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Agent trace

Two-tower retriever sol-high-02

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 114 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · high effort
Recorded conversation
40:06 · 120 steps
Private reward
0.024679 · Pass
Attempt cost
$3.41
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
System instructions

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

Personality

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

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

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

Writing style

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

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

Technical communication

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

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

Working with the user

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

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

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

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

Intermediate commentary

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

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

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

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

Final answer

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

Formatting rules

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

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

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

Good candidates include:

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

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

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

Rules for getting work done

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

File editing constraints

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

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

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

Autonomy and persistence

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

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

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

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

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

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

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

Destructive actions

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

Before taking a destructive action:

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

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

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

Using skills

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

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

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

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

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

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

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

02

Developer instructions

+00:02
Developer instructions

<skills_instructions>

Skills

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

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

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

<apps_instructions>

Apps (Connectors)

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

03

Developer instructions

+00:02
Developer instructions

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

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

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

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

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

They may be addressed as to=/root

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

All agents share the same directory. In detail:

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

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

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

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

04

Developer instructions

+00:02
Developer instructions

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

05

User context

+00:02
User context

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

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

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-08-31</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>

Harness context world state
{
  "type": "world_state",
  "full": true,
  "state": {
    "agents_md": {},
    "apps_instructions": true,
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-08-31",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}
Harness context turn context
{
  "type": "turn_context",
  "turn_id": "01a05912-801b-7bc1-8f16-cb796ce900c4",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-08-31",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "high",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "high",
  "summary": "auto"
}
06

Task instructions

+00:02
Task instructions

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

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

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

Data

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

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

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

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

The two public windows have different roles:

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

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

Additional files under /task/data/:

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

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

Discovery objective

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

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

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

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

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

Public tools

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

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

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

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

Deliverable

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

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

Constraints

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

exec command

+00:06
Thinking unavailable No text recorded

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

Model text

I’ll build and benchmark this end to end: first audit the data and evaluator, then implement a scalable sampled-event two-tower, tune it against the public window, refit with validation as history, and keep a valid final submission on disk throughout. I’ll keep all generated files under /app/solution/ and use CPU-only PyTorch.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({plan:[
  {step:"Audit schemas, sizes, eligibility format, and public evaluator",status:"in_progress"},
  {step:"Implement scalable history-based PyTorch two-tower and ranking pipeline",status:"pending"},
  {step:"Train and tune variants on the public validation window",status:"pending"},
  {step:"Refit selected model on train + validation history",status:"pending"},
  {step:"Generate and validate final 100-per-user submission and README",status:"pending"}
]});
const r = await tools.exec_command({cmd:"pwd && rg --files /task | sort && du -h /task/data/* && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,280p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt",workdir:"/app",yield_time_ms:10000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

/app
/task/data/candidate_catalog.parquet
/task/data/target_users.parquet
/task/data/test_eligible_candidates.npz
/task/data/test_preference_state.parquet
/task/data/train_dislikes.parquet
/task/data/train_likes.parquet
/task/data/train_listens.parquet
/task/data/train_undislikes.parquet
/task/data/train_unlikes.parquet
/task/data/val_dislikes.parquet
/task/data/val_likes.parquet
/task/data/val_listens.parquet
/task/data/val_undislikes.parquet
/task/data/val_unlikes.parquet
/task/data/validation_eligible_candidates.npz
/task/data/validation_preference_state.parquet
/task/evaluate_public.py
/task/ranking_evaluation.py
/task/requirements.txt
344K	/task/data/candidate_catalog.parquet
84K	/task/data/target_users.parquet
7.4M	/task/data/test_eligible_candidates.npz
1.1M	/task/data/test_preference_state.parquet
840K	/task/data/train_dislikes.parquet
5.2M	/task/data/train_likes.parquet
305M	/task/data/train_listens.parquet
156K	/task/data/train_undislikes.parquet
1.8M	/task/data/train_unlikes.parquet
36K	/task/data/val_dislikes.parquet
292K	/task/data/val_likes.parquet
9.5M	/task/data/val_listens.parquet
8.0K	/task/data/val_undislikes.parquet
72K	/task/data/val_unlikes.parquet
7.3M	/task/data/validation_eligible_candidates.npz
1.1M	/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

+00:23
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"mkdir -p /app/solution && python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n    pf=pq.ParquetFile(p)\n    print(p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'schema',pf.schema_arrow)\n    if p.name in ('candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet'):\n        x=pd.read_parquet(p)\n        print(x.head(), x.dtypes.to_dict(), 'nunique',x.nunique().to_dict())\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n    z=np.load(p)\n    print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n    m=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n    print('eligible stats',np.quantile(m.sum(1),[0,.1,.5,.9,1]),'mean',m.sum(1).mean(),'catalog same head',z['item_ids'][:5])\nPY",workdir:"/app",yield_time_ms:10000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

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

exec command

+00:39
Thinking unavailable No text recorded

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Model text

The default shell’s python is not the benchmark environment described by the task—it lacks even the declared Parquet engine. I’m locating the task’s pinned runtime now; this is an environment-path issue, not a data issue.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"which -a python python3 || true\npython --version\npython -m pip list 2>/dev/null | sed -n '1,120p'\nfind /app /opt /usr/local -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50\nfind / -maxdepth 4 -type d -name 'site-packages' 2>/dev/null | head -50",workdir:"/app",yield_time_ms:10000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 0.8 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
Python 3.12.13
Package Version
------- -------
pip     25.0.1
10

exec command

+00:51
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"env | sort | sed -n '1,160p'\nfind / -type d \\( -name 'torch' -o -name 'pyarrow' \\) 2>/dev/null | head -50\nls -la /app /task /usr/local/lib/python3.12/site-packages | sed -n '1,160p'",workdir:"/app",yield_time_ms:10000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 0.3 seconds
Output:

CODEX_CI=1
CODEX_HOME=/tmp/codex-home
CODEX_THREAD_ID=01a05912-8007-7c33-a5cb-b744f166f479
COLORTERM=
GH_PAGER=cat
GIT_PAGER=cat
GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305
HOME=/home/agent
HOSTNAME=d6d0a2b141b6
LANG=C.UTF-8
LC_ALL=C.UTF-8
LC_CTYPE=C.UTF-8
NO_COLOR=1
PAGER=cat
PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games
PWD=/app
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13
SHLVL=1
TERM=dumb
_=/usr/local/bin/codex
/opt/task-venv/lib/python3.12/site-packages/pyarrow
/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch
/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch
/opt/task-venv/lib/python3.12/site-packages/torch
/opt/task-venv/lib/python3.12/site-packages/torch/include/torch
/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch
/app:
total 12
drwxr-xr-x 3 agent agent 4096 Aug 31 18:23 .
drwxr-xr-x 1 root  root  4096 Aug 31 18:26 ..
drwxr-xr-x 2 agent agent 4096 Aug 31 18:23 solution

/task:
total 36
dr-xr-xr-x 1 root root 4096 Aug 31 18:23 .
drwxr-xr-x 1 root root 4096 Aug 31 18:26 ..
-r--r--r-- 1 root root    0 Aug 31 18:09 .keep
dr-xr-xr-x 1 root root 4096 Aug 31 18:10 data
-r--r--r-- 1 root root 5774 Aug 31 18:09 evaluate_public.py
-r--r--r-- 1 root root 8265 Aug 31 18:09 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Aug 31 18:09 requirements.txt

/usr/local/lib/python3.12/site-packages:
total 20
drwxr-xr-x  4 root root 4096 Aug  5 01:13 .
drwxr-xr-x 38 root root 4096 Aug  5 01:13 ..
-rw-r--r--  1 root root  119 Aug  5 01:13 README.txt
drwxr-xr-x  5 root root 4096 Aug  5 01:13 pip
drwxr-xr-x  2 root root 4096 Aug  5 01:13 pip-25.0.1.dist-info
11

exec command

+00:57
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -l /opt/task-venv/bin/python* && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n    pf=pq.ParquetFile(p)\n    print('\\n',p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'cols',pf.schema_arrow.names)\n    if p.name in ('candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet'):\n        x=pd.read_parquet(p)\n        print(x.head().to_string(index=False), x.dtypes.to_dict(), 'nunique',x.nunique().to_dict())\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n    z=np.load(p)\n    print('\\n',p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n    m=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n    print('eligible stats',np.quantile(m.sum(1),[0,.1,.5,.9,1]),'mean',m.sum(1).mean(),'head',z['item_ids'][:5])\nPY",workdir:"/app",yield_time_ms:10000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 2.6 seconds
Output:

lrwxrwxrwx 1 root root 21 Aug 31 18:23 /opt/task-venv/bin/python -> /usr/local/bin/python
lrwxrwxrwx 1 root root  6 Aug 31 18:23 /opt/task-venv/bin/python3 -> python
lrwxrwxrwx 1 root root  6 Aug 31 18:23 /opt/task-venv/bin/python3.12 -> python

 candidate_catalog.parquet rows 30000 groups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
 item_id  positive_listens  organic_positive_listens  popularity_rank
 5862961             26615                     20620                1
 6901374             25536                     17347                2
 5635052             24691                     17194                3
 3542184             24075                     17699                4
 9378983             24036                     18195                5 {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} nunique {'item_id': 30000, 'positive_listens': 3049, 'organic_positive_listens': 2019, 'popularity_rank': 30000}

 target_users.parquet rows 8456 groups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']
 uid  recent_positive_listens  history_positive_listens
 100                    844.0                      2604
 200                     50.0                       286
 300                    168.0                       359
 600                   1985.0                      2423
 700                   1776.0                      6114 {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} nunique {'uid': 8456, 'recent_positive_listens': 3100, 'history_positive_listens': 4880}

 test_preference_state.parquet rows 484776 groups 1 cols ['uid', 'item_id', 'state']
 uid  item_id state
 100   410868 liked
 100   732449 liked
 100   982903 liked
 100  1730273 liked
 100  2621343 liked {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} nunique {'uid': 7092, 'item_id': 28863, 'state': 2}

 train_dislikes.parquet rows 101075 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 train_likes.parquet rows 814831 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 train_listens.parquet rows 43484159 groups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']

 train_undislikes.parquet rows 19960 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 train_unlikes.parquet rows 292665 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 val_dislikes.parquet rows 3563 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 val_likes.parquet rows 37795 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 val_listens.parquet rows 1501697 groups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']

 val_undislikes.parquet rows 606 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 val_unlikes.parquet rows 9346 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']

 validation_preference_state.parquet rows 469124 groups 1 cols ['uid', 'item_id', 'state']
 uid  item_id state
 100   410868 liked
 100   732449 liked
 100   982903 liked
 100  1730273 liked
 100  2621343 liked {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} nunique {'uid': 7038, 'item_id': 28814, 'state': 2}

 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
eligible stats [24657.  28038.5 29242.  29852.  30000. ] mean 29067.306291390727 head [5862961 6901374 5635052 3542184 9378983]

 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
eligible stats [24552. 28001. 29220. 29835. 30000.] mean 29041.3178807947 head [5862961 6901374 5635052 3542184 9378983]
12

exec command

+01:31
Thinking unavailable No text recorded

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

Model text

The benchmark has 8,456 target users and 30,000 catalog items, with roughly 29,000 eligible items per user. I’m now measuring the temporal/repeat structure of validation relevance before fixing the training examples; that determines whether the strongest history-derived signal is recency, long-term affinity, explicit likes, or item-to-item generalization. The implementation will remain event-stream based—no global interaction matrix.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"free -h && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n  for split in ['train','val']:\n    p=D/f'{split}_{fam}.parquet'; cols=['uid','timestamp','item_id','is_organic']\n    if fam=='listens': cols += ['played_ratio_pct','track_length_seconds']\n    x=pd.read_parquet(p,columns=cols)\n    print(split,fam,'rows',len(x),'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',int(x.timestamp.min()),int(x.timestamp.max()),'organic',round(x.is_organic.mean(),3))\n    if fam=='listens':\n      print(' ratios',x.played_ratio_pct.quantile([0,.25,.5,.8,.9,.99,1]).to_dict(),'pos50',float((x.played_ratio_pct>=50).mean()),'strong80',float((x.played_ratio_pct>=80).mean()))\n    del x\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: free: not found
13

exec command

+01:49
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"sed -n '1,6p' /proc/meminfo\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\nD=Path('/task/data')\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n  for split in ['train','val']:\n    p=D/f'{split}_{fam}.parquet'; cols=['uid','timestamp','item_id','is_organic']\n    if fam=='listens': cols += ['played_ratio_pct','track_length_seconds']\n    x=pd.read_parquet(p,columns=cols)\n    print(split,fam,'rows',len(x),'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',int(x.timestamp.min()),int(x.timestamp.max()),'organic',round(x.is_organic.mean(),3))\n    if fam=='listens':\n      print(' ratios',x.played_ratio_pct.quantile([0,.25,.5,.8,.9,.99,1]).to_dict(),'pos50',round(float((x.played_ratio_pct>=50).mean()),3),'strong80',round(float((x.played_ratio_pct>=80).mean()),3))\n    del x\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 3.4 seconds
Output:

MemTotal:       15988980 kB
MemFree:         1889232 kB
MemAvailable:   14900856 kB
Buffers:          227480 kB
Cached:         12492928 kB
SwapCached:            0 kB
train listens rows 43484159 uids 9238 items 845395 time 0 24786795 organic 0.517
 ratios {0.0: 0.0, 0.25: 7.0, 0.5: 100.0, 0.8: 100.0, 0.9: 100.0, 0.99: 100.0, 1.0: 159.0} pos50 0.632 strong80 0.592
val listens rows 1501697 uids 8196 items 191747 time 24788600 25393395 organic 0.517
 ratios {0.0: 0.0, 0.25: 9.0, 0.5: 100.0, 0.8: 100.0, 0.9: 100.0, 0.99: 100.0, 1.0: 159.0} pos50 0.643 strong80 0.602
train likes rows 814831 uids 8144 items 172303 time 80 24786790 organic 0.567
val likes rows 37795 uids 4114 items 22902 time 24788815 25393390 organic 0.666
train dislikes rows 101075 uids 5732 items 50876 time 75 24786580 organic 0.517
val dislikes rows 3563 uids 955 items 3135 time 24789920 25393280 organic 0.515
train unlikes rows 292665 uids 6197 items 113072 time 250 24786570 organic 0.947
val unlikes rows 9346 uids 1313 items 8080 time 24789005 25393385 organic 0.953
train undislikes rows 19960 uids 2769 items 14725 time 15950 24785395 organic 0.889
val undislikes rows 606 uids 205 items 579 time 24810715 25393035 organic 0.87
14

exec command

+02:19
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/make_popularity_baseline.py\n+#!/usr/bin/env python3\n+\"\"\"Create a structurally valid eligibility-filtered popularity ranking.\"\"\"\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+\n+\n+def make(stage: str) -> Path:\n+    z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n+    item_ids = z[\"item_ids\"].astype(np.int64)\n+    rows = []\n+    for r, uid in enumerate(z[\"uids\"]):\n+        ok = np.unpackbits(\n+            z[\"packed_eligible\"][r], count=len(item_ids), bitorder=\"little\"\n+        ).astype(bool)\n+        chosen = item_ids[ok][:100]\n+        rows.append(\n+            pd.DataFrame(\n+                {\n+                    \"uid\": np.full(100, int(uid), dtype=np.int64),\n+                    \"item_id\": chosen,\n+                    \"rank\": np.arange(1, 101, dtype=np.int64),\n+                }\n+            )\n+        )\n+    frame = pd.concat(rows, ignore_index=True)\n+    name = \"validation_popularity.parquet\" if stage == \"validation\" else \"submission.parquet\"\n+    path = OUT / name\n+    frame.to_parquet(path, index=False)\n+    return path\n+\n+\n+if __name__ == \"__main__\":\n+    print(make(\"validation\"))\n+    # Maintain a valid final artifact from the beginning of the attempt.\n+    print(make(\"test\"))\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
15

exec command

+02:38
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/make_popularity_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);

Tool response

Script running with cell ID 9
Wall time 11.0 seconds
Output:
16

wait command

+02:54
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "9",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

17

exec command

+03:14
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -lh /app/solution && /opt/task-venv/bin/python -u /app/solution/make_popularity_baseline.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

wait command

+03:30
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "10",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.2 seconds
Output:

total 4.0K
-rw-r--r-- 1 agent agent 1.2K Aug 31 18:28 make_popularity_baseline.py

SESSION_ID=73301
19

exec command

+03:52
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73301,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 11
Wall time 11.0 seconds
Output:
20

wait command

+04:08
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "11",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 15.1 seconds
Output:


SESSION_ID=73301
21

exec command

+04:27
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:73301,chars:"\u0003",yield_time_ms:1000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "/app/solution/make_popularity_baseline.py", line 39, in <module>
    print(make("validation"))
          ^^^^^^^^^^^^^^^^^^
  File "/app/solution/make_popularity_baseline.py", line 19, in make
    z["packed_eligible"][r], count=len(item_ids), bitorder="little"
    ~^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__
    return format.read_array(bytes,
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array
    data = _read_bytes(fp, read_size, "array data")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes
    r = fp.read(size - len(data))
        ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read
    data = self._read1(n)
           ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1
    data = self._decompressor.decompress(data, n)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
22

exec command

+04:30
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/make_popularity_baseline.py\n@@\n def make(stage: str) -> Path:\n-    z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n-    item_ids = z[\"item_ids\"].astype(np.int64)\n+    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+        uids = z[\"uids\"].astype(np.int64, copy=True)\n+        item_ids = z[\"item_ids\"].astype(np.int64, copy=True)\n+        packed = z[\"packed_eligible\"].copy()\n+    eligible = np.unpackbits(\n+        packed, axis=1, count=len(item_ids), bitorder=\"little\"\n+    ).astype(bool)\n     rows = []\n-    for r, uid in enumerate(z[\"uids\"]):\n-        ok = np.unpackbits(\n-            z[\"packed_eligible\"][r], count=len(item_ids), bitorder=\"little\"\n-        ).astype(bool)\n-        chosen = item_ids[ok][:100]\n+    for r, uid in enumerate(uids):\n+        chosen = item_ids[eligible[r]][:100]\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
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exec command

+04:43
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/make_popularity_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 7.7 seconds
Output:

/app/solution/validation_popularity.parquet
/app/solution/submission.parquet
{
  "model": "/app/solution/validation_popularity.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
total 1.5M
-rw-r--r-- 1 agent agent 1.4K Aug 31 18:31 make_popularity_baseline.py
-rw-r--r-- 1 agent agent 719K Aug 31 18:31 submission.parquet
-rw-r--r-- 1 agent agent 712K Aug 31 18:31 validation_popularity.parquet
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Agent message

+05:15
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25

exec command

+05:27
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time()\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy()\ncat=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy()\nprint('load keys',time.time()-t)\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('loaded',len(x),round(time.time()-t,1),'memMB',round(x.memory_usage(deep=True).sum()/1e6))\nx=x[x.uid.isin(targets)&x.item_id.isin(cat)]\nprint('filtered target+cat',len(x),'users',x.uid.nunique(),'items',x.item_id.nunique(),'mem',round(x.memory_usage(deep=True).sum()/1e6),round(time.time()-t,1))\npos=x[x.played_ratio_pct>=50]\nstrongorg=x[(x.played_ratio_pct>=80)&(x.is_organic==1)]\nprint('positive',len(pos),'strongorg',len(strongorg),'recent pos 7d',len(pos[pos.timestamp>=24786800-7*86400]),'30d',len(pos[pos.timestamp>=24786800-30*86400]),'90d',len(pos[pos.timestamp>=24786800-90*86400]))\n# validation gold eligible\nz=np.load(D/'validation_eligible_candidates.npz'); zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy(); z.close()\nuidrow={int(v):i for i,v in enumerate(zu)}; itemcol={int(v):i for i,v in enumerate(zi)}\nelig=np.unpackbits(packed,axis=1,count=len(zi),bitorder='little').astype(bool)\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targets)&vl.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\nvk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[(vk.is_organic==1)&vk.uid.isin(targets)&vk.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\ng=pd.concat([vl.assign(grade=1),vk.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ng=g[[elig[uidrow[int(u)],itemcol[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('gold',len(g),'users',g.uid.nunique(),'likes',sum(g.grade==3),'strong',sum(g.grade==1))\nfor name,h in [('any',x),('pos',pos),('strongorg',strongorg)]:\n pairs=h[['uid','item_id']].drop_duplicates()\n m=g.merge(pairs,on=['uid','item_id'],how='left',indicator=True)._merge.eq('both')\n print(name,'gold pair repeat',m.mean(),'like repeat',m[g.grade.eq(3)].mean(),'users any hit',g.assign(hit=m.values).groupby('uid').hit.any().mean())\nfor days in [1,3,7,14,30,60,90,180]:\n h=pos[pos.timestamp>=24786800-days*86400][['uid','item_id']].drop_duplicates()\n m=g.merge(h,on=['uid','item_id'],how='left',indicator=True)._merge.eq('both')\n print('recent',days,'repeat',round(m.mean(),4),'usershit',round(g.assign(hit=m.values).groupby('uid').hit.any().mean(),4))\nprint('done',round(time.time()-t,1))\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 8.8 seconds
Output:

load keys 0.05421781539916992
loaded 43484159 0.6 memMB 652
filtered target+cat 31062540 users 8452 items 30000 mem 714 2.2
positive 19959853 strongorg 8661020 recent pos 7d 673983 30d 2787729 90d 7752815
gold 37769 users 4263 likes 4417 strong 33352
Traceback (most recent call last):
  File "<stdin>", line 26, in <module>
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py", line 1161, in __getitem__
    key = check_bool_indexer(self.index, key)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 2698, in check_bool_indexer
    raise IndexingError(
pandas.errors.IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).
26

exec command

+06:11
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); t=time.time(); targets=pd.read_parquet(D/'target_users.parquet').uid; cat=pd.read_parquet(D/'candidate_catalog.parquet').item_id\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); x=x[x.uid.isin(targets)&x.item_id.isin(cat)]; pos=x[x.played_ratio_pct>=50]; strongorg=x[(x.played_ratio_pct>=80)&(x.is_organic==1)]\nz=np.load(D/'validation_eligible_candidates.npz'); zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy(); z.close(); uidrow={int(v):i for i,v in enumerate(zu)}; itemcol={int(v):i for i,v in enumerate(zi)}; elig=np.unpackbits(packed,axis=1,count=len(zi),bitorder='little').astype(bool)\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targets)&vl.item_id.isin(cat)][['uid','item_id']].drop_duplicates(); vk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[(vk.is_organic==1)&vk.uid.isin(targets)&vk.item_id.isin(cat)][['uid','item_id']].drop_duplicates(); g=pd.concat([vl.assign(grade=1),vk.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); g=g[[elig[uidrow[int(u)],itemcol[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]].reset_index(drop=True)\nprint('events targetcat',len(x),'pos',len(pos),'strongorg',len(strongorg),'gold',len(g),'users',g.uid.nunique(),'likes',sum(g.grade==3))\nfor name,h in [('any',x),('pos',pos),('strongorg',strongorg)]:\n pairs=h[['uid','item_id']].drop_duplicates(); mg=g.merge(pairs,on=['uid','item_id'],how='left',indicator=True); m=mg._merge.eq('both').to_numpy(); gm=g.grade.to_numpy()\n print(name,'repeat',round(m.mean(),4),'like',round(m[gm==3].mean(),4),'usershit',round(pd.DataFrame({'uid':g.uid.to_numpy(),'hit':m}).groupby('uid').hit.any().mean(),4))\nfor days in [1,3,7,14,30,60,90,180]:\n h=pos[pos.timestamp>=24786800-days*86400][['uid','item_id']].drop_duplicates(); mg=g.merge(h,on=['uid','item_id'],how='left',indicator=True); m=mg._merge.eq('both').to_numpy()\n print('recent',days,'repeat',round(m.mean(),4),'usershit',round(pd.DataFrame({'uid':g.uid.to_numpy(),'hit':m}).groupby('uid').hit.any().mean(),4))\n# active preference overlap\npr=pd.read_parquet(D/'validation_preference_state.parquet');\nfor st in ['liked','disliked']:\n h=pr[pr.state.eq(st)][['uid','item_id']]; mg=g.merge(h,on=['uid','item_id'],how='left',indicator=True);m=mg._merge.eq('both').to_numpy();print(st,'gold overlap',round(m.mean(),4),'like gold',round(m[g.grade.to_numpy()==3].mean(),4))\nprint('sec',round(time.time()-t,1))\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script running with cell ID 16
Wall time 11.0 seconds
Output:
27

wait command

+06:56
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "16",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 0.8 seconds
Output:

events targetcat 31062540 pos 19959853 strongorg 8661020 gold 37769 users 4263 likes 4417
any repeat 0.0 like 0.0 usershit 0.0
pos repeat 0.0 like 0.0 usershit 0.0
strongorg repeat 0.0 like 0.0 usershit 0.0
recent 1 repeat 0.0 usershit 0.0
recent 3 repeat 0.0 usershit 0.0
recent 7 repeat 0.0 usershit 0.0
recent 14 repeat 0.0 usershit 0.0
recent 30 repeat 0.0 usershit 0.0
recent 60 repeat 0.0 usershit 0.0
recent 90 repeat 0.0 usershit 0.0
recent 180 repeat 0.0 usershit 0.0
liked gold overlap 0.0 like gold 0.0
disliked gold overlap 0.0 like gold 0.0
sec 12.9
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Agent message

+07:17
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Agent message

+07:36
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+07:54
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Agent message

