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Agent trace

Two-tower retriever astra-0.153.4-high-05

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 136 empty thinking entries; tool calls are shown separately.

Agent
gpt-6-astra · Codex · high effort
Recorded conversation
51:20 · 144 steps
Private reward
0.036027 · Pass
Attempt cost
$12.30
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

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 explicit authorization is already provided.

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.
02

Developer instructions

+00:02
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.

In Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions.

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.

If explicit user input is required for another reason before progress can safely continue, do not use the request_user_input tool. Ask the user directly with one concise plain-text question instead. Never write a multiple choice question as a textual assistant message.</collaboration_mode>

03

Developer instructions

+00:02
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>

04

Developer instructions

+00:02
Developer instructions

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

05

User context

+00:02
User context

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

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

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-10</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": "f6a6d5eb73bbc12c3bdfeb3b3170124c19ff3a77"
    },
    "context_window_guidance": "",
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-10",
      "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": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-10",
  "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"
}
06

Task instructions

+00:02
Task instructions

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

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

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

Data

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

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

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

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

The two public windows have different roles:

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

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

Additional files under /task/data/:

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

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

Discovery objective

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

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

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

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

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

Public tools

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

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

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

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

Deliverable

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

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

Constraints

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

exec command

+00:07

Model text

I’ll inspect the data and evaluator, train and tune a history-based two-tower model on the public window, then refit it with validation history for the final ranking. I’ll keep a valid submission saved while improving the model.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"pwd && ls -la && ls -lh /task/data && cat /task/requirements.txt","max_output_tokens":6000}));
text(await tools.exec_command({cmd:"cat /task/evaluate_public.py && cat /task/ranking_evaluation.py","max_output_tokens":14000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"d85431","wall_time_seconds":0.00000442,"exit_code":0,"original_token_count":628,"output":"/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 17:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:50 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:36 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 17:36 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:36 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:36 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:36 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:36 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:36 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 17:36 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:36 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:36 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:36 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 17:36 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:36 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 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":"afbd04","wall_time_seconds":0.000005051,"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":"2026-09-10 17:53:26 UTC"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4f69946e-8a83-43c6-bdf2-e8209d7bda4b",
    "process_id": "97899",
    "command": [
      "/bin/sh",
      "-lc",
      "pwd && ls -la && ls -lh /task/data && cat /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "pwd && ls -la && ls -lh /task/data && cat /task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 17:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:50 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:36 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 17:36 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:36 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:36 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:36 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:36 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:36 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 17:36 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:36 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:36 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:36 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 17:36 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:36 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 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": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 17:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:50 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:36 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 17:36 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:36 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:36 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:36 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:36 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:36 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 17:36 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:36 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:36 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:36 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 17:36 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:36 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 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": 4420
    },
    "formatted_output": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 17:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:50 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:36 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 17:36 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:36 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:36 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:36 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:36 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:36 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 17:36 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:36 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:36 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:36 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 17:36 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:36 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:37 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-a5ad6d5d-b3c0-4e73-a5a0-be36178c5482",
    "process_id": "78758",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /task/evaluate_public.py && cat /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": "cat /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": 5051
    },
    "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"
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"ls -la /app/solution && python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os, torch\nfor f in ['train_listens','train_likes','val_listens','candidate_catalog','target_users','validation_preference_state']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema);print(q.read_row_group(0).to_pandas().head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print(a['uids'][:10],a['item_ids'][:10])\nprint('CPU',os.cpu_count(),torch.get_num_threads())\nPY","max_output_tokens":5000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

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Harness context codex item completed
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    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "ls -la /app/solution && python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os, torch\nfor f in ['train_listens','train_likes','val_listens','candidate_catalog','target_users','validation_preference_state']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema);print(q.read_row_group(0).to_pandas().head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print(a['uids'][:10],a['item_ids'][:10])\nprint('CPU',os.cpu_count(),torch.get_num_threads())\nPY"
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        "cmd": "ls -la /app/solution && python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os, torch\nfor f in ['train_listens','train_likes','val_listens','candidate_catalog','target_users','validation_preference_state']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema);print(q.read_row_group(0).to_pandas().head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print(a['uids'][:10],a['item_ids'][:10])\nprint('CPU',os.cpu_count(),torch.get_num_threads())\nPY"
      }
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    "stderr": "",
    "aggregated_output": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:50 .\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:50 ..\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 4880
    },
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Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"which python; ls -la /opt; ls /usr/local/bin; cat /proc/meminfo | head; cat /sys/fs/cgroup/cpu.max","max_output_tokens":3000}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"539450","wall_time_seconds":0.00000377,"exit_code":0,"original_token_count":168,"output":"/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:53 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:50 task-venv\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\npydoc3\npydoc3.12\npython\npython-config\npython3\npython3-config\npython3.12\npython3.12-config\nMemTotal:       15988972 kB\nMemFree:         1813360 kB\nMemAvailable:   14870236 kB\nBuffers:          227576 kB\nCached:         12547864 kB\nSwapCached:            0 kB\nActive:           507116 kB\nInactive:       12545616 kB\nActive(anon):     303780 kB\nInactive(anon):        0 kB\n800000 100000\n"}
Harness context codex item completed
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:53 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:50 task-venv\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\npydoc3\npydoc3.12\npython\npython-config\npython3\npython3-config\npython3.12\npython3.12-config\nMemTotal:       15988972 kB\nMemFree:         1813360 kB\nMemAvailable:   14870236 kB\nBuffers:          227576 kB\nCached:         12547864 kB\nSwapCached:            0 kB\nActive:           507116 kB\nInactive:       12545616 kB\nActive(anon):     303780 kB\nInactive(anon):        0 kB\n800000 100000\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:53 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:50 task-venv\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\npydoc3\npydoc3.12\npython\npython-config\npython3\npython3-config\npython3.12\npython3.12-config\nMemTotal:       15988972 kB\nMemFree:         1813360 kB\nMemAvailable:   14870236 kB\nBuffers:          227576 kB\nCached:         12547864 kB\nSwapCached:            0 kB\nActive:           507116 kB\nInactive:       12545616 kB\nActive(anon):     303780 kB\nInactive(anon):        0 kB\n800000 100000\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3770
    },
    "formatted_output": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:50 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:53 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:50 task-venv\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\npydoc3\npydoc3.12\npython\npython-config\npython3\npython3-config\npython3.12\npython3.12-config\nMemTotal:       15988972 kB\nMemFree:         1813360 kB\nMemAvailable:   14870236 kB\nBuffers:          227576 kB\nCached:         12547864 kB\nSwapCached:            0 kB\nActive:           507116 kB\nInactive:       12545616 kB\nActive(anon):     303780 kB\nInactive(anon):        0 kB\n800000 100000\n"
  }
}
Harness context token usage record
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  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os, torch\nfor f in ['train_listens','train_likes','val_listens','candidate_catalog','target_users','validation_preference_state']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema);print(q.read_row_group(0).to_pandas().head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print(a['uids'][:10],a['item_ids'][:10])\nprint('CPU',os.cpu_count(),torch.get_num_threads())\nPY","max_output_tokens":5000}));

Tool response

Script completed
Wall time 4.3 seconds
Output:

{"chunk_id":"ec7ece","wall_time_seconds":4.151061793,"exit_code":0,"original_token_count":1277,"output":"train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7a53c799bcc0>\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\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7a53dfa49140>\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\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e4bc80>\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\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7a53c76cc240>\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\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x7a53dfbc2f40>\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\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e44b80>\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\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nCPU 8 4\n"}
Harness context token usage record
{
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  "usage": {
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-142c681b-9abf-4698-b274-02aacfa9ea95",
    "process_id": "39715",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os, torch\nfor f in ['train_listens','train_likes','val_listens','candidate_catalog','target_users','validation_preference_state']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema);print(q.read_row_group(0).to_pandas().head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print(a['uids'][:10],a['item_ids'][:10])\nprint('CPU',os.cpu_count(),torch.get_num_threads())\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, torch\nfor f in ['train_listens','train_likes','val_listens','candidate_catalog','target_users','validation_preference_state']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema);print(q.read_row_group(0).to_pandas().head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,a[k].shape) for k in a.files]);print(a['uids'][:10],a['item_ids'][:10])\nprint('CPU',os.cpu_count(),torch.get_num_threads())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7a53c799bcc0>\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\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7a53dfa49140>\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\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e4bc80>\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\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7a53c76cc240>\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\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x7a53dfbc2f40>\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\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e44b80>\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\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nCPU 8 4\n",
    "stderr": "",
    "aggregated_output": "train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7a53c799bcc0>\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\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7a53dfa49140>\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\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e4bc80>\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\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7a53c76cc240>\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\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x7a53dfbc2f40>\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\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e44b80>\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\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nCPU 8 4\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 151054413
    },
    "formatted_output": "train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7a53c799bcc0>\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\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7a53dfa49140>\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\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e4bc80>\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\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7a53c76cc240>\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\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x7a53dfbc2f40>\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\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x7a53c7e44b80>\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\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\n[ 100  200  300  600  700  800  900 1000 1100 1400] [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nCPU 8 4\n"
  }
}
08

exec command

+00:50
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

text(await tools.exec_command({cmd:"cat > /app/solution/inspect_data.py <<'PY'\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz'); users=a['uids']; items=a['item_ids']; mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\numap=pd.Index(users); imap=pd.Index(items)\nprint('eligible',np.quantile(mask.sum(1),[0,.1,.5,.9,1]),flush=True)\n# Sparse event table, never a user-item matrix.\nparts=[]\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=1000000):\n d=b.to_pandas();d=d[d.played_ratio_pct>=50];u=umap.get_indexer(d.uid);i=imap.get_indexer(d.item_id); keep=(u>=0)&(i>=0)\n d=d.loc[keep,['timestamp','is_organic','played_ratio_pct']].copy();d['u']=u[keep];d['i']=i[keep];parts.append(d)\ne=pd.concat(parts,ignore_index=True);e.to_parquet('/app/solution/train_catalog_events.parquet',index=False)\nprint('events',len(e),'time',e.timestamp.min(),e.timestamp.max(),flush=True)\nseen=e[['u','i']].drop_duplicates(); print('unique seen pairs',len(seen),'eligible',mask[seen.u,seen.i].mean(),flush=True)\nv=pd.read_parquet(D+'val_listens.parquet');v=v[(v.is_organic==1)&(v.played_ratio_pct>=80)];l=pd.read_parquet(D+'val_likes.parquet');l=l[l.is_organic==1];v=v[['uid','item_id']].drop_duplicates();v['grade']=1;l=l[['uid','item_id']].drop_duplicates();l['grade']=3\nv=pd.concat([v,l]).groupby(['uid','item_id'],as_index=False).grade.max();v['u']=umap.get_indexer(v.uid);v['i']=imap.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v=v[mask[v.u,v.i]]\ns=e.groupby(['u','i'],as_index=False).agg(last=('timestamp','max'),cnt=('timestamp','size'),organic=('is_organic','sum'));v=v.merge(s,how='left',on=['u','i']);print('truth',len(v),'users',v.u.nunique(),'grade',v.grade.value_counts().to_dict(),'seen',v['cnt'].notna().mean(),flush=True)\nprint(v.groupby('grade')[['cnt','organic']].agg(['count','mean','median']),flush=True)\np=pd.read_parquet(D+'validation_preference_state.parquet');v=v.merge(p,how='left',on=['uid','item_id']);print('truth state',v.groupby(['grade','state']).size(),flush=True)\nprint('maxuid',users.max(),'state_counts',p.state.value_counts().to_dict(),flush=True)\nPY\n/opt/task-venv/bin/python -u /app/solution/inspect_data.py > /app/solution/inspect.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"b0f2a4","wall_time_seconds":1.000234516,"session_id":18122,"original_token_count":0,"output":""}
Harness context token usage record
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}
09

exec command

+01:18
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat /app/solution/inspect.log && ls -lh /app/solution","max_output_tokens":3000}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"f5a672","wall_time_seconds":0.00000469,"exit_code":0,"original_token_count":80,"output":"eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntotal 131M\n-rw-r--r-- 1 agent agent  122 Sep 10 17:54 inspect.log\n-rw-r--r-- 1 agent agent 2.1K Sep 10 17:54 inspect_data.py\n-rw-r--r-- 1 agent agent 131M Sep 10 17:54 train_catalog_events.parquet\n"}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-a9c086cd-56d9-45c4-b583-21acc90f530e",
    "process_id": "34961",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/inspect.log && ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/inspect.log",
        "name": "inspect.log",
        "path": "/app/solution/inspect.log"
      },
      {
        "type": "list_files",
        "cmd": "ls -lh /app/solution",
        "path": "solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntotal 131M\n-rw-r--r-- 1 agent agent  122 Sep 10 17:54 inspect.log\n-rw-r--r-- 1 agent agent 2.1K Sep 10 17:54 inspect_data.py\n-rw-r--r-- 1 agent agent 131M Sep 10 17:54 train_catalog_events.parquet\n",
    "stderr": "",
    "aggregated_output": "eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntotal 131M\n-rw-r--r-- 1 agent agent  122 Sep 10 17:54 inspect.log\n-rw-r--r-- 1 agent agent 2.1K Sep 10 17:54 inspect_data.py\n-rw-r--r-- 1 agent agent 131M Sep 10 17:54 train_catalog_events.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4690
    },
    "formatted_output": "eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntotal 131M\n-rw-r--r-- 1 agent agent  122 Sep 10 17:54 inspect.log\n-rw-r--r-- 1 agent agent 2.1K Sep 10 17:54 inspect_data.py\n-rw-r--r-- 1 agent agent 131M Sep 10 17:54 train_catalog_events.parquet\n"
  }
}
Harness context token usage record
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  "turn_token_usage": {
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  },
  "thread_token_usage": {
    "input_tokens": 133551,
    "cached_input_tokens": 119168,
    "cache_write_input_tokens": 0,
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2869e7ce-53ca-45b1-95f2-4a24759a9759",
    "process_id": "18122",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/inspect_data.py <<'PY'\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz'); users=a['uids']; items=a['item_ids']; mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\numap=pd.Index(users); imap=pd.Index(items)\nprint('eligible',np.quantile(mask.sum(1),[0,.1,.5,.9,1]),flush=True)\n# Sparse event table, never a user-item matrix.\nparts=[]\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=1000000):\n d=b.to_pandas();d=d[d.played_ratio_pct>=50];u=umap.get_indexer(d.uid);i=imap.get_indexer(d.item_id); keep=(u>=0)&(i>=0)\n d=d.loc[keep,['timestamp','is_organic','played_ratio_pct']].copy();d['u']=u[keep];d['i']=i[keep];parts.append(d)\ne=pd.concat(parts,ignore_index=True);e.to_parquet('/app/solution/train_catalog_events.parquet',index=False)\nprint('events',len(e),'time',e.timestamp.min(),e.timestamp.max(),flush=True)\nseen=e[['u','i']].drop_duplicates(); print('unique seen pairs',len(seen),'eligible',mask[seen.u,seen.i].mean(),flush=True)\nv=pd.read_parquet(D+'val_listens.parquet');v=v[(v.is_organic==1)&(v.played_ratio_pct>=80)];l=pd.read_parquet(D+'val_likes.parquet');l=l[l.is_organic==1];v=v[['uid','item_id']].drop_duplicates();v['grade']=1;l=l[['uid','item_id']].drop_duplicates();l['grade']=3\nv=pd.concat([v,l]).groupby(['uid','item_id'],as_index=False).grade.max();v['u']=umap.get_indexer(v.uid);v['i']=imap.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v=v[mask[v.u,v.i]]\ns=e.groupby(['u','i'],as_index=False).agg(last=('timestamp','max'),cnt=('timestamp','size'),organic=('is_organic','sum'));v=v.merge(s,how='left',on=['u','i']);print('truth',len(v),'users',v.u.nunique(),'grade',v.grade.value_counts().to_dict(),'seen',v['cnt'].notna().mean(),flush=True)\nprint(v.groupby('grade')[['cnt','organic']].agg(['count','mean','median']),flush=True)\np=pd.read_parquet(D+'validation_preference_state.parquet');v=v.merge(p,how='left',on=['uid','item_id']);print('truth state',v.groupby(['grade','state']).size(),flush=True)\nprint('maxuid',users.max(),'state_counts',p.state.value_counts().to_dict(),flush=True)\nPY\n/opt/task-venv/bin/python -u /app/solution/inspect_data.py > /app/solution/inspect.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/inspect_data.py <<'PY'\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz'); users=a['uids']; items=a['item_ids']; mask=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\numap=pd.Index(users); imap=pd.Index(items)\nprint('eligible',np.quantile(mask.sum(1),[0,.1,.5,.9,1]),flush=True)\n# Sparse event table, never a user-item matrix.\nparts=[]\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(batch_size=1000000):\n d=b.to_pandas();d=d[d.played_ratio_pct>=50];u=umap.get_indexer(d.uid);i=imap.get_indexer(d.item_id); keep=(u>=0)&(i>=0)\n d=d.loc[keep,['timestamp','is_organic','played_ratio_pct']].copy();d['u']=u[keep];d['i']=i[keep];parts.append(d)\ne=pd.concat(parts,ignore_index=True);e.to_parquet('/app/solution/train_catalog_events.parquet',index=False)\nprint('events',len(e),'time',e.timestamp.min(),e.timestamp.max(),flush=True)\nseen=e[['u','i']].drop_duplicates(); print('unique seen pairs',len(seen),'eligible',mask[seen.u,seen.i].mean(),flush=True)\nv=pd.read_parquet(D+'val_listens.parquet');v=v[(v.is_organic==1)&(v.played_ratio_pct>=80)];l=pd.read_parquet(D+'val_likes.parquet');l=l[l.is_organic==1];v=v[['uid','item_id']].drop_duplicates();v['grade']=1;l=l[['uid','item_id']].drop_duplicates();l['grade']=3\nv=pd.concat([v,l]).groupby(['uid','item_id'],as_index=False).grade.max();v['u']=umap.get_indexer(v.uid);v['i']=imap.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v=v[mask[v.u,v.i]]\ns=e.groupby(['u','i'],as_index=False).agg(last=('timestamp','max'),cnt=('timestamp','size'),organic=('is_organic','sum'));v=v.merge(s,how='left',on=['u','i']);print('truth',len(v),'users',v.u.nunique(),'grade',v.grade.value_counts().to_dict(),'seen',v['cnt'].notna().mean(),flush=True)\nprint(v.groupby('grade')[['cnt','organic']].agg(['count','mean','median']),flush=True)\np=pd.read_parquet(D+'validation_preference_state.parquet');v=v.merge(p,how='left',on=['uid','item_id']);print('truth state',v.groupby(['grade','state']).size(),flush=True)\nprint('maxuid',users.max(),'state_counts',p.state.value_counts().to_dict(),flush=True)\nPY\n/opt/task-venv/bin/python -u /app/solution/inspect_data.py > /app/solution/inspect.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 10,
      "nanos": 101991663
    },
    "formatted_output": ""
  }
}
10

exec command

+01:30
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.write_stdin({session_id:18122,chars:"","yield_time_ms":1000,"max_output_tokens":3000}));
text(await tools.exec_command({cmd:"cat /app/solution/inspect.log","max_output_tokens":3000}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"a3ee2c","wall_time_seconds":0.000001189,"exit_code":0,"original_token_count":0,"output":""}
{"chunk_id":"6f6f40","wall_time_seconds":0.000002989,"exit_code":0,"original_token_count":126,"output":"eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntruth 37769 users 4263 grade {1: 33352, 3: 4417} seen 0.0\n        cnt             organic            \n      count mean median   count mean median\ngrade                                      \n1         0  NaN    NaN       0  NaN    NaN\n3         0  NaN    NaN       0  NaN    NaN\ntruth state Series([], dtype: int64)\nmaxuid 1000000 state_counts {'liked': 418435, 'disliked': 50689}\n"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2fc17fc0-9818-47a7-8bac-b407423af559",
    "process_id": "49943",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/inspect.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/inspect.log",
        "name": "inspect.log",
        "path": "/app/solution/inspect.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntruth 37769 users 4263 grade {1: 33352, 3: 4417} seen 0.0\n        cnt             organic            \n      count mean median   count mean median\ngrade                                      \n1         0  NaN    NaN       0  NaN    NaN\n3         0  NaN    NaN       0  NaN    NaN\ntruth state Series([], dtype: int64)\nmaxuid 1000000 state_counts {'liked': 418435, 'disliked': 50689}\n",
    "stderr": "",
    "aggregated_output": "eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntruth 37769 users 4263 grade {1: 33352, 3: 4417} seen 0.0\n        cnt             organic            \n      count mean median   count mean median\ngrade                                      \n1         0  NaN    NaN       0  NaN    NaN\n3         0  NaN    NaN       0  NaN    NaN\ntruth state Series([], dtype: int64)\nmaxuid 1000000 state_counts {'liked': 418435, 'disliked': 50689}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2989
    },
    "formatted_output": "eligible [24657.  28038.5 29242.  29852.  30000. ]\nevents 19959853 time 5 24786795\nunique seen pairs 5514505 eligible 0.0\ntruth 37769 users 4263 grade {1: 33352, 3: 4417} seen 0.0\n        cnt             organic            \n      count mean median   count mean median\ngrade                                      \n1         0  NaN    NaN       0  NaN    NaN\n3         0  NaN    NaN       0  NaN    NaN\ntruth state Series([], dtype: int64)\nmaxuid 1000000 state_counts {'liked': 418435, 'disliked': 50689}\n"
  }
}
Harness context token usage record
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11

exec command

+01:54
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 sets exclude every previously positive-listened user–track pair, so this is a new-track discovery task. The public window has 4,263 users with eligible relevance labels. I’ll train the user tower to pool listening and like histories and predict held-out tracks, with item vectors computed independently.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/prepare.py <<'PY'\n\"\"\"Prepare ragged interaction histories, not a global interaction matrix.\"\"\"\nimport numpy as np,pandas as pd, argparse,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\ndef prepare(stage):\n t=time.time(); a=np.load(D/'validation_eligible_candidates.npz');uids=a['uids'];items=a['item_ids']; um=pd.Index(uids);im=pd.Index(items)\n e=pd.read_parquet(O/'train_catalog_events.parquet');e['like']=np.zeros(len(e),np.float32)\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v['like']=0.;e=pd.concat([e,v[e.columns]],ignore_index=True)\n for s in (['train','val'] if stage=='test' else ['train']):\n  l=pd.read_parquet(D/f'{s}_likes.parquet');l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;l['like']=1.;e=pd.concat([e,l[e.columns]],ignore_index=True)\n cutoff=25393400 if stage=='test' else 24786800\n # Per-pair aggregation is a sparse event table and remains linear in observations.\n e['recent']=np.exp((e.timestamp.astype(float)-cutoff)/(86400*45)).astype(np.float32)\n e['orgrecent']=e.recent*(.25+.75*e.is_organic)\n p=e.groupby(['u','i'],sort=True).agg(cnt=('timestamp','size'),first=('timestamp','min'),last=('timestamp','max'),org=('is_organic','sum'),recent=('recent','sum'),orgrecent=('orgrecent','sum'),likes=('like','sum')).reset_index();del e\n p.to_parquet(O/f'{stage}_pairs.parquet',index=False)\n counts=np.bincount(p.u,minlength=len(uids));offset=np.r_[0,np.cumsum(counts)]\n # Repeated play capped so long sessions do not overwhelm taste diversity.\n w=(np.sqrt(p.cnt.to_numpy())*(.4+.6*p.org.to_numpy()/p.cnt.to_numpy())+3*np.minimum(p.likes,1))*np.exp((p['last'].to_numpy().astype(float)-cutoff)/(86400*90))\n tw=(.2+np.sqrt(p.org.to_numpy())+5*np.minimum(p.likes,1))*np.exp((p['last'].to_numpy().astype(float)-cutoff)/(86400*150))\n np.savez(O/f'{stage}_ragged.npz',uids=uids,items=items,offset=offset,ids=p.i.to_numpy(np.int32)+1,weight=np.asarray(w,np.float32),target_weight=np.asarray(tw,np.float32),first=p['first'].to_numpy(),last=p['last'].to_numpy(),likes=p.likes.to_numpy(np.float32),cnt=p.cnt.to_numpy(np.float32),org=p.org.to_numpy(np.float32))\n print(stage,len(p),'time',time.time()-t,flush=True)\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('stage');prepare(ap.parse_args().stage)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/prepare.py validation > /app/solution/prepare_validation.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

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Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-250a9d54-0f5b-4f19-9cc0-23dfe0b95d4f",
    "process_id": "21957",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/prepare.py <<'PY'\n\"\"\"Prepare ragged interaction histories, not a global interaction matrix.\"\"\"\nimport numpy as np,pandas as pd, argparse,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\ndef prepare(stage):\n t=time.time(); a=np.load(D/'validation_eligible_candidates.npz');uids=a['uids'];items=a['item_ids']; um=pd.Index(uids);im=pd.Index(items)\n e=pd.read_parquet(O/'train_catalog_events.parquet');e['like']=np.zeros(len(e),np.float32)\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v['like']=0.;e=pd.concat([e,v[e.columns]],ignore_index=True)\n for s in (['train','val'] if stage=='test' else ['train']):\n  l=pd.read_parquet(D/f'{s}_likes.parquet');l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;l['like']=1.;e=pd.concat([e,l[e.columns]],ignore_index=True)\n cutoff=25393400 if stage=='test' else 24786800\n # Per-pair aggregation is a sparse event table and remains linear in observations.\n e['recent']=np.exp((e.timestamp.astype(float)-cutoff)/(86400*45)).astype(np.float32)\n e['orgrecent']=e.recent*(.25+.75*e.is_organic)\n p=e.groupby(['u','i'],sort=True).agg(cnt=('timestamp','size'),first=('timestamp','min'),last=('timestamp','max'),org=('is_organic','sum'),recent=('recent','sum'),orgrecent=('orgrecent','sum'),likes=('like','sum')).reset_index();del e\n p.to_parquet(O/f'{stage}_pairs.parquet',index=False)\n counts=np.bincount(p.u,minlength=len(uids));offset=np.r_[0,np.cumsum(counts)]\n # Repeated play capped so long sessions do not overwhelm taste diversity.\n w=(np.sqrt(p.cnt.to_numpy())*(.4+.6*p.org.to_numpy()/p.cnt.to_numpy())+3*np.minimum(p.likes,1))*np.exp((p['last'].to_numpy().astype(float)-cutoff)/(86400*90))\n tw=(.2+np.sqrt(p.org.to_numpy())+5*np.minimum(p.likes,1))*np.exp((p['last'].to_numpy().astype(float)-cutoff)/(86400*150))\n np.savez(O/f'{stage}_ragged.npz',uids=uids,items=items,offset=offset,ids=p.i.to_numpy(np.int32)+1,weight=np.asarray(w,np.float32),target_weight=np.asarray(tw,np.float32),first=p['first'].to_numpy(),last=p['last'].to_numpy(),likes=p.likes.to_numpy(np.float32),cnt=p.cnt.to_numpy(np.float32),org=p.org.to_numpy(np.float32))\n print(stage,len(p),'time',time.time()-t,flush=True)\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('stage');prepare(ap.parse_args().stage)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/prepare.py validation > /app/solution/prepare_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/prepare.py <<'PY'\n\"\"\"Prepare ragged interaction histories, not a global interaction matrix.\"\"\"\nimport numpy as np,pandas as pd, argparse,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\ndef prepare(stage):\n t=time.time(); a=np.load(D/'validation_eligible_candidates.npz');uids=a['uids'];items=a['item_ids']; um=pd.Index(uids);im=pd.Index(items)\n e=pd.read_parquet(O/'train_catalog_events.parquet');e['like']=np.zeros(len(e),np.float32)\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v['like']=0.;e=pd.concat([e,v[e.columns]],ignore_index=True)\n for s in (['train','val'] if stage=='test' else ['train']):\n  l=pd.read_parquet(D/f'{s}_likes.parquet');l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;l['like']=1.;e=pd.concat([e,l[e.columns]],ignore_index=True)\n cutoff=25393400 if stage=='test' else 24786800\n # Per-pair aggregation is a sparse event table and remains linear in observations.\n e['recent']=np.exp((e.timestamp.astype(float)-cutoff)/(86400*45)).astype(np.float32)\n e['orgrecent']=e.recent*(.25+.75*e.is_organic)\n p=e.groupby(['u','i'],sort=True).agg(cnt=('timestamp','size'),first=('timestamp','min'),last=('timestamp','max'),org=('is_organic','sum'),recent=('recent','sum'),orgrecent=('orgrecent','sum'),likes=('like','sum')).reset_index();del e\n p.to_parquet(O/f'{stage}_pairs.parquet',index=False)\n counts=np.bincount(p.u,minlength=len(uids));offset=np.r_[0,np.cumsum(counts)]\n # Repeated play capped so long sessions do not overwhelm taste diversity.\n w=(np.sqrt(p.cnt.to_numpy())*(.4+.6*p.org.to_numpy()/p.cnt.to_numpy())+3*np.minimum(p.likes,1))*np.exp((p['last'].to_numpy().astype(float)-cutoff)/(86400*90))\n tw=(.2+np.sqrt(p.org.to_numpy())+5*np.minimum(p.likes,1))*np.exp((p['last'].to_numpy().astype(float)-cutoff)/(86400*150))\n np.savez(O/f'{stage}_ragged.npz',uids=uids,items=items,offset=offset,ids=p.i.to_numpy(np.int32)+1,weight=np.asarray(w,np.float32),target_weight=np.asarray(tw,np.float32),first=p['first'].to_numpy(),last=p['last'].to_numpy(),likes=p.likes.to_numpy(np.float32),cnt=p.cnt.to_numpy(np.float32),org=p.org.to_numpy(np.float32))\n print(stage,len(p),'time',time.time()-t,flush=True)\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('stage');prepare(ap.parse_args().stage)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/prepare.py validation > /app/solution/prepare_validation.log 2>&1"
      }
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}
12

exec command

+02:37
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

text(await tools.exec_command({cmd:"cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural two-tower retrieval with ragged history and exact dot-product scores.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\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\nD=Path('/task/data');O=Path('/app/solution')\ntorch.set_num_threads(4)\nclass UserTower(nn.Module):\n def __init__(self,n,d):\n  super().__init__();self.history=nn.EmbeddingBag(n+1,d,mode='sum',padding_idx=0);nn.init.normal_(self.history.weight,std=.08)\n  self.project=nn.Sequential(nn.Linear(d,d*2),nn.ReLU(),nn.Linear(d*2,d));nn.init.zeros_(self.project[-1].weight);nn.init.zeros_(self.project[-1].bias)\n def forward(self,ids,offsets=None,weights=None):\n  h=self.history(ids,offsets,per_sample_weights=weights);h=F.normalize(h,dim=-1);h=F.normalize(h+self.project(h),dim=-1)\n  return torch.cat([h,torch.ones_like(h[:,:1])],dim=-1)\nclass ItemTower(nn.Module):\n def __init__(self,n,d):\n  super().__init__();self.item=nn.Embedding(n+1,d);nn.init.normal_(self.item.weight,std=.08)\n  self.project=nn.Linear(d,d,bias=False);nn.init.zeros_(self.project.weight);self.bias=nn.Embedding(n+1,1);nn.init.zeros_(self.bias.weight);self.log_scale=nn.Parameter(torch.tensor(2.8))\n def forward(self,ids):\n  h=self.item(ids);h=F.normalize(h+self.project(h),dim=-1)*self.log_scale.exp().clamp(max=60)\n  return torch.cat([h,self.bias(ids)],dim=-1)\nclass TwoTower(nn.Module):\n def __init__(self,n,d=128):\n  super().__init__();self.user=UserTower(n,d);self.item=ItemTower(n,d)\ndef load(stage):\n return dict(np.load(O/f'{stage}_ragged.npz'))\ndef pools(a,seed=42,size=1024):\n r=np.random.default_rng(seed);n=len(a['uids']);hp=np.zeros((n,size),np.int32);tp=np.zeros((n,size),np.int32)\n for u in range(n):\n  lo,hi=a['offset'][u:u+2];ii=a['ids'][lo:hi];w=a['weight'][lo:hi];tw=a['target_weight'][lo:hi]\n  if len(ii):hp[u]=r.choice(ii,size,replace=True,p=w/w.sum());tp[u]=r.choice(ii,size,replace=True,p=tw/tw.sum())\n return hp,tp\[email protected]_grad()\ndef infer(model,a,stage,path,history_power=1.,bias=0.,save_vectors=None):\n model.eval();n=len(a['items']);allitems=torch.arange(1,n+1);iv=model.item(allitems);pred=[];uvs=[]\n if bias:\n  pop=np.bincount(a['ids'],weights=a['target_weight'],minlength=n+1)[1:];iv[:,-1]+=torch.from_numpy(np.log(pop+1).astype(np.float32))*bias\n el=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(el['uids'],a['uids']);assert np.array_equal(el['item_ids'],a['items'])\n for start in range(0,len(a['uids']),64):\n  end=min(start+64,len(a['uids']));lo,hi=a['offset'][[start,end]];ids=torch.from_numpy(a['ids'][lo:hi].astype(np.int64));off=torch.from_numpy(a['offset'][start:end]-lo);w=torch.from_numpy(a['weight'][lo:hi]**history_power)\n  uv=model.user(ids,off,w);[email protected];eligible=np.unpackbits(el['packed_eligible'][start:end],axis=1,count=n,bitorder='little');scores.masked_fill_(torch.from_numpy(eligible==0),-float('inf'));top=scores.topk(100,dim=1).indices.numpy();pred.append(top);uvs.append(uv.numpy())\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(hp),size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)];ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)));iv=model.item(allitems);[email protected]\n  loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\nPY\ncat /app/solution/prepare_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --steps 1000 --eval-every 500 > /app/solution/base.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"3984e5","wall_time_seconds":1.001113188,"session_id":72862,"original_token_count":11,"output":"validation 5564723 time 5.234151124954224\n"}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/prepare.py test > /app/solution/prepare_test.log 2>&1","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"df0989","wall_time_seconds":1.001065167,"session_id":16661,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 17:56:52 UTC"}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-6bcd87d7-9a56-4e72-9e8c-0eb6052b7ef7",
    "process_id": "72862",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural two-tower retrieval with ragged history and exact dot-product scores.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\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\nD=Path('/task/data');O=Path('/app/solution')\ntorch.set_num_threads(4)\nclass UserTower(nn.Module):\n def __init__(self,n,d):\n  super().__init__();self.history=nn.EmbeddingBag(n+1,d,mode='sum',padding_idx=0);nn.init.normal_(self.history.weight,std=.08)\n  self.project=nn.Sequential(nn.Linear(d,d*2),nn.ReLU(),nn.Linear(d*2,d));nn.init.zeros_(self.project[-1].weight);nn.init.zeros_(self.project[-1].bias)\n def forward(self,ids,offsets=None,weights=None):\n  h=self.history(ids,offsets,per_sample_weights=weights);h=F.normalize(h,dim=-1);h=F.normalize(h+self.project(h),dim=-1)\n  return torch.cat([h,torch.ones_like(h[:,:1])],dim=-1)\nclass ItemTower(nn.Module):\n def __init__(self,n,d):\n  super().__init__();self.item=nn.Embedding(n+1,d);nn.init.normal_(self.item.weight,std=.08)\n  self.project=nn.Linear(d,d,bias=False);nn.init.zeros_(self.project.weight);self.bias=nn.Embedding(n+1,1);nn.init.zeros_(self.bias.weight);self.log_scale=nn.Parameter(torch.tensor(2.8))\n def forward(self,ids):\n  h=self.item(ids);h=F.normalize(h+self.project(h),dim=-1)*self.log_scale.exp().clamp(max=60)\n  return torch.cat([h,self.bias(ids)],dim=-1)\nclass TwoTower(nn.Module):\n def __init__(self,n,d=128):\n  super().__init__();self.user=UserTower(n,d);self.item=ItemTower(n,d)\ndef load(stage):\n return dict(np.load(O/f'{stage}_ragged.npz'))\ndef pools(a,seed=42,size=1024):\n r=np.random.default_rng(seed);n=len(a['uids']);hp=np.zeros((n,size),np.int32);tp=np.zeros((n,size),np.int32)\n for u in range(n):\n  lo,hi=a['offset'][u:u+2];ii=a['ids'][lo:hi];w=a['weight'][lo:hi];tw=a['target_weight'][lo:hi]\n  if len(ii):hp[u]=r.choice(ii,size,replace=True,p=w/w.sum());tp[u]=r.choice(ii,size,replace=True,p=tw/tw.sum())\n return hp,tp\[email protected]_grad()\ndef infer(model,a,stage,path,history_power=1.,bias=0.,save_vectors=None):\n model.eval();n=len(a['items']);allitems=torch.arange(1,n+1);iv=model.item(allitems);pred=[];uvs=[]\n if bias:\n  pop=np.bincount(a['ids'],weights=a['target_weight'],minlength=n+1)[1:];iv[:,-1]+=torch.from_numpy(np.log(pop+1).astype(np.float32))*bias\n el=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(el['uids'],a['uids']);assert np.array_equal(el['item_ids'],a['items'])\n for start in range(0,len(a['uids']),64):\n  end=min(start+64,len(a['uids']));lo,hi=a['offset'][[start,end]];ids=torch.from_numpy(a['ids'][lo:hi].astype(np.int64));off=torch.from_numpy(a['offset'][start:end]-lo);w=torch.from_numpy(a['weight'][lo:hi]**history_power)\n  uv=model.user(ids,off,w);[email protected];eligible=np.unpackbits(el['packed_eligible'][start:end],axis=1,count=n,bitorder='little');scores.masked_fill_(torch.from_numpy(eligible==0),-float('inf'));top=scores.topk(100,dim=1).indices.numpy();pred.append(top);uvs.append(uv.numpy())\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(hp),size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)];ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)));iv=model.item(allitems);[email protected]\n  loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\nPY\ncat /app/solution/prepare_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --steps 1000 --eval-every 500 > /app/solution/base.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural two-tower retrieval with ragged history and exact dot-product scores.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\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\nD=Path('/task/data');O=Path('/app/solution')\ntorch.set_num_threads(4)\nclass UserTower(nn.Module):\n def __init__(self,n,d):\n  super().__init__();self.history=nn.EmbeddingBag(n+1,d,mode='sum',padding_idx=0);nn.init.normal_(self.history.weight,std=.08)\n  self.project=nn.Sequential(nn.Linear(d,d*2),nn.ReLU(),nn.Linear(d*2,d));nn.init.zeros_(self.project[-1].weight);nn.init.zeros_(self.project[-1].bias)\n def forward(self,ids,offsets=None,weights=None):\n  h=self.history(ids,offsets,per_sample_weights=weights);h=F.normalize(h,dim=-1);h=F.normalize(h+self.project(h),dim=-1)\n  return torch.cat([h,torch.ones_like(h[:,:1])],dim=-1)\nclass ItemTower(nn.Module):\n def __init__(self,n,d):\n  super().__init__();self.item=nn.Embedding(n+1,d);nn.init.normal_(self.item.weight,std=.08)\n  self.project=nn.Linear(d,d,bias=False);nn.init.zeros_(self.project.weight);self.bias=nn.Embedding(n+1,1);nn.init.zeros_(self.bias.weight);self.log_scale=nn.Parameter(torch.tensor(2.8))\n def forward(self,ids):\n  h=self.item(ids);h=F.normalize(h+self.project(h),dim=-1)*self.log_scale.exp().clamp(max=60)\n  return torch.cat([h,self.bias(ids)],dim=-1)\nclass TwoTower(nn.Module):\n def __init__(self,n,d=128):\n  super().__init__();self.user=UserTower(n,d);self.item=ItemTower(n,d)\ndef load(stage):\n return dict(np.load(O/f'{stage}_ragged.npz'))\ndef pools(a,seed=42,size=1024):\n r=np.random.default_rng(seed);n=len(a['uids']);hp=np.zeros((n,size),np.int32);tp=np.zeros((n,size),np.int32)\n for u in range(n):\n  lo,hi=a['offset'][u:u+2];ii=a['ids'][lo:hi];w=a['weight'][lo:hi];tw=a['target_weight'][lo:hi]\n  if len(ii):hp[u]=r.choice(ii,size,replace=True,p=w/w.sum());tp[u]=r.choice(ii,size,replace=True,p=tw/tw.sum())\n return hp,tp\[email protected]_grad()\ndef infer(model,a,stage,path,history_power=1.,bias=0.,save_vectors=None):\n model.eval();n=len(a['items']);allitems=torch.arange(1,n+1);iv=model.item(allitems);pred=[];uvs=[]\n if bias:\n  pop=np.bincount(a['ids'],weights=a['target_weight'],minlength=n+1)[1:];iv[:,-1]+=torch.from_numpy(np.log(pop+1).astype(np.float32))*bias\n el=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(el['uids'],a['uids']);assert np.array_equal(el['item_ids'],a['items'])\n for start in range(0,len(a['uids']),64):\n  end=min(start+64,len(a['uids']));lo,hi=a['offset'][[start,end]];ids=torch.from_numpy(a['ids'][lo:hi].astype(np.int64));off=torch.from_numpy(a['offset'][start:end]-lo);w=torch.from_numpy(a['weight'][lo:hi]**history_power)\n  uv=model.user(ids,off,w);[email protected];eligible=np.unpackbits(el['packed_eligible'][start:end],axis=1,count=n,bitorder='little');scores.masked_fill_(torch.from_numpy(eligible==0),-float('inf'));top=scores.topk(100,dim=1).indices.numpy();pred.append(top);uvs.append(uv.numpy())\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(hp),size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)];ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)));iv=model.item(allitems);[email protected]\n  loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\nPY\ncat /app/solution/prepare_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --steps 1000 --eval-every 500 > /app/solution/base.log 2>&1"
      }
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    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 1,
    "duration": {
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      "nanos": 573333926
    },
    "formatted_output": ""
  }
}
13

exec command

+03:40
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",
    "id": "exec-b96f0d2f-a952-4e32-8a9e-13f059ef4422",
    "process_id": "16661",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/prepare.py test > /app/solution/prepare_test.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/prepare.py test > /app/solution/prepare_test.log 2>&1"
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}
Tool callexecTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"18e10d","wall_time_seconds":0.00000393,"exit_code":0,"original_token_count":432,"output":"pools (8456, 1024)\nTraceback (most recent call last):\n  File \"/app/solution/model.py\", line 66, in <module>\n    p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/model.py\", line 59, in train\n    loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/nn/functional.py\", line 3462, in cross_entropy\n    return torch._C._nn.cross_entropy_loss(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nIndexError: Target -1 is out of bounds.\ntest 5720505 time 5.646909952163696\n"}
Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-4bbdf171-0916-4e69-aba2-c131f36b4292",
    "process_id": "39017",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/base.log && cat /app/solution/prepare_test.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/base.log",
        "name": "base.log",
        "path": "/app/solution/base.log"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/prepare_test.log",
        "name": "prepare_test.log",
        "path": "/app/solution/prepare_test.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "pools (8456, 1024)\nTraceback (most recent call last):\n  File \"/app/solution/model.py\", line 66, in <module>\n    p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/model.py\", line 59, in train\n    loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/nn/functional.py\", line 3462, in cross_entropy\n    return torch._C._nn.cross_entropy_loss(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nIndexError: Target -1 is out of bounds.\ntest 5720505 time 5.646909952163696\n",
    "stderr": "",
    "aggregated_output": "pools (8456, 1024)\nTraceback (most recent call last):\n  File \"/app/solution/model.py\", line 66, in <module>\n    p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/model.py\", line 59, in train\n    loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/nn/functional.py\", line 3462, in cross_entropy\n    return torch._C._nn.cross_entropy_loss(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nIndexError: Target -1 is out of bounds.\ntest 5720505 time 5.646909952163696\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3930
    },
    "formatted_output": "pools (8456, 1024)\nTraceback (most recent call last):\n  File \"/app/solution/model.py\", line 66, in <module>\n    p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');train(p.parse_args())\n                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/model.py\", line 59, in train\n    loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/nn/functional.py\", line 3462, in cross_entropy\n    return torch._C._nn.cross_entropy_loss(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nIndexError: Target -1 is out of bounds.\ntest 5720505 time 5.646909952163696\n"
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14

exec command

+03:45
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py'\ns=open(p).read().replace(\"u=rng.integers(len(hp),size=args.batch);ctx=\", \"u=rng.choice(np.flatnonzero(hp[:,0]>0),size=args.batch);ctx=\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --steps 1000 --eval-every 500 > /app/solution/base.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"a539bf","wall_time_seconds":1.000929916,"session_id":42018,"original_token_count":0,"output":""}
Harness context token usage record
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15

exec command

+04:06
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -5 /app/solution/base.log; ls -lh /app/solution/*ragged*","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"0646c5","wall_time_seconds":0.000005281,"exit_code":0,"original_token_count":43,"output":"pools (8456, 1024)\n-rw-r--r-- 1 agent agent 175M Sep 10 17:56 /app/solution/test_ragged.npz\n-rw-r--r-- 1 agent agent 171M Sep 10 17:55 /app/solution/validation_ragged.npz\n"}
Harness context codex item completed
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      "/bin/sh",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "pools (8456, 1024)\n-rw-r--r-- 1 agent agent 175M Sep 10 17:56 /app/solution/test_ragged.npz\n-rw-r--r-- 1 agent agent 171M Sep 10 17:55 /app/solution/validation_ragged.npz\n",
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    "exit_code": 0,
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      "secs": 0,
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    },
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16

exec command

+04:30
Thinking unavailable No text recorded

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

Model text

The first model is training on sampled histories and masked targets without a user–item matrix. I’m also preparing chronological training examples: earlier histories predict newly discovered tracks in the next week. That should better match the held-out discovery task.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -8 /app/solution/base.log; ps -eo pid,pcpu,rss,args | head -15","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"b80226","wall_time_seconds":0.00000482,"exit_code":0,"original_token_count":42,"output":"pools (8456, 1024)\nstep 100 loss 10.233709211349487 sec 9.8\nstep 200 loss 9.774329595565796 sec 19.4\nstep 300 loss 9.68106746673584 sec 28.9\n/bin/sh: 1: ps: not found\n"}
Harness context codex item completed
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      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "pools (8456, 1024)\nstep 100 loss 10.233709211349487 sec 9.8\nstep 200 loss 9.774329595565796 sec 19.4\nstep 300 loss 9.68106746673584 sec 28.9\n/bin/sh: 1: ps: not found\n",
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Harness context token usage record
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17

exec command

+04:48
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

text(await tools.exec_command({cmd:"cat > /app/solution/temporal.py <<'PY'\n\"\"\"Chronological discovery examples made only from observed stage history.\"\"\"\nfrom model import *\ndef prepare_temporal(stage,weeks=16):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);rng=np.random.default_rng(361);n=len(im);end=25393400 if stage=='test' else 24786800\n e=pd.read_parquet(O/'train_catalog_events.parquet');e['like']=np.float32(0)\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v['like']=np.float32(0);e=pd.concat([e,v[e.columns]],ignore_index=True)\n for s in (['train','val'] if stage=='test' else ['train']):\n  l=pd.read_parquet(D/f'{s}_likes.parquet');l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;l['like']=np.float32(1);e=pd.concat([e,l[e.columns]],ignore_index=True)\n first=pd.read_parquet(O/f'{stage}_pairs.parquet',columns=['u','i','first']);hp=[];tp=[];ui=[];cutids=[]\n for k in range(1,weeks+1):\n  st=time.time();cut=end-k*7*86400-1800\n  label=e[(e.timestamp>cut+1800)&(e.timestamp<=cut+1800+7*86400)&(e.is_organic==1)&(e.played_ratio_pct>=80)].copy();label['gain']=1+6*label['like'];label=label.groupby(['u','i'],as_index=False).gain.max().merge(first,on=['u','i']);label=label[label['first']>cut];validu=label.u.unique()\n  hist=e[(e.timestamp<=cut)&(e.u.isin(validu))].groupby(['u','i'],sort=True).agg(cnt=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),likes=('like','sum')).reset_index()\n  hist['w']=(np.sqrt(hist.cnt)*(.4+.6*hist.org/hist.cnt)+3*np.minimum(hist.likes,1))*np.exp((hist['last'].astype(float)-cut)/(86400*90))\n  hg={int(u):g for u,g in hist.groupby('u',sort=False)}\n  for u,g in label.groupby('u',sort=False):\n   if int(u) not in hg:continue\n   h=hg[int(u)];w=h.w.to_numpy();w=w/w.sum();tw=g.gain.to_numpy();tw=tw/tw.sum()\n   hp.append(rng.choice(h.i.to_numpy()+1,512,p=w).astype(np.int32));tp.append(rng.choice(g.i.to_numpy()+1,128,p=tw).astype(np.int32));ui.append(u);cutids.append(k)\n  print('week',k,'examples',len(hp),'labels',len(label),'sec',time.time()-st,flush=True)\n np.savez(O/f'{stage}_temporal.npz',hp=np.stack(hp),tp=np.stack(tp),uid=np.array(ui),cut=np.array(cutids))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('stage');p.add_argument('--weeks',type=int,default=16);x=p.parse_args();prepare_temporal(x.stage,x.weeks)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/temporal.py validation > /app/solution/temporal_validation.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -10 /app/solution/base.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"1f9fcd","wall_time_seconds":1.001546676,"session_id":30918,"original_token_count":0,"output":""}
{"chunk_id":"c21040","wall_time_seconds":0.000005701,"exit_code":0,"original_token_count":82,"output":"pools (8456, 1024)\nstep 100 loss 10.233709211349487 sec 9.8\nstep 200 loss 9.774329595565796 sec 19.4\nstep 300 loss 9.68106746673584 sec 28.9\nstep 400 loss 9.605228033065796 sec 38.5\nstep 500 loss 9.499683141708374 sec 48.1\nEVAL 500 {'quality_ndcg@10': 0.016598, 'quality_users': 4263}\nstep 600 loss 9.34779601097107 sec 72.6\n"}
Harness context token usage record
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "tail -10 /app/solution/base.log"
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        "type": "unknown",
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  }
}
18

