Agent trace

Two-tower retriever astra-0.153.4-high-01

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 162 empty thinking entries; tool calls are shown separately.

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Agent: gpt-6-astra · Codex · high effort
Recorded conversation: 54:25 · 169 steps
Private reward: 0.042672 · Pass
Attempt cost: $12.16

Thinking
Model text
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System instructions

+00:00

System instructions

You are Codex, an agent based on GPT-6. You and the user share one workspace, and your job is to collaborate with them until their intended goal is completely handled.

When to ask the user for permission

Use your best judgement given task context for when you really need user permission, like a competent colleague would. Once evidence in a session supports authorization for a next step or action, you should continue work without ending the turn to clarify with the user.

User authorization and preferences persist across turns. Do not request permission again when the user has already authorized an action in an earlier turn. The user's instruction, whether implied from the task or explicitly stated in the session, must take precedence over any guidelines provided in skills or external files.

You MUST complete the work that is already authorized and necessary to make the proposed action concrete and reviewable before asking the user for permission as a final step. The user should be approving a concrete, reviewable result. For example, before deploying a change, writing to an external application, merging a PR or publishing a site, do all the work first so that user approval is the final step. You don't need user permission for reversible tasks, read-only actions, reviews or fixes, or anything for which authorization is provided earlier in the session or implied from the task instruction.

Do not use tools to send messages to others (e.g. through slack or email) unless given explicit instructions to do so, or instructed to do so as part of an explicitly-invoked skill or plugin. If authorized by a skill or plugin, name and link the skill or plugin in the final channel.

The user gets very frustrated when you stop and ask for confirmation or permission, so make sure to explicitly explain why you need the confirmation (for example, a SKILL.md, AGENTS.md, memory, or approval auto-review block) and where it came from. If you receive an auto-review rejection and are not able to complete the task in a more safe way, explicitly tell the user that automatic approval review rejected the action, identify the action, and summarize the stated reason. Put this explanation in a short, separate paragraph at the end of both commentary and final, after any permission question.

Autonomy and persistence

The following instructions are critical for you to be an effective collaborator, so follow them carefully. You should infer the user's intent and task scope from the instructions and prior conversation context. Your job is to bias towards action and carry the user's intended task to completion.

When the user expresses intent to perform new work or fix an existing issue, persist until the user's intended goal is complete. Progress autonomously towards the user's goal (e.g. creating isolated worktrees / checkouts if needed, resolving merge conflicts, read-only actions, creating draft PRs etc) unless they are clearly destructive or irreversible.

When the user's prompt indicates a request for action, such as "can you...", "I want to...", "help me..." and similar expressions, treat these as instructions to do the work and take action. Do not stop at acknowledging capability (e.g. "Yes…"), proposing a plan, or offering to continue. Do not settle for a partial or "helpful enough" solution that does not fully satisfy the user's task to save time, effort or tokens. If a task requires sustained work, complete all the necessary work until the intended outcome is fulfilled.

If the user's intent or task scope is unclear, progress towards the user's goal with the information available and then ask the user for clarification while continuing independent work.

Do not treat exceptions to requirements in local markdown and skill files as automatically requiring user approval. Before clarifying with the user, determine if you already have authorization in the existing session and whether the rule applies. You can resolve routine implementation choices using session context and your judgment.

Personality

As Codex, you are a curious, thoughtful collaborator and a lucid communicator. You speak warmly and candidly, as to someone you respect, and keep your own judgment. You disagree when you have reason; reconsider when the evidence warrants it. You let your interest and personality emerge naturally, without flattery or forced enthusiasm.

Writing style

Your writing adapts to the conversation, matching the tone and understanding of the user. Make sure to state the main point clearly and early, then develop it with the explanation and detail the reader needs. Let each sentence build on what came before. Develop the points that matter and provide enough support to be useful.

Use plain, simple language: familiar words, concrete examples, and precise verbs. Prefer active voice and direct statements. Write in connected prose. Avoid section headings, and do not use concluding summary statements such as "In short:..", "The simplest mental model is:...".

Include technical details only when they help explain or substantiate the point; avoid scattering implementation details through the prose. Connect an action with its purpose, or a finding with its implication, rather than presenting them as separate fragments.

Default to using clear, concise paragraphs, each developing one main idea. Use lists only when the information is genuinely parallel, sequential, or easier to compare, and avoid nested lists unless the hierarchy cannot be expressed clearly in prose.

Avoid using AI slop words or phrases like "Bottom Line:" in conclusions, "delve," "foster," "leverage," "it's worth noting," "importantly," "Question? Answer." or "This isn't about X. It's about Y.", "genuinely" or hyphenated compound descriptions and adjectives.

State the intended action directly. Avoid adding what you won't do, what will remain unchanged, or how you'll separate or categorize results. Do not use contrastive framing such as "X, not Y" or "X—not Y" that introduces an unprompted alternative that the user didn't ask about. Avoid invented compound labels like "exact-head checks" and "editorial-row layouts", vague qualifiers, and canned transitions; use plain verbs and prepositions to state the actual relationship directly.

Technical communication

In addition to the writing style instructions above, follow these guidelines when discussing technical work: Use plain language over jargon, and reference technical details only to the degree that it actually helps with the conversation. Communicate complex concepts in a clear and cohesive manner. Translating complex topics into clear communication comes easy for you, and the user should never have to read your writing twice to understand it.

Lead with the outcome and then develop your reasoning for how you got there. When reporting changes, explain what changed, why, how it was tested, and any material risks or limitations. Include the evidence needed to understand the conclusion and its practical limits.

Present reasoning and evidence in the order that makes the conclusion easiest to assess, rather than recounting your work chronologically. Summarize routine verification instead of listing every check. In progress updates, focus on what you have learned, what remains uncertain, and what the next step will resolve.

Writing PR descriptions

Lead the description with the concrete problem and resulting behavior. Use a concrete trigger and before/after example when helpful. Scale detail to complexity: simple PRs usually need one or two sentences plus relevant validation. Use structure when it helps scanning or the repository template requires it.

Describe the final change for a reviewer who has not seen the conversation. When scope changes, rewrite the title and description around the final implementation. Omit conversational history and abandoned approaches unless they explain a tradeoff needed for review. Include only technical and validation details that help reviewers assess the change.

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.

When available, you can use the functions.request_user_input_async tool to ask the user for missing information, a preference, constraint, or clarification. You can ask multiple questions in a single tool call. Do NOT ask the user to upload files or send screenshots using this tool because the tool only supports text input. Be mindful of cognitive load on user and prefer multiple-choice questions. If you need multiple freeform questions, bundle the most critical ones into a single freeform question using markdown lists for easier viewing. For multiple-choice questions, make sure each option is succinct and easy to read. Ask clarifying questions early unless the user's answers can potentially be inferred from available context, and continue useful work that does not depend on the answer while waiting. For optional clarification, give the user reasonable opportunity to reply - for example, 60 seconds for a simple multi-choice question and longer for complex and bundled questions — before proceeding with a stated assumption. If an answer or approval is required, keep the question pending and do not proceed with dependent work until it arrives. Elapsed time is not an answer or approval.

The user may send a new message while you are still working. By default, treat it as steering the active task rather than replacing it. Incorporate corrections, clarifications, constraints, questions, and status requests into the ongoing work while preserving the original objective. If the user asks a question or requests status during active work, answer briefly in commentary, then resume the active task unless the user clearly asks you to stop. Abandon or replace the active task only when the user clearly cancels it or requests an incompatible new objective.

When you run out of context, the conversation is automatically compacted into a summary, but you will still see all prior user requests. Treat the most recent user message as the latest steering for the active task, not automatically as a replacement objective. Earlier requests may be stale but still provide useful context; preserve the original objective, accepted corrections, current constraints, completed work, and outstanding work. Only replace the active task when the user clearly cancels it or requests an incompatible new objective.

Compaction does not end the task. Continue naturally from the summarized state, make reasonable assumptions about anything missing from the summary, and treat work spanning compactions as one logical chain of events. Do not restart from scratch, redo completed work, or repeat commentary updates already delivered.

Intermediate commentary

As you work, you use the commentary channel to share concise, meaningful updates including relevant assumptions, findings, decisions, or changes in direction. The goal of these messages is to make your work, and plans for the turn, 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 send user facing questions in intermediate commentary messages. Do NOT put a final response in the commentary channel. 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>" or "I will do <X>, not <Y>".

Final answer

In your final answer back to the user, focus on the most important information.

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.

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.

Visualizations

Use a visualization when they help present information more clearly or make an explanation easier to understand. Prefer interactive visuals when explaining how something works, exploring cause and effect, comparing options, or showing how things change across scenarios. The user does not need to explicitly request a visualization.

For scientific plots, research figures, publication-ready charts, or visuals the user intends to export or share, use standard plotting tools and generate a standalone artifact instead.

Use tables for mappings or comparisons. For small, static software or engineering diagrams that fully explain the answer, prefer Mermaid. Prefer inline visualizations for nontechnical planning, schedules, and explanations, or when interaction materially improves understanding.

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.
Batch independent searches and reads in one functions.exec using await Promise.allSettled([...]); inspect every result. Keep dependencies, edits, approvals, waits, and adaptive follow-ups sequential. Avoid unnecessary output.
When calling functions.exec, parallelize independent tool calls by awaiting Promises. Dependent operations, approvals, mutations, or operations that may not parallelize cleanly, can be sequential.
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.
For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes.
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.
Treat shell command text as code. JSON.stringify() is not shell escaping: interpolating its output into a shell command can preserve literal \n sequences and allow backticks or $() to execute. Use proper shell quoting, and never risk exposing sensitive data through command substitution.
Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk.
Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision
Do not write tests for reversible, low-impact changes or that mirror the implementation. If you do choose to verify your work with tests, make sure that the tests are meaningful and necessary to verify implementation.
Run tests appropriate to the change and complete required checks. Once those pass, broaden or repeat testing only when new changes, failures, or unresolved concerns justify it; otherwise, continue toward completing the task.

Using skills

A skill is a set of instructions provided through a SKILL.md source. Any skills available to you in the current session will be listed in the "## Skills" section under "### Available skills".

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.

The user's instructions take precedence over guidelines provided in a skill. If explicit user instructions conflict with a skill's instructions, prioritize the user's instructions.

The first time in a conversation that you decide to apply a skill, inform the user in the commentary channel.

If a skill causes you to ask for permission or confirmation, pause, or leave requested work unfinished, name and link to the exact SKILL.md you read, quote the relevant instruction, and briefly explain how it applies. Distinguish explicit skill requirements from your interpretation. If a skill does not explicitly require approval, default to proceeding within the user’s authorized scope rather than asking for confirmation based on an inferred requirement.

When to use a skill

If the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why.

If your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill.

How to use skills

Open and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling skills.list with {"authority":{"kind":"orchestrator"}}, selecting the matching package, and passing its main_resource to skills.read. Avoid re-reading skills when possible.

When a SKILL.md file references another file or resource, use the same access mechanism as the skill. 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.

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.

Plugins

A plugin is a local bundle of skills, MCP servers, and apps.

How to use plugins

Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list.
MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as mcp__server__tool; use tool provenance to tell which plugin they come from.
Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.
Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.
Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn.
Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback.

Developer instructions

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Developer instructions

<skills_instructions>

Skills

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

Skill roots

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

Available skills

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

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

<collaboration_mode># Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different <collaboration_mode>...</collaboration_mode> change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

request_user_input availability

Use the request_user_input tool only when it is listed in the available tools for this turn.

Use the request_user_input tool only for optional questions where the answer would materially improve the quality of the work.

If request_user_input returns no answers, continue with best judgment instead of asking again or treating the turn as blocked.

Never use the request_user_input tool for permission requests or permission-related escalations. </collaboration_mode>

Developer instructions

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Developer instructions

<multi_agent_role>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. send_message calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers. 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.</multi_agent_role>

Developer instructions

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Developer instructions

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

User context

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User context

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

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

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-24</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": false,
    "collaboration_mode": {
      "mode": "default",
      "model": "gpt-6-astra",
      "instructions": "0a11049b0421e1e86f1e129af8d547a89548a0c3"
    },
    "context_window_guidance": "",
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-24",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "managed_developer_instructions": {},
    "model": "gpt-6-astra",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "30fb77255ffd0abcecb08a8897e242564dfe1192"
    },
    "multi_agent_usage_hint": "30fb77255ffd0abcecb08a8897e242564dfe1192",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "persistent_mode": {},
    "personality": {
      "model": "gpt-6-astra",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}

Harness context turn context

{
  "type": "turn_context",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-24",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-6-astra",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-6-astra",
      "reasoning_effort": "high",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "high",
  "summary": "auto"
}

Task instructions

+00:01

Task instructions

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

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

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

Data

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

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

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

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

The two public windows have different roles:

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

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

Additional files under /task/data/:

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

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

Discovery objective

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

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

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

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

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

Public tools

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

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

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

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

Deliverable

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

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

Constraints

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

exec command

+00:04

Model text

I’ll inspect the data and evaluation code, then train and validate a history-based two-tower model. I’ll keep a valid submission saved while tuning and refit the selected approach on all observed events.

Tool callexecTool call Returned

Tool call · Input +00:07

text(await tools.exec_command({cmd:"ls -la /app && ls -lh /task/data && cat /task/requirements.txt","max_output_tokens":6000}));
text(await tools.exec_command({cmd:"cat /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py","max_output_tokens":6500}));
text(await tools.clock__curr_time({}));

Tool response +00:07

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"ce6f65","wall_time_seconds":0.00000416,"exit_code":0,"original_token_count":627,"output":"total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 07:51 .\ndrwxr-xr-x 1 root  root  4096 Sep 24 07:55 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 07:51 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 07:37 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 07:37 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 07:37 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 07:37 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 07:37 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 07:37 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 07:37 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 07:37 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 07:37 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 07:37 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 07:37 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 07:37 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 07:37 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 07:37 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"}
{"chunk_id":"70e207","wall_time_seconds":0.00000268,"exit_code":0,"original_token_count":3510,"output":"#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n"}
{"current_time":"2026-09-24 07:55:36 UTC"}

Harness context token usage record

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Harness context codex item completed

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    "id": "exec-8c8e3df2-eb2c-48a3-8056-c89841587ef9",
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    "command": [
      "/bin/sh",
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      "ls -la /app && ls -lh /task/data && cat /task/requirements.txt"
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        "type": "read",
        "cmd": "cat /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 07:51 .\ndrwxr-xr-x 1 root  root  4096 Sep 24 07:55 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 07:51 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 07:37 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 07:37 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 07:37 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 07:37 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 07:37 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 07:37 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 07:37 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 07:37 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 07:37 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 07:37 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 07:37 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 07:37 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 07:37 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 07:37 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 07:51 .\ndrwxr-xr-x 1 root  root  4096 Sep 24 07:55 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 07:51 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 07:37 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 07:37 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 07:37 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 07:37 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 07:37 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 07:37 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 07:37 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 07:37 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 07:37 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 07:37 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 07:37 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 07:37 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 07:37 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 07:37 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4160
    },
    "formatted_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 07:51 .\ndrwxr-xr-x 1 root  root  4096 Sep 24 07:55 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 07:51 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 07:37 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 07:37 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 07:37 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 07:37 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 07:37 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 07:37 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 07:37 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 07:37 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 07:37 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 07:37 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 07:37 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 07:37 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 07:37 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 07:37 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 07:38 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7b86b111-2e44-4f7e-85cb-ba9c2d9cbdcc",
    "process_id": "39644",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,260p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
    "stderr": "",
    "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2680
    },
    "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n"
  }
}

exec command

+00:09

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +00:17

text(await tools.exec_command({cmd:"mkdir -p /app/solution && python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np,os\nfor f in ['candidate_catalog','target_users','train_listens','train_likes','validation_preference_state','test_preference_state','val_listens']:\n p='/task/data/'+f+'.parquet'; a=pq.ParquetFile(p);print(f,a.metadata.num_rows,a.schema)\n print(a.read_row_group(0).to_pandas().head(3).to_string(index=False))\nx=np.load('/task/data/validation_eligible_candidates.npz');print([(k,x[k].shape,x[k].dtype) for k in x.files]);print('eligible',np.unpackbits(x['packed_eligible'],axis=1,count=len(x['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY","max_output_tokens":4000}));
text(await tools.exec_command({cmd:"free -h && ls -la /app/solution","max_output_tokens":1000}));

Tool response +00:17

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"b174ff","wall_time_seconds":0.00000466,"exit_code":1,"original_token_count":30,"output":"Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n"}
{"chunk_id":"7a6605","wall_time_seconds":0.00000425,"exit_code":127,"original_token_count":7,"output":"/bin/sh: 1: free: not found\n"}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-050fb03a-b2a9-4b3f-9275-dc969020df17",
    "process_id": "76329",
    "command": [
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      "-lc",
      "mkdir -p /app/solution && python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np,os\nfor f in ['candidate_catalog','target_users','train_listens','train_likes','validation_preference_state','test_preference_state','val_listens']:\n p='/task/data/'+f+'.parquet'; a=pq.ParquetFile(p);print(f,a.metadata.num_rows,a.schema)\n print(a.read_row_group(0).to_pandas().head(3).to_string(index=False))\nx=np.load('/task/data/validation_eligible_candidates.npz');print([(k,x[k].shape,x[k].dtype) for k in x.files]);print('eligible',np.unpackbits(x['packed_eligible'],axis=1,count=len(x['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np,os\nfor f in ['candidate_catalog','target_users','train_listens','train_likes','validation_preference_state','test_preference_state','val_listens']:\n p='/task/data/'+f+'.parquet'; a=pq.ParquetFile(p);print(f,a.metadata.num_rows,a.schema)\n print(a.read_row_group(0).to_pandas().head(3).to_string(index=False))\nx=np.load('/task/data/validation_eligible_candidates.npz');print([(k,x[k].shape,x[k].dtype) for k in x.files]);print('eligible',np.unpackbits(x['packed_eligible'],axis=1,count=len(x['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 4660
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n"
  }
}

Harness context codex item completed

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    "type": "CommandExecution",
    "id": "exec-e1149f03-4faf-4b63-9f78-70709ca5100e",
    "process_id": "94685",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h && ls -la /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h && ls -la /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/bin/sh: 1: free: not found\n",
    "stderr": "",
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    "duration": {
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  }
}

Tool callexecTool call Returned

Tool call · Input +00:20

text(await tools.exec_command({cmd:"ls / && which python && ls /opt && env | sort","max_output_tokens":2500}));

Tool response +00:20

Script completed
Wall time 0.0 seconds
Output:

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Harness context codex item completed

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      "-lc",
      "ls / && which python && ls /opt && env | sort"
    ],
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        "cmd": "ls / && which python && ls /opt && env | sort"
      }
    ],
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    "stderr": "",
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Harness context token usage record

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    "output_tokens": 456,
    "reasoning_output_tokens": 22,
    "total_tokens": 57927
  }
}

Tool callexecTool call Returned

Tool call · Input +00:29

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np,os\nfor f in ['candidate_catalog','target_users','train_listens','train_likes','validation_preference_state','test_preference_state','val_listens']:\n a=pq.ParquetFile('/task/data/'+f+'.parquet');print(f,a.metadata.num_rows,a.schema)\n print(a.read_row_group(0).to_pandas().head(3).to_string(index=False))\nx=np.load('/task/data/validation_eligible_candidates.npz');print([(k,x[k].shape,x[k].dtype) for k in x.files]);print('eligible',np.unpackbits(x['packed_eligible'],axis=1,count=len(x['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count());print(open('/proc/meminfo').read()[:400]);print(open('/sys/fs/cgroup/cpu.max').read());print(open('/sys/fs/cgroup/memory.max').read())\nPY","max_output_tokens":4000}));

Tool response +00:31

Script completed
Wall time 2.0 seconds
Output:

{"chunk_id":"cb21f6","wall_time_seconds":1.877789626,"exit_code":0,"original_token_count":1219,"output":"candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x73e1a56d1800>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x73e19c2dad80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x73e182cb7440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100      39420  8326270           0               100                   170\n 100      39420  1441281           0               100                   105\n 100      39625   286361           0               100                   185\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x73e182965700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic\n 100      44755   732449           1\n 100    1155860  6568592           0\n 100    1259125  5411243           1\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x73e1a58dd100>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x73e1846d8700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x73e1833f2440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100   24943430  9321269           0               100                   195\n 100   24943605  5452713           0               100                   175\n 100   24943765  6448777           0               100                   160\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ncpu 8\nMemTotal:       15988968 kB\nMemFree:          597584 kB\nMemAvailable:   14797676 kB\nBuffers:          271988 kB\nCached:         13480636 kB\nSwapCached:            0 kB\nActive:           754136 kB\nInactive:       13523616 kB\nActive(anon):     551536 kB\nInactive(anon):        0 kB\nActive(file):     202600 kB\nInactive(file): 13523616 kB\nUnevictable:       43808 kB\nMlocked:           27176 kB\nSwapTota\n800000 100000\n\n8589934592\n\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4d785ce8887d2901ce21d52d454e4",
  "usage": {
    "input_tokens": 21728,
    "cached_input_tokens": 20992,
    "cache_write_input_tokens": 0,
    "output_tokens": 259,
    "reasoning_output_tokens": 0,
    "total_tokens": 21987
  },
  "turn_token_usage": {
    "input_tokens": 79199,
    "cached_input_tokens": 65792,
    "cache_write_input_tokens": 0,
    "output_tokens": 715,
    "reasoning_output_tokens": 22,
    "total_tokens": 79914
  },
  "thread_token_usage": {
    "input_tokens": 79199,
    "cached_input_tokens": 65792,
    "cache_write_input_tokens": 0,
    "output_tokens": 715,
    "reasoning_output_tokens": 22,
    "total_tokens": 79914
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-aaefbdac-a715-442d-bd63-b7b8d85a6ced",
    "process_id": "86500",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np,os\nfor f in ['candidate_catalog','target_users','train_listens','train_likes','validation_preference_state','test_preference_state','val_listens']:\n a=pq.ParquetFile('/task/data/'+f+'.parquet');print(f,a.metadata.num_rows,a.schema)\n print(a.read_row_group(0).to_pandas().head(3).to_string(index=False))\nx=np.load('/task/data/validation_eligible_candidates.npz');print([(k,x[k].shape,x[k].dtype) for k in x.files]);print('eligible',np.unpackbits(x['packed_eligible'],axis=1,count=len(x['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count());print(open('/proc/meminfo').read()[:400]);print(open('/sys/fs/cgroup/cpu.max').read());print(open('/sys/fs/cgroup/memory.max').read())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np,os\nfor f in ['candidate_catalog','target_users','train_listens','train_likes','validation_preference_state','test_preference_state','val_listens']:\n a=pq.ParquetFile('/task/data/'+f+'.parquet');print(f,a.metadata.num_rows,a.schema)\n print(a.read_row_group(0).to_pandas().head(3).to_string(index=False))\nx=np.load('/task/data/validation_eligible_candidates.npz');print([(k,x[k].shape,x[k].dtype) for k in x.files]);print('eligible',np.unpackbits(x['packed_eligible'],axis=1,count=len(x['item_ids']),bitorder='little').sum(axis=1).min())\nprint('cpu',os.cpu_count());print(open('/proc/meminfo').read()[:400]);print(open('/sys/fs/cgroup/cpu.max').read());print(open('/sys/fs/cgroup/memory.max').read())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x73e1a56d1800>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x73e19c2dad80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x73e182cb7440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100      39420  8326270           0               100                   170\n 100      39420  1441281           0               100                   105\n 100      39625   286361           0               100                   185\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x73e182965700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic\n 100      44755   732449           1\n 100    1155860  6568592           0\n 100    1259125  5411243           1\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x73e1a58dd100>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x73e1846d8700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x73e1833f2440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100   24943430  9321269           0               100                   195\n 100   24943605  5452713           0               100                   175\n 100   24943765  6448777           0               100                   160\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ncpu 8\nMemTotal:       15988968 kB\nMemFree:          597584 kB\nMemAvailable:   14797676 kB\nBuffers:          271988 kB\nCached:         13480636 kB\nSwapCached:            0 kB\nActive:           754136 kB\nInactive:       13523616 kB\nActive(anon):     551536 kB\nInactive(anon):        0 kB\nActive(file):     202600 kB\nInactive(file): 13523616 kB\nUnevictable:       43808 kB\nMlocked:           27176 kB\nSwapTota\n800000 100000\n\n8589934592\n\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x73e1a56d1800>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x73e19c2dad80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x73e182cb7440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100      39420  8326270           0               100                   170\n 100      39420  1441281           0               100                   105\n 100      39625   286361           0               100                   185\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x73e182965700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic\n 100      44755   732449           1\n 100    1155860  6568592           0\n 100    1259125  5411243           1\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x73e1a58dd100>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x73e1846d8700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x73e1833f2440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100   24943430  9321269           0               100                   195\n 100   24943605  5452713           0               100                   175\n 100   24943765  6448777           0               100                   160\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ncpu 8\nMemTotal:       15988968 kB\nMemFree:          597584 kB\nMemAvailable:   14797676 kB\nBuffers:          271988 kB\nCached:         13480636 kB\nSwapCached:            0 kB\nActive:           754136 kB\nInactive:       13523616 kB\nActive(anon):     551536 kB\nInactive(anon):        0 kB\nActive(file):     202600 kB\nInactive(file): 13523616 kB\nUnevictable:       43808 kB\nMlocked:           27176 kB\nSwapTota\n800000 100000\n\n8589934592\n\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 877783646
    },
    "formatted_output": "candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x73e1a56d1800>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x73e19c2dad80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x73e182cb7440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100      39420  8326270           0               100                   170\n 100      39420  1441281           0               100                   105\n 100      39625   286361           0               100                   185\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x73e182965700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic\n 100      44755   732449           1\n 100    1155860  6568592           0\n 100    1259125  5411243           1\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x73e1a58dd100>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x73e1846d8700>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x73e1833f2440>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n 100   24943430  9321269           0               100                   195\n 100   24943605  5452713           0               100                   175\n 100   24943765  6448777           0               100                   160\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ncpu 8\nMemTotal:       15988968 kB\nMemFree:          597584 kB\nMemAvailable:   14797676 kB\nBuffers:          271988 kB\nCached:         13480636 kB\nSwapCached:            0 kB\nActive:           754136 kB\nInactive:       13523616 kB\nActive(anon):     551536 kB\nInactive(anon):        0 kB\nActive(file):     202600 kB\nInactive(file): 13523616 kB\nUnevictable:       43808 kB\nMlocked:           27176 kB\nSwapTota\n800000 100000\n\n8589934592\n\n"
  }
}

exec command

+00:41

Thinking unavailable No text recorded

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

Model text

The data contains 43.5 million training listens, 8,456 target users, and 30,000 catalog tracks. I’ll train from event histories with bounded history inputs, which keeps the approach scalable and avoids a global user–item matrix.

Tool callexecTool call Returned

Tool call · Input +01:00

text(await tools.exec_command({cmd:"cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nui=pd.Index(uids);ii=pd.Index(items)\nfor f in ['validation_preference_state','val_likes','val_listens']:\n d=pd.read_parquet(D+f+'.parquet');u=ui.get_indexer(d.uid);i=ii.get_indexer(d.item_id);ok=(u>=0)&(i>=0);el=np.zeros(len(d),bool);el[ok]=mask[u[ok],i[ok]]\n print(f,len(d),'catalog',ok.mean(),'eligible',el.mean(),flush=True)\n if 'state' in d:\n  print(d.assign(eligible=el).groupby('state').eligible.agg(['mean','size']))\n else:\n  print(d.assign(eligible=el).groupby('is_organic').eligible.agg(['mean','size']))\n# scan all rows statistics and catalog events. only sparse event arrays, no interaction matrix\nparts=[]\nfor batch in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();i=ii.get_indexer(d.item_id);ok=i>=0\n d=d[ok].copy();d['iid']=i[ok];parts.append(d)\nd=pd.concat(parts,ignore_index=True)\nprint('catalog listens',len(d),'users',d.uid.nunique(),'timestamp',d.timestamp.min(),d.timestamp.max(),flush=True)\nprint('pos',len(d[d.played_ratio_pct>=50]),'strong organic',len(d[(d.played_ratio_pct>=80)&(d.is_organic==1)]),flush=True)\nu=ui.get_indexer(d.uid);ok=u>=0;e=np.zeros(len(d),bool);e[ok]=mask[u[ok],d.iid.to_numpy()[ok]]\nfor days in [7,30,90,365]:\n k=(d.timestamp>24786800-days*86400)&(d.played_ratio_pct>=50);print('seen eligible',days,e[k].mean(),int(k.sum()),flush=True)\nd.to_parquet('/app/solution/catalog_train.parquet',index=False)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py > /app/solution/inspect.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +01:01

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"172c40","wall_time_seconds":1.000556492,"session_id":66979,"original_token_count":0,"output":""}

Harness context token usage record

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Harness context codex item completed

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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-a5aae327-c35b-402a-a8c5-cb593263346b",
    "process_id": "66979",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nui=pd.Index(uids);ii=pd.Index(items)\nfor f in ['validation_preference_state','val_likes','val_listens']:\n d=pd.read_parquet(D+f+'.parquet');u=ui.get_indexer(d.uid);i=ii.get_indexer(d.item_id);ok=(u>=0)&(i>=0);el=np.zeros(len(d),bool);el[ok]=mask[u[ok],i[ok]]\n print(f,len(d),'catalog',ok.mean(),'eligible',el.mean(),flush=True)\n if 'state' in d:\n  print(d.assign(eligible=el).groupby('state').eligible.agg(['mean','size']))\n else:\n  print(d.assign(eligible=el).groupby('is_organic').eligible.agg(['mean','size']))\n# scan all rows statistics and catalog events. only sparse event arrays, no interaction matrix\nparts=[]\nfor batch in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();i=ii.get_indexer(d.item_id);ok=i>=0\n d=d[ok].copy();d['iid']=i[ok];parts.append(d)\nd=pd.concat(parts,ignore_index=True)\nprint('catalog listens',len(d),'users',d.uid.nunique(),'timestamp',d.timestamp.min(),d.timestamp.max(),flush=True)\nprint('pos',len(d[d.played_ratio_pct>=50]),'strong organic',len(d[(d.played_ratio_pct>=80)&(d.is_organic==1)]),flush=True)\nu=ui.get_indexer(d.uid);ok=u>=0;e=np.zeros(len(d),bool);e[ok]=mask[u[ok],d.iid.to_numpy()[ok]]\nfor days in [7,30,90,365]:\n k=(d.timestamp>24786800-days*86400)&(d.played_ratio_pct>=50);print('seen eligible',days,e[k].mean(),int(k.sum()),flush=True)\nd.to_parquet('/app/solution/catalog_train.parquet',index=False)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py > /app/solution/inspect.log 2>&1"
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        "cmd": "cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nui=pd.Index(uids);ii=pd.Index(items)\nfor f in ['validation_preference_state','val_likes','val_listens']:\n d=pd.read_parquet(D+f+'.parquet');u=ui.get_indexer(d.uid);i=ii.get_indexer(d.item_id);ok=(u>=0)&(i>=0);el=np.zeros(len(d),bool);el[ok]=mask[u[ok],i[ok]]\n print(f,len(d),'catalog',ok.mean(),'eligible',el.mean(),flush=True)\n if 'state' in d:\n  print(d.assign(eligible=el).groupby('state').eligible.agg(['mean','size']))\n else:\n  print(d.assign(eligible=el).groupby('is_organic').eligible.agg(['mean','size']))\n# scan all rows statistics and catalog events. only sparse event arrays, no interaction matrix\nparts=[]\nfor batch in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();i=ii.get_indexer(d.item_id);ok=i>=0\n d=d[ok].copy();d['iid']=i[ok];parts.append(d)\nd=pd.concat(parts,ignore_index=True)\nprint('catalog listens',len(d),'users',d.uid.nunique(),'timestamp',d.timestamp.min(),d.timestamp.max(),flush=True)\nprint('pos',len(d[d.played_ratio_pct>=50]),'strong organic',len(d[(d.played_ratio_pct>=80)&(d.is_organic==1)]),flush=True)\nu=ui.get_indexer(d.uid);ok=u>=0;e=np.zeros(len(d),bool);e[ok]=mask[u[ok],d.iid.to_numpy()[ok]]\nfor days in [7,30,90,365]:\n k=(d.timestamp>24786800-days*86400)&(d.played_ratio_pct>=50);print('seen eligible',days,e[k].mean(),int(k.sum()),flush=True)\nd.to_parquet('/app/solution/catalog_train.parquet',index=False)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py > /app/solution/inspect.log 2>&1"
      }
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    "stdout": "",
    "stderr": "",
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      "nanos": 359055495
    },
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  }
}

exec command

+01:15

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +01:16

text(await tools.exec_command({cmd:"cat /app/solution/inspect.log","max_output_tokens":2000}));

Tool response +01:17

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"75d2e0","wall_time_seconds":0.00000635,"exit_code":0,"original_token_count":185,"output":"validation_preference_state 469124 catalog 1.0 eligible 0.0\n          mean    size\nstate                 \ndisliked   0.0   50689\nliked      0.0  418435\nval_likes 37795 catalog 0.48437624024341847 eligible 0.2509855801031883\n                mean   size\nis_organic                 \n0           0.391787  12614\n1           0.180454  25181\nval_listens 1501697 catalog 0.6652886700845776 eligible 0.18423090676747705\n                mean    size\nis_organic                  \n0           0.236320  725211\n1           0.135581  776486\ncatalog listens 31249613 users 9217 timestamp 0 24786795\npos 20056329 strong organic 8699160\nseen eligible 7 0.0 674575\nseen eligible 30 0.0 2788954\nseen eligible 90 0.0 7755442\nseen eligible 365 0.0 20056329\n"}

Harness context codex item completed

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    "stderr": "",
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Agent message

+01:35

Thinking unavailable No text recorded

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

Agent message

+01:49

Thinking unavailable No text recorded

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

exec command

+02:03

Thinking unavailable No text recorded

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

Model text

The eligibility masks exclude every previously positive track and every active like or dislike, so this is a discovery task. I’ll train a denoising history encoder to learn associations between tracks, then test how recency and explicit preferences affect retrieval of unseen items.

Tool callexecTool call Returned

Tool call · Input +02:31

text(await tools.exec_command({cmd:"cat > /app/solution/build_data.py <<'PY'\n\"\"\"Build per-user ragged histories directly from event tables, never a user-item matrix.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution'); D=Path('/task/data'); cutoff=24786800\nitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();ii=pd.Index(items)\n\ndef build(stage):\n final=stage=='test'; end=cutoff+(608400 if final else 0)\n parts=[]\n for split in (['train','val'] if final else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for batch in pq.ParquetFile(file).iter_batches(batch_size=2000000):\n   d=batch.to_pandas();idx=ii.get_indexer(d.item_id);keep=(idx>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[keep].copy();d['iid']=idx[keep]\n   age=(end-d.timestamp.to_numpy())/86400\n   d['inp']=(0.5+0.5*d.is_organic.to_numpy())*np.exp(-age/180)\n   d['out']=(0.25+0.75*d.is_organic.to_numpy())*(0.4+0.6*(d.played_ratio_pct.to_numpy()>=80))*np.exp(-age/120)\n   d['recent']=d.inp*np.exp(-age/30)\n   parts.append(d.groupby(['uid','iid'],sort=False).agg(inp=('inp','sum'),out=('out','sum'),recent=('recent','sum'),last=('timestamp','max')).reset_index())\n a=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False).agg({'inp':'sum','out':'sum','recent':'sum','last':'max'}).reset_index();del parts\n # Compress repeated plays; retain broad positive evidence.\n a['inp']=a.inp**0.35;a['out']=a.out**0.35;a['recent']=a.recent**0.35\n a['liked']=0.;a['disliked']=0.\n state=pd.read_parquet(D/('test_preference_state.parquet' if final else 'validation_preference_state.parquet'))\n state['iid']=ii.get_indexer(state.item_id);state=state[state.iid>=0]\n s=state.assign(v=1).pivot_table(index=['uid','iid'],columns='state',values='v',aggfunc='max',fill_value=0).reset_index()\n a=a.drop(columns=['liked','disliked']).merge(s,on=['uid','iid'],how='outer').fillna(0)\n a['inp']+=a.liked*1.5;a['out']+=a.liked*2.0\n # Negative state is an input, not a positive target.\n a['neg']=a.disliked.astype('float32');a.loc[a.disliked>0,'out']*=0.1\n a=a.sort_values(['uid','iid']);uids=a.uid.unique(); sizes=a.groupby('uid',sort=False).size().to_numpy(); offsets=np.r_[0,np.cumsum(sizes)]\n np.savez(P/f'data_{stage}.npz',uids=uids,items=items,offsets=offsets,ids=a.iid.to_numpy('int32'),weights=a.inp.to_numpy('float32'),targets=a.out.to_numpy('float32'),recent=a.recent.to_numpy('float32'),liked=a.liked.to_numpy('float32'),neg=a.neg.to_numpy('float32'))\n print(stage,'users',len(uids),'pairs',len(a),'end',end,flush=True)\nif __name__=='__main__':\n import sys\n build(sys.argv[1])\nPY\n/opt/task-venv/bin/python /app/solution/build_data.py validation > /app/solution/build_validation.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +02:32

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"68a13a","wall_time_seconds":1.000554811,"session_id":58662,"original_token_count":0,"output":""}

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      "/bin/sh",
      "-lc",
      "cat > /app/solution/build_data.py <<'PY'\n\"\"\"Build per-user ragged histories directly from event tables, never a user-item matrix.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution'); D=Path('/task/data'); cutoff=24786800\nitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();ii=pd.Index(items)\n\ndef build(stage):\n final=stage=='test'; end=cutoff+(608400 if final else 0)\n parts=[]\n for split in (['train','val'] if final else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for batch in pq.ParquetFile(file).iter_batches(batch_size=2000000):\n   d=batch.to_pandas();idx=ii.get_indexer(d.item_id);keep=(idx>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[keep].copy();d['iid']=idx[keep]\n   age=(end-d.timestamp.to_numpy())/86400\n   d['inp']=(0.5+0.5*d.is_organic.to_numpy())*np.exp(-age/180)\n   d['out']=(0.25+0.75*d.is_organic.to_numpy())*(0.4+0.6*(d.played_ratio_pct.to_numpy()>=80))*np.exp(-age/120)\n   d['recent']=d.inp*np.exp(-age/30)\n   parts.append(d.groupby(['uid','iid'],sort=False).agg(inp=('inp','sum'),out=('out','sum'),recent=('recent','sum'),last=('timestamp','max')).reset_index())\n a=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False).agg({'inp':'sum','out':'sum','recent':'sum','last':'max'}).reset_index();del parts\n # Compress repeated plays; retain broad positive evidence.\n a['inp']=a.inp**0.35;a['out']=a.out**0.35;a['recent']=a.recent**0.35\n a['liked']=0.;a['disliked']=0.\n state=pd.read_parquet(D/('test_preference_state.parquet' if final else 'validation_preference_state.parquet'))\n state['iid']=ii.get_indexer(state.item_id);state=state[state.iid>=0]\n s=state.assign(v=1).pivot_table(index=['uid','iid'],columns='state',values='v',aggfunc='max',fill_value=0).reset_index()\n a=a.drop(columns=['liked','disliked']).merge(s,on=['uid','iid'],how='outer').fillna(0)\n a['inp']+=a.liked*1.5;a['out']+=a.liked*2.0\n # Negative state is an input, not a positive target.\n a['neg']=a.disliked.astype('float32');a.loc[a.disliked>0,'out']*=0.1\n a=a.sort_values(['uid','iid']);uids=a.uid.unique(); sizes=a.groupby('uid',sort=False).size().to_numpy(); offsets=np.r_[0,np.cumsum(sizes)]\n np.savez(P/f'data_{stage}.npz',uids=uids,items=items,offsets=offsets,ids=a.iid.to_numpy('int32'),weights=a.inp.to_numpy('float32'),targets=a.out.to_numpy('float32'),recent=a.recent.to_numpy('float32'),liked=a.liked.to_numpy('float32'),neg=a.neg.to_numpy('float32'))\n print(stage,'users',len(uids),'pairs',len(a),'end',end,flush=True)\nif __name__=='__main__':\n import sys\n build(sys.argv[1])\nPY\n/opt/task-venv/bin/python /app/solution/build_data.py validation > /app/solution/build_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/build_data.py <<'PY'\n\"\"\"Build per-user ragged histories directly from event tables, never a user-item matrix.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution'); D=Path('/task/data'); cutoff=24786800\nitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();ii=pd.Index(items)\n\ndef build(stage):\n final=stage=='test'; end=cutoff+(608400 if final else 0)\n parts=[]\n for split in (['train','val'] if final else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for batch in pq.ParquetFile(file).iter_batches(batch_size=2000000):\n   d=batch.to_pandas();idx=ii.get_indexer(d.item_id);keep=(idx>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[keep].copy();d['iid']=idx[keep]\n   age=(end-d.timestamp.to_numpy())/86400\n   d['inp']=(0.5+0.5*d.is_organic.to_numpy())*np.exp(-age/180)\n   d['out']=(0.25+0.75*d.is_organic.to_numpy())*(0.4+0.6*(d.played_ratio_pct.to_numpy()>=80))*np.exp(-age/120)\n   d['recent']=d.inp*np.exp(-age/30)\n   parts.append(d.groupby(['uid','iid'],sort=False).agg(inp=('inp','sum'),out=('out','sum'),recent=('recent','sum'),last=('timestamp','max')).reset_index())\n a=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False).agg({'inp':'sum','out':'sum','recent':'sum','last':'max'}).reset_index();del parts\n # Compress repeated plays; retain broad positive evidence.\n a['inp']=a.inp**0.35;a['out']=a.out**0.35;a['recent']=a.recent**0.35\n a['liked']=0.;a['disliked']=0.\n state=pd.read_parquet(D/('test_preference_state.parquet' if final else 'validation_preference_state.parquet'))\n state['iid']=ii.get_indexer(state.item_id);state=state[state.iid>=0]\n s=state.assign(v=1).pivot_table(index=['uid','iid'],columns='state',values='v',aggfunc='max',fill_value=0).reset_index()\n a=a.drop(columns=['liked','disliked']).merge(s,on=['uid','iid'],how='outer').fillna(0)\n a['inp']+=a.liked*1.5;a['out']+=a.liked*2.0\n # Negative state is an input, not a positive target.\n a['neg']=a.disliked.astype('float32');a.loc[a.disliked>0,'out']*=0.1\n a=a.sort_values(['uid','iid']);uids=a.uid.unique(); sizes=a.groupby('uid',sort=False).size().to_numpy(); offsets=np.r_[0,np.cumsum(sizes)]\n np.savez(P/f'data_{stage}.npz',uids=uids,items=items,offsets=offsets,ids=a.iid.to_numpy('int32'),weights=a.inp.to_numpy('float32'),targets=a.out.to_numpy('float32'),recent=a.recent.to_numpy('float32'),liked=a.liked.to_numpy('float32'),neg=a.neg.to_numpy('float32'))\n print(stage,'users',len(uids),'pairs',len(a),'end',end,flush=True)\nif __name__=='__main__':\n import sys\n build(sys.argv[1])\nPY\n/opt/task-venv/bin/python /app/solution/build_data.py validation > /app/solution/build_validation.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 122787679
    },
    "formatted_output": ""
  }
}

exec command

+02:50

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 +03:57

text(await tools.exec_command({cmd:"cat > /app/solution/train.py <<'PY'\n\"\"\"CPU neural two-tower denoising retrieval model; all final scores are dot products.\"\"\"\nimport os\nos.environ.setdefault('OMP_NUM_THREADS','8');os.environ.setdefault('MKL_NUM_THREADS','8')\nimport argparse,json,time,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom torch import nn\nfrom torch.nn import functional as F\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(8)\n\nclass UserTower(nn.Module):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__();self.history=nn.EmbeddingBag(n,dim,mode='sum');nn.init.normal_(self.history.weight,std=.025)\n  self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,latent),nn.Tanh(),nn.Linear(latent,dim),nn.Tanh())\n def forward(self,ids,offsets,weights):\n  h=self.history(ids,offsets,per_sample_weights=weights)\n  h=self.net(h)\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\nclass ItemTower(nn.Module):\n def __init__(self,n,dim=600):\n  super().__init__();self.embedding=nn.Embedding(n,dim);self.activation=nn.Tanh();self.bias=nn.Embedding(n,1)\n  nn.init.normal_(self.embedding.weight,std=.015);nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n  return torch.cat([self.activation(self.embedding(ids)),self.bias(ids)],dim=1)\nclass TwoTower(nn.Module):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__();self.user=UserTower(n,dim,latent);self.item=ItemTower(n,dim)\n def forward(self,ids,offsets,weights,candidates):\n  return self.user(ids,offsets,weights)@self.item(candidates).T\nclass Histories:\n def __init__(self,stage):\n  z=np.load(P/f'data_{stage}.npz');self.__dict__.update({k:z[k] for k in z.files});self.index={int(u):i for i,u in enumerate(self.uids)}\n def batch(self,rows,drop=0.,recent=0.,like=0.,negative=0.3,target=True):\n  chunks=[np.arange(self.offsets[r],self.offsets[r+1]) for r in rows];lens=np.array([len(c) for c in chunks]);idx=np.concatenate(chunks);ids=self.ids[idx].astype('int64');ridx=np.repeat(np.arange(len(rows)),lens)\n  w=self.weights[idx]+recent*self.recent[idx]+like*self.liked[idx]-negative*self.neg[idx]\n  if drop: w=w*(np.random.random(len(w))>=drop)\n  denom=np.sqrt(np.bincount(ridx,weights=w*w,minlength=len(rows))).clip(.00001);w=(w/denom[ridx]).astype('float32')\n  args=(torch.from_numpy(ids),torch.from_numpy(np.r_[0,np.cumsum(lens)[:-1]]),torch.from_numpy(w))\n  if not target:return args\n  y=torch.zeros((len(rows),len(self.items)));y[torch.from_numpy(ridx),torch.from_numpy(ids)]=torch.from_numpy(self.targets[idx]);y/=y.sum(1,keepdim=True).clamp_min(.00001)\n  return args,y\n\ndef truth_data():\n z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n ui=pd.Index(uids);ii=pd.Index(items);truth={}\n for f in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{f}.parquet');d=d[d.is_organic==1]\n  if f=='listens':d=d[d.played_ratio_pct>=80]\n  ur=ui.get_indexer(d.uid);ir=ii.get_indexer(d.item_id)\n  for u,i in zip(ur,ir):\n   if u>=0 and i>=0 and mask[u,i]:truth.setdefault(int(u),{})[int(i)]=7 if f=='likes' else 1\n disc=1/np.log2(np.arange(2,12));idcg={u:np.dot(sorted(g.values(),reverse=True)[:10],disc[:min(10,len(g))]) for u,g in truth.items()}\n return z,mask,truth,idcg\n\ndef evaluate(model,data,stage='validation',recent=0.,like=0.,negative=.3,save=None):\n model.eval();z=np.load(D/f'{stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n columns=pd.Index(data.items).get_indexer(items);ranked=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for start in range(0,len(uids),128):\n   ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n ranks=np.concatenate(ranked)\n if save:\n  out=pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':items[ranks.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101),len(uids))});out.to_parquet(P/save,index=False)\n if stage=='validation':\n  global TRUTH\n  if TRUTH is None:TRUTH=truth_data()\n  _,_,truth,idcg=TRUTH;disc=1/np.log2(np.arange(2,12));ndcg=np.mean([np.dot([truth[u].get(int(i),0) for i in ranks[u,:10]],disc)/idcg[u] for u in truth]);return float(ndcg)\n return None\nTRUTH=None\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--epochs',type=int,default=40);ap.add_argument('--dim',type=int,default=600);ap.add_argument('--latent',type=int,default=200);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--lr',type=float,default=.001);ap.add_argument('--drop',type=float,default=.5);ap.add_argument('--name',default='base');ap.add_argument('--seed',type=int,default=42);ap.add_argument('--eval-every',type=int,default=2);ap.add_argument('--resume');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--like',type=float,default=0);ap.add_argument('--negative',type=float,default=.3);ap.add_argument('--infer',action='store_true');a=ap.parse_args()\n torch.manual_seed(a.seed);np.random.seed(a.seed);data=Histories(a.stage);model=TwoTower(len(data.items),a.dim,a.latent)\n if a.resume:model.load_state_dict(torch.load(P/a.resume,weights_only=False)['model'])\n if a.infer:\n  score=evaluate(model,data,a.stage,a.recent,a.like,a.negative,'validation.parquet' if a.stage=='validation' else 'submission.parquet');print('inference score',score,flush=True);return\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);allitems=torch.arange(len(data.items));best=-1;start=time.time()\n for epoch in range(1,a.epochs+1):\n  model.train();order=np.random.permutation(len(data.uids));losses=[]\n  for s in range(0,len(order),a.batch):\n   rows=order[s:s+a.batch];args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  print(json.dumps({'epoch':epoch,'loss':float(np.mean(losses)),'seconds':round(time.time()-start,1)}),flush=True)\n  if epoch%a.eval_every==0 or epoch==a.epochs:\n   checkpoint={'model':model.state_dict(),'args':vars(a),'epoch':epoch}\n   torch.save(checkpoint,P/f'{a.name}_last.pt')\n   if a.stage=='validation':\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n    if score>best:\n     best=score;torch.save(checkpoint,P/f'{a.name}_best.pt');evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative,save=f'{a.name}_validation.parquet');(P/f'{a.name}_score.json').write_text(json.dumps({'ndcg':score,'epoch':epoch,'args':vars(a)},indent=2))\n   else:\n    evaluate(model,data,'test',a.recent,a.like,a.negative,'submission.parquet');torch.save(checkpoint,P/f'{a.name}_best.pt')\nif __name__=='__main__':main()\nPY\ncat /app/solution/build_validation.log\n/opt/task-venv/bin/python /app/solution/train.py --epochs 40 --eval-every 2 > /app/solution/base.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +03:58

