yambda-discovery-ranking-two-tower-base / codex-astra-high-base-r03-2026-09-10T16-05-15
Recorded model: gpt-6-astra

## Step 1 · system · 2026-09-10T16:27:04.393Z

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](/abs/path/app.py:12): 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.


## Step 2 · developer · 2026-09-10T16:27:06.216Z

<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>

## Step 3 · developer · 2026-09-10T16:27:06.217Z

<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>

## Step 4 · developer · 2026-09-10T16:27:06.217Z

<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>

## Step 5 · user · 2026-09-10T16:27:06.217Z

<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
{
  "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
{
  "type": "turn_context",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "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"
}

## Step 6 · user · 2026-09-10T16:27:06.236Z

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

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

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

## Data

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

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

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

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

The two public windows have different roles:

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:

```sh
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.


## Step 7 · agent · 2026-09-10T16:27:11.165Z

I’ll inspect the data and evaluation code, build a history-based two-tower model, and tune it on the public window. Then I’ll refit on train plus validation and save the final ranking with reproducible code.


### exec · 2026-09-10T16:27:13.384Z
{
  "code": "text(await tools.exec_command({cmd:\"ls -lah /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py\",max_output_tokens:10000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:27:13.477Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"48b206","wall_time_seconds":0.00000761,"exit_code":0,"original_token_count":4120,"output":"total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:10 .\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:24 ..\n-r--r--r-- 1 root root 342K Sep 10 16:09 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 16:09 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 16:09 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 16:09 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 16:09 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 16:09 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 16:09 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 16:09 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 16:09 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 16:09 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 16:09 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 16:09 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 16:09 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 16:09 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 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#!/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 16:27:13 UTC"}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-477ab0fc-06af-43d7-a7e6-63906ac91f14",
    "process_id": "41793",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lah /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lah /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "cat /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,260p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:10 .\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:24 ..\n-r--r--r-- 1 root root 342K Sep 10 16:09 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 16:09 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 16:09 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 16:09 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 16:09 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 16:09 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 16:09 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 16:09 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 16:09 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 16:09 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 16:09 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 16:09 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 16:09 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 16:09 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 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#!/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": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:10 .\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:24 ..\n-r--r--r-- 1 root root 342K Sep 10 16:09 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 16:09 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 16:09 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 16:09 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 16:09 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 16:09 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 16:09 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 16:09 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 16:09 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 16:09 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 16:09 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 16:09 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 16:09 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 16:09 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 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#!/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": 7610
    },
    "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:10 .\ndr-xr-xr-x 1 root root 4.0K Sep 10 16:24 ..\n-r--r--r-- 1 root root 342K Sep 10 16:09 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 10 16:09 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 16:09 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 16:09 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 16:09 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 16:09 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 16:09 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 16:09 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 10 16:09 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 16:09 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 16:09 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 16:09 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 10 16:09 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 16:09 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 16:10 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#!/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"
  }
}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2da5a6db087d1b809758467bd7e96",
  "usage": {
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    "cached_input_tokens": 12160,
    "cache_write_input_tokens": 0,
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  "turn_token_usage": {
    "input_tokens": 16805,
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  "thread_token_usage": {
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    "reasoning_output_tokens": 0,
    "total_tokens": 16938
  }
}

### exec · 2026-09-10T16:27:22.851Z
{
  "code": "text(await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','val_listens','train_likes']:\\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema)\\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\\na=np.load('/task/data/validation_eligible_candidates.npz'); print([(k,a[k].shape) for k in a.files]);print('eligible average',np.unpackbits(a['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1).mean())\\nprint('cpus',os.cpu_count()); print(open('/sys/fs/cgroup/memory.max').read());print(open('/sys/fs/cgroup/cpu.max').read())\\nPY\",\"max_output_tokens\":4000}));\n"
}
### Tool output · 2026-09-10T16:27:22.910Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"46335b","wall_time_seconds":0.00000431,"exit_code":1,"original_token_count":30,"output":"Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n"}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2da61be4c87d1ab6a1a69b3702d32",
  "usage": {
    "input_tokens": 21800,
    "cached_input_tokens": 16640,
    "cache_write_input_tokens": 0,
    "output_tokens": 242,
    "reasoning_output_tokens": 0,
    "total_tokens": 22042
  },
  "turn_token_usage": {
    "input_tokens": 38605,
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    "output_tokens": 375,
    "reasoning_output_tokens": 0,
    "total_tokens": 38980
  },
  "thread_token_usage": {
    "input_tokens": 38605,
    "cached_input_tokens": 28800,
    "cache_write_input_tokens": 0,
    "output_tokens": 375,
    "reasoning_output_tokens": 0,
    "total_tokens": 38980
  }
}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7a8b1202-e23c-4bb9-87af-c619bdbd6517",
    "process_id": "33126",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution && python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','val_listens','train_likes']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\na=np.load('/task/data/validation_eligible_candidates.npz'); print([(k,a[k].shape) for k in a.files]);print('eligible average',np.unpackbits(a['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1).mean())\nprint('cpus',os.cpu_count()); print(open('/sys/fs/cgroup/memory.max').read());print(open('/sys/fs/cgroup/cpu.max').read())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','val_listens','train_likes']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\na=np.load('/task/data/validation_eligible_candidates.npz'); print([(k,a[k].shape) for k in a.files]);print('eligible average',np.unpackbits(a['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1).mean())\nprint('cpus',os.cpu_count()); print(open('/sys/fs/cgroup/memory.max').read());print(open('/sys/fs/cgroup/cpu.max').read())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 4310
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n"
  }
}

### exec · 2026-09-10T16:27:26.281Z
{
  "code": "text(await tools.exec_command({cmd:\"ls -la /app /opt /usr/local/bin && which python && ls -la /task\",max_output_tokens:2000}));\n"
}
### Tool output · 2026-09-10T16:27:26.355Z
Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"52519b","wall_time_seconds":0.00000374,"exit_code":0,"original_token_count":492,"output":"/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 16:27 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 16:24 solution\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 16:23 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root  4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root  4096 Aug  3 00:00 ..\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root    53 Sep 10 16:23 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12\nlrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12\nlrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3\nlrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12\n-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config\n/usr/local/bin/python\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\n-r--r--r-- 1 root root    0 Sep 10 16:09 .keep\ndr-xr-xr-x 1 root root 4096 Sep 10 16:10 data\n-r--r--r-- 1 root root 5774 Sep 10 16:09 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 10 16:09 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 10 16:09 requirements.txt\n"}

### Harness context
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-2a09e0fa-16c0-4bb5-afa2-552d556a9ab1",