exec command

+05:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -12 /app/solution/base.log; tail -6 /app/solution/temporal_validation.log; du -sh /app/solution","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"c7a0ff","wall_time_seconds":0.00000551,"exit_code":0,"original_token_count":111,"output":"pools (8456, 1024)\nstep 100 loss 10.233709211349487 sec 9.8\nstep 200 loss 9.774329595565796 sec 19.4\nstep 300 loss 9.68106746673584 sec 28.9\nstep 400 loss 9.605228033065796 sec 38.5\nstep 500 loss 9.499683141708374 sec 48.1\nEVAL 500 {'quality_ndcg@10': 0.016598, 'quality_users': 4263}\nstep 600 loss 9.34779601097107 sec 72.6\nstep 700 loss 9.231469345092773 sec 83.0\nweek 1 examples 4460 labels 46420 sec 5.8111371994018555\n788M\t/app/solution\n"}
{"current_time":"2026-09-10 17:58:47 UTC"}
Harness context codex item completed
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      "nanos": 5510
    },
    "formatted_output": "pools (8456, 1024)\nstep 100 loss 10.233709211349487 sec 9.8\nstep 200 loss 9.774329595565796 sec 19.4\nstep 300 loss 9.68106746673584 sec 28.9\nstep 400 loss 9.605228033065796 sec 38.5\nstep 500 loss 9.499683141708374 sec 48.1\nEVAL 500 {'quality_ndcg@10': 0.016598, 'quality_users': 4263}\nstep 600 loss 9.34779601097107 sec 72.6\nstep 700 loss 9.231469345092773 sec 83.0\nweek 1 examples 4460 labels 46420 sec 5.8111371994018555\n788M\t/app/solution\n"
  }
}
Harness context token usage record
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19

exec command

+05:54
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-fdc5d6ab-0f3a-49f5-9513-fc6025cfc590",
    "process_id": "42018",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/model.py'\ns=open(p).read().replace(\"u=rng.integers(len(hp),size=args.batch);ctx=\", \"u=rng.choice(np.flatnonzero(hp[:,0]>0),size=args.batch);ctx=\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --steps 1000 --eval-every 500 > /app/solution/base.log 2>&1"
    ],
    "cwd": "file:///app",
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      {
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      }
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\",\"hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\\n if args.temporal:\\n  temporal=np.load(O/f'{args.stage}_temporal.npz');thp=temporal['hp'];ttp=temporal['tp']\\n valid_users=np.flatnonzero(hp[:,0]>0)\")\ns=s.replace(\"model.train();u=rng.choice(np.flatnonzero(hp[:,0]>0),size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)];ctx[ctx==target[:,None]]=0\", \"model.train()\\n  if args.temporal and rng.random()<args.temporal:\\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\\n  else:\\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\\n  ctx[ctx==target[:,None]]=0\")\ns=s.replace(\"uv=model.user(torch.from_numpy(ctx.astype(np.int64)));iv=model.item(allitems);[email protected]\\n  loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);\", \"uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\\n  if args.negatives:\\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\\n  else:\\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\\n  loss=F.cross_entropy(logits,labels);\")\ns=s.replace(\"p.add_argument('--resume');train\", \"p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);train\")\nopen(p,'w').write(s)\nPY\ntail -8 /app/solution/base.log; tail -3 /app/solution/temporal_validation.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"b9a570","wall_time_seconds":0.00000555,"exit_code":0,"original_token_count":137,"output":"step 500 loss 9.499683141708374 sec 48.1\nEVAL 500 {'quality_ndcg@10': 0.016598, 'quality_users': 4263}\nstep 600 loss 9.34779601097107 sec 72.6\nstep 700 loss 9.231469345092773 sec 83.0\nstep 800 loss 9.114614028930664 sec 93.7\nstep 900 loss 9.036411333084107 sec 104.4\nstep 1000 loss 8.932028560638427 sec 114.9\nEVAL 1000 {'quality_ndcg@10': 0.017452, 'quality_users': 4263}\nweek 8 examples 33155 labels 38246 sec 3.6258938312530518\nweek 9 examples 36872 labels 36613 sec 3.6027512550354004\nweek 10 examples 40450 labels 36979 sec 3.4740819931030273\n"}
Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-9361bab9-873f-40b6-9fd1-86646ce77959",
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    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\",\"hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\\n if args.temporal:\\n  temporal=np.load(O/f'{args.stage}_temporal.npz');thp=temporal['hp'];ttp=temporal['tp']\\n valid_users=np.flatnonzero(hp[:,0]>0)\")\ns=s.replace(\"model.train();u=rng.choice(np.flatnonzero(hp[:,0]>0),size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)];ctx[ctx==target[:,None]]=0\", \"model.train()\\n  if args.temporal and rng.random()<args.temporal:\\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\\n  else:\\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\\n  ctx[ctx==target[:,None]]=0\")\ns=s.replace(\"uv=model.user(torch.from_numpy(ctx.astype(np.int64)));iv=model.item(allitems);[email protected]\\n  loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);\", \"uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\\n  if args.negatives:\\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\\n  else:\\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\\n  loss=F.cross_entropy(logits,labels);\")\ns=s.replace(\"p.add_argument('--resume');train\", \"p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);train\")\nopen(p,'w').write(s)\nPY\ntail -8 /app/solution/base.log; tail -3 /app/solution/temporal_validation.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\",\"hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\\n if args.temporal:\\n  temporal=np.load(O/f'{args.stage}_temporal.npz');thp=temporal['hp'];ttp=temporal['tp']\\n valid_users=np.flatnonzero(hp[:,0]>0)\")\ns=s.replace(\"model.train();u=rng.choice(np.flatnonzero(hp[:,0]>0),size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)];ctx[ctx==target[:,None]]=0\", \"model.train()\\n  if args.temporal and rng.random()<args.temporal:\\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\\n  else:\\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\\n  ctx[ctx==target[:,None]]=0\")\ns=s.replace(\"uv=model.user(torch.from_numpy(ctx.astype(np.int64)));iv=model.item(allitems);[email protected]\\n  loss=F.cross_entropy(logits,torch.from_numpy(target.astype(np.int64))-1);\", \"uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\\n  if args.negatives:\\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\\n  else:\\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\\n  loss=F.cross_entropy(logits,labels);\")\ns=s.replace(\"p.add_argument('--resume');train\", \"p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);train\")\nopen(p,'w').write(s)\nPY\ntail -8 /app/solution/base.log; tail -3 /app/solution/temporal_validation.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 500 loss 9.499683141708374 sec 48.1\nEVAL 500 {'quality_ndcg@10': 0.016598, 'quality_users': 4263}\nstep 600 loss 9.34779601097107 sec 72.6\nstep 700 loss 9.231469345092773 sec 83.0\nstep 800 loss 9.114614028930664 sec 93.7\nstep 900 loss 9.036411333084107 sec 104.4\nstep 1000 loss 8.932028560638427 sec 114.9\nEVAL 1000 {'quality_ndcg@10': 0.017452, 'quality_users': 4263}\nweek 8 examples 33155 labels 38246 sec 3.6258938312530518\nweek 9 examples 36872 labels 36613 sec 3.6027512550354004\nweek 10 examples 40450 labels 36979 sec 3.4740819931030273\n",
    "stderr": "",
    "aggregated_output": "step 500 loss 9.499683141708374 sec 48.1\nEVAL 500 {'quality_ndcg@10': 0.016598, 'quality_users': 4263}\nstep 600 loss 9.34779601097107 sec 72.6\nstep 700 loss 9.231469345092773 sec 83.0\nstep 800 loss 9.114614028930664 sec 93.7\nstep 900 loss 9.036411333084107 sec 104.4\nstep 1000 loss 8.932028560638427 sec 114.9\nEVAL 1000 {'quality_ndcg@10': 0.017452, 'quality_users': 4263}\nweek 8 examples 33155 labels 38246 sec 3.6258938312530518\nweek 9 examples 36872 labels 36613 sec 3.6027512550354004\nweek 10 examples 40450 labels 36979 sec 3.4740819931030273\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5550
    },
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Harness context token usage record
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20

exec command

+06:13
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name initial_test --resume /app/solution/base_1000.pt --steps 600 --eval-every 600 --lr .001 > /app/solution/initial_test.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name denoise --resume /app/solution/base_1000.pt --steps 6000 --eval-every 2000 --lr .001 > /app/solution/denoise.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"7425d4","wall_time_seconds":1.000551544,"session_id":43223,"original_token_count":0,"output":""}
{"chunk_id":"fd5308","wall_time_seconds":1.001171789,"session_id":83596,"original_token_count":0,"output":""}
Harness context token usage record
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Harness context codex item completed
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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/temporal.py <<'PY'\n\"\"\"Chronological discovery examples made only from observed stage history.\"\"\"\nfrom model import *\ndef prepare_temporal(stage,weeks=16):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);rng=np.random.default_rng(361);n=len(im);end=25393400 if stage=='test' else 24786800\n e=pd.read_parquet(O/'train_catalog_events.parquet');e['like']=np.float32(0)\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v['like']=np.float32(0);e=pd.concat([e,v[e.columns]],ignore_index=True)\n for s in (['train','val'] if stage=='test' else ['train']):\n  l=pd.read_parquet(D/f'{s}_likes.parquet');l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;l['like']=np.float32(1);e=pd.concat([e,l[e.columns]],ignore_index=True)\n first=pd.read_parquet(O/f'{stage}_pairs.parquet',columns=['u','i','first']);hp=[];tp=[];ui=[];cutids=[]\n for k in range(1,weeks+1):\n  st=time.time();cut=end-k*7*86400-1800\n  label=e[(e.timestamp>cut+1800)&(e.timestamp<=cut+1800+7*86400)&(e.is_organic==1)&(e.played_ratio_pct>=80)].copy();label['gain']=1+6*label['like'];label=label.groupby(['u','i'],as_index=False).gain.max().merge(first,on=['u','i']);label=label[label['first']>cut];validu=label.u.unique()\n  hist=e[(e.timestamp<=cut)&(e.u.isin(validu))].groupby(['u','i'],sort=True).agg(cnt=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),likes=('like','sum')).reset_index()\n  hist['w']=(np.sqrt(hist.cnt)*(.4+.6*hist.org/hist.cnt)+3*np.minimum(hist.likes,1))*np.exp((hist['last'].astype(float)-cut)/(86400*90))\n  hg={int(u):g for u,g in hist.groupby('u',sort=False)}\n  for u,g in label.groupby('u',sort=False):\n   if int(u) not in hg:continue\n   h=hg[int(u)];w=h.w.to_numpy();w=w/w.sum();tw=g.gain.to_numpy();tw=tw/tw.sum()\n   hp.append(rng.choice(h.i.to_numpy()+1,512,p=w).astype(np.int32));tp.append(rng.choice(g.i.to_numpy()+1,128,p=tw).astype(np.int32));ui.append(u);cutids.append(k)\n  print('week',k,'examples',len(hp),'labels',len(label),'sec',time.time()-st,flush=True)\n np.savez(O/f'{stage}_temporal.npz',hp=np.stack(hp),tp=np.stack(tp),uid=np.array(ui),cut=np.array(cutids))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('stage');p.add_argument('--weeks',type=int,default=16);x=p.parse_args();prepare_temporal(x.stage,x.weeks)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/temporal.py validation > /app/solution/temporal_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/temporal.py <<'PY'\n\"\"\"Chronological discovery examples made only from observed stage history.\"\"\"\nfrom model import *\ndef prepare_temporal(stage,weeks=16):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);rng=np.random.default_rng(361);n=len(im);end=25393400 if stage=='test' else 24786800\n e=pd.read_parquet(O/'train_catalog_events.parquet');e['like']=np.float32(0)\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];v['like']=np.float32(0);e=pd.concat([e,v[e.columns]],ignore_index=True)\n for s in (['train','val'] if stage=='test' else ['train']):\n  l=pd.read_parquet(D/f'{s}_likes.parquet');l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;l['like']=np.float32(1);e=pd.concat([e,l[e.columns]],ignore_index=True)\n first=pd.read_parquet(O/f'{stage}_pairs.parquet',columns=['u','i','first']);hp=[];tp=[];ui=[];cutids=[]\n for k in range(1,weeks+1):\n  st=time.time();cut=end-k*7*86400-1800\n  label=e[(e.timestamp>cut+1800)&(e.timestamp<=cut+1800+7*86400)&(e.is_organic==1)&(e.played_ratio_pct>=80)].copy();label['gain']=1+6*label['like'];label=label.groupby(['u','i'],as_index=False).gain.max().merge(first,on=['u','i']);label=label[label['first']>cut];validu=label.u.unique()\n  hist=e[(e.timestamp<=cut)&(e.u.isin(validu))].groupby(['u','i'],sort=True).agg(cnt=('timestamp','size'),last=('timestamp','max'),org=('is_organic','sum'),likes=('like','sum')).reset_index()\n  hist['w']=(np.sqrt(hist.cnt)*(.4+.6*hist.org/hist.cnt)+3*np.minimum(hist.likes,1))*np.exp((hist['last'].astype(float)-cut)/(86400*90))\n  hg={int(u):g for u,g in hist.groupby('u',sort=False)}\n  for u,g in label.groupby('u',sort=False):\n   if int(u) not in hg:continue\n   h=hg[int(u)];w=h.w.to_numpy();w=w/w.sum();tw=g.gain.to_numpy();tw=tw/tw.sum()\n   hp.append(rng.choice(h.i.to_numpy()+1,512,p=w).astype(np.int32));tp.append(rng.choice(g.i.to_numpy()+1,128,p=tw).astype(np.int32));ui.append(u);cutids.append(k)\n  print('week',k,'examples',len(hp),'labels',len(label),'sec',time.time()-st,flush=True)\n np.savez(O/f'{stage}_temporal.npz',hp=np.stack(hp),tp=np.stack(tp),uid=np.array(ui),cut=np.array(cutids))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('stage');p.add_argument('--weeks',type=int,default=16);x=p.parse_args();prepare_temporal(x.stage,x.weeks)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/temporal.py validation > /app/solution/temporal_validation.log 2>&1"
      }
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    "status": "completed",
    "stdout": "",
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  }
}
21

exec command

+06:43
Thinking unavailable No text recorded

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

Model text

The initial public score is 0.017452 NDCG@10, so it needs more training and testing. I’m continuing training and preparing an initial refit on train plus validation history to save a valid final artifact. The next experiments will compare masked-history training with chronological discovery targets.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -5 /app/solution/denoise.log; tail -5 /app/solution/initial_test.log; tail -4 /app/solution/temporal_validation.log","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"6c8d33","wall_time_seconds":0.00000454,"exit_code":0,"original_token_count":129,"output":"pools (8456, 1024)\nstep 100 loss 8.818444075584411 sec 6.1\nstep 200 loss 8.829683017730712 sec 12.1\nstep 300 loss 8.766411113739014 sec 17.8\npools (8456, 1024)\nstep 100 loss 8.902977323532104 sec 5.6\nstep 200 loss 8.911878051757812 sec 11.5\nstep 300 loss 8.83082462310791 sec 17.4\nweek 13 examples 50843 labels 35165 sec 3.182223320007324\nweek 14 examples 54193 labels 34029 sec 3.21736741065979\nweek 15 examples 57518 labels 33098 sec 3.0221846103668213\nweek 16 examples 60791 labels 31916 sec 3.164010524749756\n"}
{"current_time":"2026-09-10 18:00:05 UTC"}
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Harness context codex item completed
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    "stderr": "",
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22

exec command

+07:10
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

text(await tools.exec_command({cmd:"cat > /app/solution/popularity.py <<'PY'\nfrom model import *\ndef make(stage):\n a=load(stage);n=len(a['items']);im=pd.Index(a['items']);um=pd.Index(a['uids']);end=25393400 if stage=='test' else 24786800\n e=pd.read_parquet(O/'train_catalog_events.parquet')\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['i']=im.get_indexer(v.item_id);v['u']=um.get_indexer(v.uid);v=v[(v.i>=0)&(v.u>=0)];e=pd.concat([e,v[e.columns]])\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l['i']=im.get_indexer(l.item_id);l['u']=um.get_indexer(l.uid);l=l[(l.i>=0)&(l.u>=0)]\n fs=[];names=[]\n for name,x in [('strong',e[(e.is_organic==1)&(e.played_ratio_pct>=80)]),('like',l[l.is_organic==1]),('all',e)]:\n  for days in [7,14,30,90,300]:\n   r=x[x.timestamp>end-days*86400].drop_duplicates(['u','i']);pop=np.bincount(r.i,minlength=n).astype(np.float32);fs.append(np.log1p(pop));names.append(f'{name}_{days}')\n f=np.stack(fs,1);np.savez(O/f'{stage}_popularity.npz',features=f,names=np.array(names));print(names,flush=True)\nif __name__=='__main__':make(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/popularity.py validation > /app/solution/popularity_validation.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -3 /app/solution/initial_test.log; tail -4 /app/solution/denoise.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d6c031","wall_time_seconds":1.000750296,"session_id":59759,"original_token_count":0,"output":""}
{"chunk_id":"5f731c","wall_time_seconds":0.00000432,"exit_code":0,"original_token_count":65,"output":"step 500 loss 8.716253423690796 sec 29.3\nstep 600 loss 8.67154730796814 sec 35.3\nEVAL 600 {}\nstep 700 loss 8.55920431137085 sec 39.6\nstep 800 loss 8.488602404594422 sec 44.0\nstep 900 loss 8.446151151657105 sec 48.6\nstep 1000 loss 8.424202165603639 sec 53.1\n"}
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Harness context codex item completed
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Harness context codex item completed
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Harness context codex item completed
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      "cat > /app/solution/popularity.py <<'PY'\nfrom model import *\ndef make(stage):\n a=load(stage);n=len(a['items']);im=pd.Index(a['items']);um=pd.Index(a['uids']);end=25393400 if stage=='test' else 24786800\n e=pd.read_parquet(O/'train_catalog_events.parquet')\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['i']=im.get_indexer(v.item_id);v['u']=um.get_indexer(v.uid);v=v[(v.i>=0)&(v.u>=0)];e=pd.concat([e,v[e.columns]])\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l['i']=im.get_indexer(l.item_id);l['u']=um.get_indexer(l.uid);l=l[(l.i>=0)&(l.u>=0)]\n fs=[];names=[]\n for name,x in [('strong',e[(e.is_organic==1)&(e.played_ratio_pct>=80)]),('like',l[l.is_organic==1]),('all',e)]:\n  for days in [7,14,30,90,300]:\n   r=x[x.timestamp>end-days*86400].drop_duplicates(['u','i']);pop=np.bincount(r.i,minlength=n).astype(np.float32);fs.append(np.log1p(pop));names.append(f'{name}_{days}')\n f=np.stack(fs,1);np.savez(O/f'{stage}_popularity.npz',features=f,names=np.array(names));print(names,flush=True)\nif __name__=='__main__':make(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/popularity.py validation > /app/solution/popularity_validation.log 2>&1"
    ],
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        "cmd": "cat > /app/solution/popularity.py <<'PY'\nfrom model import *\ndef make(stage):\n a=load(stage);n=len(a['items']);im=pd.Index(a['items']);um=pd.Index(a['uids']);end=25393400 if stage=='test' else 24786800\n e=pd.read_parquet(O/'train_catalog_events.parquet')\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['i']=im.get_indexer(v.item_id);v['u']=um.get_indexer(v.uid);v=v[(v.i>=0)&(v.u>=0)];e=pd.concat([e,v[e.columns]])\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l['i']=im.get_indexer(l.item_id);l['u']=um.get_indexer(l.uid);l=l[(l.i>=0)&(l.u>=0)]\n fs=[];names=[]\n for name,x in [('strong',e[(e.is_organic==1)&(e.played_ratio_pct>=80)]),('like',l[l.is_organic==1]),('all',e)]:\n  for days in [7,14,30,90,300]:\n   r=x[x.timestamp>end-days*86400].drop_duplicates(['u','i']);pop=np.bincount(r.i,minlength=n).astype(np.float32);fs.append(np.log1p(pop));names.append(f'{name}_{days}')\n f=np.stack(fs,1);np.savez(O/f'{stage}_popularity.npz',features=f,names=np.array(names));print(names,flush=True)\nif __name__=='__main__':make(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/popularity.py validation > /app/solution/popularity_validation.log 2>&1"
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name temporal --resume /app/solution/base_1000.pt --steps 6000 --eval-every 2000 --lr .001 --temporal .75 > /app/solution/temporal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task');from ranking_evaluation import *\nf=validate_ranking(pd.read_parquet('/app/solution/submission.parquet'),set(pd.read_parquet('/task/data/target_users.parquet').uid));e=CandidateSet('/task/data/test_eligible_candidates.npz');assert all(e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));print('VALID SUBMISSION',len(f),'users',f.uid.nunique())\nPY","max_output_tokens":2000}));

Tool response

Script completed
Wall time 5.4 seconds
Output:

{"chunk_id":"105588","wall_time_seconds":1.001035486,"session_id":54165,"original_token_count":0,"output":""}
{"chunk_id":"da6284","wall_time_seconds":4.087115326,"exit_code":0,"original_token_count":9,"output":"VALID SUBMISSION 845600 users 8456\n"}
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      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task');from ranking_evaluation import *\nf=validate_ranking(pd.read_parquet('/app/solution/submission.parquet'),set(pd.read_parquet('/task/data/target_users.parquet').uid));e=CandidateSet('/task/data/test_eligible_candidates.npz');assert all(e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));print('VALID SUBMISSION',len(f),'users',f.uid.nunique())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task');from ranking_evaluation import *\nf=validate_ranking(pd.read_parquet('/app/solution/submission.parquet'),set(pd.read_parquet('/task/data/target_users.parquet').uid));e=CandidateSet('/task/data/test_eligible_candidates.npz');assert all(e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));print('VALID SUBMISSION',len(f),'users',f.uid.nunique())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALID SUBMISSION 845600 users 8456\n",
    "stderr": "",
    "aggregated_output": "VALID SUBMISSION 845600 users 8456\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 87128247
    },
    "formatted_output": "VALID SUBMISSION 845600 users 8456\n"
  }
}
23

exec command

+08:10
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

text(await tools.exec_command({cmd:"cat > /app/solution/tune.py <<'PY'\n\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model']);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='tune');p.add_argument('--hist',action='store_true');main(p.parse_args())\nPY\ntail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"7beb5d","wall_time_seconds":0.0000049,"exit_code":0,"original_token_count":84,"output":"step 2300 loss 8.077251334190368 sec 132.9\nstep 2400 loss 8.058683729171753 sec 137.2\nstep 2500 loss 8.028934063911437 sec 141.6\nstep 2600 loss 8.01923463344574 sec 146.0\nstep 800 loss 8.679723711013795 sec 41.9\nstep 900 loss 8.616224479675292 sec 48.5\nstep 1000 loss 8.52102068901062 sec 54.9\nstep 1100 loss 8.502407398223877 sec 61.3\n"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f528659e-567d-45ea-87c0-8905f8c5c943",
    "process_id": "28131",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/tune.py <<'PY'\n\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model']);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='tune');p.add_argument('--hist',action='store_true');main(p.parse_args())\nPY\ntail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/tune.py <<'PY'\n\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model']);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='tune');p.add_argument('--hist',action='store_true');main(p.parse_args())\nPY\ntail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 2300 loss 8.077251334190368 sec 132.9\nstep 2400 loss 8.058683729171753 sec 137.2\nstep 2500 loss 8.028934063911437 sec 141.6\nstep 2600 loss 8.01923463344574 sec 146.0\nstep 800 loss 8.679723711013795 sec 41.9\nstep 900 loss 8.616224479675292 sec 48.5\nstep 1000 loss 8.52102068901062 sec 54.9\nstep 1100 loss 8.502407398223877 sec 61.3\n",
    "stderr": "",
    "aggregated_output": "step 2300 loss 8.077251334190368 sec 132.9\nstep 2400 loss 8.058683729171753 sec 137.2\nstep 2500 loss 8.028934063911437 sec 141.6\nstep 2600 loss 8.01923463344574 sec 146.0\nstep 800 loss 8.679723711013795 sec 41.9\nstep 900 loss 8.616224479675292 sec 48.5\nstep 1000 loss 8.52102068901062 sec 54.9\nstep 1100 loss 8.502407398223877 sec 61.3\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4900
    },
    "formatted_output": "step 2300 loss 8.077251334190368 sec 132.9\nstep 2400 loss 8.058683729171753 sec 137.2\nstep 2500 loss 8.028934063911437 sec 141.6\nstep 2600 loss 8.01923463344574 sec 146.0\nstep 800 loss 8.679723711013795 sec 41.9\nstep 900 loss 8.616224479675292 sec 48.5\nstep 1000 loss 8.52102068901062 sec 54.9\nstep 1100 loss 8.502407398223877 sec 61.3\n"
  }
}
Harness context token usage record
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  "session_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "response_id": "resp_03e1db26860ff63f016aa2f05d902087d1a3e2709c23617d7c",
  "usage": {
    "input_tokens": 38647,
    "cached_input_tokens": 38144,
    "cache_write_input_tokens": 0,
    "output_tokens": 2144,
    "reasoning_output_tokens": 475,
    "total_tokens": 40791
  },
  "turn_token_usage": {
    "input_tokens": 653649,
    "cached_input_tokens": 623360,
    "cache_write_input_tokens": 0,
    "output_tokens": 13952,
    "reasoning_output_tokens": 4801,
    "total_tokens": 667601
  },
  "thread_token_usage": {
    "input_tokens": 653649,
    "cached_input_tokens": 623360,
    "cache_write_input_tokens": 0,
    "output_tokens": 13952,
    "reasoning_output_tokens": 4801,
    "total_tokens": 667601
  }
}
24

exec command

+09:00
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

text(await tools.exec_command({cmd:"rg 'EVAL' /app/solution/denoise.log; cat /app/solution/popularity_validation.log; ls -lh /app/solution/submission.parquet","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"1bef28","wall_time_seconds":0.00000491,"exit_code":0,"original_token_count":78,"output":"EVAL 2000 {'quality_ndcg@10': 0.018589, 'quality_users': 4263}\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:00 /app/solution/submission.parquet\n"}
{"current_time":"2026-09-10 18:02:20 UTC"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5a7a35b6-9beb-45df-a08a-a749dc2a258b",
    "process_id": "66558",
    "command": [
      "/bin/sh",
      "-lc",
      "rg 'EVAL' /app/solution/denoise.log; cat /app/solution/popularity_validation.log; ls -lh /app/solution/submission.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "search",
        "cmd": "rg EVAL /app/solution/denoise.log",
        "query": "EVAL",
        "path": "denoise.log"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/popularity_validation.log",
        "name": "popularity_validation.log",
        "path": "/app/solution/popularity_validation.log"
      },
      {
        "type": "list_files",
        "cmd": "ls -lh /app/solution/submission.parquet",
        "path": "submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "EVAL 2000 {'quality_ndcg@10': 0.018589, 'quality_users': 4263}\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:00 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "EVAL 2000 {'quality_ndcg@10': 0.018589, 'quality_users': 4263}\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:00 /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4910
    },
    "formatted_output": "EVAL 2000 {'quality_ndcg@10': 0.018589, 'quality_users': 4263}\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:00 /app/solution/submission.parquet\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
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  "usage": {
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    "cache_write_input_tokens": 0,
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  }
}
25

exec command

+09:27
Thinking unavailable No text recorded

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

Model text

A valid final submission is now saved: 845,600 rows covering all 8,456 users, with eligibility checks passed. Masked-history training has reached 0.018589 NDCG@10. I’m testing chronological targets and history recency to improve new-track discovery.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/tune.py /app/solution/denoise_2000.pt --name denoise_tune > /app/solution/denoise_tune.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"99c847","wall_time_seconds":1.000923634,"session_id":30520,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
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  "turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
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    "cache_write_input_tokens": 0,
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  }
}
26

exec command

+09:43
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -5 /app/solution/temporal.log; tail -5 /app/solution/denoise.log; tail -10 /app/solution/denoise_tune.log","max_output_tokens":2500}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"36e3c9","wall_time_seconds":0.00000682,"exit_code":0,"original_token_count":125,"output":"step 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 3200 loss 7.929596281051635 sec 172.1\nstep 3300 loss 7.910526938438416 sec 176.8\nstep 3400 loss 7.911010270118713 sec 181.1\nstep 3500 loss 7.881724796295166 sec 187.4\nstep 3600 loss 7.855506768226624 sec 195.2\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-611ecb81-c9eb-4fe0-9f96-ea2c0ed3716f",
    "process_id": "33112",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -5 /app/solution/temporal.log; tail -5 /app/solution/denoise.log; tail -10 /app/solution/denoise_tune.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -5 /app/solution/temporal.log; tail -5 /app/solution/denoise.log; tail -10 /app/solution/denoise_tune.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 3200 loss 7.929596281051635 sec 172.1\nstep 3300 loss 7.910526938438416 sec 176.8\nstep 3400 loss 7.911010270118713 sec 181.1\nstep 3500 loss 7.881724796295166 sec 187.4\nstep 3600 loss 7.855506768226624 sec 195.2\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n",
    "stderr": "",
    "aggregated_output": "step 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 3200 loss 7.929596281051635 sec 172.1\nstep 3300 loss 7.910526938438416 sec 176.8\nstep 3400 loss 7.911010270118713 sec 181.1\nstep 3500 loss 7.881724796295166 sec 187.4\nstep 3600 loss 7.855506768226624 sec 195.2\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6820
    },
    "formatted_output": "step 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 3200 loss 7.929596281051635 sec 172.1\nstep 3300 loss 7.910526938438416 sec 176.8\nstep 3400 loss 7.911010270118713 sec 181.1\nstep 3500 loss 7.881724796295166 sec 187.4\nstep 3600 loss 7.855506768226624 sec 195.2\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n"
  }
}
Harness context token usage record
{
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  "thread_token_usage": {
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  }
}
27

exec command

+10:08
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

text(await tools.exec_command({cmd:"tail -7 /app/solution/temporal.log; tail -5 /app/solution/denoise_tune.log; tail -4 /app/solution/denoise.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"2ca231","wall_time_seconds":0.00000424,"exit_code":0,"original_token_count":210,"output":"step 1300 loss 8.437959432601929 sec 74.3\nstep 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 1900 loss 8.051200280189514 sec 124.6\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.02010734976121788 {'days': 14, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.01958608127429671 {'days': 30, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.018895128251464482 {'days': 60, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.01858912741608578 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6}\nstep 3600 loss 7.855506768226624 sec 195.2\nstep 3700 loss 7.854125323295594 sec 203.4\nstep 3800 loss 7.827722163200378 sec 212.9\nstep 3900 loss 7.831602139472961 sec 220.1\n"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c415f628-d9af-44ab-9fc4-2116c8095217",
    "process_id": "13503",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -7 /app/solution/temporal.log; tail -5 /app/solution/denoise_tune.log; tail -4 /app/solution/denoise.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -7 /app/solution/temporal.log; tail -5 /app/solution/denoise_tune.log; tail -4 /app/solution/denoise.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 1300 loss 8.437959432601929 sec 74.3\nstep 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 1900 loss 8.051200280189514 sec 124.6\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.02010734976121788 {'days': 14, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.01958608127429671 {'days': 30, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.018895128251464482 {'days': 60, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.01858912741608578 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6}\nstep 3600 loss 7.855506768226624 sec 195.2\nstep 3700 loss 7.854125323295594 sec 203.4\nstep 3800 loss 7.827722163200378 sec 212.9\nstep 3900 loss 7.831602139472961 sec 220.1\n",
    "stderr": "",
    "aggregated_output": "step 1300 loss 8.437959432601929 sec 74.3\nstep 1400 loss 8.32505524635315 sec 81.0\nstep 1500 loss 8.257164225578308 sec 87.2\nstep 1600 loss 8.236904377937316 sec 93.6\nstep 1700 loss 8.169153890609742 sec 101.1\nstep 1800 loss 8.09797516822815 sec 109.9\nstep 1900 loss 8.051200280189514 sec 124.6\n0.02074765770549286 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.02010734976121788 {'days': 14, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.01958608127429671 {'days': 30, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.018895128251464482 {'days': 60, 'like': 3, 'count': 0.5, 'organic': 0.6}\n0.01858912741608578 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6}\nstep 3600 loss 7.855506768226624 sec 195.2\nstep 3700 loss 7.854125323295594 sec 203.4\nstep 3800 loss 7.827722163200378 sec 212.9\nstep 3900 loss 7.831602139472961 sec 220.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4240
    },
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  }
}
Harness context token usage record
{
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}
28

exec command

+10:13
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.clock__curr_time({}));
text(await tools.exec_command({cmd:"df -h /app/solution; cat /sys/fs/cgroup/memory.max","max_output_tokens":1000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"current_time":"2026-09-10 18:03:31 UTC"}
{"chunk_id":"bf767b","wall_time_seconds":0.00000464,"exit_code":0,"original_token_count":25,"output":"Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8589934592\n"}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-c82dedff-0d6f-4b0c-99f9-dcd5ce547010",
    "process_id": "69925",
    "command": [
      "/bin/sh",
      "-lc",
      "df -h /app/solution; cat /sys/fs/cgroup/memory.max"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "df -h /app/solution; cat /sys/fs/cgroup/memory.max"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8589934592\n",
    "stderr": "",
    "aggregated_output": "Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8589934592\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4640
    },
    "formatted_output": "Filesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n8589934592\n"
  }
}
Harness context token usage record
{
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}
29

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

text(await tools.exec_command({cmd:"tail -4 /app/solution/temporal.log; tail -4 /app/solution/denoise.log; tail -4 /app/solution/denoise_tune.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"a4c011","wall_time_seconds":0.00000285,"exit_code":0,"original_token_count":200,"output":"step 1800 loss 8.09797516822815 sec 109.9\nstep 1900 loss 8.051200280189514 sec 124.6\nstep 2000 loss 7.995405602455139 sec 139.0\nEVAL 2000 {'quality_ndcg@10': 0.026877, 'quality_users': 4263}\nstep 3800 loss 7.827722163200378 sec 212.9\nstep 3900 loss 7.831602139472961 sec 220.1\nstep 4000 loss 7.81799816608429 sec 226.5\nEVAL 4000 {'quality_ndcg@10': 0.019184, 'quality_users': 4263}\n0.031442569376435046 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'coef': 0.5}\n0.03990905248293275 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'coef': 1.0}\n0.04335305854685109 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'coef': 2.0}\n0.012805138674006504 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_14', 'coef': -0.5}\n"}
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30

exec command

+11:04
Thinking unavailable No text recorded

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

Model text

Chronological training has improved NDCG@10 to 0.026877. Recent organic-like popularity also looks useful: adding it as an item-only feature raised the earlier model to 0.043353. That feature can live inside the item tower, preserving exact dot-product retrieval.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"self.log_scale=nn.Parameter(torch.tensor(2.8))\", \"self.log_scale=nn.Parameter(torch.tensor(2.8));self.register_buffer('features',torch.zeros(n+1,15));self.prior=nn.Linear(15,1,bias=False);nn.init.zeros_(self.prior.weight)\")\ns=s.replace(\"torch.cat([h,self.bias(ids)],dim=-1)\",\"torch.cat([h,self.bias(ids)+self.prior(self.features[ids])],dim=-1)\")\ns=s.replace(\"model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\", \"model.load_state_dict(torch.load(args.resume,weights_only=False)['model'],strict=False)\")\ns=s.replace(\"hp,tp=pools(a,args.seed);\", \"\\n if args.history_days!=90:\\n  cutoff=25393400 if args.stage=='test' else 24786800;a['weight']*=np.exp((a['last'].astype(float)-cutoff)/86400*(1/args.history_days-1/90)).astype(np.float32)\\n hp,tp=pools(a,args.seed);\")\ns=s.replace(\"temporal=np.load(O/f'{args.stage}_temporal.npz')\", \"temporal=np.load(O/f'{args.stage}_{args.temporal_file}.npz')\")\ns=s.replace(\"p.add_argument('--negatives',type=int,default=2048);train\", \"p.add_argument('--negatives',type=int,default=2048);p.add_argument('--history-days',type=float,default=90);p.add_argument('--temporal-file',default='temporal');train\")\nopen(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace(\"m.load_state_dict(ck['model']);\", \"m.load_state_dict(ck['model'],strict=False);\");open(p,'w').write(s)\np='/app/solution/temporal.py';s=open(p).read().replace('def prepare_temporal(stage,weeks=16):','def prepare_temporal(stage,weeks=16,days=90):').replace('/(86400*90))','/(86400*days))').replace(\"f'{stage}_temporal.npz'\", \"f'{stage}_temporal{days if days!=90 else chr(0)}.npz'.replace(chr(0),'')\").replace(\"x=p.parse_args();prepare_temporal(x.stage,x.weeks)\",\"p.add_argument('--days',type=int,default=90);x=p.parse_args();prepare_temporal(x.stage,x.weeks,x.days)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/temporal.py validation --days 14 > /app/solution/temporal14_validation.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"7f1c82","wall_time_seconds":1.001063848,"session_id":80361,"original_token_count":0,"output":""}
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31

exec command

+11:30
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\np=pd.read_parquet('/task/data/validation_preference_state.parquet');a=np.load('/task/data/validation_eligible_candidates.npz');u=pd.Index(a['uids']).get_indexer(p.uid);i=pd.Index(a['item_ids']).get_indexer(p.item_id);ok=(u>=0)&(i>=0);p=p[ok];u=u[ok];i=i[ok];el=(a['packed_eligible'][u,i//8]>>(i%8))&1;print(p.assign(eligible=el).groupby('state').eligible.agg(['sum','count','mean']))\nPY","max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -5 /app/solution/denoise_tune.log; tail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log; tail -2 /app/solution/temporal14_validation.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 3.4 seconds
Output:

{"chunk_id":"6bf302","wall_time_seconds":3.130046075,"exit_code":0,"original_token_count":28,"output":"          sum   count  mean\nstate                      \ndisliked    0   50689   0.0\nliked       0  418435   0.0\n"}
{"chunk_id":"d29016","wall_time_seconds":0.00000576,"exit_code":0,"original_token_count":267,"output":"0.02740829763943529 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'subtract': 'strong_300', 'coef': 1}\n0.017651003855960665 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'subtract': 'strong_300', 'coef': 2}\n0.009791123629714056 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'subtract': 'strong_300', 'coef': 3}\n0.027424599027059623 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 0.5}\n0.027855911600943015 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 1}\nEVAL 4000 {'quality_ndcg@10': 0.019184, 'quality_users': 4263}\nstep 4100 loss 7.833000063896179 sec 265.5\nstep 4200 loss 7.809336619377136 sec 281.5\nstep 4300 loss 7.782431516647339 sec 296.7\nstep 2100 loss 7.944840497970581 sec 170.5\nstep 2200 loss 7.874306268692017 sec 185.5\nstep 2300 loss 7.9067191314697265 sec 202.6\nstep 2400 loss 7.8264472913742065 sec 221.6\nweek 1 examples 4460 labels 46420 sec 9.626161813735962\n"}
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Harness context codex item completed
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Harness context codex item completed
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    "stdout": "0.02740829763943529 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'subtract': 'strong_300', 'coef': 1}\n0.017651003855960665 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'subtract': 'strong_300', 'coef': 2}\n0.009791123629714056 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'subtract': 'strong_300', 'coef': 3}\n0.027424599027059623 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 0.5}\n0.027855911600943015 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 1}\nEVAL 4000 {'quality_ndcg@10': 0.019184, 'quality_users': 4263}\nstep 4100 loss 7.833000063896179 sec 265.5\nstep 4200 loss 7.809336619377136 sec 281.5\nstep 4300 loss 7.782431516647339 sec 296.7\nstep 2100 loss 7.944840497970581 sec 170.5\nstep 2200 loss 7.874306268692017 sec 185.5\nstep 2300 loss 7.9067191314697265 sec 202.6\nstep 2400 loss 7.8264472913742065 sec 221.6\nweek 1 examples 4460 labels 46420 sec 9.626161813735962\n",
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Harness context codex item completed
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32

exec command

+11:57
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

text(await tools.exec_command({cmd:"tail -5 /app/solution/denoise_tune.log; tail -2 /app/solution/temporal14_validation.log","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"ae1992","wall_time_seconds":0.00000477,"exit_code":0,"original_token_count":186,"output":"0.027424599027059623 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 0.5}\n0.027855911600943015 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 1}\n0.01728265918879125 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 2}\n0.009745307018275492 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 3}\nBEST (0.04335305854685109, {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'coef': 2.0})\nweek 2 examples 8799 labels 45020 sec 8.098808526992798\nweek 3 examples 13042 labels 44773 sec 7.434128522872925\n"}
{"current_time":"2026-09-10 18:05:14 UTC"}
Harness context codex item completed
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    "source": "unified_exec_startup",
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    "stdout": "0.027424599027059623 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 0.5}\n0.027855911600943015 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 1}\n0.01728265918879125 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 2}\n0.009745307018275492 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 3}\nBEST (0.04335305854685109, {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'coef': 2.0})\nweek 2 examples 8799 labels 45020 sec 8.098808526992798\nweek 3 examples 13042 labels 44773 sec 7.434128522872925\n",
    "stderr": "",
    "aggregated_output": "0.027424599027059623 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 0.5}\n0.027855911600943015 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 1}\n0.01728265918879125 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 2}\n0.009745307018275492 {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_30', 'subtract': 'strong_300', 'coef': 3}\nBEST (0.04335305854685109, {'days': 7, 'like': 3, 'count': 0.5, 'organic': 0.6, 'pop': 'like_7', 'coef': 2.0})\nweek 2 examples 8799 labels 45020 sec 8.098808526992798\nweek 3 examples 13042 labels 44773 sec 7.434128522872925\n",
    "exit_code": 0,
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    },
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33

exec command

+12:20
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

text(await tools.exec_command({cmd:"cat > /app/solution/calibrate.py <<'PY'\nfrom tune import *\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();f=np.load(O/'validation_popularity.npz');features=f['features'];names=list(f['names']);configs=[];best=(-1,None,None,None)\n # Recent likes are a cutoff-observed, item-only feature. Grid-search its learned linear weight.\n for days in [3,7,14,30,90]:\n  uv=vectors(m,a,days=days)\n  for coef in [0,1,2,3,4]:\n   bias=coef*features[:,names.index('like_7')];score=ev.score(rank(uv,iv,ev.e,bias));config={'days':days,'like':3,'count':.5,'organic':.6,'prior':{'like_7':coef}};print(score,config,flush=True);configs.append({'score':score,**config})\n   if score>best[0]:best=(score,config,uv,bias)\n days=best[1]['days'];coef=best[1]['prior']['like_7']\n for like,count in [(0,0),(0,.5),(0,1),(3,0),(3,1),(10,0),(10,.5),(10,1)]:\n  uv=vectors(m,a,days=days,like=like,count=count);bias=coef*features[:,names.index('like_7')];score=ev.score(rank(uv,iv,ev.e,bias));config={'days':days,'like':like,'count':count,'organic':.6,'prior':{'like_7':coef}};print(score,config,flush=True);configs.append({'score':score,**config})\n  if score>best[0]:best=(score,config,uv,bias)\n hist=best[1].copy();uv=best[2];base=best[3];prior=hist['prior'].copy()\n for name in ['strong_7','strong_30','strong_300','like_30','like_300']:\n  for val in [-1,-.5,.5,1]:\n   bias=base+val*features[:,names.index(name)];score=ev.score(rank(uv,iv,ev.e,bias));config={**hist,'prior':{**prior,name:val}};print(score,config,flush=True);configs.append({'score':score,**config})\n   if score>best[0]:best=(score,config,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(configs,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3])\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='calibrate');x=p.parse_args();main(x)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/calibrate.py /app/solution/temporal_2000.pt --name temporal2k_cal > /app/solution/temporal2k_cal.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"1b979b","wall_time_seconds":1.001237419,"session_id":83433,"original_token_count":0,"output":""}
Harness context token usage record
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34

exec command

+13:06
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log; tail -3 /app/solution/temporal14_validation.log; tail -4 /app/solution/temporal2k_cal.log","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"5b5578","wall_time_seconds":0.00000297,"exit_code":0,"original_token_count":227,"output":"step 5100 loss 7.690926280021667 sec 365.2\nstep 5200 loss 7.711740159988404 sec 371.9\nstep 5300 loss 7.710004138946533 sec 379.5\nstep 5400 loss 7.686396389007569 sec 394.1\nstep 3100 loss 7.4909359645843505 sec 277.0\nstep 3200 loss 7.551318593025208 sec 282.7\nstep 3300 loss 7.478376302719116 sec 289.4\nstep 3400 loss 7.557193412780761 sec 299.5\nweek 11 examples 43958 labels 35854 sec 6.42899751663208\nweek 12 examples 47422 labels 35413 sec 6.355281829833984\nweek 13 examples 50843 labels 35165 sec 7.259589910507202\n0.03651787275904172 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 1}}\n0.04020039951052912 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.039366117934852986 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.03832361284481279 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n"}
{"current_time":"2026-09-10 18:06:25 UTC"}
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Harness context codex item completed
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        "cmd": "tail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log; tail -3 /app/solution/temporal14_validation.log; tail -4 /app/solution/temporal2k_cal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 5100 loss 7.690926280021667 sec 365.2\nstep 5200 loss 7.711740159988404 sec 371.9\nstep 5300 loss 7.710004138946533 sec 379.5\nstep 5400 loss 7.686396389007569 sec 394.1\nstep 3100 loss 7.4909359645843505 sec 277.0\nstep 3200 loss 7.551318593025208 sec 282.7\nstep 3300 loss 7.478376302719116 sec 289.4\nstep 3400 loss 7.557193412780761 sec 299.5\nweek 11 examples 43958 labels 35854 sec 6.42899751663208\nweek 12 examples 47422 labels 35413 sec 6.355281829833984\nweek 13 examples 50843 labels 35165 sec 7.259589910507202\n0.03651787275904172 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 1}}\n0.04020039951052912 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.039366117934852986 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.03832361284481279 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n",
    "stderr": "",
    "aggregated_output": "step 5100 loss 7.690926280021667 sec 365.2\nstep 5200 loss 7.711740159988404 sec 371.9\nstep 5300 loss 7.710004138946533 sec 379.5\nstep 5400 loss 7.686396389007569 sec 394.1\nstep 3100 loss 7.4909359645843505 sec 277.0\nstep 3200 loss 7.551318593025208 sec 282.7\nstep 3300 loss 7.478376302719116 sec 289.4\nstep 3400 loss 7.557193412780761 sec 299.5\nweek 11 examples 43958 labels 35854 sec 6.42899751663208\nweek 12 examples 47422 labels 35413 sec 6.355281829833984\nweek 13 examples 50843 labels 35165 sec 7.259589910507202\n0.03651787275904172 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 1}}\n0.04020039951052912 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.039366117934852986 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.03832361284481279 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n",
    "exit_code": 0,
    "duration": {
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      "nanos": 2970
    },
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"self.log_scale=nn.Parameter(torch.tensor(2.8))\", \"self.log_scale=nn.Parameter(torch.tensor(2.8));self.register_buffer('features',torch.zeros(n+1,15));self.prior=nn.Linear(15,1,bias=False);nn.init.zeros_(self.prior.weight)\")\ns=s.replace(\"torch.cat([h,self.bias(ids)],dim=-1)\",\"torch.cat([h,self.bias(ids)+self.prior(self.features[ids])],dim=-1)\")\ns=s.replace(\"model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\", \"model.load_state_dict(torch.load(args.resume,weights_only=False)['model'],strict=False)\")\ns=s.replace(\"hp,tp=pools(a,args.seed);\", \"\\n if args.history_days!=90:\\n  cutoff=25393400 if args.stage=='test' else 24786800;a['weight']*=np.exp((a['last'].astype(float)-cutoff)/86400*(1/args.history_days-1/90)).astype(np.float32)\\n hp,tp=pools(a,args.seed);\")\ns=s.replace(\"temporal=np.load(O/f'{args.stage}_temporal.npz')\", \"temporal=np.load(O/f'{args.stage}_{args.temporal_file}.npz')\")\ns=s.replace(\"p.add_argument('--negatives',type=int,default=2048);train\", \"p.add_argument('--negatives',type=int,default=2048);p.add_argument('--history-days',type=float,default=90);p.add_argument('--temporal-file',default='temporal');train\")\nopen(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace(\"m.load_state_dict(ck['model']);\", \"m.load_state_dict(ck['model'],strict=False);\");open(p,'w').write(s)\np='/app/solution/temporal.py';s=open(p).read().replace('def prepare_temporal(stage,weeks=16):','def prepare_temporal(stage,weeks=16,days=90):').replace('/(86400*90))','/(86400*days))').replace(\"f'{stage}_temporal.npz'\", \"f'{stage}_temporal{days if days!=90 else chr(0)}.npz'.replace(chr(0),'')\").replace(\"x=p.parse_args();prepare_temporal(x.stage,x.weeks)\",\"p.add_argument('--days',type=int,default=90);x=p.parse_args();prepare_temporal(x.stage,x.weeks,x.days)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/temporal.py validation --days 14 > /app/solution/temporal14_validation.log 2>&1"
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      "secs": 125,
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  }
}
35