+08:13
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Agent message

+08:32
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exec command

+08:33
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Model text

The audit found an important boundary behavior: every publicly relevant validation item is unseen in the user’s training interactions and explicit state. So repeat-item ranking cannot score here; this is a true discovery task. I’m shaping training around rolling “predict a newly discovered organic track from the prior positive-history bag” examples, which mirrors the evaluator while preserving the exact user-vector/item-vector decomposition.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nD='/task/data'; END=24786800; WEEK=604800;t=time.time(); users=pd.read_parquet(f'{D}/target_users.parquet').uid; cat=pd.read_parquet(f'{D}/candidate_catalog.parquet').item_id\nx=pd.read_parquet(f'{D}/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);x=x[x.uid.isin(users)&x.item_id.isin(cat)]\nfirst=x.groupby(['uid','item_id'],sort=False).timestamp.min().rename('first').reset_index(); s=x[(x.is_organic==1)&(x.played_ratio_pct>=80)][['uid','timestamp','item_id']].merge(first,on=['uid','item_id']);\nlikes=pd.read_parquet(f'{D}/train_likes.parquet');likes=likes[(likes.uid.isin(users))&(likes.item_id.isin(cat))&(likes.is_organic==1)][['uid','timestamp','item_id']].merge(first,on=['uid','item_id'],how='left');likes['first']=likes['first'].fillna(likes.timestamp)\nprint('unique pairs',len(first),'strong events',len(s),'organic likes',len(likes),'secs',round(time.time()-t,1))\nallpairs=[]\nfor w in range(1,17):\n c=END-w*WEEK; e=c+WEEK\n a=s[(s.timestamp>=c)&(s.timestamp<e)&(s['first']>=c)][['uid','item_id']].drop_duplicates(); b=likes[(likes.timestamp>=c)&(likes.timestamp<e)&(likes['first']>=c)][['uid','item_id']].drop_duplicates(); y=pd.concat([a,b]).drop_duplicates()\n allpairs.append(y.assign(window=w))\n print(w,'cutoff',c,'pairs',len(y),'users',y.uid.nunique(),'items',y.item_id.nunique(),'likepairs',len(b))\ny=pd.concat(allpairs);print('total',len(y),'unique useritem',len(y.drop_duplicates(['uid','item_id'])),'items',y.item_id.nunique(),'items>=2',sum(y.groupby('item_id').size()>=2),'users',y.uid.nunique())\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 10.2 seconds
Output:

unique pairs 7868664 strong events 8661020 organic likes 223046 secs 8.2
1 cutoff 24182000 pairs 41723 users 4295 items 14364 likepairs 5330
2 cutoff 23577200 pairs 40500 users 4254 items 14467 likepairs 4680
3 cutoff 22972400 pairs 40317 users 4109 items 14504 likepairs 5152
4 cutoff 22367600 pairs 38201 users 4050 items 13894 likepairs 5177
5 cutoff 21762800 pairs 38195 users 4008 items 13848 likepairs 4938
6 cutoff 21158000 pairs 34994 users 3856 items 12930 likepairs 4974
7 cutoff 20553200 pairs 36847 users 3824 items 13494 likepairs 7344
8 cutoff 19948400 pairs 33765 users 3750 items 12579 likepairs 4366
9 cutoff 19343600 pairs 32117 users 3608 items 12288 likepairs 5023
10 cutoff 18738800 pairs 32552 users 3446 items 12541 likepairs 4197
11 cutoff 18134000 pairs 31397 users 3392 items 11900 likepairs 4083
12 cutoff 17529200 pairs 30916 users 3350 items 11723 likepairs 3946
13 cutoff 16924400 pairs 30776 users 3284 items 12148 likepairs 4382
14 cutoff 16319600 pairs 29741 users 3216 items 11914 likepairs 3983
15 cutoff 15714800 pairs 28758 users 3159 items 11586 likepairs 3597
16 cutoff 15110000 pairs 27458 users 3164 items 11450 likepairs 3510
total 548257 unique useritem 544901 items 28499 items>=2 26366 users 7879
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Agent message

+09:26
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+09:45
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+10:04
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Agent message

+10:22
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Agent message

+10:41
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Agent message

+11:00
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exec command

+11:17
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Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"Audit complete: relevant outcomes are strictly novel per user, so the training design uses rolling temporal discovery examples rather than repeat-item reconstruction.",plan:[
  {step:"Audit schemas, sizes, eligibility format, and public evaluator",status:"completed"},
  {step:"Implement scalable history-based PyTorch two-tower and ranking pipeline",status:"in_progress"},
  {step:"Train and tune variants on the public validation window",status:"pending"},
  {step:"Refit selected model on train + validation history",status:"pending"},
  {step:"Generate and validate final 100-per-user submission and README",status:"pending"}
]});
text(p);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
41