    "process_id": "90104",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -la /app /opt /usr/local/bin && which python && ls -la /task"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -la /app /opt /usr/local/bin && which python && ls -la /task"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 16:27 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 16:24 solution\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 16:23 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root  4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root  4096 Aug  3 00:00 ..\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root    53 Sep 10 16:23 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12\nlrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12\nlrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3\nlrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12\n-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config\n/usr/local/bin/python\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\n-r--r--r-- 1 root root    0 Sep 10 16:09 .keep\ndr-xr-xr-x 1 root root 4096 Sep 10 16:10 data\n-r--r--r-- 1 root root 5774 Sep 10 16:09 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 10 16:09 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 10 16:09 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 16:27 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 16:24 solution\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 16:23 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root  4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root  4096 Aug  3 00:00 ..\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root    53 Sep 10 16:23 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12\nlrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12\nlrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3\nlrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12\n-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config\n/usr/local/bin/python\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\n-r--r--r-- 1 root root    0 Sep 10 16:09 .keep\ndr-xr-xr-x 1 root root 4096 Sep 10 16:10 data\n-r--r--r-- 1 root root 5774 Sep 10 16:09 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 10 16:09 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 10 16:09 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3740
    },
    "formatted_output": "/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root  root  4096 Sep 10 16:27 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 16:24 solution\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 16:23 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 task-venv\n\n/usr/local/bin:\ntotal 48\ndrwxr-xr-x 1 root root  4096 Sep 10 16:23 .\ndrwxr-xr-x 1 root root  4096 Aug  3 00:00 ..\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12\n-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12\nlrwxrwxrwx 1 root root    53 Sep 10 16:23 codex -> /opt/codex-home/packages/standalone/current/bin/codex\nlrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3\nlrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12\n-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12\nlrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3\n-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12\nlrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3\nlrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12\n-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12\nlrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3\nlrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config\nlrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12\nlrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config\n-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12\n-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config\n/usr/local/bin/python\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 10 16:24 .\ndrwxr-xr-x 1 root root 4096 Sep 10 16:27 ..\n-r--r--r-- 1 root root    0 Sep 10 16:09 .keep\ndr-xr-xr-x 1 root root 4096 Sep 10 16:10 data\n-r--r--r-- 1 root root 5774 Sep 10 16:09 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 10 16:09 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 10 16:09 requirements.txt\n"
  }
}

### Harness context
{
  "type": "token_usage_record",
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  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2da6b288c87d1b6cdaf538b5c1d9e",
  "usage": {
    "input_tokens": 22136,
    "cached_input_tokens": 21632,
    "cache_write_input_tokens": 0,
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  },
  "turn_token_usage": {
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  },
  "thread_token_usage": {
    "input_tokens": 60741,
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    "reasoning_output_tokens": 0,
    "total_tokens": 61162
  }
}

### exec · 2026-09-10T16:27:36.632Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','val_listens','train_likes']:\\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema)\\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\\na=np.load('/task/data/validation_eligible_candidates.npz'); print([(k,a[k].shape) for k in a.files]);print('eligible average',np.unpackbits(a['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1).mean())\\nprint('cpus',os.cpu_count()); print(open('/sys/fs/cgroup/memory.max').read());print(open('/sys/fs/cgroup/cpu.max').read())\\nPY\",\"max_output_tokens\":4000}));\n"
}
### Tool output · 2026-09-10T16:27:38.818Z
Script completed
Wall time 2.2 seconds
Output:

{"chunk_id":"b947af","wall_time_seconds":2.014899037,"exit_code":0,"original_token_count":1196,"output":"candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7f9d1fa38d80>\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 0x7f9d1fa86440>\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 0x7f9d01716200>\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\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x7f9d00ca2600>\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\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c84d80>\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\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7f9d0133c780>\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\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c29040>\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\n[('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible average 29067.306291390727\ncpus 8\n8589934592\n\n800000 100000\n\n"}

### Harness context
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  "usage": {
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}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-03ef8755-970d-4204-92d4-de09ccc2b798",
    "process_id": "46951",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','val_listens','train_likes']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\na=np.load('/task/data/validation_eligible_candidates.npz'); print([(k,a[k].shape) for k in a.files]);print('eligible average',np.unpackbits(a['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1).mean())\nprint('cpus',os.cpu_count()); print(open('/sys/fs/cgroup/memory.max').read());print(open('/sys/fs/cgroup/cpu.max').read())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','val_listens','train_likes']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\na=np.load('/task/data/validation_eligible_candidates.npz'); print([(k,a[k].shape) for k in a.files]);print('eligible average',np.unpackbits(a['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1).mean())\nprint('cpus',os.cpu_count()); print(open('/sys/fs/cgroup/memory.max').read());print(open('/sys/fs/cgroup/cpu.max').read())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7f9d1fa38d80>\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 0x7f9d1fa86440>\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 0x7f9d01716200>\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\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x7f9d00ca2600>\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\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c84d80>\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\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7f9d0133c780>\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\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c29040>\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\n[('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible average 29067.306291390727\ncpus 8\n8589934592\n\n800000 100000\n\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7f9d1fa38d80>\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 0x7f9d1fa86440>\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 0x7f9d01716200>\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\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x7f9d00ca2600>\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\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c84d80>\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\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7f9d0133c780>\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\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c29040>\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\n[('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible average 29067.306291390727\ncpus 8\n8589934592\n\n800000 100000\n\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 14892497
    },
    "formatted_output": "candidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x7f9d1fa38d80>\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 0x7f9d1fa86440>\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 0x7f9d01716200>\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\ntest_preference_state 484776 <pyarrow._parquet.ParquetSchema object at 0x7f9d00ca2600>\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\ntrain_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c84d80>\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\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x7f9d0133c780>\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\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x7f9d00c29040>\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\n[('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible average 29067.306291390727\ncpus 8\n8589934592\n\n800000 100000\n\n"
  }
}

## Step 8 · agent · 2026-09-10T16:27:46.752Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:28:01.267Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/inspect_data.py <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\\nD='/task/data/'\\na=np.load(D+'validation_eligible_candidates.npz'); users=a['uids'];items=a['item_ids']; um={int(x):i for i,x in enumerate(users)}; im={int(x):i for i,x in enumerate(items)}; eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little')\\nfor name in ['validation_preference_state','val_likes','val_listens']:\\n d=pd.read_parquet(D+name+'.parquet');d['u']=d.uid.map(um);d['i']=d.item_id.map(im); d=d.dropna(subset=['u','i']); d['e']=eligible[d.u.astype(int),d.i.astype(int)]\\n print(name,len(d),d.e.mean(),flush=True)\\n if 'state' in d: print(d.groupby('state').e.agg(['sum','count']),flush=True)\\n else: print(d.groupby(['is_organic','e']).size(),flush=True)\\n if name=='val_listens': print('time',d.timestamp.min(),d.timestamp.max(),flush=True)\\nprint('load train',flush=True)\\nd=pd.read_parquet(D+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('users',d.uid.nunique(),'items',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),flush=True)\\nd=d[(d.played_ratio_pct>=50)&d.item_id.isin(items)];print('catalog positives',len(d),flush=True)\\ng=d.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),organic=('is_organic','mean')).reset_index();print('pairs',len(g),flush=True)\\ng['u']=g.uid.map(um);g['i']=g.item_id.map(im);g=g.dropna();g['e']=eligible[g.u.astype(int),g.i.astype(int)]; print('seen elig',g.e.mean(),g.groupby('e').n.agg(['sum','count']),flush=True)\\ng.to_parquet('/app/solution/history_stats.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /app/solution/inspect_data.py\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}));\n"
}