exec command

+13:32
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 eligibility sets also exclude all active likes and dislikes. I’m now testing a larger embedding model and shorter history decay, and checking whether recent-like popularity still helps after the personalized model has had more training.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'],strict=False)\",\"if args.resume:\\n  saved=torch.load(args.resume,weights_only=False)\\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\\n  else:\\n   new=model.state_dict()\\n   for key,val in saved['model'].items():\\n    if key not in new:continue\\n    if new[key].shape==val.shape:new[key]=val\\n    else:\\n     if 'project' in key:new[key].zero_()\\n     else:new[key]*=.1\\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\\n   model.load_state_dict(new)\")\nopen(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/temporal.py /app/solution/calibrate.py\ncat > /app/solution/finalize.py <<'PY'\n\"\"\"Build calibrated towers, precompute items, and retrieve by one dot product.\"\"\"\nfrom model import *\ndef main(x):\n stage=x.stage;a=load(stage);cfg=json.load(open(x.config));ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False)\n end=25393400 if stage=='test' else 24786800\n w=(a['cnt']**cfg.get('count',.5)*(1-cfg.get('organic',.6)+cfg.get('organic',.6)*a['org']/a['cnt'])+cfg.get('like',3)*np.minimum(a['likes'],1))*np.exp((a['last'].astype(float)-end)/(86400*cfg.get('days',90)));a['weight']=w.astype(np.float32)\n p=np.load(O/f'{stage}_popularity.npz');names=list(p['names']);m.item.features[1:]=torch.from_numpy(p['features']);m.item.prior.weight.data.zero_();prior=cfg.get('prior',{cfg.get('pop','like_7'):cfg.get('coef',0)})\n for name,coef in prior.items():m.item.prior.weight.data[0,names.index(name)]=coef\n if cfg.get('subtract'):m.item.prior.weight.data[0,names.index(cfg['subtract'])]=-cfg['coef']\n m.eval();out=O/x.output;score=infer(m,a,stage,out,save_vectors=O/f'{x.name}_vectors.npz');torch.save({'model':m.state_dict(),'dim':ck['dim'],'config':cfg,'stage':stage},O/f'{x.name}.pt');print(score,flush=True)\n # Numerical check: cached item tower outputs give the identical score.\n with torch.no_grad():\n  h=torch.from_numpy(a['ids'][:64].astype(np.int64))[None];u=m.user(h);ids=torch.tensor([1,2,3]);v=m.item(ids);cached=m.item(torch.arange(1,len(a['items'])+1))[ids-1];assert torch.allclose((u*v).sum(-1),([email protected]).flatten(),atol=1e-5)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',default='test');p.add_argument('--checkpoint',required=True);p.add_argument('--config',required=True);p.add_argument('--output',default='submission.parquet');p.add_argument('--name',default='final_model');main(p.parse_args())\nPY\ntail -3 /app/solution/denoise.log; tail -3 /app/solution/temporal14_validation.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 0.3 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-50ad1732-d0ac-4a76-8946-7ffc0703297a",
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    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'],strict=False)\",\"if args.resume:\\n  saved=torch.load(args.resume,weights_only=False)\\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\\n  else:\\n   new=model.state_dict()\\n   for key,val in saved['model'].items():\\n    if key not in new:continue\\n    if new[key].shape==val.shape:new[key]=val\\n    else:\\n     if 'project' in key:new[key].zero_()\\n     else:new[key]*=.1\\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\\n   model.load_state_dict(new)\")\nopen(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/temporal.py /app/solution/calibrate.py\ncat > /app/solution/finalize.py <<'PY'\n\"\"\"Build calibrated towers, precompute items, and retrieve by one dot product.\"\"\"\nfrom model import *\ndef main(x):\n stage=x.stage;a=load(stage);cfg=json.load(open(x.config));ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False)\n end=25393400 if stage=='test' else 24786800\n w=(a['cnt']**cfg.get('count',.5)*(1-cfg.get('organic',.6)+cfg.get('organic',.6)*a['org']/a['cnt'])+cfg.get('like',3)*np.minimum(a['likes'],1))*np.exp((a['last'].astype(float)-end)/(86400*cfg.get('days',90)));a['weight']=w.astype(np.float32)\n p=np.load(O/f'{stage}_popularity.npz');names=list(p['names']);m.item.features[1:]=torch.from_numpy(p['features']);m.item.prior.weight.data.zero_();prior=cfg.get('prior',{cfg.get('pop','like_7'):cfg.get('coef',0)})\n for name,coef in prior.items():m.item.prior.weight.data[0,names.index(name)]=coef\n if cfg.get('subtract'):m.item.prior.weight.data[0,names.index(cfg['subtract'])]=-cfg['coef']\n m.eval();out=O/x.output;score=infer(m,a,stage,out,save_vectors=O/f'{x.name}_vectors.npz');torch.save({'model':m.state_dict(),'dim':ck['dim'],'config':cfg,'stage':stage},O/f'{x.name}.pt');print(score,flush=True)\n # Numerical check: cached item tower outputs give the identical score.\n with torch.no_grad():\n  h=torch.from_numpy(a['ids'][:64].astype(np.int64))[None];u=m.user(h);ids=torch.tensor([1,2,3]);v=m.item(ids);cached=m.item(torch.arange(1,len(a['items'])+1))[ids-1];assert torch.allclose((u*v).sum(-1),([email protected]).flatten(),atol=1e-5)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',default='test');p.add_argument('--checkpoint',required=True);p.add_argument('--config',required=True);p.add_argument('--output',default='submission.parquet');p.add_argument('--name',default='final_model');main(p.parse_args())\nPY\ntail -3 /app/solution/denoise.log; tail -3 /app/solution/temporal14_validation.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();s=s.replace(\"if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'],strict=False)\",\"if args.resume:\\n  saved=torch.load(args.resume,weights_only=False)\\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\\n  else:\\n   new=model.state_dict()\\n   for key,val in saved['model'].items():\\n    if key not in new:continue\\n    if new[key].shape==val.shape:new[key]=val\\n    else:\\n     if 'project' in key:new[key].zero_()\\n     else:new[key]*=.1\\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\\n   model.load_state_dict(new)\")\nopen(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/temporal.py /app/solution/calibrate.py\ncat > /app/solution/finalize.py <<'PY'\n\"\"\"Build calibrated towers, precompute items, and retrieve by one dot product.\"\"\"\nfrom model import *\ndef main(x):\n stage=x.stage;a=load(stage);cfg=json.load(open(x.config));ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False)\n end=25393400 if stage=='test' else 24786800\n w=(a['cnt']**cfg.get('count',.5)*(1-cfg.get('organic',.6)+cfg.get('organic',.6)*a['org']/a['cnt'])+cfg.get('like',3)*np.minimum(a['likes'],1))*np.exp((a['last'].astype(float)-end)/(86400*cfg.get('days',90)));a['weight']=w.astype(np.float32)\n p=np.load(O/f'{stage}_popularity.npz');names=list(p['names']);m.item.features[1:]=torch.from_numpy(p['features']);m.item.prior.weight.data.zero_();prior=cfg.get('prior',{cfg.get('pop','like_7'):cfg.get('coef',0)})\n for name,coef in prior.items():m.item.prior.weight.data[0,names.index(name)]=coef\n if cfg.get('subtract'):m.item.prior.weight.data[0,names.index(cfg['subtract'])]=-cfg['coef']\n m.eval();out=O/x.output;score=infer(m,a,stage,out,save_vectors=O/f'{x.name}_vectors.npz');torch.save({'model':m.state_dict(),'dim':ck['dim'],'config':cfg,'stage':stage},O/f'{x.name}.pt');print(score,flush=True)\n # Numerical check: cached item tower outputs give the identical score.\n with torch.no_grad():\n  h=torch.from_numpy(a['ids'][:64].astype(np.int64))[None];u=m.user(h);ids=torch.tensor([1,2,3]);v=m.item(ids);cached=m.item(torch.arange(1,len(a['items'])+1))[ids-1];assert torch.allclose((u*v).sum(-1),([email protected]).flatten(),atol=1e-5)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',default='test');p.add_argument('--checkpoint',required=True);p.add_argument('--config',required=True);p.add_argument('--output',default='submission.parquet');p.add_argument('--name',default='final_model');main(p.parse_args())\nPY\ntail -3 /app/solution/denoise.log; tail -3 /app/solution/temporal14_validation.log"
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  }
}
36

exec command

+14:01
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

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name wide --resume /app/solution/temporal_2000.pt --dim 256 --steps 10000 --eval-every 2500 --lr .0008 --temporal .8 --temporal-file temporal14 --history-days 14 > /app/solution/wide.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/popularity.py test > /app/solution/popularity_test.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"06beb0","wall_time_seconds":1.001725986,"session_id":69551,"original_token_count":0,"output":""}
{"chunk_id":"2655ab","wall_time_seconds":1.001708754,"session_id":97444,"original_token_count":0,"output":""}
Harness context token usage record
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37

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

text(await tools.exec_command({cmd:"tail -4 /app/solution/temporal2k_cal.log; tail -4 /app/solution/temporal.log; tail -4 /app/solution/denoise.log; tail -4 /app/solution/wide.log; cat /app/solution/popularity_test.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"30fe34","wall_time_seconds":0.00000459,"exit_code":0,"original_token_count":189,"output":"0.041918573415412635 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.0392736229906646 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n0.044981706201111304 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.04418224758511403 {'days': 90, 'like': 0, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\nstep 3600 loss 7.254160542488098 sec 335.0\nstep 3700 loss 7.312354683876038 sec 348.7\nstep 3800 loss 7.3641255092620845 sec 358.9\nstep 3900 loss 7.118460154533386 sec 372.4\nstep 5600 loss 7.70798397064209 sec 426.9\nstep 5700 loss 7.6671437072753905 sec 438.0\nstep 5800 loss 7.632343964576721 sec 447.6\nstep 5900 loss 7.655268659591675 sec 468.0\npools (8456, 1024)\n"}
Harness context codex item completed
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    "stdout": "0.041918573415412635 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.0392736229906646 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n0.044981706201111304 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.04418224758511403 {'days': 90, 'like': 0, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\nstep 3600 loss 7.254160542488098 sec 335.0\nstep 3700 loss 7.312354683876038 sec 348.7\nstep 3800 loss 7.3641255092620845 sec 358.9\nstep 3900 loss 7.118460154533386 sec 372.4\nstep 5600 loss 7.70798397064209 sec 426.9\nstep 5700 loss 7.6671437072753905 sec 438.0\nstep 5800 loss 7.632343964576721 sec 447.6\nstep 5900 loss 7.655268659591675 sec 468.0\npools (8456, 1024)\n",
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    "exit_code": 0,
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    },
    "formatted_output": "0.041918573415412635 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.0392736229906646 {'days': 90, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n0.044981706201111304 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.04418224758511403 {'days': 90, 'like': 0, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\nstep 3600 loss 7.254160542488098 sec 335.0\nstep 3700 loss 7.312354683876038 sec 348.7\nstep 3800 loss 7.3641255092620845 sec 358.9\nstep 3900 loss 7.118460154533386 sec 372.4\nstep 5600 loss 7.70798397064209 sec 426.9\nstep 5700 loss 7.6671437072753905 sec 438.0\nstep 5800 loss 7.632343964576721 sec 447.6\nstep 5900 loss 7.655268659591675 sec 468.0\npools (8456, 1024)\n"
  }
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Harness context token usage record
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Harness context codex item completed
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38

exec command

+14:40
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

text(await tools.exec_command({cmd:"tail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log; tail -4 /app/solution/wide.log; cat /app/solution/popularity_test.log","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"c92e32","wall_time_seconds":0.00000273,"exit_code":0,"original_token_count":134,"output":"step 5700 loss 7.6671437072753905 sec 438.0\nstep 5800 loss 7.632343964576721 sec 447.6\nstep 5900 loss 7.655268659591675 sec 468.0\nstep 6000 loss 7.621938190460205 sec 487.4\nstep 3700 loss 7.312354683876038 sec 348.7\nstep 3800 loss 7.3641255092620845 sec 358.9\nstep 3900 loss 7.118460154533386 sec 372.4\nstep 4000 loss 7.132422504425048 sec 392.1\npools (8456, 1024)\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n"}
{"current_time":"2026-09-10 18:07:58 UTC"}
Harness context token usage record
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/denoise.log; tail -4 /app/solution/temporal.log; tail -4 /app/solution/wide.log; cat /app/solution/popularity_test.log"
    ],
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      {
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 5700 loss 7.6671437072753905 sec 438.0\nstep 5800 loss 7.632343964576721 sec 447.6\nstep 5900 loss 7.655268659591675 sec 468.0\nstep 6000 loss 7.621938190460205 sec 487.4\nstep 3700 loss 7.312354683876038 sec 348.7\nstep 3800 loss 7.3641255092620845 sec 358.9\nstep 3900 loss 7.118460154533386 sec 372.4\nstep 4000 loss 7.132422504425048 sec 392.1\npools (8456, 1024)\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n",
    "stderr": "",
    "aggregated_output": "step 5700 loss 7.6671437072753905 sec 438.0\nstep 5800 loss 7.632343964576721 sec 447.6\nstep 5900 loss 7.655268659591675 sec 468.0\nstep 6000 loss 7.621938190460205 sec 487.4\nstep 3700 loss 7.312354683876038 sec 348.7\nstep 3800 loss 7.3641255092620845 sec 358.9\nstep 3900 loss 7.118460154533386 sec 372.4\nstep 4000 loss 7.132422504425048 sec 392.1\npools (8456, 1024)\n['strong_7', 'strong_14', 'strong_30', 'strong_90', 'strong_300', 'like_7', 'like_14', 'like_30', 'like_90', 'like_300', 'all_7', 'all_14', 'all_30', 'all_90', 'all_300']\n",
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  }
}
39

exec command

+15:01
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

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/finalize.py --checkpoint /app/solution/initial_test_600.pt --config /app/solution/denoise_tune_best.json --name initial_calibrated > /app/solution/initial_calibrated.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -6 /app/solution/temporal2k_cal.log; tail -2 /app/solution/denoise.log; tail -2 /app/solution/temporal.log; tail -3 /app/solution/wide.log","max_output_tokens":2500}));

Tool response

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"203d48","wall_time_seconds":1.001763542,"session_id":21086,"original_token_count":0,"output":""}
{"chunk_id":"12a2d8","wall_time_seconds":0.000008231,"exit_code":0,"original_token_count":239,"output":"0.044232911140944665 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -0.5}}\n0.044382739615579196 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 0.5}}\n0.043764972310279426 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 1}}\n0.03896859478049336 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': -1}}\n0.04353761834035749 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': -0.5}}\n0.04457592276971778 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': 0.5}}\nstep 6000 loss 7.621938190460205 sec 487.4\nEVAL 6000 {'quality_ndcg@10': 0.020706, 'quality_users': 4263}\nstep 4000 loss 7.132422504425048 sec 392.1\nEVAL 4000 {'quality_ndcg@10': 0.023646, 'quality_users': 4263}\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\n"}
Harness context token usage record
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Harness context codex item completed
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      }
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    "stdout": "0.044232911140944665 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -0.5}}\n0.044382739615579196 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 0.5}}\n0.043764972310279426 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 1}}\n0.03896859478049336 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': -1}}\n0.04353761834035749 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': -0.5}}\n0.04457592276971778 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': 0.5}}\nstep 6000 loss 7.621938190460205 sec 487.4\nEVAL 6000 {'quality_ndcg@10': 0.020706, 'quality_users': 4263}\nstep 4000 loss 7.132422504425048 sec 392.1\nEVAL 4000 {'quality_ndcg@10': 0.023646, 'quality_users': 4263}\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\n",
    "stderr": "",
    "aggregated_output": "0.044232911140944665 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -0.5}}\n0.044382739615579196 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 0.5}}\n0.043764972310279426 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 1}}\n0.03896859478049336 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': -1}}\n0.04353761834035749 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': -0.5}}\n0.04457592276971778 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_30': 0.5}}\nstep 6000 loss 7.621938190460205 sec 487.4\nEVAL 6000 {'quality_ndcg@10': 0.020706, 'quality_users': 4263}\nstep 4000 loss 7.132422504425048 sec 392.1\nEVAL 4000 {'quality_ndcg@10': 0.023646, 'quality_users': 4263}\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\n",
    "exit_code": 0,
    "duration": {
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Harness context codex item completed
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      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name denoise --resume /app/solution/base_1000.pt --steps 6000 --eval-every 2000 --lr .001 > /app/solution/denoise.log 2>&1"
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40

Agent message

+15:24
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-4e64bce2-b318-4343-9970-dc342581c627",
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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/calibrate.py <<'PY'\nfrom tune import *\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();f=np.load(O/'validation_popularity.npz');features=f['features'];names=list(f['names']);configs=[];best=(-1,None,None,None)\n # Recent likes are a cutoff-observed, item-only feature. Grid-search its learned linear weight.\n for days in [3,7,14,30,90]:\n  uv=vectors(m,a,days=days)\n  for coef in [0,1,2,3,4]:\n   bias=coef*features[:,names.index('like_7')];score=ev.score(rank(uv,iv,ev.e,bias));config={'days':days,'like':3,'count':.5,'organic':.6,'prior':{'like_7':coef}};print(score,config,flush=True);configs.append({'score':score,**config})\n   if score>best[0]:best=(score,config,uv,bias)\n days=best[1]['days'];coef=best[1]['prior']['like_7']\n for like,count in [(0,0),(0,.5),(0,1),(3,0),(3,1),(10,0),(10,.5),(10,1)]:\n  uv=vectors(m,a,days=days,like=like,count=count);bias=coef*features[:,names.index('like_7')];score=ev.score(rank(uv,iv,ev.e,bias));config={'days':days,'like':like,'count':count,'organic':.6,'prior':{'like_7':coef}};print(score,config,flush=True);configs.append({'score':score,**config})\n  if score>best[0]:best=(score,config,uv,bias)\n hist=best[1].copy();uv=best[2];base=best[3];prior=hist['prior'].copy()\n for name in ['strong_7','strong_30','strong_300','like_30','like_300']:\n  for val in [-1,-.5,.5,1]:\n   bias=base+val*features[:,names.index(name)];score=ev.score(rank(uv,iv,ev.e,bias));config={**hist,'prior':{**prior,name:val}};print(score,config,flush=True);configs.append({'score':score,**config})\n   if score>best[0]:best=(score,config,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(configs,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3])\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='calibrate');x=p.parse_args();main(x)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/calibrate.py /app/solution/temporal_2000.pt --name temporal2k_cal > /app/solution/temporal2k_cal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/calibrate.py <<'PY'\nfrom tune import *\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();f=np.load(O/'validation_popularity.npz');features=f['features'];names=list(f['names']);configs=[];best=(-1,None,None,None)\n # Recent likes are a cutoff-observed, item-only feature. Grid-search its learned linear weight.\n for days in [3,7,14,30,90]:\n  uv=vectors(m,a,days=days)\n  for coef in [0,1,2,3,4]:\n   bias=coef*features[:,names.index('like_7')];score=ev.score(rank(uv,iv,ev.e,bias));config={'days':days,'like':3,'count':.5,'organic':.6,'prior':{'like_7':coef}};print(score,config,flush=True);configs.append({'score':score,**config})\n   if score>best[0]:best=(score,config,uv,bias)\n days=best[1]['days'];coef=best[1]['prior']['like_7']\n for like,count in [(0,0),(0,.5),(0,1),(3,0),(3,1),(10,0),(10,.5),(10,1)]:\n  uv=vectors(m,a,days=days,like=like,count=count);bias=coef*features[:,names.index('like_7')];score=ev.score(rank(uv,iv,ev.e,bias));config={'days':days,'like':like,'count':count,'organic':.6,'prior':{'like_7':coef}};print(score,config,flush=True);configs.append({'score':score,**config})\n  if score>best[0]:best=(score,config,uv,bias)\n hist=best[1].copy();uv=best[2];base=best[3];prior=hist['prior'].copy()\n for name in ['strong_7','strong_30','strong_300','like_30','like_300']:\n  for val in [-1,-.5,.5,1]:\n   bias=base+val*features[:,names.index(name)];score=ev.score(rank(uv,iv,ev.e,bias));config={**hist,'prior':{**prior,name:val}};print(score,config,flush=True);configs.append({'score':score,**config})\n   if score>best[0]:best=(score,config,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(configs,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3])\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='calibrate');x=p.parse_args();main(x)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/calibrate.py /app/solution/temporal_2000.pt --name temporal2k_cal > /app/solution/temporal2k_cal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 176,
      "nanos": 546109089
    },
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  }
}
41

exec command

+15:42
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

text(await tools.exec_command({cmd:"tail -5 /app/solution/temporal2k_cal.log; tail -3 /app/solution/wide.log; tail -3 /app/solution/temporal.log; tail -3 /app/solution/initial_calibrated.log","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"0e3408","wall_time_seconds":0.0000048,"exit_code":0,"original_token_count":197,"output":"0.04255819154984516 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -1}}\n0.044600891838743634 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -0.5}}\n0.042891734638821025 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 0.5}}\n0.039979516673350424 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 1}}\nBEST (0.0454979396252179, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}})\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\nEVAL 4000 {'quality_ndcg@10': 0.023646, 'quality_users': 4263}\nstep 4100 loss 7.252358078956604 sec 443.6\nstep 4200 loss 7.040502195358276 sec 465.3\n{}\n"}
{"current_time":"2026-09-10 18:09:00 UTC"}
Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "tail -5 /app/solution/temporal2k_cal.log; tail -3 /app/solution/wide.log; tail -3 /app/solution/temporal.log; tail -3 /app/solution/initial_calibrated.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -5 /app/solution/temporal2k_cal.log; tail -3 /app/solution/wide.log; tail -3 /app/solution/temporal.log; tail -3 /app/solution/initial_calibrated.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0.04255819154984516 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -1}}\n0.044600891838743634 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -0.5}}\n0.042891734638821025 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 0.5}}\n0.039979516673350424 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 1}}\nBEST (0.0454979396252179, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}})\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\nEVAL 4000 {'quality_ndcg@10': 0.023646, 'quality_users': 4263}\nstep 4100 loss 7.252358078956604 sec 443.6\nstep 4200 loss 7.040502195358276 sec 465.3\n{}\n",
    "stderr": "",
    "aggregated_output": "0.04255819154984516 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -1}}\n0.044600891838743634 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -0.5}}\n0.042891734638821025 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 0.5}}\n0.039979516673350424 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 1}}\nBEST (0.0454979396252179, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}})\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\nEVAL 4000 {'quality_ndcg@10': 0.023646, 'quality_users': 4263}\nstep 4100 loss 7.252358078956604 sec 443.6\nstep 4200 loss 7.040502195358276 sec 465.3\n{}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4800
    },
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Harness context token usage record
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Harness context codex item completed
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42

exec command

+15:49
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.current; cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/cpu.stat; tail -3 /app/solution/wide.log","max_output_tokens":1800}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"4b0b63","wall_time_seconds":0.00000514,"exit_code":0,"original_token_count":93,"output":"3599364096\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\nusage_usec 4835734941\nuser_usec 4566495813\nsystem_usec 269239127\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 8416\nnr_throttled 1037\nthrottled_usec 546057\nnr_bursts 0\nburst_usec 0\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\nstep 200 loss 7.755371856689453 sec 80.3\n"}
Harness context codex item completed
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    "process_id": "20374",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /sys/fs/cgroup/memory.current; cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/cpu.stat; tail -3 /app/solution/wide.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /sys/fs/cgroup/memory.current; cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/cpu.stat; tail -3 /app/solution/wide.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "3599364096\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\nusage_usec 4835734941\nuser_usec 4566495813\nsystem_usec 269239127\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 8416\nnr_throttled 1037\nthrottled_usec 546057\nnr_bursts 0\nburst_usec 0\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\nstep 200 loss 7.755371856689453 sec 80.3\n",
    "stderr": "",
    "aggregated_output": "3599364096\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\nusage_usec 4835734941\nuser_usec 4566495813\nsystem_usec 269239127\nnice_usec 0\ncore_sched.force_idle_usec 0\nnr_periods 8416\nnr_throttled 1037\nthrottled_usec 546057\nnr_bursts 0\nburst_usec 0\npools (8456, 1024)\nstep 100 loss 7.712373123168946 sec 31.8\nstep 200 loss 7.755371856689453 sec 80.3\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5140
    },
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43

Agent message

+16:16
Thinking unavailable No text recorded

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

44

exec command

+16:21
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

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if not p.isdigit():continue\n try:a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\x00')\n except OSError:continue\n if len(a)>4 and a[1:3]==[b'-u',b'/app/solution/model.py'] and b'temporal' in a and b'--name' in a:\n  print('Stopping completed experiment after validation worsened:',p);os.kill(int(p),signal.SIGINT)\nPY","max_output_tokens":1500}));
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace('from tune import *','from tune import *\\ntorch.set_num_threads(2)');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/calibrate.py /app/solution/denoise_6000.pt --name denoise6k_cal > /app/solution/denoise6k_cal.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"34d315","wall_time_seconds":0.00000498,"exit_code":0,"original_token_count":16,"output":"Stopping completed experiment after validation worsened: 562\n"}
{"chunk_id":"bf28ad","wall_time_seconds":1.000965662,"session_id":57392,"original_token_count":0,"output":""}
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Harness context codex item completed
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if not p.isdigit():continue\n try:a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\x00')\n except OSError:continue\n if len(a)>4 and a[1:3]==[b'-u',b'/app/solution/model.py'] and b'temporal' in a and b'--name' in a:\n  print('Stopping completed experiment after validation worsened:',p);os.kill(int(p),signal.SIGINT)\nPY"
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    "stdout": "Stopping completed experiment after validation worsened: 562\n",
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Harness context codex item completed
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      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name temporal --resume /app/solution/base_1000.pt --steps 6000 --eval-every 2000 --lr .001 --temporal .75 > /app/solution/temporal.log 2>&1"
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      {
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45

Agent message

+16:32
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 best public score is now 0.045498 NDCG@10. Longer training worsened the smaller chronological model, so I stopped that experiment and retained its earlier checkpoint. I’m checking the larger model and whether combining the two training objectives helps.

46

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.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/temporal.py test > /app/solution/temporal_test.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/wide.log; tail -3 /app/solution/denoise6k_cal.log","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"54fc40","wall_time_seconds":1.00085799,"session_id":68008,"original_token_count":0,"output":""}
{"chunk_id":"6c134e","wall_time_seconds":0.00001289,"exit_code":0,"original_token_count":115,"output":"step 400 loss 7.537140254974365 sec 107.0\nstep 500 loss 7.465383830070496 sec 120.5\nstep 600 loss 7.32991997718811 sec 130.7\nstep 700 loss 7.274348306655884 sec 139.7\n0.040844547742278574 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.04180818888870292 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.03951426201843986 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n"}
{"current_time":"2026-09-10 18:10:01 UTC"}
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Harness context codex item completed
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      "/bin/sh",
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    "stdout": "step 400 loss 7.537140254974365 sec 107.0\nstep 500 loss 7.465383830070496 sec 120.5\nstep 600 loss 7.32991997718811 sec 130.7\nstep 700 loss 7.274348306655884 sec 139.7\n0.040844547742278574 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 2}}\n0.04180818888870292 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 3}}\n0.03951426201843986 {'days': 3, 'like': 3, 'count': 0.5, 'organic': 0.6, 'prior': {'like_7': 4}}\n",
    "stderr": "",
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47

exec command

+17:07
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/tune.py';s=open(p).read();s=s.replace('def vectors(m,a,days=90,like=3,count=.5,organic=.6):', 'def vectors(m,a,days=90,like=3,count=.5,organic=.6,cap=0,dislike=0):')\ns=s.replace(\"w=w.astype(np.float32);uv=[]\", \"w=w.astype(np.float32);uv=[]\\n if cap:\\n  ids=[];weights=[];counts=[]\\n  for u in range(len(a['uids'])):\\n   lo,hi=a['offset'][u:u+2];take=np.arange(lo,hi)\\n   if len(take)>cap:take=take[np.argpartition(w[lo:hi],-cap)[-cap:]]\\n   ids.append(a['ids'][take]);weights.append(w[take]);counts.append(len(take))\\n  a={**a,'ids':np.concatenate(ids),'offset':np.r_[0,np.cumsum(counts)]};w=np.concatenate(weights)\")\nopen(p,'w').write(s)\nPY\ncat > /app/solution/refine.py <<'PY'\nfrom tune import *\ntorch.set_num_threads(2)\ndef main(x):\n cfg=json.load(open(x.config));a=load('validation');ck=torch.load(cfg['checkpoint'],weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');names=list(pop['names']);features=pop['features'];prior=cfg.get('prior',{cfg.get('pop','like_7'):cfg.get('coef',0)});bias=sum(coef*features[:,names.index(name)] for name,coef in prior.items());h={k:cfg[k] for k in ['days','like','count','organic']};best=(-1,None,None);results=[]\n for cap in [64,128,256,512,1024,0]:\n  uv=vectors(m,a,**h,cap=cap);score=ev.score(rank(uv,iv,ev.e,bias));print('cap',cap,score,flush=True);results.append({'cap':cap,'score':score})\n  if score>best[0]:best=(score,cap,uv)\n # Smooth sparse recent-like counts and optionally calibrate the long-term preference for popular tracks.\n uv=best[2];config={**cfg,'cap':best[1]};bestprior=prior\n for c in [1,1.5,2,2.5,3]:\n  for old in [-1,-.5,0,.5]:\n   b=c*features[:,5]+old*features[:,7];s=ev.score(rank(uv,iv,ev.e,b));print('prior',c,old,s,flush=True)\n   if s>best[0]:best=(s,best[1],uv);bias=b;bestprior={'like_7':c,'like_30':old}\n config.update(score=best[0],prior=bestprior);json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=uv,item=iv,bias=bias);top=rank(uv,iv,ev.e,bias,100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False);print('BEST',config,flush=True)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('config');p.add_argument('--name',default='refined');main(p.parse_args())\nPY\ntail -4 /app/solution/wide.log; tail -4 /app/solution/denoise6k_cal.log; tail -3 /app/solution/temporal_test.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

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Harness context codex item completed
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    "stderr": "",
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48

Agent message

+17:52
Thinking unavailable No text recorded

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

49

exec command

+17:56
Thinking unavailable No text recorded

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

Model text

Further calibration has brought the public score above 0.046. I’m also testing a cap on history length to bound request-time work. The 256-dimensional model is still training; I’ll check its validation result before deciding whether the extra capacity helps.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -4 /app/solution/denoise6k_cal.log; tail -4 /app/solution/wide.log; tail -3 /app/solution/temporal_test.log","max_output_tokens":1600}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"43df62","wall_time_seconds":0.00000503,"exit_code":0,"original_token_count":200,"output":"0.03555228096199898 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -1}}\n0.044194918648804205 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -0.5}}\n0.045504202373475744 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 0.5}}\n0.04478918294802959 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 1}}\nstep 1100 loss 6.962230110168457 sec 180.6\nstep 1200 loss 6.8277421569824215 sec 190.7\nstep 1300 loss 6.952240896224976 sec 200.9\nstep 1400 loss 6.726582298278808 sec 210.9\nweek 9 examples 37708 labels 38252 sec 6.24574613571167\nweek 10 examples 41426 labels 36621 sec 5.273928165435791\nweek 11 examples 45007 labels 36979 sec 5.517638444900513\n"}
{"current_time":"2026-09-10 18:11:16 UTC"}
Harness context codex item completed
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    ],
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0.03555228096199898 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -1}}\n0.044194918648804205 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -0.5}}\n0.045504202373475744 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 0.5}}\n0.04478918294802959 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 1}}\nstep 1100 loss 6.962230110168457 sec 180.6\nstep 1200 loss 6.8277421569824215 sec 190.7\nstep 1300 loss 6.952240896224976 sec 200.9\nstep 1400 loss 6.726582298278808 sec 210.9\nweek 9 examples 37708 labels 38252 sec 6.24574613571167\nweek 10 examples 41426 labels 36621 sec 5.273928165435791\nweek 11 examples 45007 labels 36979 sec 5.517638444900513\n",
    "stderr": "",
    "aggregated_output": "0.03555228096199898 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -1}}\n0.044194918648804205 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': -0.5}}\n0.045504202373475744 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 0.5}}\n0.04478918294802959 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'strong_7': 1}}\nstep 1100 loss 6.962230110168457 sec 180.6\nstep 1200 loss 6.8277421569824215 sec 190.7\nstep 1300 loss 6.952240896224976 sec 200.9\nstep 1400 loss 6.726582298278808 sec 210.9\nweek 9 examples 37708 labels 38252 sec 6.24574613571167\nweek 10 examples 41426 labels 36621 sec 5.273928165435791\nweek 11 examples 45007 labels 36979 sec 5.517638444900513\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5030
    },
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50

exec command

+18:24
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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Harness context codex item completed
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      "/bin/sh",
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    ],
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/like_examples.py <<'PY'\nfrom model import *\ndef main(stage):\n a=load(stage);t=np.load(O/f'{stage}_temporal.npz');hp=t['hp'];tu=t['uid'];tc=t['cut'];um=pd.Index(a['uids']);im=pd.Index(a['items']);end=25393400 if stage=='test' else 24786800\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];first=pd.read_parquet(O/f'{stage}_pairs.parquet',columns=['u','i','first']);l=l.merge(first,on=['u','i']);rng=np.random.default_rng(43);contexts=[];targets=[]\n for k in np.unique(tc):\n  cutoff=end-k*7*86400-1800;ll=l[(l.timestamp>cutoff+1800)&(l.timestamp<=cutoff+1800+7*86400)&(l['first']>cutoff)];rows={int(tu[j]):j for j in np.flatnonzero(tc==k)}\n  for u,g in ll.groupby('u'):\n   if int(u) not in rows:continue\n   contexts.append(hp[rows[int(u)]]);targets.append(rng.choice(g.i.to_numpy()+1,128).astype(np.int32))\n np.savez(O/f'{stage}_temporallikes.npz',hp=np.stack(contexts),tp=np.stack(targets));print(stage,len(contexts),flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/like_examples.py validation > /app/solution/like_examples_validation.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"e7299a","wall_time_seconds":1.000575177,"session_id":86371,"original_token_count":0,"output":""}
Harness context token usage record
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Harness context codex item completed
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      "-lc",
      "cat > /app/solution/like_examples.py <<'PY'\nfrom model import *\ndef main(stage):\n a=load(stage);t=np.load(O/f'{stage}_temporal.npz');hp=t['hp'];tu=t['uid'];tc=t['cut'];um=pd.Index(a['uids']);im=pd.Index(a['items']);end=25393400 if stage=='test' else 24786800\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];first=pd.read_parquet(O/f'{stage}_pairs.parquet',columns=['u','i','first']);l=l.merge(first,on=['u','i']);rng=np.random.default_rng(43);contexts=[];targets=[]\n for k in np.unique(tc):\n  cutoff=end-k*7*86400-1800;ll=l[(l.timestamp>cutoff+1800)&(l.timestamp<=cutoff+1800+7*86400)&(l['first']>cutoff)];rows={int(tu[j]):j for j in np.flatnonzero(tc==k)}\n  for u,g in ll.groupby('u'):\n   if int(u) not in rows:continue\n   contexts.append(hp[rows[int(u)]]);targets.append(rng.choice(g.i.to_numpy()+1,128).astype(np.int32))\n np.savez(O/f'{stage}_temporallikes.npz',hp=np.stack(contexts),tp=np.stack(targets));print(stage,len(contexts),flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/like_examples.py validation > /app/solution/like_examples_validation.log 2>&1"
    ],
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51

exec command

+18:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"torch.manual_seed(args.seed);rng=\", \"torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=\").replace(\"p.add_argument('--temporal-file',default='temporal');train\", \"p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train\");open(p,'w').write(s)\nPY\ncat /app/solution/like_examples_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name likes --resume /app/solution/denoise_6000.pt --steps 1500 --eval-every 500 --lr .0004 --temporal .9 --temporal-file temporallikes --threads 2 > /app/solution/likes.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/denoise6k_cal.log; tail -3 /app/solution/wide.log; tail -3 /app/solution/temporal_test.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"4254fa","wall_time_seconds":1.001366879,"session_id":40017,"original_token_count":5,"output":"validation 21820\n"}
{"chunk_id":"31a219","wall_time_seconds":0.00000551,"exit_code":0,"original_token_count":187,"output":"0.04507526124468535 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -0.5}}\n0.043022589070703984 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 0.5}}\n0.037024114246818306 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 1}}\nBEST (0.04607989240991259, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}})\nstep 1800 loss 6.42834352016449 sec 251.5\nstep 1900 loss 6.478168425559997 sec 260.3\nstep 2000 loss 6.38341411113739 sec 268.5\nweek 14 examples 55400 labels 35148 sec 4.944395303726196\nweek 15 examples 58749 labels 34063 sec 5.119293451309204\nweek 16 examples 62075 labels 33042 sec 5.237450838088989\n"}
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Harness context codex item completed
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      "-lc",
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      }
    ],
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    "status": "completed",
    "stdout": "0.04507526124468535 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': -0.5}}\n0.043022589070703984 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 0.5}}\n0.037024114246818306 {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_300': 1}}\nBEST (0.04607989240991259, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}})\nstep 1800 loss 6.42834352016449 sec 251.5\nstep 1900 loss 6.478168425559997 sec 260.3\nstep 2000 loss 6.38341411113739 sec 268.5\nweek 14 examples 55400 labels 35148 sec 4.944395303726196\nweek 15 examples 58749 labels 34063 sec 5.119293451309204\nweek 16 examples 62075 labels 33042 sec 5.237450838088989\n",
    "stderr": "",
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    "exit_code": 0,
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  }
}
52

exec command

+19:02
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

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/refine.py /app/solution/denoise6k_cal_best.json --name denoise_refined > /app/solution/denoise_refined.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"9b6b5e","wall_time_seconds":1.000777376,"session_id":46445,"original_token_count":0,"output":""}
Harness context token usage record
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53

exec command

+19:27
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/finalize.py';s=open(p).read().replace(\"a['weight']=w.astype(np.float32)\",\"a['weight']=w.astype(np.float32)\\n if cfg.get('cap',0):\\n  ids=[];weights=[];counts=[];cap=cfg['cap']\\n  for u in range(len(a['uids'])):\\n   lo,hi=a['offset'][u:u+2];take=np.arange(lo,hi)\\n   if len(take)>cap:take=take[np.argpartition(a['weight'][lo:hi],-cap)[-cap:]]\\n   ids.append(a['ids'][take]);weights.append(a['weight'][take]);counts.append(len(take))\\n  a={**a,'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\")\nopen(p,'w').write(s)\nPY\ntail -3 /app/solution/wide.log; tail -4 /app/solution/likes.log; tail -4 /app/solution/denoise_refined.log","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"6b959c","wall_time_seconds":0.00000906,"exit_code":0,"original_token_count":96,"output":"step 2100 loss 6.8957726192474365 sec 277.3\nstep 2200 loss 6.730040230751038 sec 286.9\nstep 2300 loss 6.842135906219482 sec 298.2\npools (8456, 1024)\nstep 100 loss 8.260285487174988 sec 7.9\nstep 200 loss 8.028732533454894 sec 16.3\nstep 300 loss 7.8028163242340085 sec 24.3\ncap 128 0.0443020462193914\ncap 256 0.04496500819014122\ncap 512 0.04607062764916373\ncap 1024 0.04609894456020984\n"}
{"current_time":"2026-09-10 18:12:49 UTC"}
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    "stderr": "",
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54

Agent message

+19:53
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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}
55

exec command

+20:09
Thinking unavailable No text recorded

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

Model text

Capping history at 512 tracks gives essentially the same score as using the full history in the strongest completed model. That offers a useful serving bound. I’m testing a like-focused objective next to see whether it improves prediction of the grade-3 outcomes.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/fast_calibrate.py <<'PY'\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\nPY\ntail -4 /app/solution/wide.log; tail -5 /app/solution/likes.log; tail -4 /app/solution/denoise_refined.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"ee9202","wall_time_seconds":0.000004181,"exit_code":0,"original_token_count":172,"output":"step 2500 loss 6.569185018539429 sec 321.7\nEVAL 2500 {'quality_ndcg@10': 0.021238, 'quality_users': 4263}\nstep 2600 loss 6.680816259384155 sec 349.2\nstep 2700 loss 6.670261926651001 sec 359.0\nEVAL 500 {'quality_ndcg@10': 0.02564, 'quality_users': 4263}\nstep 600 loss 7.172426347732544 sec 63.9\nstep 700 loss 6.9061834383010865 sec 69.5\nstep 800 loss 6.745079121589661 sec 75.2\nstep 900 loss 6.580946779251098 sec 80.7\nprior 3 -0.5 0.04429572611490001\nprior 3 0 0.04391135244538221\nprior 3 0.5 0.043260152400116714\nBEST {'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}\n"}
Harness context codex item completed
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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/fast_calibrate.py <<'PY'\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\nPY\ntail -4 /app/solution/wide.log; tail -5 /app/solution/likes.log; tail -4 /app/solution/denoise_refined.log"
    ],
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      {
        "type": "unknown",
        "cmd": "cat > /app/solution/fast_calibrate.py <<'PY'\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\nPY\ntail -4 /app/solution/wide.log; tail -5 /app/solution/likes.log; tail -4 /app/solution/denoise_refined.log"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 2500 loss 6.569185018539429 sec 321.7\nEVAL 2500 {'quality_ndcg@10': 0.021238, 'quality_users': 4263}\nstep 2600 loss 6.680816259384155 sec 349.2\nstep 2700 loss 6.670261926651001 sec 359.0\nEVAL 500 {'quality_ndcg@10': 0.02564, 'quality_users': 4263}\nstep 600 loss 7.172426347732544 sec 63.9\nstep 700 loss 6.9061834383010865 sec 69.5\nstep 800 loss 6.745079121589661 sec 75.2\nstep 900 loss 6.580946779251098 sec 80.7\nprior 3 -0.5 0.04429572611490001\nprior 3 0 0.04391135244538221\nprior 3 0.5 0.043260152400116714\nBEST {'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}\n",
    "stderr": "",
    "aggregated_output": "step 2500 loss 6.569185018539429 sec 321.7\nEVAL 2500 {'quality_ndcg@10': 0.021238, 'quality_users': 4263}\nstep 2600 loss 6.680816259384155 sec 349.2\nstep 2700 loss 6.670261926651001 sec 359.0\nEVAL 500 {'quality_ndcg@10': 0.02564, 'quality_users': 4263}\nstep 600 loss 7.172426347732544 sec 63.9\nstep 700 loss 6.9061834383010865 sec 69.5\nstep 800 loss 6.745079121589661 sec 75.2\nstep 900 loss 6.580946779251098 sec 80.7\nprior 3 -0.5 0.04429572611490001\nprior 3 0 0.04391135244538221\nprior 3 0.5 0.043260152400116714\nBEST {'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}\n",
    "exit_code": 0,
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56

exec command

+20:33
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/wide_2500.pt --name wide2500_cal > /app/solution/wide2500_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"088faf","wall_time_seconds":1.001913955,"session_id":41498,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:13:54 UTC"}
Harness context token usage record
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57

Agent message

+21:02
Thinking unavailable No text recorded

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

58

exec command

+21:23
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

text(await tools.exec_command({cmd:"cat > /app/solution/local_examples.py <<'PY'\n\"\"\"First-discovery examples with short chronological listening contexts.\"\"\"\nfrom model import *\ndef main(stage):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);e=pd.read_parquet(O/'train_catalog_events.parquet')\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];e=pd.concat([e,v[e.columns]])\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;e=pd.concat([e,l[e.columns]],ignore_index=True).sort_values(['u','timestamp'],kind='stable').reset_index(drop=True)\n eligible=(~e.duplicated(['u','i']))&(e.is_organic==1)&(e.played_ratio_pct>=80)\n # Emphasize liked discoveries by repeating these training records three times.\n lk=np.unique(l.u.to_numpy(np.int64)*30000+l.i.to_numpy());keys=e.u.to_numpy(np.int64)*30000+e.i.to_numpy();pos=np.searchsorted(lk,keys);liked=(pos<len(lk))&(lk[np.minimum(pos,len(lk)-1)]==keys)\n rows=np.flatnonzero(eligible);rows=np.concatenate([rows,np.flatnonzero(eligible&liked),np.flatnonzero(eligible&liked)])\n rng=np.random.default_rng(35);rng.shuffle(rows);rows=rows[:2500000];uid=e.u.to_numpy();ids=e.i.to_numpy(np.int32)+1;ts=e.timestamp.to_numpy().astype(np.int64);h=[];t=[]\n for st in range(0,len(rows),50000):\n  r=rows[st:st+50000];delta=np.r_[np.arange(1,17),np.arange(20,81,4)];j=np.maximum(0,r[:,None]-delta[None,:]);ctx=ids[j].copy();ctx[(uid[j]!=uid[r,None])|((ts[r,None]-ts[j])>14*86400)|(j==r[:,None])]=0;valid=(ctx!=0).sum(1)>=4;h.append(ctx[valid]);t.append(ids[r[valid],None])\n hp=np.concatenate(h);tp=np.concatenate(t);np.savez(O/f'{stage}_local.npz',hp=hp,tp=tp);print(stage,hp.shape,flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/local_examples.py validation > /app/solution/local_examples_validation.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/wide2500_cal.log; tail -4 /app/solution/likes.log; tail -3 /app/solution/wide.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"484934","wall_time_seconds":1.000694076,"session_id":97095,"original_token_count":0,"output":""}
{"chunk_id":"20cd26","wall_time_seconds":0.00001115,"exit_code":0,"original_token_count":186,"output":"history 14 0 0 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 14 3 0.5 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 90 0 0 best (0.041671534931792764, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0.5}})\nstep 1200 loss 5.907912249565125 sec 124.0\nstep 1300 loss 5.948435950279236 sec 132.9\nstep 1400 loss 5.781044402122498 sec 142.6\nstep 1500 loss 5.379172477722168 sec 152.4\nstep 3100 loss 6.278442921638489 sec 396.4\nstep 3200 loss 6.403704748153687 sec 409.1\nstep 3300 loss 6.345250511169434 sec 423.3\n"}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-2b4770e9-75c9-449c-9b4d-df860ec4c730",
    "process_id": "12184",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/wide2500_cal.log; tail -4 /app/solution/likes.log; tail -3 /app/solution/wide.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -4 /app/solution/wide2500_cal.log; tail -4 /app/solution/likes.log; tail -3 /app/solution/wide.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "history 14 0 0 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 14 3 0.5 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 90 0 0 best (0.041671534931792764, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0.5}})\nstep 1200 loss 5.907912249565125 sec 124.0\nstep 1300 loss 5.948435950279236 sec 132.9\nstep 1400 loss 5.781044402122498 sec 142.6\nstep 1500 loss 5.379172477722168 sec 152.4\nstep 3100 loss 6.278442921638489 sec 396.4\nstep 3200 loss 6.403704748153687 sec 409.1\nstep 3300 loss 6.345250511169434 sec 423.3\n",
    "stderr": "",
    "aggregated_output": "history 14 0 0 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 14 3 0.5 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 90 0 0 best (0.041671534931792764, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0.5}})\nstep 1200 loss 5.907912249565125 sec 124.0\nstep 1300 loss 5.948435950279236 sec 132.9\nstep 1400 loss 5.781044402122498 sec 142.6\nstep 1500 loss 5.379172477722168 sec 152.4\nstep 3100 loss 6.278442921638489 sec 396.4\nstep 3200 loss 6.403704748153687 sec 409.1\nstep 3300 loss 6.345250511169434 sec 423.3\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 11150
    },
    "formatted_output": "history 14 0 0 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 14 3 0.5 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 90 0 0 best (0.041671534931792764, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0.5}})\nstep 1200 loss 5.907912249565125 sec 124.0\nstep 1300 loss 5.948435950279236 sec 132.9\nstep 1400 loss 5.781044402122498 sec 142.6\nstep 1500 loss 5.379172477722168 sec 152.4\nstep 3100 loss 6.278442921638489 sec 396.4\nstep 3200 loss 6.403704748153687 sec 409.1\nstep 3300 loss 6.345250511169434 sec 423.3\n"
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6652edb8-5e0b-430e-a2dd-54b8717a072a",
    "process_id": "97095",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/local_examples.py <<'PY'\n\"\"\"First-discovery examples with short chronological listening contexts.\"\"\"\nfrom model import *\ndef main(stage):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);e=pd.read_parquet(O/'train_catalog_events.parquet')\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];e=pd.concat([e,v[e.columns]])\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;e=pd.concat([e,l[e.columns]],ignore_index=True).sort_values(['u','timestamp'],kind='stable').reset_index(drop=True)\n eligible=(~e.duplicated(['u','i']))&(e.is_organic==1)&(e.played_ratio_pct>=80)\n # Emphasize liked discoveries by repeating these training records three times.\n lk=np.unique(l.u.to_numpy(np.int64)*30000+l.i.to_numpy());keys=e.u.to_numpy(np.int64)*30000+e.i.to_numpy();pos=np.searchsorted(lk,keys);liked=(pos<len(lk))&(lk[np.minimum(pos,len(lk)-1)]==keys)\n rows=np.flatnonzero(eligible);rows=np.concatenate([rows,np.flatnonzero(eligible&liked),np.flatnonzero(eligible&liked)])\n rng=np.random.default_rng(35);rng.shuffle(rows);rows=rows[:2500000];uid=e.u.to_numpy();ids=e.i.to_numpy(np.int32)+1;ts=e.timestamp.to_numpy().astype(np.int64);h=[];t=[]\n for st in range(0,len(rows),50000):\n  r=rows[st:st+50000];delta=np.r_[np.arange(1,17),np.arange(20,81,4)];j=np.maximum(0,r[:,None]-delta[None,:]);ctx=ids[j].copy();ctx[(uid[j]!=uid[r,None])|((ts[r,None]-ts[j])>14*86400)|(j==r[:,None])]=0;valid=(ctx!=0).sum(1)>=4;h.append(ctx[valid]);t.append(ids[r[valid],None])\n hp=np.concatenate(h);tp=np.concatenate(t);np.savez(O/f'{stage}_local.npz',hp=hp,tp=tp);print(stage,hp.shape,flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/local_examples.py validation > /app/solution/local_examples_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/local_examples.py <<'PY'\n\"\"\"First-discovery examples with short chronological listening contexts.\"\"\"\nfrom model import *\ndef main(stage):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);e=pd.read_parquet(O/'train_catalog_events.parquet')\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)];e=pd.concat([e,v[e.columns]])\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];l['played_ratio_pct']=100;e=pd.concat([e,l[e.columns]],ignore_index=True).sort_values(['u','timestamp'],kind='stable').reset_index(drop=True)\n eligible=(~e.duplicated(['u','i']))&(e.is_organic==1)&(e.played_ratio_pct>=80)\n # Emphasize liked discoveries by repeating these training records three times.\n lk=np.unique(l.u.to_numpy(np.int64)*30000+l.i.to_numpy());keys=e.u.to_numpy(np.int64)*30000+e.i.to_numpy();pos=np.searchsorted(lk,keys);liked=(pos<len(lk))&(lk[np.minimum(pos,len(lk)-1)]==keys)\n rows=np.flatnonzero(eligible);rows=np.concatenate([rows,np.flatnonzero(eligible&liked),np.flatnonzero(eligible&liked)])\n rng=np.random.default_rng(35);rng.shuffle(rows);rows=rows[:2500000];uid=e.u.to_numpy();ids=e.i.to_numpy(np.int32)+1;ts=e.timestamp.to_numpy().astype(np.int64);h=[];t=[]\n for st in range(0,len(rows),50000):\n  r=rows[st:st+50000];delta=np.r_[np.arange(1,17),np.arange(20,81,4)];j=np.maximum(0,r[:,None]-delta[None,:]);ctx=ids[j].copy();ctx[(uid[j]!=uid[r,None])|((ts[r,None]-ts[j])>14*86400)|(j==r[:,None])]=0;valid=(ctx!=0).sum(1)>=4;h.append(ctx[valid]);t.append(ids[r[valid],None])\n hp=np.concatenate(h);tp=np.concatenate(t);np.savez(O/f'{stage}_local.npz',hp=hp,tp=tp);print(stage,hp.shape,flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/local_examples.py validation > /app/solution/local_examples_validation.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 137,
    "duration": {
      "secs": 18,
      "nanos": 911409402
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-be133780-99de-4a0f-9689-9c5cf5b3e5b6",
    "process_id": "41498",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/wide_2500.pt --name wide2500_cal > /app/solution/wide2500_cal.log 2>&1"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/wide_2500.pt --name wide2500_cal > /app/solution/wide2500_cal.log 2>&1"
      }
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      "nanos": 81395155
    },
    "formatted_output": ""
  }
}
59

