12000});
text(r.output);

Tool response

Script running with cell ID 68
Wall time 11.0 seconds
Output:
150

wait command

+34:17
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "68",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 0.0 seconds
Output:

{
  "model": "/app/solution/validation_v4.parquet",
  "scores": {
    "quality_ndcg@10": 0.036834,
    "quality_users": 4263
  }
}
fallback rows 845600
valid 845600 True
151

exec command

+34:20
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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 --stage final --output /app/solution/submission.parquet --epochs 8 --dim 64 --coefficients 0.25,0.02,0.0 --cache","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=73431
152

exec command

+34:27
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 70
Wall time 11.0 seconds
Output:
153

wait command

+34:41
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "70",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 70
Wall time 11.0 seconds
Output:
154

wait command

+34:55
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "70",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.0 seconds
Output:

  histories 1000, examples 6919
155

exec command

+35:00
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 71
Wall time 11.0 seconds
Output:
156

wait command

+35:14
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "71",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 71
Wall time 11.0 seconds
Output:
157

wait command

+35:28
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "71",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.0 seconds
Output:

  histories 2000, examples 14046
  histories 3000, examples 21022
158

exec command

+35:33
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 72
Wall time 11.0 seconds
Output:
159

wait command

+35:48
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "72",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 72
Wall time 11.0 seconds
Output:
160

wait command

+36:01
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "72",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.1 seconds
Output:

  histories 4000, examples 28244
  histories 5000, examples 35374
161

exec command

+36:06
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 73
Wall time 11.0 seconds
Output:
162

wait command

+36:20
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "73",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 73
Wall time 11.0 seconds
Output:
163

wait command

+36:34
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "73",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

  histories 6000, examples 42105
  histories 7000, examples 49115
164

exec command

+36:40
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 74
Wall time 11.0 seconds
Output:
165

wait command

+36:56
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "74",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 74
Wall time 11.0 seconds
Output:
166

wait command

+37:09
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "74",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 1.7 seconds
Output:

  histories 8000, examples 56243
  histories 9000, examples 63348
167

exec command

+37:13
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 75
Wall time 11.0 seconds
Output:
168

wait command

+37:27
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "75",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 75
Wall time 11.0 seconds
Output:
169

wait command

+37:41
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "75",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.0 seconds
Output:

/app/solution/train_ranker.py:422: 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.)
  opt.step(); losses.append(float(loss))
built 64912 rolling examples and 8456 inference contexts
epoch 1/8: loss=5.5153 scale=11.21 sec=11.2
epoch 2/8: loss=5.2576 scale=10.66 sec=11.3
170

exec command

+37:46
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Model text

Final refit now includes the entire observed validation window as training history/outcomes and has produced 64,912 rolling examples. Optimization is proceeding normally; the valid momentum fallback remains untouched until the personalized file is fully written and verified.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 76
Wall time 11.0 seconds
Output:
171

wait command

+38:02
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "76",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script running with cell ID 76
Wall time 11.0 seconds
Output:
172

wait command

+38:15
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "76",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

epoch 3/8: loss=5.0133 scale=10.64 sec=11.1
epoch 4/8: loss=4.8138 scale=11.11 sec=11.2
epoch 5/8: loss=4.6385 scale=11.71 sec=11.2
173

exec command

+38:21
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73431,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script running with cell ID 77
Wall time 11.0 seconds
Output:
174

wait command

+38:36
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "77",
  "yield_time_ms": 10000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 8.1 seconds
Output:

epoch 6/8: loss=4.4644 scale=12.60 sec=11.1
epoch 7/8: loss=4.3126 scale=13.67 sec=11.3
epoch 8/8: loss=4.1699 scale=14.73 sec=11.1
wrote /app/solution/submission.parquet; coefficients=(0.25, 0.02, 0.0); validation_ndcg=0.000000
175

Agent message

+39:05
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176

exec command

+39:06
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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+`submission.parquet` is the final test ranking (845,600 rows: 100 eligible,\n+distinct items for each of 8,456 users). The selected public validation file is\n+`validation.parquet` and scores **0.036834 graded NDCG@10** on 4,263 scored\n+users with the supplied evaluator.\n+\n+## Model\n+\n+The model in `train_ranker.py` is an exact two-tower retrieval model. The user\n+tower uses no user-ID embedding. It pools four interaction-history channels\n+(recent positive listens, long-term positive taste, active likes, and active\n+dislikes) through a trainable history-item embedding table, then applies a\n+LayerNorm/GELU MLP to produce a normalized 64-dimensional vector. The item\n+tower applies a separate trainable catalog embedding and LayerNorm/GELU MLP to\n+the item ID and cutoff-correct offline features. Three additional item-only\n+bias coordinates encode seven-day all-surface momentum, seven-day organic\n+momentum, and lifetime popularity; the matching user coordinates are constants.\n+Thus both tower outputs have dimension 67 and the complete score—including the\n+selected freshness adjustment—is exactly one inner product. Catalog vectors\n+can be computed offline and placed directly in a MIPS index.\n+\n+Training streams the Parquet log by user and never constructs or decomposes a\n+global user-item matrix. It forms 18 rolling temporal next-discovery tasks,\n+where targets are previously unseen organic 80%-complete listens or organic\n+likes in the following seven days. The loss is in-batch sampled softmax with\n+duplicate positives masked, optimized by AdamW for eight CPU epochs. Validation\n+uses train history only. The final model is refit on train plus validation, and\n+uses the supplied cutoff-specific preference state and eligibility mask.\n+\n+## Reproduce the final submission\n+\n+From `/app`:\n+\n+```sh\n+/opt/task-venv/bin/python solution/train_ranker.py \\\n+  --stage final \\\n+  --output solution/submission.parquet \\\n+  --epochs 8 --dim 64 \\\n+  --coefficients 0.25,0.02,0.0 \\\n+  --cache\n+```\n+\n+Public validation check:\n+\n+```sh\n+/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\n+```\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
177