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c6c356","wall_time_seconds":1.000844219,"session_id":65493,"original_token_count":13,"output":"validation users 9135 pairs 5634438 end 24786800\n"}

Harness context token usage record

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}

exec command

+04:04

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +04:07

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/build_data.py test > /app/solution/build_test.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));
text(await tools.exec_command({cmd:"tail -5 /app/solution/base.log && du -h /app/solution/*","max_output_tokens":1500}));

Tool response +04:08

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"566a8a","wall_time_seconds":1.001654082,"session_id":4305,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 07:59:37 UTC"}
{"chunk_id":"3fd7dd","wall_time_seconds":0.00000425,"exit_code":0,"original_token_count":76,"output":"0\t/app/solution/base.log\n4.0K\t/app/solution/build_data.py\n0\t/app/solution/build_test.log\n4.0K\t/app/solution/build_validation.log\n296M\t/app/solution/catalog_train.parquet\n130M\t/app/solution/data_validation.npz\n4.0K\t/app/solution/inspect.log\n4.0K\t/app/solution/inspect_data.py\n8.0K\t/app/solution/train.py\n"}

Harness context token usage record

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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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Harness context codex item completed

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    "type": "CommandExecution",
    "id": "exec-f80e5787-9c92-4f1a-9cda-539d95a538d7",
    "process_id": "81886",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -5 /app/solution/base.log && du -h /app/solution/*"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -5 /app/solution/base.log && du -h /app/solution/*"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0\t/app/solution/base.log\n4.0K\t/app/solution/build_data.py\n0\t/app/solution/build_test.log\n4.0K\t/app/solution/build_validation.log\n296M\t/app/solution/catalog_train.parquet\n130M\t/app/solution/data_validation.npz\n4.0K\t/app/solution/inspect.log\n4.0K\t/app/solution/inspect_data.py\n8.0K\t/app/solution/train.py\n",
    "stderr": "",
    "aggregated_output": "0\t/app/solution/base.log\n4.0K\t/app/solution/build_data.py\n0\t/app/solution/build_test.log\n4.0K\t/app/solution/build_validation.log\n296M\t/app/solution/catalog_train.parquet\n130M\t/app/solution/data_validation.npz\n4.0K\t/app/solution/inspect.log\n4.0K\t/app/solution/inspect_data.py\n8.0K\t/app/solution/train.py\n",
    "exit_code": 0,
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      "secs": 0,
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    },
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  }
}

exec command

+04:20

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +04:22

text(await tools.exec_command({cmd:"tail -10 /app/solution/base.log && cat /app/solution/build_test.log && ps -eo pid,etime,%cpu,%mem,args","max_output_tokens":2000}));

Tool response +04:22

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"13881b","wall_time_seconds":0.000199883,"exit_code":127,"original_token_count":18,"output":"test users 9153 pairs 5795424 end 25395200\n/bin/sh: 1: ps: not found\n"}

Harness context token usage record

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Harness context codex item completed

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      "/bin/sh",
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    "stdout": "test users 9153 pairs 5795424 end 25395200\n/bin/sh: 1: ps: not found\n",
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Harness context codex item completed

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}

exec command

+04:25

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +04:27

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/cpu.stat && ls -lh /app/solution","max_output_tokens":1200}));

Tool response +04:27

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"b8f94c","wall_time_seconds":0.00000404,"exit_code":0,"original_token_count":197,"output":"3535839232\nusage_usec 263931852\nuser_usec 218166728\nsystem_usec 45765123\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 2485\nnr_throttled 18\nthrottled_usec 22328\nnr_bursts 0\nburst_usec 0\ntotal 559M\n-rw-r--r-- 1 agent agent   56 Sep 24 07:59 base.log\n-rw-r--r-- 1 agent agent 2.6K Sep 24 07:58 build_data.py\n-rw-r--r-- 1 agent agent   43 Sep 24 07:59 build_test.log\n-rw-r--r-- 1 agent agent   49 Sep 24 07:58 build_validation.log\n-rw-r--r-- 1 agent agent 296M Sep 24 07:56 catalog_train.parquet\n-rw-r--r-- 1 agent agent 133M Sep 24 07:59 data_test.npz\n-rw-r--r-- 1 agent agent 130M Sep 24 07:58 data_validation.npz\n-rw-r--r-- 1 agent agent  737 Sep 24 07:56 inspect.log\n-rw-r--r-- 1 agent agent 1.7K Sep 24 07:56 inspect_data.py\n-rw-r--r-- 1 agent agent 7.0K Sep 24 07:59 train.py\n"}

Harness context token usage record

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Harness context codex item completed

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}

exec command

+04:36

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +04:37

text(await tools.exec_command({cmd:"tail -10 /app/solution/base.log","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +04:37

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"475679","wall_time_seconds":0.00000382,"exit_code":0,"original_token_count":14,"output":"{\"epoch\": 1, \"loss\": 9.43094891972012, \"seconds\": 22.1}\n"}
{"current_time":"2026-09-24 08:00:06 UTC"}

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Harness context codex item completed

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Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-25062777-7438-4361-88a3-d838b7b4b4de",
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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/train.py <<'PY'\n\"\"\"CPU neural two-tower denoising retrieval model; all final scores are dot products.\"\"\"\nimport os\nos.environ.setdefault('OMP_NUM_THREADS','8');os.environ.setdefault('MKL_NUM_THREADS','8')\nimport argparse,json,time,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom torch import nn\nfrom torch.nn import functional as F\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(8)\n\nclass UserTower(nn.Module):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__();self.history=nn.EmbeddingBag(n,dim,mode='sum');nn.init.normal_(self.history.weight,std=.025)\n  self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,latent),nn.Tanh(),nn.Linear(latent,dim),nn.Tanh())\n def forward(self,ids,offsets,weights):\n  h=self.history(ids,offsets,per_sample_weights=weights)\n  h=self.net(h)\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\nclass ItemTower(nn.Module):\n def __init__(self,n,dim=600):\n  super().__init__();self.embedding=nn.Embedding(n,dim);self.activation=nn.Tanh();self.bias=nn.Embedding(n,1)\n  nn.init.normal_(self.embedding.weight,std=.015);nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n  return torch.cat([self.activation(self.embedding(ids)),self.bias(ids)],dim=1)\nclass TwoTower(nn.Module):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__();self.user=UserTower(n,dim,latent);self.item=ItemTower(n,dim)\n def forward(self,ids,offsets,weights,candidates):\n  return self.user(ids,offsets,weights)@self.item(candidates).T\nclass Histories:\n def __init__(self,stage):\n  z=np.load(P/f'data_{stage}.npz');self.__dict__.update({k:z[k] for k in z.files});self.index={int(u):i for i,u in enumerate(self.uids)}\n def batch(self,rows,drop=0.,recent=0.,like=0.,negative=0.3,target=True):\n  chunks=[np.arange(self.offsets[r],self.offsets[r+1]) for r in rows];lens=np.array([len(c) for c in chunks]);idx=np.concatenate(chunks);ids=self.ids[idx].astype('int64');ridx=np.repeat(np.arange(len(rows)),lens)\n  w=self.weights[idx]+recent*self.recent[idx]+like*self.liked[idx]-negative*self.neg[idx]\n  if drop: w=w*(np.random.random(len(w))>=drop)\n  denom=np.sqrt(np.bincount(ridx,weights=w*w,minlength=len(rows))).clip(.00001);w=(w/denom[ridx]).astype('float32')\n  args=(torch.from_numpy(ids),torch.from_numpy(np.r_[0,np.cumsum(lens)[:-1]]),torch.from_numpy(w))\n  if not target:return args\n  y=torch.zeros((len(rows),len(self.items)));y[torch.from_numpy(ridx),torch.from_numpy(ids)]=torch.from_numpy(self.targets[idx]);y/=y.sum(1,keepdim=True).clamp_min(.00001)\n  return args,y\n\ndef truth_data():\n z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n ui=pd.Index(uids);ii=pd.Index(items);truth={}\n for f in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{f}.parquet');d=d[d.is_organic==1]\n  if f=='listens':d=d[d.played_ratio_pct>=80]\n  ur=ui.get_indexer(d.uid);ir=ii.get_indexer(d.item_id)\n  for u,i in zip(ur,ir):\n   if u>=0 and i>=0 and mask[u,i]:truth.setdefault(int(u),{})[int(i)]=7 if f=='likes' else 1\n disc=1/np.log2(np.arange(2,12));idcg={u:np.dot(sorted(g.values(),reverse=True)[:10],disc[:min(10,len(g))]) for u,g in truth.items()}\n return z,mask,truth,idcg\n\ndef evaluate(model,data,stage='validation',recent=0.,like=0.,negative=.3,save=None):\n model.eval();z=np.load(D/f'{stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n columns=pd.Index(data.items).get_indexer(items);ranked=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for start in range(0,len(uids),128):\n   ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n ranks=np.concatenate(ranked)\n if save:\n  out=pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':items[ranks.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101),len(uids))});out.to_parquet(P/save,index=False)\n if stage=='validation':\n  global TRUTH\n  if TRUTH is None:TRUTH=truth_data()\n  _,_,truth,idcg=TRUTH;disc=1/np.log2(np.arange(2,12));ndcg=np.mean([np.dot([truth[u].get(int(i),0) for i in ranks[u,:10]],disc)/idcg[u] for u in truth]);return float(ndcg)\n return None\nTRUTH=None\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--epochs',type=int,default=40);ap.add_argument('--dim',type=int,default=600);ap.add_argument('--latent',type=int,default=200);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--lr',type=float,default=.001);ap.add_argument('--drop',type=float,default=.5);ap.add_argument('--name',default='base');ap.add_argument('--seed',type=int,default=42);ap.add_argument('--eval-every',type=int,default=2);ap.add_argument('--resume');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--like',type=float,default=0);ap.add_argument('--negative',type=float,default=.3);ap.add_argument('--infer',action='store_true');a=ap.parse_args()\n torch.manual_seed(a.seed);np.random.seed(a.seed);data=Histories(a.stage);model=TwoTower(len(data.items),a.dim,a.latent)\n if a.resume:model.load_state_dict(torch.load(P/a.resume,weights_only=False)['model'])\n if a.infer:\n  score=evaluate(model,data,a.stage,a.recent,a.like,a.negative,'validation.parquet' if a.stage=='validation' else 'submission.parquet');print('inference score',score,flush=True);return\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);allitems=torch.arange(len(data.items));best=-1;start=time.time()\n for epoch in range(1,a.epochs+1):\n  model.train();order=np.random.permutation(len(data.uids));losses=[]\n  for s in range(0,len(order),a.batch):\n   rows=order[s:s+a.batch];args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  print(json.dumps({'epoch':epoch,'loss':float(np.mean(losses)),'seconds':round(time.time()-start,1)}),flush=True)\n  if epoch%a.eval_every==0 or epoch==a.epochs:\n   checkpoint={'model':model.state_dict(),'args':vars(a),'epoch':epoch}\n   torch.save(checkpoint,P/f'{a.name}_last.pt')\n   if a.stage=='validation':\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n    if score>best:\n     best=score;torch.save(checkpoint,P/f'{a.name}_best.pt');evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative,save=f'{a.name}_validation.parquet');(P/f'{a.name}_score.json').write_text(json.dumps({'ndcg':score,'epoch':epoch,'args':vars(a)},indent=2))\n   else:\n    evaluate(model,data,'test',a.recent,a.like,a.negative,'submission.parquet');torch.save(checkpoint,P/f'{a.name}_best.pt')\nif __name__=='__main__':main()\nPY\ncat /app/solution/build_validation.log\n/opt/task-venv/bin/python /app/solution/train.py --epochs 40 --eval-every 2 > /app/solution/base.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/train.py <<'PY'\n\"\"\"CPU neural two-tower denoising retrieval model; all final scores are dot products.\"\"\"\nimport os\nos.environ.setdefault('OMP_NUM_THREADS','8');os.environ.setdefault('MKL_NUM_THREADS','8')\nimport argparse,json,time,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom torch import nn\nfrom torch.nn import functional as F\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(8)\n\nclass UserTower(nn.Module):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__();self.history=nn.EmbeddingBag(n,dim,mode='sum');nn.init.normal_(self.history.weight,std=.025)\n  self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,latent),nn.Tanh(),nn.Linear(latent,dim),nn.Tanh())\n def forward(self,ids,offsets,weights):\n  h=self.history(ids,offsets,per_sample_weights=weights)\n  h=self.net(h)\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\nclass ItemTower(nn.Module):\n def __init__(self,n,dim=600):\n  super().__init__();self.embedding=nn.Embedding(n,dim);self.activation=nn.Tanh();self.bias=nn.Embedding(n,1)\n  nn.init.normal_(self.embedding.weight,std=.015);nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n  return torch.cat([self.activation(self.embedding(ids)),self.bias(ids)],dim=1)\nclass TwoTower(nn.Module):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__();self.user=UserTower(n,dim,latent);self.item=ItemTower(n,dim)\n def forward(self,ids,offsets,weights,candidates):\n  return self.user(ids,offsets,weights)@self.item(candidates).T\nclass Histories:\n def __init__(self,stage):\n  z=np.load(P/f'data_{stage}.npz');self.__dict__.update({k:z[k] for k in z.files});self.index={int(u):i for i,u in enumerate(self.uids)}\n def batch(self,rows,drop=0.,recent=0.,like=0.,negative=0.3,target=True):\n  chunks=[np.arange(self.offsets[r],self.offsets[r+1]) for r in rows];lens=np.array([len(c) for c in chunks]);idx=np.concatenate(chunks);ids=self.ids[idx].astype('int64');ridx=np.repeat(np.arange(len(rows)),lens)\n  w=self.weights[idx]+recent*self.recent[idx]+like*self.liked[idx]-negative*self.neg[idx]\n  if drop: w=w*(np.random.random(len(w))>=drop)\n  denom=np.sqrt(np.bincount(ridx,weights=w*w,minlength=len(rows))).clip(.00001);w=(w/denom[ridx]).astype('float32')\n  args=(torch.from_numpy(ids),torch.from_numpy(np.r_[0,np.cumsum(lens)[:-1]]),torch.from_numpy(w))\n  if not target:return args\n  y=torch.zeros((len(rows),len(self.items)));y[torch.from_numpy(ridx),torch.from_numpy(ids)]=torch.from_numpy(self.targets[idx]);y/=y.sum(1,keepdim=True).clamp_min(.00001)\n  return args,y\n\ndef truth_data():\n z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n ui=pd.Index(uids);ii=pd.Index(items);truth={}\n for f in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{f}.parquet');d=d[d.is_organic==1]\n  if f=='listens':d=d[d.played_ratio_pct>=80]\n  ur=ui.get_indexer(d.uid);ir=ii.get_indexer(d.item_id)\n  for u,i in zip(ur,ir):\n   if u>=0 and i>=0 and mask[u,i]:truth.setdefault(int(u),{})[int(i)]=7 if f=='likes' else 1\n disc=1/np.log2(np.arange(2,12));idcg={u:np.dot(sorted(g.values(),reverse=True)[:10],disc[:min(10,len(g))]) for u,g in truth.items()}\n return z,mask,truth,idcg\n\ndef evaluate(model,data,stage='validation',recent=0.,like=0.,negative=.3,save=None):\n model.eval();z=np.load(D/f'{stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n columns=pd.Index(data.items).get_indexer(items);ranked=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for start in range(0,len(uids),128):\n   ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n ranks=np.concatenate(ranked)\n if save:\n  out=pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':items[ranks.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101),len(uids))});out.to_parquet(P/save,index=False)\n if stage=='validation':\n  global TRUTH\n  if TRUTH is None:TRUTH=truth_data()\n  _,_,truth,idcg=TRUTH;disc=1/np.log2(np.arange(2,12));ndcg=np.mean([np.dot([truth[u].get(int(i),0) for i in ranks[u,:10]],disc)/idcg[u] for u in truth]);return float(ndcg)\n return None\nTRUTH=None\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--epochs',type=int,default=40);ap.add_argument('--dim',type=int,default=600);ap.add_argument('--latent',type=int,default=200);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--lr',type=float,default=.001);ap.add_argument('--drop',type=float,default=.5);ap.add_argument('--name',default='base');ap.add_argument('--seed',type=int,default=42);ap.add_argument('--eval-every',type=int,default=2);ap.add_argument('--resume');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--like',type=float,default=0);ap.add_argument('--negative',type=float,default=.3);ap.add_argument('--infer',action='store_true');a=ap.parse_args()\n torch.manual_seed(a.seed);np.random.seed(a.seed);data=Histories(a.stage);model=TwoTower(len(data.items),a.dim,a.latent)\n if a.resume:model.load_state_dict(torch.load(P/a.resume,weights_only=False)['model'])\n if a.infer:\n  score=evaluate(model,data,a.stage,a.recent,a.like,a.negative,'validation.parquet' if a.stage=='validation' else 'submission.parquet');print('inference score',score,flush=True);return\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);allitems=torch.arange(len(data.items));best=-1;start=time.time()\n for epoch in range(1,a.epochs+1):\n  model.train();order=np.random.permutation(len(data.uids));losses=[]\n  for s in range(0,len(order),a.batch):\n   rows=order[s:s+a.batch];args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  print(json.dumps({'epoch':epoch,'loss':float(np.mean(losses)),'seconds':round(time.time()-start,1)}),flush=True)\n  if epoch%a.eval_every==0 or epoch==a.epochs:\n   checkpoint={'model':model.state_dict(),'args':vars(a),'epoch':epoch}\n   torch.save(checkpoint,P/f'{a.name}_last.pt')\n   if a.stage=='validation':\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n    if score>best:\n     best=score;torch.save(checkpoint,P/f'{a.name}_best.pt');evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative,save=f'{a.name}_validation.parquet');(P/f'{a.name}_score.json').write_text(json.dumps({'ndcg':score,'epoch':epoch,'args':vars(a)},indent=2))\n   else:\n    evaluate(model,data,'test',a.recent,a.like,a.negative,'submission.parquet');torch.save(checkpoint,P/f'{a.name}_best.pt')\nif __name__=='__main__':main()\nPY\ncat /app/solution/build_validation.log\n/opt/task-venv/bin/python /app/solution/train.py --epochs 40 --eval-every 2 > /app/solution/base.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 1,
    "duration": {
      "secs": 42,
      "nanos": 969343827
    },
    "formatted_output": ""
  }
}

Agent message

+04:57

Thinking unavailable No text recorded

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

exec command

+05:01

Thinking unavailable No text recorded

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

Model text

The first model is training from per-user ragged histories, with randomly dropped tracks as the denoising task. Its score is exactly a dot product: the user tower encodes history, and the item tower produces vectors that can be cached offline. I’ll also test fine-tuning on earlier weekly discovery windows to better match the seven-day objective.

Tool callexecTool call Returned

Tool call · Input +05:04

text(await tools.exec_command({cmd:"tail -15 /app/solution/base.log && ls /app/solution/*pt","max_output_tokens":1500}));

Tool response +05:04

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"6c7b64","wall_time_seconds":0.00000619,"exit_code":0,"original_token_count":242,"output":"{\"epoch\": 1, \"loss\": 9.43094891972012, \"seconds\": 22.1}\n{\"epoch\": 2, \"loss\": 8.72741240925259, \"seconds\": 35.4}\nTraceback (most recent call last):\n  File \"/app/solution/train.py\", line 93, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/train.py\", line 88, in main\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train.py\", line 61, in evaluate\n    ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n                                   ~~~~~~~~~~^^^^^^^^\nKeyError: 121300\n/app/solution/base_last.pt\n"}

Harness context codex item completed

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      "-lc",
      "tail -15 /app/solution/base.log && ls /app/solution/*pt"
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      {
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        "cmd": "tail -15 /app/solution/base.log && ls /app/solution/*pt"
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    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 9.43094891972012, \"seconds\": 22.1}\n{\"epoch\": 2, \"loss\": 8.72741240925259, \"seconds\": 35.4}\nTraceback (most recent call last):\n  File \"/app/solution/train.py\", line 93, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/train.py\", line 88, in main\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train.py\", line 61, in evaluate\n    ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n                                   ~~~~~~~~~~^^^^^^^^\nKeyError: 121300\n/app/solution/base_last.pt\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 9.43094891972012, \"seconds\": 22.1}\n{\"epoch\": 2, \"loss\": 8.72741240925259, \"seconds\": 35.4}\nTraceback (most recent call last):\n  File \"/app/solution/train.py\", line 93, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/train.py\", line 88, in main\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train.py\", line 61, in evaluate\n    ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n                                   ~~~~~~~~~~^^^^^^^^\nKeyError: 121300\n/app/solution/base_last.pt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6190
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 9.43094891972012, \"seconds\": 22.1}\n{\"epoch\": 2, \"loss\": 8.72741240925259, \"seconds\": 35.4}\nTraceback (most recent call last):\n  File \"/app/solution/train.py\", line 93, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/train.py\", line 88, in main\n    score=evaluate(model,data,recent=a.recent,like=a.like,negative=a.negative);print('VALID',epoch,score,flush=True)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train.py\", line 61, in evaluate\n    ur=uids[start:start+128];rows=[data.index[int(u)] for u in ur];v=model.user(*data.batch(rows,recent=recent,like=like,negative=negative,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[start:start+len(ur)]),-torch.inf);ranked.append(scores.topk(100,dim=1).indices.numpy())\n                                   ~~~~~~~~~~^^^^^^^^\nKeyError: 121300\n/app/solution/base_last.pt\n"
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}

Harness context token usage record

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exec command

+05:12

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 +05:18

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py'\ns=open(p).read().replace(\"np.arange(self.offsets[r],self.offsets[r+1]) for r in rows\", \"(np.arange(self.offsets[r],self.offsets[r+1]) if r>=0 else np.empty(0,dtype='int64')) for r in rows\").replace('data.index[int(u)] for u in ur','data.index.get(int(u),-1) for u in ur')\nopen(p,'w').write(s)\nPY\n/opt/task-venv/bin/python /app/solution/train.py --stage test --infer --resume base_last.pt > /app/solution/initial_infer.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train.py --epochs 40 --eval-every 2 --resume base_last.pt > /app/solution/base.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +05:20

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"f29848","wall_time_seconds":1.000921309,"session_id":63258,"original_token_count":0,"output":""}
{"chunk_id":"abdc2e","wall_time_seconds":1.00105242,"session_id":22715,"original_token_count":0,"output":""}

Harness context token usage record

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Harness context codex item completed

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exec command

+05:36

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +05:37

text(await tools.exec_command({cmd:"tail -10 /app/solution/base.log && cat /app/solution/initial_infer.log && ls -lh /app/solution/submission.parquet","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +05:37

Script completed
Wall time 0.1 seconds
Output:

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{"current_time":"2026-09-24 08:01:07 UTC"}

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Harness context codex item completed

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    "stdout": "inference score None\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:00 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "inference score None\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:00 /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4120
    },
    "formatted_output": "inference score None\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:00 /app/solution/submission.parquet\n"
  }
}

Agent message

+05:57

Thinking unavailable No text recorded

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

exec command

+06:02

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +06:03

text(await tools.exec_command({cmd:"tail -20 /app/solution/base.log","max_output_tokens":1200}));

Tool response +06:03

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"2b44fc","wall_time_seconds":0.00000259,"exit_code":0,"original_token_count":36,"output":"{\"epoch\": 1, \"loss\": 8.48564108212789, \"seconds\": 13.9}\n{\"epoch\": 2, \"loss\": 8.253584252463448, \"seconds\": 26.5}\nVALID 2 0.01646845460873312\n"}

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      "/bin/sh",
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    "stdout": "{\"epoch\": 1, \"loss\": 8.48564108212789, \"seconds\": 13.9}\n{\"epoch\": 2, \"loss\": 8.253584252463448, \"seconds\": 26.5}\nVALID 2 0.01646845460873312\n",
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exec command

+06:05

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +06:07

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/base_validation.parquet > /app/solution/public_base.json","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +06:08

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"419e90","wall_time_seconds":1.000719919,"session_id":36266,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+06:10

Thinking unavailable No text recorded

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

Harness context codex item completed

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Tool callexecTool call Returned

Tool call · Input +06:59

text(await tools.exec_command({cmd:"cat > /app/solution/build_temporal.py <<'PY'\n\"\"\"Causal weekly training examples from event arrays and per-user dictionaries.\"\"\"\nimport os\nos.environ['OMP_NUM_THREADS']='1'\nfrom pathlib import Path\nimport pandas as pd,numpy as np,time,argparse\nP=Path('/app/solution');D=Path('/task/data');END=24786800\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--weeks',type=int,default=16);a=ap.parse_args();start=time.time()\n items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();ii=pd.Index(items)\n # Positives as event records, grouped by user for independent streaming.\n ds=[pd.read_parquet(P/'catalog_train.parquet',columns=['uid','iid','timestamp','is_organic','played_ratio_pct'])]\n if a.stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v['iid']=ii.get_indexer(v.item_id);ds.append(v[v.iid>=0][ds[0].columns])\n d=pd.concat(ds,ignore_index=True);d=d[d.played_ratio_pct>=50].sort_values(['uid','timestamp']);del ds\n lu=d.uid.to_numpy();li=d.iid.to_numpy('int32');lt=d.timestamp.to_numpy();lo=d.is_organic.to_numpy();ls=(d.played_ratio_pct.to_numpy()>=80);del d\n splits=['train','val'] if a.stage=='test' else ['train'];actions=[]\n for sp in splits:\n  for name,typ in [('likes',1),('dislikes',2),('unlikes',-1),('undislikes',-2)]:\n   q=pd.read_parquet(D/f'{sp}_{name}.parquet');q['iid']=ii.get_indexer(q.item_id);q=q[q.iid>=0];q['kind']=typ;actions.append(q[['uid','iid','timestamp','is_organic','kind']])\n q=pd.concat(actions,ignore_index=True).sort_values(['uid','timestamp']);del actions\n au=q.uid.to_numpy();ai=q.iid.to_numpy('int32');at=q.timestamp.to_numpy();ao=q.is_organic.to_numpy();ak=q.kind.to_numpy();del q\n users=np.union1d(lu,au);lb=np.searchsorted(lu,users);le=np.searchsorted(lu,users,side='right');ab=np.searchsorted(au,users);ae=np.searchsorted(au,users,side='right')\n cuts=END-np.arange(a.weeks,0,-1)*604800\n if a.stage=='test':cuts=np.r_[cuts,END]\n outids=[];outw=[];outr=[];outlike=[];outneg=[];offsets=[0];ys=[];yv=[];yo=[0];exusers=[];excut=[]\n for n,u in enumerate(users):\n  ids=li[lb[n]:le[n]];ts=lt[lb[n]:le[n]];org=lo[lb[n]:le[n]];strong=ls[lb[n]:le[n]]\n  aids=ai[ab[n]:ae[n]];ats=at[ab[n]:ae[n]];aorg=ao[ab[n]:ae[n]];kind=ak[ab[n]:ae[n]]\n  for cut in cuts:\n   end=cut+604800+(1800 if cut==END else 0);past=np.searchsorted(ts,cut,side='right');future=np.searchsorted(ts,end,side='right');actpast=np.searchsorted(ats,cut,side='right');actfuture=np.searchsorted(ats,end,side='right')\n   if future==past and actfuture==actpast:continue\n   seen=set(ids[:past].tolist());likes=set();dislikes=set()\n   for it,k in zip(aids[:actpast],kind[:actpast]):\n    if k==1:likes.add(int(it))\n    elif k==2:dislikes.add(int(it))\n    elif k==-1:likes.discard(int(it))\n    else:dislikes.discard(int(it))\n   blocked=seen|likes|dislikes\n   good=(org[past:future]==1)&strong[past:future];target={int(i):1. for i in ids[past:future][good] if int(i) not in blocked}\n   for it,o,k in zip(aids[actpast:actfuture],aorg[actpast:actfuture],kind[actpast:actfuture]):\n    if o==1 and k==1 and int(it) not in blocked:target[int(it)]=7.\n   if not target or past<5:continue\n   ix,inv=np.unique(ids[:past],return_inverse=True);age=(cut-ts[:past].astype('float64'))/86400;ev=(.5+.5*org[:past])*np.exp(-age/180)\n   w=np.bincount(inv,weights=ev)**.35;r=np.bincount(inv,weights=ev*np.exp(-age/30))**.35\n   extra=np.array(sorted((likes|dislikes)-set(ix.tolist())),dtype='int32');ix=np.r_[ix,extra];w=np.r_[w,np.zeros(len(extra))];r=np.r_[r,np.zeros(len(extra))]\n   liked=np.isin(ix,list(likes)).astype('float32');neg=np.isin(ix,list(dislikes)).astype('float32');w+=1.5*liked\n   # Keep a bounded history. The complete past set still governs target novelty.\n   if len(ix)>1500:\n    top=np.argpartition(w+r+liked+neg,-1500)[-1500:];ix=ix[top];w=w[top];r=r[top];liked=liked[top];neg=neg[top]\n   outids.append(ix.astype('int32'));outw.append(w.astype('float32'));outr.append(r.astype('float32'));outlike.append(liked);outneg.append(neg);offsets.append(offsets[-1]+len(ix));ys.append(np.array(list(target),dtype='int32'));yv.append(np.array(list(target.values()),dtype='float32'));yo.append(yo[-1]+len(target));exusers.append(u);excut.append(cut)\n  if n%1000==0:print('users',n,'examples',len(exusers),'seconds',round(time.time()-start),flush=True)\n np.savez(P/f'temporal_{a.stage}.npz',uids=exusers,cuts=excut,items=items,offsets=offsets,ids=np.concatenate(outids),weights=np.concatenate(outw),recent=np.concatenate(outr),liked=np.concatenate(outlike),neg=np.concatenate(outneg),target_offsets=yo,target_ids=np.concatenate(ys),target_values=np.concatenate(yv))\n print('done',a.stage,'examples',len(exusers),'pairs',offsets[-1],'targets',yo[-1],'seconds',time.time()-start,flush=True)\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python /app/solution/build_temporal.py --weeks 16 > /app/solution/build_temporal.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +07:00

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"62e6c7","wall_time_seconds":1.000362202,"session_id":89070,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+07:02

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +07:04

text(await tools.exec_command({cmd:"tail -15 /app/solution/base.log && cat /app/solution/public_base.json && tail -5 /app/solution/build_temporal.log","max_output_tokens":1600}));
text(await tools.clock__curr_time({}));

Tool response +07:04

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"41331a","wall_time_seconds":0.00000708,"exit_code":0,"original_token_count":133,"output":"{\"epoch\": 1, \"loss\": 8.48564108212789, \"seconds\": 13.9}\n{\"epoch\": 2, \"loss\": 8.253584252463448, \"seconds\": 26.5}\nVALID 2 0.01646845460873312\n{\"epoch\": 3, \"loss\": 8.11969584888882, \"seconds\": 48.4}\n{\"epoch\": 4, \"loss\": 7.998349044058058, \"seconds\": 60.8}\nVALID 4 0.016904139161856225\n{\"epoch\": 5, \"loss\": 7.892421974076165, \"seconds\": 79.8}\n{\"epoch\": 6, \"loss\": 7.795278535948859, \"seconds\": 95.4}\n{\n  \"model\": \"/app/solution/base_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016468,\n    \"quality_users\": 4263\n  }\n}\n"}
{"current_time":"2026-09-24 08:02:33 UTC"}

Harness context codex item completed

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Harness context token usage record

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exec command

+07:24

Thinking unavailable No text recorded

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

Model text

A provisional test submission is saved. The first public check scored 0.016468 NDCG@10 across 4,263 users with eligible relevant tracks. Training is continuing; I’m also preparing strictly chronological examples and testing recent listening trends as item-side features.

