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Agent trace

Two-tower retriever sol-high-04

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 110 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · high effort
Recorded conversation
35:19 · 116 steps
Private reward
0.016657 · Pass
Attempt cost
$2.98
  • 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": "01a05911-4701-7dc1-9630-ab3e64138f49",
  "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

+00:05
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 this end to end: first audit the data and evaluator, then establish a valid baseline, train/tune the CPU two-tower model on the public split, and finally refit on train+validation to write the test submission. I’ll keep every artifact under /app/solution/ and preserve the best valid ranking as I iterate.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({plan:[
  {step:"Inspect schemas, data scale, eligibility, and public evaluator",status:"in_progress"},
  {step:"Implement scalable history features and PyTorch two-tower training",status:"pending"},
  {step:"Tune on validation and save the best valid ranking",status:"pending"},
  {step:"Refit with validation history and generate final test submission",status:"pending"},
  {step:"Verify artifacts and document architecture, score, and command",status:"pending"}
]}); text(p);
const r = await tools.exec_command({cmd:"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,180p' /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
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

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

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

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

Usage::

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

import json
import sys
from pathlib import Path

import pandas as pd

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


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


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

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

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

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

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


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

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

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

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

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


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

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

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


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

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


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

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
08

exec command

+00:18
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"du -h /task/data/* | sort -h\npython - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n    df=pd.read_parquet(p)\n    print(p.name, df.shape, dict(df.dtypes), 'uids', df.uid.nunique() if 'uid' in df else '-', 'items',df.item_id.nunique() if 'item_id' in df else '-')\n    if 'timestamp' in df: print('  ts',df.timestamp.min(),df.timestamp.max())\nfor p in sorted(D.glob('*.npz')):\n  with np.load(p) as z:\n    print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n    bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n    n=bits.sum(1)\n    print(' eligible min/median/mean/max',n.min(),np.median(n),n.mean(),n.max())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

8.0K	/task/data/val_undislikes.parquet
36K	/task/data/val_dislikes.parquet
72K	/task/data/val_unlikes.parquet
84K	/task/data/target_users.parquet
156K	/task/data/train_undislikes.parquet
292K	/task/data/val_likes.parquet
344K	/task/data/candidate_catalog.parquet
840K	/task/data/train_dislikes.parquet
1.1M	/task/data/test_preference_state.parquet
1.1M	/task/data/validation_preference_state.parquet
1.8M	/task/data/train_unlikes.parquet
5.2M	/task/data/train_likes.parquet
7.3M	/task/data/validation_eligible_candidates.npz
7.4M	/task/data/test_eligible_candidates.npz
9.5M	/task/data/val_listens.parquet
305M	/task/data/train_listens.parquet
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
ModuleNotFoundError: No module named 'pandas'
09

exec command

+00:32
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Model text

The data is compact enough to iterate locally, but the default shell’s python is not the task virtual environment. I’m locating the pinned PyTorch/Pandas interpreter now, then I’ll quantify event and candidate distributions before choosing history sampling and negative mining settings.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"which -a python python3 || true\nfind /opt /usr/local /app -maxdepth 4 -type f -name python 2>/dev/null | sed -n '1,80p'\nls -la /task /app","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000}); text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
/app:
total 12
drwxr-xr-x 3 agent agent 4096 Aug 31 18:22 .
drwxr-xr-x 1 root  root  4096 Aug 31 18:24 ..
drwxr-xr-x 2 agent agent 4096 Aug 31 18:22 solution

/task:
total 36
dr-xr-xr-x 1 root root 4096 Aug 31 18:22 .
drwxr-xr-x 1 root root 4096 Aug 31 18:24 ..
-r--r--r-- 1 root root    0 Aug 31 18:08 .keep
dr-xr-xr-x 1 root root 4096 Aug 31 18:08 data
-r--r--r-- 1 root root 5774 Aug 31 18:08 evaluate_public.py
-r--r--r-- 1 root root 8265 Aug 31 18:08 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Aug 31 18:08 requirements.txt
10

exec command

+00:42
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"python -m pip list --format=columns | sed -n '1,100p'\nenv | rg '^(PATH|VIRTUAL_ENV|PYTHONPATH|UV_)='","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000}); text(r.output);

Tool response

Script completed
Wall time 0.7 seconds
Output:

Package Version
------- -------
pip     25.0.1
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
11

exec command

+00:48
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"command -v uv || true\ncommand -v python3.12 || true\ncommand -v python3.13 || true\nls -la /usr/local/bin | sed -n '1,160p'\nfind /app -maxdepth 3 -type f -o -type l | sort","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000}); text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/usr/local/bin/python3.12
total 48
drwxr-xr-x 1 root root  4096 Aug 31 18:21 .
drwxr-xr-x 1 root root  4096 Aug  3 00:00 ..
lrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12
-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12
lrwxrwxrwx 1 root root    53 Aug 31 18:21 codex -> /opt/codex-home/packages/standalone/current/bin/codex
lrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3
lrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12
-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12
lrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3
-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3
-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12
lrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3
lrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12
-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12
lrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3
lrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config
lrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12
lrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config
-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12
-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config
12

exec command

+00:59
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -la /opt\nls -la /opt/venv /opt/python 2>/dev/null || true\npython - <<'PY'\nimport sys, site\nprint(sys.executable)\nprint('\\n'.join(sys.path))\nprint(site.getsitepackages())\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000}); text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

total 16
drwxr-xr-x 1 root root 4096 Aug 31 18:21 .
drwxr-xr-x 1 root root 4096 Aug 31 18:24 ..
drwxr-xr-x 4 root root 4096 Aug 31 18:21 codex-home
dr-xr-xr-x 1 root root 4096 Aug 31 18:21 task-venv
/usr/local/bin/python

/usr/local/lib/python312.zip
/usr/local/lib/python3.12
/usr/local/lib/python3.12/lib-dynload
/usr/local/lib/python3.12/site-packages
['/usr/local/lib/python3.12/site-packages']
13

exec command

+01:05
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -la /opt/task-venv/bin | sed -n '1,80p'\n/opt/task-venv/bin/python - <<'PY'\nimport torch,pandas,pyarrow,numpy\nprint('ok',torch.__version__,pandas.__version__,pyarrow.__version__,numpy.__version__)\nPY\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n    df=pd.read_parquet(p)\n    s=f'{p.name:38s} {str(df.shape):18s}'\n    if 'uid' in df: s+=f' uids={df.uid.nunique():7d}'\n    if 'item_id' in df: s+=f' items={df.item_id.nunique():7d}'\n    if 'timestamp' in df: s+=f' ts=[{df.timestamp.min()},{df.timestamp.max()}]'\n    print(s)\n    print(' ',dict(df.dtypes))\nfor p in sorted(D.glob('*.npz')):\n  with np.load(p) as z:\n    print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n    bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n    n=bits.sum(1)\n    print(' eligible min/median/mean/max',n.min(),np.median(n),round(n.mean(),1),n.max())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":30000}); text(r.output);

Tool response

Script completed
Wall time 7.5 seconds
Output:

total 124
dr-xr-xr-x 1 root root 4096 Aug 31 18:21 .
dr-xr-xr-x 1 root root 4096 Aug 31 18:21 ..
-r--r--r-- 1 root root 9033 Aug 31 18:21 Activate.ps1
-r--r--r-- 1 root root 2158 Aug 31 18:21 activate
-r--r--r-- 1 root root  920 Aug 31 18:21 activate.csh
-r--r--r-- 1 root root 2195 Aug 31 18:21 activate.fish
-r-xr-xr-x 1 root root  302 Aug 31 18:21 chardetect
-r-xr-xr-x 1 root root  304 Aug 31 18:21 dotenv
-r-xr-xr-x 1 root root  308 Aug 31 18:21 f2py
-r-xr-xr-x 1 root root  299 Aug 31 18:21 idna
-r-xr-xr-x 1 root root  297 Aug 31 18:21 isympy
-r-xr-xr-x 1 root root  305 Aug 31 18:21 jsonschema
-r-xr-xr-x 1 root root  311 Aug 31 18:21 jupyter
-r-xr-xr-x 1 root root  311 Aug 31 18:21 jupyter-migrate
-r-xr-xr-x 1 root root  316 Aug 31 18:21 jupyter-troubleshoot
-r-xr-xr-x 1 root root  344 Aug 31 18:21 jupyter-trust
-r-xr-xr-x 1 root root  311 Aug 31 18:21 jupytext
-r-xr-xr-x 1 root root  315 Aug 31 18:21 jupytext-config
-r-xr-xr-x 1 root root  301 Aug 31 18:21 kaggle
-r-xr-xr-x 1 root root  312 Aug 31 18:21 markdown-it
-r-xr-xr-x 1 root root  325 Aug 31 18:21 normalizer
-r-xr-xr-x 1 root root  308 Aug 31 18:21 numpy-config
-r-xr-xr-x 1 root root  313 Aug 31 18:21 pip
-r-xr-xr-x 1 root root  313 Aug 31 18:21 pip3
-r-xr-xr-x 1 root root  313 Aug 31 18:21 pip3.12
lrwxrwxrwx 1 root root   21 Aug 31 18:21 python -> /usr/local/bin/python
lrwxrwxrwx 1 root root    6 Aug 31 18:21 python3 -> python
lrwxrwxrwx 1 root root    6 Aug 31 18:21 python3.12 -> python
-r-xr-xr-x 1 root root  307 Aug 31 18:21 slugify
-r-xr-xr-x 1 root root  321 Aug 31 18:21 torchfrtrace
-r-xr-xr-x 1 root root  312 Aug 31 18:21 torchrun
-r-xr-xr-x 1 root root  299 Aug 31 18:21 tqdm
ok 2.8.0+cpu 2.3.3 18.1.0 2.2.6
candidate_catalog.parquet              (30000, 4)         items=  30000
  {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}
target_users.parquet                   (8456, 3)          uids=   8456
  {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}
test_preference_state.parquet          (484776, 3)        uids=   7092 items=  28863
  {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
train_dislikes.parquet                 (101075, 4)        uids=   5732 items=  50876 ts=[75,24786580]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
train_likes.parquet                    (814831, 4)        uids=   8144 items= 172303 ts=[80,24786790]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
train_listens.parquet                  (43484159, 6)      uids=   9238 items= 845395 ts=[0,24786795]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
train_undislikes.parquet               (19960, 4)         uids=   2769 items=  14725 ts=[15950,24785395]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
train_unlikes.parquet                  (292665, 4)        uids=   6197 items= 113072 ts=[250,24786570]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
val_dislikes.parquet                   (3563, 4)          uids=    955 items=   3135 ts=[24789920,25393280]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
val_likes.parquet                      (37795, 4)         uids=   4114 items=  22902 ts=[24788815,25393390]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
val_listens.parquet                    (1501697, 6)       uids=   8196 items= 191747 ts=[24788600,25393395]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
val_undislikes.parquet                 (606, 4)           uids=    205 items=    579 ts=[24810715,25393035]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
val_unlikes.parquet                    (9346, 4)          uids=   1313 items=   8080 ts=[24789005,25393385]
  {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
validation_preference_state.parquet    (469124, 3)        uids=   7038 items=  28814
  {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
 eligible min/median/mean/max 24552 29220.0 29041.3 30000
validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
 eligible min/median/mean/max 24657 29242.0 29067.3 30000
14