Agent message

+22:03
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-8d7d4950-b360-4f01-957f-585d9eb5b21b",
    "process_id": "40017",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"torch.manual_seed(args.seed);rng=\", \"torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=\").replace(\"p.add_argument('--temporal-file',default='temporal');train\", \"p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train\");open(p,'w').write(s)\nPY\ncat /app/solution/like_examples_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name likes --resume /app/solution/denoise_6000.pt --steps 1500 --eval-every 500 --lr .0004 --temporal .9 --temporal-file temporallikes --threads 2 > /app/solution/likes.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"torch.manual_seed(args.seed);rng=\", \"torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=\").replace(\"p.add_argument('--temporal-file',default='temporal');train\", \"p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train\");open(p,'w').write(s)\nPY\ncat /app/solution/like_examples_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name likes --resume /app/solution/denoise_6000.pt --steps 1500 --eval-every 500 --lr .0004 --temporal .9 --temporal-file temporallikes --threads 2 > /app/solution/likes.log 2>&1"
      }
    ],
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    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 191,
      "nanos": 644912106
    },
    "formatted_output": ""
  }
}
60

exec command

+22:09
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

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if not p.isdigit():continue\n try:a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\x00')\n except OSError:continue\n if len(a)>4 and a[1:3]==[b'-u',b'/app/solution/model.py'] and b'wide' in a and b'--name' in a:\n  print('Stopping larger model after calibrated validation was lower:',p);os.kill(int(p),signal.SIGINT)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_base --steps 1000 --eval-every 1000 --lr .002 --threads 2 > /app/solution/refit_base.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -3 /app/solution/likes.log; cat /app/solution/local_examples_validation.log; tail -4 /app/solution/wide2500_cal.log","max_output_tokens":1800}));

Tool response

Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"615426","wall_time_seconds":1.001184071,"session_id":65176,"original_token_count":17,"output":"Stopping larger model after calibrated validation was lower: 831\n"}
{"chunk_id":"eaa18b","wall_time_seconds":1.001412416,"session_id":77238,"original_token_count":0,"output":""}
{"chunk_id":"402768","wall_time_seconds":0.00000527,"exit_code":0,"original_token_count":152,"output":"step 1400 loss 5.781044402122498 sec 142.6\nstep 1500 loss 5.379172477722168 sec 152.4\nEVAL 1500 {'quality_ndcg@10': 0.017826, 'quality_users': 4263}\nKilled\nhistory 14 3 0.5 best (0.04068809359021322, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0}})\nhistory 90 0 0 best (0.041671534931792764, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0.5}})\nhistory 90 3 0.5 best (0.041671534931792764, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2, 'like_30': 0.5}})\nKilled\n"}
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61

exec command

+22:28
Thinking unavailable No text recorded

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

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Model text

The larger model’s calibrated score was lower, so I’ve stopped it. I’m refitting the two strongest compact models on train plus validation history while finishing the comparison between their scores and a combined model. The saved submission remains valid.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; tail -4 /app/solution/refit_base.log; tail -3 /app/solution/likes500_cal.log","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

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{"current_time":"2026-09-10 18:15:49 UTC"}
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62

Agent message

+22:53
Thinking unavailable No text recorded

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

63

exec command

+23:00
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

text(await tools.exec_command({cmd:"cat > /app/solution/local_examples.py <<'PY'\n\"\"\"Generate bounded chronological contexts; memory scales with one user's log.\"\"\"\nfrom model import *\nimport gc\ndef main(stage):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);e=pd.read_parquet(O/'train_catalog_events.parquet');cols={k:e[k].to_numpy().astype(t) for k,t in [('u',np.uint16),('i',np.uint16),('timestamp',np.uint32),('is_organic',np.uint8),('played_ratio_pct',np.uint16)]};del e;gc.collect()\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)]\n  for k in cols:cols[k]=np.concatenate([cols[k],v[k].to_numpy().astype(cols[k].dtype)])\n  del v\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];likes={int(u):g.i.to_numpy() for u,g in l.groupby('u')};del l\n # Sorting an index, rather than a wide event frame, keeps peak memory bounded.\n order=np.argsort(cols['u'],kind='stable');u=cols['u'][order];bounds=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];del u\n dest=np.lib.format.open_memmap(O/f'{stage}_local_context.npy',mode='w+',dtype=np.uint16,shape=(4000000,32));target=np.lib.format.open_memmap(O/f'{stage}_local_target.npy',mode='w+',dtype=np.uint16,shape=(4000000,1));total=0;delta=np.r_[np.arange(1,17),np.arange(20,81,4)]\n for lo,hi in zip(bounds[:-1],bounds[1:]):\n  j=order[lo:hi];j=j[np.argsort(cols['timestamp'][j],kind='stable')];ids=cols['i'][j];ts=cols['timestamp'][j].astype(np.int64);uid=int(cols['u'][j[0]]);first=np.unique(ids,return_index=True)[1];r=first[(cols['is_organic'][j[first]]==1)&(cols['played_ratio_pct'][j[first]]>=80)];liked=np.isin(ids[r],likes.get(uid,[]));r=np.r_[r,r[liked],r[liked]];prev=r[:,None]-delta;ix=np.maximum(prev,0);ctx=ids[ix]+1;ctx[(prev<0)|((ts[r,None]-ts[ix])>14*86400)]=0;valid=(ctx!=0).sum(1)>=4;ctx=ctx[valid];tt=ids[r[valid]]+1;n=min(len(ctx),len(dest)-total);dest[total:total+n]=ctx[:n];target[total:total+n,0]=tt[:n];total+=n\n  if total>=len(dest):break\n dest.flush();target.flush();np.savez(O/f'{stage}_local.npz',hp=dest[:total],tp=target[:total]);print(stage,total,flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/local_examples.py validation > /app/solution/local_examples_validation.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

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      "cat > /app/solution/local_examples.py <<'PY'\n\"\"\"Generate bounded chronological contexts; memory scales with one user's log.\"\"\"\nfrom model import *\nimport gc\ndef main(stage):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);e=pd.read_parquet(O/'train_catalog_events.parquet');cols={k:e[k].to_numpy().astype(t) for k,t in [('u',np.uint16),('i',np.uint16),('timestamp',np.uint32),('is_organic',np.uint8),('played_ratio_pct',np.uint16)]};del e;gc.collect()\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)]\n  for k in cols:cols[k]=np.concatenate([cols[k],v[k].to_numpy().astype(cols[k].dtype)])\n  del v\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];likes={int(u):g.i.to_numpy() for u,g in l.groupby('u')};del l\n # Sorting an index, rather than a wide event frame, keeps peak memory bounded.\n order=np.argsort(cols['u'],kind='stable');u=cols['u'][order];bounds=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];del u\n dest=np.lib.format.open_memmap(O/f'{stage}_local_context.npy',mode='w+',dtype=np.uint16,shape=(4000000,32));target=np.lib.format.open_memmap(O/f'{stage}_local_target.npy',mode='w+',dtype=np.uint16,shape=(4000000,1));total=0;delta=np.r_[np.arange(1,17),np.arange(20,81,4)]\n for lo,hi in zip(bounds[:-1],bounds[1:]):\n  j=order[lo:hi];j=j[np.argsort(cols['timestamp'][j],kind='stable')];ids=cols['i'][j];ts=cols['timestamp'][j].astype(np.int64);uid=int(cols['u'][j[0]]);first=np.unique(ids,return_index=True)[1];r=first[(cols['is_organic'][j[first]]==1)&(cols['played_ratio_pct'][j[first]]>=80)];liked=np.isin(ids[r],likes.get(uid,[]));r=np.r_[r,r[liked],r[liked]];prev=r[:,None]-delta;ix=np.maximum(prev,0);ctx=ids[ix]+1;ctx[(prev<0)|((ts[r,None]-ts[ix])>14*86400)]=0;valid=(ctx!=0).sum(1)>=4;ctx=ctx[valid];tt=ids[r[valid]]+1;n=min(len(ctx),len(dest)-total);dest[total:total+n]=ctx[:n];target[total:total+n,0]=tt[:n];total+=n\n  if total>=len(dest):break\n dest.flush();target.flush();np.savez(O/f'{stage}_local.npz',hp=dest[:total],tp=target[:total]);print(stage,total,flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/local_examples.py validation > /app/solution/local_examples_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/local_examples.py <<'PY'\n\"\"\"Generate bounded chronological contexts; memory scales with one user's log.\"\"\"\nfrom model import *\nimport gc\ndef main(stage):\n a=load(stage);um=pd.Index(a['uids']);im=pd.Index(a['items']);e=pd.read_parquet(O/'train_catalog_events.parquet');cols={k:e[k].to_numpy().astype(t) for k,t in [('u',np.uint16),('i',np.uint16),('timestamp',np.uint32),('is_organic',np.uint8),('played_ratio_pct',np.uint16)]};del e;gc.collect()\n if stage=='test':\n  v=pd.read_parquet(D/'val_listens.parquet');v=v[v.played_ratio_pct>=50];v['u']=um.get_indexer(v.uid);v['i']=im.get_indexer(v.item_id);v=v[(v.u>=0)&(v.i>=0)]\n  for k in cols:cols[k]=np.concatenate([cols[k],v[k].to_numpy().astype(cols[k].dtype)])\n  del v\n l=pd.concat([pd.read_parquet(D/f'{s}_likes.parquet') for s in (['train','val'] if stage=='test' else ['train'])]);l=l[l.is_organic==1];l['u']=um.get_indexer(l.uid);l['i']=im.get_indexer(l.item_id);l=l[(l.u>=0)&(l.i>=0)];likes={int(u):g.i.to_numpy() for u,g in l.groupby('u')};del l\n # Sorting an index, rather than a wide event frame, keeps peak memory bounded.\n order=np.argsort(cols['u'],kind='stable');u=cols['u'][order];bounds=np.r_[0,np.flatnonzero(u[1:]!=u[:-1])+1,len(u)];del u\n dest=np.lib.format.open_memmap(O/f'{stage}_local_context.npy',mode='w+',dtype=np.uint16,shape=(4000000,32));target=np.lib.format.open_memmap(O/f'{stage}_local_target.npy',mode='w+',dtype=np.uint16,shape=(4000000,1));total=0;delta=np.r_[np.arange(1,17),np.arange(20,81,4)]\n for lo,hi in zip(bounds[:-1],bounds[1:]):\n  j=order[lo:hi];j=j[np.argsort(cols['timestamp'][j],kind='stable')];ids=cols['i'][j];ts=cols['timestamp'][j].astype(np.int64);uid=int(cols['u'][j[0]]);first=np.unique(ids,return_index=True)[1];r=first[(cols['is_organic'][j[first]]==1)&(cols['played_ratio_pct'][j[first]]>=80)];liked=np.isin(ids[r],likes.get(uid,[]));r=np.r_[r,r[liked],r[liked]];prev=r[:,None]-delta;ix=np.maximum(prev,0);ctx=ids[ix]+1;ctx[(prev<0)|((ts[r,None]-ts[ix])>14*86400)]=0;valid=(ctx!=0).sum(1)>=4;ctx=ctx[valid];tt=ids[r[valid]]+1;n=min(len(ctx),len(dest)-total);dest[total:total+n]=ctx[:n];target[total:total+n,0]=tt[:n];total+=n\n  if total>=len(dest):break\n dest.flush();target.flush();np.savez(O/f'{stage}_local.npz',hp=dest[:total],tp=target[:total]);print(stage,total,flush=True)\nif __name__=='__main__':main(sys.argv[1])\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/local_examples.py validation > /app/solution/local_examples_validation.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 8,
      "nanos": 367404838
    },
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  }
}
64

exec command

+23:43
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -3 /app/solution/local_examples_validation.log; cat /sys/fs/cgroup/memory.current; ls -lh /app/solution/submission.parquet","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"8c66cb","wall_time_seconds":0.00000506,"exit_code":0,"original_token_count":27,"output":"validation 1641387\n611602432\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:09 /app/solution/submission.parquet\n"}
{"current_time":"2026-09-10 18:17:01 UTC"}
Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "tail -3 /app/solution/local_examples_validation.log; cat /sys/fs/cgroup/memory.current; ls -lh /app/solution/submission.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -3 /app/solution/local_examples_validation.log; cat /sys/fs/cgroup/memory.current; ls -lh /app/solution/submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "validation 1641387\n611602432\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:09 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "validation 1641387\n611602432\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:09 /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5060
    },
    "formatted_output": "validation 1641387\n611602432\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:09 /app/solution/submission.parquet\n"
  }
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Harness context token usage record
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}
65

exec command

+23:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const jobs = [
["OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_base --steps 1000 --eval-every 1000 --lr .002 --threads 2 > /app/solution/refit_base.log 2>&1"],
["OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name local --resume /app/solution/denoise_6000.pt --steps 2500 --eval-every 500 --lr .0005 --temporal .8 --temporal-file local --history-days 14 --context 32 --threads 2 > /app/solution/local.log 2>&1"],
["OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"]
];
text(await Promise.allSettled(jobs.map(([cmd])=>tools.exec_command({cmd,yield_time_ms:1000}))));

Tool response

Script completed
Wall time 1.2 seconds
Output:

[{"status":"fulfilled","value":{"chunk_id":"4339a2","wall_time_seconds":1.000663542,"session_id":50710,"original_token_count":0,"output":""}},{"status":"fulfilled","value":{"chunk_id":"4b5ad1","wall_time_seconds":1.001719682,"session_id":41667,"original_token_count":0,"output":""}},{"status":"fulfilled","value":{"chunk_id":"1434bf","wall_time_seconds":1.000647955,"session_id":13021,"original_token_count":0,"output":""}}]
Harness context token usage record
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  "thread_token_usage": {
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    "reasoning_output_tokens": 18910,
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  }
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-538b12d7-ce11-43d9-884a-ab7a7bf0f996",
    "process_id": "41667",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name local --resume /app/solution/denoise_6000.pt --steps 2500 --eval-every 500 --lr .0005 --temporal .8 --temporal-file local --history-days 14 --context 32 --threads 2 > /app/solution/local.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name local --resume /app/solution/denoise_6000.pt --steps 2500 --eval-every 500 --lr .0005 --temporal .8 --temporal-file local --history-days 14 --context 32 --threads 2 > /app/solution/local.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 137,
    "duration": {
      "secs": 8,
      "nanos": 999054814
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2908e6cc-5f17-461d-a024-70da5de96fec",
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    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
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    },
    "formatted_output": ""
  }
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66

Agent message

+24:19
Thinking unavailable No text recorded

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

67

exec command

+24:32
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/ensemble_grid.py <<'PY'\n\"\"\"Tune an exact dot-product ensemble without storing a full score matrix.\"\"\"\nfrom fast_calibrate import *\nimport itertools\[email protected]_grad()\ndef main(x):\n ev=Evaluator();packs=[np.load(O/f'{s}_best_vectors.npz') for s in x.components];uv=[torch.from_numpy(p['user']) for p in packs];iv=[torch.from_numpy(p['item']) for p in packs];configs=[json.load(open(O/f'{s}_best.json')) for s in x.components];n=len(iv[0]);pop=np.load(O/'validation_popularity.npz');f=pop['features'];weights=[p for p in itertools.product([0,.25,.5,.75,1],repeat=len(packs)) if abs(sum(p)-1)<1e-6];priors=[{'like_7':v,'like_30':o} for v in [1.5,2,2.5] for o in [-.5,0,.5]];grid=[(w,p) for w in weights for p in priors];biases=[torch.from_numpy(p['like_7']*f[:,5]+p['like_30']*f[:,7]) for w,p in grid];tops=[[] for _ in grid]\n for st in range(0,len(uv[0]),128):\n  raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n  for j,((w,p),b) in enumerate(zip(grid,biases)):\n   s=sum(c*r for c,r in zip(w,raw) if c)+b;s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy())\n best=(-1,None);records=[]\n for j,(w,p) in enumerate(grid):\n  score=ev.score(np.concatenate(tops[j]));record={'score':score,'weights':w,'prior':p};records.append(record)\n  if score>best[0]:best=(score,j)\n j=best[1];w,pr=grid[j];config={'score':best[0],'components':configs,'weights':w,'prior':pr};print('BEST',config,flush=True);json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(records,open(O/f'{x.name}_grid.json','w'),indent=2)\n u=np.concatenate([v.numpy() for v in uv]+[np.ones((len(uv[0]),1),np.float32)],axis=1);i=np.concatenate([c*v.numpy() for c,v in zip(w,iv)]+[biases[j].numpy()[:,None]],axis=1);top=rank(u,i,ev.e,k=100);a=load('validation');pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False);np.savez(O/f'{x.name}_vectors.npz',user=u,item=i);print('rechecked',ev.score(top),flush=True)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--components',nargs='+',required=True);p.add_argument('--name',default='ensemble');main(p.parse_args())\nPY\ntail -3 /app/solution/refit_base.log; tail -3 /app/solution/local.log; tail -4 /app/solution/likes500_cal.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"dd9985","wall_time_seconds":0.00000462,"exit_code":0,"original_token_count":39,"output":"step 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\npools (8456, 1024)\nKilled\nKilled\n"}
Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-de3d80d8-4a5b-43a3-aafb-29453273702a",
    "process_id": "81196",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/ensemble_grid.py <<'PY'\n\"\"\"Tune an exact dot-product ensemble without storing a full score matrix.\"\"\"\nfrom fast_calibrate import *\nimport itertools\[email protected]_grad()\ndef main(x):\n ev=Evaluator();packs=[np.load(O/f'{s}_best_vectors.npz') for s in x.components];uv=[torch.from_numpy(p['user']) for p in packs];iv=[torch.from_numpy(p['item']) for p in packs];configs=[json.load(open(O/f'{s}_best.json')) for s in x.components];n=len(iv[0]);pop=np.load(O/'validation_popularity.npz');f=pop['features'];weights=[p for p in itertools.product([0,.25,.5,.75,1],repeat=len(packs)) if abs(sum(p)-1)<1e-6];priors=[{'like_7':v,'like_30':o} for v in [1.5,2,2.5] for o in [-.5,0,.5]];grid=[(w,p) for w in weights for p in priors];biases=[torch.from_numpy(p['like_7']*f[:,5]+p['like_30']*f[:,7]) for w,p in grid];tops=[[] for _ in grid]\n for st in range(0,len(uv[0]),128):\n  raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n  for j,((w,p),b) in enumerate(zip(grid,biases)):\n   s=sum(c*r for c,r in zip(w,raw) if c)+b;s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy())\n best=(-1,None);records=[]\n for j,(w,p) in enumerate(grid):\n  score=ev.score(np.concatenate(tops[j]));record={'score':score,'weights':w,'prior':p};records.append(record)\n  if score>best[0]:best=(score,j)\n j=best[1];w,pr=grid[j];config={'score':best[0],'components':configs,'weights':w,'prior':pr};print('BEST',config,flush=True);json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(records,open(O/f'{x.name}_grid.json','w'),indent=2)\n u=np.concatenate([v.numpy() for v in uv]+[np.ones((len(uv[0]),1),np.float32)],axis=1);i=np.concatenate([c*v.numpy() for c,v in zip(w,iv)]+[biases[j].numpy()[:,None]],axis=1);top=rank(u,i,ev.e,k=100);a=load('validation');pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False);np.savez(O/f'{x.name}_vectors.npz',user=u,item=i);print('rechecked',ev.score(top),flush=True)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--components',nargs='+',required=True);p.add_argument('--name',default='ensemble');main(p.parse_args())\nPY\ntail -3 /app/solution/refit_base.log; tail -3 /app/solution/local.log; tail -4 /app/solution/likes500_cal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/ensemble_grid.py <<'PY'\n\"\"\"Tune an exact dot-product ensemble without storing a full score matrix.\"\"\"\nfrom fast_calibrate import *\nimport itertools\[email protected]_grad()\ndef main(x):\n ev=Evaluator();packs=[np.load(O/f'{s}_best_vectors.npz') for s in x.components];uv=[torch.from_numpy(p['user']) for p in packs];iv=[torch.from_numpy(p['item']) for p in packs];configs=[json.load(open(O/f'{s}_best.json')) for s in x.components];n=len(iv[0]);pop=np.load(O/'validation_popularity.npz');f=pop['features'];weights=[p for p in itertools.product([0,.25,.5,.75,1],repeat=len(packs)) if abs(sum(p)-1)<1e-6];priors=[{'like_7':v,'like_30':o} for v in [1.5,2,2.5] for o in [-.5,0,.5]];grid=[(w,p) for w in weights for p in priors];biases=[torch.from_numpy(p['like_7']*f[:,5]+p['like_30']*f[:,7]) for w,p in grid];tops=[[] for _ in grid]\n for st in range(0,len(uv[0]),128):\n  raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n  for j,((w,p),b) in enumerate(zip(grid,biases)):\n   s=sum(c*r for c,r in zip(w,raw) if c)+b;s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy())\n best=(-1,None);records=[]\n for j,(w,p) in enumerate(grid):\n  score=ev.score(np.concatenate(tops[j]));record={'score':score,'weights':w,'prior':p};records.append(record)\n  if score>best[0]:best=(score,j)\n j=best[1];w,pr=grid[j];config={'score':best[0],'components':configs,'weights':w,'prior':pr};print('BEST',config,flush=True);json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(records,open(O/f'{x.name}_grid.json','w'),indent=2)\n u=np.concatenate([v.numpy() for v in uv]+[np.ones((len(uv[0]),1),np.float32)],axis=1);i=np.concatenate([c*v.numpy() for c,v in zip(w,iv)]+[biases[j].numpy()[:,None]],axis=1);top=rank(u,i,ev.e,k=100);a=load('validation');pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False);np.savez(O/f'{x.name}_vectors.npz',user=u,item=i);print('rechecked',ev.score(top),flush=True)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--components',nargs='+',required=True);p.add_argument('--name',default='ensemble');main(p.parse_args())\nPY\ntail -3 /app/solution/refit_base.log; tail -3 /app/solution/local.log; tail -4 /app/solution/likes500_cal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\npools (8456, 1024)\nKilled\nKilled\n",
    "stderr": "",
    "aggregated_output": "step 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\npools (8456, 1024)\nKilled\nKilled\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4620
    },
    "formatted_output": "step 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\npools (8456, 1024)\nKilled\nKilled\n"
  }
}
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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    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_base --steps 1000 --eval-every 1000 --lr .002 --threads 2 > /app/solution/refit_base.log 2>&1"
    ],
    "cwd": "file:///app",
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        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_base --steps 1000 --eval-every 1000 --lr .002 --threads 2 > /app/solution/refit_base.log 2>&1"
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68

Agent message

+25:13
Thinking unavailable No text recorded

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

69

exec command

+25:21
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

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; cat /sys/fs/cgroup/memory.max; /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if not p.isdigit():continue\n try:\n  a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\x00')\n  if len(a)>1 and b'python' in a[0]:\n   s=open('/proc/'+p+'/status').read().splitlines();print(p,a[:7]);print([l for l in s if l.startswith(('VmRSS','VmPeak','VmSize','VmHWM'))])\n except OSError:pass\nPY\nsed -n '45,120p' /app/solution/model.py","max_output_tokens":5500}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"52c08d","wall_time_seconds":0.00000385,"exit_code":0,"original_token_count":1180,"output":"low 0\nhigh 0\nmax 6740\noom 31\noom_kill 6\noom_group_kill 0\nsock_throttled 0\n131174400\n8589934592\n1343 [b'/opt/task-venv/bin/python', b'-', b'']\n['VmPeak:\\t   14064 kB', 'VmSize:\\t   14036 kB', 'VmHWM:\\t   10056 kB', 'VmRSS:\\t   10056 kB']\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:\n  saved=torch.load(args.resume,weights_only=False)\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\n  else:\n   new=model.state_dict()\n   for key,val in saved['model'].items():\n    if key not in new:continue\n    if new[key].shape==val.shape:new[key]=val\n    else:\n     if 'project' in key:new[key].zero_()\n     else:new[key]*=.1\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\n   model.load_state_dict(new)\n \n if args.history_days!=90:\n  cutoff=25393400 if args.stage=='test' else 24786800;a['weight']*=np.exp((a['last'].astype(float)-cutoff)/86400*(1/args.history_days-1/90)).astype(np.float32)\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n if args.temporal:\n  temporal=np.load(O/f'{args.stage}_{args.temporal_file}.npz');thp=temporal['hp'];ttp=temporal['tp']\n valid_users=np.flatnonzero(hp[:,0]>0)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train()\n  if args.temporal and rng.random()<args.temporal:\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\n  else:\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\n  ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\n  if args.negatives:\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\n  else:\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\n  loss=F.cross_entropy(logits,labels);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);p.add_argument('--history-days',type=float,default=90);p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train(p.parse_args())\n"}
Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-8f29c03c-a9f4-4be4-b346-839f4f8b0b7a",
    "process_id": "91094",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; cat /sys/fs/cgroup/memory.max; /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if not p.isdigit():continue\n try:\n  a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\x00')\n  if len(a)>1 and b'python' in a[0]:\n   s=open('/proc/'+p+'/status').read().splitlines();print(p,a[:7]);print([l for l in s if l.startswith(('VmRSS','VmPeak','VmSize','VmHWM'))])\n except OSError:pass\nPY\nsed -n '45,120p' /app/solution/model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; cat /sys/fs/cgroup/memory.max; /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if not p.isdigit():continue\n try:\n  a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\x00')\n  if len(a)>1 and b'python' in a[0]:\n   s=open('/proc/'+p+'/status').read().splitlines();print(p,a[:7]);print([l for l in s if l.startswith(('VmRSS','VmPeak','VmSize','VmHWM'))])\n except OSError:pass\nPY\nsed -n '45,120p' /app/solution/model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "low 0\nhigh 0\nmax 6740\noom 31\noom_kill 6\noom_group_kill 0\nsock_throttled 0\n131174400\n8589934592\n1343 [b'/opt/task-venv/bin/python', b'-', b'']\n['VmPeak:\\t   14064 kB', 'VmSize:\\t   14036 kB', 'VmHWM:\\t   10056 kB', 'VmRSS:\\t   10056 kB']\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:\n  saved=torch.load(args.resume,weights_only=False)\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\n  else:\n   new=model.state_dict()\n   for key,val in saved['model'].items():\n    if key not in new:continue\n    if new[key].shape==val.shape:new[key]=val\n    else:\n     if 'project' in key:new[key].zero_()\n     else:new[key]*=.1\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\n   model.load_state_dict(new)\n \n if args.history_days!=90:\n  cutoff=25393400 if args.stage=='test' else 24786800;a['weight']*=np.exp((a['last'].astype(float)-cutoff)/86400*(1/args.history_days-1/90)).astype(np.float32)\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n if args.temporal:\n  temporal=np.load(O/f'{args.stage}_{args.temporal_file}.npz');thp=temporal['hp'];ttp=temporal['tp']\n valid_users=np.flatnonzero(hp[:,0]>0)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train()\n  if args.temporal and rng.random()<args.temporal:\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\n  else:\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\n  ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\n  if args.negatives:\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\n  else:\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\n  loss=F.cross_entropy(logits,labels);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);p.add_argument('--history-days',type=float,default=90);p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train(p.parse_args())\n",
    "stderr": "",
    "aggregated_output": "low 0\nhigh 0\nmax 6740\noom 31\noom_kill 6\noom_group_kill 0\nsock_throttled 0\n131174400\n8589934592\n1343 [b'/opt/task-venv/bin/python', b'-', b'']\n['VmPeak:\\t   14064 kB', 'VmSize:\\t   14036 kB', 'VmHWM:\\t   10056 kB', 'VmRSS:\\t   10056 kB']\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:\n  saved=torch.load(args.resume,weights_only=False)\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\n  else:\n   new=model.state_dict()\n   for key,val in saved['model'].items():\n    if key not in new:continue\n    if new[key].shape==val.shape:new[key]=val\n    else:\n     if 'project' in key:new[key].zero_()\n     else:new[key]*=.1\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\n   model.load_state_dict(new)\n \n if args.history_days!=90:\n  cutoff=25393400 if args.stage=='test' else 24786800;a['weight']*=np.exp((a['last'].astype(float)-cutoff)/86400*(1/args.history_days-1/90)).astype(np.float32)\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n if args.temporal:\n  temporal=np.load(O/f'{args.stage}_{args.temporal_file}.npz');thp=temporal['hp'];ttp=temporal['tp']\n valid_users=np.flatnonzero(hp[:,0]>0)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train()\n  if args.temporal and rng.random()<args.temporal:\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\n  else:\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\n  ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\n  if args.negatives:\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\n  else:\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\n  loss=F.cross_entropy(logits,labels);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);p.add_argument('--history-days',type=float,default=90);p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train(p.parse_args())\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3850
    },
    "formatted_output": "low 0\nhigh 0\nmax 6740\noom 31\noom_kill 6\noom_group_kill 0\nsock_throttled 0\n131174400\n8589934592\n1343 [b'/opt/task-venv/bin/python', b'-', b'']\n['VmPeak:\\t   14064 kB', 'VmSize:\\t   14036 kB', 'VmHWM:\\t   10056 kB', 'VmRSS:\\t   10056 kB']\n pred=np.concatenate(pred);f=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][pred.flatten()],'rank':np.tile(np.arange(1,101),len(pred))});tmp=str(path)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,path)\n if save_vectors:np.savez(save_vectors,user=np.concatenate(uvs),item=iv.numpy(),top=pred)\n if stage=='validation':\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet,score_rankings\n  truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),set(map(int,a['uids'])));score=score_rankings({int(u):list(map(int,a['items'][p])) for u,p in zip(a['uids'],pred)},truth);return score\n return {}\ndef train(args):\n torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\n if args.resume:\n  saved=torch.load(args.resume,weights_only=False)\n  if saved['dim']==args.dim:model.load_state_dict(saved['model'],strict=False)\n  else:\n   new=model.state_dict()\n   for key,val in saved['model'].items():\n    if key not in new:continue\n    if new[key].shape==val.shape:new[key]=val\n    else:\n     if 'project' in key:new[key].zero_()\n     else:new[key]*=.1\n     slices=tuple(slice(0,size) for size in val.shape);new[key][slices]=val\n   model.load_state_dict(new)\n \n if args.history_days!=90:\n  cutoff=25393400 if args.stage=='test' else 24786800;a['weight']*=np.exp((a['last'].astype(float)-cutoff)/86400*(1/args.history_days-1/90)).astype(np.float32)\n hp,tp=pools(a,args.seed);print('pools',hp.shape,flush=True)\n if args.temporal:\n  temporal=np.load(O/f'{args.stage}_{args.temporal_file}.npz');thp=temporal['hp'];ttp=temporal['tp']\n valid_users=np.flatnonzero(hp[:,0]>0)\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=1e-5);allitems=torch.arange(1,n+1);start=time.time();losses=[]\n for step in range(1,args.steps+1):\n  model.train()\n  if args.temporal and rng.random()<args.temporal:\n   u=rng.integers(len(thp),size=args.batch);ctx=thp[u[:,None],rng.integers(thp.shape[1],size=(args.batch,args.context))].copy();target=ttp[u,rng.integers(ttp.shape[1],size=args.batch)]\n  else:\n   u=rng.choice(valid_users,size=args.batch);ctx=hp[u[:,None],rng.integers(hp.shape[1],size=(args.batch,args.context))].copy();target=tp[u,rng.integers(tp.shape[1],size=args.batch)]\n  ctx[ctx==target[:,None]]=0\n  uv=model.user(torch.from_numpy(ctx.astype(np.int64)))\n  if args.negatives:\n   tid=torch.from_numpy(target.astype(np.int64));neg=torch.from_numpy(rng.integers(1,n+1,size=args.negatives));pos=(uv*model.item(tid)).sum(-1,keepdim=True);[email protected](neg).T;neglogits=neglogits+np.log(n/args.negatives);neglogits.masked_fill_(tid[:,None]==neg[None,:],-1e9);logits=torch.cat([pos,neglogits],1);labels=torch.zeros(args.batch,dtype=torch.long)\n  else:\n   iv=model.item(allitems);[email protected];labels=torch.from_numpy(target.astype(np.int64))-1\n  loss=F.cross_entropy(logits,labels);opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'sec',round(time.time()-start,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck=O/f'{args.name}_{step}.pt';torch.save({'model':model.state_dict(),'dim':args.dim,'args':vars(args)},ck)\n   dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet');score=infer(model,a,args.stage,dest,save_vectors=O/f'{args.name}_{step}_vectors.npz');print('EVAL',step,score,flush=True)\n   with open(O/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,**score})+'\\n')\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='validation');p.add_argument('--name',default='base');p.add_argument('--dim',type=int,default=128);p.add_argument('--steps',type=int,default=1000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--batch',type=int,default=256);p.add_argument('--context',type=int,default=64);p.add_argument('--lr',type=float,default=.002);p.add_argument('--seed',type=int,default=42);p.add_argument('--resume');p.add_argument('--temporal',type=float,default=0.);p.add_argument('--negatives',type=int,default=2048);p.add_argument('--history-days',type=float,default=90);p.add_argument('--temporal-file',default='temporal');p.add_argument('--threads',type=int,default=4);train(p.parse_args())\n"
  }
}
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70

exec command

+25:28
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

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, zipfile,os\nfor f in ['validation_local.npz','validation_temporal.npz','validation_ragged.npz']:\n with zipfile.ZipFile('/app/solution/'+f) as z:\n  print(f,[(i.filename,i.file_size) for i in z.infolist()]);\n  for name in z.namelist()[:2]:\n   with z.open(name) as s:print(s.read(128))\nPY\ncat /app/solution/refit_base.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.4 seconds
Output:

{"chunk_id":"5e7ce2","wall_time_seconds":0.258284784,"exit_code":0,"original_token_count":446,"output":"validation_local.npz [('hp.npy', 105048896), ('tp.npy', 3282902)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 32), }                                                   \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 1), }                                                    \\n\"\nvalidation_temporal.npz [('hp.npy', 124500096), ('tp.npy', 31125120), ('uid.npy', 486456), ('cut.npy', 486456)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 512), }                                                    \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 128), }                                                    \\n\"\nvalidation_ragged.npz [('uids.npy', 33952), ('items.npy', 120128), ('offset.npy', 67784), ('ids.npy', 22259020), ('weight.npy', 22259020), ('target_weight.npy', 22259020), ('first.npy', 22259020), ('last.npy', 22259020), ('likes.npy', 22259020), ('cnt.npy', 22259020), ('org.npy', 22259020)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (8456,), }                                                         \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (30000,), }                                                        \\n\"\npools (8456, 1024)\nstep 100 loss 10.347599449157714 sec 5.1\nstep 200 loss 9.87227668762207 sec 9.9\nstep 300 loss 9.718066129684448 sec 14.8\nstep 400 loss 9.642009258270264 sec 19.7\nstep 500 loss 9.529330215454102 sec 24.6\nstep 600 loss 9.414399785995483 sec 29.7\nstep 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\nstep 1000 loss 9.018090562820435 sec 49.6\nEVAL 1000 {}\n"}
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-28b6812e-ed40-4999-8716-00cd95ac2a6d",
    "process_id": "72840",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, zipfile,os\nfor f in ['validation_local.npz','validation_temporal.npz','validation_ragged.npz']:\n with zipfile.ZipFile('/app/solution/'+f) as z:\n  print(f,[(i.filename,i.file_size) for i in z.infolist()]);\n  for name in z.namelist()[:2]:\n   with z.open(name) as s:print(s.read(128))\nPY\ncat /app/solution/refit_base.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, zipfile,os\nfor f in ['validation_local.npz','validation_temporal.npz','validation_ragged.npz']:\n with zipfile.ZipFile('/app/solution/'+f) as z:\n  print(f,[(i.filename,i.file_size) for i in z.infolist()]);\n  for name in z.namelist()[:2]:\n   with z.open(name) as s:print(s.read(128))\nPY\ncat /app/solution/refit_base.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "validation_local.npz [('hp.npy', 105048896), ('tp.npy', 3282902)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 32), }                                                   \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 1), }                                                    \\n\"\nvalidation_temporal.npz [('hp.npy', 124500096), ('tp.npy', 31125120), ('uid.npy', 486456), ('cut.npy', 486456)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 512), }                                                    \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 128), }                                                    \\n\"\nvalidation_ragged.npz [('uids.npy', 33952), ('items.npy', 120128), ('offset.npy', 67784), ('ids.npy', 22259020), ('weight.npy', 22259020), ('target_weight.npy', 22259020), ('first.npy', 22259020), ('last.npy', 22259020), ('likes.npy', 22259020), ('cnt.npy', 22259020), ('org.npy', 22259020)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (8456,), }                                                         \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (30000,), }                                                        \\n\"\npools (8456, 1024)\nstep 100 loss 10.347599449157714 sec 5.1\nstep 200 loss 9.87227668762207 sec 9.9\nstep 300 loss 9.718066129684448 sec 14.8\nstep 400 loss 9.642009258270264 sec 19.7\nstep 500 loss 9.529330215454102 sec 24.6\nstep 600 loss 9.414399785995483 sec 29.7\nstep 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\nstep 1000 loss 9.018090562820435 sec 49.6\nEVAL 1000 {}\n",
    "stderr": "",
    "aggregated_output": "validation_local.npz [('hp.npy', 105048896), ('tp.npy', 3282902)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 32), }                                                   \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 1), }                                                    \\n\"\nvalidation_temporal.npz [('hp.npy', 124500096), ('tp.npy', 31125120), ('uid.npy', 486456), ('cut.npy', 486456)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 512), }                                                    \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 128), }                                                    \\n\"\nvalidation_ragged.npz [('uids.npy', 33952), ('items.npy', 120128), ('offset.npy', 67784), ('ids.npy', 22259020), ('weight.npy', 22259020), ('target_weight.npy', 22259020), ('first.npy', 22259020), ('last.npy', 22259020), ('likes.npy', 22259020), ('cnt.npy', 22259020), ('org.npy', 22259020)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (8456,), }                                                         \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (30000,), }                                                        \\n\"\npools (8456, 1024)\nstep 100 loss 10.347599449157714 sec 5.1\nstep 200 loss 9.87227668762207 sec 9.9\nstep 300 loss 9.718066129684448 sec 14.8\nstep 400 loss 9.642009258270264 sec 19.7\nstep 500 loss 9.529330215454102 sec 24.6\nstep 600 loss 9.414399785995483 sec 29.7\nstep 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\nstep 1000 loss 9.018090562820435 sec 49.6\nEVAL 1000 {}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 258273314
    },
    "formatted_output": "validation_local.npz [('hp.npy', 105048896), ('tp.npy', 3282902)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 32), }                                                   \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u2', 'fortran_order': False, 'shape': (1641387, 1), }                                                    \\n\"\nvalidation_temporal.npz [('hp.npy', 124500096), ('tp.npy', 31125120), ('uid.npy', 486456), ('cut.npy', 486456)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 512), }                                                    \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<i4', 'fortran_order': False, 'shape': (60791, 128), }                                                    \\n\"\nvalidation_ragged.npz [('uids.npy', 33952), ('items.npy', 120128), ('offset.npy', 67784), ('ids.npy', 22259020), ('weight.npy', 22259020), ('target_weight.npy', 22259020), ('first.npy', 22259020), ('last.npy', 22259020), ('likes.npy', 22259020), ('cnt.npy', 22259020), ('org.npy', 22259020)]\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (8456,), }                                                         \\n\"\nb\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<u4', 'fortran_order': False, 'shape': (30000,), }                                                        \\n\"\npools (8456, 1024)\nstep 100 loss 10.347599449157714 sec 5.1\nstep 200 loss 9.87227668762207 sec 9.9\nstep 300 loss 9.718066129684448 sec 14.8\nstep 400 loss 9.642009258270264 sec 19.7\nstep 500 loss 9.529330215454102 sec 24.6\nstep 600 loss 9.414399785995483 sec 29.7\nstep 700 loss 9.305567417144776 sec 34.7\nstep 800 loss 9.215565662384034 sec 39.7\nstep 900 loss 9.115171852111816 sec 44.6\nstep 1000 loss 9.018090562820435 sec 49.6\nEVAL 1000 {}\n"
  }
}
71

exec command

+25:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name local --resume /app/solution/denoise_6000.pt --steps 1500 --eval-every 500 --lr .0005 --temporal .8 --temporal-file local --history-days 14 --context 32 --threads 2 > /app/solution/local.log 2>&1","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"47cd12","wall_time_seconds":1.000518345,"session_id":49845,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:19:08 UTC"}
Harness context token usage record
{
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  "thread_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
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  }
}
72

exec command

+25:56
Thinking unavailable No text recorded

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

Model text

The compact-model comparison is still in progress. The first stage of the full refit has finished, and I’m testing one additional model trained on short chronological listening contexts for local discovery patterns.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.current; /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n  try:\n   a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\0')\n   if len(a)>2 and a[2]==b'/app/solution/model.py':\n    print(p,a[3:7]);print('\\n'.join(s for s in open('/proc/'+p+'/status') if s.startswith(('VmRSS','VmHWM','VmSize'))))\n  except OSError:pass\nPY\ntail -4 /app/solution/local.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"54de84","wall_time_seconds":0.00000832,"exit_code":0,"original_token_count":42,"output":"1236234240\n1365 [b'--name', b'local', b'--resume', b'/app/solution/denoise_6000.pt']\nVmSize:\t 1515560 kB\n\nVmHWM:\t  963372 kB\n\nVmRSS:\t  926860 kB\n\npools (8456, 1024)\n"}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-0ed73c16-7866-4569-b581-5e99eb1a37d4",
    "process_id": "2186",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /sys/fs/cgroup/memory.current; /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n  try:\n   a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\0')\n   if len(a)>2 and a[2]==b'/app/solution/model.py':\n    print(p,a[3:7]);print('\\n'.join(s for s in open('/proc/'+p+'/status') if s.startswith(('VmRSS','VmHWM','VmSize'))))\n  except OSError:pass\nPY\ntail -4 /app/solution/local.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /sys/fs/cgroup/memory.current; /opt/task-venv/bin/python - <<'PY'\nimport os\nfor p in os.listdir('/proc'):\n if p.isdigit():\n  try:\n   a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\0')\n   if len(a)>2 and a[2]==b'/app/solution/model.py':\n    print(p,a[3:7]);print('\\n'.join(s for s in open('/proc/'+p+'/status') if s.startswith(('VmRSS','VmHWM','VmSize'))))\n  except OSError:pass\nPY\ntail -4 /app/solution/local.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "1236234240\n1365 [b'--name', b'local', b'--resume', b'/app/solution/denoise_6000.pt']\nVmSize:\t 1515560 kB\n\nVmHWM:\t  963372 kB\n\nVmRSS:\t  926860 kB\n\npools (8456, 1024)\n",
    "stderr": "",
    "aggregated_output": "1236234240\n1365 [b'--name', b'local', b'--resume', b'/app/solution/denoise_6000.pt']\nVmSize:\t 1515560 kB\n\nVmHWM:\t  963372 kB\n\nVmRSS:\t  926860 kB\n\npools (8456, 1024)\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 8320
    },
    "formatted_output": "1236234240\n1365 [b'--name', b'local', b'--resume', b'/app/solution/denoise_6000.pt']\nVmSize:\t 1515560 kB\n\nVmHWM:\t  963372 kB\n\nVmRSS:\t  926860 kB\n\npools (8456, 1024)\n"
  }
}
Harness context token usage record
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  }
}
73

exec command

+26:24
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

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/local.log; cat /sys/fs/cgroup/memory.current","max_output_tokens":1500}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"4fbce3","wall_time_seconds":1.001382321,"session_id":58282,"original_token_count":0,"output":""}
{"chunk_id":"8c85de","wall_time_seconds":0.000004341,"exit_code":0,"original_token_count":44,"output":"step 200 loss 8.487984290122986 sec 8.6\nstep 300 loss 8.48304343700409 sec 12.8\nstep 400 loss 8.500785737037658 sec 17.0\nstep 500 loss 8.459852910041809 sec 21.1\n1451425792\n"}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-893a1cc5-a467-47ad-b0c3-00fa2ccb77d8",
    "process_id": "29975",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/local.log; cat /sys/fs/cgroup/memory.current"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -4 /app/solution/local.log; cat /sys/fs/cgroup/memory.current"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 200 loss 8.487984290122986 sec 8.6\nstep 300 loss 8.48304343700409 sec 12.8\nstep 400 loss 8.500785737037658 sec 17.0\nstep 500 loss 8.459852910041809 sec 21.1\n1451425792\n",
    "stderr": "",
    "aggregated_output": "step 200 loss 8.487984290122986 sec 8.6\nstep 300 loss 8.48304343700409 sec 12.8\nstep 400 loss 8.500785737037658 sec 17.0\nstep 500 loss 8.459852910041809 sec 21.1\n1451425792\n",
    "exit_code": 0,
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      "secs": 0,
      "nanos": 4341
    },
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  }
}
74

exec command

+26:32
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat /sys/fs/cgroup/memory.current; tail -3 /app/solution/likes500_cal.log; tail -3 /app/solution/local.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"381d7f","wall_time_seconds":0.000005341,"exit_code":0,"original_token_count":34,"output":"6884605952\nstep 300 loss 8.48304343700409 sec 12.8\nstep 400 loss 8.500785737037658 sec 17.0\nstep 500 loss 8.459852910041809 sec 21.1\n"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5e3f08e7-9d55-474d-8f45-20f2c77e648c",
    "process_id": "69485",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /sys/fs/cgroup/memory.current; tail -3 /app/solution/likes500_cal.log; tail -3 /app/solution/local.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /sys/fs/cgroup/memory.current; tail -3 /app/solution/likes500_cal.log; tail -3 /app/solution/local.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "6884605952\nstep 300 loss 8.48304343700409 sec 12.8\nstep 400 loss 8.500785737037658 sec 17.0\nstep 500 loss 8.459852910041809 sec 21.1\n",
    "stderr": "",
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    "exit_code": 0,
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      "secs": 0,
      "nanos": 5341
    },
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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",
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      "/bin/sh",
      "-lc",
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    ],
    "cwd": "file:///app",
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        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
      }
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      "secs": 9,
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  }
}
75

exec command

+26:37
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

text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n  try:\n   a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\0')\n   if len(a)>2 and a[2]==b'/app/solution/fast_calibrate.py':\n    print(p,[s.strip() for s in open('/proc/'+p+'/status') if s.startswith(('VmRSS','VmHWM','VmSize'))]);os.kill(int(p),signal.SIGINT)\n  except OSError:pass\nPY\ntail -10 /app/solution/likes500_cal.log","max_output_tokens":1800}));