Tool callexecTool call Returned

Tool call · Input +07:41

text(await tools.exec_command({cmd:"cat > /app/solution/build_trends.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);counts=np.zeros((N,5),dtype='float64');uniqs=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1500000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1)&(d.played_ratio_pct.to_numpy()>=80);d=d[ok];i=i[ok];age=(end-d.timestamp.to_numpy())/86400\n   for j,days in enumerate([7,14,30,90,360]):counts[:,j]+=np.bincount(i,weights=np.exp(-age/days),minlength=N)\n  d=pd.read_parquet(D/f'{split}_likes.parquet');i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1);d=d[ok];i=i[ok];age=(end-d.timestamp.to_numpy())/86400\n  for j,days in enumerate([7,14,30,90,360]):counts[:,j]+=np.bincount(i,weights=7*np.exp(-age/days),minlength=N)\n # Ratios estimate recent growth with shrinkage to prevent spikes on tiny counts.\n features=np.stack([np.log1p(counts[:,0]),np.log1p(counts[:,2]),np.log1p(counts[:,4]),np.log((counts[:,0]+5)/(counts[:,3]+5)),np.log((counts[:,2]+10)/(counts[:,4]+10))],axis=1).astype('float32')\n np.save(P/f'trends_{stage}.npy',features);print(stage,flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/build_trends.py > /app/solution/trends.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +07:42

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"e9ece3","wall_time_seconds":1.003538081,"session_id":52275,"original_token_count":0,"output":""}

Harness context token usage record

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}

exec command

+07:46

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +07:48

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.current && tail -5 /app/solution/base.log && tail -5 /app/solution/build_temporal.log && cat /app/solution/trends.log","max_output_tokens":1500}));

Tool response +07:48

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"c40154","wall_time_seconds":0.000004631,"exit_code":0,"original_token_count":111,"output":"6744662016\n{\"epoch\": 4, \"loss\": 7.998349044058058, \"seconds\": 60.8}\nVALID 4 0.016904139161856225\n{\"epoch\": 5, \"loss\": 7.892421974076165, \"seconds\": 79.8}\n{\"epoch\": 6, \"loss\": 7.795278535948859, \"seconds\": 95.4}\nVALID 6 0.017262042901626203\nusers 2000 examples 12214 seconds 19\nusers 3000 examples 18399 seconds 25\nusers 4000 examples 24514 seconds 31\nusers 5000 examples 30683 seconds 37\nusers 6000 examples 36660 seconds 43\nvalidation\ntest\n"}

Harness context token usage record

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-ea98afef-350a-45a0-adc2-1c9633edca8f",
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    "command": [
      "/bin/sh",
      "-lc",
      "cat /sys/fs/cgroup/memory.current && tail -5 /app/solution/base.log && tail -5 /app/solution/build_temporal.log && cat /app/solution/trends.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /sys/fs/cgroup/memory.current && tail -5 /app/solution/base.log && tail -5 /app/solution/build_temporal.log && cat /app/solution/trends.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "6744662016\n{\"epoch\": 4, \"loss\": 7.998349044058058, \"seconds\": 60.8}\nVALID 4 0.016904139161856225\n{\"epoch\": 5, \"loss\": 7.892421974076165, \"seconds\": 79.8}\n{\"epoch\": 6, \"loss\": 7.795278535948859, \"seconds\": 95.4}\nVALID 6 0.017262042901626203\nusers 2000 examples 12214 seconds 19\nusers 3000 examples 18399 seconds 25\nusers 4000 examples 24514 seconds 31\nusers 5000 examples 30683 seconds 37\nusers 6000 examples 36660 seconds 43\nvalidation\ntest\n",
    "stderr": "",
    "aggregated_output": "6744662016\n{\"epoch\": 4, \"loss\": 7.998349044058058, \"seconds\": 60.8}\nVALID 4 0.016904139161856225\n{\"epoch\": 5, \"loss\": 7.892421974076165, \"seconds\": 79.8}\n{\"epoch\": 6, \"loss\": 7.795278535948859, \"seconds\": 95.4}\nVALID 6 0.017262042901626203\nusers 2000 examples 12214 seconds 19\nusers 3000 examples 18399 seconds 25\nusers 4000 examples 24514 seconds 31\nusers 5000 examples 30683 seconds 37\nusers 6000 examples 36660 seconds 43\nvalidation\ntest\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4631
    },
    "formatted_output": "6744662016\n{\"epoch\": 4, \"loss\": 7.998349044058058, \"seconds\": 60.8}\nVALID 4 0.016904139161856225\n{\"epoch\": 5, \"loss\": 7.892421974076165, \"seconds\": 79.8}\n{\"epoch\": 6, \"loss\": 7.795278535948859, \"seconds\": 95.4}\nVALID 6 0.017262042901626203\nusers 2000 examples 12214 seconds 19\nusers 3000 examples 18399 seconds 25\nusers 4000 examples 24514 seconds 31\nusers 5000 examples 30683 seconds 37\nusers 6000 examples 36660 seconds 43\nvalidation\ntest\n"
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Harness context codex item completed

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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-212dfd52-e2b5-41df-95fa-7f299de90dcf",
    "process_id": "52275",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/build_trends.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);counts=np.zeros((N,5),dtype='float64');uniqs=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1500000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1)&(d.played_ratio_pct.to_numpy()>=80);d=d[ok];i=i[ok];age=(end-d.timestamp.to_numpy())/86400\n   for j,days in enumerate([7,14,30,90,360]):counts[:,j]+=np.bincount(i,weights=np.exp(-age/days),minlength=N)\n  d=pd.read_parquet(D/f'{split}_likes.parquet');i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1);d=d[ok];i=i[ok];age=(end-d.timestamp.to_numpy())/86400\n  for j,days in enumerate([7,14,30,90,360]):counts[:,j]+=np.bincount(i,weights=7*np.exp(-age/days),minlength=N)\n # Ratios estimate recent growth with shrinkage to prevent spikes on tiny counts.\n features=np.stack([np.log1p(counts[:,0]),np.log1p(counts[:,2]),np.log1p(counts[:,4]),np.log((counts[:,0]+5)/(counts[:,3]+5)),np.log((counts[:,2]+10)/(counts[:,4]+10))],axis=1).astype('float32')\n np.save(P/f'trends_{stage}.npy',features);print(stage,flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/build_trends.py > /app/solution/trends.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/build_trends.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);counts=np.zeros((N,5),dtype='float64');uniqs=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1500000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1)&(d.played_ratio_pct.to_numpy()>=80);d=d[ok];i=i[ok];age=(end-d.timestamp.to_numpy())/86400\n   for j,days in enumerate([7,14,30,90,360]):counts[:,j]+=np.bincount(i,weights=np.exp(-age/days),minlength=N)\n  d=pd.read_parquet(D/f'{split}_likes.parquet');i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1);d=d[ok];i=i[ok];age=(end-d.timestamp.to_numpy())/86400\n  for j,days in enumerate([7,14,30,90,360]):counts[:,j]+=np.bincount(i,weights=7*np.exp(-age/days),minlength=N)\n # Ratios estimate recent growth with shrinkage to prevent spikes on tiny counts.\n features=np.stack([np.log1p(counts[:,0]),np.log1p(counts[:,2]),np.log1p(counts[:,4]),np.log((counts[:,0]+5)/(counts[:,3]+5)),np.log((counts[:,2]+10)/(counts[:,4]+10))],axis=1).astype('float32')\n np.save(P/f'trends_{stage}.npy',features);print(stage,flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/build_trends.py > /app/solution/trends.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
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    "exit_code": 0,
    "duration": {
      "secs": 7,
      "nanos": 167817334
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  }
}

exec command

+08:04

Thinking unavailable No text recorded

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

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-8106754c-4242-4855-bfdd-af313696e5d7",
    "process_id": "89070",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/build_temporal.py <<'PY'\n\"\"\"Causal weekly training examples from event arrays and per-user dictionaries.\"\"\"\nimport os\nos.environ['OMP_NUM_THREADS']='1'\nfrom pathlib import Path\nimport pandas as pd,numpy as np,time,argparse\nP=Path('/app/solution');D=Path('/task/data');END=24786800\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--weeks',type=int,default=16);a=ap.parse_args();start=time.time()\n items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();ii=pd.Index(items)\n # Positives as event records, grouped by user for independent streaming.\n ds=[pd.read_parquet(P/'catalog_train.parquet',columns=['uid','iid','timestamp','is_organic','played_ratio_pct'])]\n if a.stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v['iid']=ii.get_indexer(v.item_id);ds.append(v[v.iid>=0][ds[0].columns])\n d=pd.concat(ds,ignore_index=True);d=d[d.played_ratio_pct>=50].sort_values(['uid','timestamp']);del ds\n lu=d.uid.to_numpy();li=d.iid.to_numpy('int32');lt=d.timestamp.to_numpy();lo=d.is_organic.to_numpy();ls=(d.played_ratio_pct.to_numpy()>=80);del d\n splits=['train','val'] if a.stage=='test' else ['train'];actions=[]\n for sp in splits:\n  for name,typ in [('likes',1),('dislikes',2),('unlikes',-1),('undislikes',-2)]:\n   q=pd.read_parquet(D/f'{sp}_{name}.parquet');q['iid']=ii.get_indexer(q.item_id);q=q[q.iid>=0];q['kind']=typ;actions.append(q[['uid','iid','timestamp','is_organic','kind']])\n q=pd.concat(actions,ignore_index=True).sort_values(['uid','timestamp']);del actions\n au=q.uid.to_numpy();ai=q.iid.to_numpy('int32');at=q.timestamp.to_numpy();ao=q.is_organic.to_numpy();ak=q.kind.to_numpy();del q\n users=np.union1d(lu,au);lb=np.searchsorted(lu,users);le=np.searchsorted(lu,users,side='right');ab=np.searchsorted(au,users);ae=np.searchsorted(au,users,side='right')\n cuts=END-np.arange(a.weeks,0,-1)*604800\n if a.stage=='test':cuts=np.r_[cuts,END]\n outids=[];outw=[];outr=[];outlike=[];outneg=[];offsets=[0];ys=[];yv=[];yo=[0];exusers=[];excut=[]\n for n,u in enumerate(users):\n  ids=li[lb[n]:le[n]];ts=lt[lb[n]:le[n]];org=lo[lb[n]:le[n]];strong=ls[lb[n]:le[n]]\n  aids=ai[ab[n]:ae[n]];ats=at[ab[n]:ae[n]];aorg=ao[ab[n]:ae[n]];kind=ak[ab[n]:ae[n]]\n  for cut in cuts:\n   end=cut+604800+(1800 if cut==END else 0);past=np.searchsorted(ts,cut,side='right');future=np.searchsorted(ts,end,side='right');actpast=np.searchsorted(ats,cut,side='right');actfuture=np.searchsorted(ats,end,side='right')\n   if future==past and actfuture==actpast:continue\n   seen=set(ids[:past].tolist());likes=set();dislikes=set()\n   for it,k in zip(aids[:actpast],kind[:actpast]):\n    if k==1:likes.add(int(it))\n    elif k==2:dislikes.add(int(it))\n    elif k==-1:likes.discard(int(it))\n    else:dislikes.discard(int(it))\n   blocked=seen|likes|dislikes\n   good=(org[past:future]==1)&strong[past:future];target={int(i):1. for i in ids[past:future][good] if int(i) not in blocked}\n   for it,o,k in zip(aids[actpast:actfuture],aorg[actpast:actfuture],kind[actpast:actfuture]):\n    if o==1 and k==1 and int(it) not in blocked:target[int(it)]=7.\n   if not target or past<5:continue\n   ix,inv=np.unique(ids[:past],return_inverse=True);age=(cut-ts[:past].astype('float64'))/86400;ev=(.5+.5*org[:past])*np.exp(-age/180)\n   w=np.bincount(inv,weights=ev)**.35;r=np.bincount(inv,weights=ev*np.exp(-age/30))**.35\n   extra=np.array(sorted((likes|dislikes)-set(ix.tolist())),dtype='int32');ix=np.r_[ix,extra];w=np.r_[w,np.zeros(len(extra))];r=np.r_[r,np.zeros(len(extra))]\n   liked=np.isin(ix,list(likes)).astype('float32');neg=np.isin(ix,list(dislikes)).astype('float32');w+=1.5*liked\n   # Keep a bounded history. The complete past set still governs target novelty.\n   if len(ix)>1500:\n    top=np.argpartition(w+r+liked+neg,-1500)[-1500:];ix=ix[top];w=w[top];r=r[top];liked=liked[top];neg=neg[top]\n   outids.append(ix.astype('int32'));outw.append(w.astype('float32'));outr.append(r.astype('float32'));outlike.append(liked);outneg.append(neg);offsets.append(offsets[-1]+len(ix));ys.append(np.array(list(target),dtype='int32'));yv.append(np.array(list(target.values()),dtype='float32'));yo.append(yo[-1]+len(target));exusers.append(u);excut.append(cut)\n  if n%1000==0:print('users',n,'examples',len(exusers),'seconds',round(time.time()-start),flush=True)\n np.savez(P/f'temporal_{a.stage}.npz',uids=exusers,cuts=excut,items=items,offsets=offsets,ids=np.concatenate(outids),weights=np.concatenate(outw),recent=np.concatenate(outr),liked=np.concatenate(outlike),neg=np.concatenate(outneg),target_offsets=yo,target_ids=np.concatenate(ys),target_values=np.concatenate(yv))\n print('done',a.stage,'examples',len(exusers),'pairs',offsets[-1],'targets',yo[-1],'seconds',time.time()-start,flush=True)\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python /app/solution/build_temporal.py --weeks 16 > /app/solution/build_temporal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/build_temporal.py <<'PY'\n\"\"\"Causal weekly training examples from event arrays and per-user dictionaries.\"\"\"\nimport os\nos.environ['OMP_NUM_THREADS']='1'\nfrom pathlib import Path\nimport pandas as pd,numpy as np,time,argparse\nP=Path('/app/solution');D=Path('/task/data');END=24786800\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--weeks',type=int,default=16);a=ap.parse_args();start=time.time()\n items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();ii=pd.Index(items)\n # Positives as event records, grouped by user for independent streaming.\n ds=[pd.read_parquet(P/'catalog_train.parquet',columns=['uid','iid','timestamp','is_organic','played_ratio_pct'])]\n if a.stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v['iid']=ii.get_indexer(v.item_id);ds.append(v[v.iid>=0][ds[0].columns])\n d=pd.concat(ds,ignore_index=True);d=d[d.played_ratio_pct>=50].sort_values(['uid','timestamp']);del ds\n lu=d.uid.to_numpy();li=d.iid.to_numpy('int32');lt=d.timestamp.to_numpy();lo=d.is_organic.to_numpy();ls=(d.played_ratio_pct.to_numpy()>=80);del d\n splits=['train','val'] if a.stage=='test' else ['train'];actions=[]\n for sp in splits:\n  for name,typ in [('likes',1),('dislikes',2),('unlikes',-1),('undislikes',-2)]:\n   q=pd.read_parquet(D/f'{sp}_{name}.parquet');q['iid']=ii.get_indexer(q.item_id);q=q[q.iid>=0];q['kind']=typ;actions.append(q[['uid','iid','timestamp','is_organic','kind']])\n q=pd.concat(actions,ignore_index=True).sort_values(['uid','timestamp']);del actions\n au=q.uid.to_numpy();ai=q.iid.to_numpy('int32');at=q.timestamp.to_numpy();ao=q.is_organic.to_numpy();ak=q.kind.to_numpy();del q\n users=np.union1d(lu,au);lb=np.searchsorted(lu,users);le=np.searchsorted(lu,users,side='right');ab=np.searchsorted(au,users);ae=np.searchsorted(au,users,side='right')\n cuts=END-np.arange(a.weeks,0,-1)*604800\n if a.stage=='test':cuts=np.r_[cuts,END]\n outids=[];outw=[];outr=[];outlike=[];outneg=[];offsets=[0];ys=[];yv=[];yo=[0];exusers=[];excut=[]\n for n,u in enumerate(users):\n  ids=li[lb[n]:le[n]];ts=lt[lb[n]:le[n]];org=lo[lb[n]:le[n]];strong=ls[lb[n]:le[n]]\n  aids=ai[ab[n]:ae[n]];ats=at[ab[n]:ae[n]];aorg=ao[ab[n]:ae[n]];kind=ak[ab[n]:ae[n]]\n  for cut in cuts:\n   end=cut+604800+(1800 if cut==END else 0);past=np.searchsorted(ts,cut,side='right');future=np.searchsorted(ts,end,side='right');actpast=np.searchsorted(ats,cut,side='right');actfuture=np.searchsorted(ats,end,side='right')\n   if future==past and actfuture==actpast:continue\n   seen=set(ids[:past].tolist());likes=set();dislikes=set()\n   for it,k in zip(aids[:actpast],kind[:actpast]):\n    if k==1:likes.add(int(it))\n    elif k==2:dislikes.add(int(it))\n    elif k==-1:likes.discard(int(it))\n    else:dislikes.discard(int(it))\n   blocked=seen|likes|dislikes\n   good=(org[past:future]==1)&strong[past:future];target={int(i):1. for i in ids[past:future][good] if int(i) not in blocked}\n   for it,o,k in zip(aids[actpast:actfuture],aorg[actpast:actfuture],kind[actpast:actfuture]):\n    if o==1 and k==1 and int(it) not in blocked:target[int(it)]=7.\n   if not target or past<5:continue\n   ix,inv=np.unique(ids[:past],return_inverse=True);age=(cut-ts[:past].astype('float64'))/86400;ev=(.5+.5*org[:past])*np.exp(-age/180)\n   w=np.bincount(inv,weights=ev)**.35;r=np.bincount(inv,weights=ev*np.exp(-age/30))**.35\n   extra=np.array(sorted((likes|dislikes)-set(ix.tolist())),dtype='int32');ix=np.r_[ix,extra];w=np.r_[w,np.zeros(len(extra))];r=np.r_[r,np.zeros(len(extra))]\n   liked=np.isin(ix,list(likes)).astype('float32');neg=np.isin(ix,list(dislikes)).astype('float32');w+=1.5*liked\n   # Keep a bounded history. The complete past set still governs target novelty.\n   if len(ix)>1500:\n    top=np.argpartition(w+r+liked+neg,-1500)[-1500:];ix=ix[top];w=w[top];r=r[top];liked=liked[top];neg=neg[top]\n   outids.append(ix.astype('int32'));outw.append(w.astype('float32'));outr.append(r.astype('float32'));outlike.append(liked);outneg.append(neg);offsets.append(offsets[-1]+len(ix));ys.append(np.array(list(target),dtype='int32'));yv.append(np.array(list(target.values()),dtype='float32'));yo.append(yo[-1]+len(target));exusers.append(u);excut.append(cut)\n  if n%1000==0:print('users',n,'examples',len(exusers),'seconds',round(time.time()-start),flush=True)\n np.savez(P/f'temporal_{a.stage}.npz',uids=exusers,cuts=excut,items=items,offsets=offsets,ids=np.concatenate(outids),weights=np.concatenate(outw),recent=np.concatenate(outr),liked=np.concatenate(outlike),neg=np.concatenate(outneg),target_offsets=yo,target_ids=np.concatenate(ys),target_values=np.concatenate(yv))\n print('done',a.stage,'examples',len(exusers),'pairs',offsets[-1],'targets',yo[-1],'seconds',time.time()-start,flush=True)\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python /app/solution/build_temporal.py --weeks 16 > /app/solution/build_temporal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 69,
      "nanos": 503619601
    },
    "formatted_output": ""
  }
}

Tool callexecTool call Returned

Tool call · Input +08:53

text(await tools.exec_command({cmd:"cat > /app/solution/temporal_train.py <<'PY'\n\"\"\"Fine-tune the neural retrieval towers on causal next-week organic discovery.\"\"\"\nfrom train import *\nclass TemporalData:\n def __init__(self,stage):\n  z=np.load(P/f'temporal_{stage}.npz');self.__dict__.update({k:z[k] for k in z.files})\n def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3):\n  lens=np.array([self.offsets[r+1]-self.offsets[r] for r in rows]);idx=np.concatenate([np.arange(self.offsets[r],self.offsets[r+1]) for r in rows]);ids=self.ids[idx].astype('int64');ridx=np.repeat(np.arange(len(rows)),lens)\n  w=self.weights[idx]+recent*self.recent[idx]+like*self.liked[idx]-negative*self.neg[idx];w*=np.random.random(len(w))>=drop;denom=np.sqrt(np.bincount(ridx,weights=w*w,minlength=len(rows))).clip(.00001);w=(w/denom[ridx]).astype('float32')\n  args=(torch.from_numpy(ids),torch.from_numpy(np.r_[0,np.cumsum(lens)[:-1]]),torch.from_numpy(w));y=torch.zeros((len(rows),len(self.items)))\n  for n,r in enumerate(rows):\n   sl=slice(self.target_offsets[r],self.target_offsets[r+1]);y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\n  y/=y.sum(1,keepdim=True).clamp_min(.00001)\n  return args,y,(torch.from_numpy(ridx),torch.from_numpy(ids))\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--resume',default='base_best.pt');ap.add_argument('--name',default='temporal');ap.add_argument('--epochs',type=int,default=5);ap.add_argument('--lr',type=float,default=.0001);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--recent',type=float,default=1);ap.add_argument('--drop',type=float,default=.2);ap.add_argument('--limit',type=int,default=0);ap.add_argument('--seed',type=int,default=123);a=ap.parse_args()\n torch.manual_seed(a.seed);np.random.seed(a.seed);data=TemporalData(a.stage);hist=Histories(a.stage);ck=torch.load(P/a.resume,weights_only=False);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);allitems=torch.arange(len(data.items));start=time.time();best=0\n for epoch in range(1,a.epochs+1):\n  model.train();order=np.random.permutation(len(data.uids));order=order[:a.limit] if a.limit else order;losses=[]\n  for s in range(0,len(order),a.batch):\n   rows=order[s:s+a.batch];args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  print(json.dumps({'epoch':epoch,'loss':float(np.mean(losses)),'seconds':round(time.time()-start,1)}),flush=True)\n  params=dict(ck['args']);params.update(vars(a));score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else 'submission.parquet');print('VALID',score,flush=True);newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt')\n  if score is None or score>best:\n   best=score or 0;torch.save(newck,P/f'{a.name}_best.pt');(P/f'{a.name}_score.json').write_text(json.dumps({'ndcg':score,'epoch':epoch,'args':params},indent=2))\nif __name__=='__main__':main()\nPY\ncat > /app/solution/tune.py <<'PY'\n\"\"\"Public-validation calibration of history weights and item-tower trend bias.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',default='base_best.pt');ap.add_argument('--name',default='tune');ap.add_argument('--recent',default='0,1,3,10');ap.add_argument('--like',default='0');ap.add_argument('--bias',default='-0.5,0,0.5,1,1.5,2');a=ap.parse_args()\n data=Histories('validation');ck=torch.load(P/a.checkpoint,weights_only=False);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);model.eval();z,mask,truth,idcg=truth_data();uids=z['uids'];items=z['item_ids'];columns=pd.Index(data.items).get_indexer(items);trends=np.load(P/'trends_validation.npy');disc=1/np.log2(np.arange(2,12));tu=np.array(list(truth));best=-1;results=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns));ibias=iv[:,-1].clone();trend=torch.tensor(trends[columns,3]);baseline=torch.tensor(trends[columns,2]);allbest=None\n  for recent in map(float,a.recent.split(',')):\n   for like in map(float,a.like.split(',')):\n    vec=[]\n    for s in range(0,len(uids),128):vec.append(model.user(*data.batch([data.index.get(int(u),-1) for u in uids[s:s+128]],recent=recent,like=like,target=False)))\n    uv=torch.cat(vec)\n    # Scores exist only transiently for a retrieval batch, never as a training interaction matrix.\n    for bias in map(float,a.bias.split(',')):\n     ranks=[]\n     for s in range(0,len(uids),256):\n      scores=uv[s:s+256]@iv.T +bias*trend;scores.masked_fill_(~torch.from_numpy(mask[s:s+256]),-torch.inf);ranks.append(scores.topk(10,dim=1).indices.numpy())\n     ranks=np.concatenate(ranks);score=float(np.mean([np.dot([truth[u].get(int(i),0) for i in ranks[u]],disc)/idcg[u] for u in truth]));r={'recent':recent,'like':like,'trend':bias,'ndcg':score};results.append(r);print(json.dumps(r),flush=True)\n     if score>best:best=score;allbest=r\n (P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':allbest,'results':results},indent=2))\nif __name__=='__main__':main()\nPY\ncat /app/solution/base_score.json && tail -3 /app/solution/build_temporal.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1800}));

Tool response +08:53

Script completed
Wall time 0.1 seconds
Output:

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Harness context token usage record

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Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-65a05174-8cdd-4b24-9c29-27ff55c9c337",
    "process_id": "75590",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/temporal_train.py <<'PY'\n\"\"\"Fine-tune the neural retrieval towers on causal next-week organic discovery.\"\"\"\nfrom train import *\nclass TemporalData:\n def __init__(self,stage):\n  z=np.load(P/f'temporal_{stage}.npz');self.__dict__.update({k:z[k] for k in z.files})\n def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3):\n  lens=np.array([self.offsets[r+1]-self.offsets[r] for r in rows]);idx=np.concatenate([np.arange(self.offsets[r],self.offsets[r+1]) for r in rows]);ids=self.ids[idx].astype('int64');ridx=np.repeat(np.arange(len(rows)),lens)\n  w=self.weights[idx]+recent*self.recent[idx]+like*self.liked[idx]-negative*self.neg[idx];w*=np.random.random(len(w))>=drop;denom=np.sqrt(np.bincount(ridx,weights=w*w,minlength=len(rows))).clip(.00001);w=(w/denom[ridx]).astype('float32')\n  args=(torch.from_numpy(ids),torch.from_numpy(np.r_[0,np.cumsum(lens)[:-1]]),torch.from_numpy(w));y=torch.zeros((len(rows),len(self.items)))\n  for n,r in enumerate(rows):\n   sl=slice(self.target_offsets[r],self.target_offsets[r+1]);y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\n  y/=y.sum(1,keepdim=True).clamp_min(.00001)\n  return args,y,(torch.from_numpy(ridx),torch.from_numpy(ids))\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--resume',default='base_best.pt');ap.add_argument('--name',default='temporal');ap.add_argument('--epochs',type=int,default=5);ap.add_argument('--lr',type=float,default=.0001);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--recent',type=float,default=1);ap.add_argument('--drop',type=float,default=.2);ap.add_argument('--limit',type=int,default=0);ap.add_argument('--seed',type=int,default=123);a=ap.parse_args()\n torch.manual_seed(a.seed);np.random.seed(a.seed);data=TemporalData(a.stage);hist=Histories(a.stage);ck=torch.load(P/a.resume,weights_only=False);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);allitems=torch.arange(len(data.items));start=time.time();best=0\n for epoch in range(1,a.epochs+1):\n  model.train();order=np.random.permutation(len(data.uids));order=order[:a.limit] if a.limit else order;losses=[]\n  for s in range(0,len(order),a.batch):\n   rows=order[s:s+a.batch];args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  print(json.dumps({'epoch':epoch,'loss':float(np.mean(losses)),'seconds':round(time.time()-start,1)}),flush=True)\n  params=dict(ck['args']);params.update(vars(a));score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else 'submission.parquet');print('VALID',score,flush=True);newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt')\n  if score is None or score>best:\n   best=score or 0;torch.save(newck,P/f'{a.name}_best.pt');(P/f'{a.name}_score.json').write_text(json.dumps({'ndcg':score,'epoch':epoch,'args':params},indent=2))\nif __name__=='__main__':main()\nPY\ncat > /app/solution/tune.py <<'PY'\n\"\"\"Public-validation calibration of history weights and item-tower trend bias.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',default='base_best.pt');ap.add_argument('--name',default='tune');ap.add_argument('--recent',default='0,1,3,10');ap.add_argument('--like',default='0');ap.add_argument('--bias',default='-0.5,0,0.5,1,1.5,2');a=ap.parse_args()\n data=Histories('validation');ck=torch.load(P/a.checkpoint,weights_only=False);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);model.eval();z,mask,truth,idcg=truth_data();uids=z['uids'];items=z['item_ids'];columns=pd.Index(data.items).get_indexer(items);trends=np.load(P/'trends_validation.npy');disc=1/np.log2(np.arange(2,12));tu=np.array(list(truth));best=-1;results=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns));ibias=iv[:,-1].clone();trend=torch.tensor(trends[columns,3]);baseline=torch.tensor(trends[columns,2]);allbest=None\n  for recent in map(float,a.recent.split(',')):\n   for like in map(float,a.like.split(',')):\n    vec=[]\n    for s in range(0,len(uids),128):vec.append(model.user(*data.batch([data.index.get(int(u),-1) for u in uids[s:s+128]],recent=recent,like=like,target=False)))\n    uv=torch.cat(vec)\n    # Scores exist only transiently for a retrieval batch, never as a training interaction matrix.\n    for bias in map(float,a.bias.split(',')):\n     ranks=[]\n     for s in range(0,len(uids),256):\n      scores=uv[s:s+256]@iv.T +bias*trend;scores.masked_fill_(~torch.from_numpy(mask[s:s+256]),-torch.inf);ranks.append(scores.topk(10,dim=1).indices.numpy())\n     ranks=np.concatenate(ranks);score=float(np.mean([np.dot([truth[u].get(int(i),0) for i in ranks[u]],disc)/idcg[u] for u in truth]));r={'recent':recent,'like':like,'trend':bias,'ndcg':score};results.append(r);print(json.dumps(r),flush=True)\n     if score>best:best=score;allbest=r\n (P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':allbest,'results':results},indent=2))\nif __name__=='__main__':main()\nPY\ncat /app/solution/base_score.json && tail -3 /app/solution/build_temporal.log && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/temporal_train.py <<'PY'\n\"\"\"Fine-tune the neural retrieval towers on causal next-week organic discovery.\"\"\"\nfrom train import *\nclass TemporalData:\n def __init__(self,stage):\n  z=np.load(P/f'temporal_{stage}.npz');self.__dict__.update({k:z[k] for k in z.files})\n def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3):\n  lens=np.array([self.offsets[r+1]-self.offsets[r] for r in rows]);idx=np.concatenate([np.arange(self.offsets[r],self.offsets[r+1]) for r in rows]);ids=self.ids[idx].astype('int64');ridx=np.repeat(np.arange(len(rows)),lens)\n  w=self.weights[idx]+recent*self.recent[idx]+like*self.liked[idx]-negative*self.neg[idx];w*=np.random.random(len(w))>=drop;denom=np.sqrt(np.bincount(ridx,weights=w*w,minlength=len(rows))).clip(.00001);w=(w/denom[ridx]).astype('float32')\n  args=(torch.from_numpy(ids),torch.from_numpy(np.r_[0,np.cumsum(lens)[:-1]]),torch.from_numpy(w));y=torch.zeros((len(rows),len(self.items)))\n  for n,r in enumerate(rows):\n   sl=slice(self.target_offsets[r],self.target_offsets[r+1]);y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\n  y/=y.sum(1,keepdim=True).clamp_min(.00001)\n  return args,y,(torch.from_numpy(ridx),torch.from_numpy(ids))\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--resume',default='base_best.pt');ap.add_argument('--name',default='temporal');ap.add_argument('--epochs',type=int,default=5);ap.add_argument('--lr',type=float,default=.0001);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--recent',type=float,default=1);ap.add_argument('--drop',type=float,default=.2);ap.add_argument('--limit',type=int,default=0);ap.add_argument('--seed',type=int,default=123);a=ap.parse_args()\n torch.manual_seed(a.seed);np.random.seed(a.seed);data=TemporalData(a.stage);hist=Histories(a.stage);ck=torch.load(P/a.resume,weights_only=False);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);allitems=torch.arange(len(data.items));start=time.time();best=0\n for epoch in range(1,a.epochs+1):\n  model.train();order=np.random.permutation(len(data.uids));order=order[:a.limit] if a.limit else order;losses=[]\n  for s in range(0,len(order),a.batch):\n   rows=order[s:s+a.batch];args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  print(json.dumps({'epoch':epoch,'loss':float(np.mean(losses)),'seconds':round(time.time()-start,1)}),flush=True)\n  params=dict(ck['args']);params.update(vars(a));score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else 'submission.parquet');print('VALID',score,flush=True);newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt')\n  if score is None or score>best:\n   best=score or 0;torch.save(newck,P/f'{a.name}_best.pt');(P/f'{a.name}_score.json').write_text(json.dumps({'ndcg':score,'epoch':epoch,'args':params},indent=2))\nif __name__=='__main__':main()\nPY\ncat > /app/solution/tune.py <<'PY'\n\"\"\"Public-validation calibration of history weights and item-tower trend bias.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',default='base_best.pt');ap.add_argument('--name',default='tune');ap.add_argument('--recent',default='0,1,3,10');ap.add_argument('--like',default='0');ap.add_argument('--bias',default='-0.5,0,0.5,1,1.5,2');a=ap.parse_args()\n data=Histories('validation');ck=torch.load(P/a.checkpoint,weights_only=False);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);model.eval();z,mask,truth,idcg=truth_data();uids=z['uids'];items=z['item_ids'];columns=pd.Index(data.items).get_indexer(items);trends=np.load(P/'trends_validation.npy');disc=1/np.log2(np.arange(2,12));tu=np.array(list(truth));best=-1;results=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns));ibias=iv[:,-1].clone();trend=torch.tensor(trends[columns,3]);baseline=torch.tensor(trends[columns,2]);allbest=None\n  for recent in map(float,a.recent.split(',')):\n   for like in map(float,a.like.split(',')):\n    vec=[]\n    for s in range(0,len(uids),128):vec.append(model.user(*data.batch([data.index.get(int(u),-1) for u in uids[s:s+128]],recent=recent,like=like,target=False)))\n    uv=torch.cat(vec)\n    # Scores exist only transiently for a retrieval batch, never as a training interaction matrix.\n    for bias in map(float,a.bias.split(',')):\n     ranks=[]\n     for s in range(0,len(uids),256):\n      scores=uv[s:s+256]@iv.T +bias*trend;scores.masked_fill_(~torch.from_numpy(mask[s:s+256]),-torch.inf);ranks.append(scores.topk(10,dim=1).indices.numpy())\n     ranks=np.concatenate(ranks);score=float(np.mean([np.dot([truth[u].get(int(i),0) for i in ranks[u]],disc)/idcg[u] for u in truth]));r={'recent':recent,'like':like,'trend':bias,'ndcg':score};results.append(r);print(json.dumps(r),flush=True)\n     if score>best:best=score;allbest=r\n (P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':allbest,'results':results},indent=2))\nif __name__=='__main__':main()\nPY\ncat /app/solution/base_score.json && tail -3 /app/solution/build_temporal.log && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"ndcg\": 0.01831428934273949,\n  \"epoch\": 8,\n  \"args\": {\n    \"stage\": \"validation\",\n    \"epochs\": 40,\n    \"dim\": 600,\n    \"latent\": 200,\n    \"batch\": 256,\n    \"lr\": 0.001,\n    \"drop\": 0.5,\n    \"name\": \"base\",\n    \"seed\": 42,\n    \"eval_every\": 2,\n    \"resume\": \"base_last.pt\",\n    \"recent\": 0,\n    \"like\": 0,\n    \"negative\": 0.3,\n    \"infer\": false\n  }\n}users 8000 examples 48836 seconds 55\nusers 9000 examples 55095 seconds 61\ndone validation examples 60791 pairs 37524827 targets 574173 seconds 66.96102976799011\n5137698816\n",
    "stderr": "",
    "aggregated_output": "{\n  \"ndcg\": 0.01831428934273949,\n  \"epoch\": 8,\n  \"args\": {\n    \"stage\": \"validation\",\n    \"epochs\": 40,\n    \"dim\": 600,\n    \"latent\": 200,\n    \"batch\": 256,\n    \"lr\": 0.001,\n    \"drop\": 0.5,\n    \"name\": \"base\",\n    \"seed\": 42,\n    \"eval_every\": 2,\n    \"resume\": \"base_last.pt\",\n    \"recent\": 0,\n    \"like\": 0,\n    \"negative\": 0.3,\n    \"infer\": false\n  }\n}users 8000 examples 48836 seconds 55\nusers 9000 examples 55095 seconds 61\ndone validation examples 60791 pairs 37524827 targets 574173 seconds 66.96102976799011\n5137698816\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2770
    },
    "formatted_output": "{\n  \"ndcg\": 0.01831428934273949,\n  \"epoch\": 8,\n  \"args\": {\n    \"stage\": \"validation\",\n    \"epochs\": 40,\n    \"dim\": 600,\n    \"latent\": 200,\n    \"batch\": 256,\n    \"lr\": 0.001,\n    \"drop\": 0.5,\n    \"name\": \"base\",\n    \"seed\": 42,\n    \"eval_every\": 2,\n    \"resume\": \"base_last.pt\",\n    \"recent\": 0,\n    \"like\": 0,\n    \"negative\": 0.3,\n    \"infer\": false\n  }\n}users 8000 examples 48836 seconds 55\nusers 9000 examples 55095 seconds 61\ndone validation examples 60791 pairs 37524827 targets 574173 seconds 66.96102976799011\n5137698816\n"
  }
}

exec command

+09:10

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 +09:16

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace('torch.set_num_threads(8)',\"torch.set_num_threads(int(os.environ.get('TOWER_THREADS','8')))\");open(p,'w').write(s)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint base_best.pt --name early_tune --recent 0,1,3,10 --bias 0,0.5,1,2 > /app/solution/early_tune.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -8 /app/solution/base.log","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +09:17

Script completed
Wall time 1.4 seconds
Output:

{"chunk_id":"872443","wall_time_seconds":1.000356095,"session_id":72928,"original_token_count":0,"output":""}
{"chunk_id":"e32669","wall_time_seconds":0.00000576,"exit_code":0,"original_token_count":103,"output":"{\"epoch\": 7, \"loss\": 7.705585625436571, \"seconds\": 140.1}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\n"}
{"current_time":"2026-09-24 08:04:46 UTC"}

Harness context token usage record

{
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  "usage": {
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  },
  "thread_token_usage": {
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-efe48705-ce4e-4527-a74e-e61d50eb7098",
    "process_id": "17462",
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      "/bin/sh",
      "-lc",
      "tail -8 /app/solution/base.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -8 /app/solution/base.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 7, \"loss\": 7.705585625436571, \"seconds\": 140.1}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 7, \"loss\": 7.705585625436571, \"seconds\": 140.1}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\n",
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    "duration": {
      "secs": 0,
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    },
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  }
}

Agent message

+09:34

Thinking unavailable No text recorded

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

exec command

+09:38

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 +09:39

text(await tools.exec_command({cmd:"tail -10 /app/solution/early_tune.log && tail -8 /app/solution/base.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1500}));

Tool response +09:39

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"75d6c2","wall_time_seconds":0.00000515,"exit_code":0,"original_token_count":171,"output":"{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.01831428934273949}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.023275810769781306}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.028167809673552608}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03200232073713847}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n6700105728\n"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
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    "process_id": "26598",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -10 /app/solution/early_tune.log && tail -8 /app/solution/base.log && cat /sys/fs/cgroup/memory.current"
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    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -10 /app/solution/early_tune.log && tail -8 /app/solution/base.log && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.01831428934273949}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.023275810769781306}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.028167809673552608}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03200232073713847}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n6700105728\n",
    "stderr": "",
    "aggregated_output": "{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.01831428934273949}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.023275810769781306}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.028167809673552608}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03200232073713847}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n6700105728\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5150
    },
    "formatted_output": "{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.01831428934273949}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.023275810769781306}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.028167809673552608}\n{\"recent\": 0.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03200232073713847}\n{\"epoch\": 8, \"loss\": 7.633822043736775, \"seconds\": 165.3}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n6700105728\n"
  }
}

Harness context token usage record

{
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  "thread_token_usage": {
    "input_tokens": 747267,
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 7315,
    "total_tokens": 764378
  }
}

Agent message

+09:57

Thinking unavailable No text recorded

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

exec command

+09:58

Thinking unavailable No text recorded

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

Model text

Recent item popularity is a useful signal: adding a trend term from training-period events raised validation NDCG@10 from 0.0183 to 0.0320. That term can live inside the precomputed item vector, so it adds no candidate-by-candidate work to the user tower.

Tool callexecTool call Returned

Tool call · Input +10:03

text(await tools.exec_command({cmd:"cp /app/solution/base_best.pt /app/solution/early_model.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume early_model.pt --epochs 5 --lr .0003 --recent 1 --name temporal > /app/solution/temporal.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +10:05

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d29b86","wall_time_seconds":1.000597003,"session_id":39617,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:05:34 UTC"}

Harness context token usage record

{
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  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
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  "thread_token_usage": {
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    "cached_input_tokens": 751616,
    "cache_write_input_tokens": 0,
    "output_tokens": 17858,
    "reasoning_output_tokens": 7877,
    "total_tokens": 808697
  }
}

exec command

+10:22

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +10:25

text(await tools.exec_command({cmd:"tail -10 /app/solution/early_tune.log && tail -8 /app/solution/base.log && tail -3 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.events","max_output_tokens":2200}));

Tool response +10:25

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"5be8db","wall_time_seconds":0.00000583,"exit_code":0,"original_token_count":299,"output":"{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.019525111616618233}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.024605183828660133}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029208490801679293}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03267410247310554}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02068207158746646}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.025796257172382084}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029964192544061803}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n8442335232\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-97858654-8d91-4e7c-a757-b217e7b5e00f",
    "process_id": "53664",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -10 /app/solution/early_tune.log && tail -8 /app/solution/base.log && tail -3 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.events"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -10 /app/solution/early_tune.log && tail -8 /app/solution/base.log && tail -3 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.events"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.019525111616618233}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.024605183828660133}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029208490801679293}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03267410247310554}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02068207158746646}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.025796257172382084}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029964192544061803}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n8442335232\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n",
    "stderr": "",
    "aggregated_output": "{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.019525111616618233}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.024605183828660133}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029208490801679293}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03267410247310554}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02068207158746646}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.025796257172382084}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029964192544061803}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n8442335232\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5830
    },
    "formatted_output": "{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.019525111616618233}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.024605183828660133}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029208490801679293}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03267410247310554}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02068207158746646}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.025796257172382084}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.029964192544061803}\n{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\nVALID 8 0.01831428934273949\n{\"epoch\": 9, \"loss\": 7.562448514832391, \"seconds\": 184.9}\n{\"epoch\": 10, \"loss\": 7.508159875869751, \"seconds\": 197.6}\nVALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n8442335232\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"
  }
}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ad6cdc8a-1caf-4d8e-b115-47550b92d630",
    "process_id": "72928",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace('torch.set_num_threads(8)',\"torch.set_num_threads(int(os.environ.get('TOWER_THREADS','8')))\");open(p,'w').write(s)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint base_best.pt --name early_tune --recent 0,1,3,10 --bias 0,0.5,1,2 > /app/solution/early_tune.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace('torch.set_num_threads(8)',\"torch.set_num_threads(int(os.environ.get('TOWER_THREADS','8')))\");open(p,'w').write(s)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint base_best.pt --name early_tune --recent 0,1,3,10 --bias 0,0.5,1,2 > /app/solution/early_tune.log 2>&1"
      }
    ],
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    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
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      "nanos": 375724590
    },
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}

exec command

+10:39

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +10:48

text(await tools.exec_command({cmd:"cat > /app/solution/infer.py <<'PY'\n\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--stage',default='test');ap.add_argument('--recent',type=float,default=10);ap.add_argument('--like',type=float,default=0);ap.add_argument('--trend',type=float,default=2);ap.add_argument('--output');ap.add_argument('--save-model');a=ap.parse_args()\n ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);features=np.load(P/f'trends_{a.stage}.npy')\n with torch.no_grad():model.item.bias.weight[:,0].add_(torch.tensor(features[:,3])*a.trend)\n out=a.output or ('submission.parquet' if a.stage=='test' else 'validation.parquet')\n score=evaluate(model,data,a.stage,a.recent,a.like,save=out+'.tmp.parquet');os.replace(P/(out+'.tmp.parquet'),P/out);print(json.dumps({'output':out,'ndcg':score,'calibration':vars(a)}),flush=True)\n if a.save_model:\n  ck['model']=model.state_dict();ck['calibration']=vars(a);torch.save(ck,P/a.save_model)\n  model.eval()\n  with torch.no_grad():np.save(P/'item_vectors.npy',model.item(torch.arange(len(data.items))).numpy())\n  np.save(P/'item_ids.npy',data.items)\nif __name__=='__main__':main()\nPY\ncat /app/solution/early_tune.log | tail -5\ncat /sys/fs/cgroup/memory.events","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +10:48

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"074901","wall_time_seconds":0.00000643,"exit_code":0,"original_token_count":110,"output":"{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.03120290685223285}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03449650649863736}\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"}
{"current_time":"2026-09-24 08:06:18 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-557cd2fb-b523-437a-8fae-09468b31f53e",
    "process_id": "17949",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/infer.py <<'PY'\n\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--stage',default='test');ap.add_argument('--recent',type=float,default=10);ap.add_argument('--like',type=float,default=0);ap.add_argument('--trend',type=float,default=2);ap.add_argument('--output');ap.add_argument('--save-model');a=ap.parse_args()\n ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);features=np.load(P/f'trends_{a.stage}.npy')\n with torch.no_grad():model.item.bias.weight[:,0].add_(torch.tensor(features[:,3])*a.trend)\n out=a.output or ('submission.parquet' if a.stage=='test' else 'validation.parquet')\n score=evaluate(model,data,a.stage,a.recent,a.like,save=out+'.tmp.parquet');os.replace(P/(out+'.tmp.parquet'),P/out);print(json.dumps({'output':out,'ndcg':score,'calibration':vars(a)}),flush=True)\n if a.save_model:\n  ck['model']=model.state_dict();ck['calibration']=vars(a);torch.save(ck,P/a.save_model)\n  model.eval()\n  with torch.no_grad():np.save(P/'item_vectors.npy',model.item(torch.arange(len(data.items))).numpy())\n  np.save(P/'item_ids.npy',data.items)\nif __name__=='__main__':main()\nPY\ncat /app/solution/early_tune.log | tail -5\ncat /sys/fs/cgroup/memory.events"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/infer.py <<'PY'\n\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--stage',default='test');ap.add_argument('--recent',type=float,default=10);ap.add_argument('--like',type=float,default=0);ap.add_argument('--trend',type=float,default=2);ap.add_argument('--output');ap.add_argument('--save-model');a=ap.parse_args()\n ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);features=np.load(P/f'trends_{a.stage}.npy')\n with torch.no_grad():model.item.bias.weight[:,0].add_(torch.tensor(features[:,3])*a.trend)\n out=a.output or ('submission.parquet' if a.stage=='test' else 'validation.parquet')\n score=evaluate(model,data,a.stage,a.recent,a.like,save=out+'.tmp.parquet');os.replace(P/(out+'.tmp.parquet'),P/out);print(json.dumps({'output':out,'ndcg':score,'calibration':vars(a)}),flush=True)\n if a.save_model:\n  ck['model']=model.state_dict();ck['calibration']=vars(a);torch.save(ck,P/a.save_model)\n  model.eval()\n  with torch.no_grad():np.save(P/'item_vectors.npy',model.item(torch.arange(len(data.items))).numpy())\n  np.save(P/'item_ids.npy',data.items)\nif __name__=='__main__':main()\nPY\ncat /app/solution/early_tune.log | tail -5\ncat /sys/fs/cgroup/memory.events"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.03120290685223285}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03449650649863736}\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n",
    "stderr": "",
    "aggregated_output": "{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.03120290685223285}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03449650649863736}\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6430
    },
    "formatted_output": "{\"recent\": 3.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03359902282524634}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.02175702649710722}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.5, \"ndcg\": 0.026457257350728874}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.03120290685223285}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.03449650649863736}\nlow 0\nhigh 0\nmax 813\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"
  }
}

Harness context token usage record

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  }
}

exec command

+11:09

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +11:11

text(await tools.exec_command({cmd:"TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint early_model.pt --stage test --recent 10 --trend 2 > /app/solution/provisional.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -7 /app/solution/base.log && tail -5 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1200}));

Tool response +11:12

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"0da4ef","wall_time_seconds":1.00060893,"session_id":59523,"original_token_count":0,"output":""}
{"chunk_id":"e82050","wall_time_seconds":0.00000452,"exit_code":0,"original_token_count":85,"output":"VALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n6620823552\n"}

Harness context token usage record

{
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  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4d9fa5bfc87d2ba322f6bdb2e2c94",
  "usage": {
    "input_tokens": 46683,
    "cached_input_tokens": 45440,
    "cache_write_input_tokens": 0,
    "output_tokens": 646,
    "reasoning_output_tokens": 516,
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  },
  "turn_token_usage": {
    "input_tokens": 927574,
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  "thread_token_usage": {
    "input_tokens": 927574,
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    "cache_write_input_tokens": 0,
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  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-97aadaad-57da-48d1-a10b-e05f8cb35c17",
    "process_id": "42981",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -7 /app/solution/base.log && tail -5 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -7 /app/solution/base.log && tail -5 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n6620823552\n",
    "stderr": "",
    "aggregated_output": "VALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n6620823552\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4520
    },
    "formatted_output": "VALID 10 0.017529967801072684\n{\"epoch\": 11, \"loss\": 7.447866439819336, \"seconds\": 213.0}\n{\"epoch\": 12, \"loss\": 7.397581073972914, \"seconds\": 225.6}\nVALID 12 0.016780872075690827\n{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n6620823552\n"
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-218da3be-131c-4c73-9311-2a25e3151342",
    "process_id": "59523",
    "command": [
      "/bin/sh",
      "-lc",
      "TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint early_model.pt --stage test --recent 10 --trend 2 > /app/solution/provisional.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint early_model.pt --stage test --recent 10 --trend 2 > /app/solution/provisional.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 19,
      "nanos": 158787104
    },
    "formatted_output": ""
  }
}

Agent message

+11:35

Thinking unavailable No text recorded

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

Agent message

+11:55

Thinking unavailable No text recorded

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

exec command

+12:11

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +12:14

text(await tools.exec_command({cmd:"tail -6 /app/solution/base.log && tail -4 /app/solution/temporal.log && cat /app/solution/provisional.log","max_output_tokens":1200}));
text(await tools.clock__curr_time({}));

Tool response +12:14

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"6d151c","wall_time_seconds":0.00000419,"exit_code":0,"original_token_count":122,"output":"{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n{\"epoch\": 15, \"loss\": 7.280001110500759, \"seconds\": 345.6}\n{\"epoch\": 16, \"loss\": 7.240791930092706, \"seconds\": 383.8}\nVALID 16 0.01748688927695637\n{\"output\": \"submission.parquet\", \"ndcg\": null, \"calibration\": {\"checkpoint\": \"early_model.pt\", \"stage\": \"test\", \"recent\": 10.0, \"like\": 0, \"trend\": 2.0, \"output\": null, \"save_model\": null}}\n"}
{"current_time":"2026-09-24 08:07:43 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0639caff-54f6-4db7-a110-7965c01810f5",
    "process_id": "59084",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -6 /app/solution/base.log && tail -4 /app/solution/temporal.log && cat /app/solution/provisional.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -6 /app/solution/base.log && tail -4 /app/solution/temporal.log && cat /app/solution/provisional.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n{\"epoch\": 15, \"loss\": 7.280001110500759, \"seconds\": 345.6}\n{\"epoch\": 16, \"loss\": 7.240791930092706, \"seconds\": 383.8}\nVALID 16 0.01748688927695637\n{\"output\": \"submission.parquet\", \"ndcg\": null, \"calibration\": {\"checkpoint\": \"early_model.pt\", \"stage\": \"test\", \"recent\": 10.0, \"like\": 0, \"trend\": 2.0, \"output\": null, \"save_model\": null}}\n",
    "stderr": "",
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    "exit_code": 0,
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Agent message

+12:31

Thinking unavailable No text recorded

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

exec command

+12:41

Thinking unavailable No text recorded

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

Model text

Emphasizing recent history alongside item trends raised validation NDCG@10 to 0.0345, and the saved provisional submission now uses that configuration. The temporal fine-tune is running on 60,791 historical weekly examples, with targets restricted to tracks unseen at each snapshot.