### Tool output · 2026-09-10T16:28:02.436Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d759f7","wall_time_seconds":1.001950802,"session_id":32862,"original_token_count":0,"output":""}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2da7b032c87d1919f5b31667efd24",
  "usage": {
    "input_tokens": 24995,
    "cached_input_tokens": 23040,
    "cache_write_input_tokens": 0,
    "output_tokens": 694,
    "reasoning_output_tokens": 147,
    "total_tokens": 25689
  },
  "turn_token_usage": {
    "input_tokens": 108901,
    "cached_input_tokens": 95488,
    "cache_write_input_tokens": 0,
    "output_tokens": 1356,
    "reasoning_output_tokens": 147,
    "total_tokens": 110257
  },
  "thread_token_usage": {
    "input_tokens": 108901,
    "cached_input_tokens": 95488,
    "cache_write_input_tokens": 0,
    "output_tokens": 1356,
    "reasoning_output_tokens": 147,
    "total_tokens": 110257
  }
}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ec8796cc-e1cc-405f-a08d-e5c55b929bae",
    "process_id": "32862",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz'); users=a['uids'];items=a['item_ids']; um={int(x):i for i,x in enumerate(users)}; im={int(x):i for i,x in enumerate(items)}; eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little')\nfor name in ['validation_preference_state','val_likes','val_listens']:\n d=pd.read_parquet(D+name+'.parquet');d['u']=d.uid.map(um);d['i']=d.item_id.map(im); d=d.dropna(subset=['u','i']); d['e']=eligible[d.u.astype(int),d.i.astype(int)]\n print(name,len(d),d.e.mean(),flush=True)\n if 'state' in d: print(d.groupby('state').e.agg(['sum','count']),flush=True)\n else: print(d.groupby(['is_organic','e']).size(),flush=True)\n if name=='val_listens': print('time',d.timestamp.min(),d.timestamp.max(),flush=True)\nprint('load train',flush=True)\nd=pd.read_parquet(D+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('users',d.uid.nunique(),'items',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),flush=True)\nd=d[(d.played_ratio_pct>=50)&d.item_id.isin(items)];print('catalog positives',len(d),flush=True)\ng=d.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),organic=('is_organic','mean')).reset_index();print('pairs',len(g),flush=True)\ng['u']=g.uid.map(um);g['i']=g.item_id.map(im);g=g.dropna();g['e']=eligible[g.u.astype(int),g.i.astype(int)]; print('seen elig',g.e.mean(),g.groupby('e').n.agg(['sum','count']),flush=True)\ng.to_parquet('/app/solution/history_stats.parquet',index=False)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nD='/task/data/'\na=np.load(D+'validation_eligible_candidates.npz'); users=a['uids'];items=a['item_ids']; um={int(x):i for i,x in enumerate(users)}; im={int(x):i for i,x in enumerate(items)}; eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little')\nfor name in ['validation_preference_state','val_likes','val_listens']:\n d=pd.read_parquet(D+name+'.parquet');d['u']=d.uid.map(um);d['i']=d.item_id.map(im); d=d.dropna(subset=['u','i']); d['e']=eligible[d.u.astype(int),d.i.astype(int)]\n print(name,len(d),d.e.mean(),flush=True)\n if 'state' in d: print(d.groupby('state').e.agg(['sum','count']),flush=True)\n else: print(d.groupby(['is_organic','e']).size(),flush=True)\n if name=='val_listens': print('time',d.timestamp.min(),d.timestamp.max(),flush=True)\nprint('load train',flush=True)\nd=pd.read_parquet(D+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('users',d.uid.nunique(),'items',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),flush=True)\nd=d[(d.played_ratio_pct>=50)&d.item_id.isin(items)];print('catalog positives',len(d),flush=True)\ng=d.groupby(['uid','item_id']).agg(n=('timestamp','size'),last=('timestamp','max'),organic=('is_organic','mean')).reset_index();print('pairs',len(g),flush=True)\ng['u']=g.uid.map(um);g['i']=g.item_id.map(im);g=g.dropna();g['e']=eligible[g.u.astype(int),g.i.astype(int)]; print('seen elig',g.e.mean(),g.groupby('e').n.agg(['sum','count']),flush=True)\ng.to_parquet('/app/solution/history_stats.parquet',index=False)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "validation_preference_state 469124 0.0\n          sum   count\nstate                \ndisliked    0   50689\nliked       0  418435\nval_likes 18307 0.518162451521276\nis_organic  e\n0           0    2959\n            1    4942\n1           0    5862\n            1    4544\ndtype: int64\nval_listens 999062 0.27691874978730047\nis_organic  e\n0           0    313714\n            1    171382\n1           0    408689\n            1    105277\ndtype: int64\ntime 24788600 25393395\nload train\nusers 9238 items 845395 time 0 24786795\ncatalog positives 20056329\npairs 5564926\nseen elig 0.0         sum    count\ne                   \n0  19959853  5514505\n",
    "stderr": "",
    "aggregated_output": "validation_preference_state 469124 0.0\n          sum   count\nstate                \ndisliked    0   50689\nliked       0  418435\nval_likes 18307 0.518162451521276\nis_organic  e\n0           0    2959\n            1    4942\n1           0    5862\n            1    4544\ndtype: int64\nval_listens 999062 0.27691874978730047\nis_organic  e\n0           0    313714\n            1    171382\n1           0    408689\n            1    105277\ndtype: int64\ntime 24788600 25393395\nload train\nusers 9238 items 845395 time 0 24786795\ncatalog positives 20056329\npairs 5564926\nseen elig 0.0         sum    count\ne                   \n0  19959853  5514505\n",
    "exit_code": 0,
    "duration": {
      "secs": 7,
      "nanos": 610952252
    },
    "formatted_output": "validation_preference_state 469124 0.0\n          sum   count\nstate                \ndisliked    0   50689\nliked       0  418435\nval_likes 18307 0.518162451521276\nis_organic  e\n0           0    2959\n            1    4942\n1           0    5862\n            1    4544\ndtype: int64\nval_listens 999062 0.27691874978730047\nis_organic  e\n0           0    313714\n            1    171382\n1           0    408689\n            1    105277\ndtype: int64\ntime 24788600 25393395\nload train\nusers 9238 items 845395 time 0 24786795\ncatalog positives 20056329\npairs 5564926\nseen elig 0.0         sum    count\ne                   \n0  19959853  5514505\n"
  }
}

## Step 9 · agent · 2026-09-10T16:28:10.575Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:28:11.189Z
{
  "code": "text(await tools.write_stdin({session_id:32862,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}));\n"
}
### Tool output · 2026-09-10T16:28:11.242Z
Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"75f76a","wall_time_seconds":0.00000288,"exit_code":0,"original_token_count":158,"output":"validation_preference_state 469124 0.0\n          sum   count\nstate                \ndisliked    0   50689\nliked       0  418435\nval_likes 18307 0.518162451521276\nis_organic  e\n0           0    2959\n            1    4942\n1           0    5862\n            1    4544\ndtype: int64\nval_listens 999062 0.27691874978730047\nis_organic  e\n0           0    313714\n            1    171382\n1           0    408689\n            1    105277\ndtype: int64\ntime 24788600 25393395\nload train\nusers 9238 items 845395 time 0 24786795\ncatalog positives 20056329\npairs 5564926\nseen elig 0.0         sum    count\ne                   \n0  19959853  5514505\n"}

### Harness context
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  }
}

## Step 10 · agent · 2026-09-10T16:28:34.578Z

### Thinking unavailable — no text recorded


The eligibility sets exclude prior positive listens and active explicit preferences, so this is an unseen-item discovery task. I’ll train the towers to predict held-out tracks from listening history, with stronger weight on organic engagement and likes. Training will use sampled histories and negatives, without constructing a global user–item matrix.


### exec · 2026-09-10T16:29:02.940Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/prepare.py <<'PY'\\n\\\"\\\"\\\"Create event-list/ragged-history training data; never a user-item matrix.\\\"\\\"\\\"\\nimport numpy as np,pandas as pd, argparse,time\\nfrom pathlib import Path\\nP=Path('/app/solution');D=Path('/task/data');DAY=86400\\n\\ndef prepare(stage):\\n out=P/f'data_{stage}.npz'\\n if out.exists():return\\n cutoff=24786800 if stage=='validation' else 25393400\\n fs=['train'] if stage=='validation' else ['train','val']\\n items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();imap=pd.Series(np.arange(len(items)),index=items)\\n chunks=[]\\n for f in fs:\\n  d=pd.read_parquet(D/f'{f}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\\n  d=d[d.played_ratio_pct>=50].copy();d['i']=d.item_id.map(imap);d=d.dropna(subset=['i']);d['i']=d.i.astype('int32')\\n  d['w']=(.5+.5*d.is_organic)*(0.6+0.4*(d.played_ratio_pct>=80))*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*90))\\n  d['y']=(.15+.85*d.is_organic)*(d.played_ratio_pct>=80)*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*90))\\n  g=d.groupby(['uid','i'],sort=False).agg(w=('w','sum'),y=('y','sum'),first=('timestamp','min'),last=('timestamp','max')).reset_index();chunks.append(g);del d\\n g=pd.concat(chunks).groupby(['uid','i'],sort=False).agg(w=('w','sum'),y=('y','sum'),first=('first','min'),last=('last','max')).reset_index();del chunks\\n likes=[]\\n for f in fs:\\n  d=pd.read_parquet(D/f'{f}_likes.parquet');d['i']=d.item_id.map(imap);d=d.dropna(subset=['i']);d['i']=d.i.astype('int32');d['lw']=(.5+.5*d.is_organic)*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*150));likes.append(d)\\n l=pd.concat(likes).groupby(['uid','i'],sort=False).agg(lw=('lw','sum'),first=('timestamp','min'),last=('timestamp','max')).reset_index()\\n g=g.merge(l,on=['uid','i'],how='outer',suffixes=('','_l'));g['first']=g[['first','first_l']].min(axis=1);g['last']=g[['last','last_l']].max(axis=1);g=g.fillna(0)\\n g['w']=np.power(g.w,0.5)+2*np.power(g.lw,0.5);g['y']=np.power(g.y,0.5)+4*np.power(g.lw,0.5)\\n # Explicit cutoff state only affects history weights, never eligibility.\\n s=pd.read_parquet(D/f'{stage if stage==\\\"validation\\\" else \\\"test\\\"}_preference_state.parquet');s['i']=s.item_id.map(imap);s=s.dropna(subset=['i']);s['i']=s.i.astype('int32')\\n neg=s[s.state=='disliked'][['uid','i']].assign(neg=1);g=g.merge(neg,on=['uid','i'],how='left');g.loc[g.neg==1,['w','y']]*=.1\\n g=g.sort_values(['uid','first','i']);users=np.sort(g.uid.unique());uidx=pd.Series(np.arange(len(users)),index=users);u=g.uid.map(uidx).to_numpy();lengths=np.bincount(u,minlength=len(users));offset=np.r_[0,lengths.cumsum()]\\n # Flat arrays and offsets preserve histories in O(events) space.\\n np.savez(out,users=users,items=items,offset=offset,i=g.i.to_numpy(np.int32),w=g.w.to_numpy(np.float32),y=g.y.to_numpy(np.float32),first=g['first'].to_numpy(np.int32),last=g['last'].to_numpy(np.int32),cutoff=cutoff)\\n print('prepared',stage,len(users),len(g),flush=True)\\nif __name__=='__main__':\\n p=argparse.ArgumentParser();p.add_argument('stage',choices=['validation','test']);prepare(p.parse_args().stage)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare.py validation > /app/solution/prepare_validation.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:29:04.107Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"8beae5","wall_time_seconds":1.000607101,"session_id":34997,"original_token_count":0,"output":""}

### Harness context
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  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
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### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-75fa57cc-db6c-41aa-b1f8-ca6df70eb280",