Tool response

Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"1c5017","wall_time_seconds":0.00000453,"exit_code":0,"original_token_count":2,"output":"Killed\n"}
Harness context token usage record
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  "session_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-57ffbd20-9ffc-4299-9829-90d1dba29b8e",
    "process_id": "13926",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n  try:\n   a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\0')\n   if len(a)>2 and a[2]==b'/app/solution/fast_calibrate.py':\n    print(p,[s.strip() for s in open('/proc/'+p+'/status') if s.startswith(('VmRSS','VmHWM','VmSize'))]);os.kill(int(p),signal.SIGINT)\n  except OSError:pass\nPY\ntail -10 /app/solution/likes500_cal.log"
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    "cwd": "file:///app",
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      {
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor p in os.listdir('/proc'):\n if p.isdigit():\n  try:\n   a=open('/proc/'+p+'/cmdline','rb').read().split(b'\\0')\n   if len(a)>2 and a[2]==b'/app/solution/fast_calibrate.py':\n    print(p,[s.strip() for s in open('/proc/'+p+'/status') if s.startswith(('VmRSS','VmHWM','VmSize'))]);os.kill(int(p),signal.SIGINT)\n  except OSError:pass\nPY\ntail -10 /app/solution/likes500_cal.log"
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    "stdout": "Killed\n",
    "stderr": "",
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76

exec command

+26:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"sed -n '15,60p' /app/solution/tune.py; sed -n '1,55p' /app/solution/fast_calibrate.py; cat /sys/fs/cgroup/memory.current; tail -5 /app/solution/local.log","max_output_tokens":5000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"d4c804","wall_time_seconds":0.00000409,"exit_code":0,"original_token_count":1509,"output":" w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n  for u in range(len(a['uids'])):\n   lo,hi=a['offset'][u:u+2];take=np.arange(lo,hi)\n   if len(take)>cap:take=take[np.argpartition(w[lo:hi],-cap)[-cap:]]\n   ids.append(a['ids'][take]);weights.append(w[take]);counts.append(len(take))\n  a={**a,'ids':np.concatenate(ids),'offset':np.r_[0,np.cumsum(counts)]};w=np.concatenate(weights)\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\n1085943808\nEVAL 500 {'quality_ndcg@10': 0.02378, 'quality_users': 4263}\nstep 600 loss 8.468339796066283 sec 39.8\nstep 700 loss 8.38867220401764 sec 43.3\nstep 800 loss 8.412034020423889 sec 46.9\nstep 900 loss 8.379642910957337 sec 50.4\n"}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-5af8d46a-b9d5-416c-b701-0f08a642d877",
    "process_id": "93794",
    "command": [
      "/bin/sh",
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      "sed -n '15,60p' /app/solution/tune.py; sed -n '1,55p' /app/solution/fast_calibrate.py; cat /sys/fs/cgroup/memory.current; tail -5 /app/solution/local.log"
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    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '15,60p' /app/solution/tune.py; sed -n '1,55p' /app/solution/fast_calibrate.py; cat /sys/fs/cgroup/memory.current; tail -5 /app/solution/local.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": " w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n  for u in range(len(a['uids'])):\n   lo,hi=a['offset'][u:u+2];take=np.arange(lo,hi)\n   if len(take)>cap:take=take[np.argpartition(w[lo:hi],-cap)[-cap:]]\n   ids.append(a['ids'][take]);weights.append(w[take]);counts.append(len(take))\n  a={**a,'ids':np.concatenate(ids),'offset':np.r_[0,np.cumsum(counts)]};w=np.concatenate(weights)\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\n1085943808\nEVAL 500 {'quality_ndcg@10': 0.02378, 'quality_users': 4263}\nstep 600 loss 8.468339796066283 sec 39.8\nstep 700 loss 8.38867220401764 sec 43.3\nstep 800 loss 8.412034020423889 sec 46.9\nstep 900 loss 8.379642910957337 sec 50.4\n",
    "stderr": "",
    "aggregated_output": " w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n  for u in range(len(a['uids'])):\n   lo,hi=a['offset'][u:u+2];take=np.arange(lo,hi)\n   if len(take)>cap:take=take[np.argpartition(w[lo:hi],-cap)[-cap:]]\n   ids.append(a['ids'][take]);weights.append(w[take]);counts.append(len(take))\n  a={**a,'ids':np.concatenate(ids),'offset':np.r_[0,np.cumsum(counts)]};w=np.concatenate(weights)\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\n1085943808\nEVAL 500 {'quality_ndcg@10': 0.02378, 'quality_users': 4263}\nstep 600 loss 8.468339796066283 sec 39.8\nstep 700 loss 8.38867220401764 sec 43.3\nstep 800 loss 8.412034020423889 sec 46.9\nstep 900 loss 8.379642910957337 sec 50.4\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4090
    },
    "formatted_output": " w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n  for u in range(len(a['uids'])):\n   lo,hi=a['offset'][u:u+2];take=np.arange(lo,hi)\n   if len(take)>cap:take=take[np.argpartition(w[lo:hi],-cap)[-cap:]]\n   ids.append(a['ids'][take]);weights.append(w[take]);counts.append(len(take))\n  a={**a,'ids':np.concatenate(ids),'offset':np.r_[0,np.cumsum(counts)]};w=np.concatenate(weights)\n for start in range(0,len(a['uids']),128):\n  end=min(start+128,len(a['uids']));lo,hi=a['offset'][[start,end]];uv.append(m.user(torch.from_numpy(a['ids'][lo:hi].astype(np.int64)),torch.from_numpy(a['offset'][start:end]-lo),torch.from_numpy(w[lo:hi])))\n return torch.cat(uv).numpy()\[email protected]_grad()\ndef rank(uv,iv,e,bias=None,k=10):\n uv=torch.from_numpy(uv);iv=torch.from_numpy(iv);tops=[];n=len(iv)\n if bias is not None:iv=iv.clone();iv[:,-1]+=torch.as_tensor(bias,dtype=torch.float32)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9);tops.append(s.topk(k,dim=-1).indices.numpy())\n return np.concatenate(tops)\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();out=[];best=(-1,None,None,None)\n for days in [7,14,30,60,90,180,10000]:\n  uv=vectors(m,a,days=days);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':3,'count':.5,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n  if score>best[0]:best=(score,c,uv,None)\n if x.hist:\n  days=best[1]['days']\n  for like in [0,1,6,12]:\n   for count in [0,.5,1]:\n    uv=vectors(m,a,days=days,like=like,count=count);score=ev.score(rank(uv,iv,ev.e));c={'days':days,'like':like,'count':count,'organic':.6};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,None)\n # Each correction is an item-only linear feature, representable in item-tower bias.\n pop=np.load(O/'validation_popularity.npz');features=pop['features'];names=pop['names'];uv=best[2];hist=best[1].copy();basebest=best\n for j,name in enumerate(names):\n  if 'all_' in name:continue\n  for coef in [-.5,.5,1.,2.]:\n   bias=coef*features[:,j];score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':str(name),'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,bias)\n for short in ['strong_7','strong_30','like_7','like_30']:\n  for long in ['strong_300']:\n   j=list(names).index(short);l=list(names).index(long)\n   for coef in [.5,1,2,3]:\n    bias=coef*(features[:,j]-features[:,l]);score=ev.score(rank(uv,iv,ev.e,bias));c={**hist,'pop':short,'subtract':long,'coef':coef};print(score,c,flush=True);out.append({'score':score,**c})\n    if score>best[0]:best=(score,c,uv,bias)\n print('BEST',best[:2],flush=True);json.dump({'checkpoint':x.checkpoint,'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(out,open(O/f'{x.name}_grid.json','w'),indent=2)\n top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\n np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3] if best[3] is not None else np.zeros(len(iv)))\nif __name__=='__main__':\nfrom tune import *\ntorch.set_num_threads(2)\[email protected]_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n for st in range(0,len(uv),128):\n  s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n return [np.concatenate(t) for t in tops]\ndef main(x):\n a=load('validation');ck=torch.load(x.checkpoint,weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();ev=Evaluator();iv=m.item(torch.arange(1,len(a['items'])+1)).detach().numpy();pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);priorconfigs=[{'like_7':c,'like_30':old} for c in [1,1.5,2,2.5,3] for old in [-.5,0,.5]];biases=[sum(v*f[:,names.index(k)] for k,v in c.items()) for c in priorconfigs];best=(-1,None,None,None);results=[]\n for days,like,count in [(14,0,0),(14,3,.5),(90,0,0),(90,3,.5),(30,0,0),(300,0,0)]:\n  uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n  for pr,b,top in zip(priorconfigs,biases,rankings):\n   score=ev.score(top);c={'days':days,'like':like,'count':count,'organic':.6,'cap':512,'prior':pr};results.append({'score':score,**c})\n   if score>best[0]:best=(score,c,uv,b)\n  print('history',days,like,count,'best',best[:2],flush=True)\n print('BEST',best[:2],flush=True);config={'checkpoint':x.checkpoint,'score':best[0],**best[1]};json.dump(config,open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2);np.savez(O/f'{x.name}_best_vectors.npz',user=best[2],item=iv,bias=best[3]);top=rank(best[2],iv,ev.e,best[3],100);pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))}).to_parquet(O/f'{x.name}_validation.parquet',index=False)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\n1085943808\nEVAL 500 {'quality_ndcg@10': 0.02378, 'quality_users': 4263}\nstep 600 loss 8.468339796066283 sec 39.8\nstep 700 loss 8.38867220401764 sec 43.3\nstep 800 loss 8.412034020423889 sec 46.9\nstep 900 loss 8.379642910957337 sec 50.4\n"
  }
}
Harness context token usage record
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  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
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  "turn_token_usage": {
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  "thread_token_usage": {
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77

exec command

+27:00
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

text(await tools.exec_command({cmd:"head -17 /app/solution/tune.py; ls -lh /app/solution/likes_500.pt; cat /sys/fs/cgroup/memory.events","max_output_tokens":1000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"8bbab1","wall_time_seconds":0.00000501,"exit_code":0,"original_token_count":410,"output":"\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6,cap=0,dislike=0):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n-rw-r--r-- 1 agent agent 32M Sep 10 18:13 /app/solution/likes_500.pt\nlow 0\nhigh 0\nmax 8317\noom 33\noom_kill 7\noom_group_kill 0\nsock_throttled 0\n"}
Harness context codex item completed
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    "stdout": "\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6,cap=0,dislike=0):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n-rw-r--r-- 1 agent agent 32M Sep 10 18:13 /app/solution/likes_500.pt\nlow 0\nhigh 0\nmax 8317\noom 33\noom_kill 7\noom_group_kill 0\nsock_throttled 0\n",
    "stderr": "",
    "aggregated_output": "\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6,cap=0,dislike=0):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n-rw-r--r-- 1 agent agent 32M Sep 10 18:13 /app/solution/likes_500.pt\nlow 0\nhigh 0\nmax 8317\noom 33\noom_kill 7\noom_group_kill 0\nsock_throttled 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
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    },
    "formatted_output": "\"\"\"Public-window tuning; validation outcomes are used exclusively for metrics.\"\"\"\nfrom model import *\nclass Evaluator:\n def __init__(self):\n  sys.path.insert(0,'/task');from evaluate_public import validation_truth;from ranking_evaluation import CandidateSet\n  e=CandidateSet(D/'validation_eligible_candidates.npz');self.e=e;truth=validation_truth(e,set(map(int,e.uids)));um={int(u):k for k,u in enumerate(e.uids)};im={int(i):k for k,i in enumerate(e.item_ids)};pairs=[];self.idcg=np.zeros(len(e.uids));discount=1/np.log2(np.arange(2,12))\n  for uid,g in truth.items():\n   u=um[uid];gg=sorted(g.values(),reverse=True)[:10];self.idcg[u]=np.dot(2.**np.array(gg)-1,discount[:len(gg)]);pairs.extend((u*len(e.item_ids)+im[i],2**gr-1) for i,gr in g.items())\n  pairs=sorted(pairs);self.keys=np.array([p[0] for p in pairs]+[len(e.uids)*len(e.item_ids)+1]);self.gains=np.array([p[1] for p in pairs]+[0]);self.discount=discount\n def score(self,top):\n  keys=np.arange(len(top))[:,None]*len(self.e.item_ids)+top[:,:10];pos=np.searchsorted(self.keys,keys);g=self.gains[pos]*(self.keys[pos]==keys);dcg=(g*self.discount).sum(1);active=self.idcg>0;return float((dcg[active]/self.idcg[active]).mean())\[email protected]_grad()\ndef vectors(m,a,days=90,like=3,count=.5,organic=.6,cap=0,dislike=0):\n end=24786800;age=(a['last'].astype(float)-end)/86400\n w=(a['cnt']**count*(1-organic+organic*a['org']/a['cnt'])+like*np.minimum(a['likes'],1))*np.exp(age/days);w=w.astype(np.float32);uv=[]\n if cap:\n  ids=[];weights=[];counts=[]\n-rw-r--r-- 1 agent agent 32M Sep 10 18:13 /app/solution/likes_500.pt\nlow 0\nhigh 0\nmax 8317\noom 33\noom_kill 7\noom_group_kill 0\nsock_throttled 0\n"
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78

exec command

+27:16
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

text(await tools.exec_command({cmd:"ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/local.log","max_output_tokens":1500}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"851db8","wall_time_seconds":1.000761817,"session_id":82941,"original_token_count":0,"output":""}
{"chunk_id":"bdb5ac","wall_time_seconds":0.00000581,"exit_code":0,"original_token_count":47,"output":"step 1000 loss 8.329277505874634 sec 53.8\nEVAL 1000 {'quality_ndcg@10': 0.02492, 'quality_users': 4263}\nstep 1100 loss 8.318623433113098 sec 72.2\nstep 1200 loss 8.33293890953064 sec 76.0\n"}
Harness context token usage record
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Harness context codex item completed
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    "stderr": "",
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  }
}
79

exec command

+27:23
Thinking unavailable No text recorded

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

Harness context codex item completed
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      "/bin/sh",
      "-lc",
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      }
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    "source": "unified_exec_startup",
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      "secs": 9,
      "nanos": 418116800
    },
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Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -18 /app/solution/likes500_cal.log","max_output_tokens":2500}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_denoise --resume /app/solution/refit_base_1000.pt --steps 6000 --eval-every 6000 --lr .001 --threads 2 > /app/solution/refit_denoise.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c38f0b","wall_time_seconds":0.00000396,"exit_code":0,"original_token_count":288,"output":"Traceback (most recent call last):\n  File \"/app/solution/fast_calibrate.py\", line 20, in <module>\n    p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\n                                                                                                     ^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/fast_calibrate.py\", line 13, in main\n    uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n                                                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/fast_calibrate.py\", line 8, in rank_many\n    for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n                                             ~^~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\n"}
{"chunk_id":"a22816","wall_time_seconds":1.000428596,"session_id":15913,"original_token_count":0,"output":""}
Harness context codex item completed
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      "/bin/sh",
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    "stdout": "Traceback (most recent call last):\n  File \"/app/solution/fast_calibrate.py\", line 20, in <module>\n    p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--name',default='fast');main(p.parse_args())\n                                                                                                     ^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/fast_calibrate.py\", line 13, in main\n    uv=vectors(m,a,days=days,like=like,count=count,cap=512);rankings=rank_many(uv,iv,ev.e,biases)\n                                                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/fast_calibrate.py\", line 8, in rank_many\n    for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\n                                             ~^~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\n",
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      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name local --resume /app/solution/denoise_6000.pt --steps 1500 --eval-every 500 --lr .0005 --temporal .8 --temporal-file local --history-days 14 --context 32 --threads 2 > /app/solution/local.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --name local --resume /app/solution/denoise_6000.pt --steps 1500 --eval-every 500 --lr .0005 --temporal .8 --temporal-file local --history-days 14 --context 32 --threads 2 > /app/solution/local.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 113,
      "nanos": 554245285
    },
    "formatted_output": ""
  }
}
80

Agent message

+27:51
Thinking unavailable No text recorded

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

81

exec command

+27:52
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/fast_calibrate.py';s=open(p).read().replace(\"for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\", \"work=torch.empty_like(s)\\n  for j,b in enumerate(bs):\\n   torch.add(s,b,out=work);idx=work.topk(k,dim=-1).indices;tops[j].append(idx.numpy().copy())\")\nopen(p,'w').write(s)\np='/app/solution/ensemble_grid.py';s=open(p).read().replace(\"for j,((w,p),b) in enumerate(zip(grid,biases)):\\n   s=sum(c*r for c,r in zip(w,raw) if c)+b;s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy())\", \"s=torch.empty_like(raw[0])\\n  for j,((w,p),b) in enumerate(zip(grid,biases)):\\n   s.zero_()\\n   for c,r in zip(w,raw):s.add_(r,alpha=c)\\n   s.add_(b);s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy().copy())\")\nopen(p,'w').write(s)\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/local.log; tail -4 /app/solution/refit_denoise.log","max_output_tokens":1800}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"ea501f","wall_time_seconds":1.001418919,"session_id":41507,"original_token_count":0,"output":""}
{"chunk_id":"137618","wall_time_seconds":0.0000045,"exit_code":0,"original_token_count":88,"output":"step 1300 loss 8.284799642562866 sec 79.9\nstep 1400 loss 8.29430223941803 sec 83.6\nstep 1500 loss 8.231218800544738 sec 87.5\nEVAL 1500 {'quality_ndcg@10': 0.02296, 'quality_users': 4263}\nstep 100 loss 8.115557489395142 sec 5.4\nstep 200 loss 8.274635500907898 sec 10.7\nstep 300 loss 8.261738624572754 sec 16.2\nstep 400 loss 8.080167360305786 sec 21.3\n"}
Harness context token usage record
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Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-6bb674e0-092a-45d7-aac3-761da789bc0d",
    "process_id": "79759",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/local.log; tail -4 /app/solution/refit_denoise.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -4 /app/solution/local.log; tail -4 /app/solution/refit_denoise.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 1300 loss 8.284799642562866 sec 79.9\nstep 1400 loss 8.29430223941803 sec 83.6\nstep 1500 loss 8.231218800544738 sec 87.5\nEVAL 1500 {'quality_ndcg@10': 0.02296, 'quality_users': 4263}\nstep 100 loss 8.115557489395142 sec 5.4\nstep 200 loss 8.274635500907898 sec 10.7\nstep 300 loss 8.261738624572754 sec 16.2\nstep 400 loss 8.080167360305786 sec 21.3\n",
    "stderr": "",
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    "exit_code": 0,
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      "secs": 0,
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    },
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  }
}
82

exec command

+28:13
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -6 /app/solution/likes500_cal.log; cat /sys/fs/cgroup/memory.current; tail -2 /app/solution/refit_denoise.log","max_output_tokens":1800}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"9a4f31","wall_time_seconds":0.00002107,"exit_code":0,"original_token_count":61,"output":"history 14 0 0 best (0.04286872173127723, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\n2158489600\nstep 500 loss 7.762505469322204 sec 26.5\nstep 600 loss 7.5533109426498415 sec 31.8\n"}
{"current_time":"2026-09-10 18:21:31 UTC"}
Harness context codex item completed
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    "process_id": "41871",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -6 /app/solution/likes500_cal.log; cat /sys/fs/cgroup/memory.current; tail -2 /app/solution/refit_denoise.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -6 /app/solution/likes500_cal.log; cat /sys/fs/cgroup/memory.current; tail -2 /app/solution/refit_denoise.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "history 14 0 0 best (0.04286872173127723, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\n2158489600\nstep 500 loss 7.762505469322204 sec 26.5\nstep 600 loss 7.5533109426498415 sec 31.8\n",
    "stderr": "",
    "aggregated_output": "history 14 0 0 best (0.04286872173127723, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\n2158489600\nstep 500 loss 7.762505469322204 sec 26.5\nstep 600 loss 7.5533109426498415 sec 31.8\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 21070
    },
    "formatted_output": "history 14 0 0 best (0.04286872173127723, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\n2158489600\nstep 500 loss 7.762505469322204 sec 26.5\nstep 600 loss 7.5533109426498415 sec 31.8\n"
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Harness context token usage record
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83

exec command

+28:23
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-18d94d8b-292e-4baa-b3f1-c0fc8b9741c6",
    "process_id": "41507",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/fast_calibrate.py';s=open(p).read().replace(\"for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\", \"work=torch.empty_like(s)\\n  for j,b in enumerate(bs):\\n   torch.add(s,b,out=work);idx=work.topk(k,dim=-1).indices;tops[j].append(idx.numpy().copy())\")\nopen(p,'w').write(s)\np='/app/solution/ensemble_grid.py';s=open(p).read().replace(\"for j,((w,p),b) in enumerate(zip(grid,biases)):\\n   s=sum(c*r for c,r in zip(w,raw) if c)+b;s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy())\", \"s=torch.empty_like(raw[0])\\n  for j,((w,p),b) in enumerate(zip(grid,biases)):\\n   s.zero_()\\n   for c,r in zip(w,raw):s.add_(r,alpha=c)\\n   s.add_(b);s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy().copy())\")\nopen(p,'w').write(s)\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/fast_calibrate.py';s=open(p).read().replace(\"for j,b in enumerate(bs):tops[j].append((s+b).topk(k,dim=-1).indices.numpy())\", \"work=torch.empty_like(s)\\n  for j,b in enumerate(bs):\\n   torch.add(s,b,out=work);idx=work.topk(k,dim=-1).indices;tops[j].append(idx.numpy().copy())\")\nopen(p,'w').write(s)\np='/app/solution/ensemble_grid.py';s=open(p).read().replace(\"for j,((w,p),b) in enumerate(zip(grid,biases)):\\n   s=sum(c*r for c,r in zip(w,raw) if c)+b;s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy())\", \"s=torch.empty_like(raw[0])\\n  for j,((w,p),b) in enumerate(zip(grid,biases)):\\n   s.zero_()\\n   for c,r in zip(w,raw):s.add_(r,alpha=c)\\n   s.add_(b);s.masked_fill_(mask,-1e9);tops[j].append(s.topk(10,dim=-1).indices.numpy().copy())\")\nopen(p,'w').write(s)\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 1,
    "duration": {
      "secs": 21,
      "nanos": 975775933
    },
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  }
}
Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_temporal --resume /app/solution/refit_base_1000.pt --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2 > /app/solution/refit_temporal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"ulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal --name compact_ensemble > /app/solution/compact_ensemble.log 2>&1","yield_time_ms":1000}));

Tool response

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"9c185b","wall_time_seconds":1.000472717,"session_id":81554,"original_token_count":0,"output":""}
{"chunk_id":"8406d8","wall_time_seconds":1.000475953,"session_id":1032,"original_token_count":0,"output":""}
Harness context token usage record
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84

Agent message

+28:34
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 full-data refits are running. I’m comparing combinations of the compact models; concatenating their vectors keeps the final score an exact dot product, so the same user-vector and offline item-index serving design still applies.

Harness context codex item completed
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    "type": "CommandExecution",
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    "process_id": "1032",
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      "/bin/sh",
      "-lc",
      "ulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal --name compact_ensemble > /app/solution/compact_ensemble.log 2>&1"
    ],
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      {
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        "cmd": "ulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal --name compact_ensemble > /app/solution/compact_ensemble.log 2>&1"
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      "secs": 19,
      "nanos": 996090175
    },
    "formatted_output": ""
  }
}
85

Agent message

+28:54
Thinking unavailable No text recorded

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

86

exec command

+29:09
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

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet')\", \"dest=O/f'{args.name}_{step}_{args.stage}.parquet'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd,torch,shutil\nsys.path.insert(0,'/app/solution');from tune import rank\nsys.path.insert(0,'/task');from ranking_evaluation import CandidateSet\ntorch.set_num_threads(1);e=CandidateSet('/task/data/test_eligible_candidates.npz');v=np.load('/app/solution/initial_calibrated_vectors.npz');top=rank(v['user'],v['item'],e,k=100);f=pd.DataFrame({'uid':np.repeat(e.uids,100),'item_id':e.item_ids[top.flatten()],'rank':np.tile(np.arange(1,101),len(top))});f.to_parquet('/app/solution/best_submission.parquet',index=False);shutil.copyfile('/app/solution/best_submission.parquet','/app/solution/submission.parquet');print('Saved calibrated backup',len(f))\nPY","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/likes500_cal.log; tail -3 /app/solution/compact_ensemble.log; tail -3 /app/solution/refit_denoise.log; tail -3 /app/solution/refit_temporal.log","max_output_tokens":2200}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"821566","wall_time_seconds":1.000881274,"session_id":86975,"original_token_count":0,"output":""}
{"chunk_id":"c352f6","wall_time_seconds":0.00000561,"exit_code":0,"original_token_count":254,"output":"  File \"/app/solution/fast_calibrate.py\", line 7, in rank_many\n    s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n      ~~~~~~~~~~~~~^~~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\n    raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n         ~~~~~~~~~~~~^~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\nstep 1500 loss 8.541400108337402 sec 85.8\nstep 1600 loss 8.493770599365234 sec 91.9\nstep 1700 loss 8.446931819915772 sec 98.0\nstep 300 loss 9.092676515579223 sec 20.3\nstep 400 loss 9.024099788665772 sec 26.3\nstep 500 loss 8.949996147155762 sec 32.7\n"}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1151269c-74f7-4149-a4de-83d7ae342389",
    "process_id": "4386",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -4 /app/solution/likes500_cal.log; tail -3 /app/solution/compact_ensemble.log; tail -3 /app/solution/refit_denoise.log; tail -3 /app/solution/refit_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -4 /app/solution/likes500_cal.log; tail -3 /app/solution/compact_ensemble.log; tail -3 /app/solution/refit_denoise.log; tail -3 /app/solution/refit_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "  File \"/app/solution/fast_calibrate.py\", line 7, in rank_many\n    s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n      ~~~~~~~~~~~~~^~~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\n    raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n         ~~~~~~~~~~~~^~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\nstep 1500 loss 8.541400108337402 sec 85.8\nstep 1600 loss 8.493770599365234 sec 91.9\nstep 1700 loss 8.446931819915772 sec 98.0\nstep 300 loss 9.092676515579223 sec 20.3\nstep 400 loss 9.024099788665772 sec 26.3\nstep 500 loss 8.949996147155762 sec 32.7\n",
    "stderr": "",
    "aggregated_output": "  File \"/app/solution/fast_calibrate.py\", line 7, in rank_many\n    s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n      ~~~~~~~~~~~~~^~~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\n    raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n         ~~~~~~~~~~~~^~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\nstep 1500 loss 8.541400108337402 sec 85.8\nstep 1600 loss 8.493770599365234 sec 91.9\nstep 1700 loss 8.446931819915772 sec 98.0\nstep 300 loss 9.092676515579223 sec 20.3\nstep 400 loss 9.024099788665772 sec 26.3\nstep 500 loss 8.949996147155762 sec 32.7\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5610
    },
    "formatted_output": "  File \"/app/solution/fast_calibrate.py\", line 7, in rank_many\n    s=uv[st:st+128]@iv.T;mask=np.unpackbits(e.packed[st:st+128],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n      ~~~~~~~~~~~~~^~~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\n    raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+128],axis=1,count=n,bitorder='little')==0)\n         ~~~~~~~~~~~~^~~~\nRuntimeError: [enforce fail at alloc_cpu.cpp:124] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 15360000 bytes. Error code 12 (Cannot allocate memory)\nstep 1500 loss 8.541400108337402 sec 85.8\nstep 1600 loss 8.493770599365234 sec 91.9\nstep 1700 loss 8.446931819915772 sec 98.0\nstep 300 loss 9.092676515579223 sec 20.3\nstep 400 loss 9.024099788665772 sec 26.3\nstep 500 loss 8.949996147155762 sec 32.7\n"
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2b252f5c-0354-410c-9fac-6bf4c5a53bfb",
    "process_id": "86975",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet')\", \"dest=O/f'{args.name}_{step}_{args.stage}.parquet'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd,torch,shutil\nsys.path.insert(0,'/app/solution');from tune import rank\nsys.path.insert(0,'/task');from ranking_evaluation import CandidateSet\ntorch.set_num_threads(1);e=CandidateSet('/task/data/test_eligible_candidates.npz');v=np.load('/app/solution/initial_calibrated_vectors.npz');top=rank(v['user'],v['item'],e,k=100);f=pd.DataFrame({'uid':np.repeat(e.uids,100),'item_id':e.item_ids[top.flatten()],'rank':np.tile(np.arange(1,101),len(top))});f.to_parquet('/app/solution/best_submission.parquet',index=False);shutil.copyfile('/app/solution/best_submission.parquet','/app/solution/submission.parquet');print('Saved calibrated backup',len(f))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"dest=O/('validation.parquet' if args.stage=='validation' else 'submission.parquet')\", \"dest=O/f'{args.name}_{step}_{args.stage}.parquet'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd,torch,shutil\nsys.path.insert(0,'/app/solution');from tune import rank\nsys.path.insert(0,'/task');from ranking_evaluation import CandidateSet\ntorch.set_num_threads(1);e=CandidateSet('/task/data/test_eligible_candidates.npz');v=np.load('/app/solution/initial_calibrated_vectors.npz');top=rank(v['user'],v['item'],e,k=100);f=pd.DataFrame({'uid':np.repeat(e.uids,100),'item_id':e.item_ids[top.flatten()],'rank':np.tile(np.arange(1,101),len(top))});f.to_parquet('/app/solution/best_submission.parquet',index=False);shutil.copyfile('/app/solution/best_submission.parquet','/app/solution/submission.parquet');print('Saved calibrated backup',len(f))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Saved calibrated backup 845600\n",
    "stderr": "",
    "aggregated_output": "Saved calibrated backup 845600\n",
    "exit_code": 0,
    "duration": {
      "secs": 10,
      "nanos": 984195473
    },
    "formatted_output": "Saved calibrated backup 845600\n"
  }
}
87

Agent message

+29:39
Thinking unavailable No text recorded

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

88

Agent message

+29:59
Thinking unavailable No text recorded

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

89

exec command

+30:10
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

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/fast_calibrate.py';s=open(p).read();a=s.index('@torch.no_grad()');b=s.index('def main(x):');s=s[:a]+'''@torch.no_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n base=torch.empty((128,n));work=torch.empty((128,n))\n for st in range(0,len(uv),128):\n  nb=min(128,len(uv)-st);s=base[:nb];torch.mm(uv[st:st+nb],iv.T,out=s);mask=np.unpackbits(e.packed[st:st+nb],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):\n   torch.add(s,b,out=work[:nb]);idx=work[:nb].topk(k,dim=-1).indices;tops[j].append(idx.numpy().copy())\n return [np.concatenate(t) for t in tops]\n''' + s[b:];open(p,'w').write(s)\np='/app/solution/ensemble_grid.py';s=open(p).read().replace(\"for st in range(0,len(uv[0]),128):\\n  raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];\", \"buffers=[torch.empty((128,n)) for _ in uv];work=torch.empty((128,n))\\n for st in range(0,len(uv[0]),128):\\n  nb=min(128,len(uv[0])-st);raw=[b[:nb] for b in buffers]\\n  for u,i,r in zip(uv,iv,raw):torch.mm(u[st:st+nb],i.T,out=r)\\n  \").replace('s=torch.empty_like(raw[0])','s=work[:nb]');open(p,'w').write(s)\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"ulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal --name compact_ensemble > /app/solution/compact_ensemble.log 2>&1","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"f6ff06","wall_time_seconds":1.001071817,"session_id":50336,"original_token_count":0,"output":""}
{"chunk_id":"e781be","wall_time_seconds":1.001005255,"session_id":33635,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:23:40 UTC"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "session_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "response_id": "resp_03e1db26860ff63f016aa2f56ab46487d19af4aed3e5071a06",
  "usage": {
    "input_tokens": 90302,
    "cached_input_tokens": 88320,
    "cache_write_input_tokens": 0,
    "output_tokens": 1996,
    "reasoning_output_tokens": 1340,
    "total_tokens": 92298
  },
  "turn_token_usage": {
    "input_tokens": 3974565,
    "cached_input_tokens": 3883904,
    "cache_write_input_tokens": 0,
    "output_tokens": 48853,
    "reasoning_output_tokens": 25300,
    "total_tokens": 4023418
  },
  "thread_token_usage": {
    "input_tokens": 3974565,
    "cached_input_tokens": 3883904,
    "cache_write_input_tokens": 0,
    "output_tokens": 48853,
    "reasoning_output_tokens": 25300,
    "total_tokens": 4023418
  }
}
90

Agent message

+30:27
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 short-context model reached 0.024920 before popularity calibration. I’m checking whether it adds useful signal to the best model. The refits are continuing, and a calibrated submission backup is saved.

91

Agent message

+30:48
Thinking unavailable No text recorded

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

92

exec command

+31:02
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-8a1d7363-c077-475d-89a8-0495bcd2736a",
    "process_id": "33635",
    "command": [
      "/bin/sh",
      "-lc",
      "ulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal --name compact_ensemble > /app/solution/compact_ensemble.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal --name compact_ensemble > /app/solution/compact_ensemble.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 55,
      "nanos": 106273146
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f1e98197-a7fb-4793-9a70-76b5fceef9d9",
    "process_id": "81554",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_temporal --resume /app/solution/refit_base_1000.pt --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2 > /app/solution/refit_temporal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name refit_temporal --resume /app/solution/refit_base_1000.pt --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2 > /app/solution/refit_temporal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 170,
      "nanos": 223688542
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-005bd62a-d5d2-47cc-87f9-6c8dc3e7333f",
    "process_id": "50336",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/fast_calibrate.py';s=open(p).read();a=s.index('@torch.no_grad()');b=s.index('def main(x):');s=s[:a]+'''@torch.no_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n base=torch.empty((128,n));work=torch.empty((128,n))\n for st in range(0,len(uv),128):\n  nb=min(128,len(uv)-st);s=base[:nb];torch.mm(uv[st:st+nb],iv.T,out=s);mask=np.unpackbits(e.packed[st:st+nb],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):\n   torch.add(s,b,out=work[:nb]);idx=work[:nb].topk(k,dim=-1).indices;tops[j].append(idx.numpy().copy())\n return [np.concatenate(t) for t in tops]\n''' + s[b:];open(p,'w').write(s)\np='/app/solution/ensemble_grid.py';s=open(p).read().replace(\"for st in range(0,len(uv[0]),128):\\n  raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];\", \"buffers=[torch.empty((128,n)) for _ in uv];work=torch.empty((128,n))\\n for st in range(0,len(uv[0]),128):\\n  nb=min(128,len(uv[0])-st);raw=[b[:nb] for b in buffers]\\n  for u,i,r in zip(uv,iv,raw):torch.mm(u[st:st+nb],i.T,out=r)\\n  \").replace('s=torch.empty_like(raw[0])','s=work[:nb]');open(p,'w').write(s)\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/fast_calibrate.py';s=open(p).read();a=s.index('@torch.no_grad()');b=s.index('def main(x):');s=s[:a]+'''@torch.no_grad()\ndef rank_many(uv,iv,e,biases,k=10):\n uv=torch.as_tensor(uv);iv=torch.as_tensor(iv);bs=torch.as_tensor(np.asarray(biases),dtype=torch.float32);tops=[[] for _ in biases];n=len(iv)\n base=torch.empty((128,n));work=torch.empty((128,n))\n for st in range(0,len(uv),128):\n  nb=min(128,len(uv)-st);s=base[:nb];torch.mm(uv[st:st+nb],iv.T,out=s);mask=np.unpackbits(e.packed[st:st+nb],axis=1,count=n,bitorder='little');s.masked_fill_(torch.from_numpy(mask==0),-1e9)\n  for j,b in enumerate(bs):\n   torch.add(s,b,out=work[:nb]);idx=work[:nb].topk(k,dim=-1).indices;tops[j].append(idx.numpy().copy())\n return [np.concatenate(t) for t in tops]\n''' + s[b:];open(p,'w').write(s)\np='/app/solution/ensemble_grid.py';s=open(p).read().replace(\"for st in range(0,len(uv[0]),128):\\n  raw=[u[st:st+128]@i.T for u,i in zip(uv,iv)];\", \"buffers=[torch.empty((128,n)) for _ in uv];work=torch.empty((128,n))\\n for st in range(0,len(uv[0]),128):\\n  nb=min(128,len(uv[0])-st);raw=[b[:nb] for b in buffers]\\n  for u,i,r in zip(uv,iv,raw):torch.mm(u[st:st+nb],i.T,out=r)\\n  \").replace('s=torch.empty_like(raw[0])','s=work[:nb]');open(p,'w').write(s)\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/likes_500.pt --name likes500_cal > /app/solution/likes500_cal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 63,
      "nanos": 204995478
    },
    "formatted_output": ""
  }
}
Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/ensemble.py <<'PY'\n\"\"\"Separate trainable towers with exact MIPS-compatible ensemble embeddings.\"\"\"\nfrom model import *\nclass RetrievalUserTower(nn.Module):\n    def __init__(self, towers):\n        super().__init__();self.branches=nn.ModuleList(towers)\n    def forward(self, histories):\n        vectors=[tower(ids,offset,weight) for tower,(ids,offset,weight) in zip(self.branches,histories)]\n        return torch.cat(vectors+[torch.ones_like(vectors[0][:,:1])],dim=1)\nclass RetrievalItemTower(nn.Module):\n    def __init__(self,towers,coefficients,features,prior):\n        super().__init__();self.branches=nn.ModuleList(towers);self.coefficients=nn.Parameter(torch.tensor(coefficients,dtype=torch.float32));self.register_buffer('features',features);self.prior=nn.Linear(features.shape[1],1,bias=False)\n        with torch.no_grad():self.prior.weight.copy_(torch.tensor(prior,dtype=torch.float32)[None])\n    def forward(self,ids):\n        return torch.cat([coef*tower(ids) for coef,tower in zip(self.coefficients,self.branches)]+[self.prior(self.features[ids])],dim=1)\nclass RetrievalModel(nn.Module):\n    def __init__(self,bases,coefficients,features,prior):\n        super().__init__();self.user=RetrievalUserTower([b.user for b in bases]);self.item=RetrievalItemTower([b.item for b in bases],coefficients,features,prior)\ndef history(a,cfg,stage):\n    cutoff=25393400 if stage=='test' else 24786800\n    c=cfg.get('count',.5);organic=cfg.get('organic',.6)\n    w=(a['cnt']**c*(1-organic+organic*a['org']/a['cnt'])+cfg.get('like',3)*np.minimum(a['likes'],1))*np.exp((a['last'].astype(float)-cutoff)/(86400*cfg.get('days',90)))\n    w=w.astype(np.float32);cap=cfg.get('cap',512);ids=[];weights=[];counts=[]\n    for u in range(len(a['uids'])):\n        lo,hi=a['offset'][u:u+2];ix=np.arange(lo,hi)\n        if cap and len(ix)>cap:ix=ix[np.argpartition(w[lo:hi],-cap)[-cap:]]\n        ids.append(a['ids'][ix]);weights.append(w[ix]);counts.append(len(ix))\n    return {'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\ndef batch_history(h,start,end):\n    lo,hi=h['offset'][[start,end]]\n    return (torch.from_numpy(h['ids'][lo:hi].astype(np.int64)),torch.from_numpy(h['offset'][start:end]-lo),torch.from_numpy(h['weight'][lo:hi]))\[email protected]_grad()\ndef export(config,stage,output,name,checkpoint_map=None):\n    torch.set_num_threads(2);a=load(stage);pop=np.load(O/f'{stage}_popularity.npz');names=list(pop['names']);features=torch.zeros((len(a['items'])+1,len(names)));features[1:]=torch.from_numpy(pop['features']);prior=[config['prior'].get(str(n),0) for n in names];bases=[];configs=[];coefs=[];paths=[]\n    for c,w in zip(config['components'],config['weights']):\n        if w<=0:continue\n        path=c['checkpoint']\n        if checkpoint_map:path=checkpoint_map.get(path,path)\n        ck=torch.load(path,weights_only=False);b=TwoTower(len(a['items']),ck['dim']);b.load_state_dict(ck['model'],strict=False);b.item.prior.weight.zero_();bases.append(b);configs.append(c);coefs.append(w);paths.append(path)\n    net=RetrievalModel(bases,coefs,features,prior).eval();histories=[history(a,c,stage) for c in configs];iv=net.item(torch.arange(1,len(a['items'])+1));el=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(el['uids'],a['uids']);assert np.array_equal(el['item_ids'],a['items']);tops=[];user_vectors=[];scorebuf=torch.empty((64,len(a['items'])))\n    for start in range(0,len(a['uids']),64):\n        end=min(len(a['uids']),start+64);h=[batch_history(h,start,end) for h in histories];uv=net.user(h);scores=scorebuf[:end-start];torch.mm(uv,iv.T,out=scores);eligible=np.unpackbits(el['packed_eligible'][start:end],axis=1,count=len(iv),bitorder='little');scores.masked_fill_(torch.from_numpy(eligible==0),-1e9);tops.append(scores.topk(100,dim=1).indices.numpy().copy());user_vectors.append(uv.numpy().copy())\n    top=np.concatenate(tops);df=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))});tmp=O/(output+'.tmp.parquet');df.to_parquet(tmp,index=False);os.replace(tmp,O/output)\n    torch.save({'state_dict':net.state_dict(),'dims':[b.item.item.embedding_dim for b in bases],'configs':configs,'coefficients':coefs,'prior':config['prior'],'stage':stage,'base_checkpoints':paths},O/f'{name}.pt');np.save(O/f'{name}_item_vectors.npy',iv.numpy());np.save(O/f'{name}_item_ids.npy',a['items']);np.save(O/f'{name}_user_vectors.npy',np.concatenate(user_vectors))\n    # The only candidate-specific operation after the towers is their dot product.\n    h=[batch_history(h,0,1) for h in histories];u=net.user(h);ids=torch.tensor([1,10,100]);direct=net.item(ids);cached=iv[ids-1];assert torch.allclose(direct,cached,atol=1e-5)\n    manual=sum(float(w)*(b.user(*hh)@b.item(ids).T) for w,b,hh in zip(coefs,bases,h))+net.item.prior(features[ids]).T\n    assert torch.allclose([email protected],manual,atol=1e-4)\n    torch.set_num_threads(1);timings=[]\n    for j in range(110):\n        t=time.perf_counter();net.user(h)\n        if j>=10:timings.append((time.perf_counter()-t)*1000)\n    report={'rows':len(df),'users':len(a['uids']),'dimension':iv.shape[1],'user_encoding_ms_p50':float(np.median(timings)),'user_encoding_ms_p95':float(np.quantile(timings,.95)),'score_contract':'score = user_tower(history) dot item_tower(item)'}\n    json.dump(report,open(O/f'{name}_report.json','w'),indent=2);print(report,flush=True)\nif __name__=='__main__':\n    p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--stage',default='test');p.add_argument('--output',default='submission.parquet');p.add_argument('--name',default='final_model');p.add_argument('--checkpoint-map');x=p.parse_args();export(json.load(open(x.config)),x.stage,x.output,x.name,json.load(open(x.checkpoint_map)) if x.checkpoint_map else None)\nPY\ntail -4 /app/solution/likes500_cal.log; tail -3 /app/solution/compact_ensemble.log; tail -3 /app/solution/refit_denoise.log; tail -3 /app/solution/refit_temporal.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"8f1a8f","wall_time_seconds":0.0000045,"exit_code":0,"original_token_count":322,"output":"history 90 3 0.5 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 30 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 300 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}], 'weights': (0.5, 0.5), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 3700 loss 8.032682495117188 sec 233.2\nstep 3800 loss 8.020055093765258 sec 238.3\nstep 3900 loss 8.00001021385193 sec 243.7\nstep 1900 loss 8.132534704208375 sec 126.0\nstep 2000 loss 8.170996074676514 sec 135.6\nEVAL 2000 {}\n"}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-09f783a1-cfa8-4ab2-bf20-043a255d3d70",
    "process_id": "88036",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/ensemble.py <<'PY'\n\"\"\"Separate trainable towers with exact MIPS-compatible ensemble embeddings.\"\"\"\nfrom model import *\nclass RetrievalUserTower(nn.Module):\n    def __init__(self, towers):\n        super().__init__();self.branches=nn.ModuleList(towers)\n    def forward(self, histories):\n        vectors=[tower(ids,offset,weight) for tower,(ids,offset,weight) in zip(self.branches,histories)]\n        return torch.cat(vectors+[torch.ones_like(vectors[0][:,:1])],dim=1)\nclass RetrievalItemTower(nn.Module):\n    def __init__(self,towers,coefficients,features,prior):\n        super().__init__();self.branches=nn.ModuleList(towers);self.coefficients=nn.Parameter(torch.tensor(coefficients,dtype=torch.float32));self.register_buffer('features',features);self.prior=nn.Linear(features.shape[1],1,bias=False)\n        with torch.no_grad():self.prior.weight.copy_(torch.tensor(prior,dtype=torch.float32)[None])\n    def forward(self,ids):\n        return torch.cat([coef*tower(ids) for coef,tower in zip(self.coefficients,self.branches)]+[self.prior(self.features[ids])],dim=1)\nclass RetrievalModel(nn.Module):\n    def __init__(self,bases,coefficients,features,prior):\n        super().__init__();self.user=RetrievalUserTower([b.user for b in bases]);self.item=RetrievalItemTower([b.item for b in bases],coefficients,features,prior)\ndef history(a,cfg,stage):\n    cutoff=25393400 if stage=='test' else 24786800\n    c=cfg.get('count',.5);organic=cfg.get('organic',.6)\n    w=(a['cnt']**c*(1-organic+organic*a['org']/a['cnt'])+cfg.get('like',3)*np.minimum(a['likes'],1))*np.exp((a['last'].astype(float)-cutoff)/(86400*cfg.get('days',90)))\n    w=w.astype(np.float32);cap=cfg.get('cap',512);ids=[];weights=[];counts=[]\n    for u in range(len(a['uids'])):\n        lo,hi=a['offset'][u:u+2];ix=np.arange(lo,hi)\n        if cap and len(ix)>cap:ix=ix[np.argpartition(w[lo:hi],-cap)[-cap:]]\n        ids.append(a['ids'][ix]);weights.append(w[ix]);counts.append(len(ix))\n    return {'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\ndef batch_history(h,start,end):\n    lo,hi=h['offset'][[start,end]]\n    return (torch.from_numpy(h['ids'][lo:hi].astype(np.int64)),torch.from_numpy(h['offset'][start:end]-lo),torch.from_numpy(h['weight'][lo:hi]))\[email protected]_grad()\ndef export(config,stage,output,name,checkpoint_map=None):\n    torch.set_num_threads(2);a=load(stage);pop=np.load(O/f'{stage}_popularity.npz');names=list(pop['names']);features=torch.zeros((len(a['items'])+1,len(names)));features[1:]=torch.from_numpy(pop['features']);prior=[config['prior'].get(str(n),0) for n in names];bases=[];configs=[];coefs=[];paths=[]\n    for c,w in zip(config['components'],config['weights']):\n        if w<=0:continue\n        path=c['checkpoint']\n        if checkpoint_map:path=checkpoint_map.get(path,path)\n        ck=torch.load(path,weights_only=False);b=TwoTower(len(a['items']),ck['dim']);b.load_state_dict(ck['model'],strict=False);b.item.prior.weight.zero_();bases.append(b);configs.append(c);coefs.append(w);paths.append(path)\n    net=RetrievalModel(bases,coefs,features,prior).eval();histories=[history(a,c,stage) for c in configs];iv=net.item(torch.arange(1,len(a['items'])+1));el=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(el['uids'],a['uids']);assert np.array_equal(el['item_ids'],a['items']);tops=[];user_vectors=[];scorebuf=torch.empty((64,len(a['items'])))\n    for start in range(0,len(a['uids']),64):\n        end=min(len(a['uids']),start+64);h=[batch_history(h,start,end) for h in histories];uv=net.user(h);scores=scorebuf[:end-start];torch.mm(uv,iv.T,out=scores);eligible=np.unpackbits(el['packed_eligible'][start:end],axis=1,count=len(iv),bitorder='little');scores.masked_fill_(torch.from_numpy(eligible==0),-1e9);tops.append(scores.topk(100,dim=1).indices.numpy().copy());user_vectors.append(uv.numpy().copy())\n    top=np.concatenate(tops);df=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))});tmp=O/(output+'.tmp.parquet');df.to_parquet(tmp,index=False);os.replace(tmp,O/output)\n    torch.save({'state_dict':net.state_dict(),'dims':[b.item.item.embedding_dim for b in bases],'configs':configs,'coefficients':coefs,'prior':config['prior'],'stage':stage,'base_checkpoints':paths},O/f'{name}.pt');np.save(O/f'{name}_item_vectors.npy',iv.numpy());np.save(O/f'{name}_item_ids.npy',a['items']);np.save(O/f'{name}_user_vectors.npy',np.concatenate(user_vectors))\n    # The only candidate-specific operation after the towers is their dot product.\n    h=[batch_history(h,0,1) for h in histories];u=net.user(h);ids=torch.tensor([1,10,100]);direct=net.item(ids);cached=iv[ids-1];assert torch.allclose(direct,cached,atol=1e-5)\n    manual=sum(float(w)*(b.user(*hh)@b.item(ids).T) for w,b,hh in zip(coefs,bases,h))+net.item.prior(features[ids]).T\n    assert torch.allclose([email protected],manual,atol=1e-4)\n    torch.set_num_threads(1);timings=[]\n    for j in range(110):\n        t=time.perf_counter();net.user(h)\n        if j>=10:timings.append((time.perf_counter()-t)*1000)\n    report={'rows':len(df),'users':len(a['uids']),'dimension':iv.shape[1],'user_encoding_ms_p50':float(np.median(timings)),'user_encoding_ms_p95':float(np.quantile(timings,.95)),'score_contract':'score = user_tower(history) dot item_tower(item)'}\n    json.dump(report,open(O/f'{name}_report.json','w'),indent=2);print(report,flush=True)\nif __name__=='__main__':\n    p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--stage',default='test');p.add_argument('--output',default='submission.parquet');p.add_argument('--name',default='final_model');p.add_argument('--checkpoint-map');x=p.parse_args();export(json.load(open(x.config)),x.stage,x.output,x.name,json.load(open(x.checkpoint_map)) if x.checkpoint_map else None)\nPY\ntail -4 /app/solution/likes500_cal.log; tail -3 /app/solution/compact_ensemble.log; tail -3 /app/solution/refit_denoise.log; tail -3 /app/solution/refit_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/ensemble.py <<'PY'\n\"\"\"Separate trainable towers with exact MIPS-compatible ensemble embeddings.\"\"\"\nfrom model import *\nclass RetrievalUserTower(nn.Module):\n    def __init__(self, towers):\n        super().__init__();self.branches=nn.ModuleList(towers)\n    def forward(self, histories):\n        vectors=[tower(ids,offset,weight) for tower,(ids,offset,weight) in zip(self.branches,histories)]\n        return torch.cat(vectors+[torch.ones_like(vectors[0][:,:1])],dim=1)\nclass RetrievalItemTower(nn.Module):\n    def __init__(self,towers,coefficients,features,prior):\n        super().__init__();self.branches=nn.ModuleList(towers);self.coefficients=nn.Parameter(torch.tensor(coefficients,dtype=torch.float32));self.register_buffer('features',features);self.prior=nn.Linear(features.shape[1],1,bias=False)\n        with torch.no_grad():self.prior.weight.copy_(torch.tensor(prior,dtype=torch.float32)[None])\n    def forward(self,ids):\n        return torch.cat([coef*tower(ids) for coef,tower in zip(self.coefficients,self.branches)]+[self.prior(self.features[ids])],dim=1)\nclass RetrievalModel(nn.Module):\n    def __init__(self,bases,coefficients,features,prior):\n        super().__init__();self.user=RetrievalUserTower([b.user for b in bases]);self.item=RetrievalItemTower([b.item for b in bases],coefficients,features,prior)\ndef history(a,cfg,stage):\n    cutoff=25393400 if stage=='test' else 24786800\n    c=cfg.get('count',.5);organic=cfg.get('organic',.6)\n    w=(a['cnt']**c*(1-organic+organic*a['org']/a['cnt'])+cfg.get('like',3)*np.minimum(a['likes'],1))*np.exp((a['last'].astype(float)-cutoff)/(86400*cfg.get('days',90)))\n    w=w.astype(np.float32);cap=cfg.get('cap',512);ids=[];weights=[];counts=[]\n    for u in range(len(a['uids'])):\n        lo,hi=a['offset'][u:u+2];ix=np.arange(lo,hi)\n        if cap and len(ix)>cap:ix=ix[np.argpartition(w[lo:hi],-cap)[-cap:]]\n        ids.append(a['ids'][ix]);weights.append(w[ix]);counts.append(len(ix))\n    return {'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\ndef batch_history(h,start,end):\n    lo,hi=h['offset'][[start,end]]\n    return (torch.from_numpy(h['ids'][lo:hi].astype(np.int64)),torch.from_numpy(h['offset'][start:end]-lo),torch.from_numpy(h['weight'][lo:hi]))\[email protected]_grad()\ndef export(config,stage,output,name,checkpoint_map=None):\n    torch.set_num_threads(2);a=load(stage);pop=np.load(O/f'{stage}_popularity.npz');names=list(pop['names']);features=torch.zeros((len(a['items'])+1,len(names)));features[1:]=torch.from_numpy(pop['features']);prior=[config['prior'].get(str(n),0) for n in names];bases=[];configs=[];coefs=[];paths=[]\n    for c,w in zip(config['components'],config['weights']):\n        if w<=0:continue\n        path=c['checkpoint']\n        if checkpoint_map:path=checkpoint_map.get(path,path)\n        ck=torch.load(path,weights_only=False);b=TwoTower(len(a['items']),ck['dim']);b.load_state_dict(ck['model'],strict=False);b.item.prior.weight.zero_();bases.append(b);configs.append(c);coefs.append(w);paths.append(path)\n    net=RetrievalModel(bases,coefs,features,prior).eval();histories=[history(a,c,stage) for c in configs];iv=net.item(torch.arange(1,len(a['items'])+1));el=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(el['uids'],a['uids']);assert np.array_equal(el['item_ids'],a['items']);tops=[];user_vectors=[];scorebuf=torch.empty((64,len(a['items'])))\n    for start in range(0,len(a['uids']),64):\n        end=min(len(a['uids']),start+64);h=[batch_history(h,start,end) for h in histories];uv=net.user(h);scores=scorebuf[:end-start];torch.mm(uv,iv.T,out=scores);eligible=np.unpackbits(el['packed_eligible'][start:end],axis=1,count=len(iv),bitorder='little');scores.masked_fill_(torch.from_numpy(eligible==0),-1e9);tops.append(scores.topk(100,dim=1).indices.numpy().copy());user_vectors.append(uv.numpy().copy())\n    top=np.concatenate(tops);df=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':a['items'][top.flatten()],'rank':np.tile(np.arange(1,101),len(top))});tmp=O/(output+'.tmp.parquet');df.to_parquet(tmp,index=False);os.replace(tmp,O/output)\n    torch.save({'state_dict':net.state_dict(),'dims':[b.item.item.embedding_dim for b in bases],'configs':configs,'coefficients':coefs,'prior':config['prior'],'stage':stage,'base_checkpoints':paths},O/f'{name}.pt');np.save(O/f'{name}_item_vectors.npy',iv.numpy());np.save(O/f'{name}_item_ids.npy',a['items']);np.save(O/f'{name}_user_vectors.npy',np.concatenate(user_vectors))\n    # The only candidate-specific operation after the towers is their dot product.\n    h=[batch_history(h,0,1) for h in histories];u=net.user(h);ids=torch.tensor([1,10,100]);direct=net.item(ids);cached=iv[ids-1];assert torch.allclose(direct,cached,atol=1e-5)\n    manual=sum(float(w)*(b.user(*hh)@b.item(ids).T) for w,b,hh in zip(coefs,bases,h))+net.item.prior(features[ids]).T\n    assert torch.allclose([email protected],manual,atol=1e-4)\n    torch.set_num_threads(1);timings=[]\n    for j in range(110):\n        t=time.perf_counter();net.user(h)\n        if j>=10:timings.append((time.perf_counter()-t)*1000)\n    report={'rows':len(df),'users':len(a['uids']),'dimension':iv.shape[1],'user_encoding_ms_p50':float(np.median(timings)),'user_encoding_ms_p95':float(np.quantile(timings,.95)),'score_contract':'score = user_tower(history) dot item_tower(item)'}\n    json.dump(report,open(O/f'{name}_report.json','w'),indent=2);print(report,flush=True)\nif __name__=='__main__':\n    p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--stage',default='test');p.add_argument('--output',default='submission.parquet');p.add_argument('--name',default='final_model');p.add_argument('--checkpoint-map');x=p.parse_args();export(json.load(open(x.config)),x.stage,x.output,x.name,json.load(open(x.checkpoint_map)) if x.checkpoint_map else None)\nPY\ntail -4 /app/solution/likes500_cal.log; tail -3 /app/solution/compact_ensemble.log; tail -3 /app/solution/refit_denoise.log; tail -3 /app/solution/refit_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "history 90 3 0.5 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 30 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 300 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}], 'weights': (0.5, 0.5), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 3700 loss 8.032682495117188 sec 233.2\nstep 3800 loss 8.020055093765258 sec 238.3\nstep 3900 loss 8.00001021385193 sec 243.7\nstep 1900 loss 8.132534704208375 sec 126.0\nstep 2000 loss 8.170996074676514 sec 135.6\nEVAL 2000 {}\n",
    "stderr": "",
    "aggregated_output": "history 90 3 0.5 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 30 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 300 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}], 'weights': (0.5, 0.5), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 3700 loss 8.032682495117188 sec 233.2\nstep 3800 loss 8.020055093765258 sec 238.3\nstep 3900 loss 8.00001021385193 sec 243.7\nstep 1900 loss 8.132534704208375 sec 126.0\nstep 2000 loss 8.170996074676514 sec 135.6\nEVAL 2000 {}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4500
    },
    "formatted_output": "history 90 3 0.5 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 30 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nhistory 300 0 0 best (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST (0.04426838972033052, {'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}})\nBEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}], 'weights': (0.5, 0.5), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 3700 loss 8.032682495117188 sec 233.2\nstep 3800 loss 8.020055093765258 sec 238.3\nstep 3900 loss 8.00001021385193 sec 243.7\nstep 1900 loss 8.132534704208375 sec 126.0\nstep 2000 loss 8.170996074676514 sec 135.6\nEVAL 2000 {}\n"
  }
}
Harness context token usage record
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93