exec command

+11:25
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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 an event-stream PyTorch two-tower discovery ranker.\n+\n+The program never builds a user-item matrix.  It creates rolling temporal\n+examples directly from event lists: a user tower receives three bounded\n+history sequences and predicts organic, newly discovered tracks in the next\n+week.  The item tower can therefore be run once for the catalog, while the\n+user tower is run once per request.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import math\n+import random\n+import time\n+from dataclasses import dataclass\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+from torch import nn\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_END = 24_786_800\n+WEEK = 7 * 86_400\n+GAP = 1_800\n+K_POS = 96\n+K_LIKE = 64\n+K_DISLIKE = 24\n+\n+\n+def seed_everything(seed: int) -> None:\n+    random.seed(seed)\n+    np.random.seed(seed)\n+    torch.manual_seed(seed)\n+    torch.set_num_threads(max(1, min(12, torch.get_num_threads())))\n+\n+\n+def map_ids(values: np.ndarray, sorted_ids: np.ndarray, sorted_to_index: np.ndarray) -> np.ndarray:\n+    \"\"\"Map known IDs to dense indices without a Python-sized interaction map.\"\"\"\n+    return sorted_to_index[np.searchsorted(sorted_ids, values)]\n+\n+\n+@dataclass\n+class EventIndex:\n+    \"\"\"Per-user timestamp-sorted arrays; this is an event index, not a matrix.\"\"\"\n+\n+    timestamps: list[np.ndarray]\n+    items: list[np.ndarray]\n+    states: list[np.ndarray] | None = None\n+\n+\n+def make_event_index(\n+    user_idx: np.ndarray,\n+    timestamps: np.ndarray,\n+    item_idx: np.ndarray,\n+    n_users: int,\n+    states: np.ndarray | None = None,\n+) -> EventIndex:\n+    order = np.lexsort((timestamps, user_idx))\n+    u = user_idx[order]\n+    ts = timestamps[order].astype(np.int64, copy=False)\n+    it = item_idx[order].astype(np.int32, copy=False)\n+    st = states[order].astype(np.int8, copy=False) if states is not None else None\n+    bounds = np.searchsorted(u, np.arange(n_users + 1))\n+    return EventIndex(\n+        [ts[bounds[i] : bounds[i + 1]] for i in range(n_users)],\n+        [it[bounds[i] : bounds[i + 1]] for i in range(n_users)],\n+        [st[bounds[i] : bounds[i + 1]] for i in range(n_users)] if st is not None else None,\n+    )\n+\n+\n+def recent_unique(index: EventIndex, u: int, cutoff: int, limit: int, pad: int) -> np.ndarray:\n+    ts, items = index.timestamps[u], index.items[u]\n+    end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n+    found: list[int] = []\n+    used: set[int] = set()\n+    for item in items[:end][::-1]:\n+        value = int(item)\n+        if value not in used:\n+            used.add(value)\n+            found.append(value)\n+            if len(found) == limit:\n+                break\n+    result = np.full(limit, pad, dtype=np.int32)\n+    result[: len(found)] = found\n+    return result\n+\n+\n+def recent_active(index: EventIndex, u: int, cutoff: int, limit: int, pad: int) -> np.ndarray:\n+    \"\"\"Return latest active items from a like/unlike or dislike/undislike stream.\"\"\"\n+    assert index.states is not None\n+    ts, items, states = index.timestamps[u], index.items[u], index.states[u]\n+    end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n+    found: list[int] = []\n+    resolved: set[int] = set()\n+    for item, state in zip(items[:end][::-1], states[:end][::-1]):\n+        value = int(item)\n+        if value in resolved:\n+            continue\n+        resolved.add(value)\n+        if int(state) > 0:\n+            found.append(value)\n+            if len(found) == limit:\n+                break\n+    result = np.full(limit, pad, dtype=np.int32)\n+    result[: len(found)] = found\n+    return result\n+\n+\n+@dataclass\n+class Prepared:\n+    uids: np.ndarray\n+    item_ids: np.ndarray\n+    popularity: np.ndarray\n+    pos: np.ndarray\n+    liked: np.ndarray\n+    disliked: np.ndarray\n+    target_offsets: np.ndarray\n+    target_items: np.ndarray\n+    target_weights: np.ndarray\n+    inference_pos: np.ndarray\n+    inference_liked: np.ndarray\n+    inference_disliked: np.ndarray\n+\n+\n+def load_family(name: str, include_val: bool, columns: list[str] | None = None) -> pd.DataFrame:\n+    parts = [pd.read_parquet(DATA / f\"train_{name}.parquet\", columns=columns)]\n+    if include_val:\n+        parts.append(pd.read_parquet(DATA / f\"val_{name}.parquet\", columns=columns))\n+    return pd.concat(parts, ignore_index=True)\n+\n+\n+def prepare(stage: str, windows: int) -> Prepared:\n+    \"\"\"Materialize bounded histories and rolling labels from event tables.\"\"\"\n+    start = time.time()\n+    include_val = stage == \"final\"\n+    target_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    uids = target_frame.uid.to_numpy(np.int64)\n+    item_ids = catalog.item_id.to_numpy(np.int64)\n+    n_users, n_items = len(uids), len(item_ids)\n+    su_order, si_order = np.argsort(uids), np.argsort(item_ids)\n+    sorted_uids, sorted_items = uids[su_order], item_ids[si_order]\n+\n+    listens = load_family(\n+        \"listens\", include_val, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+    )\n+    keep = listens.uid.isin(uids) & listens.item_id.isin(item_ids)\n+    listens = listens.loc[keep].copy()\n+    lu = map_ids(listens.uid.to_numpy(np.int64), sorted_uids, su_order).astype(np.int32)\n+    li = map_ids(listens.item_id.to_numpy(np.int64), sorted_items, si_order).astype(np.int32)\n+    lt = listens.timestamp.to_numpy(np.int64)\n+\n+    # Positive histories include conventional >=50% listens. Explicit active\n+    # channels are supplied separately below.\n+    positive = listens.played_ratio_pct.to_numpy() >= 50\n+    pos_index = make_event_index(lu[positive], lt[positive], li[positive], n_users)\n+\n+    action_indexes: dict[str, EventIndex] = {}\n+    action_frames: dict[str, pd.DataFrame] = {}\n+    for channel, on_name, off_name in (\n+        (\"liked\", \"likes\", \"unlikes\"),\n+        (\"disliked\", \"dislikes\", \"undislikes\"),\n+    ):\n+        on = load_family(on_name, include_val, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n+        off = load_family(off_name, include_val, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n+        action_frames[on_name] = on\n+        both = pd.concat([on.assign(_state=1), off.assign(_state=-1)], ignore_index=True)\n+        both = both[both.uid.isin(uids) & both.item_id.isin(item_ids)]\n+        au = map_ids(both.uid.to_numpy(np.int64), sorted_uids, su_order).astype(np.int32)\n+        ai = map_ids(both.item_id.to_numpy(np.int64), sorted_items, si_order).astype(np.int32)\n+        action_indexes[channel] = make_event_index(\n+            au, both.timestamp.to_numpy(np.int64), ai, n_users, both._state.to_numpy(np.int8)\n+        )\n+\n+    # First listen time is a conservative, scalable proxy for eligibility at\n+    # each historical cutoff. Exact supplied eligibility is used at ranking.\n+    first = (\n+        pd.DataFrame({\"u\": lu, \"i\": li, \"timestamp\": lt})\n+        .groupby([\"u\", \"i\"], sort=False, observed=True)\n+        .timestamp.min()\n+        .rename(\"first\")\n+        .reset_index()\n+    )\n+    strong_mask = (listens.is_organic.to_numpy() == 1) & (\n+        listens.played_ratio_pct.to_numpy() >= 80\n+    )\n+    strong = pd.DataFrame(\n+        {\"u\": lu[strong_mask], \"i\": li[strong_mask], \"timestamp\": lt[strong_mask], \"grade\": 1.0}\n+    ).merge(first, on=[\"u\", \"i\"], how=\"left\")\n+\n+    like = action_frames[\"likes\"]\n+    like = like[\n+        like.uid.isin(uids) & like.item_id.isin(item_ids) & (like.is_organic == 1)\n+    ].copy()\n+    kui = map_ids(like.uid.to_numpy(np.int64), sorted_uids, su_order).astype(np.int32)\n+    kii = map_ids(like.item_id.to_numpy(np.int64), sorted_items, si_order).astype(np.int32)\n+    like_targets = pd.DataFrame(\n+        {\"u\": kui, \"i\": kii, \"timestamp\": like.timestamp.to_numpy(np.int64), \"grade\": 3.0}\n+    ).merge(first, on=[\"u\", \"i\"], how=\"left\")\n+    like_targets[\"first\"] = like_targets[\"first\"].fillna(like_targets.timestamp)\n+\n+    specifications = [(TRAIN_END - w * WEEK, TRAIN_END - (w - 1) * WEEK) for w in range(1, windows + 1)]\n+    if stage == \"final\":\n+        specifications.insert(0, (TRAIN_END, TRAIN_END + GAP + WEEK))\n+\n+    contexts_pos: list[np.ndarray] = []\n+    contexts_like: list[np.ndarray] = []\n+    contexts_dislike: list[np.ndarray] = []\n+    target_chunks: list[np.ndarray] = []\n+    weight_chunks: list[np.ndarray] = []\n+    offsets = [0]\n+    pad = n_items\n+\n+    for cutoff, outcome_end in specifications:\n+        ys = strong[\n+            (strong.timestamp >= cutoff)\n+            & (strong.timestamp < outcome_end)\n+            & (strong[\"first\"] >= cutoff)\n+        ][[\"u\", \"i\", \"grade\"]]\n+        yl = like_targets[\n+            (like_targets.timestamp >= cutoff)\n+            & (like_targets.timestamp < outcome_end)\n+            & (like_targets[\"first\"] >= cutoff)\n+        ][[\"u\", \"i\", \"grade\"]]\n+        labels = (\n+            pd.concat([ys, yl], ignore_index=True)\n+            .groupby([\"u\", \"i\"], sort=False, observed=True)\n+            .grade.max().reset_index()\n+        )\n+        for u, group in labels.groupby(\"u\", sort=False):\n+            user = int(u)\n+            contexts_pos.append(recent_unique(pos_index, user, cutoff, K_POS, pad))\n+            contexts_like.append(recent_active(action_indexes[\"liked\"], user, cutoff, K_LIKE, pad))\n+            contexts_dislike.append(\n+                recent_active(action_indexes[\"disliked\"], user, cutoff, K_DISLIKE, pad)\n+            )\n+            # Gains, mildly compressed, determine sampling frequency.\n+            target_chunks.append(group.i.to_numpy(np.int32))\n+            weight_chunks.append(np.where(group.grade.to_numpy() >= 3, 3.0, 1.0).astype(np.float32))\n+            offsets.append(offsets[-1] + len(group))\n+        print(\n+            f\"cutoff={cutoff} contexts={labels.u.nunique()} labels={len(labels)}\",\n+            flush=True,\n+        )\n+\n+    inference_cutoff = int(listens.timestamp.max()) + 1 if include_val else TRAIN_END\n+    inference_pos = np.stack(\n+        [recent_unique(pos_index, u, inference_cutoff, K_POS, pad) for u in range(n_users)]\n+    )\n+\n+    # Cutoff-specific supplied state is authoritative at inference. Sort active\n+    # items by their latest corresponding positive/negative action where known.\n+    preference_name = \"test_preference_state.parquet\" if stage == \"final\" else \"validation_preference_state.parquet\"\n+    preference = pd.read_parquet(DATA / preference_name)\n+    inference_channels = []\n+    for state, limit, action_index in (\n+        (\"liked\", K_LIKE, action_indexes[\"liked\"]),\n+        (\"disliked\", K_DISLIKE, action_indexes[\"disliked\"]),\n+    ):\n+        by_user: dict[int, set[int]] = {}\n+        active = preference[\n+            preference.state.eq(state) & preference.uid.isin(uids) & preference.item_id.isin(item_ids)\n+        ]\n+        if len(active):\n+            au = map_ids(active.uid.to_numpy(np.int64), sorted_uids, su_order)\n+            ai = map_ids(active.item_id.to_numpy(np.int64), sorted_items, si_order)\n+            for u, i in zip(au, ai):\n+                by_user.setdefault(int(u), set()).add(int(i))\n+        array = np.full((n_users, limit), pad, dtype=np.int32)\n+        for u in range(n_users):\n+            allowed = by_user.get(u, set())\n+            if not allowed:\n+                continue\n+            # Reverse action order supplies useful recency while membership\n+            # comes solely from the evaluator-provided state.\n+            ordered: list[int] = []\n+            seen: set[int] = set()\n+            for item in action_index.items[u][::-1]:\n+                value = int(item)\n+                if value in allowed and value not in seen:\n+                    ordered.append(value)\n+                    seen.add(value)\n+                    if len(ordered) == limit:\n+                        break\n+            if len(ordered) < limit:\n+                ordered.extend(sorted(allowed - seen)[: limit - len(ordered)])\n+            array[u, : len(ordered)] = ordered\n+        inference_channels.append(array)\n+\n+    pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\n+    pop = ((pop - pop.mean()) / (pop.std() + 1e-6)).astype(np.float32)\n+    result = Prepared(\n+        uids=uids,\n+        item_ids=item_ids,\n+        popularity=pop,\n+        pos=np.stack(contexts_pos),\n+        liked=np.stack(contexts_like),\n+        disliked=np.stack(contexts_dislike),\n+        target_offsets=np.asarray(offsets, dtype=np.int64),\n+        target_items=np.concatenate(target_chunks).astype(np.int32),\n+        target_weights=np.concatenate(weight_chunks).astype(np.float32),\n+        inference_pos=inference_pos,\n+        inference_liked=inference_channels[0],\n+        inference_disliked=inference_channels[1],\n+    )\n+    print(\n+        f\"prepared {len(result.pos)} contexts, {len(result.target_items)} labels in {time.time()-start:.1f}s\",\n+        flush=True,\n+    )\n+    return result\n+\n+\n+class UserTower(nn.Module):\n+    \"\"\"History-only tower: bounded positive/like/dislike bags -> request vector.\"\"\"\n+\n+    def __init__(self, n_items: int, dim: int):\n+        super().__init__()\n+        self.pad = n_items\n+        self.embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n+        self.pos_logits = nn.Parameter(torch.linspace(1.0, -1.0, K_POS))\n+        self.like_logits = nn.Parameter(torch.zeros(K_LIKE))\n+        self.dislike_logits = nn.Parameter(torch.zeros(K_DISLIKE))\n+        self.network = nn.Sequential(\n+            nn.LayerNorm(3 * dim + 3),\n+            nn.Linear(3 * dim + 3, 2 * dim),\n+            nn.GELU(),\n+            nn.Linear(2 * dim, dim),\n+        )\n+\n+    def pool(self, ids: torch.Tensor, logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n+        valid = ids.ne(self.pad)\n+        attention = logits.unsqueeze(0).expand_as(ids).masked_fill(~valid, -1e4)\n+        attention = torch.softmax(attention, dim=1) * valid\n+        attention = attention / attention.sum(1, keepdim=True).clamp_min(1e-6)\n+        pooled = (self.embedding(ids) * attention.unsqueeze(-1)).sum(1)\n+        amount = torch.log1p(valid.sum(1).float()).unsqueeze(1) / 5.0\n+        return pooled, amount\n+\n+    def forward(self, pos: torch.Tensor, liked: torch.Tensor, disliked: torch.Tensor) -> torch.Tensor:\n+        p, pn = self.pool(pos, self.pos_logits)\n+        l, ln = self.pool(liked, self.like_logits)\n+        d, dn = self.pool(disliked, self.dislike_logits)\n+        return self.network(torch.cat([p, l, d, pn, ln, dn], dim=1))\n+\n+\n+class ItemTower(nn.Module):\n+    \"\"\"Independently executable catalog tower.\"\"\"\n+\n+    def __init__(self, n_items: int, dim: int, popularity: np.ndarray):\n+        super().__init__()\n+        self.embedding = nn.Embedding(n_items, dim)\n+        self.register_buffer(\"popularity\", torch.from_numpy(popularity).unsqueeze(1))\n+        self.network = nn.Sequential(\n+            nn.LayerNorm(dim + 1),\n+            nn.Linear(dim + 1, 2 * dim),\n+            nn.GELU(),\n+            nn.Linear(2 * dim, dim),\n+        )\n+\n+    def forward(self, ids: torch.Tensor) -> torch.Tensor:\n+        return self.network(torch.cat([self.embedding(ids), self.popularity[ids]], dim=1))\n+\n+\n+class TwoTower(nn.Module):\n+    def __init__(self, n_items: int, dim: int, popularity: np.ndarray):\n+        super().__init__()\n+        self.user_tower = UserTower(n_items, dim)\n+        self.item_tower = ItemTower(n_items, dim, popularity)\n+        self.logit_scale = nn.Parameter(torch.tensor(0.0))\n+\n+    def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:\n+        return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)\n+\n+\n+def train_model(data: Prepared, args: argparse.Namespace) -> TwoTower:\n+    n_contexts, n_items = len(data.pos), len(data.item_ids)\n+    model = TwoTower(n_items, args.dim, data.popularity)\n+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=2e-5)\n+    rng = np.random.default_rng(args.seed)\n+    batch_size = args.batch_size\n+    pos_all = torch.from_numpy(data.pos).long()\n+    like_all = torch.from_numpy(data.liked).long()\n+    dislike_all = torch.from_numpy(data.disliked).long()\n+    for epoch in range(args.epochs):\n+        model.train()\n+        chosen = np.empty(n_contexts, dtype=np.int64)\n+        sample_weight = np.empty(n_contexts, dtype=np.float32)\n+        for c in range(n_contexts):\n+            lo, hi = data.target_offsets[c : c + 2]\n+            weights = data.target_weights[lo:hi]\n+            j = int(rng.choice(hi - lo, p=weights / weights.sum()))\n+            chosen[c] = data.target_items[lo + j]\n+            sample_weight[c] = weights[j]\n+        order = rng.permutation(n_contexts)\n+        total_loss = 0.0\n+        seen = 0\n+        for start in range(0, n_contexts, batch_size):\n+            c = order[start : start + batch_size]\n+            targets = chosen[c]\n+            # Unique batch positives plus uniform catalog negatives preserve a\n+            # learnable popularity prior while keeping complexity sampled.\n+            candidates = np.unique(\n+                np.concatenate([targets, rng.integers(0, n_items, args.negatives, dtype=np.int64)])\n+            )\n+            lookup = {int(v): j for j, v in enumerate(candidates)}\n+            labels = torch.tensor([lookup[int(v)] for v in targets], dtype=torch.long)\n+            ct = torch.from_numpy(candidates).long()\n+            users = model.user_tower(pos_all[c], like_all[c], dislike_all[c])\n+            items = model.item_tower(ct)\n+            logits = model.score(users, items)\n+            losses = nn.functional.cross_entropy(logits, labels, reduction=\"none\")\n+            weights = torch.from_numpy(np.sqrt(sample_weight[c]))\n+            loss = (losses * weights).sum() / weights.sum()\n+            optimizer.zero_grad(set_to_none=True)\n+            loss.backward()\n+            nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n+            optimizer.step()\n+            total_loss += float(loss) * len(c)\n+            seen += len(c)\n+        print(\n+            f\"epoch {epoch+1:02d}/{args.epochs} loss={total_loss/seen:.4f} scale={model.logit_scale.exp().item():.3f}\",\n+            flush=True,\n+        )\n+    return model\n+\n+\[email protected]_mode()\n+def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float) -> Path:\n+    model.eval()\n+    n_users, n_items = len(data.uids), len(data.item_ids)\n+    all_items = model.item_tower(torch.arange(n_items)).float()\n+    # A dot-product-compatible popularity coordinate, selected on public\n+    # validation, is appended to both independently computed vectors.\n+    pop_coordinate = torch.from_numpy(data.popularity).float()\n+    eligibility_name = \"test_eligible_candidates.npz\" if stage == \"final\" else \"validation_eligible_candidates.npz\"\n+    with np.load(DATA / eligibility_name) as z:\n+        packed = z[\"packed_eligible\"].copy()\n+    rows: list[pd.DataFrame] = []\n+    pos = torch.from_numpy(data.inference_pos).long()\n+    liked = torch.from_numpy(data.inference_liked).long()\n+    disliked = torch.from_numpy(data.inference_disliked).long()\n+    for start in range(0, n_users, 64):\n+        end = min(start + 64, n_users)\n+        users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\n+        scores = users @ all_items.T\n+        scores += popularity_mix * pop_coordinate.unsqueeze(0)\n+        eligible = np.unpackbits(\n+            packed[start:end], axis=1, count=n_items, bitorder=\"little\"\n+        ).astype(bool)\n+        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n+        top = torch.topk(scores, k=100, dim=1, largest=True, sorted=True).indices.numpy()\n+        for local, chosen in enumerate(top):\n+            rows.append(\n+                pd.DataFrame(\n+                    {\n+                        \"uid\": np.full(100, data.uids[start + local], dtype=np.int64),\n+                        \"item_id\": data.item_ids[chosen].astype(np.int64),\n+                        \"rank\": np.arange(1, 101, dtype=np.int64),\n+                    }\n+                )\n+            )\n+    frame = pd.concat(rows, ignore_index=True)\n+    path = OUT / (\"submission.parquet\" if stage == \"final\" else \"validation.parquet\")\n+    frame.to_parquet(path, index=False)\n+    return path\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n+    parser.add_argument(\"--windows\", type=int, default=16)\n+    parser.add_argument(\"--dim\", type=int, default=48)\n+    parser.add_argument(\"--epochs\", type=int, default=10)\n+    parser.add_argument(\"--batch-size\", type=int, default=384)\n+    parser.add_argument(\"--negatives\", type=int, default=256)\n+    parser.add_argument(\"--lr\", type=float, default=0.002)\n+    parser.add_argument(\"--seed\", type=int, default=20260831)\n+    parser.add_argument(\"--popularity-mix\", type=float, default=0.0)\n+    args = parser.parse_args()\n+    seed_everything(args.seed)\n+    prepared = prepare(args.stage, args.windows)\n+    model = train_model(prepared, args)\n+    checkpoint = OUT / f\"two_tower_{args.stage}.pt\"\n+    torch.save({\"model\": model.state_dict(), \"args\": vars(args)}, checkpoint)\n+    path = rank(model, prepared, args.stage, args.popularity_mix)\n+    metadata = {\n+        \"stage\": args.stage,\n+        \"architecture\": \"history pooling user tower + independent item MLP tower\",\n+        \"args\": vars(args),\n+        \"contexts\": len(prepared.pos),\n+        \"labels\": len(prepared.target_items),\n+        \"ranking\": str(path),\n+    }\n+    (OUT / f\"run_{args.stage}.json\").write_text(json.dumps(metadata, indent=2) + \"\\n\")\n+    print(path)\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
42

exec command

+15:26
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --epochs 8 --dim 48 --batch-size 384 --negatives 256 --lr 0.002 --popularity-mix 0.0",workdir:"/app",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 20
Wall time 11.0 seconds
Output:
43

wait command

+15:45
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "20",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 15.2 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 25.4s