    "process_id": "34997",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/prepare.py <<'PY'\n\"\"\"Create event-list/ragged-history training data; never a user-item matrix.\"\"\"\nimport numpy as np,pandas as pd, argparse,time\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data');DAY=86400\n\ndef prepare(stage):\n out=P/f'data_{stage}.npz'\n if out.exists():return\n cutoff=24786800 if stage=='validation' else 25393400\n fs=['train'] if stage=='validation' else ['train','val']\n items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();imap=pd.Series(np.arange(len(items)),index=items)\n chunks=[]\n for f in fs:\n  d=pd.read_parquet(D/f'{f}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\n  d=d[d.played_ratio_pct>=50].copy();d['i']=d.item_id.map(imap);d=d.dropna(subset=['i']);d['i']=d.i.astype('int32')\n  d['w']=(.5+.5*d.is_organic)*(0.6+0.4*(d.played_ratio_pct>=80))*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*90))\n  d['y']=(.15+.85*d.is_organic)*(d.played_ratio_pct>=80)*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*90))\n  g=d.groupby(['uid','i'],sort=False).agg(w=('w','sum'),y=('y','sum'),first=('timestamp','min'),last=('timestamp','max')).reset_index();chunks.append(g);del d\n g=pd.concat(chunks).groupby(['uid','i'],sort=False).agg(w=('w','sum'),y=('y','sum'),first=('first','min'),last=('last','max')).reset_index();del chunks\n likes=[]\n for f in fs:\n  d=pd.read_parquet(D/f'{f}_likes.parquet');d['i']=d.item_id.map(imap);d=d.dropna(subset=['i']);d['i']=d.i.astype('int32');d['lw']=(.5+.5*d.is_organic)*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*150));likes.append(d)\n l=pd.concat(likes).groupby(['uid','i'],sort=False).agg(lw=('lw','sum'),first=('timestamp','min'),last=('timestamp','max')).reset_index()\n g=g.merge(l,on=['uid','i'],how='outer',suffixes=('','_l'));g['first']=g[['first','first_l']].min(axis=1);g['last']=g[['last','last_l']].max(axis=1);g=g.fillna(0)\n g['w']=np.power(g.w,0.5)+2*np.power(g.lw,0.5);g['y']=np.power(g.y,0.5)+4*np.power(g.lw,0.5)\n # Explicit cutoff state only affects history weights, never eligibility.\n s=pd.read_parquet(D/f'{stage if stage==\"validation\" else \"test\"}_preference_state.parquet');s['i']=s.item_id.map(imap);s=s.dropna(subset=['i']);s['i']=s.i.astype('int32')\n neg=s[s.state=='disliked'][['uid','i']].assign(neg=1);g=g.merge(neg,on=['uid','i'],how='left');g.loc[g.neg==1,['w','y']]*=.1\n g=g.sort_values(['uid','first','i']);users=np.sort(g.uid.unique());uidx=pd.Series(np.arange(len(users)),index=users);u=g.uid.map(uidx).to_numpy();lengths=np.bincount(u,minlength=len(users));offset=np.r_[0,lengths.cumsum()]\n # Flat arrays and offsets preserve histories in O(events) space.\n np.savez(out,users=users,items=items,offset=offset,i=g.i.to_numpy(np.int32),w=g.w.to_numpy(np.float32),y=g.y.to_numpy(np.float32),first=g['first'].to_numpy(np.int32),last=g['last'].to_numpy(np.int32),cutoff=cutoff)\n print('prepared',stage,len(users),len(g),flush=True)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('stage',choices=['validation','test']);prepare(p.parse_args().stage)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /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\"\"\"Create event-list/ragged-history training data; never a user-item matrix.\"\"\"\nimport numpy as np,pandas as pd, argparse,time\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data');DAY=86400\n\ndef prepare(stage):\n out=P/f'data_{stage}.npz'\n if out.exists():return\n cutoff=24786800 if stage=='validation' else 25393400\n fs=['train'] if stage=='validation' else ['train','val']\n items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy();imap=pd.Series(np.arange(len(items)),index=items)\n chunks=[]\n for f in fs:\n  d=pd.read_parquet(D/f'{f}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\n  d=d[d.played_ratio_pct>=50].copy();d['i']=d.item_id.map(imap);d=d.dropna(subset=['i']);d['i']=d.i.astype('int32')\n  d['w']=(.5+.5*d.is_organic)*(0.6+0.4*(d.played_ratio_pct>=80))*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*90))\n  d['y']=(.15+.85*d.is_organic)*(d.played_ratio_pct>=80)*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*90))\n  g=d.groupby(['uid','i'],sort=False).agg(w=('w','sum'),y=('y','sum'),first=('timestamp','min'),last=('timestamp','max')).reset_index();chunks.append(g);del d\n g=pd.concat(chunks).groupby(['uid','i'],sort=False).agg(w=('w','sum'),y=('y','sum'),first=('first','min'),last=('last','max')).reset_index();del chunks\n likes=[]\n for f in fs:\n  d=pd.read_parquet(D/f'{f}_likes.parquet');d['i']=d.item_id.map(imap);d=d.dropna(subset=['i']);d['i']=d.i.astype('int32');d['lw']=(.5+.5*d.is_organic)*np.exp((d.timestamp.astype(float)-cutoff)/(DAY*150));likes.append(d)\n l=pd.concat(likes).groupby(['uid','i'],sort=False).agg(lw=('lw','sum'),first=('timestamp','min'),last=('timestamp','max')).reset_index()\n g=g.merge(l,on=['uid','i'],how='outer',suffixes=('','_l'));g['first']=g[['first','first_l']].min(axis=1);g['last']=g[['last','last_l']].max(axis=1);g=g.fillna(0)\n g['w']=np.power(g.w,0.5)+2*np.power(g.lw,0.5);g['y']=np.power(g.y,0.5)+4*np.power(g.lw,0.5)\n # Explicit cutoff state only affects history weights, never eligibility.\n s=pd.read_parquet(D/f'{stage if stage==\"validation\" else \"test\"}_preference_state.parquet');s['i']=s.item_id.map(imap);s=s.dropna(subset=['i']);s['i']=s.i.astype('int32')\n neg=s[s.state=='disliked'][['uid','i']].assign(neg=1);g=g.merge(neg,on=['uid','i'],how='left');g.loc[g.neg==1,['w','y']]*=.1\n g=g.sort_values(['uid','first','i']);users=np.sort(g.uid.unique());uidx=pd.Series(np.arange(len(users)),index=users);u=g.uid.map(uidx).to_numpy();lengths=np.bincount(u,minlength=len(users));offset=np.r_[0,lengths.cumsum()]\n # Flat arrays and offsets preserve histories in O(events) space.\n np.savez(out,users=users,items=items,offset=offset,i=g.i.to_numpy(np.int32),w=g.w.to_numpy(np.float32),y=g.y.to_numpy(np.float32),first=g['first'].to_numpy(np.int32),last=g['last'].to_numpy(np.int32),cutoff=cutoff)\n print('prepared',stage,len(users),len(g),flush=True)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('stage',choices=['validation','test']);prepare(p.parse_args().stage)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare.py validation > /app/solution/prepare_validation.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 15,
      "nanos": 83939864
    },
    "formatted_output": ""
  }
}

## Step 11 · agent · 2026-09-10T16:29:28.420Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:30:39.616Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/model.py <<'PY'\\n\\\"\\\"\\\"CPU neural retrieval towers with an exact inner-product score.\\\"\\\"\\\"\\nimport torch\\nfrom torch import nn\\nimport torch.nn.functional as F\\n\\nclass UserTower(nn.Module):\\n def __init__(self,nitems,dim):\\n  super().__init__();self.history=nn.Embedding(nitems+1,dim,padding_idx=nitems);self.norm=nn.LayerNorm(dim);self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Dropout(.1),nn.Linear(dim*2,dim));self.scale=nn.Parameter(torch.tensor(3.))\\n  nn.init.normal_(self.history.weight,std=.05);nn.init.zeros_(self.history.weight[nitems]);nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\\n def project(self,x):\\n  x=self.norm(x);x=F.normalize(x+self.mlp(x),dim=-1)*self.scale.exp().clamp(max=50)\\n  return torch.cat([x,torch.ones_like(x[:,:1])],1)\\n def forward(self,ids,weight):\\n  x=(self.history(ids)*weight.unsqueeze(-1)).sum(1)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\\n\\nclass ItemTower(nn.Module):\\n def __init__(self,nitems,dim):\\n  super().__init__();self.embedding=nn.Embedding(nitems,dim);self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim));self.bias=nn.Embedding(nitems,1)\\n  nn.init.normal_(self.embedding.weight,std=.05);nn.init.zeros_(self.bias.weight);nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\\n def forward(self,ids):\\n  x=self.embedding(ids);x=F.normalize(x+self.mlp(x),dim=-1);return torch.cat([x,self.bias(ids)],-1)\\n\\nclass TwoTower(nn.Module):\\n def __init__(self,nitems,dim=128):\\n  super().__init__();self.user=UserTower(nitems,dim);self.item=ItemTower(nitems,dim)\\n def forward(self,history,weight,items):return self.user(history,weight)@self.item(items).T\\nPY\\ncat > /app/solution/train.py <<'PY'\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport argparse,time,json,sys\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,torch\\nfrom model import TwoTower\\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\\n\\ndef pools(data,seed=42,poolsize=2048):\\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\\n return ip,yp,q/q.sum()\\n\\n@torch.no_grad()\\ndef encode_users(model,data,history_power=1.,recent=0):\\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\\n w=data['w'].copy()**history_power\\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\\n us=np.repeat(np.arange(n),np.diff(data['offset']))\\n for start in range(0,len(us),100000):\\n  sl=slice(start,start+100000);ids=torch.from_numpy(data['i'][sl].astype(np.int64));weights=torch.from_numpy(w[sl]);ui=torch.from_numpy(us[sl]);out.index_add_(0,ui,model.user.history(ids)*weights[:,None]);mass.index_add_(0,ui,weights)\\n return model.user.project(out/mass[:,None].clamp_min(1e-8))\\n\\ndef truth(items,users,eligible):\\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\\n for fam in ['listens','likes']:\\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\\n for u in np.unique(keys//len(items)):\\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\\n return keys,gains,idcg\\n\\ndef metric(top,t):\\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\\n\\n@torch.no_grad()\\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\n assert np.array_equal(items,data['items'])\\n order=pd.Index(data['users']).get_indexer(users);uv=encode_users(model,data,history_power,recent)[order];iv=model.item(torch.arange(len(items)));top=[];scores=[]\\n for st in range(0,len(users),256):\\n  s=uv[st:st+256]@iv.T\\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\\n  if return_scores:scores.append(s.numpy())\\n top=np.concatenate(top)\\n if out:pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(out,index=False)\\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\\n return score,(np.concatenate(scores) if return_scores else top)\\n\\ndef main():\\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');args=ap.parse_args();torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\\n print(vars(args),flush=True)\\n for step in range(1,args.steps+1):\\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,4))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*4)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*4+torch.arange(4)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\\n  if step%args.eval_every==0 or step==args.steps:\\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\\n   if args.stage=='validation':\\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet');print('VALIDATION',step,score,flush=True)\\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\\\n')\\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\\n   else:rank(model,data,'test',P/'submission.parquet');print('saved submission',step,flush=True)\\n print('done',flush=True)\\nif __name__=='__main__':main()\\nPY\\ncat /app/solution/prepare_validation.log\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --steps 3000 --eval-every 500 > /app/solution/base.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:30:40.786Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"7413fc","wall_time_seconds":1.00104156,"session_id":18606,"original_token_count":9,"output":"prepared validation 9910 5687487\n"}

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## Step 12 · agent · 2026-09-10T16:30:44.190Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:30:47.283Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare.py test > /app/solution/prepare_test.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -10 /app/solution/base.log && du -sh /app/solution\",max_output_tokens:1000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:30:48.494Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"90d3fd","wall_time_seconds":1.000944608,"session_id":4795,"original_token_count":0,"output":""}