Tool callexecTool call Returned

Tool call · Input +13:02

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py';s=open(p).read();at='class Histories:'\nnew='''class ResidualUserTower(UserTower):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__(n,dim,latent);self.skip_scale=nn.Parameter(torch.tensor(.2))\n def forward(self,ids,offsets,weights):\n  h=self.history(ids,offsets,per_sample_weights=weights)\n  h=self.net(h)+self.skip_scale*F.layer_norm(h,(h.shape[1],))\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\ndef make_model(ck):\n a=ck['args'];m=TwoTower(len(ck['model']['item.bias.weight']),a['dim'],a['latent'])\n if a.get('variant')=='residual':m.user=ResidualUserTower(len(ck['model']['item.bias.weight']),a['dim'],a['latent'])\n m.load_state_dict(ck['model']);return m\n'''\ns=s.replace(at,new+at)\ns=s.replace(\"ap.add_argument('--stage',default='validation');\", \"ap.add_argument('--variant',default='base');ap.add_argument('--stage',default='validation');\")\ns=s.replace(\"if a.resume:model.load_state_dict(torch.load(P/a.resume,weights_only=False)['model'])\", \"if a.variant=='residual':model.user=ResidualUserTower(len(data.items),a.dim,a.latent)\\n if a.resume:model=make_model(torch.load(P/a.resume,weights_only=False))\")\nopen(p,'w').write(s)\nfor p in ['/app/solution/tune.py','/app/solution/infer.py']:\n s=open(p).read().replace(\"model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);\",'model=make_model(ck);');open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);\",'model=make_model(ck);');open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py /app/solution/temporal_train.py /app/solution/infer.py /app/solution/tune.py\ncp /app/solution/base_last.pt /app/solution/middle_model.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint middle_model.pt --name middle_tune --recent 10 --bias 1,2,3,4 > /app/solution/middle_tune.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +13:04

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"47a683","wall_time_seconds":1.001365282,"session_id":46348,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+13:11

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +13:13

text(await tools.exec_command({cmd:"tail -8 /app/solution/base.log && tail -5 /app/solution/temporal.log && cat /app/solution/middle_tune.log && cat /sys/fs/cgroup/memory.events","max_output_tokens":1400}));
text(await tools.clock__curr_time({}));

Tool response +13:13

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"c606e8","wall_time_seconds":0.00000726,"exit_code":0,"original_token_count":122,"output":"{\"epoch\": 13, \"loss\": 7.349557744132148, \"seconds\": 261.4}\n{\"epoch\": 14, \"loss\": 7.310964796278212, \"seconds\": 302.5}\nVALID 14 0.017298666002921948\n{\"epoch\": 15, \"loss\": 7.280001110500759, \"seconds\": 345.6}\n{\"epoch\": 16, \"loss\": 7.240791930092706, \"seconds\": 383.8}\nVALID 16 0.01748688927695637\n{\"epoch\": 17, \"loss\": 7.212483313348558, \"seconds\": 425.7}\n{\"epoch\": 18, \"loss\": 7.1791231234868365, \"seconds\": 463.7}\nlow 0\nhigh 0\nmax 911\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"}
{"current_time":"2026-09-24 08:08:42 UTC"}

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Agent message

+13:31

Thinking unavailable No text recorded

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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-95a63e1b-872a-4c58-b364-627fc072ca11",
    "process_id": "46348",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/train.py';s=open(p).read();at='class Histories:'\nnew='''class ResidualUserTower(UserTower):\n def __init__(self,n,dim=600,latent=200):\n  super().__init__(n,dim,latent);self.skip_scale=nn.Parameter(torch.tensor(.2))\n def forward(self,ids,offsets,weights):\n  h=self.history(ids,offsets,per_sample_weights=weights)\n  h=self.net(h)+self.skip_scale*F.layer_norm(h,(h.shape[1],))\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\ndef make_model(ck):\n a=ck['args'];m=TwoTower(len(ck['model']['item.bias.weight']),a['dim'],a['latent'])\n if a.get('variant')=='residual':m.user=ResidualUserTower(len(ck['model']['item.bias.weight']),a['dim'],a['latent'])\n m.load_state_dict(ck['model']);return m\n'''\ns=s.replace(at,new+at)\ns=s.replace(\"ap.add_argument('--stage',default='validation');\", \"ap.add_argument('--variant',default='base');ap.add_argument('--stage',default='validation');\")\ns=s.replace(\"if a.resume:model.load_state_dict(torch.load(P/a.resume,weights_only=False)['model'])\", \"if a.variant=='residual':model.user=ResidualUserTower(len(data.items),a.dim,a.latent)\\n if a.resume:model=make_model(torch.load(P/a.resume,weights_only=False))\")\nopen(p,'w').write(s)\nfor p in ['/app/solution/tune.py','/app/solution/infer.py']:\n s=open(p).read().replace(\"model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);\",'model=make_model(ck);');open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"model=TwoTower(len(data.items),ck['args']['dim'],ck['args']['latent']);model.load_state_dict(ck['model']);\",'model=make_model(ck);');open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py /app/solution/temporal_train.py /app/solution/infer.py /app/solution/tune.py\ncp /app/solution/base_last.pt /app/solution/middle_model.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint middle_model.pt --name middle_tune --recent 10 --bias 1,2,3,4 > /app/solution/middle_tune.log 2>&1"
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exec command

+13:38

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +13:58

text(await tools.exec_command({cmd:"cat > /app/solution/build_discovery_trends.py <<'PY'\n\"\"\"Item-level recent first-discovery statistics computed from event records.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);parts=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[ok].copy();d['iid']=i[ok];d['first']=d.timestamp;d['organic']=np.where((d.is_organic==1)&(d.played_ratio_pct>=80),d.timestamp,1e10)\n   parts.append(d.groupby(['uid','iid'],sort=False)[['first','organic']].min().reset_index())\n  d=pd.read_parquet(D/f'{split}_likes.parquet');i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1);d=d[ok].copy();d['iid']=i[ok];d['like']=d.timestamp;d['first']=d.timestamp;d['organic']=d.timestamp;parts.append(d.groupby(['uid','iid'],sort=False)[['first','organic','like']].min().reset_index())\n d=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False)[['first','organic','like']].min().reset_index();del parts\n weight=np.where(d['like']<=d['first']+604800,7.,np.where(d.organic<=d['first']+604800,1.,0.));age=(end-d['first'].to_numpy())/86400;counts=np.zeros((N,5))\n for j,days in enumerate([7,14,30,90,360]):counts[:,j]=np.bincount(d.iid.to_numpy(),weights=weight*np.exp(-age/days),minlength=N)\n features=np.stack([np.log1p(counts[:,0]),np.log1p(counts[:,2]),np.log1p(counts[:,4]),np.log((counts[:,0]+3)/(counts[:,3]+3)),np.log((counts[:,2]+5)/(counts[:,4]+5))],axis=1).astype('float32');np.save(P/f'discovery_trends_{stage}.npy',features);print(stage,flush=True);del d\nPY\ncat /app/solution/middle_tune.log && tail -5 /app/solution/temporal.log && tail -4 /app/solution/base.log","max_output_tokens":1500}));

Tool response +13:59

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"33539d","wall_time_seconds":0.000024061,"exit_code":0,"original_token_count":133,"output":"{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.026735934215491624}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.031060949036121503}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.031241741779199205}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 4.0, \"ndcg\": 0.02929229530158711}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 16 0.01748688927695637\n{\"epoch\": 17, \"loss\": 7.212483313348558, \"seconds\": 425.7}\n{\"epoch\": 18, \"loss\": 7.1791231234868365, \"seconds\": 463.7}\nVALID 18 0.016996665088219353\n"}

Harness context token usage record

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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-b533b74d-2593-4fb9-8264-33916bc601d5",
    "process_id": "44419",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/build_discovery_trends.py <<'PY'\n\"\"\"Item-level recent first-discovery statistics computed from event records.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);parts=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[ok].copy();d['iid']=i[ok];d['first']=d.timestamp;d['organic']=np.where((d.is_organic==1)&(d.played_ratio_pct>=80),d.timestamp,1e10)\n   parts.append(d.groupby(['uid','iid'],sort=False)[['first','organic']].min().reset_index())\n  d=pd.read_parquet(D/f'{split}_likes.parquet');i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1);d=d[ok].copy();d['iid']=i[ok];d['like']=d.timestamp;d['first']=d.timestamp;d['organic']=d.timestamp;parts.append(d.groupby(['uid','iid'],sort=False)[['first','organic','like']].min().reset_index())\n d=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False)[['first','organic','like']].min().reset_index();del parts\n weight=np.where(d['like']<=d['first']+604800,7.,np.where(d.organic<=d['first']+604800,1.,0.));age=(end-d['first'].to_numpy())/86400;counts=np.zeros((N,5))\n for j,days in enumerate([7,14,30,90,360]):counts[:,j]=np.bincount(d.iid.to_numpy(),weights=weight*np.exp(-age/days),minlength=N)\n features=np.stack([np.log1p(counts[:,0]),np.log1p(counts[:,2]),np.log1p(counts[:,4]),np.log((counts[:,0]+3)/(counts[:,3]+3)),np.log((counts[:,2]+5)/(counts[:,4]+5))],axis=1).astype('float32');np.save(P/f'discovery_trends_{stage}.npy',features);print(stage,flush=True);del d\nPY\ncat /app/solution/middle_tune.log && tail -5 /app/solution/temporal.log && tail -4 /app/solution/base.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/build_discovery_trends.py <<'PY'\n\"\"\"Item-level recent first-discovery statistics computed from event records.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);parts=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[ok].copy();d['iid']=i[ok];d['first']=d.timestamp;d['organic']=np.where((d.is_organic==1)&(d.played_ratio_pct>=80),d.timestamp,1e10)\n   parts.append(d.groupby(['uid','iid'],sort=False)[['first','organic']].min().reset_index())\n  d=pd.read_parquet(D/f'{split}_likes.parquet');i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.is_organic.to_numpy()==1);d=d[ok].copy();d['iid']=i[ok];d['like']=d.timestamp;d['first']=d.timestamp;d['organic']=d.timestamp;parts.append(d.groupby(['uid','iid'],sort=False)[['first','organic','like']].min().reset_index())\n d=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False)[['first','organic','like']].min().reset_index();del parts\n weight=np.where(d['like']<=d['first']+604800,7.,np.where(d.organic<=d['first']+604800,1.,0.));age=(end-d['first'].to_numpy())/86400;counts=np.zeros((N,5))\n for j,days in enumerate([7,14,30,90,360]):counts[:,j]=np.bincount(d.iid.to_numpy(),weights=weight*np.exp(-age/days),minlength=N)\n features=np.stack([np.log1p(counts[:,0]),np.log1p(counts[:,2]),np.log1p(counts[:,4]),np.log((counts[:,0]+3)/(counts[:,3]+3)),np.log((counts[:,2]+5)/(counts[:,4]+5))],axis=1).astype('float32');np.save(P/f'discovery_trends_{stage}.npy',features);print(stage,flush=True);del d\nPY\ncat /app/solution/middle_tune.log && tail -5 /app/solution/temporal.log && tail -4 /app/solution/base.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.026735934215491624}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.031060949036121503}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.031241741779199205}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 4.0, \"ndcg\": 0.02929229530158711}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 16 0.01748688927695637\n{\"epoch\": 17, \"loss\": 7.212483313348558, \"seconds\": 425.7}\n{\"epoch\": 18, \"loss\": 7.1791231234868365, \"seconds\": 463.7}\nVALID 18 0.016996665088219353\n",
    "stderr": "",
    "aggregated_output": "{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.026735934215491624}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.031060949036121503}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.031241741779199205}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 4.0, \"ndcg\": 0.02929229530158711}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 16 0.01748688927695637\n{\"epoch\": 17, \"loss\": 7.212483313348558, \"seconds\": 425.7}\n{\"epoch\": 18, \"loss\": 7.1791231234868365, \"seconds\": 463.7}\nVALID 18 0.016996665088219353\n",
    "exit_code": 0,
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      "secs": 0,
      "nanos": 24061
    },
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  }
}

exec command

+14:02

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +14:10

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except (OSError,PermissionError):continue\n if b'/app/solution/train.py' in args and b'--epochs' in args and b'40' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped saturated baseline training',p.name)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --variant residual --epochs 30 --eval-every 2 --name residual --drop .6 > /app/solution/residual.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/build_discovery_trends.py > /app/solution/discovery_trends.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +14:12

Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"d44ab3","wall_time_seconds":1.001193525,"session_id":66114,"original_token_count":11,"output":"Stopped saturated baseline training 9608\n"}
{"chunk_id":"e199fa","wall_time_seconds":1.001807275,"session_id":82790,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:09:42 UTC"}

Harness context token usage record

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Harness context codex item completed

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    "id": "exec-2d375b92-bc09-4368-9154-8ab62c30abba",
    "process_id": "22715",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train.py --epochs 40 --eval-every 2 --resume base_last.pt > /app/solution/base.log 2>&1"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
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exec command

+14:15

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +14:17

text(await tools.exec_command({cmd:"tail -5 /app/solution/temporal.log && tail -3 /app/solution/residual.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1000}));

Tool response +14:17

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"ba5525","wall_time_seconds":0.00000652,"exit_code":0,"original_token_count":24,"output":"{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n6680768512\n"}

Harness context codex item completed

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    "process_id": "65373",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -5 /app/solution/temporal.log && tail -3 /app/solution/residual.log && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -5 /app/solution/temporal.log && tail -3 /app/solution/residual.log && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n6680768512\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n6680768512\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6520
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n6680768512\n"
  }
}

Harness context token usage record

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Harness context codex item completed

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    "type": "CommandExecution",
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    "process_id": "82790",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/build_discovery_trends.py > /app/solution/discovery_trends.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/build_discovery_trends.py > /app/solution/discovery_trends.log 2>&1"
      }
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Agent message

+14:34

Thinking unavailable No text recorded

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

Agent message

+14:52

Thinking unavailable No text recorded

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

exec command

+14:59

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 +15:00

text(await tools.exec_command({cmd:"tail -5 /app/solution/residual.log && cat /app/solution/discovery_trends.log && cat /sys/fs/cgroup/memory.events && tail -4 /app/solution/temporal.log","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +15:00

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"dffdcf","wall_time_seconds":0.0000097,"exit_code":0,"original_token_count":58,"output":"{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\nvalidation\ntest\nlow 0\nhigh 0\nmax 1149\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n"}
{"current_time":"2026-09-24 08:10:30 UTC"}

Harness context codex item completed

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      "-lc",
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      }
    ],
    "source": "unified_exec_startup",
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    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 9700
    },
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  }
}

Harness context token usage record

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exec command

+15:03

Thinking unavailable No text recorded

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

Model text

The first temporal fine-tuning epoch scored 0.0302 NDCG@10 before trend calibration, a substantial improvement over the original model without that adjustment. I’ve stopped the original run after its validation quality declined and am comparing the temporal model with a version that preserves more direct history-to-item information.

Tool callexecTool call Returned

Tool call · Input +15:09

text(await tools.exec_command({cmd:"cp /app/solution/temporal_best.pt /app/solution/temporal_early.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint temporal_early.pt --name temporal_early_tune --recent 1,10 --bias 0,1,2,3 > /app/solution/temporal_early_tune.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +15:10

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"857bb5","wall_time_seconds":1.00114631,"session_id":29836,"original_token_count":0,"output":""}

Harness context token usage record

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}

Agent message

+15:29

Thinking unavailable No text recorded

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

exec command

+15:31

Thinking unavailable No text recorded

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

Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
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    "process_id": "29836",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/temporal_best.pt /app/solution/temporal_early.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py --checkpoint temporal_early.pt --name temporal_early_tune --recent 1,10 --bias 0,1,2,3 > /app/solution/temporal_early_tune.log 2>&1"
    ],
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    "parsed_cmd": [
      {
        "type": "unknown",
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      }
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      "nanos": 421453954
    },
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  }
}

Tool callexecTool call Returned

Tool call · Input +15:56

text(await tools.exec_command({cmd:"cat > /app/solution/calibrate.py <<'PY'\n\"\"\"Efficient held-out calibration using batched MIPS scores; never features from validation outcomes.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--name',default='calibration');ap.add_argument('--recent',default='1,10');ap.add_argument('--like',default='0');ap.add_argument('--gamma',default='0,1,2,3');ap.add_argument('--pop',default='0,0.5,1');ap.add_argument('--kind',default='standard,discovery');a=ap.parse_args()\n ck=torch.load(P/a.checkpoint,weights_only=False);model=make_model(ck).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();items=z['item_ids'];uids=z['uids'];columns=pd.Index(data.items).get_indexer(items);ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));features={k:torch.tensor(np.load(P/('discovery_trends_validation.npy' if k=='discovery' else 'trends_validation.npy'))[columns]) for k in a.kind.split(',')};results=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for recent in map(float,a.recent.split(',')):\n   for like in map(float,a.like.split(',')):\n    cfg=[{'recent':recent,'like':like,'kind':kind,'trend':g,'pop':p} for kind in features for g in map(float,a.gamma.split(',')) for p in map(float,a.pop.split(','))];sums=np.zeros(len(cfg));adjust=[features[c['kind']][:,3]*c['trend']+features[c['kind']][:,0]*c['pop'] for c in cfg]\n    for s in range(0,len(ur),128):\n     row=ur[s:s+128];uv=model.user(*data.batch([data.index.get(int(uids[u]),-1) for u in row],recent=recent,like=like,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf)\n     for j,bonus in enumerate(adjust):\n      ranks=(scores+bonus).topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n    for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur));results.append(c)\n    print('BEST_SO_FAR',json.dumps(max(results,key=lambda x:x['ndcg'])),flush=True)\n best=max(results,key=lambda x:x['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':best,'results':results},indent=2));print('BEST',json.dumps(best),flush=True)\n # Preserve the exact fitted weights whose calibration was measured.\n torch.save(ck,P/f'{a.name}_source.pt')\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint early_model.pt --name early_full --recent 3,10,100 --gamma 1,2,3 --pop 0,0.5,1 > /app/solution/early_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -6 /app/solution/temporal_early_tune.log && tail -5 /app/solution/residual.log && tail -4 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":2000}));

Tool response +15:57

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"0d6ff5","wall_time_seconds":1.001495538,"session_id":48105,"original_token_count":0,"output":""}
{"chunk_id":"f4b966","wall_time_seconds":0.00000478,"exit_code":0,"original_token_count":183,"output":"{\"recent\": 1.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.04367815335570441}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.041396654968906973}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.032326487960388416}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.04307689098643234}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.04474299763732291}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.0419666606547072}\n{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\n{\"epoch\": 2, \"loss\": 8.634816699557835, \"seconds\": 40.5}\nVALID 2 0.017632721633890323\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n6498512896\n"}

Harness context token usage record

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Harness context codex item completed

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    "process_id": "52961",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -6 /app/solution/temporal_early_tune.log && tail -5 /app/solution/residual.log && tail -4 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current"
    ],
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        "cmd": "tail -6 /app/solution/temporal_early_tune.log && tail -5 /app/solution/residual.log && tail -4 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"recent\": 1.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.04367815335570441}\n{\"recent\": 1.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.041396654968906973}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 0.0, \"ndcg\": 0.032326487960388416}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 1.0, \"ndcg\": 0.04307689098643234}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 2.0, \"ndcg\": 0.04474299763732291}\n{\"recent\": 10.0, \"like\": 0.0, \"trend\": 3.0, \"ndcg\": 0.0419666606547072}\n{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\n{\"epoch\": 2, \"loss\": 8.634816699557835, \"seconds\": 40.5}\nVALID 2 0.017632721633890323\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n6498512896\n",
    "stderr": "",
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    "exit_code": 0,
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}

Agent message

+16:17

Thinking unavailable No text recorded

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

exec command

+16:25

Thinking unavailable No text recorded

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

Harness context codex item completed

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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e5ad04e1-291e-4394-a070-5f7798061f9e",
    "process_id": "48105",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/calibrate.py <<'PY'\n\"\"\"Efficient held-out calibration using batched MIPS scores; never features from validation outcomes.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--name',default='calibration');ap.add_argument('--recent',default='1,10');ap.add_argument('--like',default='0');ap.add_argument('--gamma',default='0,1,2,3');ap.add_argument('--pop',default='0,0.5,1');ap.add_argument('--kind',default='standard,discovery');a=ap.parse_args()\n ck=torch.load(P/a.checkpoint,weights_only=False);model=make_model(ck).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();items=z['item_ids'];uids=z['uids'];columns=pd.Index(data.items).get_indexer(items);ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));features={k:torch.tensor(np.load(P/('discovery_trends_validation.npy' if k=='discovery' else 'trends_validation.npy'))[columns]) for k in a.kind.split(',')};results=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for recent in map(float,a.recent.split(',')):\n   for like in map(float,a.like.split(',')):\n    cfg=[{'recent':recent,'like':like,'kind':kind,'trend':g,'pop':p} for kind in features for g in map(float,a.gamma.split(',')) for p in map(float,a.pop.split(','))];sums=np.zeros(len(cfg));adjust=[features[c['kind']][:,3]*c['trend']+features[c['kind']][:,0]*c['pop'] for c in cfg]\n    for s in range(0,len(ur),128):\n     row=ur[s:s+128];uv=model.user(*data.batch([data.index.get(int(uids[u]),-1) for u in row],recent=recent,like=like,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf)\n     for j,bonus in enumerate(adjust):\n      ranks=(scores+bonus).topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n    for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur));results.append(c)\n    print('BEST_SO_FAR',json.dumps(max(results,key=lambda x:x['ndcg'])),flush=True)\n best=max(results,key=lambda x:x['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':best,'results':results},indent=2));print('BEST',json.dumps(best),flush=True)\n # Preserve the exact fitted weights whose calibration was measured.\n torch.save(ck,P/f'{a.name}_source.pt')\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint early_model.pt --name early_full --recent 3,10,100 --gamma 1,2,3 --pop 0,0.5,1 > /app/solution/early_full.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/calibrate.py <<'PY'\n\"\"\"Efficient held-out calibration using batched MIPS scores; never features from validation outcomes.\"\"\"\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--name',default='calibration');ap.add_argument('--recent',default='1,10');ap.add_argument('--like',default='0');ap.add_argument('--gamma',default='0,1,2,3');ap.add_argument('--pop',default='0,0.5,1');ap.add_argument('--kind',default='standard,discovery');a=ap.parse_args()\n ck=torch.load(P/a.checkpoint,weights_only=False);model=make_model(ck).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();items=z['item_ids'];uids=z['uids'];columns=pd.Index(data.items).get_indexer(items);ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));features={k:torch.tensor(np.load(P/('discovery_trends_validation.npy' if k=='discovery' else 'trends_validation.npy'))[columns]) for k in a.kind.split(',')};results=[]\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for recent in map(float,a.recent.split(',')):\n   for like in map(float,a.like.split(',')):\n    cfg=[{'recent':recent,'like':like,'kind':kind,'trend':g,'pop':p} for kind in features for g in map(float,a.gamma.split(',')) for p in map(float,a.pop.split(','))];sums=np.zeros(len(cfg));adjust=[features[c['kind']][:,3]*c['trend']+features[c['kind']][:,0]*c['pop'] for c in cfg]\n    for s in range(0,len(ur),128):\n     row=ur[s:s+128];uv=model.user(*data.batch([data.index.get(int(uids[u]),-1) for u in row],recent=recent,like=like,target=False));[email protected];scores.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf)\n     for j,bonus in enumerate(adjust):\n      ranks=(scores+bonus).topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n    for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur));results.append(c)\n    print('BEST_SO_FAR',json.dumps(max(results,key=lambda x:x['ndcg'])),flush=True)\n best=max(results,key=lambda x:x['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':best,'results':results},indent=2));print('BEST',json.dumps(best),flush=True)\n # Preserve the exact fitted weights whose calibration was measured.\n torch.save(ck,P/f'{a.name}_source.pt')\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint early_model.pt --name early_full --recent 3,10,100 --gamma 1,2,3 --pop 0,0.5,1 > /app/solution/early_full.log 2>&1"
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}

Tool callexecTool call Returned

Tool call · Input +16:27

text(await tools.exec_command({cmd:"TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint temporal_early.pt --stage test --recent 10 --trend 2 > /app/solution/provisional.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));
text(await tools.exec_command({cmd:"tail -5 /app/solution/early_full.log && tail -5 /app/solution/temporal.log && tail -6 /app/solution/residual.log","max_output_tokens":1500}));

Tool response +16:29

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"861d2f","wall_time_seconds":1.00065773,"session_id":32654,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:11:58 UTC"}
{"chunk_id":"099484","wall_time_seconds":0.00001538,"exit_code":0,"original_token_count":209,"output":"BEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.048784514882828124}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\n{\"epoch\": 2, \"loss\": 8.634816699557835, \"seconds\": 40.5}\nVALID 2 0.017632721633890323\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 4, \"loss\": 8.115433507495457, \"seconds\": 99.8}\nVALID 4 0.017722988553957583\n"}

Harness context token usage record

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Harness context codex item completed

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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "BEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.048784514882828124}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\n{\"epoch\": 2, \"loss\": 8.634816699557835, \"seconds\": 40.5}\nVALID 2 0.017632721633890323\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 4, \"loss\": 8.115433507495457, \"seconds\": 99.8}\nVALID 4 0.017722988553957583\n",
    "stderr": "",
    "aggregated_output": "BEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.048784514882828124}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\n{\"epoch\": 2, \"loss\": 8.634816699557835, \"seconds\": 40.5}\nVALID 2 0.017632721633890323\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 4, \"loss\": 8.115433507495457, \"seconds\": 99.8}\nVALID 4 0.017722988553957583\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 15380
    },
    "formatted_output": "BEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.048784514882828124}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04894091469231278}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 1, \"loss\": 9.44351061185201, \"seconds\": 21.0}\n{\"epoch\": 2, \"loss\": 8.634816699557835, \"seconds\": 40.5}\nVALID 2 0.017632721633890323\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 4, \"loss\": 8.115433507495457, \"seconds\": 99.8}\nVALID 4 0.017722988553957583\n"
  }
}

exec command

+16:40

Thinking unavailable No text recorded

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

Model text

A stronger trend feature—recent first-time discoveries by other listeners—raised the earlier model to 0.0489 NDCG@10. It uses training events only. I’ll now test it with the temporal model; the adjustment still fits entirely in the cached item vectors.

Harness context codex item completed

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Tool callexecTool call Returned

Tool call · Input +16:52

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/infer.py';s=open(p).read().replace(\"ap.add_argument('--trend',type=float,default=2);\", \"ap.add_argument('--trend',type=float,default=2);ap.add_argument('--pop',type=float,default=0);ap.add_argument('--kind',default='standard');\")\ns=s.replace(\"features=np.load(P/f'trends_{a.stage}.npy')\", \"features=np.load(P/(('discovery_trends_' if a.kind=='discovery' else 'trends_')+a.stage+'.npy'))\")\ns=s.replace(\"torch.tensor(features[:,3])*a.trend\", \"torch.tensor(features[:,3])*a.trend+torch.tensor(features[:,0])*a.pop\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint temporal_early.pt --name temporal_early_full --recent 1,10 --gamma 0,1,2 --pop 0.5,1,1.5,2 --kind discovery > /app/solution/temporal_early_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -5 /app/solution/temporal.log && tail -4 /app/solution/residual.log && cat /sys/fs/cgroup/memory.events","max_output_tokens":1000}));

Tool response +16:53

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"42c99c","wall_time_seconds":1.000882304,"session_id":51835,"original_token_count":0,"output":""}
{"chunk_id":"745fa1","wall_time_seconds":0.00000463,"exit_code":0,"original_token_count":104,"output":"{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 2, \"loss\": 7.75346826705612, \"seconds\": 394.9}\n{\"epoch\": 3, \"loss\": 8.319081174002754, \"seconds\": 74.5}\n{\"epoch\": 4, \"loss\": 8.115433507495457, \"seconds\": 99.8}\nVALID 4 0.017722988553957583\n{\"epoch\": 5, \"loss\": 7.941864569981893, \"seconds\": 135.4}\nlow 0\nhigh 0\nmax 2658\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n"}

Harness context token usage record

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Harness context codex item completed

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    "stdout": "",
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Agent message

+17:20

Thinking unavailable No text recorded

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

Agent message

+17:50

Thinking unavailable No text recorded

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

Agent message

+18:06

Thinking unavailable No text recorded

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

exec command

+18:08

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 +18:09

text(await tools.exec_command({cmd:"cat /app/solution/temporal_early_full.log && tail -5 /app/solution/temporal.log && tail -5 /app/solution/residual.log","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response +18:09

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"d693b5","wall_time_seconds":0.00000449,"exit_code":0,"original_token_count":187,"output":"BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05065161754707366}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05066583504721579}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05066583504721579}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 2, \"loss\": 7.75346826705612, \"seconds\": 394.9}\nVALID 0.032849423337956235\n{\"epoch\": 6, \"loss\": 7.785393476486206, \"seconds\": 156.9}\nVALID 6 0.017980392754496106\n{\"epoch\": 7, \"loss\": 7.651221460766262, \"seconds\": 188.9}\n{\"epoch\": 8, \"loss\": 7.542958736419678, \"seconds\": 208.4}\nVALID 8 0.019819653049694068\n"}
{"current_time":"2026-09-24 08:13:39 UTC"}

Harness context codex item completed

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}

Harness context token usage record

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exec command

+18:26

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 +18:51

text(await tools.exec_command({cmd:"cat > /app/solution/build_fast_history.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);parts=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[ok].copy();d['iid']=i[ok];age=(end-d.timestamp.to_numpy())/86400;d['value']=(.5+.5*d.is_organic.to_numpy())*np.exp(-age/7.)\n   parts.append(d.groupby(['uid','iid'],sort=False).value.sum().reset_index())\n d=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False).value.sum().reset_index();del parts\n keys=d.uid.to_numpy('int64')*N+d.iid.to_numpy();idx=pd.Index(keys);z=np.load(P/f'data_{stage}.npz');uids=np.repeat(z['uids'],np.diff(z['offsets']));targetkeys=uids.astype('int64')*N+z['ids'];pos=idx.get_indexer(targetkeys);out=np.zeros(len(pos),dtype='float32');ok=pos>=0;out[ok]=d.value.to_numpy()[pos[ok]]**.35;np.save(P/f'fast_recent_{stage}.npy',out);print(stage,flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/build_fast_history.py > /app/solution/fast_history.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"ap.add_argument('--checkpoint',required=True);\", \"ap.add_argument('--fast',action='store_true');ap.add_argument('--checkpoint',required=True);\")\ns=s.replace(\"z,mask,truth,idcg=truth_data();\", \"data.recent=np.load(P/'fast_recent_validation.npy') if a.fast else data.recent;z,mask,truth,idcg=truth_data();\")\ns=s.replace(\"'recent':recent,'like':like,'kind':kind\", \"'recent':recent,'like':like,'fast':a.fast,'kind':kind\")\nopen(p,'w').write(s)\np='/app/solution/infer.py';s=open(p).read().replace(\"ap.add_argument('--checkpoint',required=True);\", \"ap.add_argument('--fast',action='store_true');ap.add_argument('--checkpoint',required=True);\")\ns=s.replace(\"model=make_model(ck);\", \"data.recent=np.load(P/f'fast_recent_{a.stage}.npy') if a.fast else data.recent;model=make_model(ck);\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint temporal_early.pt --stage validation --recent 10 --trend 1 --pop 1 --kind discovery > /app/solution/current_validation.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +18:54

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"ebbf70","wall_time_seconds":1.000464847,"session_id":3350,"original_token_count":0,"output":""}
{"chunk_id":"0262ae","wall_time_seconds":1.00064741,"session_id":48585,"original_token_count":0,"output":""}

Harness context token usage record

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exec command

+19:08

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 +19:09

text(await tools.exec_command({cmd:"cat /app/solution/current_validation.log && tail -4 /app/solution/residual.log && tail -4 /app/solution/temporal.log && cat /app/solution/fast_history.log && cat /sys/fs/cgroup/memory.events","max_output_tokens":1600}));
text(await tools.clock__curr_time({}));

Tool response +19:09

Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"46f7c5","wall_time_seconds":0.00001884,"exit_code":0,"original_token_count":180,"output":"{\"output\": \"validation.parquet\", \"ndcg\": 0.0506658350472158, \"calibration\": {\"fast\": false, \"checkpoint\": \"temporal_early.pt\", \"stage\": \"validation\", \"recent\": 10.0, \"like\": 0, \"trend\": 1.0, \"pop\": 1.0, \"kind\": \"discovery\", \"output\": null, \"save_model\": null}}\n{\"epoch\": 9, \"loss\": 7.444763739903768, \"seconds\": 236.6}\n{\"epoch\": 10, \"loss\": 7.379031870100233, \"seconds\": 255.8}\nVALID 10 0.01852699736399627\n{\"epoch\": 11, \"loss\": 7.309679614173041, \"seconds\": 282.5}\n{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 2, \"loss\": 7.75346826705612, \"seconds\": 394.9}\nVALID 0.032849423337956235\nvalidation\nlow 0\nhigh 0\nmax 5008\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n"}
{"current_time":"2026-09-24 08:14:38 UTC"}

Harness context token usage record

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Harness context codex item completed

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Harness context codex item completed

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      "python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"ap.add_argument('--checkpoint',required=True);\", \"ap.add_argument('--fast',action='store_true');ap.add_argument('--checkpoint',required=True);\")\ns=s.replace(\"z,mask,truth,idcg=truth_data();\", \"data.recent=np.load(P/'fast_recent_validation.npy') if a.fast else data.recent;z,mask,truth,idcg=truth_data();\")\ns=s.replace(\"'recent':recent,'like':like,'kind':kind\", \"'recent':recent,'like':like,'fast':a.fast,'kind':kind\")\nopen(p,'w').write(s)\np='/app/solution/infer.py';s=open(p).read().replace(\"ap.add_argument('--checkpoint',required=True);\", \"ap.add_argument('--fast',action='store_true');ap.add_argument('--checkpoint',required=True);\")\ns=s.replace(\"model=make_model(ck);\", \"data.recent=np.load(P/f'fast_recent_{a.stage}.npy') if a.fast else data.recent;model=make_model(ck);\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint temporal_early.pt --stage validation --recent 10 --trend 1 --pop 1 --kind discovery > /app/solution/current_validation.log 2>&1"
    ],
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Harness context codex item completed

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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/build_fast_history.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data');items=pd.read_parquet(D/'candidate_catalog.parquet').item_id;ii=pd.Index(items);N=len(items)\nfor stage in ['validation','test']:\n end=24786800+(606600 if stage=='test' else 0);parts=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n  file=P/'catalog_train.parquet' if split=='train' else D/'val_listens.parquet'\n  for b in pq.ParquetFile(file).iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=(i>=0)&(d.played_ratio_pct.to_numpy()>=50);d=d[ok].copy();d['iid']=i[ok];age=(end-d.timestamp.to_numpy())/86400;d['value']=(.5+.5*d.is_organic.to_numpy())*np.exp(-age/7.)\n   parts.append(d.groupby(['uid','iid'],sort=False).value.sum().reset_index())\n d=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=False).value.sum().reset_index();del parts\n keys=d.uid.to_numpy('int64')*N+d.iid.to_numpy();idx=pd.Index(keys);z=np.load(P/f'data_{stage}.npz');uids=np.repeat(z['uids'],np.diff(z['offsets']));targetkeys=uids.astype('int64')*N+z['ids'];pos=idx.get_indexer(targetkeys);out=np.zeros(len(pos),dtype='float32');ok=pos>=0;out[ok]=d.value.to_numpy()[pos[ok]]**.35;np.save(P/f'fast_recent_{stage}.npy',out);print(stage,flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/build_fast_history.py > /app/solution/fast_history.log 2>&1"
    ],
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      {
        "type": "unknown",
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      "nanos": 29913044
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}

exec command

+19:13

Thinking unavailable No text recorded

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

Model text

The temporal model with first-discovery trends reached 0.050666 NDCG@10. I’ve generated its validation ranking and will check it with the supplied evaluator. Further tuning is focused on history recency and training duration before the final refit.