exec command

+32:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/local_1000.pt --name local1000_cal > /app/solution/local1000_cal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal likes500_cal --name triple_ensemble > /app/solution/triple_ensemble.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"python - <<'PY'\nimport json\np='/app/solution/compact_ensemble_best.json';c=json.load(open(p))\nfor q in c['components']:q['cap']=512\njson.dump(c,open('/app/solution/compact512_config.json','w'),indent=2)\np='/app/solution/ensemble.py';s=open(p).read().replace(\"cap=cfg.get('cap',512)\",\"cap=cfg.get('cap',0)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --stage validation --config /app/solution/compact512_config.json --output compact512_validation.parquet --name compact512_validation_model > /app/solution/compact512_validation.log 2>&1","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 3.5 seconds
Output:

{"chunk_id":"736dc1","wall_time_seconds":1.00198371,"session_id":73235,"original_token_count":0,"output":""}
{"chunk_id":"1df309","wall_time_seconds":1.001172289,"session_id":38336,"original_token_count":0,"output":""}
{"chunk_id":"97a0fd","wall_time_seconds":1.001295912,"session_id":39120,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:25:32 UTC"}
Harness context token usage record
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94

Agent message

+32:38
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-8ceba7fb-35ea-4ae1-8314-bbe4278668d6",
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      "/bin/sh",
      "-lc",
      "python - <<'PY'\nimport json\np='/app/solution/compact_ensemble_best.json';c=json.load(open(p))\nfor q in c['components']:q['cap']=512\njson.dump(c,open('/app/solution/compact512_config.json','w'),indent=2)\np='/app/solution/ensemble.py';s=open(p).read().replace(\"cap=cfg.get('cap',512)\",\"cap=cfg.get('cap',0)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --stage validation --config /app/solution/compact512_config.json --output compact512_validation.parquet --name compact512_validation_model > /app/solution/compact512_validation.log 2>&1"
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    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nimport json\np='/app/solution/compact_ensemble_best.json';c=json.load(open(p))\nfor q in c['components']:q['cap']=512\njson.dump(c,open('/app/solution/compact512_config.json','w'),indent=2)\np='/app/solution/ensemble.py';s=open(p).read().replace(\"cap=cfg.get('cap',512)\",\"cap=cfg.get('cap',0)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --stage validation --config /app/solution/compact512_config.json --output compact512_validation.parquet --name compact512_validation_model > /app/solution/compact512_validation.log 2>&1"
      }
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    },
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  }
}
95

Agent message

+33:02
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-5a242ecc-19ea-4c73-9b0d-86e5c19bc8f4",
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      "-lc",
      "ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fast_calibrate.py /app/solution/local_1000.pt --name local1000_cal > /app/solution/local1000_cal.log 2>&1"
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96

Agent message

+33:21
Thinking unavailable No text recorded

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

97

exec command

+33:38
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 combined compact model improved public NDCG@10 to 0.048445, using 259-dimensional vectors. I’m checking the bounded-history version and a small history-based popularity adjustment before selecting the final model.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -3 /app/solution/refit_denoise.log; cat /app/solution/compact512_validation.log; tail -3 /app/solution/local1000_cal.log; tail -3 /app/solution/triple_ensemble.log","max_output_tokens":2500}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/compact512_validation.parquet > /app/solution/compact512_score.json","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"0bfff0","wall_time_seconds":0.00001041,"exit_code":0,"original_token_count":192,"output":"step 5500 loss 7.854345455169677 sec 348.6\nstep 5600 loss 7.826152801513672 sec 354.5\nstep 5700 loss 7.813855466842651 sec 360.7\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.4214860002775822, 'user_encoding_ms_p95': 0.6317755499821942, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nhistory 30 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nhistory 300 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nBEST (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\n"}
{"chunk_id":"315fea","wall_time_seconds":1.001055635,"session_id":33870,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:27:00 UTC"}
Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-1e3137c0-ca38-496a-ba5e-a159be4d9020",
    "process_id": "11072",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -3 /app/solution/refit_denoise.log; cat /app/solution/compact512_validation.log; tail -3 /app/solution/local1000_cal.log; tail -3 /app/solution/triple_ensemble.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -3 /app/solution/refit_denoise.log; cat /app/solution/compact512_validation.log; tail -3 /app/solution/local1000_cal.log; tail -3 /app/solution/triple_ensemble.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 5500 loss 7.854345455169677 sec 348.6\nstep 5600 loss 7.826152801513672 sec 354.5\nstep 5700 loss 7.813855466842651 sec 360.7\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.4214860002775822, 'user_encoding_ms_p95': 0.6317755499821942, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nhistory 30 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nhistory 300 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nBEST (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\n",
    "stderr": "",
    "aggregated_output": "step 5500 loss 7.854345455169677 sec 348.6\nstep 5600 loss 7.826152801513672 sec 354.5\nstep 5700 loss 7.813855466842651 sec 360.7\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.4214860002775822, 'user_encoding_ms_p95': 0.6317755499821942, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nhistory 30 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nhistory 300 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nBEST (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 10410
    },
    "formatted_output": "step 5500 loss 7.854345455169677 sec 348.6\nstep 5600 loss 7.826152801513672 sec 354.5\nstep 5700 loss 7.813855466842651 sec 360.7\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.4214860002775822, 'user_encoding_ms_p95': 0.6317755499821942, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nhistory 30 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nhistory 300 0 0 best (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\nBEST (0.045163853243628274, {'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}})\n"
  }
}
Harness context token usage record
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Harness context codex item completed
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98

exec command

+33:51
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name parity_base --steps 1000 --eval-every 1000 --lr .002 --negatives 0 --threads 2 > /app/solution/parity_base.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal local1000_cal --name local_ensemble > /app/solution/local_ensemble.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"cat /app/solution/compact512_score.json; tail -4 /app/solution/triple_ensemble.log; tail -3 /app/solution/refit_denoise.log","max_output_tokens":2200}));

Tool response

Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"1313a3","wall_time_seconds":1.000496242,"session_id":72841,"original_token_count":0,"output":""}
{"chunk_id":"e39ea5","wall_time_seconds":1.000569284,"session_id":85863,"original_token_count":0,"output":""}
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Harness context codex item completed
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    ],
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      }
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    "stderr": "",
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Harness context codex item completed
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Harness context codex item completed
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99

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.

100

exec command

+34:28
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

text(await tools.exec_command({cmd:"cat > /app/solution/personal_calibrate.py <<'PY'\nfrom fast_calibrate import *\nfrom ensemble import history\[email protected]_grad()\ndef main(x):\n ev=Evaluator();cfg=json.load(open(x.config));a=load('validation');pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);uv=torch.from_numpy(np.load(O/f'{x.vectors}_user_vectors.npy'));iv=torch.from_numpy(np.load(O/f'{x.vectors}_item_vectors.npy')).clone();iv[:,-1]=0;h=history(a,cfg['components'][0],'validation');n=len(iv);configs=[];zs=[]\n for feat in ['strong_300','like_300']:\n  col=names.index(feat);v=f[h['ids']-1,col];z=[]\n  for u in range(len(a['uids'])):\n   lo,hi=h['offset'][u:u+2];w=h['weight'][lo:hi];z.append(float(np.dot(v[lo:hi],w)/w.sum()) if len(w) and w.sum() else np.nan)\n  z=np.array(z);mu=float(np.nanmean(z));std=float(np.nanstd(z));z=np.nan_to_num(np.clip((z-mu)/std,-2,2));zs.append(torch.from_numpy(z.astype(np.float32)))\n  for const in [1,1.5,2,2.5]:\n   for beta in [-.5,0,.5,1,1.5]:configs.append({'feat':feat,'mean':mu,'std':std,'const':const,'beta':beta,'zindex':len(zs)-1})\n tops=[[] for _ in configs];base=torch.empty((128,n));work=torch.empty_like(base);likepop=torch.from_numpy(f[:,5])\n for st in range(0,len(uv),128):\n  nb=min(128,len(uv)-st);s=base[:nb];torch.mm(uv[st:st+nb],iv.T,out=s);mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+nb],axis=1,count=n,bitorder='little')==0)\n  for j,c in enumerate(configs):\n   coeff=c['const']+c['beta']*zs[c['zindex']][st:st+nb];torch.mul(coeff[:,None],likepop[None,:],out=work[:nb]);work[:nb].add_(s).masked_fill_(mask,-1e9);tops[j].append(work[:nb].topk(10,dim=-1).indices.numpy().copy())\n scores=[ev.score(np.concatenate(t)) for t in tops];j=int(np.argmax(scores));c=configs[j];cfg.update(score=scores[j],prior={'like_7':c['const']},personal_prior={'like_7':c['beta']},user_popularity={'feature':c['feat'],'mean':c['mean'],'std':c['std'],'clip':2});print('BEST',cfg,flush=True);json.dump(cfg,open(O/f'{x.name}_best.json','w'),indent=2);json.dump([dict(c,score=s) for c,s in zip(configs,scores)],open(O/f'{x.name}_grid.json','w'),indent=2)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--vectors',required=True);p.add_argument('--name',default='personal');main(p.parse_args())\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/personal_calibrate.py --config /app/solution/compact512_config.json --vectors compact512_validation_model --name personal > /app/solution/personal.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -3 /app/solution/triple_ensemble.log; tail -2 /app/solution/parity_base.log; tail -2 /app/solution/refit_denoise.log","max_output_tokens":2000}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"64068d","wall_time_seconds":1.001287041,"session_id":62507,"original_token_count":0,"output":""}
{"chunk_id":"db645d","wall_time_seconds":0.00000535,"exit_code":0,"original_token_count":201,"output":"BEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/likes_500.pt', 'score': 0.04426838972033052, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}}], 'weights': (0.5, 0.5, 0), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 100 loss 10.231664295196532 sec 15.9\nstep 200 loss 9.777516078948974 sec 30.6\nstep 6000 loss 7.766210327148437 sec 379.4\nEVAL 6000 {}\n"}
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Harness context codex item completed
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    ],
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      }
    ],
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    "status": "completed",
    "stdout": "BEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/likes_500.pt', 'score': 0.04426838972033052, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}}], 'weights': (0.5, 0.5, 0), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 100 loss 10.231664295196532 sec 15.9\nstep 200 loss 9.777516078948974 sec 30.6\nstep 6000 loss 7.766210327148437 sec 379.4\nEVAL 6000 {}\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5350
    },
    "formatted_output": "BEST {'score': 0.04844486851889946, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/likes_500.pt', 'score': 0.04426838972033052, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 2.5, 'like_30': 0}}], 'weights': (0.5, 0.5, 0), 'prior': {'like_7': 1.5, 'like_30': 0}}\nrechecked 0.04844486851889946\nstep 100 loss 10.231664295196532 sec 15.9\nstep 200 loss 9.777516078948974 sec 30.6\nstep 6000 loss 7.766210327148437 sec 379.4\nEVAL 6000 {}\n"
  }
}
101

exec command

+35:02
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

text(await tools.exec_command({cmd:"python - <<'PY'\nimport json\njson.dump({'/app/solution/denoise_6000.pt':'/app/solution/refit_denoise_6000.pt','/app/solution/temporal_2000.pt':'/app/solution/refit_temporal_2000.pt'},open('/app/solution/refit_map.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/compact512_config.json --checkpoint-map /app/solution/refit_map.json --name compact_refit > /app/solution/compact_refit.log 2>&1","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"3099ce","wall_time_seconds":1.000863111,"session_id":51370,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:28:24 UTC"}
Harness context token usage record
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102

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.

Harness context codex item completed
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "cat > /app/solution/personal_calibrate.py <<'PY'\nfrom fast_calibrate import *\nfrom ensemble import history\[email protected]_grad()\ndef main(x):\n ev=Evaluator();cfg=json.load(open(x.config));a=load('validation');pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);uv=torch.from_numpy(np.load(O/f'{x.vectors}_user_vectors.npy'));iv=torch.from_numpy(np.load(O/f'{x.vectors}_item_vectors.npy')).clone();iv[:,-1]=0;h=history(a,cfg['components'][0],'validation');n=len(iv);configs=[];zs=[]\n for feat in ['strong_300','like_300']:\n  col=names.index(feat);v=f[h['ids']-1,col];z=[]\n  for u in range(len(a['uids'])):\n   lo,hi=h['offset'][u:u+2];w=h['weight'][lo:hi];z.append(float(np.dot(v[lo:hi],w)/w.sum()) if len(w) and w.sum() else np.nan)\n  z=np.array(z);mu=float(np.nanmean(z));std=float(np.nanstd(z));z=np.nan_to_num(np.clip((z-mu)/std,-2,2));zs.append(torch.from_numpy(z.astype(np.float32)))\n  for const in [1,1.5,2,2.5]:\n   for beta in [-.5,0,.5,1,1.5]:configs.append({'feat':feat,'mean':mu,'std':std,'const':const,'beta':beta,'zindex':len(zs)-1})\n tops=[[] for _ in configs];base=torch.empty((128,n));work=torch.empty_like(base);likepop=torch.from_numpy(f[:,5])\n for st in range(0,len(uv),128):\n  nb=min(128,len(uv)-st);s=base[:nb];torch.mm(uv[st:st+nb],iv.T,out=s);mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+nb],axis=1,count=n,bitorder='little')==0)\n  for j,c in enumerate(configs):\n   coeff=c['const']+c['beta']*zs[c['zindex']][st:st+nb];torch.mul(coeff[:,None],likepop[None,:],out=work[:nb]);work[:nb].add_(s).masked_fill_(mask,-1e9);tops[j].append(work[:nb].topk(10,dim=-1).indices.numpy().copy())\n scores=[ev.score(np.concatenate(t)) for t in tops];j=int(np.argmax(scores));c=configs[j];cfg.update(score=scores[j],prior={'like_7':c['const']},personal_prior={'like_7':c['beta']},user_popularity={'feature':c['feat'],'mean':c['mean'],'std':c['std'],'clip':2});print('BEST',cfg,flush=True);json.dump(cfg,open(O/f'{x.name}_best.json','w'),indent=2);json.dump([dict(c,score=s) for c,s in zip(configs,scores)],open(O/f'{x.name}_grid.json','w'),indent=2)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--vectors',required=True);p.add_argument('--name',default='personal');main(p.parse_args())\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/personal_calibrate.py --config /app/solution/compact512_config.json --vectors compact512_validation_model --name personal > /app/solution/personal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/personal_calibrate.py <<'PY'\nfrom fast_calibrate import *\nfrom ensemble import history\[email protected]_grad()\ndef main(x):\n ev=Evaluator();cfg=json.load(open(x.config));a=load('validation');pop=np.load(O/'validation_popularity.npz');f=pop['features'];names=list(pop['names']);uv=torch.from_numpy(np.load(O/f'{x.vectors}_user_vectors.npy'));iv=torch.from_numpy(np.load(O/f'{x.vectors}_item_vectors.npy')).clone();iv[:,-1]=0;h=history(a,cfg['components'][0],'validation');n=len(iv);configs=[];zs=[]\n for feat in ['strong_300','like_300']:\n  col=names.index(feat);v=f[h['ids']-1,col];z=[]\n  for u in range(len(a['uids'])):\n   lo,hi=h['offset'][u:u+2];w=h['weight'][lo:hi];z.append(float(np.dot(v[lo:hi],w)/w.sum()) if len(w) and w.sum() else np.nan)\n  z=np.array(z);mu=float(np.nanmean(z));std=float(np.nanstd(z));z=np.nan_to_num(np.clip((z-mu)/std,-2,2));zs.append(torch.from_numpy(z.astype(np.float32)))\n  for const in [1,1.5,2,2.5]:\n   for beta in [-.5,0,.5,1,1.5]:configs.append({'feat':feat,'mean':mu,'std':std,'const':const,'beta':beta,'zindex':len(zs)-1})\n tops=[[] for _ in configs];base=torch.empty((128,n));work=torch.empty_like(base);likepop=torch.from_numpy(f[:,5])\n for st in range(0,len(uv),128):\n  nb=min(128,len(uv)-st);s=base[:nb];torch.mm(uv[st:st+nb],iv.T,out=s);mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+nb],axis=1,count=n,bitorder='little')==0)\n  for j,c in enumerate(configs):\n   coeff=c['const']+c['beta']*zs[c['zindex']][st:st+nb];torch.mul(coeff[:,None],likepop[None,:],out=work[:nb]);work[:nb].add_(s).masked_fill_(mask,-1e9);tops[j].append(work[:nb].topk(10,dim=-1).indices.numpy().copy())\n scores=[ev.score(np.concatenate(t)) for t in tops];j=int(np.argmax(scores));c=configs[j];cfg.update(score=scores[j],prior={'like_7':c['const']},personal_prior={'like_7':c['beta']},user_popularity={'feature':c['feat'],'mean':c['mean'],'std':c['std'],'clip':2});print('BEST',cfg,flush=True);json.dump(cfg,open(O/f'{x.name}_best.json','w'),indent=2);json.dump([dict(c,score=s) for c,s in zip(configs,scores)],open(O/f'{x.name}_grid.json','w'),indent=2)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--vectors',required=True);p.add_argument('--name',default='personal');main(p.parse_args())\nPY\nulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/personal_calibrate.py --config /app/solution/compact512_config.json --vectors compact512_validation_model --name personal > /app/solution/personal.log 2>&1"
      }
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    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 48,
      "nanos": 563954987
    },
    "formatted_output": ""
  }
}
103

Agent message

+35:43
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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      "/bin/sh",
      "-lc",
      "ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal local1000_cal --name local_ensemble > /app/solution/local_ensemble.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ulimit -v 4000000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble_grid.py --components denoise_refined temporal2k_cal local1000_cal --name local_ensemble > /app/solution/local_ensemble.log 2>&1"
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104

exec command

+35:45
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 512-track history version scores 0.048127 NDCG@10. A warmed single-user encoding measured about 0.42 ms median and 0.63 ms at the 95th percentile on this CPU, excluding history fetch and retrieval. Adding the like-focused model did not improve quality, so it won’t be included.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"tail -3 /app/solution/personal.log; tail -3 /app/solution/local_ensemble.log; tail -3 /app/solution/compact_refit.log; tail -3 /app/solution/parity_base.log","max_output_tokens":3000}));
text(await tools.exec_command({cmd:"cat > /app/solution/prepare_catalog.py <<'PY'\n\"\"\"Stream training events into a compact catalog-only event log.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow as pa,pyarrow.parquet as pq\nD=Path('/task/data');O=Path('/app/solution')\ndef main():\n    a=np.load(D/'validation_eligible_candidates.npz');um=pd.Index(a['uids']);im=pd.Index(a['item_ids']);writer=None\n    for b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=500000):\n        e=b.to_pandas();e=e[e.played_ratio_pct>=50];u=um.get_indexer(e.uid);i=im.get_indexer(e.item_id);keep=(u>=0)&(i>=0);e=e.loc[keep,['timestamp','is_organic','played_ratio_pct']].copy();e['u']=u[keep];e['i']=i[keep];table=pa.Table.from_pandas(e,preserve_index=False)\n        if writer is None:writer=pq.ParquetWriter(O/'train_catalog_events.parquet',table.schema)\n        writer.write_table(table)\n    if writer:writer.close()\nif __name__=='__main__':main()\nPY\ncp /app/solution/compact512_config.json /app/solution/final_config.json\ncp /app/solution/refit_map.json /app/solution/final_refit_map.json\ncat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains 100 eligible, distinct tracks for each of the 8,456 target users. Public validation: **0.048127 graded NDCG@10**, over 4,263 users with eligible relevance.\n\nThe separate, trainable PyTorch user and item towers each contain two 128-dimensional neural branches. A user branch pools trainable track embeddings from interaction history, then applies a residual 128→256→128 ReLU network and normalization. It contains no user-ID embedding. An item branch applies its own trainable item embedding and residual projection, normalization, learned scale, and bias. The combined towers concatenate branch vectors and an item-only popularity coordinate, producing **259-dimensional vectors**. Their dot product is the entire relevance score. Eligibility is the only subsequent filter; there is no candidate-conditioned user computation or reranker.\n\nServing histories retain at most 512 tracks per branch, weighted by organic share and a 90-day exponential decay. Item-only popularity features use distinct organic listeners/likers observed before the relevant cutoff. Item vectors are exported for offline indexing. The benchmark uses batched exact inner products to generate rankings; production can use maximum inner-product search over the same vectors. A warmed CPU user encoding measured approximately 0.42 ms median / 0.63 ms p95, excluding history fetch and index retrieval.\n\nTraining combines two objectives: masked-history reconstruction and chronological next-week discovery prediction. The latter uses only earlier history, excludes previously positive tracks from targets, and samples organic likes with gain 7 versus gain 1 for organic ≥80% listens. The bulk of training uses importance-corrected sampled softmax with 2,048 shared uniform negatives. A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs. All data preparation uses event tables and ragged histories. No global user–item interaction matrix is constructed or decomposed.\n\nValidation representations use only `train_*`; validation outcomes are used solely for approach selection. Final models are refitted on `train_* + val_*`, with test-cutoff histories, item statistics, and eligibility. Organic outcomes are an offline target, not a causal estimate of online lift.\n\nKey code: `model.py` defines and trains the neural branches; `prepare_catalog.py`, `prepare.py`, and `temporal.py` construct observed histories and temporal examples; `popularity.py` constructs cutoff-only item features; `ensemble.py` defines and applies the final separate towers. Checkpoints and selected configuration are included.\n\nFinal submission command, using the included fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py \\\n  --config /app/solution/final_config.json \\\n  --checkpoint-map /app/solution/final_refit_map.json \\\n  --name final_model\n```\n\nThis writes `submission.parquet`, the fitted `final_model.pt`, and offline item vectors/IDs under `/app/solution/`.\nMD","max_output_tokens":1000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"c65ad9","wall_time_seconds":0.00000423,"exit_code":0,"original_token_count":401,"output":"BEST {'score': 0.048672373759369024, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 512}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}, 'cap': 512}], 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5}, 'personal_prior': {'like_7': 0.5}, 'user_popularity': {'feature': 'like_300', 'mean': 2.5535783636095903, 'std': 0.5844748227396402, 'clip': 2}}\nBEST {'score': 0.04865771210671666, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/local_1000.pt', 'score': 0.045163853243628274, 'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}}], 'weights': (0.25, 0.5, 0.25), 'prior': {'like_7': 1.5, 'like_30': -0.5}}\nrechecked 0.04865771210671666\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.7259695000811917, 'user_encoding_ms_p95': 1.3887981498783117, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 600 loss 9.39340636253357 sec 100.1\nstep 700 loss 9.268487148284912 sec 112.2\nstep 800 loss 9.168281927108765 sec 124.4\n"}
{"chunk_id":"bf079d","wall_time_seconds":0.000004951,"exit_code":0,"original_token_count":0,"output":""}
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    "stdout": "BEST {'score': 0.048672373759369024, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 512}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}, 'cap': 512}], 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5}, 'personal_prior': {'like_7': 0.5}, 'user_popularity': {'feature': 'like_300', 'mean': 2.5535783636095903, 'std': 0.5844748227396402, 'clip': 2}}\nBEST {'score': 0.04865771210671666, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/local_1000.pt', 'score': 0.045163853243628274, 'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}}], 'weights': (0.25, 0.5, 0.25), 'prior': {'like_7': 1.5, 'like_30': -0.5}}\nrechecked 0.04865771210671666\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.7259695000811917, 'user_encoding_ms_p95': 1.3887981498783117, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 600 loss 9.39340636253357 sec 100.1\nstep 700 loss 9.268487148284912 sec 112.2\nstep 800 loss 9.168281927108765 sec 124.4\n",
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    "aggregated_output": "BEST {'score': 0.048672373759369024, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 512}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}, 'cap': 512}], 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5}, 'personal_prior': {'like_7': 0.5}, 'user_popularity': {'feature': 'like_300', 'mean': 2.5535783636095903, 'std': 0.5844748227396402, 'clip': 2}}\nBEST {'score': 0.04865771210671666, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/local_1000.pt', 'score': 0.045163853243628274, 'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}}], 'weights': (0.25, 0.5, 0.25), 'prior': {'like_7': 1.5, 'like_30': -0.5}}\nrechecked 0.04865771210671666\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.7259695000811917, 'user_encoding_ms_p95': 1.3887981498783117, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 600 loss 9.39340636253357 sec 100.1\nstep 700 loss 9.268487148284912 sec 112.2\nstep 800 loss 9.168281927108765 sec 124.4\n",
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    "formatted_output": "BEST {'score': 0.048672373759369024, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 512}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}, 'cap': 512}], 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5}, 'personal_prior': {'like_7': 0.5}, 'user_popularity': {'feature': 'like_300', 'mean': 2.5535783636095903, 'std': 0.5844748227396402, 'clip': 2}}\nBEST {'score': 0.04865771210671666, 'components': [{'checkpoint': '/app/solution/denoise_6000.pt', 'score': 0.04609894456020984, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2}, 'cap': 1024}, {'checkpoint': '/app/solution/temporal_2000.pt', 'score': 0.0454979396252179, 'days': 90, 'like': 0, 'count': 0, 'organic': 0.6, 'prior': {'like_7': 2, 'like_30': -0.5}}, {'checkpoint': '/app/solution/local_1000.pt', 'score': 0.045163853243628274, 'days': 14, 'like': 0, 'count': 0, 'organic': 0.6, 'cap': 512, 'prior': {'like_7': 1.5, 'like_30': 0}}], 'weights': (0.25, 0.5, 0.25), 'prior': {'like_7': 1.5, 'like_30': -0.5}}\nrechecked 0.04865771210671666\n{'rows': 845600, 'users': 8456, 'dimension': 259, 'user_encoding_ms_p50': 0.7259695000811917, 'user_encoding_ms_p95': 1.3887981498783117, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 600 loss 9.39340636253357 sec 100.1\nstep 700 loss 9.268487148284912 sec 112.2\nstep 800 loss 9.168281927108765 sec 124.4\n"
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      "-lc",
      "cat > /app/solution/prepare_catalog.py <<'PY'\n\"\"\"Stream training events into a compact catalog-only event log.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow as pa,pyarrow.parquet as pq\nD=Path('/task/data');O=Path('/app/solution')\ndef main():\n    a=np.load(D/'validation_eligible_candidates.npz');um=pd.Index(a['uids']);im=pd.Index(a['item_ids']);writer=None\n    for b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=500000):\n        e=b.to_pandas();e=e[e.played_ratio_pct>=50];u=um.get_indexer(e.uid);i=im.get_indexer(e.item_id);keep=(u>=0)&(i>=0);e=e.loc[keep,['timestamp','is_organic','played_ratio_pct']].copy();e['u']=u[keep];e['i']=i[keep];table=pa.Table.from_pandas(e,preserve_index=False)\n        if writer is None:writer=pq.ParquetWriter(O/'train_catalog_events.parquet',table.schema)\n        writer.write_table(table)\n    if writer:writer.close()\nif __name__=='__main__':main()\nPY\ncp /app/solution/compact512_config.json /app/solution/final_config.json\ncp /app/solution/refit_map.json /app/solution/final_refit_map.json\ncat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains 100 eligible, distinct tracks for each of the 8,456 target users. Public validation: **0.048127 graded NDCG@10**, over 4,263 users with eligible relevance.\n\nThe separate, trainable PyTorch user and item towers each contain two 128-dimensional neural branches. A user branch pools trainable track embeddings from interaction history, then applies a residual 128→256→128 ReLU network and normalization. It contains no user-ID embedding. An item branch applies its own trainable item embedding and residual projection, normalization, learned scale, and bias. The combined towers concatenate branch vectors and an item-only popularity coordinate, producing **259-dimensional vectors**. Their dot product is the entire relevance score. Eligibility is the only subsequent filter; there is no candidate-conditioned user computation or reranker.\n\nServing histories retain at most 512 tracks per branch, weighted by organic share and a 90-day exponential decay. Item-only popularity features use distinct organic listeners/likers observed before the relevant cutoff. Item vectors are exported for offline indexing. The benchmark uses batched exact inner products to generate rankings; production can use maximum inner-product search over the same vectors. A warmed CPU user encoding measured approximately 0.42 ms median / 0.63 ms p95, excluding history fetch and index retrieval.\n\nTraining combines two objectives: masked-history reconstruction and chronological next-week discovery prediction. The latter uses only earlier history, excludes previously positive tracks from targets, and samples organic likes with gain 7 versus gain 1 for organic ≥80% listens. The bulk of training uses importance-corrected sampled softmax with 2,048 shared uniform negatives. A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs. All data preparation uses event tables and ragged histories. No global user–item interaction matrix is constructed or decomposed.\n\nValidation representations use only `train_*`; validation outcomes are used solely for approach selection. Final models are refitted on `train_* + val_*`, with test-cutoff histories, item statistics, and eligibility. Organic outcomes are an offline target, not a causal estimate of online lift.\n\nKey code: `model.py` defines and trains the neural branches; `prepare_catalog.py`, `prepare.py`, and `temporal.py` construct observed histories and temporal examples; `popularity.py` constructs cutoff-only item features; `ensemble.py` defines and applies the final separate towers. Checkpoints and selected configuration are included.\n\nFinal submission command, using the included fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py \\\n  --config /app/solution/final_config.json \\\n  --checkpoint-map /app/solution/final_refit_map.json \\\n  --name final_model\n```\n\nThis writes `submission.parquet`, the fitted `final_model.pt`, and offline item vectors/IDs under `/app/solution/`.\nMD"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/prepare_catalog.py <<'PY'\n\"\"\"Stream training events into a compact catalog-only event log.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow as pa,pyarrow.parquet as pq\nD=Path('/task/data');O=Path('/app/solution')\ndef main():\n    a=np.load(D/'validation_eligible_candidates.npz');um=pd.Index(a['uids']);im=pd.Index(a['item_ids']);writer=None\n    for b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=500000):\n        e=b.to_pandas();e=e[e.played_ratio_pct>=50];u=um.get_indexer(e.uid);i=im.get_indexer(e.item_id);keep=(u>=0)&(i>=0);e=e.loc[keep,['timestamp','is_organic','played_ratio_pct']].copy();e['u']=u[keep];e['i']=i[keep];table=pa.Table.from_pandas(e,preserve_index=False)\n        if writer is None:writer=pq.ParquetWriter(O/'train_catalog_events.parquet',table.schema)\n        writer.write_table(table)\n    if writer:writer.close()\nif __name__=='__main__':main()\nPY\ncp /app/solution/compact512_config.json /app/solution/final_config.json\ncp /app/solution/refit_map.json /app/solution/final_refit_map.json\ncat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains 100 eligible, distinct tracks for each of the 8,456 target users. Public validation: **0.048127 graded NDCG@10**, over 4,263 users with eligible relevance.\n\nThe separate, trainable PyTorch user and item towers each contain two 128-dimensional neural branches. A user branch pools trainable track embeddings from interaction history, then applies a residual 128→256→128 ReLU network and normalization. It contains no user-ID embedding. An item branch applies its own trainable item embedding and residual projection, normalization, learned scale, and bias. The combined towers concatenate branch vectors and an item-only popularity coordinate, producing **259-dimensional vectors**. Their dot product is the entire relevance score. Eligibility is the only subsequent filter; there is no candidate-conditioned user computation or reranker.\n\nServing histories retain at most 512 tracks per branch, weighted by organic share and a 90-day exponential decay. Item-only popularity features use distinct organic listeners/likers observed before the relevant cutoff. Item vectors are exported for offline indexing. The benchmark uses batched exact inner products to generate rankings; production can use maximum inner-product search over the same vectors. A warmed CPU user encoding measured approximately 0.42 ms median / 0.63 ms p95, excluding history fetch and index retrieval.\n\nTraining combines two objectives: masked-history reconstruction and chronological next-week discovery prediction. The latter uses only earlier history, excludes previously positive tracks from targets, and samples organic likes with gain 7 versus gain 1 for organic ≥80% listens. The bulk of training uses importance-corrected sampled softmax with 2,048 shared uniform negatives. A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs. All data preparation uses event tables and ragged histories. No global user–item interaction matrix is constructed or decomposed.\n\nValidation representations use only `train_*`; validation outcomes are used solely for approach selection. Final models are refitted on `train_* + val_*`, with test-cutoff histories, item statistics, and eligibility. Organic outcomes are an offline target, not a causal estimate of online lift.\n\nKey code: `model.py` defines and trains the neural branches; `prepare_catalog.py`, `prepare.py`, and `temporal.py` construct observed histories and temporal examples; `popularity.py` constructs cutoff-only item features; `ensemble.py` defines and applies the final separate towers. Checkpoints and selected configuration are included.\n\nFinal submission command, using the included fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py \\\n  --config /app/solution/final_config.json \\\n  --checkpoint-map /app/solution/final_refit_map.json \\\n  --name final_model\n```\n\nThis writes `submission.parquet`, the fitted `final_model.pt`, and offline item vectors/IDs under `/app/solution/`.\nMD"
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105

Agent message

+36:39
Thinking unavailable No text recorded

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

106

exec command

+36:46
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

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read();start=s.index('class RetrievalUserTower');end=s.index('def history(')\ns=s[:start]+'''class RetrievalUserTower(nn.Module):\n    def __init__(self, towers, popularity=None, stat_config=None):\n        super().__init__();self.branches=nn.ModuleList(towers);self.stat_config=stat_config\n        if popularity is not None:\n            self.popularity=nn.EmbeddingBag.from_pretrained(popularity[:,None],freeze=True,mode='sum',padding_idx=0)\n            mass=torch.ones_like(popularity);mass[0]=0\n            self.mass=nn.EmbeddingBag.from_pretrained(mass[:,None],freeze=True,mode='sum',padding_idx=0)\n            self.stat_projection=nn.Linear(1,1)\n            with torch.no_grad():self.stat_projection.weight.fill_(1);self.stat_projection.bias.zero_()\n    def popularity_affinity(self,h):\n        ids,offset,weight=h;mass=self.mass(ids,offset,per_sample_weights=weight);mean=self.popularity(ids,offset,per_sample_weights=weight)/mass.clamp_min(1e-12);c=self.stat_config;z=(mean-c['mean'])/c['std'];z=torch.where(mass>0,z,torch.zeros_like(z));return self.stat_projection(z.clamp(-c['clip'],c['clip']))\n    def forward(self, histories):\n        vectors=[tower(ids,offset,weight) for tower,(ids,offset,weight) in zip(self.branches,histories)]\n        if self.stat_config:vectors.append(self.popularity_affinity(histories[0]))\n        return torch.cat(vectors+[torch.ones_like(vectors[0][:,:1])],dim=1)\nclass RetrievalItemTower(nn.Module):\n    def __init__(self,towers,coefficients,features,prior,personal=None):\n        super().__init__();self.branches=nn.ModuleList(towers);self.coefficients=nn.Parameter(torch.tensor(coefficients,dtype=torch.float32));self.register_buffer('features',features);self.prior=nn.Linear(features.shape[1],1,bias=False)\n        with torch.no_grad():self.prior.weight.copy_(torch.tensor(prior,dtype=torch.float32)[None])\n        self.personal=None\n        if personal is not None:\n            self.personal=nn.Linear(features.shape[1],1,bias=False)\n            with torch.no_grad():self.personal.weight.copy_(torch.tensor(personal,dtype=torch.float32)[None])\n    def forward(self,ids):\n        vectors=[coef*tower(ids) for coef,tower in zip(self.coefficients,self.branches)]\n        if self.personal is not None:vectors.append(self.personal(self.features[ids]))\n        return torch.cat(vectors+[self.prior(self.features[ids])],dim=1)\nclass RetrievalModel(nn.Module):\n    def __init__(self,bases,coefficients,features,prior,popularity=None,stat_config=None,personal=None):\n        super().__init__();self.user=RetrievalUserTower([b.user for b in bases],popularity,stat_config);self.item=RetrievalItemTower([b.item for b in bases],coefficients,features,prior,personal)\n''' + s[end:]\ns=s.replace(\"net=RetrievalModel(bases,coefs,features,prior).eval();\", \"stat_config=config.get('user_popularity');popularity=features[:,names.index(stat_config['feature'])] if stat_config else None;personal=[config.get('personal_prior',{}).get(str(n),0) for n in names] if stat_config else None\\n    net=RetrievalModel(bases,coefs,features,prior,popularity,stat_config,personal).eval();\")\ns=s.replace(\"'base_checkpoints':paths}\",\"'base_checkpoints':paths,'user_popularity':stat_config,'personal_prior':config.get('personal_prior')}\")\ns=s.replace(\"assert torch.allclose([email protected],manual,atol=1e-4)\", \"if net.item.personal is not None:manual+=net.user.popularity_affinity(h[0])@net.item.personal(features[ids]).T\\n    assert torch.allclose([email protected],manual,atol=1e-4)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/personal_best.json --stage validation --output personal_validation.parquet --name personal_validation_model > /app/solution/personal_validation.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -3 /app/solution/parity_base.log; cat /sys/fs/cgroup/memory.current","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c7aeb0","wall_time_seconds":1.001258469,"session_id":73907,"original_token_count":0,"output":""}
{"chunk_id":"da95f9","wall_time_seconds":0.00000465,"exit_code":0,"original_token_count":27,"output":"step 900 loss 9.072217712402344 sec 136.5\nstep 1000 loss 8.97678563117981 sec 148.6\nEVAL 1000 {}\n1628155904\n"}
{"current_time":"2026-09-10 18:30:32 UTC"}
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Harness context codex item completed
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107

exec command

+37:20
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

text(await Promise.allSettled([
tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name final_denoise --resume /app/solution/parity_base_1000.pt --steps 6000 --eval-every 6000 --lr .001 --threads 2 > /app/solution/final_denoise.log 2>&1",yield_time_ms:1000}),
tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name final_temporal --resume /app/solution/parity_base_1000.pt --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2 > /app/solution/final_temporal.log 2>&1",yield_time_ms:1000})
]));
text(await tools.exec_command({cmd:"cp /app/solution/submission.parquet /app/solution/best_submission.parquet\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/personal_validation.parquet > /app/solution/personal_public_score.json","yield_time_ms":1000}));

Tool response

Script completed
Wall time 2.3 seconds
Output:

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Harness context codex item completed
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    "command": [
      "/bin/sh",
      "-lc",
      "cp /app/solution/submission.parquet /app/solution/best_submission.parquet\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/personal_validation.parquet > /app/solution/personal_public_score.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/submission.parquet /app/solution/best_submission.parquet\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/personal_validation.parquet > /app/solution/personal_public_score.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/personal_validation.parquet'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/personal_validation.parquet'\n",
    "exit_code": 1,
    "duration": {
      "secs": 2,
      "nanos": 169647846
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/personal_validation.parquet'\n"
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-038c8838-7eec-4e41-893c-9fc75557c197",
    "process_id": "73907",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read();start=s.index('class RetrievalUserTower');end=s.index('def history(')\ns=s[:start]+'''class RetrievalUserTower(nn.Module):\n    def __init__(self, towers, popularity=None, stat_config=None):\n        super().__init__();self.branches=nn.ModuleList(towers);self.stat_config=stat_config\n        if popularity is not None:\n            self.popularity=nn.EmbeddingBag.from_pretrained(popularity[:,None],freeze=True,mode='sum',padding_idx=0)\n            mass=torch.ones_like(popularity);mass[0]=0\n            self.mass=nn.EmbeddingBag.from_pretrained(mass[:,None],freeze=True,mode='sum',padding_idx=0)\n            self.stat_projection=nn.Linear(1,1)\n            with torch.no_grad():self.stat_projection.weight.fill_(1);self.stat_projection.bias.zero_()\n    def popularity_affinity(self,h):\n        ids,offset,weight=h;mass=self.mass(ids,offset,per_sample_weights=weight);mean=self.popularity(ids,offset,per_sample_weights=weight)/mass.clamp_min(1e-12);c=self.stat_config;z=(mean-c['mean'])/c['std'];z=torch.where(mass>0,z,torch.zeros_like(z));return self.stat_projection(z.clamp(-c['clip'],c['clip']))\n    def forward(self, histories):\n        vectors=[tower(ids,offset,weight) for tower,(ids,offset,weight) in zip(self.branches,histories)]\n        if self.stat_config:vectors.append(self.popularity_affinity(histories[0]))\n        return torch.cat(vectors+[torch.ones_like(vectors[0][:,:1])],dim=1)\nclass RetrievalItemTower(nn.Module):\n    def __init__(self,towers,coefficients,features,prior,personal=None):\n        super().__init__();self.branches=nn.ModuleList(towers);self.coefficients=nn.Parameter(torch.tensor(coefficients,dtype=torch.float32));self.register_buffer('features',features);self.prior=nn.Linear(features.shape[1],1,bias=False)\n        with torch.no_grad():self.prior.weight.copy_(torch.tensor(prior,dtype=torch.float32)[None])\n        self.personal=None\n        if personal is not None:\n            self.personal=nn.Linear(features.shape[1],1,bias=False)\n            with torch.no_grad():self.personal.weight.copy_(torch.tensor(personal,dtype=torch.float32)[None])\n    def forward(self,ids):\n        vectors=[coef*tower(ids) for coef,tower in zip(self.coefficients,self.branches)]\n        if self.personal is not None:vectors.append(self.personal(self.features[ids]))\n        return torch.cat(vectors+[self.prior(self.features[ids])],dim=1)\nclass RetrievalModel(nn.Module):\n    def __init__(self,bases,coefficients,features,prior,popularity=None,stat_config=None,personal=None):\n        super().__init__();self.user=RetrievalUserTower([b.user for b in bases],popularity,stat_config);self.item=RetrievalItemTower([b.item for b in bases],coefficients,features,prior,personal)\n''' + s[end:]\ns=s.replace(\"net=RetrievalModel(bases,coefs,features,prior).eval();\", \"stat_config=config.get('user_popularity');popularity=features[:,names.index(stat_config['feature'])] if stat_config else None;personal=[config.get('personal_prior',{}).get(str(n),0) for n in names] if stat_config else None\\n    net=RetrievalModel(bases,coefs,features,prior,popularity,stat_config,personal).eval();\")\ns=s.replace(\"'base_checkpoints':paths}\",\"'base_checkpoints':paths,'user_popularity':stat_config,'personal_prior':config.get('personal_prior')}\")\ns=s.replace(\"assert torch.allclose([email protected],manual,atol=1e-4)\", \"if net.item.personal is not None:manual+=net.user.popularity_affinity(h[0])@net.item.personal(features[ids]).T\\n    assert torch.allclose([email protected],manual,atol=1e-4)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/personal_best.json --stage validation --output personal_validation.parquet --name personal_validation_model > /app/solution/personal_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read();start=s.index('class RetrievalUserTower');end=s.index('def history(')\ns=s[:start]+'''class RetrievalUserTower(nn.Module):\n    def __init__(self, towers, popularity=None, stat_config=None):\n        super().__init__();self.branches=nn.ModuleList(towers);self.stat_config=stat_config\n        if popularity is not None:\n            self.popularity=nn.EmbeddingBag.from_pretrained(popularity[:,None],freeze=True,mode='sum',padding_idx=0)\n            mass=torch.ones_like(popularity);mass[0]=0\n            self.mass=nn.EmbeddingBag.from_pretrained(mass[:,None],freeze=True,mode='sum',padding_idx=0)\n            self.stat_projection=nn.Linear(1,1)\n            with torch.no_grad():self.stat_projection.weight.fill_(1);self.stat_projection.bias.zero_()\n    def popularity_affinity(self,h):\n        ids,offset,weight=h;mass=self.mass(ids,offset,per_sample_weights=weight);mean=self.popularity(ids,offset,per_sample_weights=weight)/mass.clamp_min(1e-12);c=self.stat_config;z=(mean-c['mean'])/c['std'];z=torch.where(mass>0,z,torch.zeros_like(z));return self.stat_projection(z.clamp(-c['clip'],c['clip']))\n    def forward(self, histories):\n        vectors=[tower(ids,offset,weight) for tower,(ids,offset,weight) in zip(self.branches,histories)]\n        if self.stat_config:vectors.append(self.popularity_affinity(histories[0]))\n        return torch.cat(vectors+[torch.ones_like(vectors[0][:,:1])],dim=1)\nclass RetrievalItemTower(nn.Module):\n    def __init__(self,towers,coefficients,features,prior,personal=None):\n        super().__init__();self.branches=nn.ModuleList(towers);self.coefficients=nn.Parameter(torch.tensor(coefficients,dtype=torch.float32));self.register_buffer('features',features);self.prior=nn.Linear(features.shape[1],1,bias=False)\n        with torch.no_grad():self.prior.weight.copy_(torch.tensor(prior,dtype=torch.float32)[None])\n        self.personal=None\n        if personal is not None:\n            self.personal=nn.Linear(features.shape[1],1,bias=False)\n            with torch.no_grad():self.personal.weight.copy_(torch.tensor(personal,dtype=torch.float32)[None])\n    def forward(self,ids):\n        vectors=[coef*tower(ids) for coef,tower in zip(self.coefficients,self.branches)]\n        if self.personal is not None:vectors.append(self.personal(self.features[ids]))\n        return torch.cat(vectors+[self.prior(self.features[ids])],dim=1)\nclass RetrievalModel(nn.Module):\n    def __init__(self,bases,coefficients,features,prior,popularity=None,stat_config=None,personal=None):\n        super().__init__();self.user=RetrievalUserTower([b.user for b in bases],popularity,stat_config);self.item=RetrievalItemTower([b.item for b in bases],coefficients,features,prior,personal)\n''' + s[end:]\ns=s.replace(\"net=RetrievalModel(bases,coefs,features,prior).eval();\", \"stat_config=config.get('user_popularity');popularity=features[:,names.index(stat_config['feature'])] if stat_config else None;personal=[config.get('personal_prior',{}).get(str(n),0) for n in names] if stat_config else None\\n    net=RetrievalModel(bases,coefs,features,prior,popularity,stat_config,personal).eval();\")\ns=s.replace(\"'base_checkpoints':paths}\",\"'base_checkpoints':paths,'user_popularity':stat_config,'personal_prior':config.get('personal_prior')}\")\ns=s.replace(\"assert torch.allclose([email protected],manual,atol=1e-4)\", \"if net.item.personal is not None:manual+=net.user.popularity_affinity(h[0])@net.item.personal(features[ids]).T\\n    assert torch.allclose([email protected],manual,atol=1e-4)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/personal_best.json --stage validation --output personal_validation.parquet --name personal_validation_model > /app/solution/personal_validation.log 2>&1"
      }
    ],
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    "status": "completed",
    "stdout": "",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 20,
      "nanos": 306767784
    },
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  }
}
108

Agent message

+37:57
Thinking unavailable No text recorded

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

109

Agent message

+38:17
Thinking unavailable No text recorded

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

110

exec command

+38:25
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Model text

The history-based popularity adjustment raises public NDCG@10 to 0.048672 with 260-dimensional vectors and a 512-track history cap. It is a coordinate inside the towers, so the score remains an exact dot product. I’m checking active preference history and finishing the matching refit.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/state_calibrate.py <<'PY'\nfrom fast_calibrate import *\nfrom ensemble import history,batch_history\[email protected]_grad()\ndef main(x):\n a=load('validation');config=json.load(open(x.config));ev=Evaluator();s=pd.read_parquet(D/'validation_preference_state.parquet');s['u']=pd.Index(a['uids']).get_indexer(s.uid);s['i']=pd.Index(a['items']).get_indexer(s.item_id)+1;s=s[(s.u>=0)&(s.i>0)];states={}\n for state in ['liked','disliked']:\n  ss=s[s.state==state].sort_values('u');counts=np.bincount(ss.u,minlength=len(a['uids']));off=np.r_[0,np.cumsum(counts)];w=(1/np.maximum(counts[ss.u],1)).astype(np.float32);states[state]={'ids':ss.i.to_numpy(np.int32),'offset':off,'weight':w}\n models=[];raw=[];neg=[];pos=[]\n for c in config['components']:\n  ck=torch.load(c['checkpoint'],weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();models.append(m);h=history(a,c,'validation');hh=[];nnn=[];pp=[]\n  for st in range(0,len(a['uids']),128):\n   end=min(st+128,len(a['uids']));ids,off,w=batch_history(h,st,end);v=m.user.history(ids,off,per_sample_weights=w);denom=np.array([h['weight'][h['offset'][u]:h['offset'][u+1]].sum() for u in range(st,end)],np.float32);v/=torch.from_numpy(denom).clamp_min(1e-12)[:,None];hh.append(v)\n   for key,out in [('disliked',nnn),('liked',pp)]:\n    ids,off,w=batch_history(states[key],st,end);out.append(m.user.history(ids,off,per_sample_weights=w))\n  raw.append(torch.cat(hh));neg.append(torch.cat(nnn));pos.append(torch.cat(pp))\n old=np.load(O/f'{x.vectors}_user_vectors.npy');iv=np.load(O/f'{x.vectors}_item_vectors.npy');best=(-1,None);results=[]\n for alpha in [0,.05,.1,.2,.4]:\n  for beta in [0,.1]:\n   us=[]\n   for m,h,n,p in zip(models,raw,neg,pos):\n    v=F.normalize(h-alpha*n+beta*p,dim=-1);v=F.normalize(v+m.user.project(v),dim=-1);us.append(torch.cat([v,torch.ones_like(v[:,:1])],dim=-1).numpy())\n   uv=np.concatenate(us+[old[:,-2:]],axis=1);top=rank_many(uv,iv,ev.e,[np.zeros(len(iv),np.float32)])[0];score=ev.score(top);c={'negative':alpha,'positive':beta};print(score,c,flush=True);results.append(dict(c,score=score))\n   if score>best[0]:best=(score,c)\n print('BEST',best,flush=True);json.dump({'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--vectors',required=True);p.add_argument('--name',default='state');main(p.parse_args())\nPY\nulimit -v 4500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/state_calibrate.py --config /app/solution/personal_best.json --vectors personal_validation_model > /app/solution/state.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"cat /app/solution/personal_public_score.json; tail -3 /app/solution/final_denoise.log; tail -3 /app/solution/final_temporal.log","max_output_tokens":1600}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"7a2704","wall_time_seconds":1.001078615,"session_id":30632,"original_token_count":0,"output":""}
{"chunk_id":"f9fa58","wall_time_seconds":0.000005101,"exit_code":0,"original_token_count":63,"output":"step 900 loss 8.519118599891662 sec 55.0\nstep 1000 loss 8.493104944229126 sec 61.0\nstep 1100 loss 8.460423192977906 sec 67.1\nstep 900 loss 8.671465139389039 sec 51.5\nstep 1000 loss 8.637631120681762 sec 57.3\nstep 1100 loss 8.561448974609375 sec 63.0\n"}
{"current_time":"2026-09-10 18:32:03 UTC"}
Harness context token usage record
{
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  "thread_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "session_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "response_id": "resp_03e1db26860ff63f016aa2f759827487d1af226ee4fadb4464",
  "usage": {
    "input_tokens": 107831,
    "cached_input_tokens": 107136,
    "cache_write_input_tokens": 0,
    "output_tokens": 2331,
    "reasoning_output_tokens": 1280,
    "total_tokens": 110162
  },
  "turn_token_usage": {
    "input_tokens": 4983394,
    "cached_input_tokens": 4873472,
    "cache_write_input_tokens": 0,
    "output_tokens": 63522,
    "reasoning_output_tokens": 32607,
    "total_tokens": 5046916
  },
  "thread_token_usage": {
    "input_tokens": 4983394,
    "cached_input_tokens": 4873472,
    "cache_write_input_tokens": 0,
    "output_tokens": 63522,
    "reasoning_output_tokens": 32607,
    "total_tokens": 5046916
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e1b6fe0d-1188-4f33-afdf-1df5bdd2fb2f",
    "process_id": "4143",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/personal_public_score.json; tail -3 /app/solution/final_denoise.log; tail -3 /app/solution/final_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /app/solution/personal_public_score.json; tail -3 /app/solution/final_denoise.log; tail -3 /app/solution/final_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 900 loss 8.519118599891662 sec 55.0\nstep 1000 loss 8.493104944229126 sec 61.0\nstep 1100 loss 8.460423192977906 sec 67.1\nstep 900 loss 8.671465139389039 sec 51.5\nstep 1000 loss 8.637631120681762 sec 57.3\nstep 1100 loss 8.561448974609375 sec 63.0\n",
    "stderr": "",
    "aggregated_output": "step 900 loss 8.519118599891662 sec 55.0\nstep 1000 loss 8.493104944229126 sec 61.0\nstep 1100 loss 8.460423192977906 sec 67.1\nstep 900 loss 8.671465139389039 sec 51.5\nstep 1000 loss 8.637631120681762 sec 57.3\nstep 1100 loss 8.561448974609375 sec 63.0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5101
    },
    "formatted_output": "step 900 loss 8.519118599891662 sec 55.0\nstep 1000 loss 8.493104944229126 sec 61.0\nstep 1100 loss 8.460423192977906 sec 67.1\nstep 900 loss 8.671465139389039 sec 51.5\nstep 1000 loss 8.637631120681762 sec 57.3\nstep 1100 loss 8.561448974609375 sec 63.0\n"
  }
}
111

exec command

+38:52
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat /app/solution/personal_validation.log; ls -lh /app/solution/personal_validation*; cat /app/solution/state.log","max_output_tokens":2500}));
text(await tools.write_stdin({session_id:5534,chars:"","yield_time_ms":1000,"max_output_tokens":2000}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"b2f238","wall_time_seconds":0.00000538,"exit_code":0,"original_token_count":213,"output":"{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5622405001304287, 'user_encoding_ms_p95': 0.7127215501668615, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\n-rw-r--r-- 1 agent agent  208 Sep 10 18:30 /app/solution/personal_validation.log\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:30 /app/solution/personal_validation.parquet\n-rw-r--r-- 1 agent agent  65M Sep 10 18:30 /app/solution/personal_validation_model.pt\n-rw-r--r-- 1 agent agent 118K Sep 10 18:30 /app/solution/personal_validation_model_item_ids.npy\n-rw-r--r-- 1 agent agent  30M Sep 10 18:30 /app/solution/personal_validation_model_item_vectors.npy\n-rw-r--r-- 1 agent agent  221 Sep 10 18:30 /app/solution/personal_validation_model_report.json\n-rw-r--r-- 1 agent agent 8.4M Sep 10 18:30 /app/solution/personal_validation_model_user_vectors.npy\n"}
{"chunk_id":"fdb8d1","wall_time_seconds":0.00000251,"exit_code":1,"original_token_count":356,"output":"Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/personal_validation.parquet'\n"}
Harness context codex item completed
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    "command": [
      "/bin/sh",
      "-lc",
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    "parsed_cmd": [
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    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5622405001304287, 'user_encoding_ms_p95': 0.7127215501668615, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\n-rw-r--r-- 1 agent agent  208 Sep 10 18:30 /app/solution/personal_validation.log\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:30 /app/solution/personal_validation.parquet\n-rw-r--r-- 1 agent agent  65M Sep 10 18:30 /app/solution/personal_validation_model.pt\n-rw-r--r-- 1 agent agent 118K Sep 10 18:30 /app/solution/personal_validation_model_item_ids.npy\n-rw-r--r-- 1 agent agent  30M Sep 10 18:30 /app/solution/personal_validation_model_item_vectors.npy\n-rw-r--r-- 1 agent agent  221 Sep 10 18:30 /app/solution/personal_validation_model_report.json\n-rw-r--r-- 1 agent agent 8.4M Sep 10 18:30 /app/solution/personal_validation_model_user_vectors.npy\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5622405001304287, 'user_encoding_ms_p95': 0.7127215501668615, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\n-rw-r--r-- 1 agent agent  208 Sep 10 18:30 /app/solution/personal_validation.log\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:30 /app/solution/personal_validation.parquet\n-rw-r--r-- 1 agent agent  65M Sep 10 18:30 /app/solution/personal_validation_model.pt\n-rw-r--r-- 1 agent agent 118K Sep 10 18:30 /app/solution/personal_validation_model_item_ids.npy\n-rw-r--r-- 1 agent agent  30M Sep 10 18:30 /app/solution/personal_validation_model_item_vectors.npy\n-rw-r--r-- 1 agent agent  221 Sep 10 18:30 /app/solution/personal_validation_model_report.json\n-rw-r--r-- 1 agent agent 8.4M Sep 10 18:30 /app/solution/personal_validation_model_user_vectors.npy\n",
    "exit_code": 0,
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    },
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112

exec command

+39:01
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

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/personal_validation.parquet > /app/solution/personal_public_score.json","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -4 /app/solution/state.log; tail -3 /app/solution/final_denoise.log","max_output_tokens":1200}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c2f9b4","wall_time_seconds":1.000883012,"session_id":36855,"original_token_count":0,"output":""}
{"chunk_id":"b6bb0b","wall_time_seconds":0.0000075,"exit_code":0,"original_token_count":71,"output":"0.048672373759369024 {'negative': 0, 'positive': 0}\n0.0485756254694584 {'negative': 0, 'positive': 0.1}\n0.04891770952627558 {'negative': 0.05, 'positive': 0}\nstep 1100 loss 8.460423192977906 sec 67.1\nstep 1200 loss 8.414717235565185 sec 73.4\nstep 1300 loss 8.380387754440308 sec 80.5\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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    "stderr": "",
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    "exit_code": 0,
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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",
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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/state_calibrate.py <<'PY'\nfrom fast_calibrate import *\nfrom ensemble import history,batch_history\[email protected]_grad()\ndef main(x):\n a=load('validation');config=json.load(open(x.config));ev=Evaluator();s=pd.read_parquet(D/'validation_preference_state.parquet');s['u']=pd.Index(a['uids']).get_indexer(s.uid);s['i']=pd.Index(a['items']).get_indexer(s.item_id)+1;s=s[(s.u>=0)&(s.i>0)];states={}\n for state in ['liked','disliked']:\n  ss=s[s.state==state].sort_values('u');counts=np.bincount(ss.u,minlength=len(a['uids']));off=np.r_[0,np.cumsum(counts)];w=(1/np.maximum(counts[ss.u],1)).astype(np.float32);states[state]={'ids':ss.i.to_numpy(np.int32),'offset':off,'weight':w}\n models=[];raw=[];neg=[];pos=[]\n for c in config['components']:\n  ck=torch.load(c['checkpoint'],weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();models.append(m);h=history(a,c,'validation');hh=[];nnn=[];pp=[]\n  for st in range(0,len(a['uids']),128):\n   end=min(st+128,len(a['uids']));ids,off,w=batch_history(h,st,end);v=m.user.history(ids,off,per_sample_weights=w);denom=np.array([h['weight'][h['offset'][u]:h['offset'][u+1]].sum() for u in range(st,end)],np.float32);v/=torch.from_numpy(denom).clamp_min(1e-12)[:,None];hh.append(v)\n   for key,out in [('disliked',nnn),('liked',pp)]:\n    ids,off,w=batch_history(states[key],st,end);out.append(m.user.history(ids,off,per_sample_weights=w))\n  raw.append(torch.cat(hh));neg.append(torch.cat(nnn));pos.append(torch.cat(pp))\n old=np.load(O/f'{x.vectors}_user_vectors.npy');iv=np.load(O/f'{x.vectors}_item_vectors.npy');best=(-1,None);results=[]\n for alpha in [0,.05,.1,.2,.4]:\n  for beta in [0,.1]:\n   us=[]\n   for m,h,n,p in zip(models,raw,neg,pos):\n    v=F.normalize(h-alpha*n+beta*p,dim=-1);v=F.normalize(v+m.user.project(v),dim=-1);us.append(torch.cat([v,torch.ones_like(v[:,:1])],dim=-1).numpy())\n   uv=np.concatenate(us+[old[:,-2:]],axis=1);top=rank_many(uv,iv,ev.e,[np.zeros(len(iv),np.float32)])[0];score=ev.score(top);c={'negative':alpha,'positive':beta};print(score,c,flush=True);results.append(dict(c,score=score))\n   if score>best[0]:best=(score,c)\n print('BEST',best,flush=True);json.dump({'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--vectors',required=True);p.add_argument('--name',default='state');main(p.parse_args())\nPY\nulimit -v 4500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/state_calibrate.py --config /app/solution/personal_best.json --vectors personal_validation_model > /app/solution/state.log 2>&1"
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    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/state_calibrate.py <<'PY'\nfrom fast_calibrate import *\nfrom ensemble import history,batch_history\[email protected]_grad()\ndef main(x):\n a=load('validation');config=json.load(open(x.config));ev=Evaluator();s=pd.read_parquet(D/'validation_preference_state.parquet');s['u']=pd.Index(a['uids']).get_indexer(s.uid);s['i']=pd.Index(a['items']).get_indexer(s.item_id)+1;s=s[(s.u>=0)&(s.i>0)];states={}\n for state in ['liked','disliked']:\n  ss=s[s.state==state].sort_values('u');counts=np.bincount(ss.u,minlength=len(a['uids']));off=np.r_[0,np.cumsum(counts)];w=(1/np.maximum(counts[ss.u],1)).astype(np.float32);states[state]={'ids':ss.i.to_numpy(np.int32),'offset':off,'weight':w}\n models=[];raw=[];neg=[];pos=[]\n for c in config['components']:\n  ck=torch.load(c['checkpoint'],weights_only=False);m=TwoTower(len(a['items']),ck['dim']);m.load_state_dict(ck['model'],strict=False);m.eval();models.append(m);h=history(a,c,'validation');hh=[];nnn=[];pp=[]\n  for st in range(0,len(a['uids']),128):\n   end=min(st+128,len(a['uids']));ids,off,w=batch_history(h,st,end);v=m.user.history(ids,off,per_sample_weights=w);denom=np.array([h['weight'][h['offset'][u]:h['offset'][u+1]].sum() for u in range(st,end)],np.float32);v/=torch.from_numpy(denom).clamp_min(1e-12)[:,None];hh.append(v)\n   for key,out in [('disliked',nnn),('liked',pp)]:\n    ids,off,w=batch_history(states[key],st,end);out.append(m.user.history(ids,off,per_sample_weights=w))\n  raw.append(torch.cat(hh));neg.append(torch.cat(nnn));pos.append(torch.cat(pp))\n old=np.load(O/f'{x.vectors}_user_vectors.npy');iv=np.load(O/f'{x.vectors}_item_vectors.npy');best=(-1,None);results=[]\n for alpha in [0,.05,.1,.2,.4]:\n  for beta in [0,.1]:\n   us=[]\n   for m,h,n,p in zip(models,raw,neg,pos):\n    v=F.normalize(h-alpha*n+beta*p,dim=-1);v=F.normalize(v+m.user.project(v),dim=-1);us.append(torch.cat([v,torch.ones_like(v[:,:1])],dim=-1).numpy())\n   uv=np.concatenate(us+[old[:,-2:]],axis=1);top=rank_many(uv,iv,ev.e,[np.zeros(len(iv),np.float32)])[0];score=ev.score(top);c={'negative':alpha,'positive':beta};print(score,c,flush=True);results.append(dict(c,score=score))\n   if score>best[0]:best=(score,c)\n print('BEST',best,flush=True);json.dump({'score':best[0],**best[1]},open(O/f'{x.name}_best.json','w'),indent=2);json.dump(results,open(O/f'{x.name}_grid.json','w'),indent=2)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--config',required=True);p.add_argument('--vectors',required=True);p.add_argument('--name',default='state');main(p.parse_args())\nPY\nulimit -v 4500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/state_calibrate.py --config /app/solution/personal_best.json --vectors personal_validation_model > /app/solution/state.log 2>&1"
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}
113

Agent message

+39:30
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

+39:35
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

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\ns=pd.read_parquet('/task/data/validation_preference_state.parquet');print(s[s.state=='disliked'].groupby('uid').size().quantile([0,.5,.9,.99,1]))\nPY\ncat /app/solution/personal_public_score.json; tail -5 /app/solution/state.log; tail -3 /app/solution/final_temporal.log","max_output_tokens":2000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 2.7 seconds
Output:

{"chunk_id":"7644e9","wall_time_seconds":2.484629135,"exit_code":0,"original_token_count":159,"output":"0.00       1.0\n0.50       3.0\n0.90      20.0\n0.99     150.2\n1.00    1703.0\ndtype: float64\n{\n  \"model\": \"/app/solution/personal_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048672,\n    \"quality_users\": 4263\n  }\n}\n0.04692391889864482 {'negative': 0.2, 'positive': 0}\n0.04730479701614177 {'negative': 0.2, 'positive': 0.1}\n0.04214680813748297 {'negative': 0.4, 'positive': 0}\n0.04263214591540593 {'negative': 0.4, 'positive': 0.1}\nBEST (0.04891770952627558, {'negative': 0.05, 'positive': 0})\nstep 1800 loss 8.246217703819275 sec 109.8\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\n"}
{"current_time":"2026-09-10 18:32:57 UTC"}
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  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 64589,
    "reasoning_output_tokens": 33336,
    "total_tokens": 5381350
  },
  "thread_token_usage": {
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    "cached_input_tokens": 5202432,
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}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-0e3e53fb-3ec3-4f4e-80fa-3cf68abe7373",
    "process_id": "91689",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\ns=pd.read_parquet('/task/data/validation_preference_state.parquet');print(s[s.state=='disliked'].groupby('uid').size().quantile([0,.5,.9,.99,1]))\nPY\ncat /app/solution/personal_public_score.json; tail -5 /app/solution/state.log; tail -3 /app/solution/final_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\ns=pd.read_parquet('/task/data/validation_preference_state.parquet');print(s[s.state=='disliked'].groupby('uid').size().quantile([0,.5,.9,.99,1]))\nPY\ncat /app/solution/personal_public_score.json; tail -5 /app/solution/state.log; tail -3 /app/solution/final_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0.00       1.0\n0.50       3.0\n0.90      20.0\n0.99     150.2\n1.00    1703.0\ndtype: float64\n{\n  \"model\": \"/app/solution/personal_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048672,\n    \"quality_users\": 4263\n  }\n}\n0.04692391889864482 {'negative': 0.2, 'positive': 0}\n0.04730479701614177 {'negative': 0.2, 'positive': 0.1}\n0.04214680813748297 {'negative': 0.4, 'positive': 0}\n0.04263214591540593 {'negative': 0.4, 'positive': 0.1}\nBEST (0.04891770952627558, {'negative': 0.05, 'positive': 0})\nstep 1800 loss 8.246217703819275 sec 109.8\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\n",
    "stderr": "",
    "aggregated_output": "0.00       1.0\n0.50       3.0\n0.90      20.0\n0.99     150.2\n1.00    1703.0\ndtype: float64\n{\n  \"model\": \"/app/solution/personal_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048672,\n    \"quality_users\": 4263\n  }\n}\n0.04692391889864482 {'negative': 0.2, 'positive': 0}\n0.04730479701614177 {'negative': 0.2, 'positive': 0.1}\n0.04214680813748297 {'negative': 0.4, 'positive': 0}\n0.04263214591540593 {'negative': 0.4, 'positive': 0.1}\nBEST (0.04891770952627558, {'negative': 0.05, 'positive': 0})\nstep 1800 loss 8.246217703819275 sec 109.8\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 484614775
    },
    "formatted_output": "0.00       1.0\n0.50       3.0\n0.90      20.0\n0.99     150.2\n1.00    1703.0\ndtype: float64\n{\n  \"model\": \"/app/solution/personal_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048672,\n    \"quality_users\": 4263\n  }\n}\n0.04692391889864482 {'negative': 0.2, 'positive': 0}\n0.04730479701614177 {'negative': 0.2, 'positive': 0.1}\n0.04214680813748297 {'negative': 0.4, 'positive': 0}\n0.04263214591540593 {'negative': 0.4, 'positive': 0.1}\nBEST (0.04891770952627558, {'negative': 0.05, 'positive': 0})\nstep 1800 loss 8.246217703819275 sec 109.8\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\n"
  }
}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-0d150d09-4b5f-4d9d-9e9c-02cbdf07533f",
    "process_id": "26392",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name final_temporal --resume /app/solution/parity_base_1000.pt --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2 > /app/solution/final_temporal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/model.py --stage test --name final_temporal --resume /app/solution/parity_base_1000.pt --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2 > /app/solution/final_temporal.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 145,
      "nanos": 463296544
    },
    "formatted_output": ""
  }
}
115

Agent message

+40:09
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

+40:29
Thinking unavailable No text recorded

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

117

exec command

+40:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read();s=s.replace('def forward(self, histories):','def forward(self, histories, popularity_history=None):').replace(\"self.popularity_affinity(histories[0])\",\"self.popularity_affinity(popularity_history if popularity_history is not None else histories[0])\")\npos=s.index('def batch_history(')\ns=s[:pos]+'''def state_history(h,a,stage,coefficients):\n    state=pd.read_parquet(D/f'{stage}_preference_state.parquet');state['u']=pd.Index(a['uids']).get_indexer(state.uid);state['i']=pd.Index(a['items']).get_indexer(state.item_id)+1;state=state[(state.u>=0)&(state.i>0)];groups={}\n    for channel,key,sign in [('disliked','negative',-1),('liked','positive',1)]:\n        coef=coefficients.get(key,0)*sign\n        if not coef:continue\n        for u,g in state[state.state==channel].groupby('u'):\n            groups.setdefault(int(u),[]).append((g.i.to_numpy(np.int32),coef))\n    ids=[];weights=[];counts=[]\n    for u in range(len(a['uids'])):\n        lo,hi=h['offset'][u:u+2];ii=[h['ids'][lo:hi]];ww=[h['weight'][lo:hi]];mass=float(ww[0].sum()) or 1.\n        for items,coef in groups.get(u,[]):\n            ii.append(items);ww.append(np.full(len(items),coef*mass/len(items),np.float32))\n        ii=np.concatenate(ii);ww=np.concatenate(ww);ids.append(ii);weights.append(ww);counts.append(len(ii))\n    return {'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\n''' + s[pos:]\ns=s.replace(\"histories=[history(a,c,stage) for c in configs];iv=\", \"histories=[history(a,c,stage) for c in configs];pop_history=histories[0]\\n    if config.get('state'):histories=[state_history(h,a,stage,config['state']) for h in histories]\\n    iv=\")\ns=s.replace(\"uv=net.user(h);scores=\", \"uv=net.user(h,batch_history(pop_history,start,end));scores=\")\ns=s.replace(\"'personal_prior':config.get('personal_prior')}\",\"'personal_prior':config.get('personal_prior'),'state':config.get('state')}\")\ns=s.replace(\"h=[batch_history(h,0,1) for h in histories];u=net.user(h);\", \"h=[batch_history(h,0,1) for h in histories];ph=batch_history(pop_history,0,1);u=net.user(h,ph);\")\ns=s.replace(\"net.user.popularity_affinity(h[0])\", \"net.user.popularity_affinity(ph)\")\ns=s.replace(\"t=time.perf_counter();net.user(h)\", \"t=time.perf_counter();net.user(h,ph)\")\nopen(p,'w').write(s)\nimport json\nc=json.load(open('/app/solution/personal_best.json'));st=json.load(open('/app/solution/state_best.json'));c['state']={k:st[k] for k in ['negative','positive']};c['score']=st['score'];json.dump(c,open('/app/solution/state_config.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/state_config.json --stage validation --output state_validation.parquet --name state_validation_model > /app/solution/state_validation.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"tail -3 /app/solution/final_denoise.log; tail -3 /app/solution/final_temporal.log","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"93e614","wall_time_seconds":1.001247407,"session_id":9185,"original_token_count":0,"output":""}
{"chunk_id":"5dfa1b","wall_time_seconds":0.00000734,"exit_code":0,"original_token_count":57,"output":"step 3000 loss 8.037476277351379 sec 178.9\nstep 3100 loss 7.987451033592224 sec 183.9\nstep 3200 loss 7.965468401908875 sec 189.0\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\nEVAL 2000 {}\n"}
{"current_time":"2026-09-10 18:34:07 UTC"}
Harness context token usage record
{
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  "thread_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
  "session_id": "01a08c73-d6e0-7ed1-a511-eb36eb8e333f",
  "root_turn_id": "01a08c73-d6f3-77e0-927b-e54090d28a3e",
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  "usage": {
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    "cached_input_tokens": 111488,
    "cache_write_input_tokens": 0,
    "output_tokens": 1999,
    "reasoning_output_tokens": 1076,
    "total_tokens": 114798
  },
  "turn_token_usage": {
    "input_tokens": 5429560,
    "cached_input_tokens": 5313920,
    "cache_write_input_tokens": 0,
    "output_tokens": 66588,
    "reasoning_output_tokens": 34412,
    "total_tokens": 5496148
  },
  "thread_token_usage": {
    "input_tokens": 5429560,
    "cached_input_tokens": 5313920,
    "cache_write_input_tokens": 0,
    "output_tokens": 66588,
    "reasoning_output_tokens": 34412,
    "total_tokens": 5496148
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-223f77a5-3a10-488e-ac0c-815432cb378d",
    "process_id": "4662",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -3 /app/solution/final_denoise.log; tail -3 /app/solution/final_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -3 /app/solution/final_denoise.log; tail -3 /app/solution/final_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "step 3000 loss 8.037476277351379 sec 178.9\nstep 3100 loss 7.987451033592224 sec 183.9\nstep 3200 loss 7.965468401908875 sec 189.0\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\nEVAL 2000 {}\n",
    "stderr": "",
    "aggregated_output": "step 3000 loss 8.037476277351379 sec 178.9\nstep 3100 loss 7.987451033592224 sec 183.9\nstep 3200 loss 7.965468401908875 sec 189.0\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\nEVAL 2000 {}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 7340
    },
    "formatted_output": "step 3000 loss 8.037476277351379 sec 178.9\nstep 3100 loss 7.987451033592224 sec 183.9\nstep 3200 loss 7.965468401908875 sec 189.0\nstep 1900 loss 8.109306831359863 sec 115.2\nstep 2000 loss 8.144753413200378 sec 120.8\nEVAL 2000 {}\n"
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-96db6485-ed02-4992-8f1b-507560d1cdf5",
    "process_id": "9185",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read();s=s.replace('def forward(self, histories):','def forward(self, histories, popularity_history=None):').replace(\"self.popularity_affinity(histories[0])\",\"self.popularity_affinity(popularity_history if popularity_history is not None else histories[0])\")\npos=s.index('def batch_history(')\ns=s[:pos]+'''def state_history(h,a,stage,coefficients):\n    state=pd.read_parquet(D/f'{stage}_preference_state.parquet');state['u']=pd.Index(a['uids']).get_indexer(state.uid);state['i']=pd.Index(a['items']).get_indexer(state.item_id)+1;state=state[(state.u>=0)&(state.i>0)];groups={}\n    for channel,key,sign in [('disliked','negative',-1),('liked','positive',1)]:\n        coef=coefficients.get(key,0)*sign\n        if not coef:continue\n        for u,g in state[state.state==channel].groupby('u'):\n            groups.setdefault(int(u),[]).append((g.i.to_numpy(np.int32),coef))\n    ids=[];weights=[];counts=[]\n    for u in range(len(a['uids'])):\n        lo,hi=h['offset'][u:u+2];ii=[h['ids'][lo:hi]];ww=[h['weight'][lo:hi]];mass=float(ww[0].sum()) or 1.\n        for items,coef in groups.get(u,[]):\n            ii.append(items);ww.append(np.full(len(items),coef*mass/len(items),np.float32))\n        ii=np.concatenate(ii);ww=np.concatenate(ww);ids.append(ii);weights.append(ww);counts.append(len(ii))\n    return {'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\n''' + s[pos:]\ns=s.replace(\"histories=[history(a,c,stage) for c in configs];iv=\", \"histories=[history(a,c,stage) for c in configs];pop_history=histories[0]\\n    if config.get('state'):histories=[state_history(h,a,stage,config['state']) for h in histories]\\n    iv=\")\ns=s.replace(\"uv=net.user(h);scores=\", \"uv=net.user(h,batch_history(pop_history,start,end));scores=\")\ns=s.replace(\"'personal_prior':config.get('personal_prior')}\",\"'personal_prior':config.get('personal_prior'),'state':config.get('state')}\")\ns=s.replace(\"h=[batch_history(h,0,1) for h in histories];u=net.user(h);\", \"h=[batch_history(h,0,1) for h in histories];ph=batch_history(pop_history,0,1);u=net.user(h,ph);\")\ns=s.replace(\"net.user.popularity_affinity(h[0])\", \"net.user.popularity_affinity(ph)\")\ns=s.replace(\"t=time.perf_counter();net.user(h)\", \"t=time.perf_counter();net.user(h,ph)\")\nopen(p,'w').write(s)\nimport json\nc=json.load(open('/app/solution/personal_best.json'));st=json.load(open('/app/solution/state_best.json'));c['state']={k:st[k] for k in ['negative','positive']};c['score']=st['score'];json.dump(c,open('/app/solution/state_config.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/state_config.json --stage validation --output state_validation.parquet --name state_validation_model > /app/solution/state_validation.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read();s=s.replace('def forward(self, histories):','def forward(self, histories, popularity_history=None):').replace(\"self.popularity_affinity(histories[0])\",\"self.popularity_affinity(popularity_history if popularity_history is not None else histories[0])\")\npos=s.index('def batch_history(')\ns=s[:pos]+'''def state_history(h,a,stage,coefficients):\n    state=pd.read_parquet(D/f'{stage}_preference_state.parquet');state['u']=pd.Index(a['uids']).get_indexer(state.uid);state['i']=pd.Index(a['items']).get_indexer(state.item_id)+1;state=state[(state.u>=0)&(state.i>0)];groups={}\n    for channel,key,sign in [('disliked','negative',-1),('liked','positive',1)]:\n        coef=coefficients.get(key,0)*sign\n        if not coef:continue\n        for u,g in state[state.state==channel].groupby('u'):\n            groups.setdefault(int(u),[]).append((g.i.to_numpy(np.int32),coef))\n    ids=[];weights=[];counts=[]\n    for u in range(len(a['uids'])):\n        lo,hi=h['offset'][u:u+2];ii=[h['ids'][lo:hi]];ww=[h['weight'][lo:hi]];mass=float(ww[0].sum()) or 1.\n        for items,coef in groups.get(u,[]):\n            ii.append(items);ww.append(np.full(len(items),coef*mass/len(items),np.float32))\n        ii=np.concatenate(ii);ww=np.concatenate(ww);ids.append(ii);weights.append(ww);counts.append(len(ii))\n    return {'ids':np.concatenate(ids),'weight':np.concatenate(weights),'offset':np.r_[0,np.cumsum(counts)]}\n''' + s[pos:]\ns=s.replace(\"histories=[history(a,c,stage) for c in configs];iv=\", \"histories=[history(a,c,stage) for c in configs];pop_history=histories[0]\\n    if config.get('state'):histories=[state_history(h,a,stage,config['state']) for h in histories]\\n    iv=\")\ns=s.replace(\"uv=net.user(h);scores=\", \"uv=net.user(h,batch_history(pop_history,start,end));scores=\")\ns=s.replace(\"'personal_prior':config.get('personal_prior')}\",\"'personal_prior':config.get('personal_prior'),'state':config.get('state')}\")\ns=s.replace(\"h=[batch_history(h,0,1) for h in histories];u=net.user(h);\", \"h=[batch_history(h,0,1) for h in histories];ph=batch_history(pop_history,0,1);u=net.user(h,ph);\")\ns=s.replace(\"net.user.popularity_affinity(h[0])\", \"net.user.popularity_affinity(ph)\")\ns=s.replace(\"t=time.perf_counter();net.user(h)\", \"t=time.perf_counter();net.user(h,ph)\")\nopen(p,'w').write(s)\nimport json\nc=json.load(open('/app/solution/personal_best.json'));st=json.load(open('/app/solution/state_best.json'));c['state']={k:st[k] for k in ['negative','positive']};c['score']=st['score'];json.dump(c,open('/app/solution/state_config.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/state_config.json --stage validation --output state_validation.parquet --name state_validation_model > /app/solution/state_validation.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 22,
      "nanos": 805540079
    },
    "formatted_output": ""
  }
}
118

Agent message

+41:15
Thinking unavailable No text recorded

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

119

Agent message

+41:38
Thinking unavailable No text recorded

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

120

Agent message

+41:59
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

+42:00
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

text(await tools.exec_command({cmd:"tail -2 /app/solution/state_validation.log; tail -3 /app/solution/final_denoise.log; du -sh /app/solution","max_output_tokens":1500}));
text(await tools.write_stdin({session_id:9185,chars:"","yield_time_ms":1000,"max_output_tokens":1000}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"e8edae","wall_time_seconds":0.00000512,"exit_code":0,"original_token_count":90,"output":"{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5728554999677726, 'user_encoding_ms_p95': 0.6165115503563356, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 4400 loss 7.826632709503174 sec 249.8\nstep 4500 loss 7.861194019317627 sec 254.6\nstep 4600 loss 7.8106281328201295 sec 259.7\n3.3G\t/app/solution\n"}
{"chunk_id":"e6c27b","wall_time_seconds":0.00000236,"exit_code":0,"original_token_count":0,"output":""}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d4d46b84-c2ab-4f35-b766-60d5a750b500",
    "process_id": "23151",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -2 /app/solution/state_validation.log; tail -3 /app/solution/final_denoise.log; du -sh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "tail -2 /app/solution/state_validation.log; tail -3 /app/solution/final_denoise.log; du -sh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5728554999677726, 'user_encoding_ms_p95': 0.6165115503563356, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 4400 loss 7.826632709503174 sec 249.8\nstep 4500 loss 7.861194019317627 sec 254.6\nstep 4600 loss 7.8106281328201295 sec 259.7\n3.3G\t/app/solution\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5728554999677726, 'user_encoding_ms_p95': 0.6165115503563356, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 4400 loss 7.826632709503174 sec 249.8\nstep 4500 loss 7.861194019317627 sec 254.6\nstep 4600 loss 7.8106281328201295 sec 259.7\n3.3G\t/app/solution\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5120
    },
    "formatted_output": "{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5728554999677726, 'user_encoding_ms_p95': 0.6165115503563356, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\nstep 4400 loss 7.826632709503174 sec 249.8\nstep 4500 loss 7.861194019317627 sec 254.6\nstep 4600 loss 7.8106281328201295 sec 259.7\n3.3G\t/app/solution\n"
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122

exec command

+42:13
Thinking unavailable No text recorded

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

Model text

A small contribution from active dislike history raises public NDCG@10 to about 0.048918. The selected model remains 260-dimensional. I’m verifying that ranking with the public evaluator and completing the final refit.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/state_validation.parquet > /app/solution/state_public_score.json","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/ensemble.py';s=open(p).read().replace(\"ck=torch.load(path,weights_only=False);b=\", \"ck=torch.load(path,weights_only=False)\\n        if stage=='test':assert ck.get('args',{}).get('stage')=='test', 'Final retrieval requires a model refitted on train plus validation history'\\n        b=\");open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/ensemble.py\nls -lh /app/solution/*pairs* /app/solution/*ragged*","max_output_tokens":1500}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"07189e","wall_time_seconds":1.00087524,"session_id":7523,"original_token_count":0,"output":""}
{"chunk_id":"837fba","wall_time_seconds":0.014322511,"exit_code":0,"original_token_count":78,"output":"-rw-r--r-- 1 agent agent 130M Sep 10 17:56 /app/solution/test_pairs.parquet\n-rw-r--r-- 1 agent agent 175M Sep 10 17:56 /app/solution/test_ragged.npz\n-rw-r--r-- 1 agent agent 126M Sep 10 17:55 /app/solution/validation_pairs.parquet\n-rw-r--r-- 1 agent agent 171M Sep 10 17:55 /app/solution/validation_ragged.npz\n"}
{"current_time":"2026-09-10 18:35:40 UTC"}
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Harness context codex item completed
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    ],
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
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    "exit_code": 0,
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Harness context codex item completed
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      "-lc",
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123

Agent message

+42:44
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

+43:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/fine_calibrate.py <<'PY'\nfrom fast_calibrate import *\[email protected]_grad()\ndef main():\n ev=Evaluator();c=json.load(open(O/'state_config.json'));u=torch.from_numpy(np.load(O/'state_validation_model_user_vectors.npy'));i=torch.from_numpy(np.load(O/'state_validation_model_item_vectors.npy'));f=np.load(O/'validation_popularity.npz')['features'];like7=torch.from_numpy(f[:,5]);like30=torch.from_numpy(f[:,7]);grid=[(a,p,g,old) for a in [.375,.5,.625] for p in [1.25,1.5,1.75] for g in [.25,.5,.75] for old in [-.25,0]];tops=[[] for _ in grid];n=len(i);b1=torch.empty((128,n));b2=torch.empty_like(b1);s=torch.empty_like(b1);bias=torch.empty_like(b1)\n for st in range(0,len(u),128):\n  nb=min(128,len(u)-st);torch.mm(u[st:st+nb,:129],i[:,:129].T,out=b1[:nb]);torch.mm(u[st:st+nb,129:258],i[:,129:258].T,out=b2[:nb]);z=u[st:st+nb,-2];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+nb],axis=1,count=n,bitorder='little')==0)\n  for j,(a,p,g,old) in enumerate(grid):\n   torch.mul((p+g*z)[:,None],like7[None,:],out=bias[:nb]);s[:nb].copy_(b1[:nb]).mul_(a/c['weights'][0]).add_(b2[:nb],alpha=(1-a)/c['weights'][1]).add_(bias[:nb]).add_(like30,alpha=old).masked_fill_(mask,-1e9);tops[j].append(s[:nb].topk(10,dim=-1).indices.numpy().copy())\n records=[{'score':ev.score(np.concatenate(t)),'weights':[a,1-a],'prior':{'like_7':p,'like_30':old},'personal_prior':{'like_7':g}} for t,(a,p,g,old) in zip(tops,grid)];records.sort(key=lambda r:r['score'],reverse=True);best=records[0];c.update(best);json.dump(c,open(O/'fine_best.json','w'),indent=2);json.dump(records,open(O/'fine_grid.json','w'),indent=2);print('TOP',records[:5],flush=True)\nif __name__=='__main__':main()\nPY\nulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fine_calibrate.py > /app/solution/fine.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"cat /app/solution/state_public_score.json; tail -3 /app/solution/final_denoise.log","max_output_tokens":1600}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"52efd8","wall_time_seconds":1.000950451,"session_id":27259,"original_token_count":0,"output":""}
{"chunk_id":"c3c71e","wall_time_seconds":0.00000453,"exit_code":0,"original_token_count":66,"output":"{\n  \"model\": \"/app/solution/state_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048918,\n    \"quality_users\": 4263\n  }\n}\nstep 5800 loss 7.739403395652771 sec 319.9\nstep 5900 loss 7.71438054561615 sec 325.1\nstep 6000 loss 7.7004862833023076 sec 330.2\n"}
{"current_time":"2026-09-10 18:36:34 UTC"}
Harness context token usage record
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Harness context codex item completed
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    "stdout": "{\n  \"model\": \"/app/solution/state_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048918,\n    \"quality_users\": 4263\n  }\n}\nstep 5800 loss 7.739403395652771 sec 319.9\nstep 5900 loss 7.71438054561615 sec 325.1\nstep 6000 loss 7.7004862833023076 sec 330.2\n",
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Harness context codex item completed
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125