{"chunk_id":"94c576","wall_time_seconds":0.00000495,"exit_code":0,"original_token_count":5,"output":"161M\t/app/solution\n"}
{"current_time":"2026-09-10 16:30:48 UTC"}

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## Step 13 · agent · 2026-09-10T16:30:57.012Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:30:58.437Z
{
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}
### Tool output · 2026-09-10T16:30:58.568Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"db65c1","wall_time_seconds":0.00000352,"exit_code":0,"original_token_count":57,"output":"{'stage': 'validation', 'name': 'base', 'steps': 3000, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None}\nstep 100 loss 7.950010967254639 seconds 5.1\n/bin/sh: 1: ps: not found\n"}

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## Step 14 · agent · 2026-09-10T16:31:07.300Z

### Thinking unavailable — no text recorded


The first model is training on weighted, sampled histories. Its score is an exact dot product between a neural user vector and a separately computed neural item vector; item bias is included as an extra vector coordinate. I’ll check public NDCG at successive checkpoints to select the training duration.


### exec · 2026-09-10T16:31:10.339Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -20 /app/solution/base.log && tail -4 /app/solution/prepare_test.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:31:10.451Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"811363","wall_time_seconds":0.000017831,"exit_code":0,"original_token_count":73,"output":"{'stage': 'validation', 'name': 'base', 'steps': 3000, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None}\nstep 100 loss 7.950010967254639 seconds 5.1\nstep 200 loss 7.2673363637924195 seconds 10.0\nstep 300 loss 6.776273746490478 seconds 14.9\n"}

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## Step 15 · agent · 2026-09-10T16:31:35.906Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:31:36.621Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -16 /app/solution/base.log && tail -4 /app/solution/prepare_test.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:31:36.685Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"d488e6","wall_time_seconds":0.0000042,"exit_code":0,"original_token_count":156,"output":"{'stage': 'validation', 'name': 'base', 'steps': 3000, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None}\nstep 100 loss 7.950010967254639 seconds 5.1\nstep 200 loss 7.2673363637924195 seconds 10.0\nstep 300 loss 6.776273746490478 seconds 14.9\nstep 400 loss 6.477748832702637 seconds 19.7\nstep 500 loss 6.257651553153992 seconds 24.5\nVALIDATION 500 0.01581648684718543\nstep 600 loss 6.1199998903274535 seconds 31.2\nstep 700 loss 6.01055992603302 seconds 35.0\nstep 800 loss 5.933208084106445 seconds 38.9\nstep 900 loss 5.87111831665039 seconds 42.8\nprepared test 9924 5850135\n"}

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## Step 16 · agent · 2026-09-10T16:32:01.324Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:32:01.952Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -20 /app/solution/base.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:32:02.022Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"9a76c1","wall_time_seconds":0.00000538,"exit_code":0,"original_token_count":227,"output":"{'stage': 'validation', 'name': 'base', 'steps': 3000, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None}\nstep 100 loss 7.950010967254639 seconds 5.1\nstep 200 loss 7.2673363637924195 seconds 10.0\nstep 300 loss 6.776273746490478 seconds 14.9\nstep 400 loss 6.477748832702637 seconds 19.7\nstep 500 loss 6.257651553153992 seconds 24.5\nVALIDATION 500 0.01581648684718543\nstep 600 loss 6.1199998903274535 seconds 31.2\nstep 700 loss 6.01055992603302 seconds 35.0\nstep 800 loss 5.933208084106445 seconds 38.9\nstep 900 loss 5.87111831665039 seconds 42.8\nstep 1000 loss 5.817206716537475 seconds 46.8\nVALIDATION 1000 0.020323768283159634\nstep 1100 loss 5.768835463523865 seconds 53.5\nstep 1200 loss 5.723997344970703 seconds 57.4\nstep 1300 loss 5.689145021438598 seconds 61.3\nstep 1400 loss 5.665953326225281 seconds 65.2\nstep 1500 loss 5.631428956985474 seconds 69.1\n"}
{"current_time":"2026-09-10 16:32:02 UTC"}

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## Step 17 · agent · 2026-09-10T16:32:29.042Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:32:54.700Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/prepare_temporal.py <<'PY'\\n\\\"\\\"\\\"Strict prefix-history, subsequent-week discovery training examples.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np,argparse,time\\nP=Path('/app/solution');D=Path('/task/data');DAY=86400\\n\\ndef main(stage,weeks):\\n rng=np.random.default_rng(733);data=np.load(P/f'data_{stage}.npz');users=data['users'];items=data['items'];um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);cutoff=int(data['cutoff']);frames=[]\\n for part in (['train'] if stage=='validation' else ['train','val']):\\n  d=pd.read_parquet(D/f'{part}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct']);d=d[d.played_ratio_pct>=50].copy();d['i']=d.item_id.map(im);d=d.dropna(subset=['i']);d['u']=d.uid.map(um);d['i']=d.i.astype(np.int32);d['u']=d.u.astype(np.int32)\\n  d['w']=(.5+.5*d.is_organic)*(0.6+0.4*(d.played_ratio_pct>=80));d['gain']=((d.is_organic==1)&(d.played_ratio_pct>=80)).astype(np.float32);d['like']=0.;frames.append(d[['u','i','timestamp','w','gain','like']]);del d\\n  d=pd.read_parquet(D/f'{part}_likes.parquet');d['i']=d.item_id.map(im);d=d.dropna(subset=['i']);d['u']=d.uid.map(um);d['i']=d.i.astype(np.int32);d['u']=d.u.astype(np.int32);d['w']=0.;d['gain']=np.where(d.is_organic==1,7.,0.);d['like']=.5+.5*d.is_organic;frames.append(d[['u','i','timestamp','w','gain','like']]);del d\\n e=pd.concat(frames,ignore_index=True);del frames;e['key']=e.u.astype(np.int64)*len(items)+e.i\\n first=e.groupby('key').timestamp.min();inputs=[];outputs=[];folds=[];uids=[];qs=np.zeros(len(items));t0=time.time()\\n for fold in range(weeks):\\n  end=cutoff-fold*7*DAY;start=end-7*DAY-1800\\n  target=e[(e.timestamp>start+1800)&(e.timestamp<=end)&(e.gain>0)][['key','gain']].groupby('key').gain.max();target=target[first.reindex(target.index)>start]\\n  tu=np.unique(target.index.to_numpy()//len(items));h=e[(e.timestamp<=start)&e.u.isin(tu)][['u','i','timestamp','w','like']].copy()\\n  h['w']*=np.exp((h.timestamp.astype(float)-start)/(DAY*90));h['like']*=np.exp((h.timestamp.astype(float)-start)/(DAY*150));h=h.groupby(['u','i'],sort=True)[['w','like']].sum();hw=np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy());hu=h.index.get_level_values(0).to_numpy();hi=h.index.get_level_values(1).to_numpy();unique,starts,counts=np.unique(hu,return_index=True,return_counts=True)\\n  tk=target.index.to_numpy();tg=target.to_numpy();ty=tk//len(items);ti=tk%len(items)\\n  for u,a,c in zip(unique,starts,counts):\\n   if c<10:continue\\n   inds=ti[ty==u];weights=tg[ty==u];weights=weights/weights.sum();iw=hw[a:a+c]+1e-5;iw/=iw.sum();inputs.append(rng.choice(hi[a:a+c],1024,p=iw).astype(np.int32));outputs.append(rng.choice(inds,256,p=weights).astype(np.int32));folds.append(fold);uids.append(u);np.add.at(qs,inds,weights)\\n  print(stage,'fold',fold,'targets',len(target),'examples',len(inputs),'sec',round(time.time()-t0),flush=True)\\n np.savez(P/f'temporal_{stage}.npz',ip=np.array(inputs),yp=np.array(outputs),q=qs/qs.sum(),fold=np.array(folds),u=np.array(uids))\\nif __name__=='__main__':\\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--weeks',type=int,default=12);a=ap.parse_args();main(a.stage,a.weeks)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare_temporal.py > /app/solution/prepare_temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:32:55.881Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"3dcd4c","wall_time_seconds":1.001578711,"session_id":20563,"original_token_count":0,"output":""}

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## Step 18 · agent · 2026-09-10T16:33:00.310Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:33:10.944Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -12 /app/solution/base.log\",\"max_output_tokens\":1500}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/train.py'\\ns=open(p).read().replace(\\\"ap.add_argument('--resume');\\\", \\\"ap.add_argument('--resume');ap.add_argument('--temporal',action='store_true');ap.add_argument('--targets',type=int,default=4);ap.add_argument('--threads',type=int,default=6);\\\")\\ns=s.replace(\\\"args=ap.parse_args();torch.manual_seed\\\", \\\"args=ap.parse_args();torch.set_num_threads(args.threads);torch.manual_seed\\\")\\ns=s.replace(\\\"ip,yp,q=pools(data,args.seed);data['pop']=q;\\\", \\\"ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'temporal_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;\\\")\\ns=s.replace(\\\"size=(args.batch,4)\\\",\\\"size=(args.batch,args.targets)\\\").replace(\\\"size=args.batch*4)\\\",\\\"size=args.batch*args.targets)\\\").replace(\\\"[:,None]*4+torch.arange(4)\\\",\\\"[:,None]*args.targets+torch.arange(args.targets)\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name initial_final --steps 3000 --eval-every 3000 --threads 3 > /app/solution/initial_final.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
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### Tool output · 2026-09-10T16:33:12.148Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"9c637c","wall_time_seconds":0.00000457,"exit_code":0,"original_token_count":126,"output":"step 2200 loss 5.489059772491455 seconds 101.3\nstep 2300 loss 5.467156000137329 seconds 105.1\nstep 2400 loss 5.465627541542053 seconds 109.0\nstep 2500 loss 5.427786140441895 seconds 113.0\nVALIDATION 2500 0.01923550823806045\nstep 2600 loss 5.425414652824402 seconds 119.6\nstep 2700 loss 5.409049339294434 seconds 123.6\nstep 2800 loss 5.413222961425781 seconds 128.1\nstep 2900 loss 5.398502388000488 seconds 132.9\nstep 3000 loss 5.378040037155151 seconds 137.1\nVALIDATION 3000 0.019768211370602435\ndone\n"}
{"chunk_id":"7c29ee","wall_time_seconds":1.000742846,"session_id":24343,"original_token_count":0,"output":""}

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      "nanos": 4570
    },
    "formatted_output": "step 2200 loss 5.489059772491455 seconds 101.3\nstep 2300 loss 5.467156000137329 seconds 105.1\nstep 2400 loss 5.465627541542053 seconds 109.0\nstep 2500 loss 5.427786140441895 seconds 113.0\nVALIDATION 2500 0.01923550823806045\nstep 2600 loss 5.425414652824402 seconds 119.6\nstep 2700 loss 5.409049339294434 seconds 123.6\nstep 2800 loss 5.413222961425781 seconds 128.1\nstep 2900 loss 5.398502388000488 seconds 132.9\nstep 3000 loss 5.378040037155151 seconds 137.1\nVALIDATION 3000 0.019768211370602435\ndone\n"
  }
}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-44f323b5-ca9f-4176-9ef8-65b9bca2ebe8",