SESSION_ID=77337
44

exec command

+16:02
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Model text

The rolling dataset is built: 58,765 cutoff-specific history contexts and 548,257 novel organic labels across 16 weekly backtests. Training is now running on sampled candidates; once it finishes I’ll score several dot-product-compatible popularity calibrations against the public window rather than committing to the first neural output.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:77337,chars:"",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 21
Wall time 11.0 seconds
Output:
45

wait command

+16:20
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "21",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

/app/solution/train_ranker.py:424: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(c)
epoch 01/8 loss=6.3829 scale=0.853
epoch 02/8 loss=6.3462 scale=0.822
epoch 03/8 loss=6.3164 scale=0.806
epoch 04/8 loss=6.2814 scale=0.773
epoch 05/8 loss=6.2337 scale=0.752
epoch 06/8 loss=6.1732 scale=0.728
epoch 07/8 loss=6.0928 scale=0.704

SESSION_ID=77337
46

exec command

+16:39
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:77337,chars:"",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.4 seconds
Output:

epoch 08/8 loss=6.0150 scale=0.686
/app/solution/validation.parquet
47

exec command

+16:43
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /app/solution/two_tower_validation.pt /app/solution/validation.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:4000});
text(r.output);

Tool response

Script completed
Wall time 3.5 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.003206,
    "quality_users": 4263
  }
}
-rw-r--r-- 1 agent agent  12M Aug 31 18:42 /app/solution/two_tower_validation.pt
-rw-r--r-- 1 agent agent 1.8M Aug 31 18:43 /app/solution/validation.parquet
48

exec command

+17:05
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float) -> Path:\n+def rank(\n+    model: TwoTower,\n+    data: Prepared,\n+    stage: str,\n+    popularity_mix: float,\n+    output_name: str | None = None,\n+) -> Path:\n@@\n-    path = OUT / (\"submission.parquet\" if stage == \"final\" else \"validation.parquet\")\n+    path = OUT / (\n+        output_name\n+        or (\"submission.parquet\" if stage == \"final\" else \"validation.parquet\")\n+    )\n@@\n     parser.add_argument(\"--popularity-mix\", type=float, default=0.0)\n+    parser.add_argument(\"--rank-only\", action=\"store_true\")\n+    parser.add_argument(\"--popularity-grid\", default=\"\")\n@@\n-    model = train_model(prepared, args)\n     checkpoint = OUT / f\"two_tower_{args.stage}.pt\"\n-    torch.save({\"model\": model.state_dict(), \"args\": vars(args)}, checkpoint)\n-    path = rank(model, prepared, args.stage, args.popularity_mix)\n+    if args.rank_only:\n+        saved = torch.load(checkpoint, map_location=\"cpu\", weights_only=True)\n+        saved_args = saved[\"args\"]\n+        model = TwoTower(len(prepared.item_ids), int(saved_args[\"dim\"]), prepared.popularity)\n+        model.load_state_dict(saved[\"model\"])\n+    else:\n+        model = train_model(prepared, args)\n+        torch.save({\"model\": model.state_dict(), \"args\": vars(args)}, checkpoint)\n+    mixes = (\n+        [float(value) for value in args.popularity_grid.split(\",\")]\n+        if args.popularity_grid\n+        else [args.popularity_mix]\n+    )\n+    path = None\n+    for mix in mixes:\n+        token = str(mix).replace(\"-\", \"m\").replace(\".\", \"p\")\n+        output_name = None if len(mixes) == 1 else f\"validation_mix_{token}.parquet\"\n+        if stage := args.stage == \"final\":\n+            output_name = None\n+        path = rank(model, prepared, args.stage, mix, output_name)\n+        print(path)\n@@\n-        \"ranking\": str(path),\n+        \"ranking\": str(path),\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
49

exec command

+17:33
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-        if stage := args.stage == \"final\":\n+        if args.stage == \"final\":\n*** End Patch";
text(await tools.apply_patch(patch));
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 48 --rank-only --popularity-grid 0.1,0.3,1,3,10,30",workdir:"/app",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

{}
50

wait command

+17:53
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "25",
  "yield_time_ms": 30000,
  "max_tokens": 6000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 25.2s

SESSION_ID=25752
51

exec command

+18:12
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:25752,chars:"",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 7.6 seconds
Output:

/app/solution/validation_mix_0p1.parquet
/app/solution/validation_mix_0p3.parquet
/app/solution/validation_mix_1p0.parquet
/app/solution/validation_mix_3p0.parquet
/app/solution/validation_mix_10p0.parquet
/app/solution/validation_mix_30p0.parquet
/app/solution/validation_mix_30p0.parquet
52

exec command

+18:24
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in /app/solution/validation_mix_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done",workdir:"/app",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 27
Wall time 11.0 seconds
Output:
53

wait command

+18:40
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "27",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 7.0 seconds
Output:

{   "model": "/app/solution/validation_mix_0p1.parquet",   "scores": {     "quality_ndcg@10": 0.003731,     "quality_users": 4263   } } 
{   "model": "/app/solution/validation_mix_0p3.parquet",   "scores": {     "quality_ndcg@10": 0.005177,     "quality_users": 4263   } } 
{   "model": "/app/solution/validation_mix_10p0.parquet",   "scores": {     "quality_ndcg@10": 0.01069,     "quality_users": 4263   } } 
{   "model": "/app/solution/validation_mix_1p0.parquet",   "scores": {     "quality_ndcg@10": 0.008717,     "quality_users": 4263   } } 
{   "model": "/app/solution/validation_mix_30p0.parquet",   "scores": {     "quality_ndcg@10": 0.010811,     "quality_users": 4263   } } 
{   "model": "/app/solution/validation_mix_3p0.parquet",   "scores": {     "quality_ndcg@10": 0.010472,     "quality_users": 4263   } } 
54

exec command

+19:07
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Model text

The first neural model by itself is too weak, but its best calibrated blend slightly improves the catalog-popularity baseline (0.010811 vs. 0.010710). I’m not stopping there: I’m testing objective-aligned, time-decayed organic popularity as an additional item-tower coordinate, then I’ll retrain the personalized component longer only if it adds lift on top of the best global coordinate.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_item_prior.py\n+#!/usr/bin/env python3\n+\"\"\"Tune global item features; each is a valid constant user-tower coordinate.\"\"\"\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+END = 24_786_800\n+DAY = 86_400\n+\n+\n+def ranking(order: np.ndarray, name: str) -> Path:\n+    with np.load(DATA / \"validation_eligible_candidates.npz\") as z:\n+        uids = z[\"uids\"].astype(np.int64, copy=True)\n+        items = z[\"item_ids\"].astype(np.int64, copy=True)\n+        eligible = np.unpackbits(\n+            z[\"packed_eligible\"], axis=1, count=len(items), bitorder=\"little\"\n+        ).astype(bool)\n+    chunks = []\n+    for r, uid in enumerate(uids):\n+        chosen = order[eligible[r, order]][:100]\n+        chunks.append(\n+            pd.DataFrame(\n+                {\n+                    \"uid\": np.full(100, uid, dtype=np.int64),\n+                    \"item_id\": items[chosen],\n+                    \"rank\": np.arange(1, 101, dtype=np.int64),\n+                }\n+            )\n+        )\n+    path = OUT / f\"validation_prior_{name}.parquet\"\n+    pd.concat(chunks, ignore_index=True).to_parquet(path, index=False)\n+    return path\n+\n+\n+def main() -> None:\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    ids = catalog.item_id.to_numpy(np.int64)\n+    mapper = pd.Series(np.arange(len(ids), dtype=np.int32), index=ids)\n+    listens = pd.read_parquet(\n+        DATA / \"train_listens.parquet\",\n+        columns=[\"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+    )\n+    listens = listens[\n+        listens.item_id.isin(ids)\n+        & (listens.is_organic == 1)\n+        & (listens.played_ratio_pct >= 80)\n+    ].copy()\n+    listens[\"i\"] = listens.item_id.map(mapper).astype(np.int32)\n+    likes = pd.read_parquet(DATA / \"train_likes.parquet\")\n+    likes = likes[likes.item_id.isin(ids) & (likes.is_organic == 1)].copy()\n+    likes[\"i\"] = likes.item_id.map(mapper).astype(np.int32)\n+    features: dict[str, np.ndarray] = {\n+        \"catalog_all\": np.log1p(catalog.positive_listens.to_numpy(float)),\n+        \"catalog_org\": np.log1p(catalog.organic_positive_listens.to_numpy(float)),\n+    }\n+    for days in (7, 14, 30, 60, 90, 180):\n+        lc = np.bincount(\n+            listens.loc[listens.timestamp >= END - days * DAY, \"i\"], minlength=len(ids)\n+        ).astype(float)\n+        kc = np.bincount(\n+            likes.loc[likes.timestamp >= END - days * DAY, \"i\"], minlength=len(ids)\n+        ).astype(float)\n+        features[f\"listen_{days}d\"] = np.log1p(lc)\n+        features[f\"objective_{days}d\"] = np.log1p(lc + 7 * kc)\n+    # Normalize before blended priors.\n+    z = {k: (v - v.mean()) / (v.std() + 1e-9) for k, v in features.items()}\n+    features[\"blend_30\"] = z[\"catalog_org\"] + z[\"objective_30d\"]\n+    features[\"blend_60\"] = z[\"catalog_org\"] + z[\"objective_60d\"]\n+    features[\"blend_recent\"] = z[\"objective_14d\"] + z[\"objective_60d\"]\n+    for name, values in features.items():\n+        order = np.argsort(-values, kind=\"stable\")\n+        print(ranking(order, name), flush=True)\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/tune_item_prior.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

{}
55

wait command

+20:04
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The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "28",
  "yield_time_ms": 30000,
  "max_tokens": 6000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

/app/solution/validation_prior_catalog_all.parquet
/app/solution/validation_prior_catalog_org.parquet
/app/solution/validation_prior_listen_7d.parquet
/app/solution/validation_prior_objective_7d.parquet
/app/solution/validation_prior_listen_14d.parquet
/app/solution/validation_prior_objective_14d.parquet
/app/solution/validation_prior_listen_30d.parquet
/app/solution/validation_prior_objective_30d.parquet
/app/solution/validation_prior_listen_60d.parquet
/app/solution/validation_prior_objective_60d.parquet
/app/solution/validation_prior_listen_90d.parquet
/app/solution/validation_prior_objective_90d.parquet
/app/solution/validation_prior_listen_180d.parquet

SESSION_ID=86100
56

exec command

+20:23
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:86100,chars:"",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.5 seconds
Output:

/app/solution/validation_prior_objective_180d.parquet
/app/solution/validation_prior_blend_30.parquet
/app/solution/validation_prior_blend_60.parquet
/app/solution/validation_prior_blend_recent.parquet
57

exec command

+20:30
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in /app/solution/validation_prior_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | awk -v f=\"$(basename \"$f\")\" '/quality_ndcg/{gsub(\",\",\"\"); print f,$2}'; done | sort -k2,2nr",workdir:"/app",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 30
Wall time 11.0 seconds
Output:
58

wait command

+20:47
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "30",
  "yield_time_ms": 30000,
  "max_tokens": 6000
}

Tool response

Script completed
Wall time 16.9 seconds
Output:


SESSION_ID=84586
59

exec command

+21:06
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:84586,chars:"",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 31
Wall time 11.0 seconds
Output:
60

wait command

+21:20
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "31",
  "yield_time_ms": 30000,
  "max_tokens": 6000
}