exec command

+39:34
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Model text

The final personalized submission has been written. I’m doing the last independent contract check now—exact user set, 100 ranks per user, no duplicates, test eligibility for all 845,600 rows—then I’ll align the canonical validation file with the selected 0.036834 run and report the artifact paths.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"cp /app/solution/validation_v4.parquet /app/solution/validation.parquet\n/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,json\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data');S=Path('/app/solution')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=pd.read_parquet(S/'submission.parquet'); n=validate_ranking(f,targets)\nc=CandidateSet(D/'test_eligible_candidates.npz')\nelig=all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None))\nck=torch.load(S/'model_final.pt',weights_only=False)\nprint(json.dumps({'rows':len(n),'users':n.uid.nunique(),'min_rank':int(n['rank'].min()),'max_rank':int(n['rank'].max()),'duplicate_pairs':int(n.duplicated(['uid','item_id']).sum()),'all_test_eligible':elig,'model_stage':ck['stage'],'coefficients':ck['coefficients'],'checkpoint_bytes':(S/'model_final.pt').stat().st_size,'submission_bytes':(S/'submission.parquet').stat().st_size},indent=2))\nPY\nls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/model_final.pt /app/solution/train_ranker.py /app/solution/README.md /app/solution/metrics_final.json\ncat /app/solution/metrics_final.json","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 8.7 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.036834,
    "quality_users": 4263
  }
}
{
  "rows": 845600,
  "users": 8456,
  "min_rank": 1,
  "max_rank": 100,
  "duplicate_pairs": 0,
  "all_test_eligible": true,
  "model_stage": "final",
  "coefficients": [
    0.25,
    0.02,
    0.0
  ],
  "checkpoint_bytes": 16207052,
  "submission_bytes": 1394896
}
-rw-r--r-- 1 agent agent 2.1K Aug 31 19:05 /app/solution/README.md
-rw-r--r-- 1 agent agent  131 Aug 31 19:04 /app/solution/metrics_final.json
-rw-r--r-- 1 agent agent  16M Aug 31 19:04 /app/solution/model_final.pt
-rw-r--r-- 1 agent agent 1.4M Aug 31 19:04 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  26K Aug 31 18:56 /app/solution/train_ranker.py
-rw-r--r-- 1 agent agent 1.3M Aug 31 19:06 /app/solution/validation.parquet
{
  "coefficients": [
    0.25,
    0.02,
    0.0
  ],
  "validation_ndcg": 0.0,
  "examples": 64912,
  "epochs": 8,
  "dim": 64
}
178

exec command

+40:11
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Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"Implemented, tuned, refit, and independently validated all deliverables.",plan:[
  {step:"Inspect data, schemas, environment, and public evaluator",status:"completed"},
  {step:"Implement scalable history-feature two-tower training and ranking pipeline",status:"completed"},
  {step:"Train validation models, evaluate, and tune",status:"completed"},
  {step:"Refit on train+validation and write a valid test submission",status:"completed"},
  {step:"Verify artifacts and document the solution",status:"completed"}
]});
text(p);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
179

Final response

+40:18
Thinking unavailable No text recorded

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

Model text

Implemented and verified the complete CPU PyTorch two-tower ranker.

Results:

  • Public NDCG@10: 0.036834
  • 845,600 submission rows
  • Exactly 100 distinct items for all 8,456 users
  • Ranks 1–100 with no ties
  • All submitted items test-eligible
  • Final model refit on train + validation
  • Exact 67-dimensional user/item vectors with dot-product scoring and offline-precomputable item embeddings
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-009-gpt-5-6-sol-high-2026-08-31T11-04-49
Recorded model
gpt-5.6-sol
Harness version
0.147.0
Session
01a05912-4f4c-7c83-8d0f-badb40e6a17c
Source SHA-256
83b75e0e8dc57c9c7e0f19bd91d127035d06f14390838a1ce994ecf92afc4ca9

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