    "process_id": "18606",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural retrieval towers with an exact inner-product score.\"\"\"\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nclass UserTower(nn.Module):\n def __init__(self,nitems,dim):\n  super().__init__();self.history=nn.Embedding(nitems+1,dim,padding_idx=nitems);self.norm=nn.LayerNorm(dim);self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Dropout(.1),nn.Linear(dim*2,dim));self.scale=nn.Parameter(torch.tensor(3.))\n  nn.init.normal_(self.history.weight,std=.05);nn.init.zeros_(self.history.weight[nitems]);nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def project(self,x):\n  x=self.norm(x);x=F.normalize(x+self.mlp(x),dim=-1)*self.scale.exp().clamp(max=50)\n  return torch.cat([x,torch.ones_like(x[:,:1])],1)\n def forward(self,ids,weight):\n  x=(self.history(ids)*weight.unsqueeze(-1)).sum(1)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\n\nclass ItemTower(nn.Module):\n def __init__(self,nitems,dim):\n  super().__init__();self.embedding=nn.Embedding(nitems,dim);self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim));self.bias=nn.Embedding(nitems,1)\n  nn.init.normal_(self.embedding.weight,std=.05);nn.init.zeros_(self.bias.weight);nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def forward(self,ids):\n  x=self.embedding(ids);x=F.normalize(x+self.mlp(x),dim=-1);return torch.cat([x,self.bias(ids)],-1)\n\nclass TwoTower(nn.Module):\n def __init__(self,nitems,dim=128):\n  super().__init__();self.user=UserTower(nitems,dim);self.item=ItemTower(nitems,dim)\n def forward(self,history,weight,items):return self.user(history,weight)@self.item(items).T\nPY\ncat > /app/solution/train.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom model import TwoTower\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\n\ndef pools(data,seed=42,poolsize=2048):\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\n return ip,yp,q/q.sum()\n\n@torch.no_grad()\ndef encode_users(model,data,history_power=1.,recent=0):\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\n w=data['w'].copy()**history_power\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\n us=np.repeat(np.arange(n),np.diff(data['offset']))\n for start in range(0,len(us),100000):\n  sl=slice(start,start+100000);ids=torch.from_numpy(data['i'][sl].astype(np.int64));weights=torch.from_numpy(w[sl]);ui=torch.from_numpy(us[sl]);out.index_add_(0,ui,model.user.history(ids)*weights[:,None]);mass.index_add_(0,ui,weights)\n return model.user.project(out/mass[:,None].clamp_min(1e-8))\n\ndef truth(items,users,eligible):\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\n for fam in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\n for u in np.unique(keys//len(items)):\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\n return keys,gains,idcg\n\ndef metric(top,t):\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\n\n@torch.no_grad()\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n assert np.array_equal(items,data['items'])\n order=pd.Index(data['users']).get_indexer(users);uv=encode_users(model,data,history_power,recent)[order];iv=model.item(torch.arange(len(items)));top=[];scores=[]\n for st in range(0,len(users),256):\n  s=uv[st:st+256]@iv.T\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\n  if return_scores:scores.append(s.numpy())\n top=np.concatenate(top)\n if out:pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(out,index=False)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n return score,(np.concatenate(scores) if return_scores else top)\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');args=ap.parse_args();torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\n print(vars(args),flush=True)\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,4))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*4)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*4+torch.arange(4)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\n   if args.stage=='validation':\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet');print('VALIDATION',step,score,flush=True)\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\n')\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\n   else:rank(model,data,'test',P/'submission.parquet');print('saved submission',step,flush=True)\n print('done',flush=True)\nif __name__=='__main__':main()\nPY\ncat /app/solution/prepare_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --steps 3000 --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 retrieval towers with an exact inner-product score.\"\"\"\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nclass UserTower(nn.Module):\n def __init__(self,nitems,dim):\n  super().__init__();self.history=nn.Embedding(nitems+1,dim,padding_idx=nitems);self.norm=nn.LayerNorm(dim);self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Dropout(.1),nn.Linear(dim*2,dim));self.scale=nn.Parameter(torch.tensor(3.))\n  nn.init.normal_(self.history.weight,std=.05);nn.init.zeros_(self.history.weight[nitems]);nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def project(self,x):\n  x=self.norm(x);x=F.normalize(x+self.mlp(x),dim=-1)*self.scale.exp().clamp(max=50)\n  return torch.cat([x,torch.ones_like(x[:,:1])],1)\n def forward(self,ids,weight):\n  x=(self.history(ids)*weight.unsqueeze(-1)).sum(1)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\n\nclass ItemTower(nn.Module):\n def __init__(self,nitems,dim):\n  super().__init__();self.embedding=nn.Embedding(nitems,dim);self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim));self.bias=nn.Embedding(nitems,1)\n  nn.init.normal_(self.embedding.weight,std=.05);nn.init.zeros_(self.bias.weight);nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def forward(self,ids):\n  x=self.embedding(ids);x=F.normalize(x+self.mlp(x),dim=-1);return torch.cat([x,self.bias(ids)],-1)\n\nclass TwoTower(nn.Module):\n def __init__(self,nitems,dim=128):\n  super().__init__();self.user=UserTower(nitems,dim);self.item=ItemTower(nitems,dim)\n def forward(self,history,weight,items):return self.user(history,weight)@self.item(items).T\nPY\ncat > /app/solution/train.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom model import TwoTower\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\n\ndef pools(data,seed=42,poolsize=2048):\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\n return ip,yp,q/q.sum()\n\n@torch.no_grad()\ndef encode_users(model,data,history_power=1.,recent=0):\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\n w=data['w'].copy()**history_power\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\n us=np.repeat(np.arange(n),np.diff(data['offset']))\n for start in range(0,len(us),100000):\n  sl=slice(start,start+100000);ids=torch.from_numpy(data['i'][sl].astype(np.int64));weights=torch.from_numpy(w[sl]);ui=torch.from_numpy(us[sl]);out.index_add_(0,ui,model.user.history(ids)*weights[:,None]);mass.index_add_(0,ui,weights)\n return model.user.project(out/mass[:,None].clamp_min(1e-8))\n\ndef truth(items,users,eligible):\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\n for fam in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\n for u in np.unique(keys//len(items)):\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\n return keys,gains,idcg\n\ndef metric(top,t):\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\n\n@torch.no_grad()\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n assert np.array_equal(items,data['items'])\n order=pd.Index(data['users']).get_indexer(users);uv=encode_users(model,data,history_power,recent)[order];iv=model.item(torch.arange(len(items)));top=[];scores=[]\n for st in range(0,len(users),256):\n  s=uv[st:st+256]@iv.T\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\n  if return_scores:scores.append(s.numpy())\n top=np.concatenate(top)\n if out:pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(out,index=False)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n return score,(np.concatenate(scores) if return_scores else top)\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');args=ap.parse_args();torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\n print(vars(args),flush=True)\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,4))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*4)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*4+torch.arange(4)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\n   if args.stage=='validation':\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet');print('VALIDATION',step,score,flush=True)\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\n')\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\n   else:rank(model,data,'test',P/'submission.parquet');print('saved submission',step,flush=True)\n print('done',flush=True)\nif __name__=='__main__':main()\nPY\ncat /app/solution/prepare_validation.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --steps 3000 --eval-every 500 > /app/solution/base.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 151,
      "nanos": 209869464
    },
    "formatted_output": ""
  }
}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2dbb80ffc87d19965f48c0a1cb17e",
  "usage": {
    "input_tokens": 35838,
    "cached_input_tokens": 33920,
    "cache_write_input_tokens": 0,
    "output_tokens": 456,
    "reasoning_output_tokens": 70,
    "total_tokens": 36294
  },
  "turn_token_usage": {
    "input_tokens": 418587,
    "cached_input_tokens": 373504,
    "cache_write_input_tokens": 0,
    "output_tokens": 10241,
    "reasoning_output_tokens": 3315,
    "total_tokens": 428828
  },
  "thread_token_usage": {
    "input_tokens": 418587,
    "cached_input_tokens": 373504,
    "cache_write_input_tokens": 0,
    "output_tokens": 10241,
    "reasoning_output_tokens": 3315,
    "total_tokens": 428828
  }
}

## Step 19 · agent · 2026-09-10T16:33:24.917Z

### Thinking unavailable — no text recorded


The first validation checkpoints are around 0.02 NDCG@10, with no clear gain from longer training so far. I’m testing how much recent history should influence the user vector. I’m also preparing training examples that predict new organic engagement in the next week from strictly earlier history, to better match the discovery objective.


### exec · 2026-09-10T16:33:43.634Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/tune.py <<'PY'\\nimport argparse,json,time\\nimport numpy as np,pandas as pd,torch\\nfrom train import P,D,encode_users,truth,metric\\nfrom model import TwoTower\\nap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='tune');a=ap.parse_args();torch.set_num_threads(3)\\nck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);best=-1;results=[]\\nwith torch.no_grad():\\n iv=m.item(torch.arange(30000));lp=torch.from_numpy(np.log(ck['pop']+1e-9)).float()\\n for hp in [.5,1.]:\\n  for recent in [0,90,30,14,7,3]:\\n   uv=encode_users(m,data,hp,recent)[order];tops={pop:[] for pop in [-1.,-.5,0.,.5,1.]}\\n   for st in range(0,len(users),256):\\n    s=uv[st:st+256]@iv.T\\n    for pop in tops:\\n     v=s+pop*lp;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[pop].append(v.topk(100,1).indices.numpy())\\n   for pop,top in tops.items():\\n    top=np.concatenate(top);score=metric(top,tt);row={'history_power':hp,'recent':recent,'pop':pop,'score':score};results.append(row);print(row,flush=True)\\n    if score>best:\\n     best=score;pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\\n json.dump(sorted(results,key=lambda x:-x['score']),open(P/f'{a.output}.json','w'),indent=2)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py base_best.pt --output base_tune > /app/solution/base_tune.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/prepare_temporal.log && cat /app/solution/base_metrics.jsonl\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:33:44.857Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"9cdb4d","wall_time_seconds":1.000994646,"session_id":97046,"original_token_count":0,"output":""}
{"chunk_id":"5658f4","wall_time_seconds":0.00000631,"exit_code":0,"original_token_count":137,"output":"validation fold 7 targets 40209 examples 35742 sec 26\nvalidation fold 8 targets 38404 examples 39732 sec 29\nvalidation fold 9 targets 38925 examples 43612 sec 32\nvalidation fold 10 targets 37630 examples 47405 sec 35\nvalidation fold 11 targets 37250 examples 51167 sec 37\n{\"step\": 500, \"score\": 0.01581648684718543}\n{\"step\": 1000, \"score\": 0.020323768283159634}\n{\"step\": 1500, \"score\": 0.020468694312302374}\n{\"step\": 2000, \"score\": 0.020450135630845148}\n{\"step\": 2500, \"score\": 0.01923550823806045}\n{\"step\": 3000, \"score\": 0.019768211370602435}\n"}

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      "/bin/sh",
      "-lc",