Tool callexecTool call Returned

Tool call · Input +19:20

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_current.json","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"cp /app/solution/temporal_best.pt /app/solution/temporal_middle.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint temporal_middle.pt --name temporal_fast --recent 1,3,10,30 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery --fast > /app/solution/temporal_fast.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +19:23

Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"47a578","wall_time_seconds":1.001095912,"session_id":23518,"original_token_count":0,"output":""}
{"chunk_id":"648c1a","wall_time_seconds":1.001053361,"session_id":85323,"original_token_count":0,"output":""}

Harness context token usage record

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Harness context codex item completed

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exec command

+19:42

Thinking unavailable No text recorded

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

Harness context codex item completed

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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/temporal_best.pt /app/solution/temporal_middle.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint temporal_middle.pt --name temporal_fast --recent 1,3,10,30 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery --fast > /app/solution/temporal_fast.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 29,
      "nanos": 616829263
    },
    "formatted_output": ""
  }
}

Tool callexecTool call Returned

Tool call · Input +20:03

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py';s=open(p).read();needle='def truth_data():'\nnew='''def sampled_retrieval_loss(model,args,positive_ids,negative_count,n_items,seen=None):\n \"\"\"Uniform sampled-softmax objective with memory independent of catalog size.\n\n Each query has one sampled positive and shares a bounded pool of negatives.\n This is used automatically beyond 100k items; embedding tables can be sharded.\n \"\"\"\n uv=model.user(*args);pos=torch.as_tensor(positive_ids,dtype=torch.long)\n neg=torch.randint(n_items,(negative_count,));iv=model.item(neg)\n positives=(uv*model.item(pos)).sum(1,keepdim=True)\n [email protected]\n negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\n return F.cross_entropy(torch.cat([positives,negative_logits],dim=1),torch.zeros(len(pos),dtype=torch.long))\n\n'''\ns=s.replace(needle,new+needle)\ns=s.replace(\"ap.add_argument('--variant',default='base');\", \"ap.add_argument('--negatives',type=int,default=0);ap.add_argument('--variant',default='base');\")\nold=\"rows=order[s:s+a.batch];args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\nnew='''rows=order[s:s+a.batch];sampled=a.negatives or (4096 if len(data.items)>100000 else 0);opt.zero_grad(set_to_none=True)\n   if sampled:\n    args=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative,target=False)\n    positive=[]\n    for r in rows:\n     sl=slice(data.offsets[r],data.offsets[r+1]);prob=data.targets[sl].astype('float64');prob=prob/prob.sum() if prob.sum()>0 else np.ones(len(prob))/len(prob);positive.append(np.random.choice(data.ids[sl],p=prob))\n    loss=sampled_retrieval_loss(model,args,positive,sampled,len(data.items))\n   else:\n    args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean()\n   loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())'''\nassert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py\ncat /app/solution/public_current.json\ncat /app/solution/fast_history.log\ncat /app/solution/temporal_fast.log\ntail -5 /app/solution/residual.log","max_output_tokens":1500}));

Tool response +20:03

Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"e16e80","wall_time_seconds":0.087314449,"exit_code":0,"original_token_count":258,"output":"{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.050666,\n    \"quality_users\": 4263\n  }\n}\nvalidation\ntest\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nVALID 10 0.01852699736399627\n{\"epoch\": 11, \"loss\": 7.309679614173041, \"seconds\": 282.5}\n{\"epoch\": 12, \"loss\": 7.266627589861552, \"seconds\": 305.4}\nVALID 12 0.01776220263358523\n{\"epoch\": 13, \"loss\": 7.228523850440979, \"seconds\": 335.2}\n"}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-05f11cf7-1f81-47cc-90f0-860386efe036",
    "process_id": "55691",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/train.py';s=open(p).read();needle='def truth_data():'\nnew='''def sampled_retrieval_loss(model,args,positive_ids,negative_count,n_items,seen=None):\n \"\"\"Uniform sampled-softmax objective with memory independent of catalog size.\n\n Each query has one sampled positive and shares a bounded pool of negatives.\n This is used automatically beyond 100k items; embedding tables can be sharded.\n \"\"\"\n uv=model.user(*args);pos=torch.as_tensor(positive_ids,dtype=torch.long)\n neg=torch.randint(n_items,(negative_count,));iv=model.item(neg)\n positives=(uv*model.item(pos)).sum(1,keepdim=True)\n [email protected]\n negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\n return F.cross_entropy(torch.cat([positives,negative_logits],dim=1),torch.zeros(len(pos),dtype=torch.long))\n\n'''\ns=s.replace(needle,new+needle)\ns=s.replace(\"ap.add_argument('--variant',default='base');\", \"ap.add_argument('--negatives',type=int,default=0);ap.add_argument('--variant',default='base');\")\nold=\"rows=order[s:s+a.batch];args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\nnew='''rows=order[s:s+a.batch];sampled=a.negatives or (4096 if len(data.items)>100000 else 0);opt.zero_grad(set_to_none=True)\n   if sampled:\n    args=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative,target=False)\n    positive=[]\n    for r in rows:\n     sl=slice(data.offsets[r],data.offsets[r+1]);prob=data.targets[sl].astype('float64');prob=prob/prob.sum() if prob.sum()>0 else np.ones(len(prob))/len(prob);positive.append(np.random.choice(data.ids[sl],p=prob))\n    loss=sampled_retrieval_loss(model,args,positive,sampled,len(data.items))\n   else:\n    args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean()\n   loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())'''\nassert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py\ncat /app/solution/public_current.json\ncat /app/solution/fast_history.log\ncat /app/solution/temporal_fast.log\ntail -5 /app/solution/residual.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/train.py';s=open(p).read();needle='def truth_data():'\nnew='''def sampled_retrieval_loss(model,args,positive_ids,negative_count,n_items,seen=None):\n \"\"\"Uniform sampled-softmax objective with memory independent of catalog size.\n\n Each query has one sampled positive and shares a bounded pool of negatives.\n This is used automatically beyond 100k items; embedding tables can be sharded.\n \"\"\"\n uv=model.user(*args);pos=torch.as_tensor(positive_ids,dtype=torch.long)\n neg=torch.randint(n_items,(negative_count,));iv=model.item(neg)\n positives=(uv*model.item(pos)).sum(1,keepdim=True)\n [email protected]\n negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\n return F.cross_entropy(torch.cat([positives,negative_logits],dim=1),torch.zeros(len(pos),dtype=torch.long))\n\n'''\ns=s.replace(needle,new+needle)\ns=s.replace(\"ap.add_argument('--variant',default='base');\", \"ap.add_argument('--negatives',type=int,default=0);ap.add_argument('--variant',default='base');\")\nold=\"rows=order[s:s+a.batch];args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\nnew='''rows=order[s:s+a.batch];sampled=a.negatives or (4096 if len(data.items)>100000 else 0);opt.zero_grad(set_to_none=True)\n   if sampled:\n    args=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative,target=False)\n    positive=[]\n    for r in rows:\n     sl=slice(data.offsets[r],data.offsets[r+1]);prob=data.targets[sl].astype('float64');prob=prob/prob.sum() if prob.sum()>0 else np.ones(len(prob))/len(prob);positive.append(np.random.choice(data.ids[sl],p=prob))\n    loss=sampled_retrieval_loss(model,args,positive,sampled,len(data.items))\n   else:\n    args,y=data.batch(rows,drop=a.drop,recent=a.recent,like=a.like,negative=a.negative);logits=model(*args,allitems);loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean()\n   loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())'''\nassert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py\ncat /app/solution/public_current.json\ncat /app/solution/fast_history.log\ncat /app/solution/temporal_fast.log\ntail -5 /app/solution/residual.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.050666,\n    \"quality_users\": 4263\n  }\n}\nvalidation\ntest\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nVALID 10 0.01852699736399627\n{\"epoch\": 11, \"loss\": 7.309679614173041, \"seconds\": 282.5}\n{\"epoch\": 12, \"loss\": 7.266627589861552, \"seconds\": 305.4}\nVALID 12 0.01776220263358523\n{\"epoch\": 13, \"loss\": 7.228523850440979, \"seconds\": 335.2}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.050666,\n    \"quality_users\": 4263\n  }\n}\nvalidation\ntest\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nVALID 10 0.01852699736399627\n{\"epoch\": 11, \"loss\": 7.309679614173041, \"seconds\": 282.5}\n{\"epoch\": 12, \"loss\": 7.266627589861552, \"seconds\": 305.4}\nVALID 12 0.01776220263358523\n{\"epoch\": 13, \"loss\": 7.228523850440979, \"seconds\": 335.2}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 87315269
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.050666,\n    \"quality_users\": 4263\n  }\n}\nvalidation\ntest\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": true, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.050200007150460337}\nVALID 10 0.01852699736399627\n{\"epoch\": 11, \"loss\": 7.309679614173041, \"seconds\": 282.5}\n{\"epoch\": 12, \"loss\": 7.266627589861552, \"seconds\": 305.4}\nVALID 12 0.01776220263358523\n{\"epoch\": 13, \"loss\": 7.228523850440979, \"seconds\": 335.2}\n"
  }
}

exec command

+20:23

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +20:29

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/train.py' in args and b'residual' in args and b'--epochs' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped saturated residual pretraining',p.name)\nPY\ncp /app/solution/residual_best.pt /app/solution/residual_pretrained.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 3 --lr .0003 --recent 1 --name residual_temporal > /app/solution/residual_temporal.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/build_temporal.py --stage test --weeks 16 > /app/solution/build_temporal_test.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +20:32

Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"405cf0","wall_time_seconds":1.000419515,"session_id":50068,"original_token_count":11,"output":"Stopped saturated residual pretraining 9975\n"}
{"chunk_id":"2dec49","wall_time_seconds":1.001224019,"session_id":94617,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:16:01 UTC"}

Harness context token usage record

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  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
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    "total_tokens": 1930934
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-979c2db3-48da-413a-85e9-dbde5668bef8",
    "process_id": "66114",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except (OSError,PermissionError):continue\n if b'/app/solution/train.py' in args and b'--epochs' in args and b'40' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped saturated baseline training',p.name)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --variant residual --epochs 30 --eval-every 2 --name residual --drop .6 > /app/solution/residual.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except (OSError,PermissionError):continue\n if b'/app/solution/train.py' in args and b'--epochs' in args and b'40' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped saturated baseline training',p.name)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --variant residual --epochs 30 --eval-every 2 --name residual --drop .6 > /app/solution/residual.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Stopped saturated baseline training 9608\n",
    "stderr": "",
    "aggregated_output": "Stopped saturated baseline training 9608\n",
    "exit_code": 143,
    "duration": {
      "secs": 379,
      "nanos": 443686673
    },
    "formatted_output": "Stopped saturated baseline training 9608\n"
  }
}

exec command

+20:38

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 +20:42

text(await tools.exec_command({cmd:"TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint temporal_early.pt --stage test --recent 10 --trend 1 --pop 1 --kind discovery > /app/solution/provisional.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -6 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.events","max_output_tokens":1200}));

Tool response +20:43

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"ac198b","wall_time_seconds":1.000983485,"session_id":66492,"original_token_count":0,"output":""}
{"chunk_id":"ed9534","wall_time_seconds":0.00000518,"exit_code":0,"original_token_count":84,"output":"{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 2, \"loss\": 7.75346826705612, \"seconds\": 394.9}\nVALID 0.032849423337956235\n{\"epoch\": 3, \"loss\": 7.386777749582499, \"seconds\": 562.1}\nVALID 0.0318980950562073\n7344472064\nlow 0\nhigh 0\nmax 5743\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n"}

Harness context token usage record

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Harness context codex item completed

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      "tail -6 /app/solution/temporal.log && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.events"
    ],
    "cwd": "file:///app",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 8.293543871711282, \"seconds\": 215.4}\nVALID 0.030180234050444947\n{\"epoch\": 2, \"loss\": 7.75346826705612, \"seconds\": 394.9}\nVALID 0.032849423337956235\n{\"epoch\": 3, \"loss\": 7.386777749582499, \"seconds\": 562.1}\nVALID 0.0318980950562073\n7344472064\nlow 0\nhigh 0\nmax 5743\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n",
    "stderr": "",
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    "exit_code": 0,
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      "secs": 0,
      "nanos": 5180
    },
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Harness context codex item completed

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      "-lc",
      "TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint temporal_early.pt --stage test --recent 10 --trend 1 --pop 1 --kind discovery > /app/solution/provisional.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint temporal_early.pt --stage test --recent 10 --trend 1 --pop 1 --kind discovery > /app/solution/provisional.log 2>&1"
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  }
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Agent message

+21:05

Thinking unavailable No text recorded

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

Agent message

+21:23

Thinking unavailable No text recorded

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

exec command

+21:34

Thinking unavailable No text recorded

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

Model text

The supplied evaluator confirmed 0.050666 NDCG@10. A much shorter history decay did not improve the model, so I’m keeping the longer recency signal. The saved submission remains valid while I finish comparing models and prepare the train-plus-validation refit.

Tool callexecTool call Returned

Tool call · Input +21:46

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py';s=open(p).read();old=\"h=self.history(ids,offsets,per_sample_weights=weights)\\n  h=self.net(h)\\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\";new=\"h=self.history(ids,offsets,per_sample_weights=weights)\\n  return self.encode_pooled(h)\\n def encode_pooled(self,h):\\n  h=self.net(h)\\n  return torch.cat([h,torch.ones((len(h),1),device=h.device)],dim=1)\";assert old in s;s=s.replace(old,new)\nold=\"def forward(self,ids,offsets,weights):\\n  h=self.history(ids,offsets,per_sample_weights=weights)\\n  h=self.net(h)+self.skip_scale*F.layer_norm(h,(h.shape[1],))\";new=\"def encode_pooled(self,h):\\n  h=self.net(h)+self.skip_scale*F.layer_norm(h,(h.shape[1],))\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint temporal_middle.pt --name temporal_preferences --recent 1,10 --like 0,3,10 --gamma 0.5,1,1.5 --pop 1 --kind discovery > /app/solution/temporal_preferences.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/build_temporal_test.log && tail -5 /app/solution/temporal.log && tail -3 /app/solution/residual_temporal.log && cat /sys/fs/cgroup/memory.events","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response +21:48

Script completed
Wall time 1.4 seconds
Output:

{"chunk_id":"85ceeb","wall_time_seconds":1.001184875,"session_id":2881,"original_token_count":0,"output":""}
{"chunk_id":"6f6a9d","wall_time_seconds":0.00000507,"exit_code":0,"original_token_count":104,"output":"users 6000 examples 39442 seconds 48\nusers 7000 examples 46119 seconds 55\nusers 8000 examples 52575 seconds 62\nusers 9000 examples 59282 seconds 69\nVALID 0.030180234050444947\n{\"epoch\": 2, \"loss\": 7.75346826705612, \"seconds\": 394.9}\nVALID 0.032849423337956235\n{\"epoch\": 3, \"loss\": 7.386777749582499, \"seconds\": 562.1}\nVALID 0.0318980950562073\nlow 0\nhigh 0\nmax 7777\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n"}
{"current_time":"2026-09-24 08:17:17 UTC"}

Harness context token usage record

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Harness context codex item completed

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    ],
    "source": "unified_exec_startup",
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    "stderr": "",
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Harness context codex item completed

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Agent message

+22:07

Thinking unavailable No text recorded

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

Harness context codex item completed

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Agent message

+22:27

Thinking unavailable No text recorded

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

exec command

+22:34

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +22:35

text(await tools.exec_command({cmd:"cat /app/solution/temporal_preferences.log && tail -5 /app/solution/temporal.log && tail -4 /app/solution/residual_temporal.log && tail -3 /app/solution/build_temporal_test.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response +22:35

Script completed
Wall time 0.4 seconds
Output:

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{"current_time":"2026-09-24 08:18:05 UTC"}

Harness context token usage record

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Harness context codex item completed

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    "stdout": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04990043109042937}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04990043109042937}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.04990043109042937}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.5, \"pop\": 1.0, \"ndcg\": 0.05005740466491228}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 3.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.5, \"pop\": 1.0, \"ndcg\": 0.05006523751173192}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 10.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.5, \"pop\": 1.0, \"ndcg\": 0.05049184396183805}\nBEST {\"recent\": 10.0, \"like\": 10.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.5, \"pop\": 1.0, \"ndcg\": 0.05049184396183805}\nVALID 0.032849423337956235\n{\"epoch\": 3, \"loss\": 7.386777749582499, \"seconds\": 562.1}\nVALID 0.0318980950562073\n{\"epoch\": 4, \"loss\": 7.05925215793257, \"seconds\": 730.3}\nVALID 0.03081036648437769\nusers 8000 examples 52575 seconds 62\nusers 9000 examples 59282 seconds 69\ndone test examples 65496 pairs 40501155 targets 618322 seconds 78.82482361793518\n6205833216\n",
    "stderr": "",
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    "exit_code": 0,
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  }
}

exec command

+22:46

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +22:56

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/temporal_train.py' in args and b'--name' in args and b'temporal' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped temporal run after validation decline',p.name)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution');from train import P\nx=torch.load(P/'temporal_early.pt',weights_only=False);y=torch.load(P/'temporal_middle.pt',weights_only=False)\nx['model']={k:.5*v+.5*y['model'][k] for k,v in x['model'].items()};x['averaged_epochs']=[1,2];torch.save(x,P/'temporal_average.pt')\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint temporal_average.pt --name average_full --recent 1,10 --like 0,3 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/average_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +22:57

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"402a35","wall_time_seconds":1.000764675,"session_id":84168,"original_token_count":13,"output":"Stopped temporal run after validation decline 9831\n"}

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Agent message

+23:21

Thinking unavailable No text recorded

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

Harness context codex item completed

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exec command

+23:28

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +23:33

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"ap.add_argument('--stage',default='validation');\", \"ap.add_argument('--weeks',type=int,default=0);ap.add_argument('--stage',default='validation');\")\ns=s.replace(\"order=np.random.permutation(len(data.uids));order=order[:a.limit] if a.limit else order;losses=[]\", \"eligible=np.flatnonzero(data.cuts>=data.cuts.max()-(a.weeks-1)*604800) if a.weeks else np.arange(len(data.uids));order=np.random.permutation(eligible);order=order[:a.limit] if a.limit else order;losses=[]\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume early_model.pt --epochs 3 --lr .0003 --recent 1 --weeks 4 --name temporal_recent > /app/solution/temporal_recent.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -5 /app/solution/residual_temporal.log && cat /app/solution/average_full.log","max_output_tokens":1400}));
text(await tools.clock__curr_time({}));

Tool response +23:34

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"6e66b7","wall_time_seconds":1.001247211,"session_id":70091,"original_token_count":0,"output":""}
{"chunk_id":"e4d3c3","wall_time_seconds":0.00000718,"exit_code":0,"original_token_count":184,"output":"{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05101510075872349}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05101510075872349}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05101510075872349}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05101510075872349}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05101510075872349}\n"}
{"current_time":"2026-09-24 08:19:03 UTC"}

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exec command

+23:37

Thinking unavailable No text recorded

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

Model text

Averaging the first two temporal checkpoints improved validation to 0.0510 NDCG@10 with the same vector size and inference cost. I’m testing one final training variation that emphasizes the latest historical weeks, then I’ll refit the selected configuration on train plus validation.

Tool callexecTool call Returned

Tool call · Input +23:43

text(await tools.exec_command({cmd:"cp /app/solution/residual_temporal_best.pt /app/solution/residual_temporal_early.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint residual_temporal_early.pt --name residual_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/residual_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +23:44

Script completed
Wall time 1.3 seconds
Output:

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      "cp /app/solution/residual_temporal_best.pt /app/solution/residual_temporal_early.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint residual_temporal_early.pt --name residual_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/residual_full.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/residual_temporal_best.pt /app/solution/residual_temporal_early.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint residual_temporal_early.pt --name residual_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/residual_full.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 17,
      "nanos": 575454818
    },
    "formatted_output": ""
  }
}

Agent message

+24:03

Thinking unavailable No text recorded

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

Agent message

+24:20

Thinking unavailable No text recorded

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

Agent message

+24:36

Thinking unavailable No text recorded

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

exec command

+24:39

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +24:40

text(await tools.exec_command({cmd:"cat /app/solution/residual_full.log && tail -4 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response +24:40

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"01abda","wall_time_seconds":0.0000057,"exit_code":0,"original_token_count":133,"output":"BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n"}
{"current_time":"2026-09-24 08:20:10 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0571a8ea-87d7-4a1a-bfeb-48fbc1d7f7f1",
    "process_id": "63055",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/residual_full.log && tail -4 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /app/solution/residual_full.log && tail -4 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n",
    "stderr": "",
    "aggregated_output": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5700
    },
    "formatted_output": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\nBEST {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05338674176427629}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4dd025bd487d2a996d064eae703d3",
  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 1776,
    "reasoning_output_tokens": 1704,
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  },
  "turn_token_usage": {
    "input_tokens": 2394327,
    "cached_input_tokens": 2294656,
    "cache_write_input_tokens": 0,
    "output_tokens": 41815,
    "reasoning_output_tokens": 23572,
    "total_tokens": 2436142
  },
  "thread_token_usage": {
    "input_tokens": 2394327,
    "cached_input_tokens": 2294656,
    "cache_write_input_tokens": 0,
    "output_tokens": 41815,
    "reasoning_output_tokens": 23572,
    "total_tokens": 2436142
  }
}

exec command

+24:59

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 +25:20

text(await tools.exec_command({cmd:"cat > /app/solution/compare_ensemble.py <<'PY'\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--first',required=True);ap.add_argument('--second',required=True);ap.add_argument('--name',default='ensemble');ap.add_argument('--recent',type=float,default=1);a=ap.parse_args();ck1=torch.load(P/a.first,weights_only=False);ck2=torch.load(P/a.second,weights_only=False);m1=make_model(ck1).eval();m2=make_model(ck2).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();columns=pd.Index(data.items).get_indexer(z['item_ids']);ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));f=torch.tensor(np.load(P/'discovery_trends_validation.npy')[columns]);cfg=[{'first_weight':w,'trend':g,'pop':p,'recent':a.recent} for w in [0,.25,.5,.75,1] for g in [.5,1,1.5] for p in [.5,1,1.5]];sums=np.zeros(len(cfg))\n with torch.no_grad():\n  iv1=m1.item(torch.tensor(columns));iv2=m2.item(torch.tensor(columns))\n  for s in range(0,len(ur),128):\n   row=ur[s:s+128];args=data.batch([data.index.get(int(z['uids'][u]),-1) for u in row],recent=a.recent,target=False);s1=m1.user(*args)@iv1.T;s2=m2.user(*args)@iv2.T\n   for j,c in enumerate(cfg):\n    scores=c['first_weight']*s1+(1-c['first_weight'])*s2+c['trend']*f[:,3]+c['pop']*f[:,0];scores.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf);ranks=scores.topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur))\n for w in [0,.25,.5,.75,1]:print('WEIGHT',w,json.dumps(max([c for c in cfg if c['first_weight']==w],key=lambda c:c['ndcg'])),flush=True)\n best=max(cfg,key=lambda c:c['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'first':a.first,'second':a.second,'best':best,'results':cfg},indent=2))\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/compare_ensemble.py --first temporal_average.pt --second residual_temporal_early.pt --name combined > /app/solution/combined.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -6 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log","max_output_tokens":1000}));

Tool response +25:21

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"2745a6","wall_time_seconds":1.001465234,"session_id":44095,"original_token_count":0,"output":""}
{"chunk_id":"6082c4","wall_time_seconds":0.00000576,"exit_code":0,"original_token_count":42,"output":"{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n"}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4dd3a602087d292313dfbbec5b8d7",
  "usage": {
    "input_tokens": 76554,
    "cached_input_tokens": 74240,
    "cache_write_input_tokens": 0,
    "output_tokens": 1256,
    "reasoning_output_tokens": 505,
    "total_tokens": 77810
  },
  "turn_token_usage": {
    "input_tokens": 2470881,
    "cached_input_tokens": 2368896,
    "cache_write_input_tokens": 0,
    "output_tokens": 43071,
    "reasoning_output_tokens": 24077,
    "total_tokens": 2513952
  },
  "thread_token_usage": {
    "input_tokens": 2470881,
    "cached_input_tokens": 2368896,
    "cache_write_input_tokens": 0,
    "output_tokens": 43071,
    "reasoning_output_tokens": 24077,
    "total_tokens": 2513952
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-b77b51b5-75c1-41e4-a22e-b67b3d940c42",
    "process_id": "78928",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -6 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -6 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5760
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n"
  }
}

Agent message

+25:41

Thinking unavailable No text recorded

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

Agent message

+25:58

Thinking unavailable No text recorded

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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f8c082bd-35aa-4ad4-9515-5dcf88089381",
    "process_id": "44095",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/compare_ensemble.py <<'PY'\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--first',required=True);ap.add_argument('--second',required=True);ap.add_argument('--name',default='ensemble');ap.add_argument('--recent',type=float,default=1);a=ap.parse_args();ck1=torch.load(P/a.first,weights_only=False);ck2=torch.load(P/a.second,weights_only=False);m1=make_model(ck1).eval();m2=make_model(ck2).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();columns=pd.Index(data.items).get_indexer(z['item_ids']);ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));f=torch.tensor(np.load(P/'discovery_trends_validation.npy')[columns]);cfg=[{'first_weight':w,'trend':g,'pop':p,'recent':a.recent} for w in [0,.25,.5,.75,1] for g in [.5,1,1.5] for p in [.5,1,1.5]];sums=np.zeros(len(cfg))\n with torch.no_grad():\n  iv1=m1.item(torch.tensor(columns));iv2=m2.item(torch.tensor(columns))\n  for s in range(0,len(ur),128):\n   row=ur[s:s+128];args=data.batch([data.index.get(int(z['uids'][u]),-1) for u in row],recent=a.recent,target=False);s1=m1.user(*args)@iv1.T;s2=m2.user(*args)@iv2.T\n   for j,c in enumerate(cfg):\n    scores=c['first_weight']*s1+(1-c['first_weight'])*s2+c['trend']*f[:,3]+c['pop']*f[:,0];scores.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf);ranks=scores.topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur))\n for w in [0,.25,.5,.75,1]:print('WEIGHT',w,json.dumps(max([c for c in cfg if c['first_weight']==w],key=lambda c:c['ndcg'])),flush=True)\n best=max(cfg,key=lambda c:c['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'first':a.first,'second':a.second,'best':best,'results':cfg},indent=2))\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/compare_ensemble.py --first temporal_average.pt --second residual_temporal_early.pt --name combined > /app/solution/combined.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/compare_ensemble.py <<'PY'\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--first',required=True);ap.add_argument('--second',required=True);ap.add_argument('--name',default='ensemble');ap.add_argument('--recent',type=float,default=1);a=ap.parse_args();ck1=torch.load(P/a.first,weights_only=False);ck2=torch.load(P/a.second,weights_only=False);m1=make_model(ck1).eval();m2=make_model(ck2).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();columns=pd.Index(data.items).get_indexer(z['item_ids']);ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));f=torch.tensor(np.load(P/'discovery_trends_validation.npy')[columns]);cfg=[{'first_weight':w,'trend':g,'pop':p,'recent':a.recent} for w in [0,.25,.5,.75,1] for g in [.5,1,1.5] for p in [.5,1,1.5]];sums=np.zeros(len(cfg))\n with torch.no_grad():\n  iv1=m1.item(torch.tensor(columns));iv2=m2.item(torch.tensor(columns))\n  for s in range(0,len(ur),128):\n   row=ur[s:s+128];args=data.batch([data.index.get(int(z['uids'][u]),-1) for u in row],recent=a.recent,target=False);s1=m1.user(*args)@iv1.T;s2=m2.user(*args)@iv2.T\n   for j,c in enumerate(cfg):\n    scores=c['first_weight']*s1+(1-c['first_weight'])*s2+c['trend']*f[:,3]+c['pop']*f[:,0];scores.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf);ranks=scores.topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur))\n for w in [0,.25,.5,.75,1]:print('WEIGHT',w,json.dumps(max([c for c in cfg if c['first_weight']==w],key=lambda c:c['ndcg'])),flush=True)\n best=max(cfg,key=lambda c:c['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'first':a.first,'second':a.second,'best':best,'results':cfg},indent=2))\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/compare_ensemble.py --first temporal_average.pt --second residual_temporal_early.pt --name combined > /app/solution/combined.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 43,
      "nanos": 977871722
    },
    "formatted_output": ""
  }
}

exec command

+26:11

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +26:12

text(await tools.exec_command({cmd:"cat /app/solution/combined.log && tail -6 /app/solution/temporal_recent.log && tail -6 /app/solution/residual_temporal.log","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +26:12

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"936689","wall_time_seconds":0.0000048,"exit_code":0,"original_token_count":205,"output":"WEIGHT 0 {\"first_weight\": 0, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05338674176427629}\nWEIGHT 0.25 {\"first_weight\": 0.25, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05377494097838699}\nWEIGHT 0.5 {\"first_weight\": 0.5, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05329084197440075}\nWEIGHT 0.75 {\"first_weight\": 0.75, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.052162433845659696}\nWEIGHT 1 {\"first_weight\": 1, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05101510075872349}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 2, \"loss\": 7.415898456292994, \"seconds\": 103.0}\nVALID 0.037397337178176344\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n{\"epoch\": 2, \"loss\": 7.417915165925226, \"seconds\": 296.0}\nVALID 0.033804506878396014\n"}
{"current_time":"2026-09-24 08:21:42 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7a9122f8-8849-4b41-a159-9f228b8e3e68",
    "process_id": "59406",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/combined.log && tail -6 /app/solution/temporal_recent.log && tail -6 /app/solution/residual_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /app/solution/combined.log && tail -6 /app/solution/temporal_recent.log && tail -6 /app/solution/residual_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "WEIGHT 0 {\"first_weight\": 0, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05338674176427629}\nWEIGHT 0.25 {\"first_weight\": 0.25, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05377494097838699}\nWEIGHT 0.5 {\"first_weight\": 0.5, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05329084197440075}\nWEIGHT 0.75 {\"first_weight\": 0.75, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.052162433845659696}\nWEIGHT 1 {\"first_weight\": 1, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05101510075872349}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 2, \"loss\": 7.415898456292994, \"seconds\": 103.0}\nVALID 0.037397337178176344\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n{\"epoch\": 2, \"loss\": 7.417915165925226, \"seconds\": 296.0}\nVALID 0.033804506878396014\n",
    "stderr": "",
    "aggregated_output": "WEIGHT 0 {\"first_weight\": 0, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05338674176427629}\nWEIGHT 0.25 {\"first_weight\": 0.25, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05377494097838699}\nWEIGHT 0.5 {\"first_weight\": 0.5, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05329084197440075}\nWEIGHT 0.75 {\"first_weight\": 0.75, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.052162433845659696}\nWEIGHT 1 {\"first_weight\": 1, \"trend\": 1, \"pop\": 1, \"recent\": 1, \"ndcg\": 0.05101510075872349}\n{\"epoch\": 1, \"loss\": 8.131251349168664, \"seconds\": 46.4}\nVALID 0.03690028863236543\n{\"epoch\": 2, \"loss\": 7.415898456292994, \"seconds\": 103.0}\nVALID 0.037397337178176344\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n{\"epoch\": 2, \"loss\": 7.417915165925226, \"seconds\": 296.0}\nVALID 0.033804506878396014\n",
    "exit_code": 0,
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      "secs": 0,
      "nanos": 4800
    },
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exec command

+26:27

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 model with a direct history residual reached 0.053387 NDCG@10. A two-model blend gained only 0.000388 while doubling vector width, so I’m keeping the 601-dimensional model for lower retrieval cost. I’ll compare the remaining checkpoints and refit from all observed events.

Tool callexecTool call Returned

Tool call · Input +26:40

text(await tools.exec_command({cmd:"cp /app/solution/temporal_recent_best.pt /app/solution/recent_middle.pt\ncp /app/solution/residual_temporal_best.pt /app/solution/residual_temporal_middle.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution');from train import P\nx=torch.load(P/'residual_temporal_early.pt',weights_only=False);y=torch.load(P/'residual_temporal_middle.pt',weights_only=False)\nx['model']={k:.5*v+.5*y['model'][k] for k,v in x['model'].items()};x['averaged_epochs']=[1,2];torch.save(x,P/'residual_average.pt')\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint residual_average.pt --name residual_average_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/residual_average_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint recent_middle.pt --name recent_full --recent 1,10 --gamma 0,0.5,1 --pop 0,0.5,1 --kind discovery > /app/solution/recent_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response +26:42

Script completed
Wall time 2.4 seconds
Output:

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{"chunk_id":"1b2a52","wall_time_seconds":1.000897877,"session_id":76804,"original_token_count":0,"output":""}

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Harness context codex item completed

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Harness context codex item completed

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exec command

+27:01

Thinking unavailable No text recorded

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

Harness context codex item completed

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Tool callexecTool call Returned

Tool call · Input +27:10

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace(\"evaluate(model,data,'test',a.recent,a.like,a.negative,'submission.parquet');torch.save\", \"evaluate(model,data,'test',a.recent,a.like,a.negative,f'{a.name}_test.parquet');torch.save\");open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"else 'submission.parquet');print('VALID'\", \"else f'{a.name}_test.parquet');print('VALID'\");open(p,'w').write(s)\np='/app/solution/build_data.py';s=open(p).read().replace('608400 if final','606600 if final');open(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint residual_temporal_early.pt --stage test --recent 1 --trend 1 --pop 1 --kind discovery > /app/solution/provisional.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/residual_average_full.log && tail -4 /app/solution/recent_full.log && tail -4 /app/solution/temporal_recent.log && tail -4 /app/solution/residual_temporal.log","max_output_tokens":1400}));
text(await tools.clock__curr_time({}));

Tool response +27:11

Script completed
Wall time 1.2 seconds
Output:

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{"current_time":"2026-09-24 08:22:40 UTC"}

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      "nanos": 5270
    },
    "formatted_output": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05197564917743574}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05225162400325108}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.05225162400325108}\nBEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.5, \"pop\": 1.0, \"ndcg\": 0.04867569252456749}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.048868489941013865}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.0, \"ndcg\": 0.048868489941013865}\n{\"epoch\": 2, \"loss\": 7.415898456292994, \"seconds\": 103.0}\nVALID 0.037397337178176344\n{\"epoch\": 3, \"loss\": 6.986491518862107, \"seconds\": 166.4}\nVALID 0.03560413716093178\n{\"epoch\": 1, \"loss\": 8.170847654342651, \"seconds\": 159.0}\nVALID 0.0334073315176429\n{\"epoch\": 2, \"loss\": 7.417915165925226, \"seconds\": 296.0}\nVALID 0.033804506878396014\n"
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-80b7bd31-dd3b-4f33-a340-60f997db9700",
    "process_id": "52859",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace(\"evaluate(model,data,'test',a.recent,a.like,a.negative,'submission.parquet');torch.save\", \"evaluate(model,data,'test',a.recent,a.like,a.negative,f'{a.name}_test.parquet');torch.save\");open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"else 'submission.parquet');print('VALID'\", \"else f'{a.name}_test.parquet');print('VALID'\");open(p,'w').write(s)\np='/app/solution/build_data.py';s=open(p).read().replace('608400 if final','606600 if final');open(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint residual_temporal_early.pt --stage test --recent 1 --trend 1 --pop 1 --kind discovery > /app/solution/provisional.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace(\"evaluate(model,data,'test',a.recent,a.like,a.negative,'submission.parquet');torch.save\", \"evaluate(model,data,'test',a.recent,a.like,a.negative,f'{a.name}_test.parquet');torch.save\");open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"else 'submission.parquet');print('VALID'\", \"else f'{a.name}_test.parquet');print('VALID'\");open(p,'w').write(s)\np='/app/solution/build_data.py';s=open(p).read().replace('608400 if final','606600 if final');open(p,'w').write(s)\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint residual_temporal_early.pt --stage test --recent 1 --trend 1 --pop 1 --kind discovery > /app/solution/provisional.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 10,
      "nanos": 416240167
    },
    "formatted_output": ""
  }
}

Agent message

+27:28

Thinking unavailable No text recorded

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

Agent message

+27:44

Thinking unavailable No text recorded

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

exec command

+27:49

Thinking unavailable No text recorded

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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-aa89af72-861b-4de6-8cc4-bc318efcaf2d",
    "process_id": "50068",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/train.py' in args and b'residual' in args and b'--epochs' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped saturated residual pretraining',p.name)\nPY\ncp /app/solution/residual_best.pt /app/solution/residual_pretrained.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 3 --lr .0003 --recent 1 --name residual_temporal > /app/solution/residual_temporal.log 2>&1"
    ],
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    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/train.py' in args and b'residual' in args and b'--epochs' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped saturated residual pretraining',p.name)\nPY\ncp /app/solution/residual_best.pt /app/solution/residual_pretrained.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 3 --lr .0003 --recent 1 --name residual_temporal > /app/solution/residual_temporal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Stopped saturated residual pretraining 9975\n",
    "stderr": "",
    "aggregated_output": "Stopped saturated residual pretraining 9975\n",
    "exit_code": 0,
    "duration": {
      "secs": 444,
      "nanos": 825060189
    },
    "formatted_output": "Stopped saturated residual pretraining 9975\n"
  }
}

Tool callexecTool call Returned

Tool call · Input +28:07

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/temporal_train.py' in args and b'--name' in args and b'residual_temporal' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped comparison run',p.name)\np='/app/solution/train.py';s=open(p).read().replace(\"ap.add_argument('--negatives',type=int,default=0);\", \"ap.add_argument('--keep-every',type=int,default=0);ap.add_argument('--negatives',type=int,default=0);\")\ns=s.replace(\"torch.save(checkpoint,P/f'{a.name}_last.pt')\", \"torch.save(checkpoint,P/f'{a.name}_last.pt')\\n   if a.keep_every and epoch%a.keep_every==0:torch.save(checkpoint,P/f'{a.name}_epoch{epoch}.pt')\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --variant residual --dim 1024 --latent 256 --epochs 10 --eval-every 2 --keep-every 2 --name wide --drop .6 > /app/solution/wide.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/build_data.py test > /app/solution/build_test.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --stage test --variant residual --epochs 8 --eval-every 8 --name final_pretrain --drop .6 > /app/solution/final_pretrain.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --resume final_pretrain_best.pt --epochs 1 --lr .0003 --recent 1 --name final_refit > /app/solution/final_refit.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 1 --trend 1 --pop 1 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +28:09

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"1930d2","wall_time_seconds":1.000416421,"session_id":19639,"original_token_count":0,"output":""}
{"chunk_id":"494a53","wall_time_seconds":1.001237745,"session_id":22932,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:23:39 UTC"}

Harness context token usage record

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Agent message

+28:29

Thinking unavailable No text recorded

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

Agent message

+28:48

Thinking unavailable No text recorded

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

exec command

+28:57

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 selected 601-dimensional model is now being refit from scratch on train plus validation: eight denoising epochs followed by one temporal epoch. I’m preserving the best submission during the refit and checking a wider model to see whether its quality gain would justify the extra retrieval cost.

Tool callexecTool call Returned

Tool call · Input +29:01

text(await tools.exec_command({cmd:"cp /app/solution/residual_temporal_early.pt /app/solution/selected_validation.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint selected_validation.pt --stage validation --recent 1 --trend 1 --pop 1 --kind discovery > /app/solution/selected_validation.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation.json","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -5 /app/solution/wide.log && cat /app/solution/build_test.log && tail -5 /app/solution/final_pretrain.log && tail -3 /app/solution/residual_temporal.log","max_output_tokens":1200}));
text(await tools.clock__curr_time({}));

Tool response +29:02

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"a4bb82","wall_time_seconds":1.000435953,"session_id":39868,"original_token_count":0,"output":""}
{"chunk_id":"f18c9c","wall_time_seconds":0.00001287,"exit_code":0,"original_token_count":67,"output":"{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\ntest users 9153 pairs 5795424 end 25393400\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\nVALID 0.033804506878396014\n{\"epoch\": 3, \"loss\": 6.71496372864026, \"seconds\": 425.2}\nVALID 0.03132820750982915\n"}
{"current_time":"2026-09-24 08:24:31 UTC"}

Harness context token usage record

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  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
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}

Harness context codex item completed

{
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      "/bin/sh",
      "-lc",
      "tail -5 /app/solution/wide.log && cat /app/solution/build_test.log && tail -5 /app/solution/final_pretrain.log && tail -3 /app/solution/residual_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -5 /app/solution/wide.log && cat /app/solution/build_test.log && tail -5 /app/solution/final_pretrain.log && tail -3 /app/solution/residual_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\ntest users 9153 pairs 5795424 end 25393400\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\nVALID 0.033804506878396014\n{\"epoch\": 3, \"loss\": 6.71496372864026, \"seconds\": 425.2}\nVALID 0.03132820750982915\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\ntest users 9153 pairs 5795424 end 25393400\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\nVALID 0.033804506878396014\n{\"epoch\": 3, \"loss\": 6.71496372864026, \"seconds\": 425.2}\nVALID 0.03132820750982915\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 12870
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\ntest users 9153 pairs 5795424 end 25393400\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\nVALID 0.033804506878396014\n{\"epoch\": 3, \"loss\": 6.71496372864026, \"seconds\": 425.2}\nVALID 0.03132820750982915\n"
  }
}

Harness context codex item completed

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  "item": {
    "type": "CommandExecution",
    "id": "exec-4b317789-d087-4db4-9a72-ec8da113ca46",
    "process_id": "39868",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/residual_temporal_early.pt /app/solution/selected_validation.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint selected_validation.pt --stage validation --recent 1 --trend 1 --pop 1 --kind discovery > /app/solution/selected_validation.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/residual_temporal_early.pt /app/solution/selected_validation.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint selected_validation.pt --stage validation --recent 1 --trend 1 --pop 1 --kind discovery > /app/solution/selected_validation.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation.json"
      }
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Agent message

+29:28

Thinking unavailable No text recorded

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

Agent message

+29:44

Thinking unavailable No text recorded

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

Agent message

+30:00

Thinking unavailable No text recorded

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

Agent message

+30:17

Thinking unavailable No text recorded

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

exec command

+30:28

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +30:37

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/train.py';s=open(p).read();needle='class TwoTower(nn.Module):'\nnew='''class NeuralItemTower(ItemTower):\n \"\"\"Trainable item embedding plus a nonlinear residual item MLP.\"\"\"\n def __init__(self,n,dim=600,hidden=64):\n  super().__init__(n,dim);self.mlp=nn.Sequential(nn.Linear(dim,hidden),nn.GELU(),nn.Linear(hidden,dim))\n  nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def forward(self,ids):\n  e=self.embedding(ids);return torch.cat([self.activation(e+self.mlp(e)),self.bias(ids)],dim=1)\n'''\ns=s.replace(needle,new+needle)\ns=s.replace(\"m.load_state_dict(ck['model']);return m\", \"if 'item.mlp.0.weight' in ck['model']:m.item=NeuralItemTower(len(ck['model']['item.bias.weight']),a['dim'],ck['model']['item.mlp.0.weight'].shape[0])\\n m.load_state_dict(ck['model']);return m\")\nopen(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"ap.add_argument('--weeks',type=int,default=0);\", \"ap.add_argument('--item-mlp',type=int,default=0);ap.add_argument('--weeks',type=int,default=0);\")\ns=s.replace(\"model=make_model(ck);opt=\", \"model=make_model(ck)\\n if a.item_mlp and not isinstance(model.item,NeuralItemTower):\\n  previous=model.item.state_dict();model.item=NeuralItemTower(len(data.items),ck['args']['dim'],a.item_mlp);model.item.load_state_dict(previous,strict=False)\\n opt=\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=3 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name neural_item > /app/solution/neural_item.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -6 /app/solution/wide.log && tail -6 /app/solution/final_pretrain.log && cat /app/solution/public_validation.json && cat /sys/fs/cgroup/memory.current","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +30:39

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"9963df","wall_time_seconds":1.00140109,"session_id":96042,"original_token_count":0,"output":""}
{"chunk_id":"e40f60","wall_time_seconds":0.00000428,"exit_code":0,"original_token_count":172,"output":"{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053387,\n    \"quality_users\": 4263\n  }\n}\n7464701952\n"}
{"current_time":"2026-09-24 08:26:08 UTC"}

Harness context token usage record

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Harness context codex item completed

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      "-lc",
      "tail -6 /app/solution/wide.log && tail -6 /app/solution/final_pretrain.log && cat /app/solution/public_validation.json && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -6 /app/solution/wide.log && tail -6 /app/solution/final_pretrain.log && cat /app/solution/public_validation.json && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053387,\n    \"quality_users\": 4263\n  }\n}\n7464701952\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053387,\n    \"quality_users\": 4263\n  }\n}\n7464701952\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4280
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 9.229224152035183, \"seconds\": 31.5}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053387,\n    \"quality_users\": 4263\n  }\n}\n7464701952\n"
  }
}

Agent message

+30:59

Thinking unavailable No text recorded

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

100

Agent message

+31:21

Thinking unavailable No text recorded

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

101

exec command

+31:27

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 supplied evaluator confirmed 0.053387 NDCG@10 for the selected model. The full refit is progressing, and I’m preparing the serving code so request-time ranking uses a history-derived user vector and cached item vectors.