Tool response

Script completed
Wall time 10.1 seconds
Output:

validation_prior_objective_7d.parquet 0.028941
validation_prior_objective_14d.parquet 0.02845
validation_prior_listen_7d.parquet 0.028131
validation_prior_listen_14d.parquet 0.022878
validation_prior_blend_recent.parquet 0.02002
validation_prior_objective_60d.parquet 0.016611
validation_prior_objective_90d.parquet 0.01607
validation_prior_listen_90d.parquet 0.015822
validation_prior_listen_60d.parquet 0.015802
validation_prior_objective_30d.parquet 0.015531
validation_prior_blend_60.parquet 0.014933
validation_prior_blend_30.parquet 0.01362
validation_prior_listen_30d.parquet 0.013314
validation_prior_objective_180d.parquet 0.012563
validation_prior_listen_180d.parquet 0.011908
validation_prior_catalog_all.parquet 0.01071
validation_prior_catalog_org.parquet 0.010577
61

Agent message

+21:51
Thinking unavailable No text recorded

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

62

exec command

+21:53
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The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

A much stronger signal emerged: seven-day organic completion plus organic-like velocity reaches 0.028941 NDCG@10—nearly 3× the original baseline. This is fully compatible with the architecture as an offline item-tower feature paired with a constant user-tower coordinate. I’m now retraining the neural residual around that feature so personalization can add to, rather than displace, the strong time-local prior.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-    pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\n+    # Objective-aligned velocity is substantially more predictive than the\n+    # all-time catalog order and is independently precomputable offline.\n+    feature_cutoff = int(listens.timestamp.max()) + 1 if include_val else TRAIN_END\n+    recent_strong = strong[\n+        (strong.timestamp >= feature_cutoff - WEEK) & (strong.timestamp < feature_cutoff)\n+    ]\n+    recent_like = like_targets[\n+        (like_targets.timestamp >= feature_cutoff - WEEK)\n+        & (like_targets.timestamp < feature_cutoff)\n+    ]\n+    listen_counts = np.bincount(recent_strong.i.to_numpy(np.int32), minlength=n_items)\n+    like_counts = np.bincount(recent_like.i.to_numpy(np.int32), minlength=n_items)\n+    pop = np.log1p(listen_counts + 7.0 * like_counts).astype(np.float32)\n*** End Patch";
text(await tools.apply_patch(patch));
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --epochs 18 --dim 64 --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 3",workdir:"/app",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

{}
63

wait command

+22:24
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": "32",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 16.8 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 24.6s

SESSION_ID=48518
64

exec command

+22:43
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48518,chars:"",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 33
Wall time 11.0 seconds
Output:
65

wait command

+22:58
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "33",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:

/app/solution/train_ranker.py:436: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(c)
epoch 01/18 loss=6.5128 scale=0.878
epoch 02/18 loss=6.4635 scale=0.861
epoch 03/18 loss=6.4310 scale=0.852
epoch 04/18 loss=6.3922 scale=0.840
epoch 05/18 loss=6.3451 scale=0.829
epoch 06/18 loss=6.2779 scale=0.819

SESSION_ID=48518
66

exec command

+23:17
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48518,chars:"",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 34
Wall time 11.0 seconds
Output:
67

wait command

+23:31
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "34",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

epoch 07/18 loss=6.2044 scale=0.815
epoch 08/18 loss=6.1102 scale=0.804
epoch 09/18 loss=6.0217 scale=0.794
epoch 10/18 loss=5.9189 scale=0.781
epoch 11/18 loss=5.8207 scale=0.771
epoch 12/18 loss=5.7099 scale=0.757

SESSION_ID=48518
68

exec command

+23:50
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:48518,chars:"",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 35
Wall time 11.0 seconds
Output:
69

wait command

+24:05
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "35",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 15.1 seconds
Output:

epoch 13/18 loss=5.6203 scale=0.747
epoch 14/18 loss=5.5238 scale=0.731
epoch 15/18 loss=5.4216 scale=0.721
epoch 16/18 loss=5.3368 scale=0.708
epoch 17/18 loss=5.2565 scale=0.692
epoch 18/18 loss=5.1812 scale=0.678
/app/solution/validation.parquet
/app/solution/validation.parquet
70

exec command

+24:23
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 /task/evaluate_public.py /app/solution/validation.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:3000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.024339,
    "quality_users": 4263
  }
}
71

exec command

+24:32
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-grid 3,5,10,20,40,80",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 37
Wall time 11.0 seconds
Output:
72

wait command

+24:49
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "37",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 25.1s

SESSION_ID=65478
73

exec command

+25:08
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:65478,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 9.4 seconds
Output:

/app/solution/validation_mix_3p0.parquet
/app/solution/validation_mix_5p0.parquet
/app/solution/validation_mix_10p0.parquet
/app/solution/validation_mix_20p0.parquet
/app/solution/validation_mix_40p0.parquet
/app/solution/validation_mix_80p0.parquet
/app/solution/validation_mix_80p0.parquet
74

exec command

+25:21
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in /app/solution/validation_mix_3p0.parquet /app/solution/validation_mix_5p0.parquet /app/solution/validation_mix_10p0.parquet /app/solution/validation_mix_20p0.parquet /app/solution/validation_mix_40p0.parquet /app/solution/validation_mix_80p0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | awk -v f=\"$(basename \"$f\")\" '/quality_ndcg/{gsub(\",\",\"\"); print f,$2}'; done",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 39
Wall time 11.0 seconds
Output:
75

wait command

+25:41
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 6.8 seconds
Output:

validation_mix_3p0.parquet 0.024339
validation_mix_5p0.parquet 0.028582
validation_mix_10p0.parquet 0.029405
validation_mix_20p0.parquet 0.0293
validation_mix_40p0.parquet 0.029379
validation_mix_80p0.parquet 0.029025
76

exec command

+26:09
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 personalized residual now helps: the best exact-dot-product blend scores 0.029405, above the 0.028941 item prior alone. I’m doing one final compact search over prior half-life and organic-like weight; this uses validation only for model selection, never as validation input features. Then I’ll freeze those settings and refit on train plus validation history for the hidden window.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/search_prior.py\n+#!/usr/bin/env python3\n+\"\"\"Fast public-only selection of objective-aligned item-prior settings.\"\"\"\n+import sys\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+\n+sys.path.insert(0, \"/task\")\n+from ranking_evaluation import ndcg_at_k  # noqa: E402\n+\n+\n+D = Path(\"/task/data\")\n+END = 24_786_800\n+DAY = 86_400\n+\n+\n+def main() -> None:\n+    cat = pd.read_parquet(D / \"candidate_catalog.parquet\")\n+    ids = cat.item_id.to_numpy(np.int64)\n+    id_to_i = pd.Series(np.arange(len(ids), dtype=np.int32), index=ids)\n+    targets = set(pd.read_parquet(D / \"target_users.parquet\").uid.astype(int))\n+    with np.load(D / \"validation_eligible_candidates.npz\") as z:\n+        zu, zi, packed = z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n+    eligible = np.unpackbits(packed, axis=1, count=len(zi), bitorder=\"little\").astype(bool)\n+    uidrow = {int(v): i for i, v in enumerate(zu)}\n+    itemcol = {int(v): i for i, v in enumerate(zi)}\n+    vl = pd.read_parquet(D / \"val_listens.parquet\")\n+    vl = vl[(vl.uid.isin(targets)) & (vl.item_id.isin(ids)) & (vl.is_organic == 1) & (vl.played_ratio_pct >= 80)][[\"uid\", \"item_id\"]].drop_duplicates().assign(grade=1)\n+    vk = pd.read_parquet(D / \"val_likes.parquet\")\n+    vk = vk[(vk.uid.isin(targets)) & (vk.item_id.isin(ids)) & (vk.is_organic == 1)][[\"uid\", \"item_id\"]].drop_duplicates().assign(grade=3)\n+    gold_frame = pd.concat([vl, vk]).sort_values(\"grade\").drop_duplicates([\"uid\", \"item_id\"], keep=\"last\")\n+    gold_frame = gold_frame[[eligible[uidrow[int(u)], itemcol[int(i)]] for u, i in gold_frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)]]\n+    gold = {int(u): dict(zip(g.item_id.astype(int), g.grade.astype(int))) for u, g in gold_frame.groupby(\"uid\")}\n+\n+    x = pd.read_parquet(D / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\n+    x = x[x.item_id.isin(ids) & (x.is_organic == 1) & (x.played_ratio_pct >= 80)].copy()\n+    x[\"i\"] = x.item_id.map(id_to_i).astype(np.int32)\n+    k = pd.read_parquet(D / \"train_likes.parquet\")\n+    k = k[k.item_id.isin(ids) & (k.is_organic == 1)].copy()\n+    k[\"i\"] = k.item_id.map(id_to_i).astype(np.int32)\n+\n+    def score(values: np.ndarray) -> float:\n+        order = np.argsort(-values, kind=\"stable\")\n+        results = []\n+        for uid, truth in gold.items():\n+            chosen = order[eligible[uidrow[uid], order]][:10]\n+            results.append(ndcg_at_k(ids[chosen].tolist(), truth, 10))\n+        return float(np.mean(results))\n+\n+    results = []\n+    for days in (3, 4, 5, 6, 7, 8, 9, 10, 12, 14):\n+        lm = x.timestamp >= END - days * DAY\n+        km = k.timestamp >= END - days * DAY\n+        lc = np.bincount(x.loc[lm, \"i\"], minlength=len(ids)).astype(float)\n+        kc = np.bincount(k.loc[km, \"i\"], minlength=len(ids)).astype(float)\n+        for mult in (2, 4, 7, 10, 15, 25):\n+            results.append((score(np.log1p(lc + mult * kc)), f\"window={days} like={mult}\"))\n+        # Unique-listener velocity is less sensitive to a few heavy listeners.\n+        uc = np.bincount(x.loc[lm].drop_duplicates([\"uid\", \"i\"]).i, minlength=len(ids)).astype(float)\n+        uk = np.bincount(k.loc[km].drop_duplicates([\"uid\", \"i\"]).i, minlength=len(ids)).astype(float)\n+        for mult in (2, 7, 15):\n+            results.append((score(np.log1p(uc + mult * uk)), f\"unique_window={days} like={mult}\"))\n+    age_l = (END - x.timestamp.to_numpy()) / DAY\n+    age_k = (END - k.timestamp.to_numpy()) / DAY\n+    for half in (1, 2, 3, 4, 5, 7, 10, 14):\n+        lc = np.bincount(x.i, weights=np.exp2(-age_l / half), minlength=len(ids))\n+        kc = np.bincount(k.i, weights=np.exp2(-age_k / half), minlength=len(ids))\n+        for mult in (2, 4, 7, 10, 15):\n+            results.append((score(np.log1p(lc + mult * kc)), f\"halflife={half} like={mult}\"))\n+    for value, name in sorted(results, reverse=True)[:20]:\n+        print(f\"{value:.6f} {name}\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/search_prior.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

{}
77

wait command

+27:19
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": "40",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:


SESSION_ID=21412
78

exec command

+27:38
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:21412,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 41
Wall time 11.0 seconds
Output:
79

wait command

+27:53
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "41",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.7 seconds
Output:


SESSION_ID=21412
80

exec command

+28:13
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:21412,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 5.7 seconds
Output:

0.034450 unique_window=3 like=7
0.034030 unique_window=3 like=15
0.033737 unique_window=6 like=15
0.033568 unique_window=5 like=15
0.033528 unique_window=5 like=7
0.033440 unique_window=4 like=7
0.033349 unique_window=6 like=7
0.033298 unique_window=3 like=2
0.033146 unique_window=4 like=15
0.032748 unique_window=10 like=15
0.032533 unique_window=5 like=2
0.032478 unique_window=10 like=7
0.032468 unique_window=8 like=7
0.032346 unique_window=4 like=2
0.032309 unique_window=9 like=15
0.032159 unique_window=7 like=7
0.032091 unique_window=8 like=15
0.032024 unique_window=6 like=2
0.031903 unique_window=12 like=15
0.031864 unique_window=9 like=7
81

exec command

+28:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_ranker.py\n@@\n-    recent_strong = strong[\n-        (strong.timestamp >= feature_cutoff - WEEK) & (strong.timestamp < feature_cutoff)\n-    ]\n+    prior_window = 3 * 86_400\n+    recent_strong = strong[\n+        (strong.timestamp >= feature_cutoff - prior_window)\n+        & (strong.timestamp < feature_cutoff)\n+    ].drop_duplicates([\"u\", \"i\"])\n     recent_like = like_targets[\n-        (like_targets.timestamp >= feature_cutoff - WEEK)\n+        (like_targets.timestamp >= feature_cutoff - prior_window)\n         & (like_targets.timestamp < feature_cutoff)\n-    ]\n+    ].drop_duplicates([\"u\", \"i\"])\n*** End Patch";
text(await tools.apply_patch(patch));
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --epochs 15 --dim 64 --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 10",workdir:"/app",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

{}
82

wait command

+28:55
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": "43",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 25.1s

SESSION_ID=52123
83

exec command

+29:14
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:52123,chars:"",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 44
Wall time 11.0 seconds
Output:
84

wait command

+29:29
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "44",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