      "cat > /app/solution/prepare_temporal.py <<'PY'\n\"\"\"Strict prefix-history, subsequent-week discovery training examples.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nfrom pathlib import Path\nimport pandas as pd,numpy as np,argparse,time\nP=Path('/app/solution');D=Path('/task/data');DAY=86400\n\ndef main(stage,weeks):\n rng=np.random.default_rng(733);data=np.load(P/f'data_{stage}.npz');users=data['users'];items=data['items'];um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);cutoff=int(data['cutoff']);frames=[]\n for part in (['train'] if stage=='validation' else ['train','val']):\n  d=pd.read_parquet(D/f'{part}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct']);d=d[d.played_ratio_pct>=50].copy();d['i']=d.item_id.map(im);d=d.dropna(subset=['i']);d['u']=d.uid.map(um);d['i']=d.i.astype(np.int32);d['u']=d.u.astype(np.int32)\n  d['w']=(.5+.5*d.is_organic)*(0.6+0.4*(d.played_ratio_pct>=80));d['gain']=((d.is_organic==1)&(d.played_ratio_pct>=80)).astype(np.float32);d['like']=0.;frames.append(d[['u','i','timestamp','w','gain','like']]);del d\n  d=pd.read_parquet(D/f'{part}_likes.parquet');d['i']=d.item_id.map(im);d=d.dropna(subset=['i']);d['u']=d.uid.map(um);d['i']=d.i.astype(np.int32);d['u']=d.u.astype(np.int32);d['w']=0.;d['gain']=np.where(d.is_organic==1,7.,0.);d['like']=.5+.5*d.is_organic;frames.append(d[['u','i','timestamp','w','gain','like']]);del d\n e=pd.concat(frames,ignore_index=True);del frames;e['key']=e.u.astype(np.int64)*len(items)+e.i\n first=e.groupby('key').timestamp.min();inputs=[];outputs=[];folds=[];uids=[];qs=np.zeros(len(items));t0=time.time()\n for fold in range(weeks):\n  end=cutoff-fold*7*DAY;start=end-7*DAY-1800\n  target=e[(e.timestamp>start+1800)&(e.timestamp<=end)&(e.gain>0)][['key','gain']].groupby('key').gain.max();target=target[first.reindex(target.index)>start]\n  tu=np.unique(target.index.to_numpy()//len(items));h=e[(e.timestamp<=start)&e.u.isin(tu)][['u','i','timestamp','w','like']].copy()\n  h['w']*=np.exp((h.timestamp.astype(float)-start)/(DAY*90));h['like']*=np.exp((h.timestamp.astype(float)-start)/(DAY*150));h=h.groupby(['u','i'],sort=True)[['w','like']].sum();hw=np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy());hu=h.index.get_level_values(0).to_numpy();hi=h.index.get_level_values(1).to_numpy();unique,starts,counts=np.unique(hu,return_index=True,return_counts=True)\n  tk=target.index.to_numpy();tg=target.to_numpy();ty=tk//len(items);ti=tk%len(items)\n  for u,a,c in zip(unique,starts,counts):\n   if c<10:continue\n   inds=ti[ty==u];weights=tg[ty==u];weights=weights/weights.sum();iw=hw[a:a+c]+1e-5;iw/=iw.sum();inputs.append(rng.choice(hi[a:a+c],1024,p=iw).astype(np.int32));outputs.append(rng.choice(inds,256,p=weights).astype(np.int32));folds.append(fold);uids.append(u);np.add.at(qs,inds,weights)\n  print(stage,'fold',fold,'targets',len(target),'examples',len(inputs),'sec',round(time.time()-t0),flush=True)\n np.savez(P/f'temporal_{stage}.npz',ip=np.array(inputs),yp=np.array(outputs),q=qs/qs.sum(),fold=np.array(folds),u=np.array(uids))\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--weeks',type=int,default=12);a=ap.parse_args();main(a.stage,a.weeks)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare_temporal.py > /app/solution/prepare_temporal.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/prepare_temporal.py <<'PY'\n\"\"\"Strict prefix-history, subsequent-week discovery training examples.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nfrom pathlib import Path\nimport pandas as pd,numpy as np,argparse,time\nP=Path('/app/solution');D=Path('/task/data');DAY=86400\n\ndef main(stage,weeks):\n rng=np.random.default_rng(733);data=np.load(P/f'data_{stage}.npz');users=data['users'];items=data['items'];um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);cutoff=int(data['cutoff']);frames=[]\n for part in (['train'] if stage=='validation' else ['train','val']):\n  d=pd.read_parquet(D/f'{part}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct']);d=d[d.played_ratio_pct>=50].copy();d['i']=d.item_id.map(im);d=d.dropna(subset=['i']);d['u']=d.uid.map(um);d['i']=d.i.astype(np.int32);d['u']=d.u.astype(np.int32)\n  d['w']=(.5+.5*d.is_organic)*(0.6+0.4*(d.played_ratio_pct>=80));d['gain']=((d.is_organic==1)&(d.played_ratio_pct>=80)).astype(np.float32);d['like']=0.;frames.append(d[['u','i','timestamp','w','gain','like']]);del d\n  d=pd.read_parquet(D/f'{part}_likes.parquet');d['i']=d.item_id.map(im);d=d.dropna(subset=['i']);d['u']=d.uid.map(um);d['i']=d.i.astype(np.int32);d['u']=d.u.astype(np.int32);d['w']=0.;d['gain']=np.where(d.is_organic==1,7.,0.);d['like']=.5+.5*d.is_organic;frames.append(d[['u','i','timestamp','w','gain','like']]);del d\n e=pd.concat(frames,ignore_index=True);del frames;e['key']=e.u.astype(np.int64)*len(items)+e.i\n first=e.groupby('key').timestamp.min();inputs=[];outputs=[];folds=[];uids=[];qs=np.zeros(len(items));t0=time.time()\n for fold in range(weeks):\n  end=cutoff-fold*7*DAY;start=end-7*DAY-1800\n  target=e[(e.timestamp>start+1800)&(e.timestamp<=end)&(e.gain>0)][['key','gain']].groupby('key').gain.max();target=target[first.reindex(target.index)>start]\n  tu=np.unique(target.index.to_numpy()//len(items));h=e[(e.timestamp<=start)&e.u.isin(tu)][['u','i','timestamp','w','like']].copy()\n  h['w']*=np.exp((h.timestamp.astype(float)-start)/(DAY*90));h['like']*=np.exp((h.timestamp.astype(float)-start)/(DAY*150));h=h.groupby(['u','i'],sort=True)[['w','like']].sum();hw=np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy());hu=h.index.get_level_values(0).to_numpy();hi=h.index.get_level_values(1).to_numpy();unique,starts,counts=np.unique(hu,return_index=True,return_counts=True)\n  tk=target.index.to_numpy();tg=target.to_numpy();ty=tk//len(items);ti=tk%len(items)\n  for u,a,c in zip(unique,starts,counts):\n   if c<10:continue\n   inds=ti[ty==u];weights=tg[ty==u];weights=weights/weights.sum();iw=hw[a:a+c]+1e-5;iw/=iw.sum();inputs.append(rng.choice(hi[a:a+c],1024,p=iw).astype(np.int32));outputs.append(rng.choice(inds,256,p=weights).astype(np.int32));folds.append(fold);uids.append(u);np.add.at(qs,inds,weights)\n  print(stage,'fold',fold,'targets',len(target),'examples',len(inputs),'sec',round(time.time()-t0),flush=True)\n np.savez(P/f'temporal_{stage}.npz',ip=np.array(inputs),yp=np.array(outputs),q=qs/qs.sum(),fold=np.array(folds),u=np.array(uids))\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--weeks',type=int,default=12);a=ap.parse_args();main(a.stage,a.weeks)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare_temporal.py > /app/solution/prepare_temporal.log 2>&1"
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    "stderr": "",
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  }
}

## Step 20 · agent · 2026-09-10T16:33:48.293Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:33:52.302Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name temporal --temporal --resume /app/solution/base_best.pt --lr .0005 --steps 4000 --eval-every 500 --threads 3 > /app/solution/temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -10 /app/solution/base_tune.log && tail -4 /app/solution/initial_final.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:33:53.551Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c1f70e","wall_time_seconds":1.001219686,"session_id":29886,"original_token_count":0,"output":""}
{"chunk_id":"8548f8","wall_time_seconds":0.000019521,"exit_code":0,"original_token_count":46,"output":"step 200 loss 7.271602039337158 seconds 11.0\nstep 300 loss 6.8050933980941775 seconds 16.0\nstep 400 loss 6.492786951065064 seconds 21.5\nstep 500 loss 6.292131471633911 seconds 27.0\n"}

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}

## Step 21 · agent · 2026-09-10T16:34:19.186Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:34:20.299Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/base_tune.log && tail -5 /app/solution/initial_final.log && tail -6 /app/solution/temporal.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:34:20.467Z
Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"adf723","wall_time_seconds":0.00001055,"exit_code":0,"original_token_count":356,"output":"{'history_power': 0.5, 'recent': 30, 'pop': -1.0, 'score': np.float64(0.0076453890685472875)}\n{'history_power': 0.5, 'recent': 30, 'pop': -0.5, 'score': np.float64(0.015805536692550838)}\n{'history_power': 0.5, 'recent': 30, 'pop': 0.0, 'score': np.float64(0.02334337200163229)}\n{'history_power': 0.5, 'recent': 30, 'pop': 0.5, 'score': np.float64(0.02495071896613443)}\n{'history_power': 0.5, 'recent': 30, 'pop': 1.0, 'score': np.float64(0.022717764030988064)}\n{'history_power': 0.5, 'recent': 14, 'pop': -1.0, 'score': np.float64(0.008949984315570834)}\n{'history_power': 0.5, 'recent': 14, 'pop': -0.5, 'score': np.float64(0.016746435518580283)}\n{'history_power': 0.5, 'recent': 14, 'pop': 0.0, 'score': np.float64(0.024821294489146634)}\n{'history_power': 0.5, 'recent': 14, 'pop': 0.5, 'score': np.float64(0.02693835051495864)}\n{'history_power': 0.5, 'recent': 14, 'pop': 1.0, 'score': np.float64(0.02383054426295032)}\nstep 400 loss 6.492786951065064 seconds 21.5\nstep 500 loss 6.292131471633911 seconds 27.0\nstep 600 loss 6.1417447328567505 seconds 33.1\nstep 700 loss 6.050289430618286 seconds 40.0\nstep 800 loss 5.965232706069946 seconds 48.1\n{'stage': 'validation', 'name': 'temporal', 'steps': 4000, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.0005, 'eval_every': 500, 'seed': 42, 'resume': '/app/solution/base_best.pt', 'temporal': True, 'targets': 4, 'threads': 3}\nstep 100 loss 6.626254868507385 seconds 10.4\n"}
{"current_time":"2026-09-10 16:34:20 UTC"}

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}

## Step 22 · agent · 2026-09-10T16:34:32.353Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:34:34.160Z
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}
### Tool output · 2026-09-10T16:34:35.458Z
Script completed
Wall time 1.3 seconds
Output:

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## Step 23 · agent · 2026-09-10T16:34:44.359Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:34:46.718Z
{
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}
### Tool output · 2026-09-10T16:34:54.134Z
Script completed
Wall time 7.4 seconds
Output:

{"chunk_id":"99473d","wall_time_seconds":7.129959836,"exit_code":0,"original_token_count":9,"output":"1500 tensor(20.7190)\ntensor(0.0687)\n"}
{"chunk_id":"af663b","wall_time_seconds":0.00000262,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/base_tune_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.026938,\n    \"quality_users\": 4263\n  }\n}\n"}

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## Step 24 · agent · 2026-09-10T16:35:14.788Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:35:19.561Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/model.py';s=open(p).read();s=s.replace(\\\"self.mlp=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim));self.bias\\\", \\\"self.mlp=nn.Sequential(nn.Linear(dim,min(dim*2,128) if dim>128 else dim*2),nn.GELU(),nn.Linear(min(dim*2,128) if dim>128 else dim*2,dim));self.bias\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 5000 --eval-every 1000 --threads 3 > /app/solution/wide.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/initial_final.log && tail -6 /app/solution/temporal.log && tail -6 /app/solution/base_tune.log\",\"max_output_tokens\":2000}));\n"
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### Tool output · 2026-09-10T16:35:20.884Z
Script completed
Wall time 1.3 seconds
Output:

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## Step 25 · agent · 2026-09-10T16:35:44.865Z

### Thinking unavailable — no text recorded


Temporal fine-tuning has raised public NDCG@10 to 0.0322, from about 0.0205 for the initial model. Recency weighting also helped that initial model. I’m now testing a wider representation, while a train-plus-validation refit runs to produce the first valid final submission.