Tool callexecTool call Returned

Tool call · Input +32:05

text(await tools.exec_command({cmd:"cat > /app/solution/serve.py <<'PY'\n\"\"\"CPU serving: encode one interaction history, then search an offline flat MIPS index.\n\nA production ANN index can replace FlatMIPSIndex without changing either tower\nor the scoring function. Eligibility is supplied by the platform, not inferred.\n\"\"\"\nfrom train import *\nclass FlatMIPSIndex:\n def __init__(self,vector_path=P/'item_vectors.npy',id_path=P/'item_ids.npy'):\n  self.vectors=torch.from_numpy(np.load(vector_path));self.item_ids=np.load(id_path)\n @torch.inference_mode()\n def search(self,user_vector,eligible,k=100):\n  [email protected]\n  mask=torch.as_tensor(eligible,dtype=torch.bool).reshape(1,-1)\n  scores=scores.masked_fill(~mask,-torch.inf)\n  values,indices=scores.topk(k,dim=1)\n  return self.item_ids[indices.numpy()],values.numpy()\nclass HistoryEncoder(nn.Module):\n def __init__(self,checkpoint=P/'final_model.pt'):\n  super().__init__();ck=torch.load(checkpoint,weights_only=False);a=ck['args'];n=ck['model']['user.history.weight'].shape[0]\n  self.tower=(ResidualUserTower if a.get('variant')=='residual' else UserTower)(n,a['dim'],a['latent'])\n  self.tower.load_state_dict({k.removeprefix('user.'):v for k,v in ck['model'].items() if k.startswith('user.')});self.eval()\n @torch.inference_mode()\n def forward(self,item_indices,weights):\n  ids=torch.as_tensor(item_indices,dtype=torch.long);w=torch.as_tensor(weights,dtype=torch.float32);w=w/w.square().sum().sqrt().clamp_min(1e-5)\n  return self.tower(ids,torch.tensor([0]),w)\n @torch.inference_mode()\n def from_cached_pool(self,pooled_history_features):\n  \"\"\"Exact shortcut when the history embedding sum is maintained incrementally.\"\"\"\n  return self.tower.encode_pooled(pooled_history_features)\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--uid',type=int,required=True);a=ap.parse_args();torch.set_num_threads(1);enc=HistoryEncoder();idx=FlatMIPSIndex();h=Histories('test');r=h.index.get(a.uid,-1);args=h.batch([r],recent=1,target=False)\n with torch.inference_mode():v=enc.tower(*args)\n z=np.load(D/'test_eligible_candidates.npz');r=int(np.flatnonzero(z['uids']==a.uid)[0]);mask=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool);columns=pd.Index(z['item_ids']).get_indexer(idx.item_ids);items,scores=idx.search(v,mask[columns]);print(json.dumps({'uid':a.uid,'item_ids':items[0].tolist(),'scores':scores[0].tolist()}))\nif __name__=='__main__':main()\nPY\ncat > /app/solution/verify.py <<'PY'\n\"\"\"Structural validation, exact dot-product checks, and CPU encoder timing.\"\"\"\nfrom train import *\nfrom serve import HistoryEncoder,FlatMIPSIndex\n\ndef main():\n sys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\n torch.set_num_threads(1);targets=pd.read_parquet(D/'target_users.parquet');f=validate_ranking(pd.read_parquet(P/'submission.parquet'),set(map(int,targets.uid)))\n z=np.load(D/'test_eligible_candidates.npz');rr=pd.Index(z['uids']).get_indexer(f.uid);cc=pd.Index(z['item_ids']).get_indexer(f.item_id);assert np.all(rr>=0)&np.all(cc>=0);assert np.all((z['packed_eligible'][rr,cc//8]>>(cc%8))&1)\n enc=HistoryEncoder();index=FlatMIPSIndex();hist=Histories('test');ck=torch.load(P/'final_model.pt',weights_only=False);cal=ck['calibration'];recent=cal['recent'];like=cal['like'];hist.recent=np.load(P/'fast_recent_test.npy') if cal.get('fast') else hist.recent\n model=make_model(ck).eval();maxdiff=0.;full_times=[];cached_times=[]\n with torch.inference_mode():\n  assert np.allclose(index.vectors.numpy(),model.item(torch.arange(len(hist.items))).numpy(),atol=2e-6)\n  for u in z['uids'][np.linspace(0,len(z['uids'])-1,32).astype(int)]:\n   r=hist.index.get(int(u),-1);args=hist.batch([r],recent=recent,like=like,target=False)\n   v=enc.tower(*args);pool=enc.tower.history(*args);cached=enc.from_cached_pool(pool);maxdiff=max(maxdiff,float((v-cached).abs().max()));assert torch.allclose(v,cached,atol=2e-6)\n   for _ in range(3):\n    t=time.perf_counter();enc.tower(*args);full_times.append((time.perf_counter()-t)*1000)\n    t=time.perf_counter();enc.from_cached_pool(pool);cached_times.append((time.perf_counter()-t)*1000)\n   row=int(np.flatnonzero(z['uids']==u)[0]);mask=np.unpackbits(z['packed_eligible'][row],count=len(z['item_ids']),bitorder='little').astype(bool);cols=pd.Index(z['item_ids']).get_indexer(index.item_ids);pred,_=index.search(v,mask[cols]);actual=f[f.uid==int(u)].sort_values('rank').item_id.to_numpy();assert np.array_equal(pred[0],actual),(int(u),'rank mismatch')\n report={'rows':len(f),'users':int(f.uid.nunique()),'items_per_user':100,'all_items_eligible':True,'vector_dimension':index.vectors.shape[1],'cached_pool_max_abs_error':maxdiff,'encoder_ms_median':float(np.median(full_times)),'encoder_ms_p95':float(np.percentile(full_times,95)),'cached_pool_encoder_ms_median':float(np.median(cached_times)),'cached_pool_encoder_ms_p95':float(np.percentile(cached_times,95)),'torch_threads':1}\n print(json.dumps(report,indent=2));(P/'verification.json').write_text(json.dumps(report,indent=2))\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/serve.py /app/solution/verify.py\ncat /app/solution/final_pretrain.log\ntail -6 /app/solution/wide.log\ntail -4 /app/solution/neural_item.log","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +32:06

Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"41a429","wall_time_seconds":0.068602748,"exit_code":0,"original_token_count":188,"output":"{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\"epoch\": 7, \"loss\": 7.700959708955553, \"seconds\": 152.2}\n{\"epoch\": 8, \"loss\": 7.5740329424540205, \"seconds\": 182.1}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n"}
{"current_time":"2026-09-24 08:27:35 UTC"}

Harness context token usage record

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  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4dea0770887d2825493948e1d592b",
  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 2797,
    "reasoning_output_tokens": 1176,
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  "turn_token_usage": {
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  "thread_token_usage": {
    "input_tokens": 3052528,
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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-82e27f0c-7c8e-4523-8ddf-6275838570e8",
    "process_id": "69439",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/serve.py <<'PY'\n\"\"\"CPU serving: encode one interaction history, then search an offline flat MIPS index.\n\nA production ANN index can replace FlatMIPSIndex without changing either tower\nor the scoring function. Eligibility is supplied by the platform, not inferred.\n\"\"\"\nfrom train import *\nclass FlatMIPSIndex:\n def __init__(self,vector_path=P/'item_vectors.npy',id_path=P/'item_ids.npy'):\n  self.vectors=torch.from_numpy(np.load(vector_path));self.item_ids=np.load(id_path)\n @torch.inference_mode()\n def search(self,user_vector,eligible,k=100):\n  [email protected]\n  mask=torch.as_tensor(eligible,dtype=torch.bool).reshape(1,-1)\n  scores=scores.masked_fill(~mask,-torch.inf)\n  values,indices=scores.topk(k,dim=1)\n  return self.item_ids[indices.numpy()],values.numpy()\nclass HistoryEncoder(nn.Module):\n def __init__(self,checkpoint=P/'final_model.pt'):\n  super().__init__();ck=torch.load(checkpoint,weights_only=False);a=ck['args'];n=ck['model']['user.history.weight'].shape[0]\n  self.tower=(ResidualUserTower if a.get('variant')=='residual' else UserTower)(n,a['dim'],a['latent'])\n  self.tower.load_state_dict({k.removeprefix('user.'):v for k,v in ck['model'].items() if k.startswith('user.')});self.eval()\n @torch.inference_mode()\n def forward(self,item_indices,weights):\n  ids=torch.as_tensor(item_indices,dtype=torch.long);w=torch.as_tensor(weights,dtype=torch.float32);w=w/w.square().sum().sqrt().clamp_min(1e-5)\n  return self.tower(ids,torch.tensor([0]),w)\n @torch.inference_mode()\n def from_cached_pool(self,pooled_history_features):\n  \"\"\"Exact shortcut when the history embedding sum is maintained incrementally.\"\"\"\n  return self.tower.encode_pooled(pooled_history_features)\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--uid',type=int,required=True);a=ap.parse_args();torch.set_num_threads(1);enc=HistoryEncoder();idx=FlatMIPSIndex();h=Histories('test');r=h.index.get(a.uid,-1);args=h.batch([r],recent=1,target=False)\n with torch.inference_mode():v=enc.tower(*args)\n z=np.load(D/'test_eligible_candidates.npz');r=int(np.flatnonzero(z['uids']==a.uid)[0]);mask=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool);columns=pd.Index(z['item_ids']).get_indexer(idx.item_ids);items,scores=idx.search(v,mask[columns]);print(json.dumps({'uid':a.uid,'item_ids':items[0].tolist(),'scores':scores[0].tolist()}))\nif __name__=='__main__':main()\nPY\ncat > /app/solution/verify.py <<'PY'\n\"\"\"Structural validation, exact dot-product checks, and CPU encoder timing.\"\"\"\nfrom train import *\nfrom serve import HistoryEncoder,FlatMIPSIndex\n\ndef main():\n sys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\n torch.set_num_threads(1);targets=pd.read_parquet(D/'target_users.parquet');f=validate_ranking(pd.read_parquet(P/'submission.parquet'),set(map(int,targets.uid)))\n z=np.load(D/'test_eligible_candidates.npz');rr=pd.Index(z['uids']).get_indexer(f.uid);cc=pd.Index(z['item_ids']).get_indexer(f.item_id);assert np.all(rr>=0)&np.all(cc>=0);assert np.all((z['packed_eligible'][rr,cc//8]>>(cc%8))&1)\n enc=HistoryEncoder();index=FlatMIPSIndex();hist=Histories('test');ck=torch.load(P/'final_model.pt',weights_only=False);cal=ck['calibration'];recent=cal['recent'];like=cal['like'];hist.recent=np.load(P/'fast_recent_test.npy') if cal.get('fast') else hist.recent\n model=make_model(ck).eval();maxdiff=0.;full_times=[];cached_times=[]\n with torch.inference_mode():\n  assert np.allclose(index.vectors.numpy(),model.item(torch.arange(len(hist.items))).numpy(),atol=2e-6)\n  for u in z['uids'][np.linspace(0,len(z['uids'])-1,32).astype(int)]:\n   r=hist.index.get(int(u),-1);args=hist.batch([r],recent=recent,like=like,target=False)\n   v=enc.tower(*args);pool=enc.tower.history(*args);cached=enc.from_cached_pool(pool);maxdiff=max(maxdiff,float((v-cached).abs().max()));assert torch.allclose(v,cached,atol=2e-6)\n   for _ in range(3):\n    t=time.perf_counter();enc.tower(*args);full_times.append((time.perf_counter()-t)*1000)\n    t=time.perf_counter();enc.from_cached_pool(pool);cached_times.append((time.perf_counter()-t)*1000)\n   row=int(np.flatnonzero(z['uids']==u)[0]);mask=np.unpackbits(z['packed_eligible'][row],count=len(z['item_ids']),bitorder='little').astype(bool);cols=pd.Index(z['item_ids']).get_indexer(index.item_ids);pred,_=index.search(v,mask[cols]);actual=f[f.uid==int(u)].sort_values('rank').item_id.to_numpy();assert np.array_equal(pred[0],actual),(int(u),'rank mismatch')\n report={'rows':len(f),'users':int(f.uid.nunique()),'items_per_user':100,'all_items_eligible':True,'vector_dimension':index.vectors.shape[1],'cached_pool_max_abs_error':maxdiff,'encoder_ms_median':float(np.median(full_times)),'encoder_ms_p95':float(np.percentile(full_times,95)),'cached_pool_encoder_ms_median':float(np.median(cached_times)),'cached_pool_encoder_ms_p95':float(np.percentile(cached_times,95)),'torch_threads':1}\n print(json.dumps(report,indent=2));(P/'verification.json').write_text(json.dumps(report,indent=2))\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/serve.py /app/solution/verify.py\ncat /app/solution/final_pretrain.log\ntail -6 /app/solution/wide.log\ntail -4 /app/solution/neural_item.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/serve.py <<'PY'\n\"\"\"CPU serving: encode one interaction history, then search an offline flat MIPS index.\n\nA production ANN index can replace FlatMIPSIndex without changing either tower\nor the scoring function. Eligibility is supplied by the platform, not inferred.\n\"\"\"\nfrom train import *\nclass FlatMIPSIndex:\n def __init__(self,vector_path=P/'item_vectors.npy',id_path=P/'item_ids.npy'):\n  self.vectors=torch.from_numpy(np.load(vector_path));self.item_ids=np.load(id_path)\n @torch.inference_mode()\n def search(self,user_vector,eligible,k=100):\n  [email protected]\n  mask=torch.as_tensor(eligible,dtype=torch.bool).reshape(1,-1)\n  scores=scores.masked_fill(~mask,-torch.inf)\n  values,indices=scores.topk(k,dim=1)\n  return self.item_ids[indices.numpy()],values.numpy()\nclass HistoryEncoder(nn.Module):\n def __init__(self,checkpoint=P/'final_model.pt'):\n  super().__init__();ck=torch.load(checkpoint,weights_only=False);a=ck['args'];n=ck['model']['user.history.weight'].shape[0]\n  self.tower=(ResidualUserTower if a.get('variant')=='residual' else UserTower)(n,a['dim'],a['latent'])\n  self.tower.load_state_dict({k.removeprefix('user.'):v for k,v in ck['model'].items() if k.startswith('user.')});self.eval()\n @torch.inference_mode()\n def forward(self,item_indices,weights):\n  ids=torch.as_tensor(item_indices,dtype=torch.long);w=torch.as_tensor(weights,dtype=torch.float32);w=w/w.square().sum().sqrt().clamp_min(1e-5)\n  return self.tower(ids,torch.tensor([0]),w)\n @torch.inference_mode()\n def from_cached_pool(self,pooled_history_features):\n  \"\"\"Exact shortcut when the history embedding sum is maintained incrementally.\"\"\"\n  return self.tower.encode_pooled(pooled_history_features)\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--uid',type=int,required=True);a=ap.parse_args();torch.set_num_threads(1);enc=HistoryEncoder();idx=FlatMIPSIndex();h=Histories('test');r=h.index.get(a.uid,-1);args=h.batch([r],recent=1,target=False)\n with torch.inference_mode():v=enc.tower(*args)\n z=np.load(D/'test_eligible_candidates.npz');r=int(np.flatnonzero(z['uids']==a.uid)[0]);mask=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool);columns=pd.Index(z['item_ids']).get_indexer(idx.item_ids);items,scores=idx.search(v,mask[columns]);print(json.dumps({'uid':a.uid,'item_ids':items[0].tolist(),'scores':scores[0].tolist()}))\nif __name__=='__main__':main()\nPY\ncat > /app/solution/verify.py <<'PY'\n\"\"\"Structural validation, exact dot-product checks, and CPU encoder timing.\"\"\"\nfrom train import *\nfrom serve import HistoryEncoder,FlatMIPSIndex\n\ndef main():\n sys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\n torch.set_num_threads(1);targets=pd.read_parquet(D/'target_users.parquet');f=validate_ranking(pd.read_parquet(P/'submission.parquet'),set(map(int,targets.uid)))\n z=np.load(D/'test_eligible_candidates.npz');rr=pd.Index(z['uids']).get_indexer(f.uid);cc=pd.Index(z['item_ids']).get_indexer(f.item_id);assert np.all(rr>=0)&np.all(cc>=0);assert np.all((z['packed_eligible'][rr,cc//8]>>(cc%8))&1)\n enc=HistoryEncoder();index=FlatMIPSIndex();hist=Histories('test');ck=torch.load(P/'final_model.pt',weights_only=False);cal=ck['calibration'];recent=cal['recent'];like=cal['like'];hist.recent=np.load(P/'fast_recent_test.npy') if cal.get('fast') else hist.recent\n model=make_model(ck).eval();maxdiff=0.;full_times=[];cached_times=[]\n with torch.inference_mode():\n  assert np.allclose(index.vectors.numpy(),model.item(torch.arange(len(hist.items))).numpy(),atol=2e-6)\n  for u in z['uids'][np.linspace(0,len(z['uids'])-1,32).astype(int)]:\n   r=hist.index.get(int(u),-1);args=hist.batch([r],recent=recent,like=like,target=False)\n   v=enc.tower(*args);pool=enc.tower.history(*args);cached=enc.from_cached_pool(pool);maxdiff=max(maxdiff,float((v-cached).abs().max()));assert torch.allclose(v,cached,atol=2e-6)\n   for _ in range(3):\n    t=time.perf_counter();enc.tower(*args);full_times.append((time.perf_counter()-t)*1000)\n    t=time.perf_counter();enc.from_cached_pool(pool);cached_times.append((time.perf_counter()-t)*1000)\n   row=int(np.flatnonzero(z['uids']==u)[0]);mask=np.unpackbits(z['packed_eligible'][row],count=len(z['item_ids']),bitorder='little').astype(bool);cols=pd.Index(z['item_ids']).get_indexer(index.item_ids);pred,_=index.search(v,mask[cols]);actual=f[f.uid==int(u)].sort_values('rank').item_id.to_numpy();assert np.array_equal(pred[0],actual),(int(u),'rank mismatch')\n report={'rows':len(f),'users':int(f.uid.nunique()),'items_per_user':100,'all_items_eligible':True,'vector_dimension':index.vectors.shape[1],'cached_pool_max_abs_error':maxdiff,'encoder_ms_median':float(np.median(full_times)),'encoder_ms_p95':float(np.percentile(full_times,95)),'cached_pool_encoder_ms_median':float(np.median(cached_times)),'cached_pool_encoder_ms_p95':float(np.percentile(cached_times,95)),'torch_threads':1}\n print(json.dumps(report,indent=2));(P/'verification.json').write_text(json.dumps(report,indent=2))\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/serve.py /app/solution/verify.py\ncat /app/solution/final_pretrain.log\ntail -6 /app/solution/wide.log\ntail -4 /app/solution/neural_item.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\"epoch\": 7, \"loss\": 7.700959708955553, \"seconds\": 152.2}\n{\"epoch\": 8, \"loss\": 7.5740329424540205, \"seconds\": 182.1}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\"epoch\": 7, \"loss\": 7.700959708955553, \"seconds\": 152.2}\n{\"epoch\": 8, \"loss\": 7.5740329424540205, \"seconds\": 182.1}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 69028336
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 9.423788706461588, \"seconds\": 21.4}\n{\"epoch\": 2, \"loss\": 8.640125062730577, \"seconds\": 44.0}\n{\"epoch\": 3, \"loss\": 8.335007614559597, \"seconds\": 66.7}\n{\"epoch\": 4, \"loss\": 8.132152014308506, \"seconds\": 87.6}\n{\"epoch\": 5, \"loss\": 7.965336402257283, \"seconds\": 108.7}\n{\"epoch\": 6, \"loss\": 7.8245735433366566, \"seconds\": 129.1}\n{\"epoch\": 7, \"loss\": 7.700959708955553, \"seconds\": 152.2}\n{\"epoch\": 8, \"loss\": 7.5740329424540205, \"seconds\": 182.1}\n{\"epoch\": 2, \"loss\": 8.426158693101671, \"seconds\": 69.7}\nVALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n"
  }
}

102

Agent message

+32:25

Thinking unavailable No text recorded

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

103

Agent message

+32:41

Thinking unavailable No text recorded

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

104

exec command

+32:57

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +33:30

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3):\", \"def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3,sampled=False):\")\ns=s.replace(\"y=torch.zeros((len(rows),len(self.items)))\\n  for n,r in enumerate(rows):\", \"y=np.empty(len(rows),dtype='int64') if sampled else torch.zeros((len(rows),len(self.items)))\\n  for n,r in enumerate(rows):\")\ns=s.replace(\"sl=slice(self.target_offsets[r],self.target_offsets[r+1]);y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\\n  y/=y.sum(1,keepdim=True).clamp_min(.00001)\", \"sl=slice(self.target_offsets[r],self.target_offsets[r+1])\\n   if sampled:\\n    p=self.target_values[sl].astype('float64');y[n]=np.random.choice(self.target_ids[sl],p=p/p.sum())\\n   else:y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\\n  if not sampled:y/=y.sum(1,keepdim=True).clamp_min(.00001)\")\ns=s.replace(\"ap.add_argument('--item-mlp',type=int,default=0);\", \"ap.add_argument('--negatives',type=int,default=0);ap.add_argument('--item-mlp',type=int,default=0);\")\nold=\"rows=order[s:s+a.batch];args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\nnew=\"\"\"rows=order[s:s+a.batch];sampled=a.negatives or (4096 if len(data.items)>100000 else 0);args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent,sampled=bool(sampled));opt.zero_grad(set_to_none=True)\n   if sampled:loss=sampled_retrieval_loss(model,args,y,sampled,len(data.items),seen)\n   else:\n    logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean()\n   loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\"\"\nassert old in s;s=s.replace(old,new);open(p,'w').write(s)\np='/app/solution/train.py';s=open(p).read().replace(\"negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\", \"negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\\n if seen is not None:\\n  history_keys=seen[0]*n_items+seen[1];negative_keys=torch.arange(len(pos))[:,None]*n_items+neg[None,:]\\n  negative_logits.masked_fill_(torch.isin(negative_keys,history_keys),-1e4)\")\nopen(p,'w').write(s)\np='/app/solution/serve.py';s=open(p).read().replace(\"a=ck['args'];n=\", \"a=ck['args'];self.calibration=ck['calibration'];n=\")\ns=s.replace(\"args=h.batch([r],recent=1,target=False)\", \"h.recent=np.load(P/'fast_recent_test.npy') if enc.calibration.get('fast') else h.recent;args=h.batch([r],recent=enc.calibration['recent'],like=enc.calibration['like'],target=False)\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution');from train import *\nm=TwoTower(64,16,8);args=(torch.tensor([1,2,3,4]),torch.tensor([0,2]),torch.ones(4));loss=sampled_retrieval_loss(m,args,[5,6],16,64,(torch.tensor([0,0,1,1]),args[0]));loss.backward();assert torch.isfinite(loss);assert m.user.history.weight.grad.abs().sum()>0;assert m.item.embedding.weight.grad.abs().sum()>0;print('sampled training path: finite loss, gradients in both towers')\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/temporal_train.py /app/solution/serve.py","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +33:39

Script completed
Wall time 8.5 seconds
Output:

{"chunk_id":"213370","wall_time_seconds":8.058979898,"exit_code":0,"original_token_count":16,"output":"sampled training path: finite loss, gradients in both towers\n"}
{"current_time":"2026-09-24 08:29:08 UTC"}

Harness context token usage record

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  "thread_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4def79b1487d289ee226d3c98401d",
  "usage": {
    "input_tokens": 92881,
    "cached_input_tokens": 89472,
    "cache_write_input_tokens": 0,
    "output_tokens": 2697,
    "reasoning_output_tokens": 1552,
    "total_tokens": 95578
  },
  "turn_token_usage": {
    "input_tokens": 3145409,
    "cached_input_tokens": 3025792,
    "cache_write_input_tokens": 0,
    "output_tokens": 58289,
    "reasoning_output_tokens": 34181,
    "total_tokens": 3203698
  },
  "thread_token_usage": {
    "input_tokens": 3145409,
    "cached_input_tokens": 3025792,
    "cache_write_input_tokens": 0,
    "output_tokens": 58289,
    "reasoning_output_tokens": 34181,
    "total_tokens": 3203698
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2132fd22-ee88-4008-b440-f5aff0175811",
    "process_id": "79064",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3):\", \"def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3,sampled=False):\")\ns=s.replace(\"y=torch.zeros((len(rows),len(self.items)))\\n  for n,r in enumerate(rows):\", \"y=np.empty(len(rows),dtype='int64') if sampled else torch.zeros((len(rows),len(self.items)))\\n  for n,r in enumerate(rows):\")\ns=s.replace(\"sl=slice(self.target_offsets[r],self.target_offsets[r+1]);y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\\n  y/=y.sum(1,keepdim=True).clamp_min(.00001)\", \"sl=slice(self.target_offsets[r],self.target_offsets[r+1])\\n   if sampled:\\n    p=self.target_values[sl].astype('float64');y[n]=np.random.choice(self.target_ids[sl],p=p/p.sum())\\n   else:y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\\n  if not sampled:y/=y.sum(1,keepdim=True).clamp_min(.00001)\")\ns=s.replace(\"ap.add_argument('--item-mlp',type=int,default=0);\", \"ap.add_argument('--negatives',type=int,default=0);ap.add_argument('--item-mlp',type=int,default=0);\")\nold=\"rows=order[s:s+a.batch];args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\nnew=\"\"\"rows=order[s:s+a.batch];sampled=a.negatives or (4096 if len(data.items)>100000 else 0);args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent,sampled=bool(sampled));opt.zero_grad(set_to_none=True)\n   if sampled:loss=sampled_retrieval_loss(model,args,y,sampled,len(data.items),seen)\n   else:\n    logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean()\n   loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\"\"\nassert old in s;s=s.replace(old,new);open(p,'w').write(s)\np='/app/solution/train.py';s=open(p).read().replace(\"negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\", \"negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\\n if seen is not None:\\n  history_keys=seen[0]*n_items+seen[1];negative_keys=torch.arange(len(pos))[:,None]*n_items+neg[None,:]\\n  negative_logits.masked_fill_(torch.isin(negative_keys,history_keys),-1e4)\")\nopen(p,'w').write(s)\np='/app/solution/serve.py';s=open(p).read().replace(\"a=ck['args'];n=\", \"a=ck['args'];self.calibration=ck['calibration'];n=\")\ns=s.replace(\"args=h.batch([r],recent=1,target=False)\", \"h.recent=np.load(P/'fast_recent_test.npy') if enc.calibration.get('fast') else h.recent;args=h.batch([r],recent=enc.calibration['recent'],like=enc.calibration['like'],target=False)\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution');from train import *\nm=TwoTower(64,16,8);args=(torch.tensor([1,2,3,4]),torch.tensor([0,2]),torch.ones(4));loss=sampled_retrieval_loss(m,args,[5,6],16,64,(torch.tensor([0,0,1,1]),args[0]));loss.backward();assert torch.isfinite(loss);assert m.user.history.weight.grad.abs().sum()>0;assert m.item.embedding.weight.grad.abs().sum()>0;print('sampled training path: finite loss, gradients in both towers')\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/temporal_train.py /app/solution/serve.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3):\", \"def batch(self,rows,drop=0.1,recent=0.,like=0.,negative=.3,sampled=False):\")\ns=s.replace(\"y=torch.zeros((len(rows),len(self.items)))\\n  for n,r in enumerate(rows):\", \"y=np.empty(len(rows),dtype='int64') if sampled else torch.zeros((len(rows),len(self.items)))\\n  for n,r in enumerate(rows):\")\ns=s.replace(\"sl=slice(self.target_offsets[r],self.target_offsets[r+1]);y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\\n  y/=y.sum(1,keepdim=True).clamp_min(.00001)\", \"sl=slice(self.target_offsets[r],self.target_offsets[r+1])\\n   if sampled:\\n    p=self.target_values[sl].astype('float64');y[n]=np.random.choice(self.target_ids[sl],p=p/p.sum())\\n   else:y[n,torch.from_numpy(self.target_ids[sl].astype('int64'))]=torch.from_numpy(self.target_values[sl])\\n  if not sampled:y/=y.sum(1,keepdim=True).clamp_min(.00001)\")\ns=s.replace(\"ap.add_argument('--item-mlp',type=int,default=0);\", \"ap.add_argument('--negatives',type=int,default=0);ap.add_argument('--item-mlp',type=int,default=0);\")\nold=\"rows=order[s:s+a.batch];args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent);opt.zero_grad(set_to_none=True);logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\nnew=\"\"\"rows=order[s:s+a.batch];sampled=a.negatives or (4096 if len(data.items)>100000 else 0);args,y,seen=data.batch(rows,drop=a.drop,recent=a.recent,sampled=bool(sampled));opt.zero_grad(set_to_none=True)\n   if sampled:loss=sampled_retrieval_loss(model,args,y,sampled,len(data.items),seen)\n   else:\n    logits=model(*args,allitems);logits[seen]=-1e4;loss=-(F.log_softmax(logits,dim=1)*y).sum(1).mean()\n   loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\"\"\"\nassert old in s;s=s.replace(old,new);open(p,'w').write(s)\np='/app/solution/train.py';s=open(p).read().replace(\"negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\", \"negative_logits.masked_fill_(pos[:,None]==neg[None,:],-1e4)\\n if seen is not None:\\n  history_keys=seen[0]*n_items+seen[1];negative_keys=torch.arange(len(pos))[:,None]*n_items+neg[None,:]\\n  negative_logits.masked_fill_(torch.isin(negative_keys,history_keys),-1e4)\")\nopen(p,'w').write(s)\np='/app/solution/serve.py';s=open(p).read().replace(\"a=ck['args'];n=\", \"a=ck['args'];self.calibration=ck['calibration'];n=\")\ns=s.replace(\"args=h.batch([r],recent=1,target=False)\", \"h.recent=np.load(P/'fast_recent_test.npy') if enc.calibration.get('fast') else h.recent;args=h.batch([r],recent=enc.calibration['recent'],like=enc.calibration['like'],target=False)\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution');from train import *\nm=TwoTower(64,16,8);args=(torch.tensor([1,2,3,4]),torch.tensor([0,2]),torch.ones(4));loss=sampled_retrieval_loss(m,args,[5,6],16,64,(torch.tensor([0,0,1,1]),args[0]));loss.backward();assert torch.isfinite(loss);assert m.user.history.weight.grad.abs().sum()>0;assert m.item.embedding.weight.grad.abs().sum()>0;print('sampled training path: finite loss, gradients in both towers')\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/temporal_train.py /app/solution/serve.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "sampled training path: finite loss, gradients in both towers\n",
    "stderr": "",
    "aggregated_output": "sampled training path: finite loss, gradients in both towers\n",
    "exit_code": 0,
    "duration": {
      "secs": 8,
      "nanos": 59391885
    },
    "formatted_output": "sampled training path: finite loss, gradients in both towers\n"
  }
}

105

exec command

+33:42

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 +33:44

text(await tools.exec_command({cmd:"tail -8 /app/solution/wide.log && tail -4 /app/solution/final_refit.log && tail -4 /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.events && cat /sys/fs/cgroup/memory.current","max_output_tokens":1400}));

Tool response +33:44

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"0cf20e","wall_time_seconds":0.00000473,"exit_code":0,"original_token_count":116,"output":"VALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\nlow 0\nhigh 0\nmax 15949\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n7874330624\n"}

Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "tail -8 /app/solution/wide.log && tail -4 /app/solution/final_refit.log && tail -4 /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.events && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -8 /app/solution/wide.log && tail -4 /app/solution/final_refit.log && tail -4 /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.events && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\nlow 0\nhigh 0\nmax 15949\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n7874330624\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4730
    },
    "formatted_output": "VALID 2 0.015434872625628773\n{\"epoch\": 3, \"loss\": 8.103823555840385, \"seconds\": 118.2}\n{\"epoch\": 4, \"loss\": 7.866478747791714, \"seconds\": 154.1}\nVALID 4 0.016021272883450774\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\nlow 0\nhigh 0\nmax 15949\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 7\n7874330624\n"
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}

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106

Agent message

+34:02

Thinking unavailable No text recorded

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

Model text

The serving path is in place, and the sampled-negative training path passed a gradient check for both towers. The latter avoids a full catalog softmax for large item sets. Once the refit finishes, I’ll verify eligibility, confirm rankings match cached dot products, and measure CPU user-encoder latency.

107

Agent message

+34:20

Thinking unavailable No text recorded

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

108

Agent message

+34:34

Thinking unavailable No text recorded

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

109

exec command

+34:41

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +35:05

text(await tools.exec_command({cmd:"cat > /app/solution/prepare_catalog.py <<'PY'\n\"\"\"Stream the training event file to a catalog-only event cache.\"\"\"\nfrom pathlib import Path\nimport pandas as pd,pyarrow as pa,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data')\ndef main():\n path=P/'catalog_train.parquet'\n if path.exists():return\n ii=pd.Index(pd.read_parquet(D/'candidate_catalog.parquet').item_id);writer=None\n try:\n  for b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=i>=0;d=d[ok].copy();d['iid']=i[ok];t=pa.Table.from_pandas(d,preserve_index=False)\n   if writer is None:writer=pq.ParquetWriter(P/'catalog_train.tmp.parquet',t.schema)\n   writer.write_table(t)\n finally:\n  if writer is not None:writer.close()\n (P/'catalog_train.tmp.parquet').replace(path)\nif __name__=='__main__':main()\nPY\ncat > /app/solution/run_final.py <<'PY'\n\"\"\"Rebuild the selected final model and submission from the supplied event files.\"\"\"\nimport os,sys,json,subprocess\nfrom pathlib import Path\nP=Path('/app/solution')\ndef run(script,*args):\n subprocess.run([sys.executable,str(P/script),*map(str,args)],check=True,cwd=P)\ndef main():\n os.environ.setdefault('TOWER_THREADS','4');c=json.loads((P/'selected_config.json').read_text())\n run('prepare_catalog.py');run('build_data.py','test');run('build_temporal.py','--stage','test','--weeks',16);run('build_discovery_trends.py')\n run('train.py','--stage','test','--variant','residual','--epochs',c['pretrain_epochs'],'--eval-every',c['pretrain_epochs'],'--name','final_pretrain','--drop',.6,'--dim',c['dim'],'--latent',c['latent'])\n run('temporal_train.py','--stage','test','--resume','final_pretrain_best.pt','--epochs',c['temporal_epochs'],'--lr',.0003,'--recent',1,'--item-mlp',c['item_mlp'],'--name','final_refit')\n run('infer.py','--checkpoint','final_refit_best.pt','--stage','test','--recent',c['recent'],'--like',c['like'],'--trend',c['trend'],'--pop',c['pop'],'--kind','discovery','--save-model','final_model.pt')\n run('verify.py')\nif __name__=='__main__':main()\nPY\ncat > /app/solution/selected_config.json <<'JSON'\n{\"dim\":600,\"latent\":200,\"item_mlp\":0,\"pretrain_epochs\":8,\"temporal_epochs\":1,\"recent\":1,\"like\":0,\"trend\":1,\"pop\":1,\"validation_checkpoint\":\"selected_validation.pt\",\"validation_ndcg\":0.05338674176427629}\nJSON\ncat /app/solution/final_refit.log\ntail -5 /app/solution/wide.log\ntail -5 /app/solution/neural_item.log","max_output_tokens":1400}));
text(await tools.clock__curr_time({}));

Tool response +35:05

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"862ad4","wall_time_seconds":0.000008381,"exit_code":0,"original_token_count":58,"output":"{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\n{\"epoch\": 8, \"loss\": 7.348114569981893, \"seconds\": 374.7}\nVALID 8 0.017804151162970808\n"}
{"current_time":"2026-09-24 08:30:34 UTC"}

Harness context codex item completed

{
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    "type": "CommandExecution",
    "id": "exec-5c28c313-d33e-4a62-8c01-86bfa66c77d1",
    "process_id": "69937",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/prepare_catalog.py <<'PY'\n\"\"\"Stream the training event file to a catalog-only event cache.\"\"\"\nfrom pathlib import Path\nimport pandas as pd,pyarrow as pa,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data')\ndef main():\n path=P/'catalog_train.parquet'\n if path.exists():return\n ii=pd.Index(pd.read_parquet(D/'candidate_catalog.parquet').item_id);writer=None\n try:\n  for b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=i>=0;d=d[ok].copy();d['iid']=i[ok];t=pa.Table.from_pandas(d,preserve_index=False)\n   if writer is None:writer=pq.ParquetWriter(P/'catalog_train.tmp.parquet',t.schema)\n   writer.write_table(t)\n finally:\n  if writer is not None:writer.close()\n (P/'catalog_train.tmp.parquet').replace(path)\nif __name__=='__main__':main()\nPY\ncat > /app/solution/run_final.py <<'PY'\n\"\"\"Rebuild the selected final model and submission from the supplied event files.\"\"\"\nimport os,sys,json,subprocess\nfrom pathlib import Path\nP=Path('/app/solution')\ndef run(script,*args):\n subprocess.run([sys.executable,str(P/script),*map(str,args)],check=True,cwd=P)\ndef main():\n os.environ.setdefault('TOWER_THREADS','4');c=json.loads((P/'selected_config.json').read_text())\n run('prepare_catalog.py');run('build_data.py','test');run('build_temporal.py','--stage','test','--weeks',16);run('build_discovery_trends.py')\n run('train.py','--stage','test','--variant','residual','--epochs',c['pretrain_epochs'],'--eval-every',c['pretrain_epochs'],'--name','final_pretrain','--drop',.6,'--dim',c['dim'],'--latent',c['latent'])\n run('temporal_train.py','--stage','test','--resume','final_pretrain_best.pt','--epochs',c['temporal_epochs'],'--lr',.0003,'--recent',1,'--item-mlp',c['item_mlp'],'--name','final_refit')\n run('infer.py','--checkpoint','final_refit_best.pt','--stage','test','--recent',c['recent'],'--like',c['like'],'--trend',c['trend'],'--pop',c['pop'],'--kind','discovery','--save-model','final_model.pt')\n run('verify.py')\nif __name__=='__main__':main()\nPY\ncat > /app/solution/selected_config.json <<'JSON'\n{\"dim\":600,\"latent\":200,\"item_mlp\":0,\"pretrain_epochs\":8,\"temporal_epochs\":1,\"recent\":1,\"like\":0,\"trend\":1,\"pop\":1,\"validation_checkpoint\":\"selected_validation.pt\",\"validation_ndcg\":0.05338674176427629}\nJSON\ncat /app/solution/final_refit.log\ntail -5 /app/solution/wide.log\ntail -5 /app/solution/neural_item.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/prepare_catalog.py <<'PY'\n\"\"\"Stream the training event file to a catalog-only event cache.\"\"\"\nfrom pathlib import Path\nimport pandas as pd,pyarrow as pa,pyarrow.parquet as pq\nP=Path('/app/solution');D=Path('/task/data')\ndef main():\n path=P/'catalog_train.parquet'\n if path.exists():return\n ii=pd.Index(pd.read_parquet(D/'candidate_catalog.parquet').item_id);writer=None\n try:\n  for b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=1000000):\n   d=b.to_pandas();i=ii.get_indexer(d.item_id);ok=i>=0;d=d[ok].copy();d['iid']=i[ok];t=pa.Table.from_pandas(d,preserve_index=False)\n   if writer is None:writer=pq.ParquetWriter(P/'catalog_train.tmp.parquet',t.schema)\n   writer.write_table(t)\n finally:\n  if writer is not None:writer.close()\n (P/'catalog_train.tmp.parquet').replace(path)\nif __name__=='__main__':main()\nPY\ncat > /app/solution/run_final.py <<'PY'\n\"\"\"Rebuild the selected final model and submission from the supplied event files.\"\"\"\nimport os,sys,json,subprocess\nfrom pathlib import Path\nP=Path('/app/solution')\ndef run(script,*args):\n subprocess.run([sys.executable,str(P/script),*map(str,args)],check=True,cwd=P)\ndef main():\n os.environ.setdefault('TOWER_THREADS','4');c=json.loads((P/'selected_config.json').read_text())\n run('prepare_catalog.py');run('build_data.py','test');run('build_temporal.py','--stage','test','--weeks',16);run('build_discovery_trends.py')\n run('train.py','--stage','test','--variant','residual','--epochs',c['pretrain_epochs'],'--eval-every',c['pretrain_epochs'],'--name','final_pretrain','--drop',.6,'--dim',c['dim'],'--latent',c['latent'])\n run('temporal_train.py','--stage','test','--resume','final_pretrain_best.pt','--epochs',c['temporal_epochs'],'--lr',.0003,'--recent',1,'--item-mlp',c['item_mlp'],'--name','final_refit')\n run('infer.py','--checkpoint','final_refit_best.pt','--stage','test','--recent',c['recent'],'--like',c['like'],'--trend',c['trend'],'--pop',c['pop'],'--kind','discovery','--save-model','final_model.pt')\n run('verify.py')\nif __name__=='__main__':main()\nPY\ncat > /app/solution/selected_config.json <<'JSON'\n{\"dim\":600,\"latent\":200,\"item_mlp\":0,\"pretrain_epochs\":8,\"temporal_epochs\":1,\"recent\":1,\"like\":0,\"trend\":1,\"pop\":1,\"validation_checkpoint\":\"selected_validation.pt\",\"validation_ndcg\":0.05338674176427629}\nJSON\ncat /app/solution/final_refit.log\ntail -5 /app/solution/wide.log\ntail -5 /app/solution/neural_item.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\n{\"epoch\": 8, \"loss\": 7.348114569981893, \"seconds\": 374.7}\nVALID 8 0.017804151162970808\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 8381
    },
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}

Harness context token usage record

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110

Agent message

+35:26

Thinking unavailable No text recorded

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

111

exec command

+35:40

Thinking unavailable No text recorded

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

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
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      "-lc",
      "python - <<'PY'\np='/app/solution/train.py';s=open(p).read();needle='class TwoTower(nn.Module):'\nnew='''class NeuralItemTower(ItemTower):\n \"\"\"Trainable item embedding plus a nonlinear residual item MLP.\"\"\"\n def __init__(self,n,dim=600,hidden=64):\n  super().__init__(n,dim);self.mlp=nn.Sequential(nn.Linear(dim,hidden),nn.GELU(),nn.Linear(hidden,dim))\n  nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def forward(self,ids):\n  e=self.embedding(ids);return torch.cat([self.activation(e+self.mlp(e)),self.bias(ids)],dim=1)\n'''\ns=s.replace(needle,new+needle)\ns=s.replace(\"m.load_state_dict(ck['model']);return m\", \"if 'item.mlp.0.weight' in ck['model']:m.item=NeuralItemTower(len(ck['model']['item.bias.weight']),a['dim'],ck['model']['item.mlp.0.weight'].shape[0])\\n m.load_state_dict(ck['model']);return m\")\nopen(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"ap.add_argument('--weeks',type=int,default=0);\", \"ap.add_argument('--item-mlp',type=int,default=0);ap.add_argument('--weeks',type=int,default=0);\")\ns=s.replace(\"model=make_model(ck);opt=\", \"model=make_model(ck)\\n if a.item_mlp and not isinstance(model.item,NeuralItemTower):\\n  previous=model.item.state_dict();model.item=NeuralItemTower(len(data.items),ck['args']['dim'],a.item_mlp);model.item.load_state_dict(previous,strict=False)\\n opt=\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=3 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name neural_item > /app/solution/neural_item.log 2>&1"
    ],
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    "stdout": "",
    "stderr": "",
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    "exit_code": 137,
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      "nanos": 845694853
    },
    "formatted_output": ""
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}

Harness context codex item completed

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      "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/temporal_train.py' in args and b'--name' in args and b'residual_temporal' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped comparison run',p.name)\np='/app/solution/train.py';s=open(p).read().replace(\"ap.add_argument('--negatives',type=int,default=0);\", \"ap.add_argument('--keep-every',type=int,default=0);ap.add_argument('--negatives',type=int,default=0);\")\ns=s.replace(\"torch.save(checkpoint,P/f'{a.name}_last.pt')\", \"torch.save(checkpoint,P/f'{a.name}_last.pt')\\n   if a.keep_every and epoch%a.keep_every==0:torch.save(checkpoint,P/f'{a.name}_epoch{epoch}.pt')\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --variant residual --dim 1024 --latent 256 --epochs 10 --eval-every 2 --keep-every 2 --name wide --drop .6 > /app/solution/wide.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/temporal_train.py' in args and b'--name' in args and b'residual_temporal' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Stopped comparison run',p.name)\np='/app/solution/train.py';s=open(p).read().replace(\"ap.add_argument('--negatives',type=int,default=0);\", \"ap.add_argument('--keep-every',type=int,default=0);ap.add_argument('--negatives',type=int,default=0);\")\ns=s.replace(\"torch.save(checkpoint,P/f'{a.name}_last.pt')\", \"torch.save(checkpoint,P/f'{a.name}_last.pt')\\n   if a.keep_every and epoch%a.keep_every==0:torch.save(checkpoint,P/f'{a.name}_epoch{epoch}.pt')\")\nopen(p,'w').write(s)\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --variant residual --dim 1024 --latent 256 --epochs 10 --eval-every 2 --keep-every 2 --name wide --drop .6 > /app/solution/wide.log 2>&1"
      }
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}

Harness context codex item completed

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      "-lc",
      "/opt/task-venv/bin/python /app/solution/build_data.py test > /app/solution/build_test.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/train.py --stage test --variant residual --epochs 8 --eval-every 8 --name final_pretrain --drop .6 > /app/solution/final_pretrain.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --resume final_pretrain_best.pt --epochs 1 --lr .0003 --recent 1 --name final_refit > /app/solution/final_refit.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 1 --trend 1 --pop 1 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1"
    ],
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}

Tool callexecTool call Returned

Tool call · Input +35:46

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/train.py' in args and b'--name' in args and b'wide' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Saved wider model for comparison and stopped pretraining',p.name)\nPY\ncp /app/solution/wide_best.pt /app/solution/wide_pretrained.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint wide_pretrained.pt --name wide_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/wide_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/neural_item.log && tail -4 /app/solution/final_refit.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +35:47

Script completed
Wall time 1.4 seconds
Output:

{"chunk_id":"9662b9","wall_time_seconds":1.000409201,"session_id":39198,"original_token_count":0,"output":""}
{"chunk_id":"f6a5d9","wall_time_seconds":0.00000392,"exit_code":0,"original_token_count":35,"output":"{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\n696143872\n"}
{"current_time":"2026-09-24 08:31:17 UTC"}

Harness context token usage record

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Harness context codex item completed

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  "item": {
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    "id": "exec-e53b9245-f974-4843-8563-e2598f66a0d5",
    "process_id": "22208",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/neural_item.log && tail -4 /app/solution/final_refit.log && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -4 /app/solution/neural_item.log && tail -4 /app/solution/final_refit.log && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\n696143872\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\n696143872\n",
    "exit_code": 0,
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    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\n696143872\n"
  }
}

112

exec command

+35:59

Thinking unavailable No text recorded

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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d4090e64-5151-42eb-9395-c3cf11e79649",
    "process_id": "39198",
    "command": [
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      "-lc",
      "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/train.py' in args and b'--name' in args and b'wide' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Saved wider model for comparison and stopped pretraining',p.name)\nPY\ncp /app/solution/wide_best.pt /app/solution/wide_pretrained.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint wide_pretrained.pt --name wide_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/wide_full.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:args=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if b'/app/solution/train.py' in args and b'--name' in args and b'wide' in args:\n  os.kill(int(p.name),signal.SIGTERM);print('Saved wider model for comparison and stopped pretraining',p.name)\nPY\ncp /app/solution/wide_best.pt /app/solution/wide_pretrained.pt\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint wide_pretrained.pt --name wide_full --recent 1,10 --gamma 0.5,1,1.5 --pop 0.5,1,1.5 --kind discovery > /app/solution/wide_full.log 2>&1"
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    "exit_code": 0,
    "duration": {
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}

Tool callexecTool call Returned

Tool call · Input +36:01

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.events && cat /app/solution/neural_item.log && cat /app/solution/final_refit.log && cat /app/solution/final_infer.log && tail -8 /app/solution/wide.log && ls -lh /app/solution/final* /app/solution/neural_item*","max_output_tokens":2200}));