Agent message

+01:46
Thinking unavailable No text recorded

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15

Agent message

+02:05
Thinking unavailable No text recorded

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

16

exec command

+02:13
Thinking unavailable No text recorded

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

Model text

The retrieval boundary is broad (median ≈29.2k of 30k tracks per user), so the model—not eligibility—must do nearly all of the top-10 work. I’m measuring the public label density and a popularity-only floor next; this also validates the submission writer before neural training. The neural design will use rolling temporal snapshots, history pooling in the user tower, and sampled/in-batch negatives—no global interaction matrix.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/baseline.py\n+#!/usr/bin/env python3\n+\"\"\"Write an eligibility-correct popularity baseline for either cutoff.\"\"\"\n+from pathlib import Path\n+import argparse\n+import numpy as np\n+import pandas as pd\n+\n+DATA = Path(\"/task/data\")\n+\n+\n+def write(stage: str, output: Path) -> None:\n+    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+        uids = z[\"uids\"].astype(np.int64)\n+        items = z[\"item_ids\"].astype(np.int64)\n+        packed = z[\"packed_eligible\"]\n+        chunks = []\n+        for row, uid in enumerate(uids):\n+            ok = np.unpackbits(packed[row], count=len(items), bitorder=\"little\").astype(bool)\n+            # Archive/catalog columns are already ordered by training popularity.\n+            top = items[ok][:100]\n+            chunks.append(pd.DataFrame({\"uid\": uid, \"item_id\": top,\n+                                        \"rank\": np.arange(1, 101, dtype=np.int64)}))\n+    output.parent.mkdir(parents=True, exist_ok=True)\n+    pd.concat(chunks, ignore_index=True).to_parquet(output, index=False)\n+\n+\n+if __name__ == \"__main__\":\n+    ap = argparse.ArgumentParser()\n+    ap.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n+    ap.add_argument(\"--output\", type=Path, required=True)\n+    args = ap.parse_args()\n+    write(args.stage, args.output)\n*** End Patch";
const a = await tools.apply_patch(patch); text(a);
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/baseline.py --stage validation --output /app/solution/validation_popularity.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data/'\nt=pd.read_parquet(D+'target_users.parquet').uid\nwith np.load(D+'validation_eligible_candidates.npz') as z:\n  urow={int(u):i for i,u in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}; p=z['packed_eligible']\n  rel={}\n  L=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n  L=L[L.uid.isin(t)&(L.is_organic==1)&(L.played_ratio_pct>=80)].drop_duplicates(['uid','item_id'])\n  K=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\n  K=K[K.uid.isin(t)&(K.is_organic==1)].drop_duplicates(['uid','item_id'])\n  for grade,df in [(1,L),(3,K)]:\n    for u,x in df[['uid','item_id']].itertuples(index=False,name=None):\n      r=urow.get(int(u)); c=icol.get(int(x))\n      if r is not None and c is not None and ((int(p[r,c//8])>>(c%8))&1): rel.setdefault(int(u),{})[int(x)]=grade\nprint('scored users',len(rel),'relevant pairs',sum(map(len,rel.values())),'likes',sum(sum(g==3 for g in d.values()) for d in rel.values()),'median rel',np.median([len(x) for x in rel.values()]))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output);

Tool response

Script completed
Wall time 8.3 seconds
Output:

{}
{
  "model": "/app/solution/validation_popularity.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
scored users 4263 relevant pairs 37769 likes 4417 median rel 4.0
17

exec command

+03:09
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd\np='/task/data/train_listens.parquet'; f=pq.ParquetFile(p)\nprint('rowgroups',f.num_row_groups)\nfor i in [0,1,f.num_row_groups-2,f.num_row_groups-1]:\n rg=f.metadata.row_group(i)\n print(i,rg.num_rows,[(rg.column(j).path_in_schema,rg.column(j).statistics.min,rg.column(j).statistics.max) for j in [0,1,2,3,4]])\nfor n in ['train_listens','train_likes','train_dislikes']:\n d=pd.read_parquet('/task/data/'+n+'.parquet').head(10); print(n); print(d.to_string(index=False))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000}); text(r.output);

Tool response

Script completed
Wall time 2.3 seconds
Output:

rowgroups 42
0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)]
1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)]
40 1048576 [('uid', 965500, 989500), ('timestamp', 230, 24786710), ('item_id', 50, 9390541), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)]
41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508), ('is_organic', 0, 1), ('played_ratio_pct', 0, 159)]
train_listens
 uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
 100      39420  8326270           0               100                   170
 100      39420  1441281           0               100                   105
 100      39625   286361           0               100                   185
 100      40110   732449           0               100                   240
 100      40360  3397170           0                46                   130
 100      40380  7849270           0               100                   205
 100      40640  1449307           0               100                   205
 100      40785  6887232           0               100                   145
 100      40885  2996197           0               100                    95
 100      41130  6474571           0               100                   245
train_likes
 uid  timestamp  item_id  is_organic
 100      44755   732449           1
 100    1155860  6568592           0
 100    1259125  5411243           1
 100    1260005  7371186           0
 100    1263935  4943655           0
 100    2055015   410868           1
 100    2494620  1730273           1
 100    3939350  2621343           0
 100    3940755  6399268           0
 100    6902675  3982388           0
train_dislikes
 uid  timestamp  item_id  is_organic
 100    3087560  9170134           1
 100    3936560  8661238           1
 100   20432825  4927727           1
 400    2980800   399811           1
 400    3157520  1575108           1
 400    3417600  9229946           1
 400    4968885  6593949           1
 400    4968905  9198404           1
 400    5519800   999732           1
 400    5754475  1254798           1
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+04:17
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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-based PyTorch two-tower ranker.\n+\n+The trainer uses rolling, cutoff-correct user snapshots and sampled pairwise\n+negatives.  It never builds a user-item matrix.  At serving time the item\n+tower is evaluated once for the catalog and each request evaluates one user\n+tower vector followed by inner-product retrieval.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import math\n+import random\n+from dataclasses import dataclass\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+from torch import nn\n+from torch.nn import functional as F\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_END = 24_786_800\n+WEEK = 7 * 24 * 3600\n+GAP = 1800\n+HASH_SIZE = 131_071\n+HIST_N = 128\n+PREF_N = 64\n+\n+\n+def seed_all(seed: int) -> None:\n+    random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\n+\n+\n+def hash_items(values: np.ndarray) -> np.ndarray:\n+    \"\"\"Stable multiplicative hash; zero is reserved for padding.\"\"\"\n+    x = values.astype(np.uint64, copy=False)\n+    return ((x * np.uint64(11400714819323198485) % np.uint64(HASH_SIZE)) + 1).astype(np.int32)\n+\n+\n+def read_listens(include_val: bool) -> pd.DataFrame:\n+    cols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+    frames = [pd.read_parquet(DATA / \"train_listens.parquet\", columns=cols)]\n+    if include_val:\n+        frames.append(pd.read_parquet(DATA / \"val_listens.parquet\", columns=cols))\n+    x = pd.concat(frames, ignore_index=True)\n+    # Conventional positive history. Very short/skipped plays are deliberately omitted.\n+    x = x[x.played_ratio_pct >= 50].copy()\n+    x.sort_values([\"uid\", \"timestamp\"], inplace=True, kind=\"stable\")\n+    return x\n+\n+\n+def read_actions(family: str, include_val: bool) -> pd.DataFrame:\n+    opposite = \"un\" + family\n+    cols = [\"uid\", \"timestamp\", \"item_id\"]\n+    frames = []\n+    for prefix in [\"train\"] + ([\"val\"] if include_val else []):\n+        a = pd.read_parquet(DATA / f\"{prefix}_{family}.parquet\", columns=cols)\n+        a[\"active\"] = np.uint8(1); frames.append(a)\n+        a = pd.read_parquet(DATA / f\"{prefix}_{opposite}.parquet\", columns=cols)\n+        a[\"active\"] = np.uint8(0); frames.append(a)\n+    x = pd.concat(frames, ignore_index=True)\n+    x.sort_values([\"uid\", \"timestamp\"], inplace=True, kind=\"stable\")\n+    return x\n+\n+\n+def grouped_arrays(df: pd.DataFrame, columns: list[str], targets: set[int]) -> dict[int, tuple[np.ndarray, ...]]:\n+    df = df[df.uid.isin(targets)]\n+    ans = {}\n+    for uid, g in df.groupby(\"uid\", sort=False):\n+        ans[int(uid)] = tuple(g[c].to_numpy(copy=True) for c in columns)\n+    return ans\n+\n+\n+def latest_distinct(ids: np.ndarray, ts: np.ndarray, end: int, n: int,\n+                    extra_weight: np.ndarray | None = None) -> tuple[np.ndarray, np.ndarray]:\n+    k = int(np.searchsorted(ts, end, side=\"right\"))\n+    # Inspect a generous recent suffix; repeated tracks are deduplicated.\n+    lo = max(0, k - n * 8)\n+    seen: set[int] = set(); chosen: list[int] = []; weights: list[float] = []\n+    for j in range(k - 1, lo - 1, -1):\n+        item = int(ids[j])\n+        if item in seen: continue\n+        seen.add(item); chosen.append(item)\n+        age = max(0, end - int(ts[j]))\n+        recency = math.exp(-age / (45.0 * 86400.0))\n+        weights.append(recency * (float(extra_weight[j]) if extra_weight is not None else 1.0))\n+        if len(chosen) == n: break\n+    if not chosen:\n+        return np.zeros(n, np.int32), np.zeros(n, np.float32)\n+    h = hash_items(np.asarray(chosen, dtype=np.uint64))\n+    out = np.zeros(n, np.int32); w = np.zeros(n, np.float32)\n+    out[:len(h)] = h; w[:len(h)] = np.asarray(weights, np.float32)\n+    return out, w\n+\n+\n+def active_distinct(events: tuple[np.ndarray, ...] | None, end: int, n: int) -> tuple[np.ndarray, np.ndarray]:\n+    if events is None:\n+        return np.zeros(n, np.int32), np.zeros(n, np.float32)\n+    ts, ids, active = events\n+    k = int(np.searchsorted(ts, end, side=\"right\"))\n+    seen: set[int] = set(); chosen: list[int] = []; weights: list[float] = []\n+    for j in range(k - 1, -1, -1):\n+        item = int(ids[j])\n+        if item in seen: continue\n+        seen.add(item)\n+        if int(active[j]):\n+            chosen.append(item)\n+            weights.append(math.exp(-max(0, end-int(ts[j])) / (180.0*86400.0)))\n+            if len(chosen) == n: break\n+    h = hash_items(np.asarray(chosen, dtype=np.uint64)) if chosen else np.empty(0, np.int32)\n+    out = np.zeros(n, np.int32); w = np.zeros(n, np.float32)\n+    out[:len(h)] = h; w[:len(h)] = np.asarray(weights, np.float32)\n+    return out, w\n+\n+\n+@dataclass\n+class Prepared:\n+    uids: np.ndarray\n+    hist: np.ndarray\n+    hist_w: np.ndarray\n+    likes: np.ndarray\n+    likes_w: np.ndarray\n+    dislikes: np.ndarray\n+    dislikes_w: np.ndarray\n+    numeric: np.ndarray\n+    sample_snapshot: np.ndarray\n+    sample_item: np.ndarray\n+    sample_weight: np.ndarray\n+    inference_rows: np.ndarray\n+\n+\n+def prepare(refit: bool, snapshots: int, cache: Path) -> Prepared:\n+    if cache.exists():\n+        z = np.load(cache)\n+        return Prepared(**{k: z[k] for k in Prepared.__annotations__})\n+    targets_df = pd.read_parquet(DATA / \"target_users.parquet\")\n+    uids = targets_df.uid.to_numpy(np.int64); targets = set(map(int, uids))\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    item_to_idx = {int(x): i for i, x in enumerate(catalog.item_id)}\n+    listens = read_listens(include_val=refit)\n+    # Per-event history strength preserves organic intent and completion.\n+    listens[\"strength\"] = (0.55 + 0.45*np.minimum(listens.played_ratio_pct,100)/100.0) * (1.0 + 0.35*listens.is_organic)\n+    lg = grouped_arrays(listens, [\"timestamp\", \"item_id\", \"strength\", \"is_organic\"], targets)\n+    del listens\n+    likes = grouped_arrays(read_actions(\"likes\", refit), [\"timestamp\", \"item_id\", \"active\"], targets)\n+    dislikes = grouped_arrays(read_actions(\"dislikes\", refit), [\"timestamp\", \"item_id\", \"active\"], targets)\n+\n+    # Rolling train-only windows, plus validation as a clean label window when refitting.\n+    cutoffs = [TRAIN_END - (snapshots-i)*(WEEK+GAP) for i in range(snapshots)]\n+    if refit:\n+        cutoffs.append(TRAIN_END)\n+    infer_end = TRAIN_END if not refit else int(pd.read_parquet(DATA / \"val_listens.parquet\", columns=[\"timestamp\"]).timestamp.max())\n+    all_cutoffs = cutoffs + [infer_end]\n+    nrows = len(all_cutoffs)*len(uids)\n+    hist=np.zeros((nrows,HIST_N),np.int32); hist_w=np.zeros((nrows,HIST_N),np.float32)\n+    li=np.zeros((nrows,PREF_N),np.int32); li_w=np.zeros((nrows,PREF_N),np.float32)\n+    di=np.zeros((nrows,PREF_N),np.int32); di_w=np.zeros((nrows,PREF_N),np.float32)\n+    numeric=np.zeros((nrows,6),np.float32)\n+    for ci, cutoff in enumerate(all_cutoffs):\n+        for ui, uid in enumerate(uids):\n+            r=ci*len(uids)+ui; ev=lg.get(int(uid))\n+            if ev is not None:\n+                ts, ids, strength, organic = ev\n+                hist[r],hist_w[r]=latest_distinct(ids,ts,cutoff,HIST_N,strength)\n+                k=int(np.searchsorted(ts,cutoff,\"right\")); lo=max(0,k-512)\n+                numeric[r,0]=math.log1p(k)\n+                numeric[r,1]=math.log1p(max(0,k-lo))\n+                numeric[r,2]=float(np.mean(organic[lo:k])) if k>lo else 0\n+                numeric[r,3]=float(np.mean(strength[lo:k])) if k>lo else 0\n+            li[r],li_w[r]=active_distinct(likes.get(int(uid)),cutoff,PREF_N)\n+            di[r],di_w[r]=active_distinct(dislikes.get(int(uid)),cutoff,PREF_N)\n+            numeric[r,4]=math.log1p(float((li_w[r]>0).sum()))\n+            numeric[r,5]=math.log1p(float((di_w[r]>0).sum()))\n+\n+    # Labels are organic >=80% listens and organic likes in the post-gap week.\n+    train_l = pd.read_parquet(DATA/\"train_listens.parquet\",columns=[\"uid\",\"timestamp\",\"item_id\",\"is_organic\",\"played_ratio_pct\"])\n+    train_k = pd.read_parquet(DATA/\"train_likes.parquet\",columns=[\"uid\",\"timestamp\",\"item_id\",\"is_organic\"])\n+    if refit:\n+        train_l=pd.concat([train_l,pd.read_parquet(DATA/\"val_listens.parquet\",columns=train_l.columns)],ignore_index=True)\n+        train_k=pd.concat([train_k,pd.read_parquet(DATA/\"val_likes.parquet\",columns=train_k.columns)],ignore_index=True)\n+    train_l=train_l[(train_l.uid.isin(targets))&(train_l.is_organic==1)&(train_l.played_ratio_pct>=80)]\n+    train_k=train_k[(train_k.uid.isin(targets))&(train_k.is_organic==1)]\n+    uid_to_pos={int(u):i for i,u in enumerate(uids)}\n+    ss=[]; si=[]; sw=[]\n+    for ci,cutoff in enumerate(cutoffs):\n+        hi=cutoff+GAP+WEEK\n+        a=train_l[(train_l.timestamp>=cutoff+GAP)&(train_l.timestamp<hi)][[\"uid\",\"item_id\"]].drop_duplicates()\n+        b=train_k[(train_k.timestamp>=cutoff+GAP)&(train_k.timestamp<hi)][[\"uid\",\"item_id\"]].drop_duplicates()\n+        grades={(int(u),int(x)):1 for u,x in a.itertuples(index=False,name=None) if int(x) in item_to_idx}\n+        grades.update({(int(u),int(x)):3 for u,x in b.itertuples(index=False,name=None) if int(x) in item_to_idx})\n+        for (u,x),g in grades.items():\n+            ss.append(ci*len(uids)+uid_to_pos[u]); si.append(item_to_idx[x]); sw.append(3.5 if g==3 else 1.0)\n+    arr=Prepared(uids,hist,hist_w,li,li_w,di,di_w,numeric,\n+                 np.asarray(ss,np.int32),np.asarray(si,np.int32),np.asarray(sw,np.float32),\n+                 np.arange((len(all_cutoffs)-1)*len(uids),len(all_cutoffs)*len(uids),dtype=np.int32))\n+    np.savez_compressed(cache,**arr.__dict__)\n+    print(f\"prepared {len(cutoffs)} snapshots, {len(si):,} positive pairs\")\n+    return arr\n+\n+\n+class WeightedPool(nn.Module):\n+    def __init__(self, emb: nn.Embedding): super().__init__(); self.emb=emb\n+    def forward(self,x,w):\n+        e=self.emb(x); den=w.sum(1,keepdim=True).clamp_min(1e-6)\n+        return (e*w.unsqueeze(-1)).sum(1)/den\n+\n+\n+class UserTower(nn.Module):\n+    def __init__(self, dim=64):\n+        super().__init__()\n+        self.history_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\n+        self.like_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\n+        self.dislike_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\n+        self.hp=WeightedPool(self.history_embedding); self.lp=WeightedPool(self.like_embedding); self.dp=WeightedPool(self.dislike_embedding)\n+        self.net=nn.Sequential(nn.Linear(dim*3+6,192),nn.LayerNorm(192),nn.GELU(),nn.Dropout(.08),nn.Linear(192,dim))\n+    def forward(self,h,hw,l,lw,d,dw,n):\n+        x=torch.cat([self.hp(h,hw),self.lp(l,lw),self.dp(d,dw),n],1)\n+        return F.normalize(self.net(x),dim=1)\n+\n+\n+class ItemTower(nn.Module):\n+    def __init__(self, features: torch.Tensor, dim=64):\n+        super().__init__(); self.embedding=nn.Embedding(len(features),dim); self.register_buffer(\"features\",features)\n+        self.net=nn.Sequential(nn.Linear(dim+3,128),nn.LayerNorm(128),nn.GELU(),nn.Linear(128,dim))\n+    def forward(self,idx):\n+        return F.normalize(self.net(torch.cat([self.embedding(idx),self.features[idx]],1)),dim=1)\n+\n+\n+class TwoTower(nn.Module):\n+    def __init__(self, features,dim=64): super().__init__(); self.user_tower=UserTower(dim); self.item_tower=ItemTower(features,dim)\n+\n+\n+def item_features(catalog: pd.DataFrame) -> torch.Tensor:\n+    x=np.column_stack([np.log1p(catalog.positive_listens),np.log1p(catalog.organic_positive_listens),-np.log1p(catalog.popularity_rank)]).astype(np.float32)\n+    x=(x-x.mean(0))/x.std(0).clip(1e-5)\n+    return torch.from_numpy(x)\n+\n+\n+def train_model(prep: Prepared, epochs:int, seed:int, checkpoint:Path) -> TwoTower:\n+    seed_all(seed); torch.set_num_threads(min(12,torch.get_num_threads()))\n+    catalog=pd.read_parquet(DATA/\"candidate_catalog.parquet\")\n+    model=TwoTower(item_features(catalog)); opt=torch.optim.AdamW(model.parameters(),lr=2.5e-3,weight_decay=2e-5)\n+    # A smoothed popularity sampler yields both hard head negatives and tail coverage.\n+    prob=torch.from_numpy(np.power(catalog.positive_listens.to_numpy(np.float64)+20,.55)); prob=prob/prob.sum()\n+    A={k:torch.from_numpy(getattr(prep,k)) for k in [\"hist\",\"hist_w\",\"likes\",\"likes_w\",\"dislikes\",\"dislikes_w\",\"numeric\"]}\n+    snap=torch.from_numpy(prep.sample_snapshot.astype(np.int64)); pos=torch.from_numpy(prep.sample_item.astype(np.int64)); wt=torch.from_numpy(prep.sample_weight)\n+    batch=384; neg_n=24\n+    for ep in range(epochs):\n+        order=torch.randperm(len(pos)); total=0.; seen=0\n+        model.train()\n+        for st in range(0,len(order),batch):\n+            q=order[st:st+batch]; r=snap[q]; p=pos[q]\n+            u=model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r])\n+            pv=model.item_tower(p)\n+            neg=torch.multinomial(prob,len(q)*neg_n,replacement=True).reshape(len(q),neg_n)\n+            nv=model.item_tower(neg.flatten()).reshape(len(q),neg_n,-1)\n+            ps=(u*pv).sum(1,keepdim=True); ns=torch.einsum('bd,bnd->bn',u,nv)\n+            loss=(F.softplus((ns-ps)/.09).mean(1)*wt[q]).sum()/wt[q].sum().clamp_min(1)\n+            opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5); opt.step()\n+            total+=float(loss)*len(q);seen+=len(q)\n+        print(f\"epoch {ep+1}/{epochs} loss={total/seen:.5f}\")\n+    torch.save({\"state_dict\":model.state_dict(),\"seed\":seed},checkpoint)\n+    return model\n+\n+\[email protected]_mode()\n+def rank(model:TwoTower, prep:Prepared, stage:str, output:Path) -> None:\n+    model.eval(); rows=torch.from_numpy(prep.inference_rows.astype(np.int64))\n+    A={k:torch.from_numpy(getattr(prep,k)) for k in [\"hist\",\"hist_w\",\"likes\",\"likes_w\",\"dislikes\",\"dislikes_w\",\"numeric\"]}\n+    user=[]\n+    for st in range(0,len(rows),256):\n+        r=rows[st:st+256]; user.append(model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]))\n+    user=torch.cat(user).numpy(); nitems=len(model.item_tower.features); item=[]\n+    for st in range(0,nitems,2048): item.append(model.item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\n+    item=np.concatenate(item)\n+    with np.load(DATA/f\"{stage}_eligible_candidates.npz\") as z:\n+        euids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\n+        assert np.array_equal(euids,prep.uids)\n+        frames=[]\n+        for st in range(0,len(euids),128):\n+            en=min(st+128,len(euids)); scores=user[st:en]@item.T\n+            ok=np.unpackbits(packed[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\n+            scores[~ok]=-np.inf\n+            ix=np.argpartition(scores,-100,axis=1)[:,-100:]\n+            ix=ix[np.arange(en-st)[:,None],np.argsort(-scores[np.arange(en-st)[:,None],ix],axis=1)]\n+            for j in range(en-st):\n+                frames.append(pd.DataFrame({'uid':euids[st+j],'item_id':items[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\n+    pd.concat(frames,ignore_index=True).to_parquet(output,index=False)\n+    print(f\"wrote {output}\")\n+\n+\n+def main():\n+    ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],required=True)\n+    ap.add_argument('--snapshots',type=int,default=10);ap.add_argument('--epochs',type=int,default=5);ap.add_argument('--seed',type=int,default=17)\n+    ap.add_argument('--output',type=Path,required=True);ap.add_argument('--rebuild-cache',action='store_true')\n+    a=ap.parse_args();OUT.mkdir(exist_ok=True)\n+    refit=a.stage=='test'; cache=OUT/f\"prepared_{'refit' if refit else 'validation'}_{a.snapshots}.npz\"\n+    if a.rebuild_cache and cache.exists(): cache.unlink()\n+    prep=prepare(refit,a.snapshots,cache); ckpt=OUT/f\"model_{a.stage}.pt\"\n+    model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output)\n+    (OUT/f\"run_{a.stage}.json\").write_text(json.dumps(vars(a),default=str,indent=2))\n+\n+\n+if __name__=='__main__': main()\n*** End Patch";
const result = await tools.apply_patch(patch); text(result);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
23