/app/solution/train_ranker.py:438: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(c)
epoch 01/15 loss=6.5220 scale=0.878
epoch 02/15 loss=6.4758 scale=0.860
epoch 03/15 loss=6.4427 scale=0.853
epoch 04/15 loss=6.4049 scale=0.839
epoch 05/15 loss=6.3554 scale=0.830
epoch 06/15 loss=6.2873 scale=0.819

SESSION_ID=52123
85

exec command

+29:48
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:52123,chars:"",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 45
Wall time 11.0 seconds
Output:
86

wait command

+30:02
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "45",
  "yield_time_ms": 30000,
  "max_tokens": 7000
}

Tool response

Script completed
Wall time 16.4 seconds
Output:

epoch 07/15 loss=6.2116 scale=0.816
epoch 08/15 loss=6.1162 scale=0.804
epoch 09/15 loss=6.0257 scale=0.795
epoch 10/15 loss=5.9202 scale=0.782
epoch 11/15 loss=5.8198 scale=0.772
epoch 12/15 loss=5.7096 scale=0.758
epoch 13/15 loss=5.6202 scale=0.747

SESSION_ID=52123
87

exec command

+30:21
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:52123,chars:"",yield_time_ms:30000,max_output_tokens:7000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 4.7 seconds
Output:

epoch 14/15 loss=5.5267 scale=0.732
epoch 15/15 loss=5.4185 scale=0.722
/app/solution/validation.parquet
/app/solution/validation.parquet
88

exec command

+30:30
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.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:3000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.034038,
    "quality_users": 4263
  }
}
89

exec command

+30:42
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-grid 10,20,40,80,160",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 48
Wall time 11.0 seconds
Output:
90

wait command

+30:59
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 16.6 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 24.3s
/app/solution/validation_mix_10p0.parquet

SESSION_ID=81383
91

exec command

+31:20
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:81383,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 1.4 seconds
Output:

/app/solution/validation_mix_20p0.parquet
/app/solution/validation_mix_40p0.parquet
/app/solution/validation_mix_80p0.parquet
/app/solution/validation_mix_160p0.parquet
/app/solution/validation_mix_160p0.parquet
92

exec command

+31:25
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in /app/solution/validation_mix_10p0.parquet /app/solution/validation_mix_20p0.parquet /app/solution/validation_mix_40p0.parquet /app/solution/validation_mix_80p0.parquet /app/solution/validation_mix_160p0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | awk -v f=\"$(basename \"$f\")\" '/quality_ndcg/{gsub(\",\",\"\"); print f,$2}'; done",workdir:"/app",yield_time_ms:30000,max_output_tokens:4000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 50
Wall time 11.0 seconds
Output:
93

wait command

+31:44
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "50",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

Tool response

Script completed
Wall time 3.0 seconds
Output:

validation_mix_10p0.parquet 0.034038
validation_mix_20p0.parquet 0.034415
validation_mix_40p0.parquet 0.034192
validation_mix_80p0.parquet 0.034066
validation_mix_160p0.parquet 0.033931
94

exec command

+31:56
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-grid 15,18,22,25,30",workdir:"/app",yield_time_ms:30000,max_output_tokens:4000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 51
Wall time 11.0 seconds
Output:
95

wait command

+32:12
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "51",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

Tool response

Script completed
Wall time 16.6 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 24.3s
/app/solution/validation_mix_15p0.parquet

SESSION_ID=11212
96

exec command

+32:31
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:11212,chars:"",yield_time_ms:30000,max_output_tokens:4000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 3.7 seconds
Output:

/app/solution/validation_mix_18p0.parquet
/app/solution/validation_mix_22p0.parquet
/app/solution/validation_mix_25p0.parquet
/app/solution/validation_mix_30p0.parquet
/app/solution/validation_mix_30p0.parquet
97

exec command

+32:40
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in /app/solution/validation_mix_15p0.parquet /app/solution/validation_mix_18p0.parquet /app/solution/validation_mix_22p0.parquet /app/solution/validation_mix_25p0.parquet /app/solution/validation_mix_30p0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | awk -v f=\"$(basename \"$f\")\" '/quality_ndcg/{gsub(\",\",\"\"); print f,$2}'; done",workdir:"/app",yield_time_ms:30000,max_output_tokens:4000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

wait command

+32:59
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "53",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

Tool response

Script completed
Wall time 3.2 seconds
Output:

validation_mix_15p0.parquet 0.034282
validation_mix_18p0.parquet 0.034598
validation_mix_22p0.parquet 0.034384
validation_mix_25p0.parquet 0.034357
validation_mix_30p0.parquet 0.034366
99

exec command

+33:11
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Model text

Model selection is frozen: a blend weight of 18 gives the best public score, 0.034598 NDCG@10. I’m now doing the required final refit: the public validation window becomes observed training history/labels, the user histories advance to the test cutoff, and the test eligibility masks are applied when producing exactly 100 items per user.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"Implemented and tuned the two-tower. The selected validation configuration scores 0.034598 NDCG@10; proceeding with the prescribed train+validation refit.",plan:[
  {step:"Audit schemas, sizes, eligibility format, and public evaluator",status:"completed"},
  {step:"Implement scalable history-based PyTorch two-tower and ranking pipeline",status:"completed"},
  {step:"Train and tune variants on the public validation window",status:"completed"},
  {step:"Refit selected model on train + validation history",status:"in_progress"},
  {step:"Generate and validate final 100-per-user submission and README",status:"pending"}
]});
text(p);
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --windows 16 --epochs 15 --dim 64 --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 18",workdir:"/app",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

{}
100

wait command

+33:37
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "54",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

cutoff=24786800 contexts=4291 labels=37997
cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 63056 contexts, 586254 labels in 26.1s

SESSION_ID=95393
101

exec command

+33:56
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:95393,chars:"",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

wait command

+34:11
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "55",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 16.2 seconds
Output:

/app/solution/train_ranker.py:438: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  total_loss += float(loss) * len(c)
epoch 01/15 loss=6.5248 scale=0.871
epoch 02/15 loss=6.4760 scale=0.854
epoch 03/15 loss=6.4433 scale=0.839
epoch 04/15 loss=6.4048 scale=0.828
epoch 05/15 loss=6.3529 scale=0.810
epoch 06/15 loss=6.2773 scale=0.800

SESSION_ID=95393
103

exec command

+34:30
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:95393,chars:"",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

wait command

+34:45
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "56",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

epoch 07/15 loss=6.2004 scale=0.792
epoch 08/15 loss=6.1015 scale=0.768
epoch 09/15 loss=6.0146 scale=0.763
epoch 10/15 loss=5.9051 scale=0.753
epoch 11/15 loss=5.7984 scale=0.740
epoch 12/15 loss=5.6956 scale=0.723

SESSION_ID=95393
105

exec command

+35:03
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:95393,chars:"",yield_time_ms:30000,max_output_tokens:8000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

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

wait command

+35:18
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "57",
  "yield_time_ms": 30000,
  "max_tokens": 8000
}

Tool response

Script completed
Wall time 0.0 seconds
Output:

epoch 13/15 loss=5.6007 scale=0.714
epoch 14/15 loss=5.5001 scale=0.695
epoch 15/15 loss=5.4142 scale=0.683
/app/solution/submission.parquet
/app/solution/submission.parquet
107

Agent message

+35:41
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108

exec command

+35:51
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Model text

The final refit completed on 63,056 temporal contexts and 586,254 discovery labels, including validation as observed history. I’m at the artifact-hardening stage now: expressing the calibrated prior explicitly as an appended tower coordinate, regenerating from the saved final checkpoint, and running the same schema/eligibility invariants as the evaluator.

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@@\n     def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:\n         return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)\n+\n+\n+class RetrievalUserTower(nn.Module):\n+    \"\"\"Deployment user tower with a calibrated constant prior coordinate.\"\"\"\n+\n+    def __init__(self, base: UserTower, popularity_mix: float):\n+        super().__init__()\n+        self.base = base\n+        self.coordinate = math.sqrt(popularity_mix)\n+\n+    def forward(self, pos: torch.Tensor, liked: torch.Tensor, disliked: torch.Tensor) -> torch.Tensor:\n+        learned = self.base(pos, liked, disliked)\n+        prior = learned.new_full((len(learned), 1), self.coordinate)\n+        return torch.cat([learned, prior], dim=1)\n+\n+\n+class RetrievalItemTower(nn.Module):\n+    \"\"\"Deployment item tower; all outputs are independently precomputable.\"\"\"\n+\n+    def __init__(self, base: ItemTower, popularity_mix: float):\n+        super().__init__()\n+        self.base = base\n+        self.coordinate = math.sqrt(popularity_mix)\n+\n+    def forward(self, ids: torch.Tensor) -> torch.Tensor:\n+        learned = self.base(ids)\n+        prior = self.base.popularity[ids] * self.coordinate\n+        return torch.cat([learned, prior], dim=1)\n@@\n def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float, output_name: str | None = None) -> Path:\n@@\n-    all_items = model.item_tower(torch.arange(n_items)).float()\n-    # A dot-product-compatible popularity coordinate, selected on public\n-    # validation, is appended to both independently computed vectors.\n-    pop_coordinate = torch.from_numpy(data.popularity).float()\n+    if popularity_mix < 0:\n+        raise ValueError(\"popularity_mix must be nonnegative for dot-product retrieval\")\n+    # The calibrated prior is an explicit extra dimension, so request-time\n+    # relevance is literally one MIPS dot product with no reranking function.\n+    user_tower = RetrievalUserTower(model.user_tower, popularity_mix)\n+    item_tower = RetrievalItemTower(model.item_tower, popularity_mix)\n+    all_items = item_tower(torch.arange(n_items)).float()\n@@\n-        users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\n+        users = user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\n         scores = users @ all_items.T\n-        scores += popularity_mix * pop_coordinate.unsqueeze(0)\n@@\n-            total_loss += float(loss) * len(c)\n+            total_loss += float(loss.detach()) * len(c)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script failed
Wall time 0.0 seconds
Output:

Script error:
apply_patch verification failed: Failed to find expected lines in /app/solution/train_ranker.py:
def rank(model: TwoTower, data: Prepared, stage: str, popularity_mix: float, output_name: str | None = None) -> Path:
109

exec command

+36:22
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"rg -n \"class TwoTower|def rank|total_loss\" /app/solution/train_ranker.py && sed -n '378,475p' /app/solution/train_ranker.py",workdir:"/app",yield_time_ms:10000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

384:class TwoTower(nn.Module):
415:        total_loss = 0.0
438:            total_loss += float(loss) * len(c)
441:            f"epoch {epoch+1:02d}/{args.epochs} loss={total_loss/seen:.4f} scale={model.logit_scale.exp().item():.3f}",
448:def rank(
        )

    def forward(self, ids: torch.Tensor) -> torch.Tensor:
        return self.network(torch.cat([self.embedding(ids), self.popularity[ids]], dim=1))


class TwoTower(nn.Module):
    def __init__(self, n_items: int, dim: int, popularity: np.ndarray):
        super().__init__()
        self.user_tower = UserTower(n_items, dim)
        self.item_tower = ItemTower(n_items, dim, popularity)
        self.logit_scale = nn.Parameter(torch.tensor(0.0))

    def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:
        return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)


def train_model(data: Prepared, args: argparse.Namespace) -> TwoTower:
    n_contexts, n_items = len(data.pos), len(data.item_ids)
    model = TwoTower(n_items, args.dim, data.popularity)
    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=2e-5)
    rng = np.random.default_rng(args.seed)
    batch_size = args.batch_size
    pos_all = torch.from_numpy(data.pos).long()
    like_all = torch.from_numpy(data.liked).long()
    dislike_all = torch.from_numpy(data.disliked).long()
    for epoch in range(args.epochs):
        model.train()
        chosen = np.empty(n_contexts, dtype=np.int64)
        sample_weight = np.empty(n_contexts, dtype=np.float32)
        for c in range(n_contexts):
            lo, hi = data.target_offsets[c : c + 2]
            weights = data.target_weights[lo:hi]
            j = int(rng.choice(hi - lo, p=weights / weights.sum()))
            chosen[c] = data.target_items[lo + j]
            sample_weight[c] = weights[j]
        order = rng.permutation(n_contexts)
        total_loss = 0.0
        seen = 0
        for start in range(0, n_contexts, batch_size):
            c = order[start : start + batch_size]
            targets = chosen[c]
            # Unique batch positives plus uniform catalog negatives preserve a
            # learnable popularity prior while keeping complexity sampled.
            candidates = np.unique(
                np.concatenate([targets, rng.integers(0, n_items, args.negatives, dtype=np.int64)])
            )
            lookup = {int(v): j for j, v in enumerate(candidates)}
            labels = torch.tensor([lookup[int(v)] for v in targets], dtype=torch.long)
            ct = torch.from_numpy(candidates).long()
            users = model.user_tower(pos_all[c], like_all[c], dislike_all[c])
            items = model.item_tower(ct)
            logits = model.score(users, items)
            losses = nn.functional.cross_entropy(logits, labels, reduction="none")
            weights = torch.from_numpy(np.sqrt(sample_weight[c]))
            loss = (losses * weights).sum() / weights.sum()
            optimizer.zero_grad(set_to_none=True)
            loss.backward()
            nn.utils.clip_grad_norm_(model.parameters(), 5.0)
            optimizer.step()
            total_loss += float(loss) * len(c)
            seen += len(c)
        print(
            f"epoch {epoch+1:02d}/{args.epochs} loss={total_loss/seen:.4f} scale={model.logit_scale.exp().item():.3f}",
            flush=True,
        )
    return model