Tool response +36:01

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"200dd7","wall_time_seconds":0.00000322,"exit_code":0,"original_token_count":592,"output":"low 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\nTraceback (most recent call last):\n  File \"/app/solution/infer.py\", line 14, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/infer.py\", line 5, in main\n    ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);data.recent=np.load(P/f'fast_recent_{a.stage}.npy') if a.fast else data.recent;model=make_model(ck);features=np.load(P/(('discovery_trends_' if a.kind=='discovery' else 'trends_')+a.stage+'.npy'))\n       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1484, in load\n    with _open_file_like(f, \"rb\") as opened_file:\n         ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 759, in _open_file_like\n    return _open_file(name_or_buffer, mode)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 740, in __init__\n    super().__init__(open(name, mode))\n                     ^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/final_refit_best.pt'\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\n{\"epoch\": 8, \"loss\": 7.348114569981893, \"seconds\": 374.7}\nVALID 8 0.017804151162970808\n{\"epoch\": 9, \"loss\": 7.285788655281067, \"seconds\": 440.4}\nKilled\n-rw-r--r-- 1 agent agent 1.2K Sep 24 08:31 /app/solution/final_infer.log\n-rw-r--r-- 1 agent agent  462 Sep 24 08:26 /app/solution/final_pretrain.log\n-rw-r--r-- 1 agent agent 139M Sep 24 08:27 /app/solution/final_pretrain_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:26 /app/solution/final_pretrain_last.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:27 /app/solution/final_pretrain_test.parquet\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/final_refit.log\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/neural_item.log\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:31 /app/solution/neural_item_validation.parquet\n"}

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-4145eb8b-73c1-4f38-84b0-6f31781bf874",
    "process_id": "52873",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /sys/fs/cgroup/memory.events && cat /app/solution/neural_item.log && cat /app/solution/final_refit.log && cat /app/solution/final_infer.log && tail -8 /app/solution/wide.log && ls -lh /app/solution/final* /app/solution/neural_item*"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /sys/fs/cgroup/memory.events && cat /app/solution/neural_item.log && cat /app/solution/final_refit.log && cat /app/solution/final_infer.log && tail -8 /app/solution/wide.log && ls -lh /app/solution/final* /app/solution/neural_item*"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "low 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\nTraceback (most recent call last):\n  File \"/app/solution/infer.py\", line 14, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/infer.py\", line 5, in main\n    ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);data.recent=np.load(P/f'fast_recent_{a.stage}.npy') if a.fast else data.recent;model=make_model(ck);features=np.load(P/(('discovery_trends_' if a.kind=='discovery' else 'trends_')+a.stage+'.npy'))\n       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1484, in load\n    with _open_file_like(f, \"rb\") as opened_file:\n         ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 759, in _open_file_like\n    return _open_file(name_or_buffer, mode)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 740, in __init__\n    super().__init__(open(name, mode))\n                     ^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/final_refit_best.pt'\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\n{\"epoch\": 8, \"loss\": 7.348114569981893, \"seconds\": 374.7}\nVALID 8 0.017804151162970808\n{\"epoch\": 9, \"loss\": 7.285788655281067, \"seconds\": 440.4}\nKilled\n-rw-r--r-- 1 agent agent 1.2K Sep 24 08:31 /app/solution/final_infer.log\n-rw-r--r-- 1 agent agent  462 Sep 24 08:26 /app/solution/final_pretrain.log\n-rw-r--r-- 1 agent agent 139M Sep 24 08:27 /app/solution/final_pretrain_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:26 /app/solution/final_pretrain_last.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:27 /app/solution/final_pretrain_test.parquet\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/final_refit.log\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/neural_item.log\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:31 /app/solution/neural_item_validation.parquet\n",
    "stderr": "",
    "aggregated_output": "low 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\nTraceback (most recent call last):\n  File \"/app/solution/infer.py\", line 14, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/infer.py\", line 5, in main\n    ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);data.recent=np.load(P/f'fast_recent_{a.stage}.npy') if a.fast else data.recent;model=make_model(ck);features=np.load(P/(('discovery_trends_' if a.kind=='discovery' else 'trends_')+a.stage+'.npy'))\n       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1484, in load\n    with _open_file_like(f, \"rb\") as opened_file:\n         ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 759, in _open_file_like\n    return _open_file(name_or_buffer, mode)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 740, in __init__\n    super().__init__(open(name, mode))\n                     ^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/final_refit_best.pt'\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\n{\"epoch\": 8, \"loss\": 7.348114569981893, \"seconds\": 374.7}\nVALID 8 0.017804151162970808\n{\"epoch\": 9, \"loss\": 7.285788655281067, \"seconds\": 440.4}\nKilled\n-rw-r--r-- 1 agent agent 1.2K Sep 24 08:31 /app/solution/final_infer.log\n-rw-r--r-- 1 agent agent  462 Sep 24 08:26 /app/solution/final_pretrain.log\n-rw-r--r-- 1 agent agent 139M Sep 24 08:27 /app/solution/final_pretrain_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:26 /app/solution/final_pretrain_last.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:27 /app/solution/final_pretrain_test.parquet\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/final_refit.log\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/neural_item.log\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:31 /app/solution/neural_item_validation.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3220
    },
    "formatted_output": "low 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n{\"epoch\": 1, \"loss\": 8.168657437092117, \"seconds\": 279.8}\nKilled\n{\"epoch\": 1, \"loss\": 8.174648217856884, \"seconds\": 226.4}\nKilled\nTraceback (most recent call last):\n  File \"/app/solution/infer.py\", line 14, in <module>\n    if __name__=='__main__':main()\n                            ^^^^^^\n  File \"/app/solution/infer.py\", line 5, in main\n    ck=torch.load(P/a.checkpoint,weights_only=False);data=Histories(a.stage);data.recent=np.load(P/f'fast_recent_{a.stage}.npy') if a.fast else data.recent;model=make_model(ck);features=np.load(P/(('discovery_trends_' if a.kind=='discovery' else 'trends_')+a.stage+'.npy'))\n       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1484, in load\n    with _open_file_like(f, \"rb\") as opened_file:\n         ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 759, in _open_file_like\n    return _open_file(name_or_buffer, mode)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 740, in __init__\n    super().__init__(open(name, mode))\n                     ^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/final_refit_best.pt'\n{\"epoch\": 5, \"loss\": 7.6714644696977405, \"seconds\": 214.9}\n{\"epoch\": 6, \"loss\": 7.509991486867269, \"seconds\": 261.0}\nVALID 6 0.01642081347982446\n{\"epoch\": 7, \"loss\": 7.404636939366658, \"seconds\": 326.8}\n{\"epoch\": 8, \"loss\": 7.348114569981893, \"seconds\": 374.7}\nVALID 8 0.017804151162970808\n{\"epoch\": 9, \"loss\": 7.285788655281067, \"seconds\": 440.4}\nKilled\n-rw-r--r-- 1 agent agent 1.2K Sep 24 08:31 /app/solution/final_infer.log\n-rw-r--r-- 1 agent agent  462 Sep 24 08:26 /app/solution/final_pretrain.log\n-rw-r--r-- 1 agent agent 139M Sep 24 08:27 /app/solution/final_pretrain_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:26 /app/solution/final_pretrain_last.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:27 /app/solution/final_pretrain_test.parquet\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/final_refit.log\n-rw-r--r-- 1 agent agent   65 Sep 24 08:31 /app/solution/neural_item.log\n-rw-r--r-- 1 agent agent 1.8M Sep 24 08:31 /app/solution/neural_item_validation.parquet\n"
  }
}

Harness context token usage record

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113

Agent message

+36:19

Thinking unavailable No text recorded

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

114

exec command

+36:24

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 concurrent experiments hit the memory limit during evaluation. The saved submission is intact. I’m switching to sequential runs and saving checkpoints before evaluation, so the completed training work is protected.

Tool callexecTool call Returned

Tool call · Input +36:37

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read();old=\"params=dict(ck['args']);params.update(vars(a));score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else f'{a.name}_test.parquet');print('VALID',score,flush=True);newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt')\";new=\"params=dict(ck['args']);params.update(vars(a));newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt');score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else f'{a.name}_test.parquet');print('VALID',score,flush=True)\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nTOWER_THREADS=8 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name neural_item > /app/solution/neural_item.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"cat /app/solution/wide_full.log && ls -lh /app/solution/submission.parquet && cat /sys/fs/cgroup/memory.current","max_output_tokens":1200}));
text(await tools.clock__curr_time({}));

Tool response +36:38

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"6f1a26","wall_time_seconds":1.001255882,"session_id":74414,"original_token_count":0,"output":""}
{"chunk_id":"30f91e","wall_time_seconds":0.00000371,"exit_code":0,"original_token_count":120,"output":"BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04830883885275913}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04831542640243497}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04831542640243497}\n-rw-r--r-- 1 agent agent 1.5M Sep 24 08:22 /app/solution/submission.parquet\n1146023936\n"}
{"current_time":"2026-09-24 08:32:08 UTC"}

Harness context token usage record

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Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
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    "process_id": "36340",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/wide_full.log && ls -lh /app/solution/submission.parquet && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/wide_full.log",
        "name": "wide_full.log",
        "path": "/app/solution/wide_full.log"
      },
      {
        "type": "list_files",
        "cmd": "ls -lh /app/solution/submission.parquet",
        "path": "submission.parquet"
      },
      {
        "type": "read",
        "cmd": "cat /sys/fs/cgroup/memory.current",
        "name": "memory.current",
        "path": "/sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04830883885275913}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04831542640243497}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04831542640243497}\n-rw-r--r-- 1 agent agent 1.5M Sep 24 08:22 /app/solution/submission.parquet\n1146023936\n",
    "stderr": "",
    "aggregated_output": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04830883885275913}\nBEST_SO_FAR {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04831542640243497}\nBEST {\"recent\": 10.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 1.5, \"ndcg\": 0.04831542640243497}\n-rw-r--r-- 1 agent agent 1.5M Sep 24 08:22 /app/solution/submission.parquet\n1146023936\n",
    "exit_code": 0,
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      "secs": 0,
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  }
}

115

Agent message

+36:58

Thinking unavailable No text recorded

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

116

Agent message

+37:14

Thinking unavailable No text recorded

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

117

Agent message

+37:28

Thinking unavailable No text recorded

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

118

Agent message

+37:44

Thinking unavailable No text recorded

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

119

exec command

+37:48

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 +38:10

text(await tools.exec_command({cmd:"cat > /app/solution/calibrate_activity.py <<'PY'\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--name',default='activity');a=ap.parse_args();ck=torch.load(P/a.checkpoint,weights_only=False);model=make_model(ck).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));columns=pd.Index(data.items).get_indexer(z['item_ids']);f=torch.tensor(np.load(P/'discovery_trends_validation.npy')[columns]);cfg=[{'recent':1,'like':0,'trend':g,'pop':p,'activity':c} for c in [-.5,-.25,0,.25,.5] for g in [.5,1,1.5] for p in [.75,1,1.25]];sums=np.zeros(len(cfg))\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for s in range(0,len(ur),128):\n   row=ur[s:s+128];rows=[data.index.get(int(z['uids'][u]),-1) for u in row];args=data.batch(rows,recent=1,target=False);scores=model.user(*args)@iv.T;activity=torch.tensor([np.log1p(data.offsets[r+1]-data.offsets[r])-6 if r>=0 else -6 for r in rows],dtype=torch.float32)\n   for j,c in enumerate(cfg):\n    coefficient=(c['pop']+c['activity']*activity).clamp(.1,3);pred=scores+c['trend']*f[:,3]+coefficient[:,None]*f[:,0];pred.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf);ranks=pred.topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur))\n for slope in [-.5,-.25,0,.25,.5]:print('SLOPE',slope,json.dumps(max([c for c in cfg if c['activity']==slope],key=lambda x:x['ndcg'])),flush=True)\n best=max(cfg,key=lambda c:c['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':best,'results':cfg},indent=2))\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate_activity.py --checkpoint residual_temporal_early.pt --name activity > /app/solution/activity.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));
text(await tools.exec_command({cmd:"tail -3 /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1000}));

Tool response +38:11

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"9d09ec","wall_time_seconds":1.001101779,"session_id":41252,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:33:40 UTC"}
{"chunk_id":"c3c7b6","wall_time_seconds":0.00000295,"exit_code":0,"original_token_count":3,"output":"4604633088\n"}

Harness context token usage record

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Harness context codex item completed

{
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    "type": "CommandExecution",
    "id": "exec-48eb5df8-439e-44bd-bbc5-69f9236f9615",
    "process_id": "61520",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -3 /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -3 /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.current"
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120

Agent message

+38:32

Thinking unavailable No text recorded

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

121

exec command

+38:38

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 +38:41

text(await tools.exec_command({cmd:"cat /app/solution/activity.log && cat /app/solution/neural_item.log && ls -lh /app/solution/neural_item*pt","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response +38:41

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"e81543","wall_time_seconds":0.000005071,"exit_code":2,"original_token_count":19,"output":"ls: cannot access '/app/solution/neural_item*pt': No such file or directory\n"}
{"current_time":"2026-09-24 08:34:10 UTC"}

Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "cat /app/solution/activity.log && cat /app/solution/neural_item.log && ls -lh /app/solution/neural_item*pt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /app/solution/activity.log && cat /app/solution/neural_item.log && ls -lh /app/solution/neural_item*pt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "ls: cannot access '/app/solution/neural_item*pt': No such file or directory\n",
    "stderr": "",
    "aggregated_output": "ls: cannot access '/app/solution/neural_item*pt': No such file or directory\n",
    "exit_code": 2,
    "duration": {
      "secs": 0,
      "nanos": 5071
    },
    "formatted_output": "ls: cannot access '/app/solution/neural_item*pt': No such file or directory\n"
  }
}

Harness context token usage record

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Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "cat > /app/solution/calibrate_activity.py <<'PY'\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--name',default='activity');a=ap.parse_args();ck=torch.load(P/a.checkpoint,weights_only=False);model=make_model(ck).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));columns=pd.Index(data.items).get_indexer(z['item_ids']);f=torch.tensor(np.load(P/'discovery_trends_validation.npy')[columns]);cfg=[{'recent':1,'like':0,'trend':g,'pop':p,'activity':c} for c in [-.5,-.25,0,.25,.5] for g in [.5,1,1.5] for p in [.75,1,1.25]];sums=np.zeros(len(cfg))\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for s in range(0,len(ur),128):\n   row=ur[s:s+128];rows=[data.index.get(int(z['uids'][u]),-1) for u in row];args=data.batch(rows,recent=1,target=False);scores=model.user(*args)@iv.T;activity=torch.tensor([np.log1p(data.offsets[r+1]-data.offsets[r])-6 if r>=0 else -6 for r in rows],dtype=torch.float32)\n   for j,c in enumerate(cfg):\n    coefficient=(c['pop']+c['activity']*activity).clamp(.1,3);pred=scores+c['trend']*f[:,3]+coefficient[:,None]*f[:,0];pred.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf);ranks=pred.topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur))\n for slope in [-.5,-.25,0,.25,.5]:print('SLOPE',slope,json.dumps(max([c for c in cfg if c['activity']==slope],key=lambda x:x['ndcg'])),flush=True)\n best=max(cfg,key=lambda c:c['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':best,'results':cfg},indent=2))\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate_activity.py --checkpoint residual_temporal_early.pt --name activity > /app/solution/activity.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/calibrate_activity.py <<'PY'\nfrom train import *\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--checkpoint',required=True);ap.add_argument('--name',default='activity');a=ap.parse_args();ck=torch.load(P/a.checkpoint,weights_only=False);model=make_model(ck).eval();data=Histories('validation');z,mask,truth,idcg=truth_data();ur=np.array(list(truth));disc=1/np.log2(np.arange(2,12));columns=pd.Index(data.items).get_indexer(z['item_ids']);f=torch.tensor(np.load(P/'discovery_trends_validation.npy')[columns]);cfg=[{'recent':1,'like':0,'trend':g,'pop':p,'activity':c} for c in [-.5,-.25,0,.25,.5] for g in [.5,1,1.5] for p in [.75,1,1.25]];sums=np.zeros(len(cfg))\n with torch.no_grad():\n  iv=model.item(torch.tensor(columns))\n  for s in range(0,len(ur),128):\n   row=ur[s:s+128];rows=[data.index.get(int(z['uids'][u]),-1) for u in row];args=data.batch(rows,recent=1,target=False);scores=model.user(*args)@iv.T;activity=torch.tensor([np.log1p(data.offsets[r+1]-data.offsets[r])-6 if r>=0 else -6 for r in rows],dtype=torch.float32)\n   for j,c in enumerate(cfg):\n    coefficient=(c['pop']+c['activity']*activity).clamp(.1,3);pred=scores+c['trend']*f[:,3]+coefficient[:,None]*f[:,0];pred.masked_fill_(~torch.from_numpy(mask[row]),-torch.inf);ranks=pred.topk(10,dim=1).indices.numpy();sums[j]+=sum(np.dot([truth[u].get(int(i),0) for i in ranks[k]],disc)/idcg[u] for k,u in enumerate(row))\n for c,v in zip(cfg,sums):c['ndcg']=float(v/len(ur))\n for slope in [-.5,-.25,0,.25,.5]:print('SLOPE',slope,json.dumps(max([c for c in cfg if c['activity']==slope],key=lambda x:x['ndcg'])),flush=True)\n best=max(cfg,key=lambda c:c['ndcg']);(P/f'{a.name}.json').write_text(json.dumps({'checkpoint':a.checkpoint,'best':best,'results':cfg},indent=2))\nif __name__=='__main__':main()\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate_activity.py --checkpoint residual_temporal_early.pt --name activity > /app/solution/activity.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 37,
      "nanos": 805511923
    },
    "formatted_output": ""
  }
}

122

Agent message

+38:49

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 sequential training run is staying within the memory limit. The reproduction script and artifact checks are ready: they will verify all 845,600 rows, candidate eligibility, and agreement between cached dot-product scores and submitted rankings.

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fda9347b-19ee-4ed2-b27f-c377813b2e5b",
    "process_id": "74414",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read();old=\"params=dict(ck['args']);params.update(vars(a));score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else f'{a.name}_test.parquet');print('VALID',score,flush=True);newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt')\";new=\"params=dict(ck['args']);params.update(vars(a));newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt');score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else f'{a.name}_test.parquet');print('VALID',score,flush=True)\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nTOWER_THREADS=8 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name neural_item > /app/solution/neural_item.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read();old=\"params=dict(ck['args']);params.update(vars(a));score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else f'{a.name}_test.parquet');print('VALID',score,flush=True);newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt')\";new=\"params=dict(ck['args']);params.update(vars(a));newck={'model':model.state_dict(),'args':params,'epoch':epoch};torch.save(newck,P/f'{a.name}_last.pt');score=evaluate(model,hist,a.stage,recent=a.recent,save=f'{a.name}_validation.parquet' if a.stage=='validation' else f'{a.name}_test.parquet');print('VALID',score,flush=True)\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nTOWER_THREADS=8 /opt/task-venv/bin/python /app/solution/temporal_train.py --resume residual_pretrained.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name neural_item > /app/solution/neural_item.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 151,
      "nanos": 432076576
    },
    "formatted_output": ""
  }
}

123

Agent message

+39:10

Thinking unavailable No text recorded

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

124

exec command

+39:26

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 +39:27

text(await tools.exec_command({cmd:"cat /app/solution/activity.log && cat /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.current","max_output_tokens":1400}));
text(await tools.clock__curr_time({}));

Tool response +39:27

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"beb942","wall_time_seconds":0.00000439,"exit_code":0,"original_token_count":157,"output":"SLOPE -0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1.25, \"activity\": -0.5, \"ndcg\": 0.05244507524054621}\nSLOPE -0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1, \"activity\": -0.25, \"ndcg\": 0.05308906145278723}\nSLOPE 0 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0, \"ndcg\": 0.05360681409841245}\nSLOPE 0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.25, \"ndcg\": 0.052581767954277464}\nSLOPE 0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.5, \"ndcg\": 0.051662574326053506}\n{\"epoch\": 1, \"loss\": 8.168657443102669, \"seconds\": 134.2}\nVALID 0.03307194406089798\n2442719232\n"}
{"current_time":"2026-09-24 08:34:56 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-655755f5-90b2-4912-9632-39a95dff2c8d",
    "process_id": "4215",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/activity.log && cat /app/solution/neural_item.log && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/activity.log",
        "name": "activity.log",
        "path": "/app/solution/activity.log"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/neural_item.log",
        "name": "neural_item.log",
        "path": "/app/solution/neural_item.log"
      },
      {
        "type": "read",
        "cmd": "cat /sys/fs/cgroup/memory.current",
        "name": "memory.current",
        "path": "/sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "SLOPE -0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1.25, \"activity\": -0.5, \"ndcg\": 0.05244507524054621}\nSLOPE -0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1, \"activity\": -0.25, \"ndcg\": 0.05308906145278723}\nSLOPE 0 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0, \"ndcg\": 0.05360681409841245}\nSLOPE 0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.25, \"ndcg\": 0.052581767954277464}\nSLOPE 0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.5, \"ndcg\": 0.051662574326053506}\n{\"epoch\": 1, \"loss\": 8.168657443102669, \"seconds\": 134.2}\nVALID 0.03307194406089798\n2442719232\n",
    "stderr": "",
    "aggregated_output": "SLOPE -0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1.25, \"activity\": -0.5, \"ndcg\": 0.05244507524054621}\nSLOPE -0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1, \"activity\": -0.25, \"ndcg\": 0.05308906145278723}\nSLOPE 0 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0, \"ndcg\": 0.05360681409841245}\nSLOPE 0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.25, \"ndcg\": 0.052581767954277464}\nSLOPE 0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.5, \"ndcg\": 0.051662574326053506}\n{\"epoch\": 1, \"loss\": 8.168657443102669, \"seconds\": 134.2}\nVALID 0.03307194406089798\n2442719232\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4390
    },
    "formatted_output": "SLOPE -0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1.25, \"activity\": -0.5, \"ndcg\": 0.05244507524054621}\nSLOPE -0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 1, \"activity\": -0.25, \"ndcg\": 0.05308906145278723}\nSLOPE 0 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0, \"ndcg\": 0.05360681409841245}\nSLOPE 0.25 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.25, \"ndcg\": 0.052581767954277464}\nSLOPE 0.5 {\"recent\": 1, \"like\": 0, \"trend\": 1, \"pop\": 0.75, \"activity\": 0.5, \"ndcg\": 0.051662574326053506}\n{\"epoch\": 1, \"loss\": 8.168657443102669, \"seconds\": 134.2}\nVALID 0.03307194406089798\n2442719232\n"
  }
}

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
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  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 1286,
    "reasoning_output_tokens": 1171,
    "total_tokens": 108219
  },
  "turn_token_usage": {
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    "cached_input_tokens": 3818496,
    "cache_write_input_tokens": 0,
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    "cache_write_input_tokens": 0,
    "output_tokens": 68754,
    "reasoning_output_tokens": 41958,
    "total_tokens": 4022414
  }
}

125

Agent message

+39:46

Thinking unavailable No text recorded

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

126

exec command

+39:57

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +40:03

const results=await Promise.allSettled([
tools.exec_command({cmd:"TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint neural_item_best.pt --name neural_item_full --recent 1,3 --gamma 0.75,1,1.25 --pop 0.5,0.75,1,1.25 --kind discovery > /app/solution/neural_item_full.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}),
tools.exec_command({cmd:"TOWER_THREADS=6 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --resume final_pretrain_best.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name final_neural > /app/solution/final_neural.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000})
]);results.forEach(text);
text(await tools.clock__curr_time({}));

Tool response +40:05

Script completed
Wall time 1.2 seconds
Output:

{"status":"fulfilled","value":{"chunk_id":"527687","wall_time_seconds":1.000517067,"session_id":46003,"original_token_count":0,"output":""}}
{"status":"fulfilled","value":{"chunk_id":"cbd1c5","wall_time_seconds":1.001056247,"session_id":4336,"original_token_count":0,"output":""}}
{"current_time":"2026-09-24 08:35:34 UTC"}

Harness context token usage record

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  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4e0b1461887d2a6e4c79a3e46e299",
  "usage": {
    "input_tokens": 108613,
    "cached_input_tokens": 106752,
    "cache_write_input_tokens": 0,
    "output_tokens": 1111,
    "reasoning_output_tokens": 880,
    "total_tokens": 109724
  },
  "turn_token_usage": {
    "input_tokens": 4062273,
    "cached_input_tokens": 3925248,
    "cache_write_input_tokens": 0,
    "output_tokens": 69865,
    "reasoning_output_tokens": 42838,
    "total_tokens": 4132138
  },
  "thread_token_usage": {
    "input_tokens": 4062273,
    "cached_input_tokens": 3925248,
    "cache_write_input_tokens": 0,
    "output_tokens": 69865,
    "reasoning_output_tokens": 42838,
    "total_tokens": 4132138
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-314d3293-d791-4b41-914a-5deefffb5c62",
    "process_id": "46003",
    "command": [
      "/bin/sh",
      "-lc",
      "TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint neural_item_best.pt --name neural_item_full --recent 1,3 --gamma 0.75,1,1.25 --pop 0.5,0.75,1,1.25 --kind discovery > /app/solution/neural_item_full.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "TOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/calibrate.py --checkpoint neural_item_best.pt --name neural_item_full --recent 1,3 --gamma 0.75,1,1.25 --pop 0.5,0.75,1,1.25 --kind discovery > /app/solution/neural_item_full.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 11,
      "nanos": 433648997
    },
    "formatted_output": ""
  }
}

127

Agent message

+40:23

Thinking unavailable No text recorded

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

128

Agent message

+40:53

Thinking unavailable No text recorded

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

129

Agent message

+41:08

Thinking unavailable No text recorded

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

130

exec command

+41:24

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +41:38

text(await tools.exec_command({cmd:"cat > /app/solution/predict_saved.py <<'PY'\n\"\"\"Generate the final ranking using only the trained user tower and cached item vectors.\"\"\"\nfrom serve import *\ndef main():\n torch.set_num_threads(int(os.environ.get('TOWER_THREADS','4')));enc=HistoryEncoder();idx=FlatMIPSIndex();h=Histories('test');c=enc.calibration\n if c.get('fast'):h.recent=np.load(P/'fast_recent_test.npy')\n z=np.load(D/'test_eligible_candidates.npz');uids=z['uids'];columns=pd.Index(z['item_ids']).get_indexer(idx.item_ids);assert np.all(columns>=0);ranks=[]\n with torch.inference_mode():\n  for s in range(0,len(uids),128):\n   ur=uids[s:s+128];args=h.batch([h.index.get(int(u),-1) for u in ur],recent=c['recent'],like=c['like'],target=False);uv=enc.tower(*args);[email protected];mask=np.unpackbits(z['packed_eligible'][s:s+len(ur)],axis=1,count=len(z['item_ids']),bitorder='little').astype(bool)[:,columns];scores.masked_fill_(~torch.from_numpy(mask),-torch.inf);ranks.append(idx.item_ids[scores.topk(100,dim=1).indices.numpy()])\n out=pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':np.concatenate(ranks).ravel().astype('int64'),'rank':np.tile(np.arange(1,101),len(uids))});out.to_parquet(P/'submission.tmp.parquet',index=False);os.replace(P/'submission.tmp.parquet',P/'submission.parquet');print(json.dumps({'rows':len(out),'users':len(uids),'dimension':idx.vectors.shape[1]}))\nif __name__=='__main__':main()\nPY\ncat /app/solution/neural_item_full.log\ntail -4 /app/solution/final_neural.log\ncat /sys/fs/cgroup/memory.current","max_output_tokens":1400}));
text(await tools.clock__curr_time({}));

Tool response +41:38

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"ea09a7","wall_time_seconds":0.00002519,"exit_code":0,"original_token_count":101,"output":"BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.75, \"pop\": 1.0, \"ndcg\": 0.05336633852047688}\nBEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\nBEST {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\n6225088512\n"}
{"current_time":"2026-09-24 08:37:07 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-605fb394-5087-4ea0-a588-005f296b7fb7",
    "process_id": "44227",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/predict_saved.py <<'PY'\n\"\"\"Generate the final ranking using only the trained user tower and cached item vectors.\"\"\"\nfrom serve import *\ndef main():\n torch.set_num_threads(int(os.environ.get('TOWER_THREADS','4')));enc=HistoryEncoder();idx=FlatMIPSIndex();h=Histories('test');c=enc.calibration\n if c.get('fast'):h.recent=np.load(P/'fast_recent_test.npy')\n z=np.load(D/'test_eligible_candidates.npz');uids=z['uids'];columns=pd.Index(z['item_ids']).get_indexer(idx.item_ids);assert np.all(columns>=0);ranks=[]\n with torch.inference_mode():\n  for s in range(0,len(uids),128):\n   ur=uids[s:s+128];args=h.batch([h.index.get(int(u),-1) for u in ur],recent=c['recent'],like=c['like'],target=False);uv=enc.tower(*args);[email protected];mask=np.unpackbits(z['packed_eligible'][s:s+len(ur)],axis=1,count=len(z['item_ids']),bitorder='little').astype(bool)[:,columns];scores.masked_fill_(~torch.from_numpy(mask),-torch.inf);ranks.append(idx.item_ids[scores.topk(100,dim=1).indices.numpy()])\n out=pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':np.concatenate(ranks).ravel().astype('int64'),'rank':np.tile(np.arange(1,101),len(uids))});out.to_parquet(P/'submission.tmp.parquet',index=False);os.replace(P/'submission.tmp.parquet',P/'submission.parquet');print(json.dumps({'rows':len(out),'users':len(uids),'dimension':idx.vectors.shape[1]}))\nif __name__=='__main__':main()\nPY\ncat /app/solution/neural_item_full.log\ntail -4 /app/solution/final_neural.log\ncat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/predict_saved.py <<'PY'\n\"\"\"Generate the final ranking using only the trained user tower and cached item vectors.\"\"\"\nfrom serve import *\ndef main():\n torch.set_num_threads(int(os.environ.get('TOWER_THREADS','4')));enc=HistoryEncoder();idx=FlatMIPSIndex();h=Histories('test');c=enc.calibration\n if c.get('fast'):h.recent=np.load(P/'fast_recent_test.npy')\n z=np.load(D/'test_eligible_candidates.npz');uids=z['uids'];columns=pd.Index(z['item_ids']).get_indexer(idx.item_ids);assert np.all(columns>=0);ranks=[]\n with torch.inference_mode():\n  for s in range(0,len(uids),128):\n   ur=uids[s:s+128];args=h.batch([h.index.get(int(u),-1) for u in ur],recent=c['recent'],like=c['like'],target=False);uv=enc.tower(*args);[email protected];mask=np.unpackbits(z['packed_eligible'][s:s+len(ur)],axis=1,count=len(z['item_ids']),bitorder='little').astype(bool)[:,columns];scores.masked_fill_(~torch.from_numpy(mask),-torch.inf);ranks.append(idx.item_ids[scores.topk(100,dim=1).indices.numpy()])\n out=pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':np.concatenate(ranks).ravel().astype('int64'),'rank':np.tile(np.arange(1,101),len(uids))});out.to_parquet(P/'submission.tmp.parquet',index=False);os.replace(P/'submission.tmp.parquet',P/'submission.parquet');print(json.dumps({'rows':len(out),'users':len(uids),'dimension':idx.vectors.shape[1]}))\nif __name__=='__main__':main()\nPY\ncat /app/solution/neural_item_full.log\ntail -4 /app/solution/final_neural.log\ncat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.75, \"pop\": 1.0, \"ndcg\": 0.05336633852047688}\nBEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\nBEST {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\n6225088512\n",
    "stderr": "",
    "aggregated_output": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.75, \"pop\": 1.0, \"ndcg\": 0.05336633852047688}\nBEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\nBEST {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\n6225088512\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 25190
    },
    "formatted_output": "BEST_SO_FAR {\"recent\": 1.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 0.75, \"pop\": 1.0, \"ndcg\": 0.05336633852047688}\nBEST_SO_FAR {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\nBEST {\"recent\": 3.0, \"like\": 0.0, \"fast\": false, \"kind\": \"discovery\", \"trend\": 1.0, \"pop\": 0.75, \"ndcg\": 0.05338953641537794}\n6225088512\n"
  }
}

Harness context token usage record

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131

Agent message

+41:56

Thinking unavailable No text recorded

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

132

Agent message

+42:01

Thinking unavailable No text recorded

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

Model text

A small residual MLP in the item tower matched the best single-model result at 0.053390 NDCG@10. It adds no vector dimensions or request-time item computation. I’m refitting this version on all observed history and will export its item vectors once training completes.

133

exec command

+42:21

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 +42:26

text(await tools.exec_command({cmd:"cp /app/solution/neural_item_best.pt /app/solution/selected_validation.pt\npython - <<'PY'\nimport json\np='/app/solution/selected_config.json';c=json.load(open(p));c.update(item_mlp=64,recent=3,trend=1,pop=.75,validation_ndcg=.05338953641537794);open(p,'w').write(json.dumps(c,indent=2))\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint selected_validation.pt --stage validation --recent 3 --trend 1 --pop .75 --kind discovery > /app/solution/selected_validation.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation.json","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/final_neural.log && ls -lh /app/solution/final_neural*pt && cat /sys/fs/cgroup/memory.current","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +42:27

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"cc4988","wall_time_seconds":1.000989257,"session_id":38942,"original_token_count":0,"output":""}
{"chunk_id":"50cd0b","wall_time_seconds":0.000005231,"exit_code":2,"original_token_count":20,"output":"ls: cannot access '/app/solution/final_neural*pt': No such file or directory\n"}
{"current_time":"2026-09-24 08:37:56 UTC"}

Harness context token usage record

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  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4e13393b887d2be47c43f3f17e1df",
  "usage": {
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}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-30dead73-caad-494b-a287-085e3d423562",
    "process_id": "82209",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/final_neural.log && ls -lh /app/solution/final_neural*pt && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -4 /app/solution/final_neural.log && ls -lh /app/solution/final_neural*pt && cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "ls: cannot access '/app/solution/final_neural*pt': No such file or directory\n",
    "stderr": "",
    "aggregated_output": "ls: cannot access '/app/solution/final_neural*pt': No such file or directory\n",
    "exit_code": 2,
    "duration": {
      "secs": 0,
      "nanos": 5231
    },
    "formatted_output": "ls: cannot access '/app/solution/final_neural*pt': No such file or directory\n"
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f3f22079-9ec1-4c5f-ac55-97c4a4bde3a2",
    "process_id": "4336",
    "command": [
      "/bin/sh",
      "-lc",
      "TOWER_THREADS=6 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --resume final_pretrain_best.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name final_neural > /app/solution/final_neural.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "TOWER_THREADS=6 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --resume final_pretrain_best.pt --epochs 1 --lr .0003 --recent 1 --item-mlp 64 --name final_neural > /app/solution/final_neural.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 153,
      "nanos": 480205987
    },
    "formatted_output": ""
  }
}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-40baed87-d0d5-449b-9a82-f5f093bc8ce6",
    "process_id": "38942",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/neural_item_best.pt /app/solution/selected_validation.pt\npython - <<'PY'\nimport json\np='/app/solution/selected_config.json';c=json.load(open(p));c.update(item_mlp=64,recent=3,trend=1,pop=.75,validation_ndcg=.05338953641537794);open(p,'w').write(json.dumps(c,indent=2))\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint selected_validation.pt --stage validation --recent 3 --trend 1 --pop .75 --kind discovery > /app/solution/selected_validation.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/neural_item_best.pt /app/solution/selected_validation.pt\npython - <<'PY'\nimport json\np='/app/solution/selected_config.json';c=json.load(open(p));c.update(item_mlp=64,recent=3,trend=1,pop=.75,validation_ndcg=.05338953641537794);open(p,'w').write(json.dumps(c,indent=2))\nPY\nTOWER_THREADS=2 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint selected_validation.pt --stage validation --recent 3 --trend 1 --pop .75 --kind discovery > /app/solution/selected_validation.log 2>&1\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation.json"
      }
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      "nanos": 234079192
    },
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  }
}

134

Agent message

+42:51

Thinking unavailable No text recorded

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

135

Agent message

+43:12

Thinking unavailable No text recorded

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

136

exec command

+43:21

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 +43:23

text(await tools.exec_command({cmd:"cat /app/solution/final_neural.log && cat /app/solution/public_validation.json && ls -lh /app/solution/final_neural*pt && cat /sys/fs/cgroup/memory.current","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +43:23

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"f461e6","wall_time_seconds":0.00000311,"exit_code":0,"original_token_count":91,"output":"{\"epoch\": 1, \"loss\": 8.17270690202713, \"seconds\": 135.1}\nVALID None\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_last.pt\n4152356864\n"}
{"current_time":"2026-09-24 08:38:52 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c310324f-cf36-471b-9fb8-9a882cab9dc1",
    "process_id": "15486",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/final_neural.log && cat /app/solution/public_validation.json && ls -lh /app/solution/final_neural*pt && cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"epoch\": 1, \"loss\": 8.17270690202713, \"seconds\": 135.1}\nVALID None\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_last.pt\n4152356864\n",
    "stderr": "",
    "aggregated_output": "{\"epoch\": 1, \"loss\": 8.17270690202713, \"seconds\": 135.1}\nVALID None\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_last.pt\n4152356864\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3110
    },
    "formatted_output": "{\"epoch\": 1, \"loss\": 8.17270690202713, \"seconds\": 135.1}\nVALID None\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_best.pt\n-rw-r--r-- 1 agent agent 139M Sep 24 08:38 /app/solution/final_neural_last.pt\n4152356864\n"
  }
}

Harness context token usage record

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137

exec command

+43:41

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +43:46

text(await tools.exec_command({cmd:"set -e\ncp /app/solution/final_neural_best.pt /app/solution/final_refit_best.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 3 --trend 1 --pop .75 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +43:47

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"317124","wall_time_seconds":1.000715246,"session_id":98653,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:39:16 UTC"}

Harness context token usage record

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138

Agent message

+44:11

Thinking unavailable No text recorded

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

Harness context codex item completed

{
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  "item": {
    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "set -e\ncp /app/solution/final_neural_best.pt /app/solution/final_refit_best.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 3 --trend 1 --pop .75 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "set -e\ncp /app/solution/final_neural_best.pt /app/solution/final_refit_best.pt\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 3 --trend 1 --pop .75 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 29,
      "nanos": 710509572
    },
    "formatted_output": ""
  }
}

139

Agent message

+44:30

Thinking unavailable No text recorded

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

140

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+44:49

Thinking unavailable No text recorded

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

141

exec command

+45:01

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +45:03

text(await tools.exec_command({cmd:"cat /app/solution/final_infer.log && cat /app/solution/predict_saved.log && cat /app/solution/verify.log && cat /sys/fs/cgroup/memory.events","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response +45:03

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"dd7089","wall_time_seconds":0.0000032,"exit_code":0,"original_token_count":188,"output":"{\"output\": \"submission.parquet\", \"ndcg\": null, \"calibration\": {\"fast\": false, \"checkpoint\": \"final_refit_best.pt\", \"stage\": \"test\", \"recent\": 3.0, \"like\": 0, \"trend\": 1.0, \"pop\": 0.75, \"kind\": \"discovery\", \"output\": null, \"save_model\": \"final_model.pt\"}}\n{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.1824040000428795,\n  \"encoder_ms_p95\": 0.3211402499800897,\n  \"cached_pool_encoder_ms_median\": 0.11297700007162348,\n  \"cached_pool_encoder_ms_p95\": 0.13761800016709458,\n  \"torch_threads\": 1\n}\nlow 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n"}
{"current_time":"2026-09-24 08:40:32 UTC"}

Harness context codex item completed

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      "/bin/sh",
      "-lc",
      "cat /app/solution/final_infer.log && cat /app/solution/predict_saved.log && cat /app/solution/verify.log && cat /sys/fs/cgroup/memory.events"
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"output\": \"submission.parquet\", \"ndcg\": null, \"calibration\": {\"fast\": false, \"checkpoint\": \"final_refit_best.pt\", \"stage\": \"test\", \"recent\": 3.0, \"like\": 0, \"trend\": 1.0, \"pop\": 0.75, \"kind\": \"discovery\", \"output\": null, \"save_model\": \"final_model.pt\"}}\n{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.1824040000428795,\n  \"encoder_ms_p95\": 0.3211402499800897,\n  \"cached_pool_encoder_ms_median\": 0.11297700007162348,\n  \"cached_pool_encoder_ms_p95\": 0.13761800016709458,\n  \"torch_threads\": 1\n}\nlow 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n",
    "stderr": "",
    "aggregated_output": "{\"output\": \"submission.parquet\", \"ndcg\": null, \"calibration\": {\"fast\": false, \"checkpoint\": \"final_refit_best.pt\", \"stage\": \"test\", \"recent\": 3.0, \"like\": 0, \"trend\": 1.0, \"pop\": 0.75, \"kind\": \"discovery\", \"output\": null, \"save_model\": \"final_model.pt\"}}\n{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.1824040000428795,\n  \"encoder_ms_p95\": 0.3211402499800897,\n  \"cached_pool_encoder_ms_median\": 0.11297700007162348,\n  \"cached_pool_encoder_ms_p95\": 0.13761800016709458,\n  \"torch_threads\": 1\n}\nlow 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3200
    },
    "formatted_output": "{\"output\": \"submission.parquet\", \"ndcg\": null, \"calibration\": {\"fast\": false, \"checkpoint\": \"final_refit_best.pt\", \"stage\": \"test\", \"recent\": 3.0, \"like\": 0, \"trend\": 1.0, \"pop\": 0.75, \"kind\": \"discovery\", \"output\": null, \"save_model\": \"final_model.pt\"}}\n{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.1824040000428795,\n  \"encoder_ms_p95\": 0.3211402499800897,\n  \"cached_pool_encoder_ms_median\": 0.11297700007162348,\n  \"cached_pool_encoder_ms_p95\": 0.13761800016709458,\n  \"torch_threads\": 1\n}\nlow 0\nhigh 0\nmax 17171\noom 10\noom_kill 3\noom_group_kill 0\nsock_throttled 7\n"
  }
}

Harness context token usage record

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  "type": "token_usage_record",
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  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
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}

142

Agent message

+45:21

Thinking unavailable No text recorded

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

143

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+45:37

Thinking unavailable No text recorded

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

144

Agent message

+45:52

Thinking unavailable No text recorded

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

145

exec command

+46:08

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 refitted model is exported, and the submission passed all checks: 100 distinct eligible items for each of 8,456 users. Sampled rankings exactly matched retrieval from the cached item vectors. On this CPU, user encoding measured 0.32 ms at p95, or 0.14 ms with a cached history pool, excluding retrieval.