exec command

+07:59
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The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"Data and evaluator audit are complete; the popularity floor is 0.010710 NDCG@10 over 4,263 scored users. The scalable two-tower implementation is now in place and I’m beginning temporal training.","plan":[
  {step:"Inspect schemas, data scale, eligibility, and public evaluator",status:"completed"},
  {step:"Implement scalable history features and PyTorch two-tower training",status:"in_progress"},
  {step:"Tune on validation and save the best valid ranking",status:"pending"},
  {step:"Refit with validation history and generate final test submission",status:"pending"},
  {step:"Verify artifacts and document architecture, score, and command",status:"pending"}
]}); text(p);
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/baseline.py\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --snapshots 10 --epochs 4 --seed 17 --output /app/solution/validation_twotower.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); 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=35831
24

exec command

+08:13
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:35831,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);

Tool response

Script running with cell ID 12
Wall time 11.0 seconds
Output:
25

wait command

+08:28
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "12",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 15.8 seconds
Output:

26

exec command

+08:47
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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:35831,chars:"",yield_time_ms:1000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"5d69f0","wall_time_seconds":5.000996939,"session_id":35831,"original_token_count":0,"output":""}
27

exec command

+08:54
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The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The implementation has passed syntax checks and is building ten weekly, cutoff-correct training snapshots. This preprocessing is a one-time cached step; subsequent hyperparameter runs reuse it. The baseline artifact remains saved while the first neural run trains.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:35831,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 14
Wall time 11.0 seconds
Output:
28

wait command

+09:11
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "14",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"93161e","wall_time_seconds":30.00120954,"session_id":35831,"original_token_count":92,"output":"prepared 10 snapshots, 1,651,352 positive pairs\r\n/app/solution/train_ranker.py:264: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n  total+=float(loss)*len(q);seen+=len(q)\r\n"}
29

exec command

+09:31
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:35831,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 15
Wall time 11.0 seconds
Output:
30

wait command

+09:45
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "15",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.9 seconds
Output:

{"chunk_id":"beac5e","wall_time_seconds":30.001837851,"session_id":35831,"original_token_count":0,"output":""}
31

exec command

+10:13
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:35831,chars:"\u0003",yield_time_ms:1000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 0.5 seconds
Output:

{"chunk_id":"2884b9","wall_time_seconds":0.411952634,"exit_code":1,"original_token_count":402,"output":"^CTraceback (most recent call last):\r\n  File \"/app/solution/train_ranker.py\", line 308, in <module>\r\n    if __name__=='__main__': main()\r\n                             ^^^^^^\r\n  File \"/app/solution/train_ranker.py\", line 304, in main\r\n    model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output)\r\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n  File \"/app/solution/train_ranker.py\", line 263, in train_model\r\n    opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5); opt.step()\r\n                                                                                                      ^^^^^^^^^^\r\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 516, in wrapper\r\n    out = func(*args, **kwargs)\r\n          ^^^^^^^^^^^^^^^^^^^^^\r\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 81, in _use_grad\r\n    ret = func(*args, **kwargs)\r\n          ^^^^^^^^^^^^^^^^^^^^^\r\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 247, in step\r\n    adam(\r\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 149, in maybe_fallback\r\n    return func(*args, **kwargs)\r\n           ^^^^^^^^^^^^^^^^^^^^^\r\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 949, in adam\r\n    func(\r\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 533, in _single_tensor_adam\r\n    denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)\r\n             ^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n"}
32

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+10:35
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33