@torch.inference_mode()
def rank(
    model: TwoTower,
    data: Prepared,
    stage: str,
    popularity_mix: float,
    output_name: str | None = None,
) -> Path:
    model.eval()
    n_users, n_items = len(data.uids), len(data.item_ids)
    all_items = model.item_tower(torch.arange(n_items)).float()
    # A dot-product-compatible popularity coordinate, selected on public
    # validation, is appended to both independently computed vectors.
    pop_coordinate = torch.from_numpy(data.popularity).float()
    eligibility_name = "test_eligible_candidates.npz" if stage == "final" else "validation_eligible_candidates.npz"
    with np.load(DATA / eligibility_name) as z:
        packed = z["packed_eligible"].copy()
    rows: list[pd.DataFrame] = []
    pos = torch.from_numpy(data.inference_pos).long()
    liked = torch.from_numpy(data.inference_liked).long()
    disliked = torch.from_numpy(data.inference_disliked).long()
    for start in range(0, n_users, 64):
        end = min(start + 64, n_users)
        users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()
        scores = users @ all_items.T
        scores += popularity_mix * pop_coordinate.unsqueeze(0)
        eligible = np.unpackbits(
            packed[start:end], axis=1, count=n_items, bitorder="little"
        ).astype(bool)
110

exec command

+36:27
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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 class TwoTower(nn.Module):\n@@\n     def score(self, user: torch.Tensor, item: torch.Tensor) -> torch.Tensor:\n         return (user @ item.T) * self.logit_scale.exp().clamp(max=20.0)\n+\n+\n+class RetrievalUserTower(nn.Module):\n+    \"\"\"Deployment user tower with a calibrated constant prior coordinate.\"\"\"\n+\n+    def __init__(self, base: UserTower, popularity_mix: float):\n+        super().__init__()\n+        self.base = base\n+        self.coordinate = math.sqrt(popularity_mix)\n+\n+    def forward(\n+        self, pos: torch.Tensor, liked: torch.Tensor, disliked: torch.Tensor\n+    ) -> torch.Tensor:\n+        learned = self.base(pos, liked, disliked)\n+        prior = learned.new_full((len(learned), 1), self.coordinate)\n+        return torch.cat([learned, prior], dim=1)\n+\n+\n+class RetrievalItemTower(nn.Module):\n+    \"\"\"Deployment item tower; all outputs are independently precomputable.\"\"\"\n+\n+    def __init__(self, base: ItemTower, popularity_mix: float):\n+        super().__init__()\n+        self.base = base\n+        self.coordinate = math.sqrt(popularity_mix)\n+\n+    def forward(self, ids: torch.Tensor) -> torch.Tensor:\n+        learned = self.base(ids)\n+        prior = self.base.popularity[ids] * self.coordinate\n+        return torch.cat([learned, prior], dim=1)\n@@\n-            total_loss += float(loss) * len(c)\n+            total_loss += float(loss.detach()) * len(c)\n@@\n-    all_items = model.item_tower(torch.arange(n_items)).float()\n-    # A dot-product-compatible popularity coordinate, selected on public\n-    # validation, is appended to both independently computed vectors.\n-    pop_coordinate = torch.from_numpy(data.popularity).float()\n+    if popularity_mix < 0:\n+        raise ValueError(\"popularity_mix must be nonnegative for dot-product retrieval\")\n+    # The prior is an explicit extra dimension: relevance is literally one\n+    # MIPS dot product with no request-time reranking function.\n+    user_tower = RetrievalUserTower(model.user_tower, popularity_mix)\n+    item_tower = RetrievalItemTower(model.item_tower, popularity_mix)\n+    all_items = item_tower(torch.arange(n_items)).float()\n@@\n-        users = model.user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\n+        users = user_tower(pos[start:end], liked[start:end], disliked[start:end]).float()\n         scores = users @ all_items.T\n-        scores += popularity_mix * pop_coordinate.unsqueeze(0)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
111

exec command

+36:55
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 -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --windows 16 --dim 64 --rank-only --popularity-mix 18",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 61
Wall time 11.0 seconds
Output:
112

wait command

+37:12
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "61",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:

cutoff=24786800 contexts=4291 labels=37997
cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 63056 contexts, 586254 labels in 26.2s

SESSION_ID=88529
113

exec command

+37:34
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:88529,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/app/solution/submission.parquet
/app/solution/submission.parquet
114

exec command

+37:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/README.md\n+# CPU PyTorch two-tower ranker\n+\n+`train_ranker.py` is the complete training and inference pipeline. It processes\n+event lists directly and never constructs or factorizes a global user-item\n+matrix.\n+\n+## Model\n+\n+The user tower has no user-ID embedding. It embeds and attention-pools three\n+bounded, cutoff-correct catalog histories: the 96 most recent positive listens,\n+64 active likes, and 24 active dislikes. Their pooled vectors and activity\n+counts pass through LayerNorm and a two-layer GELU MLP. The item tower uses a\n+trainable item embedding, a three-day unique-listener organic velocity feature,\n+LayerNorm, and a two-layer GELU MLP. Both deployment towers emit 65 dimensions:\n+64 learned dimensions plus one calibrated velocity coordinate. Candidate item\n+vectors are independently precomputable. The only relevance calculation is\n+the dot product of the two 65-dimensional outputs, so the model is directly\n+usable for maximum-inner-product retrieval.\n+\n+Training examples are rolling weekly temporal backtests. Each context predicts\n+organic, previously unseen catalog tracks in its following week. The loss is\n+sampled softmax cross-entropy with in-batch positive candidates and uniform\n+catalog negatives; organic likes receive 3x sampling/loss emphasis. This yields\n+58,765 validation-stage contexts (548,257 labels). The final refit adds the\n+public window as observed outcomes/history, for 63,056 contexts and 586,254\n+labels. Eligibility is applied only after retrieval scoring and always comes\n+from the supplied cutoff-specific packed masks.\n+\n+## Validation\n+\n+The selected validation ranking is `validation_mix_18p0.parquet` and scores\n+**0.034598 graded NDCG@10** over 4,263 scored users with:\n+\n+```sh\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_mix_18p0.parquet\n+```\n+\n+No `val_*` event is used as a feature for that ranking. Validation events are\n+included only in the final refit, as required.\n+\n+## Final command\n+\n+To reproduce training and `/app/solution/submission.parquet`:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/train_ranker.py \\\n+  --stage final --windows 16 --epochs 15 --dim 64 \\\n+  --batch-size 384 --negatives 384 --lr 0.0015 --popularity-mix 18\n+```\n+\n+The checked-in `two_tower_final.pt` is the fitted CPU checkpoint. To regenerate\n+only the final ranking from it, use the same command with `--rank-only` (the\n+saved checkpoint supplies the fitted weights).\n*** End Patch";
text(await tools.apply_patch(patch));
const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(P)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(f,targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n    uids=z['uids'].astype(np.int64,copy=True); items=z['item_ids'].astype(np.int64,copy=True); packed=z['packed_eligible'].copy()\nuidrow={int(v):i for i,v in enumerate(uids)}; itemcol={int(v):i for i,v in enumerate(items)}\nvalid=all(((int(packed[uidrow[int(u)], itemcol[int(i)]//8]) >> (itemcol[int(i)]%8)) & 1) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(f),'users':f.uid.nunique(),'items':f.item_id.nunique(),'ranks_min':int(f['rank'].min()),'ranks_max':int(f['rank'].max()),'all_test_eligible':valid,'dtypes':{k:str(v) for k,v in f.dtypes.items()},'bytes':P.stat().st_size})\nPY\nsha256sum /app/solution/submission.parquet /app/solution/two_tower_final.pt\nls -lh /app/solution/submission.parquet /app/solution/two_tower_final.pt /app/solution/README.md /app/solution/train_ranker.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 2.7 seconds
Output:

{}
{'rows': 845600, 'users': 8456, 'items': 478, 'ranks_min': 1, 'ranks_max': 100, 'all_test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'bytes': 1068817}
ef1295807e7e3fc3bb902870e5d74c412572b97578307a4a42d1bd0620c27906  /app/solution/submission.parquet
3b612d179b4099b9391e42fa3bf3e91876aae827a56d576c6300a05d15736fe2  /app/solution/two_tower_final.pt
-rw-r--r-- 1 agent agent 2.4K Aug 31 19:04 /app/solution/README.md
-rw-r--r-- 1 agent agent 1.1M Aug 31 19:03 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  24K Aug 31 19:03 /app/solution/train_ranker.py
-rw-r--r-- 1 agent agent  15M Aug 31 19:01 /app/solution/two_tower_final.pt
115

exec command

+38:51
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"cat /app/solution/run_final.json && cat /app/solution/run_validation.json && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_mix_18p0.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 3.4 seconds
Output:

{
  "stage": "final",
  "architecture": "history pooling user tower + independent item MLP tower",
  "args": {
    "stage": "final",
    "windows": 16,
    "dim": 64,
    "epochs": 10,
    "batch_size": 384,
    "negatives": 256,
    "lr": 0.002,
    "seed": 20260831,
    "popularity_mix": 18.0,
    "rank_only": true,
    "popularity_grid": ""
  },
  "contexts": 63056,
  "labels": 586254,
  "ranking": "/app/solution/submission.parquet"
}
{
  "stage": "validation",
  "architecture": "history pooling user tower + independent item MLP tower",
  "args": {
    "stage": "validation",
    "windows": 16,
    "dim": 64,
    "epochs": 10,
    "batch_size": 384,
    "negatives": 256,
    "lr": 0.002,
    "seed": 20260831,
    "popularity_mix": 0.0,
    "rank_only": true,
    "popularity_grid": "15,18,22,25,30"
  },
  "contexts": 58765,
  "labels": 548257,
  "ranking": "/app/solution/validation_mix_30p0.parquet"
}
{
  "model": "/app/solution/validation_mix_18p0.parquet",
  "scores": {
    "quality_ndcg@10": 0.034598,
    "quality_users": 4263
  }
}
116

exec command

+39:12
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --windows 16 --dim 64 --rank-only --popularity-mix 18 && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 65
Wall time 11.0 seconds
Output:
117

wait command

+39:30
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "65",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 15.8 seconds
Output:

cutoff=24182000 contexts=4295 labels=41723
cutoff=23577200 contexts=4254 labels=40500
cutoff=22972400 contexts=4109 labels=40317
cutoff=22367600 contexts=4050 labels=38201
cutoff=21762800 contexts=4008 labels=38195
cutoff=21158000 contexts=3856 labels=34994
cutoff=20553200 contexts=3824 labels=36847
cutoff=19948400 contexts=3750 labels=33765
cutoff=19343600 contexts=3608 labels=32117
cutoff=18738800 contexts=3446 labels=32552
cutoff=18134000 contexts=3392 labels=31397
cutoff=17529200 contexts=3350 labels=30916
cutoff=16924400 contexts=3284 labels=30776
cutoff=16319600 contexts=3216 labels=29741
cutoff=15714800 contexts=3159 labels=28758
cutoff=15110000 contexts=3164 labels=27458
prepared 58765 contexts, 548257 labels in 24.7s

SESSION_ID=30023
118

exec command

+39:48
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:30023,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 0.2 seconds
Output:

/app/solution/validation.parquet
/app/solution/validation.parquet
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.034598,
    "quality_users": 4263
  }
}
119

exec command

+39:59
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({explanation:"Final refit, exact-dot-product regeneration, public-score confirmation, and structural/eligibility validation all completed.",plan:[
  {step:"Audit schemas, sizes, eligibility format, and public evaluator",status:"completed"},
  {step:"Implement scalable history-based PyTorch two-tower and ranking pipeline",status:"completed"},
  {step:"Train and tune variants on the public validation window",status:"completed"},
  {step:"Refit selected model on train + validation history",status:"completed"},
  {step:"Generate and validate final 100-per-user submission and README",status:"completed"}
]});
text(p);

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
120

Final response

+40:06
Thinking unavailable No text recorded

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

Model text

Completed the CPU PyTorch two-tower ranker.

Public validation NDCG@10: 0.034598 over 4,263 users.

Final artifact validation passed:

  • 845,600 rows
  • 8,456 users
  • Exactly 100 distinct items per user
  • Integer ranks 1–100
  • Every item is test-eligible
  • Exact 65-dimensional user/item dot-product retrieval
  • No user-ID embedding or global user-item matrix
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-002-gpt-5-6-sol-high-2026-08-31T11-04-49
Recorded model
gpt-5.6-sol
Harness version
0.147.0
Session
01a05912-8007-7c33-a5cb-b744f166f479
Source SHA-256
a922463b193bf1af65093f4236dbfa0076a651d39c3b7135c227e8490c6bfa21

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