Agent message

+43:45
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": "unknown",
        "cmd": "cat > /app/solution/fine_calibrate.py <<'PY'\nfrom fast_calibrate import *\[email protected]_grad()\ndef main():\n ev=Evaluator();c=json.load(open(O/'state_config.json'));u=torch.from_numpy(np.load(O/'state_validation_model_user_vectors.npy'));i=torch.from_numpy(np.load(O/'state_validation_model_item_vectors.npy'));f=np.load(O/'validation_popularity.npz')['features'];like7=torch.from_numpy(f[:,5]);like30=torch.from_numpy(f[:,7]);grid=[(a,p,g,old) for a in [.375,.5,.625] for p in [1.25,1.5,1.75] for g in [.25,.5,.75] for old in [-.25,0]];tops=[[] for _ in grid];n=len(i);b1=torch.empty((128,n));b2=torch.empty_like(b1);s=torch.empty_like(b1);bias=torch.empty_like(b1)\n for st in range(0,len(u),128):\n  nb=min(128,len(u)-st);torch.mm(u[st:st+nb,:129],i[:,:129].T,out=b1[:nb]);torch.mm(u[st:st+nb,129:258],i[:,129:258].T,out=b2[:nb]);z=u[st:st+nb,-2];mask=torch.from_numpy(np.unpackbits(ev.e.packed[st:st+nb],axis=1,count=n,bitorder='little')==0)\n  for j,(a,p,g,old) in enumerate(grid):\n   torch.mul((p+g*z)[:,None],like7[None,:],out=bias[:nb]);s[:nb].copy_(b1[:nb]).mul_(a/c['weights'][0]).add_(b2[:nb],alpha=(1-a)/c['weights'][1]).add_(bias[:nb]).add_(like30,alpha=old).masked_fill_(mask,-1e9);tops[j].append(s[:nb].topk(10,dim=-1).indices.numpy().copy())\n records=[{'score':ev.score(np.concatenate(t)),'weights':[a,1-a],'prior':{'like_7':p,'like_30':old},'personal_prior':{'like_7':g}} for t,(a,p,g,old) in zip(tops,grid)];records.sort(key=lambda r:r['score'],reverse=True);best=records[0];c.update(best);json.dump(c,open(O/'fine_best.json','w'),indent=2);json.dump(records,open(O/'fine_grid.json','w'),indent=2);print('TOP',records[:5],flush=True)\nif __name__=='__main__':main()\nPY\nulimit -v 3500000\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/fine_calibrate.py > /app/solution/fine.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 42,
      "nanos": 14700313
    },
    "formatted_output": ""
  }
}
126

Agent message

+44:07
Thinking unavailable No text recorded

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

127

exec command

+44:19
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.write_stdin({session_id:5935,chars:"","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"tail -2 /app/solution/fine.log; cat /app/solution/final_temporal_metrics.jsonl; cat /app/solution/final_denoise_metrics.jsonl","max_output_tokens":1600}));
text(await tools.exec_command({cmd:"python - <<'PY'\nimport json\njson.dump({'/app/solution/denoise_6000.pt':'/app/solution/final_denoise_6000.pt','/app/solution/temporal_2000.pt':'/app/solution/final_temporal_2000.pt'},open('/app/solution/final_refit_map.json','w'),indent=2)\nPY","max_output_tokens":500}));

Tool response

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"b9e2b2","wall_time_seconds":0.00000226,"exit_code":0,"original_token_count":0,"output":""}
{"chunk_id":"9dff66","wall_time_seconds":0.00000341,"exit_code":0,"original_token_count":175,"output":"TOP [{'score': 0.04891770952627558, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04886762625221446, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04881032564338565, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.048756442308703206, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.04875310206234905, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.5}}]\n{\"step\": 2000}\n{\"step\": 6000}\n"}
{"chunk_id":"ec353d","wall_time_seconds":0.000002671,"exit_code":0,"original_token_count":0,"output":""}
Harness context codex item completed
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      "/bin/sh",
      "-lc",
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    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "tail -2 /app/solution/fine.log; cat /app/solution/final_temporal_metrics.jsonl; cat /app/solution/final_denoise_metrics.jsonl"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "TOP [{'score': 0.04891770952627558, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04886762625221446, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04881032564338565, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.048756442308703206, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.04875310206234905, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.5}}]\n{\"step\": 2000}\n{\"step\": 6000}\n",
    "stderr": "",
    "aggregated_output": "TOP [{'score': 0.04891770952627558, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04886762625221446, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04881032564338565, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.048756442308703206, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.04875310206234905, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.5}}]\n{\"step\": 2000}\n{\"step\": 6000}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3410
    },
    "formatted_output": "TOP [{'score': 0.04891770952627558, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.5, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04886762625221446, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.5}}, {'score': 0.04881032564338565, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': 0}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.048756442308703206, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.25}}, {'score': 0.04875310206234905, 'weights': [0.5, 0.5], 'prior': {'like_7': 1.75, 'like_30': -0.25}, 'personal_prior': {'like_7': 0.5}}]\n{\"step\": 2000}\n{\"step\": 6000}\n"
  }
}
Harness context token usage record
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "python - <<'PY'\nimport json\njson.dump({'/app/solution/denoise_6000.pt':'/app/solution/final_denoise_6000.pt','/app/solution/temporal_2000.pt':'/app/solution/final_temporal_2000.pt'},open('/app/solution/final_refit_map.json','w'),indent=2)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nimport json\njson.dump({'/app/solution/denoise_6000.pt':'/app/solution/final_denoise_6000.pt','/app/solution/temporal_2000.pt':'/app/solution/final_temporal_2000.pt'},open('/app/solution/final_refit_map.json','w'),indent=2)\nPY"
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  }
}
128

exec command

+44:28
Thinking unavailable No text recorded

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

Model text

The finer calibration check did not improve the selected settings. The public evaluator confirms 0.048918 NDCG@10, and the matching train-plus-validation refit is complete. I’m generating and auditing the final submission now.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cp /app/solution/fine_best.json /app/solution/final_config.json\ncp /app/solution/state_validation.parquet /app/solution/validation.parquet\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/final_config.json --checkpoint-map /app/solution/final_refit_map.json --name final_model > /app/solution/final_export.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/README.md';s=open(p).read().replace('0.048127','0.048918').replace('**259-dimensional vectors**','**260-dimensional vectors**').replace('and an item-only popularity coordinate','and two popularity coordinates').replace('Serving histories retain at most 512 tracks per branch, weighted by organic share and a 90-day exponential decay.', 'Each branch uses at most 512 positive history tracks, weighted by organic share and a 90-day exponential decay. A small negative contribution (0.05 times the mean embedding) uses the cutoff-correct active dislike state. One additional user coordinate summarizes the historical popularity of tracks in that positive history; the item tower learns its matching recent-like popularity coordinate.').replace('0.42 ms median / 0.63 ms p95','0.57 ms median / 0.62 ms p95')\nopen(p,'w').write(s)\nPY\ncat > /app/solution/retrain.sh <<'SH'\n#!/bin/sh\n# All generated files remain under /app/solution. /task is input-only.\nset -eu\nexport OPENBLAS_NUM_THREADS=1\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nSOL=/app/solution\nif [ ! -f \"$SOL/train_catalog_events.parquet\" ]; then\n  \"$PY\" \"$SOL/prepare_catalog.py\"\nfi\n\"$PY\" \"$SOL/prepare.py\" test\n\"$PY\" \"$SOL/temporal.py\" test\n\"$PY\" \"$SOL/popularity.py\" test\n# Exact benchmark warmup used for the selected validation experiment.\n# For million-item catalogs use --negatives 2048 here, as in subsequent training.\n\"$PY\" \"$SOL/model.py\" --stage test --name parity_base --steps 1000 --eval-every 1000 --lr .002 --negatives 0 --threads 2\n\"$PY\" \"$SOL/model.py\" --stage test --name final_denoise --resume \"$SOL/parity_base_1000.pt\" --steps 6000 --eval-every 6000 --lr .001 --threads 2\n\"$PY\" \"$SOL/model.py\" --stage test --name final_temporal --resume \"$SOL/parity_base_1000.pt\" --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2\n\"$PY\" \"$SOL/ensemble.py\" --config \"$SOL/final_config.json\" --checkpoint-map \"$SOL/final_refit_map.json\" --name final_model\nSH\nchmod +x /app/solution/retrain.sh","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"35a9ca","wall_time_seconds":1.000181405,"session_id":7937,"original_token_count":0,"output":""}
{"chunk_id":"6702f6","wall_time_seconds":0.00000362,"exit_code":0,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:38:13 UTC"}
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-d6962e0d-8b40-4981-9178-15e0d7136135",
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      "/bin/sh",
      "-lc",
      "python - <<'PY'\np='/app/solution/README.md';s=open(p).read().replace('0.048127','0.048918').replace('**259-dimensional vectors**','**260-dimensional vectors**').replace('and an item-only popularity coordinate','and two popularity coordinates').replace('Serving histories retain at most 512 tracks per branch, weighted by organic share and a 90-day exponential decay.', 'Each branch uses at most 512 positive history tracks, weighted by organic share and a 90-day exponential decay. A small negative contribution (0.05 times the mean embedding) uses the cutoff-correct active dislike state. One additional user coordinate summarizes the historical popularity of tracks in that positive history; the item tower learns its matching recent-like popularity coordinate.').replace('0.42 ms median / 0.63 ms p95','0.57 ms median / 0.62 ms p95')\nopen(p,'w').write(s)\nPY\ncat > /app/solution/retrain.sh <<'SH'\n#!/bin/sh\n# All generated files remain under /app/solution. /task is input-only.\nset -eu\nexport OPENBLAS_NUM_THREADS=1\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nSOL=/app/solution\nif [ ! -f \"$SOL/train_catalog_events.parquet\" ]; then\n  \"$PY\" \"$SOL/prepare_catalog.py\"\nfi\n\"$PY\" \"$SOL/prepare.py\" test\n\"$PY\" \"$SOL/temporal.py\" test\n\"$PY\" \"$SOL/popularity.py\" test\n# Exact benchmark warmup used for the selected validation experiment.\n# For million-item catalogs use --negatives 2048 here, as in subsequent training.\n\"$PY\" \"$SOL/model.py\" --stage test --name parity_base --steps 1000 --eval-every 1000 --lr .002 --negatives 0 --threads 2\n\"$PY\" \"$SOL/model.py\" --stage test --name final_denoise --resume \"$SOL/parity_base_1000.pt\" --steps 6000 --eval-every 6000 --lr .001 --threads 2\n\"$PY\" \"$SOL/model.py\" --stage test --name final_temporal --resume \"$SOL/parity_base_1000.pt\" --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2\n\"$PY\" \"$SOL/ensemble.py\" --config \"$SOL/final_config.json\" --checkpoint-map \"$SOL/final_refit_map.json\" --name final_model\nSH\nchmod +x /app/solution/retrain.sh"
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    "parsed_cmd": [
      {
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        "cmd": "python - <<'PY'\np='/app/solution/README.md';s=open(p).read().replace('0.048127','0.048918').replace('**259-dimensional vectors**','**260-dimensional vectors**').replace('and an item-only popularity coordinate','and two popularity coordinates').replace('Serving histories retain at most 512 tracks per branch, weighted by organic share and a 90-day exponential decay.', 'Each branch uses at most 512 positive history tracks, weighted by organic share and a 90-day exponential decay. A small negative contribution (0.05 times the mean embedding) uses the cutoff-correct active dislike state. One additional user coordinate summarizes the historical popularity of tracks in that positive history; the item tower learns its matching recent-like popularity coordinate.').replace('0.42 ms median / 0.63 ms p95','0.57 ms median / 0.62 ms p95')\nopen(p,'w').write(s)\nPY\ncat > /app/solution/retrain.sh <<'SH'\n#!/bin/sh\n# All generated files remain under /app/solution. /task is input-only.\nset -eu\nexport OPENBLAS_NUM_THREADS=1\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nSOL=/app/solution\nif [ ! -f \"$SOL/train_catalog_events.parquet\" ]; then\n  \"$PY\" \"$SOL/prepare_catalog.py\"\nfi\n\"$PY\" \"$SOL/prepare.py\" test\n\"$PY\" \"$SOL/temporal.py\" test\n\"$PY\" \"$SOL/popularity.py\" test\n# Exact benchmark warmup used for the selected validation experiment.\n# For million-item catalogs use --negatives 2048 here, as in subsequent training.\n\"$PY\" \"$SOL/model.py\" --stage test --name parity_base --steps 1000 --eval-every 1000 --lr .002 --negatives 0 --threads 2\n\"$PY\" \"$SOL/model.py\" --stage test --name final_denoise --resume \"$SOL/parity_base_1000.pt\" --steps 6000 --eval-every 6000 --lr .001 --threads 2\n\"$PY\" \"$SOL/model.py\" --stage test --name final_temporal --resume \"$SOL/parity_base_1000.pt\" --steps 2000 --eval-every 2000 --lr .001 --temporal .75 --threads 2\n\"$PY\" \"$SOL/ensemble.py\" --config \"$SOL/final_config.json\" --checkpoint-map \"$SOL/final_refit_map.json\" --name final_model\nSH\nchmod +x /app/solution/retrain.sh"
      }
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    "status": "completed",
    "stdout": "",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3620
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    "formatted_output": ""
  }
}
Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-4d269dbd-4481-4f9f-9743-e5caa22b4550",
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      "/bin/sh",
      "-lc",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /app/solution/fine_best.json /app/solution/final_config.json\ncp /app/solution/state_validation.parquet /app/solution/validation.parquet\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/ensemble.py --config /app/solution/final_config.json --checkpoint-map /app/solution/final_refit_map.json --name final_model > /app/solution/final_export.log 2>&1"
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129

Agent message

+45:20
Thinking unavailable No text recorded

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

130

Agent message

+45:43
Thinking unavailable No text recorded

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

131

Agent message

+46:07
Thinking unavailable No text recorded

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

132

exec command

+46:21
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

text(await tools.write_stdin({session_id:7937,chars:"","yield_time_ms":1000,"max_output_tokens":1500}));
text(await tools.exec_command({cmd:"cat /app/solution/final_export.log; ls -lh /app/solution/submission.parquet /app/solution/final_model.pt /app/solution/final_model_item_vectors.npy","max_output_tokens":1600}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"82ec7f","wall_time_seconds":0.000001181,"exit_code":0,"original_token_count":0,"output":""}
{"chunk_id":"7b1918","wall_time_seconds":0.00000559,"exit_code":0,"original_token_count":111,"output":"{'rows': 845600, 'users': 8456, 'dimension': 260, 'user_encoding_ms_p50': 0.5601149998710753, 'user_encoding_ms_p95': 0.5831474501292178, 'score_contract': 'score = user_tower(history) dot item_tower(item)'}\n-rw-r--r-- 1 agent agent  65M Sep 10 18:38 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent  30M Sep 10 18:38 /app/solution/final_model_item_vectors.npy\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:38 /app/solution/submission.parquet\n"}
{"current_time":"2026-09-10 18:39:40 UTC"}
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133

exec command

+46:29
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 final refitted model and ranking are written. The export checks passed for independently cached item vectors and exact dot-product scoring. I’m checking every submitted row for target-user coverage, ranks, duplicates, and test-cutoff eligibility.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"cat > /app/solution/audit.py <<'PY'\n\"\"\"Validate the submission and independently reload/test the final two towers.\"\"\"\nfrom ensemble import *\nimport sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\[email protected]_grad()\ndef main():\n    torch.set_num_threads(1);targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(O/'submission.parquet'),targets);eligible=CandidateSet(D/'test_eligible_candidates.npz')\n    assert all(eligible.is_eligible(int(u),int(i)) for u,i in df[['uid','item_id']].itertuples(index=False,name=None))\n    ck=torch.load(O/'final_model.pt',weights_only=False);assert ck['stage']=='test';n=len(eligible.item_ids);bases=[TwoTower(n,d) for d in ck['dims']];pop=np.load(O/'test_popularity.npz');names=list(pop['names']);features=torch.zeros((n+1,len(names)));features[1:]=torch.from_numpy(pop['features']);stat=ck['user_popularity'];prior=[ck['prior'].get(str(name),0) for name in names];personal=[ck['personal_prior'].get(str(name),0) for name in names];net=RetrievalModel(bases,ck['coefficients'],features,prior,features[:,names.index(stat['feature'])],stat,personal);net.load_state_dict(ck['state_dict']);net.eval()\n    for p in ck['base_checkpoints']:assert torch.load(p,weights_only=False)['args']['stage']=='test'\n    a=load('test');histories=[history(a,c,'test') for c in ck['configs']];ph=histories[0];histories=[state_history(h,a,'test',ck['state']) for h in histories];cached_users=np.load(O/'final_model_user_vectors.npy');cached_items=np.load(O/'final_model_item_vectors.npy');assert np.array_equal(np.load(O/'final_model_item_ids.npy'),eligible.item_ids)\n    rng=np.random.default_rng(917);users=np.unique(np.r_[rng.choice(len(a['uids']),100,replace=False),np.argmax(np.diff(histories[0]['offset'])),np.argmin(np.diff(histories[0]['offset']))]);itemids=rng.integers(1,n+1,100);fresh_items=net.item(torch.from_numpy(itemids));assert np.allclose(fresh_items.numpy(),cached_items[itemids-1],atol=2e-5)\n    errors=[];timings=[]\n    for u in users:\n        h=[batch_history(h,int(u),int(u)+1) for h in histories];pp=batch_history(ph,int(u),int(u)+1);net.user(h,pp);start=time.perf_counter();v=net.user(h,pp);timings.append((time.perf_counter()-start)*1000);errors.append(float(np.max(np.abs(v.numpy()[0]-cached_users[u]))));assert np.allclose(v.numpy()[0],cached_users[u],atol=2e-5)\n        direct=(v@fresh_items.T).numpy()[0];cached=cached_users[u]@cached_items[itemids-1].T;assert np.allclose(direct,cached,atol=1e-4)\n    report={'rows':len(df),'target_users':len(targets),'ranks_per_user':100,'eligible_fraction':1.,'duplicate_pairs':0,'vector_dimension':cached_items.shape[1],'user_tower_trainable_parameters':sum(p.numel() for p in net.user.parameters() if p.requires_grad),'item_tower_trainable_parameters':sum(p.numel() for p in net.item.parameters() if p.requires_grad),'reloaded_user_vector_max_error':max(errors),'sampled_users_for_latency':len(users),'history_encoding_ms_p50':float(np.median(timings)),'history_encoding_ms_p95':float(np.quantile(timings,.95)),'history_encoding_ms_max':float(np.max(timings)),'validation_ndcg_at_10':0.048918}\n    json.dump(report,open(O/'audit.json','w'),indent=2);print(json.dumps(report,indent=2))\nif __name__=='__main__':main()\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/audit.py > /app/solution/audit.log 2>&1","yield_time_ms":1000}));
text(await tools.exec_command({cmd:"cp /app/solution/submission.parquet /app/solution/best_submission.parquet\npython - <<'PY'\np='/app/solution/README.md';s=open(p).read().replace('0.57 ms median / 0.62 ms p95','0.56 ms median / 0.58 ms p95').replace('A warmed CPU user encoding measured', 'A warmed single-user CPU encoding measured');s+='\\nTo regenerate training artifacts and refit both neural branches from the raw inputs, run `sh /app/solution/retrain.sh`. To rerun the structural and independent-vector checks, run `/opt/task-venv/bin/python /app/solution/audit.py`.\\n';open(p,'w').write(s)\nPY","max_output_tokens":500}));

Tool response

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"7bfc43","wall_time_seconds":1.000669828,"session_id":4165,"original_token_count":0,"output":""}
{"chunk_id":"d00249","wall_time_seconds":0.000003591,"exit_code":0,"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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      "cat > /app/solution/audit.py <<'PY'\n\"\"\"Validate the submission and independently reload/test the final two towers.\"\"\"\nfrom ensemble import *\nimport sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\[email protected]_grad()\ndef main():\n    torch.set_num_threads(1);targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(O/'submission.parquet'),targets);eligible=CandidateSet(D/'test_eligible_candidates.npz')\n    assert all(eligible.is_eligible(int(u),int(i)) for u,i in df[['uid','item_id']].itertuples(index=False,name=None))\n    ck=torch.load(O/'final_model.pt',weights_only=False);assert ck['stage']=='test';n=len(eligible.item_ids);bases=[TwoTower(n,d) for d in ck['dims']];pop=np.load(O/'test_popularity.npz');names=list(pop['names']);features=torch.zeros((n+1,len(names)));features[1:]=torch.from_numpy(pop['features']);stat=ck['user_popularity'];prior=[ck['prior'].get(str(name),0) for name in names];personal=[ck['personal_prior'].get(str(name),0) for name in names];net=RetrievalModel(bases,ck['coefficients'],features,prior,features[:,names.index(stat['feature'])],stat,personal);net.load_state_dict(ck['state_dict']);net.eval()\n    for p in ck['base_checkpoints']:assert torch.load(p,weights_only=False)['args']['stage']=='test'\n    a=load('test');histories=[history(a,c,'test') for c in ck['configs']];ph=histories[0];histories=[state_history(h,a,'test',ck['state']) for h in histories];cached_users=np.load(O/'final_model_user_vectors.npy');cached_items=np.load(O/'final_model_item_vectors.npy');assert np.array_equal(np.load(O/'final_model_item_ids.npy'),eligible.item_ids)\n    rng=np.random.default_rng(917);users=np.unique(np.r_[rng.choice(len(a['uids']),100,replace=False),np.argmax(np.diff(histories[0]['offset'])),np.argmin(np.diff(histories[0]['offset']))]);itemids=rng.integers(1,n+1,100);fresh_items=net.item(torch.from_numpy(itemids));assert np.allclose(fresh_items.numpy(),cached_items[itemids-1],atol=2e-5)\n    errors=[];timings=[]\n    for u in users:\n        h=[batch_history(h,int(u),int(u)+1) for h in histories];pp=batch_history(ph,int(u),int(u)+1);net.user(h,pp);start=time.perf_counter();v=net.user(h,pp);timings.append((time.perf_counter()-start)*1000);errors.append(float(np.max(np.abs(v.numpy()[0]-cached_users[u]))));assert np.allclose(v.numpy()[0],cached_users[u],atol=2e-5)\n        direct=(v@fresh_items.T).numpy()[0];cached=cached_users[u]@cached_items[itemids-1].T;assert np.allclose(direct,cached,atol=1e-4)\n    report={'rows':len(df),'target_users':len(targets),'ranks_per_user':100,'eligible_fraction':1.,'duplicate_pairs':0,'vector_dimension':cached_items.shape[1],'user_tower_trainable_parameters':sum(p.numel() for p in net.user.parameters() if p.requires_grad),'item_tower_trainable_parameters':sum(p.numel() for p in net.item.parameters() if p.requires_grad),'reloaded_user_vector_max_error':max(errors),'sampled_users_for_latency':len(users),'history_encoding_ms_p50':float(np.median(timings)),'history_encoding_ms_p95':float(np.quantile(timings,.95)),'history_encoding_ms_max':float(np.max(timings)),'validation_ndcg_at_10':0.048918}\n    json.dump(report,open(O/'audit.json','w'),indent=2);print(json.dumps(report,indent=2))\nif __name__=='__main__':main()\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/audit.py > /app/solution/audit.log 2>&1"
    ],
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      {
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        "cmd": "cat > /app/solution/audit.py <<'PY'\n\"\"\"Validate the submission and independently reload/test the final two towers.\"\"\"\nfrom ensemble import *\nimport sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\[email protected]_grad()\ndef main():\n    torch.set_num_threads(1);targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));df=validate_ranking(pd.read_parquet(O/'submission.parquet'),targets);eligible=CandidateSet(D/'test_eligible_candidates.npz')\n    assert all(eligible.is_eligible(int(u),int(i)) for u,i in df[['uid','item_id']].itertuples(index=False,name=None))\n    ck=torch.load(O/'final_model.pt',weights_only=False);assert ck['stage']=='test';n=len(eligible.item_ids);bases=[TwoTower(n,d) for d in ck['dims']];pop=np.load(O/'test_popularity.npz');names=list(pop['names']);features=torch.zeros((n+1,len(names)));features[1:]=torch.from_numpy(pop['features']);stat=ck['user_popularity'];prior=[ck['prior'].get(str(name),0) for name in names];personal=[ck['personal_prior'].get(str(name),0) for name in names];net=RetrievalModel(bases,ck['coefficients'],features,prior,features[:,names.index(stat['feature'])],stat,personal);net.load_state_dict(ck['state_dict']);net.eval()\n    for p in ck['base_checkpoints']:assert torch.load(p,weights_only=False)['args']['stage']=='test'\n    a=load('test');histories=[history(a,c,'test') for c in ck['configs']];ph=histories[0];histories=[state_history(h,a,'test',ck['state']) for h in histories];cached_users=np.load(O/'final_model_user_vectors.npy');cached_items=np.load(O/'final_model_item_vectors.npy');assert np.array_equal(np.load(O/'final_model_item_ids.npy'),eligible.item_ids)\n    rng=np.random.default_rng(917);users=np.unique(np.r_[rng.choice(len(a['uids']),100,replace=False),np.argmax(np.diff(histories[0]['offset'])),np.argmin(np.diff(histories[0]['offset']))]);itemids=rng.integers(1,n+1,100);fresh_items=net.item(torch.from_numpy(itemids));assert np.allclose(fresh_items.numpy(),cached_items[itemids-1],atol=2e-5)\n    errors=[];timings=[]\n    for u in users:\n        h=[batch_history(h,int(u),int(u)+1) for h in histories];pp=batch_history(ph,int(u),int(u)+1);net.user(h,pp);start=time.perf_counter();v=net.user(h,pp);timings.append((time.perf_counter()-start)*1000);errors.append(float(np.max(np.abs(v.numpy()[0]-cached_users[u]))));assert np.allclose(v.numpy()[0],cached_users[u],atol=2e-5)\n        direct=(v@fresh_items.T).numpy()[0];cached=cached_users[u]@cached_items[itemids-1].T;assert np.allclose(direct,cached,atol=1e-4)\n    report={'rows':len(df),'target_users':len(targets),'ranks_per_user':100,'eligible_fraction':1.,'duplicate_pairs':0,'vector_dimension':cached_items.shape[1],'user_tower_trainable_parameters':sum(p.numel() for p in net.user.parameters() if p.requires_grad),'item_tower_trainable_parameters':sum(p.numel() for p in net.item.parameters() if p.requires_grad),'reloaded_user_vector_max_error':max(errors),'sampled_users_for_latency':len(users),'history_encoding_ms_p50':float(np.median(timings)),'history_encoding_ms_p95':float(np.quantile(timings,.95)),'history_encoding_ms_max':float(np.max(timings)),'validation_ndcg_at_10':0.048918}\n    json.dump(report,open(O/'audit.json','w'),indent=2);print(json.dumps(report,indent=2))\nif __name__=='__main__':main()\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -u /app/solution/audit.py > /app/solution/audit.log 2>&1"
      }
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  }
}
134

Agent message

+47:31
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

+47:50
Thinking unavailable No text recorded

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

136

Agent message

+48:13
Thinking unavailable No text recorded

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

137

exec command

+48:28
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

text(await tools.write_stdin({session_id:4165,chars:"","yield_time_ms":1000,"max_output_tokens":1500}));
text(await tools.exec_command({cmd:"cat /app/solution/audit.log; cat /app/solution/README.md","max_output_tokens":5000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"68534c","wall_time_seconds":0.000001501,"exit_code":0,"original_token_count":0,"output":""}
{"chunk_id":"650910","wall_time_seconds":0.00000363,"exit_code":0,"original_token_count":1036,"output":"{\n  \"rows\": 845600,\n  \"target_users\": 8456,\n  \"ranks_per_user\": 100,\n  \"eligible_fraction\": 1.0,\n  \"duplicate_pairs\": 0,\n  \"vector_dimension\": 260,\n  \"user_tower_trainable_parameters\": 7812098,\n  \"item_tower_trainable_parameters\": 7773090,\n  \"reloaded_user_vector_max_error\": 8.940696716308594e-08,\n  \"sampled_users_for_latency\": 102,\n  \"history_encoding_ms_p50\": 0.6014304999553133,\n  \"history_encoding_ms_p95\": 0.7021484997039805,\n  \"history_encoding_ms_max\": 1.2993749996894621,\n  \"validation_ndcg_at_10\": 0.048918\n}\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains 100 eligible, distinct tracks for each of the 8,456 target users. Public validation: **0.048918 graded NDCG@10**, over 4,263 users with eligible relevance.\n\nThe separate, trainable PyTorch user and item towers each contain two 128-dimensional neural branches. A user branch pools trainable track embeddings from interaction history, then applies a residual 128→256→128 ReLU network and normalization. It contains no user-ID embedding. An item branch applies its own trainable item embedding and residual projection, normalization, learned scale, and bias. The combined towers concatenate branch vectors and two popularity coordinates, producing **260-dimensional vectors**. Their dot product is the entire relevance score. Eligibility is the only subsequent filter; there is no candidate-conditioned user computation or reranker.\n\nEach branch uses at most 512 positive history tracks, weighted by organic share and a 90-day exponential decay. A small negative contribution (0.05 times the mean embedding) uses the cutoff-correct active dislike state. One additional user coordinate summarizes the historical popularity of tracks in that positive history; the item tower learns its matching recent-like popularity coordinate. Item-only popularity features use distinct organic listeners/likers observed before the relevant cutoff. Item vectors are exported for offline indexing. The benchmark uses batched exact inner products to generate rankings; production can use maximum inner-product search over the same vectors. A warmed single-user CPU encoding measured approximately 0.56 ms median / 0.58 ms p95, excluding history fetch and index retrieval.\n\nTraining combines two objectives: masked-history reconstruction and chronological next-week discovery prediction. The latter uses only earlier history, excludes previously positive tracks from targets, and samples organic likes with gain 7 versus gain 1 for organic ≥80% listens. The bulk of training uses importance-corrected sampled softmax with 2,048 shared uniform negatives. A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs. All data preparation uses event tables and ragged histories. No global user–item interaction matrix is constructed or decomposed.\n\nValidation representations use only `train_*`; validation outcomes are used solely for approach selection. Final models are refitted on `train_* + val_*`, with test-cutoff histories, item statistics, and eligibility. Organic outcomes are an offline target, not a causal estimate of online lift.\n\nKey code: `model.py` defines and trains the neural branches; `prepare_catalog.py`, `prepare.py`, and `temporal.py` construct observed histories and temporal examples; `popularity.py` constructs cutoff-only item features; `ensemble.py` defines and applies the final separate towers. Checkpoints and selected configuration are included.\n\nFinal submission command, using the included fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py \\\n  --config /app/solution/final_config.json \\\n  --checkpoint-map /app/solution/final_refit_map.json \\\n  --name final_model\n```\n\nThis writes `submission.parquet`, the fitted `final_model.pt`, and offline item vectors/IDs under `/app/solution/`.\n\nTo regenerate training artifacts and refit both neural branches from the raw inputs, run `sh /app/solution/retrain.sh`. To rerun the structural and independent-vector checks, run `/opt/task-venv/bin/python /app/solution/audit.py`.\n"}
{"current_time":"2026-09-10 18:41:47 UTC"}
Harness context codex item completed
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    "stdout": "{\n  \"rows\": 845600,\n  \"target_users\": 8456,\n  \"ranks_per_user\": 100,\n  \"eligible_fraction\": 1.0,\n  \"duplicate_pairs\": 0,\n  \"vector_dimension\": 260,\n  \"user_tower_trainable_parameters\": 7812098,\n  \"item_tower_trainable_parameters\": 7773090,\n  \"reloaded_user_vector_max_error\": 8.940696716308594e-08,\n  \"sampled_users_for_latency\": 102,\n  \"history_encoding_ms_p50\": 0.6014304999553133,\n  \"history_encoding_ms_p95\": 0.7021484997039805,\n  \"history_encoding_ms_max\": 1.2993749996894621,\n  \"validation_ndcg_at_10\": 0.048918\n}\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains 100 eligible, distinct tracks for each of the 8,456 target users. Public validation: **0.048918 graded NDCG@10**, over 4,263 users with eligible relevance.\n\nThe separate, trainable PyTorch user and item towers each contain two 128-dimensional neural branches. A user branch pools trainable track embeddings from interaction history, then applies a residual 128→256→128 ReLU network and normalization. It contains no user-ID embedding. An item branch applies its own trainable item embedding and residual projection, normalization, learned scale, and bias. The combined towers concatenate branch vectors and two popularity coordinates, producing **260-dimensional vectors**. Their dot product is the entire relevance score. Eligibility is the only subsequent filter; there is no candidate-conditioned user computation or reranker.\n\nEach branch uses at most 512 positive history tracks, weighted by organic share and a 90-day exponential decay. A small negative contribution (0.05 times the mean embedding) uses the cutoff-correct active dislike state. One additional user coordinate summarizes the historical popularity of tracks in that positive history; the item tower learns its matching recent-like popularity coordinate. Item-only popularity features use distinct organic listeners/likers observed before the relevant cutoff. Item vectors are exported for offline indexing. The benchmark uses batched exact inner products to generate rankings; production can use maximum inner-product search over the same vectors. A warmed single-user CPU encoding measured approximately 0.56 ms median / 0.58 ms p95, excluding history fetch and index retrieval.\n\nTraining combines two objectives: masked-history reconstruction and chronological next-week discovery prediction. The latter uses only earlier history, excludes previously positive tracks from targets, and samples organic likes with gain 7 versus gain 1 for organic ≥80% listens. The bulk of training uses importance-corrected sampled softmax with 2,048 shared uniform negatives. A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs. All data preparation uses event tables and ragged histories. No global user–item interaction matrix is constructed or decomposed.\n\nValidation representations use only `train_*`; validation outcomes are used solely for approach selection. Final models are refitted on `train_* + val_*`, with test-cutoff histories, item statistics, and eligibility. Organic outcomes are an offline target, not a causal estimate of online lift.\n\nKey code: `model.py` defines and trains the neural branches; `prepare_catalog.py`, `prepare.py`, and `temporal.py` construct observed histories and temporal examples; `popularity.py` constructs cutoff-only item features; `ensemble.py` defines and applies the final separate towers. Checkpoints and selected configuration are included.\n\nFinal submission command, using the included fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py \\\n  --config /app/solution/final_config.json \\\n  --checkpoint-map /app/solution/final_refit_map.json \\\n  --name final_model\n```\n\nThis writes `submission.parquet`, the fitted `final_model.pt`, and offline item vectors/IDs under `/app/solution/`.\n\nTo regenerate training artifacts and refit both neural branches from the raw inputs, run `sh /app/solution/retrain.sh`. To rerun the structural and independent-vector checks, run `/opt/task-venv/bin/python /app/solution/audit.py`.\n",
    "stderr": "",
    "aggregated_output": "{\n  \"rows\": 845600,\n  \"target_users\": 8456,\n  \"ranks_per_user\": 100,\n  \"eligible_fraction\": 1.0,\n  \"duplicate_pairs\": 0,\n  \"vector_dimension\": 260,\n  \"user_tower_trainable_parameters\": 7812098,\n  \"item_tower_trainable_parameters\": 7773090,\n  \"reloaded_user_vector_max_error\": 8.940696716308594e-08,\n  \"sampled_users_for_latency\": 102,\n  \"history_encoding_ms_p50\": 0.6014304999553133,\n  \"history_encoding_ms_p95\": 0.7021484997039805,\n  \"history_encoding_ms_max\": 1.2993749996894621,\n  \"validation_ndcg_at_10\": 0.048918\n}\n# CPU two-tower discovery ranker\n\n`submission.parquet` contains 100 eligible, distinct tracks for each of the 8,456 target users. Public validation: **0.048918 graded NDCG@10**, over 4,263 users with eligible relevance.\n\nThe separate, trainable PyTorch user and item towers each contain two 128-dimensional neural branches. A user branch pools trainable track embeddings from interaction history, then applies a residual 128→256→128 ReLU network and normalization. It contains no user-ID embedding. An item branch applies its own trainable item embedding and residual projection, normalization, learned scale, and bias. The combined towers concatenate branch vectors and two popularity coordinates, producing **260-dimensional vectors**. Their dot product is the entire relevance score. Eligibility is the only subsequent filter; there is no candidate-conditioned user computation or reranker.\n\nEach branch uses at most 512 positive history tracks, weighted by organic share and a 90-day exponential decay. A small negative contribution (0.05 times the mean embedding) uses the cutoff-correct active dislike state. One additional user coordinate summarizes the historical popularity of tracks in that positive history; the item tower learns its matching recent-like popularity coordinate. Item-only popularity features use distinct organic listeners/likers observed before the relevant cutoff. Item vectors are exported for offline indexing. The benchmark uses batched exact inner products to generate rankings; production can use maximum inner-product search over the same vectors. A warmed single-user CPU encoding measured approximately 0.56 ms median / 0.58 ms p95, excluding history fetch and index retrieval.\n\nTraining combines two objectives: masked-history reconstruction and chronological next-week discovery prediction. The latter uses only earlier history, excludes previously positive tracks from targets, and samples organic likes with gain 7 versus gain 1 for organic ≥80% listens. The bulk of training uses importance-corrected sampled softmax with 2,048 shared uniform negatives. A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs. All data preparation uses event tables and ragged histories. No global user–item interaction matrix is constructed or decomposed.\n\nValidation representations use only `train_*`; validation outcomes are used solely for approach selection. Final models are refitted on `train_* + val_*`, with test-cutoff histories, item statistics, and eligibility. Organic outcomes are an offline target, not a causal estimate of online lift.\n\nKey code: `model.py` defines and trains the neural branches; `prepare_catalog.py`, `prepare.py`, and `temporal.py` construct observed histories and temporal examples; `popularity.py` constructs cutoff-only item features; `ensemble.py` defines and applies the final separate towers. Checkpoints and selected configuration are included.\n\nFinal submission command, using the included fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py \\\n  --config /app/solution/final_config.json \\\n  --checkpoint-map /app/solution/final_refit_map.json \\\n  --name final_model\n```\n\nThis writes `submission.parquet`, the fitted `final_model.pt`, and offline item vectors/IDs under `/app/solution/`.\n\nTo regenerate training artifacts and refit both neural branches from the raw inputs, run `sh /app/solution/retrain.sh`. To rerun the structural and independent-vector checks, run `/opt/task-venv/bin/python /app/solution/audit.py`.\n",
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138

Agent message

+48:53
Thinking unavailable No text recorded

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

139

Agent message

+49:13
Thinking unavailable No text recorded

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

140

exec command

+49:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\", \"a=load(args.stage);n=len(a['items']);model=TwoTower(n,args.dim)\\n # Exact warmup is bounded to small catalogs; large catalogs always sample.\\n if args.negatives==0 and n>32768:args.negatives=2048\")\nopen(p,'w').write(s)\np='/app/solution/ensemble.py';s=open(p).read().replace(\"iv=net.item(torch.arange(1,len(a['items'])+1));el=\", \"# Candidate vectors are item-only and can be exported/indexed in bounded shards.\\n    if len(a['items'])<=32768:iv=net.item(torch.arange(1,len(a['items'])+1))\\n    else:iv=torch.cat([net.item(torch.arange(lo,min(lo+4096,len(a['items'])+1))) for lo in range(1,len(a['items'])+1,4096)])\\n    el=\")\nopen(p,'w').write(s)\np='/app/solution/README.md';s=open(p).read().replace('A full-catalog softmax warmup was tested on this small catalog; the implementation also supports sampled warmup for large catalogs.', 'The selected small-catalog warmup uses exact minibatch softmax; above 32,768 items the same code automatically switches warmup to sampled softmax. Offline item encoding likewise supports bounded batches.').replace('A warmed single-user CPU encoding measured approximately 0.56 ms median / 0.58 ms p95, excluding history fetch and index retrieval.', 'Across 102 sampled users (including the largest history), warmed CPU encoding measured approximately 0.60 ms median / 0.70 ms p95, excluding history fetch and index retrieval.');open(p,'w').write(s)\np='/app/solution/retrain.sh';s=open(p).read().replace('# For million-item catalogs use --negatives 2048 here, as in subsequent training.', '# Above 32,768 items the model automatically uses 2,048 sampled negatives.');open(p,'w').write(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/ensemble.py /app/solution/audit.py","max_output_tokens":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 0.2 seconds
Output:

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{"current_time":"2026-09-10 18:43:00 UTC"}
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141

Agent message

+50:06
Thinking unavailable No text recorded

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

142

exec command

+50:25
Thinking unavailable No text recorded

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

Model text

All final checks passed: 845,600 rows, 100 distinct eligible items per user, and ranks 1–100 for all 8,456 users. Reloaded towers reproduced the cached user vectors to within 9×10⁻⁸. I’m removing unused experiment artifacts while retaining the final models, code, validation ranking, and reproduction inputs.

Tool callexecTool call Returned

Tool call · Input

text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\np=Path('/app/solution')\nkeep={'submission.parquet','best_submission.parquet','validation.parquet','final_model.pt','final_model_item_vectors.npy','final_model_item_ids.npy','final_model_user_vectors.npy','final_denoise_6000.pt','final_temporal_2000.pt','parity_base_1000.pt','denoise_6000.pt','temporal_2000.pt','base_1000.pt','state_validation_model.pt','state_validation_model_item_vectors.npy','state_validation_model_item_ids.npy','state_validation_model_user_vectors.npy','test_ragged.npz','validation_ragged.npz','test_popularity.npz','validation_popularity.npz','train_catalog_events.parquet'}\na=hashlib.sha256((p/'submission.parquet').read_bytes()).hexdigest();b=hashlib.sha256((p/'best_submission.parquet').read_bytes()).hexdigest();assert a==b\nremoved=0;size=0\nfor f in p.iterdir():\n if f.is_file() and f.suffix in {'.pt','.npy','.npz','.parquet'} and f.name not in keep:\n  size+=f.stat().st_size;f.unlink();removed+=1\nfor name in keep:assert (p/name).exists(),name\njson.dump({'submission_sha256':a,'retained_model_files':['final_model.pt','final_denoise_6000.pt','final_temporal_2000.pt'],'public_ndcg_at_10':.048918},open(p/'manifest.json','w'),indent=2)\nprint('Removed unused binary artifacts:',removed,'MB:',round(size/1e6))\nPY\ndu -sh /app/solution","max_output_tokens":1200}));
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation_score.json","yield_time_ms":1000}));
text(await tools.clock__curr_time({}));

Tool response

Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"b325f7","wall_time_seconds":0.014119505,"exit_code":0,"original_token_count":16,"output":"Removed unused binary artifacts: 83 MB: 2951\n874M\t/app/solution\n"}
{"chunk_id":"aabbb5","wall_time_seconds":1.000979616,"session_id":83761,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 18:43:58 UTC"}
Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-ef112186-7564-455d-a42e-3451bdebeab4",
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    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\np=Path('/app/solution')\nkeep={'submission.parquet','best_submission.parquet','validation.parquet','final_model.pt','final_model_item_vectors.npy','final_model_item_ids.npy','final_model_user_vectors.npy','final_denoise_6000.pt','final_temporal_2000.pt','parity_base_1000.pt','denoise_6000.pt','temporal_2000.pt','base_1000.pt','state_validation_model.pt','state_validation_model_item_vectors.npy','state_validation_model_item_ids.npy','state_validation_model_user_vectors.npy','test_ragged.npz','validation_ragged.npz','test_popularity.npz','validation_popularity.npz','train_catalog_events.parquet'}\na=hashlib.sha256((p/'submission.parquet').read_bytes()).hexdigest();b=hashlib.sha256((p/'best_submission.parquet').read_bytes()).hexdigest();assert a==b\nremoved=0;size=0\nfor f in p.iterdir():\n if f.is_file() and f.suffix in {'.pt','.npy','.npz','.parquet'} and f.name not in keep:\n  size+=f.stat().st_size;f.unlink();removed+=1\nfor name in keep:assert (p/name).exists(),name\njson.dump({'submission_sha256':a,'retained_model_files':['final_model.pt','final_denoise_6000.pt','final_temporal_2000.pt'],'public_ndcg_at_10':.048918},open(p/'manifest.json','w'),indent=2)\nprint('Removed unused binary artifacts:',removed,'MB:',round(size/1e6))\nPY\ndu -sh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\np=Path('/app/solution')\nkeep={'submission.parquet','best_submission.parquet','validation.parquet','final_model.pt','final_model_item_vectors.npy','final_model_item_ids.npy','final_model_user_vectors.npy','final_denoise_6000.pt','final_temporal_2000.pt','parity_base_1000.pt','denoise_6000.pt','temporal_2000.pt','base_1000.pt','state_validation_model.pt','state_validation_model_item_vectors.npy','state_validation_model_item_ids.npy','state_validation_model_user_vectors.npy','test_ragged.npz','validation_ragged.npz','test_popularity.npz','validation_popularity.npz','train_catalog_events.parquet'}\na=hashlib.sha256((p/'submission.parquet').read_bytes()).hexdigest();b=hashlib.sha256((p/'best_submission.parquet').read_bytes()).hexdigest();assert a==b\nremoved=0;size=0\nfor f in p.iterdir():\n if f.is_file() and f.suffix in {'.pt','.npy','.npz','.parquet'} and f.name not in keep:\n  size+=f.stat().st_size;f.unlink();removed+=1\nfor name in keep:assert (p/name).exists(),name\njson.dump({'submission_sha256':a,'retained_model_files':['final_model.pt','final_denoise_6000.pt','final_temporal_2000.pt'],'public_ndcg_at_10':.048918},open(p/'manifest.json','w'),indent=2)\nprint('Removed unused binary artifacts:',removed,'MB:',round(size/1e6))\nPY\ndu -sh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Removed unused binary artifacts: 83 MB: 2951\n874M\t/app/solution\n",
    "stderr": "",
    "aggregated_output": "Removed unused binary artifacts: 83 MB: 2951\n874M\t/app/solution\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 14126485
    },
    "formatted_output": "Removed unused binary artifacts: 83 MB: 2951\n874M\t/app/solution\n"
  }
}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-0bd25898-2395-45d9-b09d-94911e33fc23",
    "process_id": "83761",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation_score.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_validation_score.json"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
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      "secs": 3,
      "nanos": 222831197
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}
143

exec command

+51:11
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

text(await tools.write_stdin({session_id:83761,chars:"","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"cat /app/solution/public_validation_score.json; cat /app/solution/manifest.json; ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/final_model.pt","max_output_tokens":1500}));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"e67042","wall_time_seconds":0.00000107,"exit_code":0,"original_token_count":0,"output":""}
{"chunk_id":"7be474","wall_time_seconds":0.00000511,"exit_code":0,"original_token_count":146,"output":"{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048918,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"submission_sha256\": \"8a18b11886b2e2eed7ded9b7648e38766f444e03fd0932743fe8a0c8acdd3b0a\",\n  \"retained_model_files\": [\n    \"final_model.pt\",\n    \"final_denoise_6000.pt\",\n    \"final_temporal_2000.pt\"\n  ],\n  \"public_ndcg_at_10\": 0.048918\n}-rw-r--r-- 1 agent agent 3.7K Sep 10 18:43 /app/solution/README.md\n-rw-r--r-- 1 agent agent  65M Sep 10 18:38 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:38 /app/solution/submission.parquet\n"}
Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-e7026bc9-e652-424b-99cf-7ceb48fdc7dc",
    "process_id": "56355",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/public_validation_score.json; cat /app/solution/manifest.json; ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/final_model.pt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/public_validation_score.json",
        "name": "public_validation_score.json",
        "path": "/app/solution/public_validation_score.json"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/manifest.json",
        "name": "manifest.json",
        "path": "/app/solution/manifest.json"
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        "type": "list_files",
        "cmd": "ls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/final_model.pt",
        "path": "submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048918,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"submission_sha256\": \"8a18b11886b2e2eed7ded9b7648e38766f444e03fd0932743fe8a0c8acdd3b0a\",\n  \"retained_model_files\": [\n    \"final_model.pt\",\n    \"final_denoise_6000.pt\",\n    \"final_temporal_2000.pt\"\n  ],\n  \"public_ndcg_at_10\": 0.048918\n}-rw-r--r-- 1 agent agent 3.7K Sep 10 18:43 /app/solution/README.md\n-rw-r--r-- 1 agent agent  65M Sep 10 18:38 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:38 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048918,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"submission_sha256\": \"8a18b11886b2e2eed7ded9b7648e38766f444e03fd0932743fe8a0c8acdd3b0a\",\n  \"retained_model_files\": [\n    \"final_model.pt\",\n    \"final_denoise_6000.pt\",\n    \"final_temporal_2000.pt\"\n  ],\n  \"public_ndcg_at_10\": 0.048918\n}-rw-r--r-- 1 agent agent 3.7K Sep 10 18:43 /app/solution/README.md\n-rw-r--r-- 1 agent agent  65M Sep 10 18:38 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:38 /app/solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5110
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.048918,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"submission_sha256\": \"8a18b11886b2e2eed7ded9b7648e38766f444e03fd0932743fe8a0c8acdd3b0a\",\n  \"retained_model_files\": [\n    \"final_model.pt\",\n    \"final_denoise_6000.pt\",\n    \"final_temporal_2000.pt\"\n  ],\n  \"public_ndcg_at_10\": 0.048918\n}-rw-r--r-- 1 agent agent 3.7K Sep 10 18:43 /app/solution/README.md\n-rw-r--r-- 1 agent agent  65M Sep 10 18:38 /app/solution/final_model.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:38 /app/solution/submission.parquet\n"
  }
}
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144

Final response

+51:20

Model text

Created submission.parquet: 100 distinct eligible items for all 8,456 users.

Public NDCG@10: 0.048918. Eligibility, rank, and exact dot-product checks passed.

Architecture, training code, and reproduction instructions are in README.md.

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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
codex-astra-high-base-r05-2026-09-10T17-31-17
Recorded model
gpt-6-astra
Harness version
0.153.4
Session
01a08c73-d6e0-7ed1-a511-eb36eb8e333f
Source SHA-256
480ed8884fb1d4322a1796d6a4634debf4019006be3c03a9644c42c181137035

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