Agent message

+10:54
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34

exec command

+11:00
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Model text

The first cache exposed an important modeling issue: raw rolling labels contain about 1.65M repeat-play pairs, while the evaluation only rewards tracks that survive platform eligibility. I stopped that run before it could overwrite the saved valid baseline. I’m tightening snapshot labels to tracks not previously consumed at that cutoff and switching the large embedding tables to sparse optimization, which better matches both the discovery target and production-scale training.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-    listens = read_listens(include_val=refit)\n+    listens = read_listens(include_val=refit)\n+    # Approximate the platform's historical-consumption exclusion at each old\n+    # snapshot. Only catalog tracks need this compact first-seen lookup.\n+    first_seen = (listens[listens.item_id.isin(item_to_idx)]\n+                  .groupby([\"uid\", \"item_id\"], sort=False).timestamp.min())\n@@\n-        for (u,x),g in grades.items():\n-            ss.append(ci*len(uids)+uid_to_pos[u]); si.append(item_to_idx[x]); sw.append(3.5 if g==3 else 1.0)\n+        if grades:\n+            keys=list(grades)\n+            seen_at=first_seen.reindex(pd.MultiIndex.from_tuples(keys,names=[\"uid\",\"item_id\"])).to_numpy()\n+            for (u,x),seen in zip(keys,seen_at):\n+                # Discovery labels must be novel as of the snapshot cutoff.\n+                if not np.isnan(seen) and seen <= cutoff:\n+                    continue\n+                g=grades[(u,x)]\n+                ss.append(ci*len(uids)+uid_to_pos[u]); si.append(item_to_idx[x]); sw.append(3.5 if g==3 else 1.0)\n@@\n-        self.history_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\n-        self.like_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\n-        self.dislike_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0)\n+        self.history_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0,sparse=True)\n+        self.like_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0,sparse=True)\n+        self.dislike_embedding=nn.Embedding(HASH_SIZE+1,dim,padding_idx=0,sparse=True)\n@@\n-        super().__init__(); self.embedding=nn.Embedding(len(features),dim); self.register_buffer(\"features\",features)\n+        super().__init__(); self.embedding=nn.Embedding(len(features),dim,sparse=True); self.register_buffer(\"features\",features)\n@@\n-    model=TwoTower(item_features(catalog)); opt=torch.optim.AdamW(model.parameters(),lr=2.5e-3,weight_decay=2e-5)\n+    model=TwoTower(item_features(catalog))\n+    sparse=[model.user_tower.history_embedding.weight,model.user_tower.like_embedding.weight,\n+            model.user_tower.dislike_embedding.weight,model.item_tower.embedding.weight]\n+    sparse_ids={id(p) for p in sparse}; dense=[p for p in model.parameters() if id(p) not in sparse_ids]\n+    dense_opt=torch.optim.AdamW(dense,lr=2.5e-3,weight_decay=2e-5)\n+    sparse_opt=torch.optim.SparseAdam(sparse,lr=6e-3)\n@@\n-    batch=384; neg_n=24\n+    batch=512; neg_n=16\n@@\n-            opt.zero_grad(set_to_none=True); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5); opt.step()\n-            total+=float(loss)*len(q);seen+=len(q)\n+            dense_opt.zero_grad(set_to_none=True); sparse_opt.zero_grad(set_to_none=True)\n+            loss.backward(); dense_opt.step(); sparse_opt.step()\n+            total+=float(loss.detach())*len(q);seen+=len(q)\n*** End Patch";
const r = await tools.apply_patch(patch); text(r);
const e = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\n/opt/task-venv/bin/python /app/solution/train_ranker.py --stage validation --snapshots 10 --epochs 4 --seed 17 --rebuild-cache --output /app/solution/validation_twotower.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); text(e.output); if(e.session_id) text(`SESSION_ID=${e.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:

{}

SESSION_ID=90209
35

exec command

+11:42
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 18
Wall time 11.0 seconds
Output:
36

wait command

+11:55
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "18",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"779594","wall_time_seconds":30.000779315,"session_id":90209,"original_token_count":0,"output":""}
37

exec command

+12:14
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 19
Wall time 11.0 seconds
Output:
38

wait command

+12:28
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "19",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.8 seconds
Output:

{"chunk_id":"474f87","wall_time_seconds":30.001527641,"session_id":90209,"original_token_count":12,"output":"prepared 10 snapshots, 429,825 positive pairs\r\n"}
39

exec command

+12:47
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 20
Wall time 11.0 seconds
Output:
40

wait command

+13:01
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "20",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"e1a340","wall_time_seconds":30.001606743,"session_id":90209,"original_token_count":0,"output":""}
41

exec command

+13:20
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 21
Wall time 11.0 seconds
Output:
42

wait command

+13:34
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "21",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"ecefdd","wall_time_seconds":30.000411758,"session_id":90209,"original_token_count":6,"output":"epoch 1/4 loss=0.52608\r\n"}
43

exec command

+13:53
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 22
Wall time 11.0 seconds
Output:
44

wait command

+14:07
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "22",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"5c4056","wall_time_seconds":30.000317509,"session_id":90209,"original_token_count":6,"output":"epoch 2/4 loss=0.41360\r\n"}
45

exec command

+14:26
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Model text

The revised dataset now has 429,825 novel discovery pairs across ten weeks—much closer to the public window’s cutoff-correct density. Sparse training is progressing normally: pairwise loss fell from 0.526 to 0.414 by epoch 2. Once this run scores, I’ll use the result to decide whether a short second seed/epoch variant is worth the remaining time.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 23
Wall time 11.0 seconds
Output:
46

wait command

+14:43
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "23",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"89f64c","wall_time_seconds":30.001121858,"session_id":90209,"original_token_count":6,"output":"epoch 3/4 loss=0.33438\r\n"}
47

exec command

+15:02
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 24
Wall time 11.0 seconds
Output:
48

wait command

+15:16
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "24",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.8 seconds
Output:

{"chunk_id":"7e2228","wall_time_seconds":30.001005755,"session_id":90209,"original_token_count":0,"output":""}
49

exec command

+15:35
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:90209,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 4.9 seconds
Output:

{"chunk_id":"b23e9c","wall_time_seconds":4.854972228,"exit_code":0,"original_token_count":19,"output":"epoch 4/4 loss=0.27620\r\nwrote /app/solution/validation_twotower.parquet\r\n"}
50

exec command

+15:42
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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_twotower.parquet\nls -lh /app/solution","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation_twotower.parquet",
  "scores": {
    "quality_ndcg@10": 0.014333,
    "quality_users": 4263
  }
}
total 182M
drwxr-xr-x 2 agent agent 4.0K Aug 31 18:36 __pycache__
-rw-r--r-- 1 agent agent 1.3K Aug 31 18:27 baseline.py
-rw-r--r-- 1 agent agent 104M Aug 31 18:40 model_validation.pt
-rw-r--r-- 1 agent agent  76M Aug 31 18:37 prepared_validation_10.npz
-rw-r--r-- 1 agent agent  157 Aug 31 18:40 run_validation.json
-rw-r--r-- 1 agent agent  17K Aug 31 18:36 train_ranker.py
-rw-r--r-- 1 agent agent 712K Aug 31 18:27 validation_popularity.parquet
-rw-r--r-- 1 agent agent 1.8M Aug 31 18:40 validation_twotower.parquet
51

Agent message

+16:08
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52

exec command

+16:14
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Model text

The first neural ranker scores 0.014333 NDCG@10, a 33.8% lift over popularity’s 0.010710, and it is saved intact. I’m now calibrating a small catalog-popularity coordinate alongside the learned 64-D taste vector. This remains an exact dot product: the user tower emits a constant gate coordinate and the item tower emits the corresponding popularity feature, so offline item precomputation and one-vector request-time retrieval are preserved.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\[email protected]_mode()\n-def rank(model:TwoTower, prep:Prepared, stage:str, output:Path) -> None:\[email protected]_mode()\n+def rank(model:TwoTower, prep:Prepared, stage:str, output:Path, pop_blend:float=0.0) -> None:\n@@\n-    item=np.concatenate(item)\n+    item=np.concatenate(item)\n+    # This calibrated prior is still an exact inner product: conceptually append\n+    # sqrt(alpha) to every user vector and sqrt(alpha)*popularity to each item.\n+    # Centering has no effect on ranks but keeps scales numerically convenient.\n+    pop=-np.log1p(np.arange(1,nitems+1,dtype=np.float32)); pop=(pop-pop.mean())/pop.std()\n@@\n-            en=min(st+128,len(euids)); scores=user[st:en]@item.T\n+            en=min(st+128,len(euids)); scores=user[st:en]@item.T + pop_blend*pop[None,:]\n@@\n-    ap.add_argument('--output',type=Path,required=True);ap.add_argument('--rebuild-cache',action='store_true')\n+    ap.add_argument('--output',type=Path,required=True);ap.add_argument('--rebuild-cache',action='store_true')\n+    ap.add_argument('--pop-blend',type=float,default=0.0)\n@@\n-    model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output)\n+    model=train_model(prep,a.epochs,a.seed,ckpt);rank(model,prep,a.stage,a.output,a.pop_blend)\n*** End Patch";
const a=await tools.apply_patch(patch); text(a);
const r=await tools.exec_command({cmd:"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport torch, pandas as pd\nfrom train_ranker import *\nprep=prepare(False,10,OUT/'prepared_validation_10.npz')\ncat=pd.read_parquet(DATA/'candidate_catalog.parquet')\nmodel=TwoTower(item_features(cat));model.load_state_dict(torch.load(OUT/'model_validation.pt',weights_only=True)['state_dict'])\nfor alpha in [.01,.02,.04,.07,.10,.15,.25]:\n rank(model,prep,'validation',OUT/f'validation_blend_{alpha:.2f}.parquet',alpha)\nPY\nfor f in /app/solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:

{}

SESSION_ID=13333
53

exec command

+16:44
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:13333,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 28
Wall time 11.0 seconds
Output:
54

wait command

+16:58
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Tool callwaitTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"a2c3f8","wall_time_seconds":30.001642831,"session_id":13333,"original_token_count":195,"output":"wrote /app/solution/validation_blend_0.01.parquet\r\nwrote /app/solution/validation_blend_0.02.parquet\r\nwrote /app/solution/validation_blend_0.04.parquet\r\nwrote /app/solution/validation_blend_0.07.parquet\r\nwrote /app/solution/validation_blend_0.10.parquet\r\nwrote /app/solution/validation_blend_0.15.parquet\r\nwrote /app/solution/validation_blend_0.25.parquet\r\n{   \"model\": \"/app/solution/validation_blend_0.01.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.016628,     \"quality_users\": 4263   } } \r\n{   \"model\": \"/app/solution/validation_blend_0.02.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019111,     \"quality_users\": 4263   } } \r\n{   \"model\": \"/app/solution/validation_blend_0.04.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.019848,     \"quality_users\": 4263   } } \r\n"}
55

exec command

+17:17
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:13333,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 10.2 seconds
Output:

{"chunk_id":"0b275f","wall_time_seconds":10.208251013,"exit_code":0,"original_token_count":141,"output":"{   \"model\": \"/app/solution/validation_blend_0.07.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.018106,     \"quality_users\": 4263   } } \r\n{   \"model\": \"/app/solution/validation_blend_0.10.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.01585,     \"quality_users\": 4263   } } \r\n{   \"model\": \"/app/solution/validation_blend_0.15.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.013763,     \"quality_users\": 4263   } } \r\n{   \"model\": \"/app/solution/validation_blend_0.25.parquet\",   \"scores\": {     \"quality_ndcg@10\": 0.01249,     \"quality_users\": 4263   } } \r\n"}
56

exec command

+17:38
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom train_ranker import *\nfrom pathlib import Path\nimport torch,pandas as pd\nprep=prepare(False,10,OUT/'prepared_validation_10.npz');cat=pd.read_parquet(DATA/'candidate_catalog.parquet')\nm=TwoTower(item_features(cat));m.load_state_dict(torch.load(OUT/'model_validation.pt',weights_only=True)['state_dict'])\nfor a in [.028,.034,.040,.046,.052]:rank(m,prep,'validation',OUT/f'validation_blend_{a:.3f}.parquet',a)\nPY\nfor f in /app/solution/validation_blend_0.0??.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -n 4 | head -n 1 | tr '\\n' ' '; echo \" $f\"; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=22818
57

exec command

+17:50
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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:22818,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 31
Wall time 11.0 seconds
Output:
58

wait command

+18:04
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "31",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"0f0169","wall_time_seconds":30.001366132,"session_id":22818,"original_token_count":145,"output":"wrote /app/solution/validation_blend_0.028.parquet\r\nwrote /app/solution/validation_blend_0.034.parquet\r\nwrote /app/solution/validation_blend_0.040.parquet\r\nwrote /app/solution/validation_blend_0.046.parquet\r\nwrote /app/solution/validation_blend_0.052.parquet\r\n    \"quality_ndcg@10\": 0.020288,  /app/solution/validation_blend_0.028.parquet\r\n    \"quality_ndcg@10\": 0.020108,  /app/solution/validation_blend_0.034.parquet\r\n    \"quality_ndcg@10\": 0.019848,  /app/solution/validation_blend_0.040.parquet\r\n    \"quality_ndcg@10\": 0.01979,  /app/solution/validation_blend_0.046.parquet\r\n"}
59

exec command

+18:23
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:22818,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"225e2a","wall_time_seconds":0.00000246,"exit_code":0,"original_token_count":20,"output":"    \"quality_ndcg@10\": 0.019272,  /app/solution/validation_blend_0.052.parquet\r\n"}
60

exec command

+18:30
Thinking unavailable No text recorded

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

Model text

The calibrated dot-product model reaches 0.020288 at a popularity-coordinate weight of 0.028—about 89% above the floor and 42% above the uncalibrated tower. I’m testing one independent initialization next; if averaging the two learned vector spaces is better, the final serving vector will simply concatenate the two tower outputs (plus the scalar popularity coordinate), still allowing one exact MIPS query.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom train_ranker import *\nprep=prepare(False,10,OUT/'prepared_validation_10.npz')\ntrain_model(prep,4,29,OUT/'model_validation_seed29.pt')\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); 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=15548
61

exec command

+18:41
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15548,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

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

wait command

+18:55
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": "34",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"efc962","wall_time_seconds":30.001552879,"session_id":15548,"original_token_count":0,"output":""}
63

exec command

+19:14
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15548,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

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

wait command

+19:28
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "35",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"be6264","wall_time_seconds":30.001009003,"session_id":15548,"original_token_count":6,"output":"epoch 1/4 loss=0.52253\r\n"}
65

exec command

+19:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15548,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 36
Wall time 11.0 seconds
Output:
66

wait command

+20:01
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "36",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"299da0","wall_time_seconds":30.001089428,"session_id":15548,"original_token_count":6,"output":"epoch 2/4 loss=0.41007\r\n"}
67

exec command

+20:20
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15548,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 37
Wall time 11.0 seconds
Output:
68

wait command

+20:34
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": "37",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"b89f0a","wall_time_seconds":30.004557047,"session_id":15548,"original_token_count":0,"output":""}
69

exec command

+20:52
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15548,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 38
Wall time 11.0 seconds
Output:
70

wait command

+21:06
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "38",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"d8b613","wall_time_seconds":30.000392921,"session_id":15548,"original_token_count":6,"output":"epoch 3/4 loss=0.33473\r\n"}
71

exec command

+21:25
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15548,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 39
Wall time 11.0 seconds
Output:
72

wait 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 callwaitTool call Returned

Tool call · Input

{
  "cell_id": "39",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 3.1 seconds
Output:

{"chunk_id":"c51ec2","wall_time_seconds":16.498065902,"exit_code":0,"original_token_count":6,"output":"epoch 4/4 loss=0.27702\r\n"}
73

exec command

+21:54
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n @torch.inference_mode()\n def rank(model:TwoTower, prep:Prepared, stage:str, output:Path, pop_blend:float=0.0) -> None:\n@@\n     print(f\"wrote {output}\")\n+\n+\[email protected]_mode()\n+def rank_ensemble(models:list[TwoTower], prep:Prepared, stage:str, output:Path,\n+                  pop_blend:float=0.0) -> None:\n+    \"\"\"Rank by a concatenated retrieval vector from independent tower seeds.\"\"\"\n+    A={k:torch.from_numpy(getattr(prep,k)) for k in [\"hist\",\"hist_w\",\"likes\",\"likes_w\",\"dislikes\",\"dislikes_w\",\"numeric\"]}\n+    rows=torch.from_numpy(prep.inference_rows.astype(np.int64)); users=[]; items_all=[]\n+    for model in models:\n+        model.eval(); ub=[]\n+        for st in range(0,len(rows),256):\n+            r=rows[st:st+256];ub.append(model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]).numpy())\n+        users.append(np.concatenate(ub)/math.sqrt(len(models)))\n+        ib=[];nitems=len(model.item_tower.features)\n+        for st in range(0,nitems,2048):ib.append(model.item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\n+        items_all.append(np.concatenate(ib)/math.sqrt(len(models)))\n+    user=np.concatenate(users,axis=1);item=np.concatenate(items_all,axis=1)\n+    pop=-np.log1p(np.arange(1,nitems+1,dtype=np.float32));pop=(pop-pop.mean())/pop.std()\n+    with np.load(DATA/f\"{stage}_eligible_candidates.npz\") as z:\n+        euids=z['uids'].astype(np.int64);catalog_items=z['item_ids'].astype(np.int64);packed=z['packed_eligible'];frames=[]\n+        assert np.array_equal(euids,prep.uids)\n+        for st in range(0,len(euids),128):\n+            en=min(st+128,len(euids));scores=user[st:en]@item.T+pop_blend*pop[None,:]\n+            ok=np.unpackbits(packed[st:en],axis=1,count=nitems,bitorder='little').astype(bool);scores[~ok]=-np.inf\n+            ix=np.argpartition(scores,-100,axis=1)[:,-100:]\n+            ix=ix[np.arange(en-st)[:,None],np.argsort(-scores[np.arange(en-st)[:,None],ix],axis=1)]\n+            for j in range(en-st):frames.append(pd.DataFrame({'uid':euids[st+j],'item_id':catalog_items[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\n+    pd.concat(frames,ignore_index=True).to_parquet(output,index=False);print(f\"wrote {output}\")\n*** End Patch";
const a=await tools.apply_patch(patch); text(a);
const r=await tools.exec_command({cmd:"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom train_ranker import *\nimport torch,pandas as pd\nprep=prepare(False,10,OUT/'prepared_validation_10.npz');cat=pd.read_parquet(DATA/'candidate_catalog.parquet');models=[]\nfor f in ['model_validation.pt','model_validation_seed29.pt']:\n m=TwoTower(item_features(cat));m.load_state_dict(torch.load(OUT/f,weights_only=True)['state_dict']);models.append(m)\nfor a in [.018,.024,.028,.034,.040]:rank_ensemble(models,prep,'validation',OUT/f'validation_ensemble_{a:.3f}.parquet',a)\nPY\nfor f in /app/solution/validation_ensemble_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -n 4 | head -n 1 | tr '\\n' ' '; echo \" $f\"; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.2 seconds
Output:

{}

SESSION_ID=21377
74

exec command

+22:34
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:21377,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 41
Wall time 11.0 seconds
Output:
75

wait command

+22:48
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": "41",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"1a49d7","wall_time_seconds":30.000728567,"session_id":21377,"original_token_count":152,"output":"wrote /app/solution/validation_ensemble_0.018.parquet\r\nwrote /app/solution/validation_ensemble_0.024.parquet\r\nwrote /app/solution/validation_ensemble_0.028.parquet\r\nwrote /app/solution/validation_ensemble_0.034.parquet\r\nwrote /app/solution/validation_ensemble_0.040.parquet\r\n    \"quality_ndcg@10\": 0.023698,  /app/solution/validation_ensemble_0.018.parquet\r\n    \"quality_ndcg@10\": 0.024197,  /app/solution/validation_ensemble_0.024.parquet\r\n    \"quality_ndcg@10\": 0.024346,  /app/solution/validation_ensemble_0.028.parquet\r\n    \"quality_ndcg@10\": 0.024173,  /app/solution/validation_ensemble_0.034.parquet\r\n"}
76

exec command

+23:07
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:21377,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"cdbe39","wall_time_seconds":0.00000213,"exit_code":0,"original_token_count":21,"output":"    \"quality_ndcg@10\": 0.024254,  /app/solution/validation_ensemble_0.040.parquet\r\n"}
77

exec command

+23:25
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_submission.py\n+#!/usr/bin/env python3\n+\"\"\"Refit the selected two-seed model and write the hidden-test ranking.\"\"\"\n+import pandas as pd\n+from train_ranker import DATA, OUT, TwoTower, item_features, prepare, rank_ensemble, train_model\n+\n+\n+def main() -> None:\n+    snapshots, epochs, alpha = 10, 4, 0.028\n+    prep = prepare(True, snapshots, OUT / \"prepared_refit_10.npz\")\n+    models = []\n+    for seed in (17, 29):\n+        models.append(train_model(prep, epochs, seed, OUT / f\"model_test_seed{seed}.pt\"))\n+    rank_ensemble(models, prep, \"test\", OUT / \"submission.parquet\", alpha)\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
const a=await tools.apply_patch(patch); text(a);
const p=await tools.update_plan({explanation:"The selected two-seed concatenated tower plus dot-product popularity coordinate scores 0.024346 public NDCG@10. I’m preserving a structurally valid test fallback, then refitting the selected model with validation as observed history and labels.","plan":[
  {step:"Inspect schemas, data scale, eligibility, and public evaluator",status:"completed"},
  {step:"Implement scalable history features and PyTorch two-tower training",status:"completed"},
  {step:"Tune on validation and save the best valid ranking",status:"completed"},
  {step:"Refit with validation history and generate final test submission",status:"in_progress"},
  {step:"Verify artifacts and document architecture, score, and command",status:"pending"}
]}); text(p);
const r=await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/baseline.py --stage test --output /app/solution/submission.parquet\n/opt/task-venv/bin/python -m py_compile /app/solution/build_submission.py\n/opt/task-venv/bin/python /app/solution/build_submission.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); 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=60523
78

exec command

+23:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

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

wait command

+24:01
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "44",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"dd7aab","wall_time_seconds":30.000513583,"session_id":60523,"original_token_count":0,"output":""}
80

exec command

+24:21
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

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

wait command

+24:35
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "45",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"fa934d","wall_time_seconds":30.001703822,"session_id":60523,"original_token_count":12,"output":"prepared 11 snapshots, 474,619 positive pairs\r\n"}
82

exec command

+24:54
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Model text

The selected public model is 0.024346 NDCG@10 (127% above popularity). Final refit preparation is complete with 474,619 cutoff-correct pairs across the ten historical windows plus the public validation window. A valid popularity-based submission.parquet is already in place as a fallback while the two final seeds train.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 46
Wall time 11.0 seconds
Output:
83

wait command

+25:12
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "46",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.3 seconds
Output:

{"chunk_id":"20935b","wall_time_seconds":30.000782588,"session_id":60523,"original_token_count":0,"output":""}
84

exec command

+25:30
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 47
Wall time 11.0 seconds
Output:
85

wait command

+25:44
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "47",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"a4a799","wall_time_seconds":30.000296883,"session_id":60523,"original_token_count":6,"output":"epoch 1/4 loss=0.52378\r\n"}
86

exec command

+26:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 48
Wall time 11.0 seconds
Output:
87

wait command

+26:19
Thinking unavailable No text recorded

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "48",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 14.5 seconds
Output:

{"chunk_id":"402fbd","wall_time_seconds":30.003642902,"session_id":60523,"original_token_count":6,"output":"epoch 2/4 loss=0.40924\r\n"}
88

exec command

+26:36
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 49
Wall time 11.0 seconds
Output:
89

wait command

+26:50
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "49",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"4ef7d8","wall_time_seconds":30.000507496,"session_id":60523,"original_token_count":0,"output":""}
90

exec command

+27:09
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 50
Wall time 11.0 seconds
Output:
91

wait command

+27:24
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": "50",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 15.8 seconds
Output:

{"chunk_id":"6f638b","wall_time_seconds":30.004452874,"session_id":60523,"original_token_count":6,"output":"epoch 3/4 loss=0.32919\r\n"}
92

exec command

+27:42
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 51
Wall time 11.0 seconds
Output:
93

wait command

+27:56
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "51",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"45da10","wall_time_seconds":30.000683923,"session_id":60523,"original_token_count":6,"output":"epoch 4/4 loss=0.27473\r\n"}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 52
Wall time 11.0 seconds
Output:
95

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "52",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"7c65fb","wall_time_seconds":30.000858457,"session_id":60523,"original_token_count":0,"output":""}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 53
Wall time 11.0 seconds
Output:
97

wait command

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "53",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"8cb1b1","wall_time_seconds":30.000323916,"session_id":60523,"original_token_count":6,"output":"epoch 1/4 loss=0.52018\r\n"}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 54
Wall time 11.0 seconds
Output:
99

wait command

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "54",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"70bd1b","wall_time_seconds":30.001329793,"session_id":60523,"original_token_count":6,"output":"epoch 2/4 loss=0.40396\r\n"}
100

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 55
Wall time 11.0 seconds
Output:
101

wait command

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "55",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"97433f","wall_time_seconds":30.000873149,"session_id":60523,"original_token_count":0,"output":""}
102

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 56
Wall time 11.0 seconds
Output:
103

wait command

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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "56",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"a172c0","wall_time_seconds":30.001199924,"session_id":60523,"original_token_count":6,"output":"epoch 3/4 loss=0.32787\r\n"}
104

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script running with cell ID 57
Wall time 11.0 seconds
Output:
105

wait command

+31:15
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "57",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"1a1f4c","wall_time_seconds":30.000992509,"session_id":60523,"original_token_count":6,"output":"epoch 4/4 loss=0.27493\r\n"}
106

exec command

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:60523,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"d7b5cc","wall_time_seconds":0.000002449,"exit_code":0,"original_token_count":10,"output":"wrote /app/solution/submission.parquet\r\n"}
107

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Model text

Final refit and test ranking are complete. Both seeds converged consistently (final losses 0.2747 and 0.2749), and the neural ranking has replaced the fallback submission. I’m doing the last contract audit now: exact 100×8,456 shape, integer ranks, uniqueness, per-user eligibility, and an explicit serving wrapper whose returned 129-D user/item vectors reproduce the ranking by dot product alone.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n class TwoTower(nn.Module):\n     def __init__(self, features,dim=64): super().__init__(); self.user_tower=UserTower(dim); self.item_tower=ItemTower(features,dim)\n+\n+\n+class ServingUserTower(nn.Module):\n+    \"\"\"Concatenate seed towers and the calibrated global-prior coordinate.\"\"\"\n+    def __init__(self, models:list[TwoTower], pop_blend:float):\n+        super().__init__();self.towers=nn.ModuleList([m.user_tower for m in models])\n+        self.register_buffer(\"gate\",torch.tensor(math.sqrt(pop_blend),dtype=torch.float32))\n+    def forward(self,h,hw,l,lw,d,dw,n):\n+        scale=math.sqrt(len(self.towers))\n+        parts=[tower(h,hw,l,lw,d,dw,n)/scale for tower in self.towers]\n+        return torch.cat(parts+[self.gate.expand(len(h),1)],dim=1)\n+\n+\n+class ServingItemTower(nn.Module):\n+    \"\"\"Return independently precomputable item vectors matching ServingUserTower.\"\"\"\n+    def __init__(self, models:list[TwoTower], pop_blend:float):\n+        super().__init__();self.towers=nn.ModuleList([m.item_tower for m in models])\n+        n=len(models[0].item_tower.features)\n+        pop=-torch.log1p(torch.arange(1,n+1,dtype=torch.float32));pop=(pop-pop.mean())/pop.std()\n+        self.register_buffer(\"pop_coordinate\",math.sqrt(pop_blend)*pop)\n+    def forward(self,idx):\n+        scale=math.sqrt(len(self.towers));parts=[tower(idx)/scale for tower in self.towers]\n+        return torch.cat(parts+[self.pop_coordinate[idx,None]],dim=1)\n@@\n def rank_ensemble(models:list[TwoTower], prep:Prepared, stage:str, output:Path,\n                   pop_blend:float=0.0) -> None:\n     \"\"\"Rank by a concatenated retrieval vector from independent tower seeds.\"\"\"\n@@\n-    for model in models:\n-        model.eval(); ub=[]\n-        for st in range(0,len(rows),256):\n-            r=rows[st:st+256];ub.append(model.user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]).numpy())\n-        users.append(np.concatenate(ub)/math.sqrt(len(models)))\n-        ib=[];nitems=len(model.item_tower.features)\n-        for st in range(0,nitems,2048):ib.append(model.item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\n-        items_all.append(np.concatenate(ib)/math.sqrt(len(models)))\n-    user=np.concatenate(users,axis=1);item=np.concatenate(items_all,axis=1)\n-    pop=-np.log1p(np.arange(1,nitems+1,dtype=np.float32));pop=(pop-pop.mean())/pop.std()\n+    user_tower=ServingUserTower(models,pop_blend).eval();item_tower=ServingItemTower(models,pop_blend).eval()\n+    ub=[]\n+    for st in range(0,len(rows),256):\n+        r=rows[st:st+256];ub.append(user_tower(A['hist'][r].long(),A['hist_w'][r],A['likes'][r].long(),A['likes_w'][r],A['dislikes'][r].long(),A['dislikes_w'][r],A['numeric'][r]).numpy())\n+    user=np.concatenate(ub);nitems=len(models[0].item_tower.features);ib=[]\n+    for st in range(0,nitems,2048):ib.append(item_tower(torch.arange(st,min(st+2048,nitems))).numpy())\n+    item=np.concatenate(ib)\n@@\n-            en=min(st+128,len(euids));scores=user[st:en]@item.T+pop_blend*pop[None,:]\n+            en=min(st+128,len(euids));scores=user[st:en]@item.T\n*** Add File: /app/solution/README.md\n+# CPU PyTorch two-tower discovery ranker\n+\n+## Model\n+\n+The request-time representation is a 129-dimensional vector returned by\n+`ServingUserTower`; the independently precomputable catalog representation is\n+the same size and is returned by `ServingItemTower`. The relevance score used\n+to produce the submission is exactly `user_vector @ item_vector`.\n+\n+Each of two seeded 64-D user towers pools a user's latest 128 positive-listen\n+history tokens plus up to 64 active likes and 64 active dislikes. Raw item IDs\n+are multiplicatively hashed into sparse trainable history embeddings; event\n+strength, organic intent, recency, and six activity statistics feed a\n+192-unit GELU MLP. There is deliberately no user-ID embedding. Each matching\n+item tower combines a trainable 30,000-track catalog embedding with log listen,\n+organic-listen, and popularity features in a 128-unit GELU MLP. Seed outputs\n+are normalized and concatenated; the final coordinate is the validation-tuned\n+catalog-popularity prior (weight 0.028). This coordinate is emitted by both\n+PyTorch serving towers, so it does not require post-retrieval reranking.\n+\n+## Training\n+\n+Training uses ten rolling seven-day temporal targets, each separated from its\n+history by the required 30-minute gap. Targets are organic >=80% listens and\n+organic likes; likes receive 3.5x loss weight. Previously consumed targets are\n+removed to approximate the cutoff candidate boundary. The sampled pairwise\n+softplus objective uses 16 popularity-smoothed negatives per positive. Sparse\n+embedding parameters use `SparseAdam`; dense MLP parameters use `AdamW`.\n+This event/snapshot pipeline does not construct or factorize a global\n+user-item interaction matrix.\n+\n+The selected two-seed ensemble scored **0.024346 graded NDCG@10** on 4,263\n+public validation users (popularity baseline: 0.010710). The final model is\n+refit with validation interactions as observed history and its outcomes as the\n+last training window before applying test-cutoff eligibility.\n+\n+## Reproduce the final submission\n+\n+From `/app/solution` with the supplied read-only task environment:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_submission.py\n+```\n+\n+This writes `/app/solution/submission.parquet` with columns `uid`, `item_id`,\n+and `rank`.\n*** End Patch";
const r=await tools.apply_patch(patch); text(r);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
109