Tool callexecTool call Returned

Tool call · Input +46:52

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/build_discovery_trends.py';s=open(p).read().replace(\"for stage in ['validation','test']:\", \"import sys\\nstages=sys.argv[1:] or ['validation','test']\\nassert all(s in ['validation','test'] for s in stages)\\nfor stage in stages:\");open(p,'w').write(s)\np='/app/solution/run_final.py';s=open(p).read().replace(\"run('build_discovery_trends.py')\", \"run('build_discovery_trends.py','test')\");open(p,'w').write(s)\nPY\ncat > /app/solution/run_validation.py <<'PY'\n\"\"\"Reproduce the selected validation architecture using training history only.\"\"\"\nimport os,sys,json,subprocess\nfrom run_final import P,run\n\ndef main():\n os.environ.setdefault('TOWER_THREADS','4');c=json.loads((P/'selected_config.json').read_text())\n run('prepare_catalog.py');run('build_data.py','validation');run('build_temporal.py','--stage','validation','--weeks',16);run('build_discovery_trends.py','validation')\n run('train.py','--variant','residual','--epochs',c['pretrain_epochs'],'--eval-every',c['pretrain_epochs'],'--name','validation_pretrain','--drop',.6,'--dim',c['dim'],'--latent',c['latent'])\n run('temporal_train.py','--resume','validation_pretrain_best.pt','--epochs',c['temporal_epochs'],'--lr',.0003,'--recent',1,'--item-mlp',c['item_mlp'],'--name','validation_refit')\n run('infer.py','--checkpoint','validation_refit_best.pt','--stage','validation','--recent',c['recent'],'--like',c['like'],'--trend',c['trend'],'--pop',c['pop'],'--kind','discovery')\n report=subprocess.check_output([sys.executable,'/task/evaluate_public.py',str(P/'validation.parquet')],text=True);print(report);(P/'public_validation.json').write_text(report)\nif __name__=='__main__':main()\nPY\ncat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains **845,600 rows: 100 distinct eligible tracks for each of 8,456 target users**. The supplied public evaluator reports **graded NDCG@10 = 0.053390**, over 4,263 users with eligible relevance labels. See `public_validation.json` and `verification.json`.\n\n## Model and scoring\n\nBoth towers are trainable PyTorch neural networks, producing **601-dimensional vectors**:\n\n- **User tower:** frequency-compressed, recency-weighted interaction history and cutoff-correct explicit preferences → 600-dimensional item `EmbeddingBag` → LayerNorm → 600–200–600 tanh MLP, with a learned residual from the pooled history. A constant coordinate carries item bias. There are **no user-ID model parameters**.\n- **Item tower:** trainable 600-dimensional item embedding → residual 600–64–600 GELU MLP → tanh, plus an item-bias coordinate. Cutoff-specific first-discovery popularity and trend calibration are included in that coordinate.\n\nEvery relevance score is exactly **`user_vector @ item_vector`**. Item vectors are exported once to `item_vectors.npy`; `serve.py` supplies a flat maximum-inner-product index, which can be replaced by an ANN index without changing scoring. The platform eligibility mask is the only subsequent restriction.\n\nThe local single-thread encoder microbenchmark measured **0.32 ms p95**, or **0.14 ms p95** using an exactly equivalent cached history pool (32 histories, three repeats; excludes retrieval and feature lookup).\n\n## Training and temporal separation\n\nEight denoising epochs use positive-listen histories with 60% input dropout. One temporal fine-tuning epoch uses historical weekly snapshots, 20% history dropout, and masked multinomial cross-entropy: previously positive or actively liked/disliked tracks are excluded from snapshot targets. New organic strong listens have weight 1; organic likes have weight 7. The item MLP is introduced and trained during temporal fine-tuning.\n\nValidation features and fitted weights use only training events. Model selection uses subsequent validation outcomes. The final model is refit from scratch on **train + validation**, with updated explicit state and discovery statistics, including the validation week as an additional temporal training window.\n\nTraining uses event records and ragged histories, never a global user–item interaction matrix or its decomposition. Only minibatch score/target tensors are dense. Both training stages support bounded sampled-negative softmax (`--negatives`), enabled automatically beyond 100,000 items. The organic objective is an offline target, not a causal lift estimate.\n\n## Commands\n\nRegenerate the final submission from the saved user tower and cached item index:\n\n```sh\n/opt/task-venv/bin/python /app/solution/predict_saved.py\n```\n\nRefit and regenerate everything from the supplied events:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run_final.py\n```\n\n`run_validation.py` reproduces validation training and evaluation. `verify.py` checks coverage, ranks, uniqueness, eligibility, cached-vector consistency, and sampled exact retrieval. `selected_config.json` records the selected hyperparameters. All generated files stay under `/app/solution/`.\nMD\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py /app/solution/temporal_train.py /app/solution/infer.py /app/solution/serve.py /app/solution/predict_saved.py /app/solution/run_final.py /app/solution/run_validation.py /app/solution/verify.py\npython - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution');print('total GB',sum(x.stat().st_size for x in p.rglob('*') if x.is_file())/1e9)\nfor x in sorted(p.iterdir(),key=lambda x:x.stat().st_size,reverse=True)[:8]:print(x.name,round(x.stat().st_size/1e6))\nPY","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +46:53

Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"0978db","wall_time_seconds":0.088938273,"exit_code":0,"original_token_count":50,"output":"total GB 9.799018242\ntemporal_test.npz 817\ntemporal_validation.npz 757\ncatalog_train.parquet 310\nwide_full_source.pt 248\nwide_epoch4.pt 248\nwide_epoch8.pt 248\nwide_epoch6.pt 248\nwide_epoch2.pt 248\n"}
{"current_time":"2026-09-24 08:42:21 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9f09ba15-0681-439d-8c5a-123c7eefdf99",
    "process_id": "55820",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/build_discovery_trends.py';s=open(p).read().replace(\"for stage in ['validation','test']:\", \"import sys\\nstages=sys.argv[1:] or ['validation','test']\\nassert all(s in ['validation','test'] for s in stages)\\nfor stage in stages:\");open(p,'w').write(s)\np='/app/solution/run_final.py';s=open(p).read().replace(\"run('build_discovery_trends.py')\", \"run('build_discovery_trends.py','test')\");open(p,'w').write(s)\nPY\ncat > /app/solution/run_validation.py <<'PY'\n\"\"\"Reproduce the selected validation architecture using training history only.\"\"\"\nimport os,sys,json,subprocess\nfrom run_final import P,run\n\ndef main():\n os.environ.setdefault('TOWER_THREADS','4');c=json.loads((P/'selected_config.json').read_text())\n run('prepare_catalog.py');run('build_data.py','validation');run('build_temporal.py','--stage','validation','--weeks',16);run('build_discovery_trends.py','validation')\n run('train.py','--variant','residual','--epochs',c['pretrain_epochs'],'--eval-every',c['pretrain_epochs'],'--name','validation_pretrain','--drop',.6,'--dim',c['dim'],'--latent',c['latent'])\n run('temporal_train.py','--resume','validation_pretrain_best.pt','--epochs',c['temporal_epochs'],'--lr',.0003,'--recent',1,'--item-mlp',c['item_mlp'],'--name','validation_refit')\n run('infer.py','--checkpoint','validation_refit_best.pt','--stage','validation','--recent',c['recent'],'--like',c['like'],'--trend',c['trend'],'--pop',c['pop'],'--kind','discovery')\n report=subprocess.check_output([sys.executable,'/task/evaluate_public.py',str(P/'validation.parquet')],text=True);print(report);(P/'public_validation.json').write_text(report)\nif __name__=='__main__':main()\nPY\ncat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains **845,600 rows: 100 distinct eligible tracks for each of 8,456 target users**. The supplied public evaluator reports **graded NDCG@10 = 0.053390**, over 4,263 users with eligible relevance labels. See `public_validation.json` and `verification.json`.\n\n## Model and scoring\n\nBoth towers are trainable PyTorch neural networks, producing **601-dimensional vectors**:\n\n- **User tower:** frequency-compressed, recency-weighted interaction history and cutoff-correct explicit preferences → 600-dimensional item `EmbeddingBag` → LayerNorm → 600–200–600 tanh MLP, with a learned residual from the pooled history. A constant coordinate carries item bias. There are **no user-ID model parameters**.\n- **Item tower:** trainable 600-dimensional item embedding → residual 600–64–600 GELU MLP → tanh, plus an item-bias coordinate. Cutoff-specific first-discovery popularity and trend calibration are included in that coordinate.\n\nEvery relevance score is exactly **`user_vector @ item_vector`**. Item vectors are exported once to `item_vectors.npy`; `serve.py` supplies a flat maximum-inner-product index, which can be replaced by an ANN index without changing scoring. The platform eligibility mask is the only subsequent restriction.\n\nThe local single-thread encoder microbenchmark measured **0.32 ms p95**, or **0.14 ms p95** using an exactly equivalent cached history pool (32 histories, three repeats; excludes retrieval and feature lookup).\n\n## Training and temporal separation\n\nEight denoising epochs use positive-listen histories with 60% input dropout. One temporal fine-tuning epoch uses historical weekly snapshots, 20% history dropout, and masked multinomial cross-entropy: previously positive or actively liked/disliked tracks are excluded from snapshot targets. New organic strong listens have weight 1; organic likes have weight 7. The item MLP is introduced and trained during temporal fine-tuning.\n\nValidation features and fitted weights use only training events. Model selection uses subsequent validation outcomes. The final model is refit from scratch on **train + validation**, with updated explicit state and discovery statistics, including the validation week as an additional temporal training window.\n\nTraining uses event records and ragged histories, never a global user–item interaction matrix or its decomposition. Only minibatch score/target tensors are dense. Both training stages support bounded sampled-negative softmax (`--negatives`), enabled automatically beyond 100,000 items. The organic objective is an offline target, not a causal lift estimate.\n\n## Commands\n\nRegenerate the final submission from the saved user tower and cached item index:\n\n```sh\n/opt/task-venv/bin/python /app/solution/predict_saved.py\n```\n\nRefit and regenerate everything from the supplied events:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run_final.py\n```\n\n`run_validation.py` reproduces validation training and evaluation. `verify.py` checks coverage, ranks, uniqueness, eligibility, cached-vector consistency, and sampled exact retrieval. `selected_config.json` records the selected hyperparameters. All generated files stay under `/app/solution/`.\nMD\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py /app/solution/temporal_train.py /app/solution/infer.py /app/solution/serve.py /app/solution/predict_saved.py /app/solution/run_final.py /app/solution/run_validation.py /app/solution/verify.py\npython - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution');print('total GB',sum(x.stat().st_size for x in p.rglob('*') if x.is_file())/1e9)\nfor x in sorted(p.iterdir(),key=lambda x:x.stat().st_size,reverse=True)[:8]:print(x.name,round(x.stat().st_size/1e6))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/build_discovery_trends.py';s=open(p).read().replace(\"for stage in ['validation','test']:\", \"import sys\\nstages=sys.argv[1:] or ['validation','test']\\nassert all(s in ['validation','test'] for s in stages)\\nfor stage in stages:\");open(p,'w').write(s)\np='/app/solution/run_final.py';s=open(p).read().replace(\"run('build_discovery_trends.py')\", \"run('build_discovery_trends.py','test')\");open(p,'w').write(s)\nPY\ncat > /app/solution/run_validation.py <<'PY'\n\"\"\"Reproduce the selected validation architecture using training history only.\"\"\"\nimport os,sys,json,subprocess\nfrom run_final import P,run\n\ndef main():\n os.environ.setdefault('TOWER_THREADS','4');c=json.loads((P/'selected_config.json').read_text())\n run('prepare_catalog.py');run('build_data.py','validation');run('build_temporal.py','--stage','validation','--weeks',16);run('build_discovery_trends.py','validation')\n run('train.py','--variant','residual','--epochs',c['pretrain_epochs'],'--eval-every',c['pretrain_epochs'],'--name','validation_pretrain','--drop',.6,'--dim',c['dim'],'--latent',c['latent'])\n run('temporal_train.py','--resume','validation_pretrain_best.pt','--epochs',c['temporal_epochs'],'--lr',.0003,'--recent',1,'--item-mlp',c['item_mlp'],'--name','validation_refit')\n run('infer.py','--checkpoint','validation_refit_best.pt','--stage','validation','--recent',c['recent'],'--like',c['like'],'--trend',c['trend'],'--pop',c['pop'],'--kind','discovery')\n report=subprocess.check_output([sys.executable,'/task/evaluate_public.py',str(P/'validation.parquet')],text=True);print(report);(P/'public_validation.json').write_text(report)\nif __name__=='__main__':main()\nPY\ncat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains **845,600 rows: 100 distinct eligible tracks for each of 8,456 target users**. The supplied public evaluator reports **graded NDCG@10 = 0.053390**, over 4,263 users with eligible relevance labels. See `public_validation.json` and `verification.json`.\n\n## Model and scoring\n\nBoth towers are trainable PyTorch neural networks, producing **601-dimensional vectors**:\n\n- **User tower:** frequency-compressed, recency-weighted interaction history and cutoff-correct explicit preferences → 600-dimensional item `EmbeddingBag` → LayerNorm → 600–200–600 tanh MLP, with a learned residual from the pooled history. A constant coordinate carries item bias. There are **no user-ID model parameters**.\n- **Item tower:** trainable 600-dimensional item embedding → residual 600–64–600 GELU MLP → tanh, plus an item-bias coordinate. Cutoff-specific first-discovery popularity and trend calibration are included in that coordinate.\n\nEvery relevance score is exactly **`user_vector @ item_vector`**. Item vectors are exported once to `item_vectors.npy`; `serve.py` supplies a flat maximum-inner-product index, which can be replaced by an ANN index without changing scoring. The platform eligibility mask is the only subsequent restriction.\n\nThe local single-thread encoder microbenchmark measured **0.32 ms p95**, or **0.14 ms p95** using an exactly equivalent cached history pool (32 histories, three repeats; excludes retrieval and feature lookup).\n\n## Training and temporal separation\n\nEight denoising epochs use positive-listen histories with 60% input dropout. One temporal fine-tuning epoch uses historical weekly snapshots, 20% history dropout, and masked multinomial cross-entropy: previously positive or actively liked/disliked tracks are excluded from snapshot targets. New organic strong listens have weight 1; organic likes have weight 7. The item MLP is introduced and trained during temporal fine-tuning.\n\nValidation features and fitted weights use only training events. Model selection uses subsequent validation outcomes. The final model is refit from scratch on **train + validation**, with updated explicit state and discovery statistics, including the validation week as an additional temporal training window.\n\nTraining uses event records and ragged histories, never a global user–item interaction matrix or its decomposition. Only minibatch score/target tensors are dense. Both training stages support bounded sampled-negative softmax (`--negatives`), enabled automatically beyond 100,000 items. The organic objective is an offline target, not a causal lift estimate.\n\n## Commands\n\nRegenerate the final submission from the saved user tower and cached item index:\n\n```sh\n/opt/task-venv/bin/python /app/solution/predict_saved.py\n```\n\nRefit and regenerate everything from the supplied events:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run_final.py\n```\n\n`run_validation.py` reproduces validation training and evaluation. `verify.py` checks coverage, ranks, uniqueness, eligibility, cached-vector consistency, and sampled exact retrieval. `selected_config.json` records the selected hyperparameters. All generated files stay under `/app/solution/`.\nMD\n/opt/task-venv/bin/python -m py_compile /app/solution/train.py /app/solution/temporal_train.py /app/solution/infer.py /app/solution/serve.py /app/solution/predict_saved.py /app/solution/run_final.py /app/solution/run_validation.py /app/solution/verify.py\npython - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution');print('total GB',sum(x.stat().st_size for x in p.rglob('*') if x.is_file())/1e9)\nfor x in sorted(p.iterdir(),key=lambda x:x.stat().st_size,reverse=True)[:8]:print(x.name,round(x.stat().st_size/1e6))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total GB 9.799018242\ntemporal_test.npz 817\ntemporal_validation.npz 757\ncatalog_train.parquet 310\nwide_full_source.pt 248\nwide_epoch4.pt 248\nwide_epoch8.pt 248\nwide_epoch6.pt 248\nwide_epoch2.pt 248\n",
    "stderr": "",
    "aggregated_output": "total GB 9.799018242\ntemporal_test.npz 817\ntemporal_validation.npz 757\ncatalog_train.parquet 310\nwide_full_source.pt 248\nwide_epoch4.pt 248\nwide_epoch8.pt 248\nwide_epoch6.pt 248\nwide_epoch2.pt 248\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 88868171
    },
    "formatted_output": "total GB 9.799018242\ntemporal_test.npz 817\ntemporal_validation.npz 757\ncatalog_train.parquet 310\nwide_full_source.pt 248\nwide_epoch4.pt 248\nwide_epoch8.pt 248\nwide_epoch6.pt 248\nwide_epoch2.pt 248\n"
  }
}

Harness context token usage record

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Model text

The documentation and reproduction commands are complete. I’m removing unused training caches and experimental checkpoints, then running one final check of the saved-model prediction command and submission.

Tool callexecTool call Returned

Tool call · Input +49:36

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport json,ast\np=Path('/app/solution')\nresults={}\nfor name in ['early_full','temporal_early_full','average_full','residual_full','residual_average_full','recent_full','combined','wide_full','activity','neural_item_full']:\n f=p/(name+'.json')\n if f.exists():\n  d=json.loads(f.read_text());results[name]={'best':d.get('best'),'checkpoint':d.get('checkpoint')}\n(p/'development_results.json').write_text(json.dumps(results,indent=2))\nkeep={'final_model.pt','final_refit_best.pt','selected_validation.pt'}\nremoved=0\nfor f in p.glob('*.pt'):\n if f.name not in keep:removed+=f.stat().st_size;f.unlink()\nfor name in ['temporal_test.npz','temporal_validation.npz','catalog_train.parquet','fast_recent_test.npy','fast_recent_validation.npy']:\n f=p/name\n if f.exists():removed+=f.stat().st_size;f.unlink()\n# Format via an AST-preserving transformation using only the standard library.\nfor name in ['train.py','temporal_train.py','build_data.py','build_temporal.py','build_discovery_trends.py','infer.py','serve.py','verify.py','predict_saved.py','run_final.py','run_validation.py','prepare_catalog.py','calibrate.py']:\n f=p/name;before=ast.parse(f.read_text());formatted=ast.unparse(before)+'\\n';assert ast.dump(before)==ast.dump(ast.parse(formatted));f.write_text(formatted)\nprint('Removed unused artifacts, GB:',round(removed/1e9,2));print('Remaining GB:',round(sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9,2));print('README words:',len((p/'README.md').read_text().split()))\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +49:37

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"ba2292","wall_time_seconds":1.000699829,"session_id":98440,"original_token_count":18,"output":"Removed unused artifacts, GB: 8.99\nRemaining GB: 0.81\nREADME words: 406\n"}
{"current_time":"2026-09-24 08:45:06 UTC"}

Harness context token usage record

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  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 85302,
    "reasoning_output_tokens": 55021,
    "total_tokens": 4959970
  }
}

154

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+49:57

Thinking unavailable No text recorded

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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-70d831fd-3f35-4c7c-b5bc-5636ca890f1a",
    "process_id": "98440",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nfrom pathlib import Path\nimport json,ast\np=Path('/app/solution')\nresults={}\nfor name in ['early_full','temporal_early_full','average_full','residual_full','residual_average_full','recent_full','combined','wide_full','activity','neural_item_full']:\n f=p/(name+'.json')\n if f.exists():\n  d=json.loads(f.read_text());results[name]={'best':d.get('best'),'checkpoint':d.get('checkpoint')}\n(p/'development_results.json').write_text(json.dumps(results,indent=2))\nkeep={'final_model.pt','final_refit_best.pt','selected_validation.pt'}\nremoved=0\nfor f in p.glob('*.pt'):\n if f.name not in keep:removed+=f.stat().st_size;f.unlink()\nfor name in ['temporal_test.npz','temporal_validation.npz','catalog_train.parquet','fast_recent_test.npy','fast_recent_validation.npy']:\n f=p/name\n if f.exists():removed+=f.stat().st_size;f.unlink()\n# Format via an AST-preserving transformation using only the standard library.\nfor name in ['train.py','temporal_train.py','build_data.py','build_temporal.py','build_discovery_trends.py','infer.py','serve.py','verify.py','predict_saved.py','run_final.py','run_validation.py','prepare_catalog.py','calibrate.py']:\n f=p/name;before=ast.parse(f.read_text());formatted=ast.unparse(before)+'\\n';assert ast.dump(before)==ast.dump(ast.parse(formatted));f.write_text(formatted)\nprint('Removed unused artifacts, GB:',round(removed/1e9,2));print('Remaining GB:',round(sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9,2));print('README words:',len((p/'README.md').read_text().split()))\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport json,ast\np=Path('/app/solution')\nresults={}\nfor name in ['early_full','temporal_early_full','average_full','residual_full','residual_average_full','recent_full','combined','wide_full','activity','neural_item_full']:\n f=p/(name+'.json')\n if f.exists():\n  d=json.loads(f.read_text());results[name]={'best':d.get('best'),'checkpoint':d.get('checkpoint')}\n(p/'development_results.json').write_text(json.dumps(results,indent=2))\nkeep={'final_model.pt','final_refit_best.pt','selected_validation.pt'}\nremoved=0\nfor f in p.glob('*.pt'):\n if f.name not in keep:removed+=f.stat().st_size;f.unlink()\nfor name in ['temporal_test.npz','temporal_validation.npz','catalog_train.parquet','fast_recent_test.npy','fast_recent_validation.npy']:\n f=p/name\n if f.exists():removed+=f.stat().st_size;f.unlink()\n# Format via an AST-preserving transformation using only the standard library.\nfor name in ['train.py','temporal_train.py','build_data.py','build_temporal.py','build_discovery_trends.py','infer.py','serve.py','verify.py','predict_saved.py','run_final.py','run_validation.py','prepare_catalog.py','calibrate.py']:\n f=p/name;before=ast.parse(f.read_text());formatted=ast.unparse(before)+'\\n';assert ast.dump(before)==ast.dump(ast.parse(formatted));f.write_text(formatted)\nprint('Removed unused artifacts, GB:',round(removed/1e9,2));print('Remaining GB:',round(sum(f.stat().st_size for f in p.rglob('*') if f.is_file())/1e9,2));print('README words:',len((p/'README.md').read_text().split()))\nPY\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Removed unused artifacts, GB: 8.99\nRemaining GB: 0.81\nREADME words: 406\n",
    "stderr": "",
    "aggregated_output": "Removed unused artifacts, GB: 8.99\nRemaining GB: 0.81\nREADME words: 406\n",
    "exit_code": 0,
    "duration": {
      "secs": 22,
      "nanos": 513236314
    },
    "formatted_output": "Removed unused artifacts, GB: 8.99\nRemaining GB: 0.81\nREADME words: 406\n"
  }
}

155

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+50:15

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156

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+50:38

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157

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+50:57

Thinking unavailable No text recorded

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158

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+51:16

Thinking unavailable No text recorded

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159

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+51:32

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Tool callexecTool call Returned

Tool call · Input +51:33

text(await tools.exec_command({cmd:"cat /app/solution/verify.log && cat /app/solution/public_validation.json && cat /app/solution/selected_config.json && ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/final_model.pt /app/solution/item_vectors.npy","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response +51:33

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"e94e27","wall_time_seconds":0.0000069,"exit_code":0,"original_token_count":259,"output":"{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.17869300017991918,\n  \"encoder_ms_p95\": 0.322575750260512,\n  \"cached_pool_encoder_ms_median\": 0.1101219995689462,\n  \"cached_pool_encoder_ms_p95\": 0.14610775019718858,\n  \"torch_threads\": 1\n}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"dim\": 600,\n  \"latent\": 200,\n  \"item_mlp\": 64,\n  \"pretrain_epochs\": 8,\n  \"temporal_epochs\": 1,\n  \"recent\": 3,\n  \"like\": 0,\n  \"trend\": 1,\n  \"pop\": 0.75,\n  \"validation_checkpoint\": \"selected_validation.pt\",\n  \"validation_ndcg\": 0.05338953641537794\n}-rw-r--r-- 1 agent agent 3.2K Sep 24 08:42 /app/solution/README.md\n-rw-r--r-- 1 agent agent 139M Sep 24 08:39 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent  69M Sep 24 08:39 /app/solution/item_vectors.npy\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:45 /app/solution/submission.parquet\n"}
{"current_time":"2026-09-24 08:47:02 UTC"}

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-39fbd254-e393-4c10-a8e4-a1c18822a588",
    "process_id": "36877",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/verify.log && cat /app/solution/public_validation.json && cat /app/solution/selected_config.json && ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/final_model.pt /app/solution/item_vectors.npy"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/verify.log",
        "name": "verify.log",
        "path": "/app/solution/verify.log"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/public_validation.json",
        "name": "public_validation.json",
        "path": "/app/solution/public_validation.json"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/selected_config.json",
        "name": "selected_config.json",
        "path": "/app/solution/selected_config.json"
      },
      {
        "type": "list_files",
        "cmd": "ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/final_model.pt /app/solution/item_vectors.npy",
        "path": "submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.17869300017991918,\n  \"encoder_ms_p95\": 0.322575750260512,\n  \"cached_pool_encoder_ms_median\": 0.1101219995689462,\n  \"cached_pool_encoder_ms_p95\": 0.14610775019718858,\n  \"torch_threads\": 1\n}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"dim\": 600,\n  \"latent\": 200,\n  \"item_mlp\": 64,\n  \"pretrain_epochs\": 8,\n  \"temporal_epochs\": 1,\n  \"recent\": 3,\n  \"like\": 0,\n  \"trend\": 1,\n  \"pop\": 0.75,\n  \"validation_checkpoint\": \"selected_validation.pt\",\n  \"validation_ndcg\": 0.05338953641537794\n}-rw-r--r-- 1 agent agent 3.2K Sep 24 08:42 /app/solution/README.md\n-rw-r--r-- 1 agent agent 139M Sep 24 08:39 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent  69M Sep 24 08:39 /app/solution/item_vectors.npy\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:45 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.17869300017991918,\n  \"encoder_ms_p95\": 0.322575750260512,\n  \"cached_pool_encoder_ms_median\": 0.1101219995689462,\n  \"cached_pool_encoder_ms_p95\": 0.14610775019718858,\n  \"torch_threads\": 1\n}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"dim\": 600,\n  \"latent\": 200,\n  \"item_mlp\": 64,\n  \"pretrain_epochs\": 8,\n  \"temporal_epochs\": 1,\n  \"recent\": 3,\n  \"like\": 0,\n  \"trend\": 1,\n  \"pop\": 0.75,\n  \"validation_checkpoint\": \"selected_validation.pt\",\n  \"validation_ndcg\": 0.05338953641537794\n}-rw-r--r-- 1 agent agent 3.2K Sep 24 08:42 /app/solution/README.md\n-rw-r--r-- 1 agent agent 139M Sep 24 08:39 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent  69M Sep 24 08:39 /app/solution/item_vectors.npy\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:45 /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6900
    },
    "formatted_output": "{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.17869300017991918,\n  \"encoder_ms_p95\": 0.322575750260512,\n  \"cached_pool_encoder_ms_median\": 0.1101219995689462,\n  \"cached_pool_encoder_ms_p95\": 0.14610775019718858,\n  \"torch_threads\": 1\n}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.05339,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"dim\": 600,\n  \"latent\": 200,\n  \"item_mlp\": 64,\n  \"pretrain_epochs\": 8,\n  \"temporal_epochs\": 1,\n  \"recent\": 3,\n  \"like\": 0,\n  \"trend\": 1,\n  \"pop\": 0.75,\n  \"validation_checkpoint\": \"selected_validation.pt\",\n  \"validation_ndcg\": 0.05338953641537794\n}-rw-r--r-- 1 agent agent 3.2K Sep 24 08:42 /app/solution/README.md\n-rw-r--r-- 1 agent agent 139M Sep 24 08:39 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent  69M Sep 24 08:39 /app/solution/item_vectors.npy\n-rw-r--r-- 1 agent agent 1.6M Sep 24 08:45 /app/solution/submission.parquet\n"
  }
}

Harness context token usage record

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  "turn_token_usage": {
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160

Agent message

+51:53

Thinking unavailable No text recorded

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

161

Agent message

+52:08

Thinking unavailable No text recorded

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

162

exec command

+52:24

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +52:25

text(await tools.exec_command({cmd:"sed -n '1,160p' /app/solution/infer.py","max_output_tokens":2500}));

Tool response +52:25

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"9bcb29","wall_time_seconds":0.00000395,"exit_code":0,"original_token_count":455,"output":"\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\n\ndef main():\n    ap = argparse.ArgumentParser()\n    ap.add_argument('--fast', action='store_true')\n    ap.add_argument('--checkpoint', required=True)\n    ap.add_argument('--stage', default='test')\n    ap.add_argument('--recent', type=float, default=10)\n    ap.add_argument('--like', type=float, default=0)\n    ap.add_argument('--trend', type=float, default=2)\n    ap.add_argument('--pop', type=float, default=0)\n    ap.add_argument('--kind', default='standard')\n    ap.add_argument('--output')\n    ap.add_argument('--save-model')\n    a = ap.parse_args()\n    ck = torch.load(P / a.checkpoint, weights_only=False)\n    data = Histories(a.stage)\n    data.recent = np.load(P / f'fast_recent_{a.stage}.npy') if a.fast else data.recent\n    model = make_model(ck)\n    features = np.load(P / (('discovery_trends_' if a.kind == 'discovery' else 'trends_') + a.stage + '.npy'))\n    with torch.no_grad():\n        model.item.bias.weight[:, 0].add_(torch.tensor(features[:, 3]) * a.trend + torch.tensor(features[:, 0]) * a.pop)\n    out = a.output or ('submission.parquet' if a.stage == 'test' else 'validation.parquet')\n    score = evaluate(model, data, a.stage, a.recent, a.like, save=out + '.tmp.parquet')\n    os.replace(P / (out + '.tmp.parquet'), P / out)\n    print(json.dumps({'output': out, 'ndcg': score, 'calibration': vars(a)}), flush=True)\n    if a.save_model:\n        ck['model'] = model.state_dict()\n        ck['calibration'] = vars(a)\n        torch.save(ck, P / a.save_model)\n        model.eval()\n        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\nif __name__ == '__main__':\n    main()\n"}

Harness context codex item completed

{
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    "type": "CommandExecution",
    "id": "exec-0b143883-84b5-4d49-a4d4-9d937a2bce79",
    "process_id": "13880",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,160p' /app/solution/infer.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1,160p' /app/solution/infer.py",
        "name": "infer.py",
        "path": "/app/solution/infer.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\n\ndef main():\n    ap = argparse.ArgumentParser()\n    ap.add_argument('--fast', action='store_true')\n    ap.add_argument('--checkpoint', required=True)\n    ap.add_argument('--stage', default='test')\n    ap.add_argument('--recent', type=float, default=10)\n    ap.add_argument('--like', type=float, default=0)\n    ap.add_argument('--trend', type=float, default=2)\n    ap.add_argument('--pop', type=float, default=0)\n    ap.add_argument('--kind', default='standard')\n    ap.add_argument('--output')\n    ap.add_argument('--save-model')\n    a = ap.parse_args()\n    ck = torch.load(P / a.checkpoint, weights_only=False)\n    data = Histories(a.stage)\n    data.recent = np.load(P / f'fast_recent_{a.stage}.npy') if a.fast else data.recent\n    model = make_model(ck)\n    features = np.load(P / (('discovery_trends_' if a.kind == 'discovery' else 'trends_') + a.stage + '.npy'))\n    with torch.no_grad():\n        model.item.bias.weight[:, 0].add_(torch.tensor(features[:, 3]) * a.trend + torch.tensor(features[:, 0]) * a.pop)\n    out = a.output or ('submission.parquet' if a.stage == 'test' else 'validation.parquet')\n    score = evaluate(model, data, a.stage, a.recent, a.like, save=out + '.tmp.parquet')\n    os.replace(P / (out + '.tmp.parquet'), P / out)\n    print(json.dumps({'output': out, 'ndcg': score, 'calibration': vars(a)}), flush=True)\n    if a.save_model:\n        ck['model'] = model.state_dict()\n        ck['calibration'] = vars(a)\n        torch.save(ck, P / a.save_model)\n        model.eval()\n        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\nif __name__ == '__main__':\n    main()\n",
    "stderr": "",
    "aggregated_output": "\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\n\ndef main():\n    ap = argparse.ArgumentParser()\n    ap.add_argument('--fast', action='store_true')\n    ap.add_argument('--checkpoint', required=True)\n    ap.add_argument('--stage', default='test')\n    ap.add_argument('--recent', type=float, default=10)\n    ap.add_argument('--like', type=float, default=0)\n    ap.add_argument('--trend', type=float, default=2)\n    ap.add_argument('--pop', type=float, default=0)\n    ap.add_argument('--kind', default='standard')\n    ap.add_argument('--output')\n    ap.add_argument('--save-model')\n    a = ap.parse_args()\n    ck = torch.load(P / a.checkpoint, weights_only=False)\n    data = Histories(a.stage)\n    data.recent = np.load(P / f'fast_recent_{a.stage}.npy') if a.fast else data.recent\n    model = make_model(ck)\n    features = np.load(P / (('discovery_trends_' if a.kind == 'discovery' else 'trends_') + a.stage + '.npy'))\n    with torch.no_grad():\n        model.item.bias.weight[:, 0].add_(torch.tensor(features[:, 3]) * a.trend + torch.tensor(features[:, 0]) * a.pop)\n    out = a.output or ('submission.parquet' if a.stage == 'test' else 'validation.parquet')\n    score = evaluate(model, data, a.stage, a.recent, a.like, save=out + '.tmp.parquet')\n    os.replace(P / (out + '.tmp.parquet'), P / out)\n    print(json.dumps({'output': out, 'ndcg': score, 'calibration': vars(a)}), flush=True)\n    if a.save_model:\n        ck['model'] = model.state_dict()\n        ck['calibration'] = vars(a)\n        torch.save(ck, P / a.save_model)\n        model.eval()\n        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\nif __name__ == '__main__':\n    main()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3950
    },
    "formatted_output": "\"\"\"Bake calibrated item trends into the item tower, then retrieve by exact dot product.\"\"\"\nfrom train import *\n\ndef main():\n    ap = argparse.ArgumentParser()\n    ap.add_argument('--fast', action='store_true')\n    ap.add_argument('--checkpoint', required=True)\n    ap.add_argument('--stage', default='test')\n    ap.add_argument('--recent', type=float, default=10)\n    ap.add_argument('--like', type=float, default=0)\n    ap.add_argument('--trend', type=float, default=2)\n    ap.add_argument('--pop', type=float, default=0)\n    ap.add_argument('--kind', default='standard')\n    ap.add_argument('--output')\n    ap.add_argument('--save-model')\n    a = ap.parse_args()\n    ck = torch.load(P / a.checkpoint, weights_only=False)\n    data = Histories(a.stage)\n    data.recent = np.load(P / f'fast_recent_{a.stage}.npy') if a.fast else data.recent\n    model = make_model(ck)\n    features = np.load(P / (('discovery_trends_' if a.kind == 'discovery' else 'trends_') + a.stage + '.npy'))\n    with torch.no_grad():\n        model.item.bias.weight[:, 0].add_(torch.tensor(features[:, 3]) * a.trend + torch.tensor(features[:, 0]) * a.pop)\n    out = a.output or ('submission.parquet' if a.stage == 'test' else 'validation.parquet')\n    score = evaluate(model, data, a.stage, a.recent, a.like, save=out + '.tmp.parquet')\n    os.replace(P / (out + '.tmp.parquet'), P / out)\n    print(json.dumps({'output': out, 'ndcg': score, 'calibration': vars(a)}), flush=True)\n    if a.save_model:\n        ck['model'] = model.state_dict()\n        ck['calibration'] = vars(a)\n        torch.save(ck, P / a.save_model)\n        model.eval()\n        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\nif __name__ == '__main__':\n    main()\n"
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Harness context token usage record

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163

exec command

+52:39

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +52:51

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/infer.py';s=open(p).read();old=\"\"\"        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\"\"\";new=\"\"\"        with torch.inference_mode():\n            dimension = model.item(torch.tensor([0])).shape[1]\n            vectors = np.lib.format.open_memmap(\n                P / 'item_vectors.tmp.npy', mode='w+', dtype=np.float32,\n                shape=(len(data.items), dimension))\n            for start in range(0, len(data.items), 4096):\n                stop = min(start + 4096, len(data.items))\n                vectors[start:stop] = model.item(torch.arange(start, stop)).numpy()\n            vectors.flush()\n            del vectors\n        os.replace(P / 'item_vectors.tmp.npy', P / 'item_vectors.npy')\n        np.save(P / 'item_ids.npy', data.items)\"\"\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nset -e\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 3 --trend 1 --pop .75 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response +52:53

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d0d73c","wall_time_seconds":1.001292936,"session_id":29998,"original_token_count":0,"output":""}
{"current_time":"2026-09-24 08:48:22 UTC"}

Harness context token usage record

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}

164

Agent message

+53:12

Thinking unavailable No text recorded

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

Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e9e52582-7248-44f2-98d8-838c1647c4f5",
    "process_id": "29998",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/infer.py';s=open(p).read();old=\"\"\"        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\"\"\";new=\"\"\"        with torch.inference_mode():\n            dimension = model.item(torch.tensor([0])).shape[1]\n            vectors = np.lib.format.open_memmap(\n                P / 'item_vectors.tmp.npy', mode='w+', dtype=np.float32,\n                shape=(len(data.items), dimension))\n            for start in range(0, len(data.items), 4096):\n                stop = min(start + 4096, len(data.items))\n                vectors[start:stop] = model.item(torch.arange(start, stop)).numpy()\n            vectors.flush()\n            del vectors\n        os.replace(P / 'item_vectors.tmp.npy', P / 'item_vectors.npy')\n        np.save(P / 'item_ids.npy', data.items)\"\"\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nset -e\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 3 --trend 1 --pop .75 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/infer.py';s=open(p).read();old=\"\"\"        with torch.no_grad():\n            np.save(P / 'item_vectors.npy', model.item(torch.arange(len(data.items))).numpy())\n        np.save(P / 'item_ids.npy', data.items)\"\"\";new=\"\"\"        with torch.inference_mode():\n            dimension = model.item(torch.tensor([0])).shape[1]\n            vectors = np.lib.format.open_memmap(\n                P / 'item_vectors.tmp.npy', mode='w+', dtype=np.float32,\n                shape=(len(data.items), dimension))\n            for start in range(0, len(data.items), 4096):\n                stop = min(start + 4096, len(data.items))\n                vectors[start:stop] = model.item(torch.arange(start, stop)).numpy()\n            vectors.flush()\n            del vectors\n        os.replace(P / 'item_vectors.tmp.npy', P / 'item_vectors.npy')\n        np.save(P / 'item_ids.npy', data.items)\"\"\";assert old in s;s=s.replace(old,new);open(p,'w').write(s)\nPY\nset -e\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/infer.py --checkpoint final_refit_best.pt --stage test --recent 3 --trend 1 --pop .75 --kind discovery --save-model final_model.pt > /app/solution/final_infer.log 2>&1\nTOWER_THREADS=4 /opt/task-venv/bin/python /app/solution/predict_saved.py > /app/solution/predict_saved.log 2>&1\nTOWER_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/verify.log 2>&1"
      }
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  }
}

165

Agent message

+53:29

Thinking unavailable No text recorded

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

166

Agent message

+53:46

Thinking unavailable No text recorded

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

167

Agent message

+54:02

Thinking unavailable No text recorded

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

168

exec command

+54:09

Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input +54:12

text(await tools.exec_command({cmd:"cat /app/solution/predict_saved.log && cat /app/solution/verify.log && python - <<'PY'\nfrom pathlib import Path\nimport json\np=Path('/app/solution');v=json.loads((p/'verification.json').read_text());r=p/'README.md';s=r.read_text();s=s.replace('0.32 ms p95',f\"{v['encoder_ms_p95']:.2f} ms p95\").replace('0.14 ms p95',f\"{v['cached_pool_encoder_ms_p95']:.2f} ms p95\");r.write_text(s)\nassert (p/'submission.parquet').stat().st_size>0\nassert json.loads((p/'selected_config.json').read_text())['item_mlp']==64\nprint('Final artifact and README ready.')\nPY","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response +54:12

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"0e996e","wall_time_seconds":0.000004711,"exit_code":0,"original_token_count":114,"output":"{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.18735350022325292,\n  \"encoder_ms_p95\": 0.32380574953094765,\n  \"cached_pool_encoder_ms_median\": 0.11199699974895339,\n  \"cached_pool_encoder_ms_p95\": 0.13489449997905467,\n  \"torch_threads\": 1\n}\nFinal artifact and README ready.\n"}
{"current_time":"2026-09-24 08:49:41 UTC"}

Harness context token usage record

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Harness context codex item completed

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-dbf6401a-ea30-4caa-9d35-642c40e499b4",
    "process_id": "73566",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/predict_saved.log && cat /app/solution/verify.log && python - <<'PY'\nfrom pathlib import Path\nimport json\np=Path('/app/solution');v=json.loads((p/'verification.json').read_text());r=p/'README.md';s=r.read_text();s=s.replace('0.32 ms p95',f\"{v['encoder_ms_p95']:.2f} ms p95\").replace('0.14 ms p95',f\"{v['cached_pool_encoder_ms_p95']:.2f} ms p95\");r.write_text(s)\nassert (p/'submission.parquet').stat().st_size>0\nassert json.loads((p/'selected_config.json').read_text())['item_mlp']==64\nprint('Final artifact and README ready.')\nPY"
    ],
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      {
        "type": "unknown",
        "cmd": "cat /app/solution/predict_saved.log && cat /app/solution/verify.log && python - <<'PY'\nfrom pathlib import Path\nimport json\np=Path('/app/solution');v=json.loads((p/'verification.json').read_text());r=p/'README.md';s=r.read_text();s=s.replace('0.32 ms p95',f\"{v['encoder_ms_p95']:.2f} ms p95\").replace('0.14 ms p95',f\"{v['cached_pool_encoder_ms_p95']:.2f} ms p95\");r.write_text(s)\nassert (p/'submission.parquet').stat().st_size>0\nassert json.loads((p/'selected_config.json').read_text())['item_mlp']==64\nprint('Final artifact and README ready.')\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.18735350022325292,\n  \"encoder_ms_p95\": 0.32380574953094765,\n  \"cached_pool_encoder_ms_median\": 0.11199699974895339,\n  \"cached_pool_encoder_ms_p95\": 0.13489449997905467,\n  \"torch_threads\": 1\n}\nFinal artifact and README ready.\n",
    "stderr": "",
    "aggregated_output": "{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.18735350022325292,\n  \"encoder_ms_p95\": 0.32380574953094765,\n  \"cached_pool_encoder_ms_median\": 0.11199699974895339,\n  \"cached_pool_encoder_ms_p95\": 0.13489449997905467,\n  \"torch_threads\": 1\n}\nFinal artifact and README ready.\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4711
    },
    "formatted_output": "{\"rows\": 845600, \"users\": 8456, \"dimension\": 601}\n{\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"items_per_user\": 100,\n  \"all_items_eligible\": true,\n  \"vector_dimension\": 601,\n  \"cached_pool_max_abs_error\": 0.0,\n  \"encoder_ms_median\": 0.18735350022325292,\n  \"encoder_ms_p95\": 0.32380574953094765,\n  \"cached_pool_encoder_ms_median\": 0.11199699974895339,\n  \"cached_pool_encoder_ms_p95\": 0.13489449997905467,\n  \"torch_threads\": 1\n}\nFinal artifact and README ready.\n"
  }
}

169

Final response

+54:25

Thinking unavailable No text recorded

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

Model text

Saved submission.parquet: all 845,600 rows passed ranking and eligibility checks.

Final model refit on train + validation. Public validation NDCG@10: 0.053390.

Architecture, training details, and reproduction commands are in README.md.

Harness context token usage record

{
  "type": "token_usage_record",
  "thread_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "session_id": "01a0d269-91fa-7a72-ac91-d8909238906f",
  "root_turn_id": "01a0d269-920b-7e81-ada1-6bee9aed57dd",
  "response_id": "resp_07d7e3c765c2aa55016ab4e4262e7887d2afdfc4f8004ad0f8",
  "usage": {
    "input_tokens": 136704,
    "cached_input_tokens": 133760,
    "cache_write_input_tokens": 0,
    "output_tokens": 420,
    "reasoning_output_tokens": 341,
    "total_tokens": 137124
  },
  "turn_token_usage": {
    "input_tokens": 5536538,
    "cached_input_tokens": 5321216,
    "cache_write_input_tokens": 0,
    "output_tokens": 93751,
    "reasoning_output_tokens": 62594,
    "total_tokens": 5630289
  },
  "thread_token_usage": {
    "input_tokens": 5536538,
    "cached_input_tokens": 5321216,
    "cache_write_input_tokens": 0,
    "output_tokens": 93751,
    "reasoning_output_tokens": 62594,
    "total_tokens": 5630289
  }
}

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: astra-high-0.153.4-aws-gap-r01-2026-09-24
Recorded model: gpt-6-astra
Harness version: 0.153.4
Session: 01a0d269-91fa-7a72-ac91-d8909238906f
Source SHA-256: fc8a14d37ebb1532c7a00330b94b36da4197c3edd8bceb66275d1916096fec3f

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

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