Agent message

+33:19
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110

exec command

+33:21
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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/*.py\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\np=Path('/app/solution/submission.parquet');df=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nn=validate_ranking(df,targets);cs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nelig=sum(cs.is_eligible(int(u),int(x)) for u,x in n[['uid','item_id']].itertuples(index=False,name=None))\nprint('shape',n.shape,'users',n.uid.nunique(),'items',n.item_id.nunique(),'eligible',elig,'expected',len(n))\nprint('dtypes',n.dtypes.to_dict(),'bytes',p.stat().st_size)\n# Check that event-derived active state covers the authoritative supplied state.\nz=np.load('/app/solution/prepared_refit_10.npz');rows=z['inference_rows'];uids=z['uids'];state=pd.read_parquet('/task/data/test_preference_state.parquet')\nHASH=131071\ndef h(a): return ((a.astype(np.uint64)*np.uint64(11400714819323198485)%np.uint64(HASH))+1).astype(np.int32)\nby={(int(u),s):set(h(g.item_id.to_numpy())) for (u,s),g in state.groupby(['uid','state'])}\nfor typ,key in [('liked','likes'),('disliked','dislikes')]:\n overlaps=[]\n for j,u in enumerate(uids):\n  got=set(z[key][rows[j]]);got.discard(0);truth=by.get((int(u),typ),set())\n  overlaps.append(len(got&truth)/max(1,len(got)))\n print(typ,'derived tokens in authoritative state mean',round(float(np.mean(overlaps)),4))\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet /app/solution/model_test_seed*.pt /app/solution/README.md","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response

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

wait command

+33:53
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "60",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.8 seconds
Output:

112

exec command

+34:15
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ps -ef | rg 'python|sha256' | rg -v 'rg|server' || true\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\np=Path('/app/solution/submission.parquet');df=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));n=validate_ranking(df,targets);cs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nbad=[(int(u),int(x)) for u,x in n[['uid','item_id']].itertuples(index=False,name=None) if not cs.is_eligible(int(u),int(x))]\nprint('shape',n.shape,'users',n.uid.nunique(),'bad eligibility',len(bad));print(n.dtypes.to_dict())\nPY\nsha256sum /app/solution/submission.parquet\nls -lh /app/solution/submission.parquet /app/solution/model_test_seed*.pt /app/solution/README.md","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response

Script completed
Wall time 2.9 seconds
Output:

/bin/sh: 1: ps: not found
shape (845600, 3) users 8456 bad eligibility 0
{'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}
a887ec7180971bda190d3b0eb5f09b6375db6f638112b7f3e6201e349502c3aa  /app/solution/submission.parquet
-rw-r--r-- 1 agent agent 2.2K Aug 31 18:57 /app/solution/README.md
-rw-r--r-- 1 agent agent 104M Aug 31 18:53 /app/solution/model_test_seed17.pt
-rw-r--r-- 1 agent agent 104M Aug 31 18:56 /app/solution/model_test_seed29.pt
-rw-r--r-- 1 agent agent 1.6M Aug 31 18:56 /app/solution/submission.parquet
113

exec command

+34:34
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas as pd\nfrom train_ranker import *\nprep=prepare(True,10,OUT/'prepared_refit_10.npz');cat=pd.read_parquet(DATA/'candidate_catalog.parquet');models=[]\nfor seed in (17,29):\n m=TwoTower(item_features(cat));m.load_state_dict(torch.load(OUT/f'model_test_seed{seed}.pt',weights_only=True)['state_dict']);models.append(m)\nrank_ensemble(models,prep,'test',OUT/'submission.parquet',0.028)\nPY\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\np=Path('/app/solution/submission.parquet');d=validate_ranking(pd.read_parquet(p),set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)));c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nassert all(c.is_eligible(u,x) for u,x in d[['uid','item_id']].itertuples(index=False,name=None))\nassert not d.duplicated(['uid','item_id']).any()\nprint(f'VALID rows={len(d)} users={d.uid.nunique()} ranks={d.groupby(\"uid\").size().unique().tolist()}')\nPY\nsha256sum /app/solution/submission.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000,"tty":true}); 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=92378
114

exec command

+34:51
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:92378,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(JSON.stringify(r));

Tool response

Script completed
Wall time 7.2 seconds
Output:

{"chunk_id":"2164b1","wall_time_seconds":7.18348442,"exit_code":0,"original_token_count":46,"output":"wrote /app/solution/submission.parquet\r\nVALID rows=845600 users=8456 ranks=[100]\r\n7e565127afb62e2e3b0db808b53e2eeada6bea05114ce3605f5929f25fd52796  /app/solution/submission.parquet\r\n"}
115

exec command

+35:04
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Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"The final two-seed model was refit on train plus validation history, its serving-vector path regenerated the test ranking, and the submission passed all structural and eligibility checks.","plan":[
  {step:"Inspect schemas, data scale, eligibility, and public evaluator",status:"completed"},
  {step:"Implement scalable history features and PyTorch two-tower training",status:"completed"},
  {step:"Tune on validation and save the best valid ranking",status:"completed"},
  {step:"Refit with validation history and generate final test submission",status:"completed"},
  {step:"Verify artifacts and document architecture, score, and command",status:"completed"}
]}); text(p);
const r=await tools.exec_command({cmd:"find /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort | sed -n '1,120p'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_0.028.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000}); text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{}
README.md 2243 bytes
baseline.py 1256 bytes
build_submission.py 595 bytes
model_test_seed17.pt 108983254 bytes
model_test_seed29.pt 108983254 bytes
model_validation.pt 108983231 bytes
model_validation_seed29.pt 108983456 bytes
prepared_refit_10.npz 86778697 bytes
prepared_validation_10.npz 78787326 bytes
run_validation.json 157 bytes
submission.parquet 1652916 bytes
train_ranker.py 20367 bytes
validation_blend_0.01.parquet 1719948 bytes
validation_blend_0.02.parquet 1676528 bytes
validation_blend_0.028.parquet 1668686 bytes
validation_blend_0.034.parquet 1662246 bytes
validation_blend_0.04.parquet 1655783 bytes
validation_blend_0.040.parquet 1655783 bytes
validation_blend_0.046.parquet 1649353 bytes
validation_blend_0.052.parquet 1643239 bytes
validation_blend_0.07.parquet 1521386 bytes
validation_blend_0.10.parquet 1399371 bytes
validation_blend_0.15.parquet 1284968 bytes
validation_blend_0.25.parquet 1176584 bytes
validation_ensemble_0.018.parquet 1667107 bytes
validation_ensemble_0.024.parquet 1661907 bytes
validation_ensemble_0.028.parquet 1658385 bytes
validation_ensemble_0.034.parquet 1652647 bytes
validation_ensemble_0.040.parquet 1647202 bytes
validation_popularity.parquet 728599 bytes
validation_twotower.parquet 1794497 bytes
{
  "model": "/app/solution/validation_ensemble_0.028.parquet",
  "scores": {
    "quality_ndcg@10": 0.024346,
    "quality_users": 4263
  }
}
116

Final response

+35:19
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Model text

Completed the CPU PyTorch two-tower ranker.

  • Final submission: submission.parquet
  • Training/application code: train_ranker.py
  • Reproduction command: build_submission.py
  • Documentation: README.md
  • Public validation NDCG@10: 0.024346
  • Validated: 845,600 rows, 8,456 users, 100 unique eligible items per user
  • Serving score: exact dot product between independently computed 129-D user and item vectors
  • Submission SHA-256: 7e565127afb62e2e3b0db808b53e2eeada6bea05114ce3605f5929f25fd52796
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-004-gpt-5-6-sol-high-2026-08-31T11-04-49
Recorded model
gpt-5.6-sol
Harness version
0.147.0
Session
01a05911-46ec-7791-9c00-3dabb370c360
Source SHA-256
6e573cfca1466efe460ae48f429333887ac9b8505bc550785649438c2849afa8

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