### exec · 2026-09-10T16:35:48.589Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal.log && tail -5 /app/solution/wide.log && tail -4 /app/solution/initial_final.log && tail -5 /app/solution/base_tune.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:35:48.736Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"fbac33","wall_time_seconds":0.00000286,"exit_code":0,"original_token_count":278,"output":"step 600 loss 6.027982339859009 seconds 66.7\nstep 700 loss 5.904402203559876 seconds 74.4\nstep 800 loss 5.822391176223755 seconds 81.1\nstep 900 loss 5.73159462928772 seconds 87.3\nstep 1000 loss 5.630548515319824 seconds 93.9\nVALIDATION 1000 0.03123566830155113\n{'stage': 'validation', 'name': 'wide', 'steps': 5000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 1000, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 3}\nstep 1500 loss 5.653708753585815 seconds 116.6\nstep 1600 loss 5.627542352676391 seconds 125.4\nstep 1700 loss 5.616691589355469 seconds 133.6\nstep 1800 loss 5.589112677574158 seconds 141.6\n{'history_power': 1.0, 'recent': 3, 'pop': -1.0, 'score': np.float64(0.009287842923965369)}\n{'history_power': 1.0, 'recent': 3, 'pop': -0.5, 'score': np.float64(0.01697324404548726)}\n{'history_power': 1.0, 'recent': 3, 'pop': 0.0, 'score': np.float64(0.023304253116789585)}\n{'history_power': 1.0, 'recent': 3, 'pop': 0.5, 'score': np.float64(0.024817877104716873)}\n{'history_power': 1.0, 'recent': 3, 'pop': 1.0, 'score': np.float64(0.022451051635858237)}\n"}
{"current_time":"2026-09-10 16:35:48 UTC"}

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  }
}

## Step 26 · agent · 2026-09-10T16:36:02.710Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:36:04.384Z
{
  "code": "text(await tools.exec_command({cmd:\"cp /app/solution/temporal_best.pt /app/solution/temporal_early.pt\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py temporal_early.pt --output temporal_tune > /app/solution/temporal_tune.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:36:05.619Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"74fa97","wall_time_seconds":1.002586254,"session_id":7143,"original_token_count":0,"output":""}

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## Step 27 · agent · 2026-09-10T16:36:31.321Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:36:32.416Z
{
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}
### Tool output · 2026-09-10T16:36:32.545Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"a83932","wall_time_seconds":0.000010581,"exit_code":0,"original_token_count":269,"output":"VALIDATION 1000 0.03123566830155113\nstep 1100 loss 5.5801558017730715 seconds 109.0\nstep 1200 loss 5.489655380249023 seconds 117.0\nstep 1300 loss 5.427938432693481 seconds 126.4\nstep 1400 loss 5.325127763748169 seconds 138.1\n{'stage': 'validation', 'name': 'wide', 'steps': 5000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 1000, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 3}\nstep 1900 loss 5.5652602005004885 seconds 150.8\nstep 2000 loss 5.541696834564209 seconds 160.8\nstep 2100 loss 5.531633749008178 seconds 171.8\nstep 2200 loss 5.513475685119629 seconds 188.7\n{'history_power': 0.5, 'recent': 0, 'pop': -1.0, 'score': np.float64(0.00040165549252140344)}\n{'history_power': 0.5, 'recent': 0, 'pop': -0.5, 'score': np.float64(0.002702856948777951)}\n{'history_power': 0.5, 'recent': 0, 'pop': 0.0, 'score': np.float64(0.03141250856416356)}\n{'history_power': 0.5, 'recent': 0, 'pop': 0.5, 'score': np.float64(0.030019054988487035)}\n{'history_power': 0.5, 'recent': 0, 'pop': 1.0, 'score': np.float64(0.02735471042597222)}\n"}

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## Step 28 · agent · 2026-09-10T16:36:38.331Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:36:39.688Z
{
  "code": "text(await tools.write_stdin({session_id:46307,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.events && tail -2 /app/solution/wide.log\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:36:44.846Z
Script completed
Wall time 5.1 seconds
Output:

{"chunk_id":"068c71","wall_time_seconds":5.001225569,"session_id":46307,"original_token_count":0,"output":""}
{"chunk_id":"b2ef6d","wall_time_seconds":0.00001289,"exit_code":0,"original_token_count":83,"output":"3304693760\nlow 0\nhigh 0\nmax 741\noom 5\noom_kill 1\noom_group_kill 0\nsock_throttled 0\n{'stage': 'validation', 'name': 'wide', 'steps': 5000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 1000, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 3}\nstep 100 loss 7.75424699306488 seconds 56.3\n"}

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## Step 29 · agent · 2026-09-10T16:36:59.394Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:37:00.794Z
{
  "code": "text(await tools.exec_command({cmd:\"ls -lh /app/solution/*.npz /app/solution/*.pt && tail -6 /app/solution/initial_final.log && tail -5 /app/solution/temporal.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:37:00.939Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"d4fed5","wall_time_seconds":0.000030641,"exit_code":0,"original_token_count":276,"output":"-rw-r--r-- 1 agent agent  31M Sep 10 16:32 /app/solution/base_best.pt\n-rw-r--r-- 1 agent agent  31M Sep 10 16:33 /app/solution/base_last.pt\n-rw-r--r-- 1 agent agent 112M Sep 10 16:31 /app/solution/data_test.npz\n-rw-r--r-- 1 agent agent 109M Sep 10 16:29 /app/solution/data_validation.npz\n-rw-r--r-- 1 agent agent  31M Sep 10 16:35 /app/solution/temporal_best.pt\n-rw-r--r-- 1 agent agent  31M Sep 10 16:36 /app/solution/temporal_early.pt\n-rw-r--r-- 1 agent agent  31M Sep 10 16:36 /app/solution/temporal_last.pt\n-rw-r--r-- 1 agent agent 251M Sep 10 16:33 /app/solution/temporal_validation.npz\nstep 1900 loss 5.5652602005004885 seconds 150.8\nstep 2000 loss 5.541696834564209 seconds 160.8\nstep 2100 loss 5.531633749008178 seconds 171.8\nstep 2200 loss 5.513475685119629 seconds 188.7\nstep 2300 loss 5.516394329071045 seconds 199.1\nstep 2400 loss 5.491465501785278 seconds 208.7\nstep 1400 loss 5.325127763748169 seconds 138.1\nstep 1500 loss 5.2650589179992675 seconds 150.1\nVALIDATION 1500 0.030410085464525633\nstep 1600 loss 5.2234703731536865 seconds 165.9\nstep 1700 loss 5.126646208763122 seconds 174.1\n"}
{"current_time":"2026-09-10 16:37:00 UTC"}

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}

## Step 30 · agent · 2026-09-10T16:37:05.398Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:37:06.350Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -12 /app/solution/temporal_tune.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:37:06.467Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"c58d62","wall_time_seconds":0.00000713,"exit_code":0,"original_token_count":116,"output":"{'history_power': 0.5, 'recent': 0, 'pop': -1.0, 'score': np.float64(0.00040165549252140344)}\n{'history_power': 0.5, 'recent': 0, 'pop': -0.5, 'score': np.float64(0.002702856948777951)}\n{'history_power': 0.5, 'recent': 0, 'pop': 0.0, 'score': np.float64(0.03141250856416356)}\n{'history_power': 0.5, 'recent': 0, 'pop': 0.5, 'score': np.float64(0.030019054988487035)}\n{'history_power': 0.5, 'recent': 0, 'pop': 1.0, 'score': np.float64(0.02735471042597222)}\nKilled\n"}

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    "stderr": "",
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    "exit_code": 0,
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      "secs": 0,
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    "formatted_output": "{'history_power': 0.5, 'recent': 0, 'pop': -1.0, 'score': np.float64(0.00040165549252140344)}\n{'history_power': 0.5, 'recent': 0, 'pop': -0.5, 'score': np.float64(0.002702856948777951)}\n{'history_power': 0.5, 'recent': 0, 'pop': 0.0, 'score': np.float64(0.03141250856416356)}\n{'history_power': 0.5, 'recent': 0, 'pop': 0.5, 'score': np.float64(0.030019054988487035)}\n{'history_power': 0.5, 'recent': 0, 'pop': 1.0, 'score': np.float64(0.02735471042597222)}\nKilled\n"
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## Step 31 · agent · 2026-09-10T16:37:31.452Z

### Thinking unavailable — no text recorded


## Step 32 · agent · 2026-09-10T16:37:53.965Z

### Thinking unavailable — no text recorded


## Step 33 · agent · 2026-09-10T16:37:55.865Z

### Thinking unavailable — no text recorded


The temporal model performs best early in fine-tuning; further training lowers loss but has reduced validation quality so far. I’m retaining the best checkpoint. One tuning process was killed by the memory limit, so I’m reducing concurrent jobs before continuing.


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### exec · 2026-09-10T16:37:58.819Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -7 /app/solution/initial_final.log && tail -6 /app/solution/temporal.log && tail -4 /app/solution/wide.log && cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:37:58.955Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"d4aca9","wall_time_seconds":0.00000648,"exit_code":0,"original_token_count":210,"output":"step 2600 loss 5.452418131828308 seconds 228.6\nstep 2700 loss 5.454132661819458 seconds 238.7\nstep 2800 loss 5.41808249950409 seconds 247.2\nstep 2900 loss 5.427879958152771 seconds 256.4\nstep 3000 loss 5.40817174911499 seconds 267.2\nsaved submission 3000\ndone\nstep 1900 loss 5.003690552711487 seconds 190.0\nstep 2000 loss 4.961255092620849 seconds 198.3\nVALIDATION 2000 0.029676565578582705\nstep 2100 loss 4.921529669761657 seconds 213.5\nstep 2200 loss 4.876661148071289 seconds 221.2\nstep 2300 loss 4.828880982398987 seconds 228.5\n{'stage': 'validation', 'name': 'wide', 'steps': 5000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 1000, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 3}\nstep 100 loss 7.75424699306488 seconds 56.3\nstep 200 loss 7.117884016036987 seconds 105.1\n2589057024\n"}
{"current_time":"2026-09-10 16:37:58 UTC"}

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## Step 34 · agent · 2026-09-10T16:38:21.631Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:38:32.992Z
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  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport os,signal\\nfor p in Path('/proc').iterdir():\\n if not p.name.isdigit():continue\\n try:c=(p/'cmdline').read_bytes().split(b'\\\\0')\\n except OSError:continue\\n if len(c)>4 and c[1]==b'/app/solution/train.py' and b'wide' in c:os.kill(int(p.name),signal.SIGTERM);print('stopped wide',p.name)\\np=Path('/app/solution/model.py');s=p.read_text().replace(\\\"x=(self.history(ids)*weight.unsqueeze(-1)).sum(1)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\\\",\\\"x=F.embedding_bag(ids,self.history.weight,mode='sum',per_sample_weights=weight,padding_idx=self.history.padding_idx)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\\\");p.write_text(s)\\np=Path('/app/solution/train.py');s=p.read_text();start=s.index(' us=np.repeat');end=s.index('\\\\ndef truth',start);s=s[:start]+''' for st in range(0,n,256):\\n  end=min(st+256,n);a=int(data['offset'][st]);b=int(data['offset'][end]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64));ww=torch.from_numpy(w[a:b]);offsets=torch.from_numpy((data['offset'][st:end]-a).astype(np.int64))\\n  out[st:end]=torch.nn.functional.embedding_bag(ids,model.user.history.weight,offsets,mode='sum',per_sample_weights=ww)\\n  mass[st:end]=torch.from_numpy(np.add.reduceat(w[a:b],data['offset'][st:end]-a))\\n return model.user.project(out/mass[:,None].clamp_min(1e-8))\\n''' + s[end:];p.write_text(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4 > /app/solution/wide.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
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### Tool output · 2026-09-10T16:38:34.196Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"63c0d6","wall_time_seconds":1.00114513,"session_id":49405,"original_token_count":5,"output":"stopped wide 612\n"}

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## Step 35 · agent · 2026-09-10T16:38:37.412Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:38:45.884Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py temporal_early.pt --output temporal_tune > /app/solution/temporal_tune.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/validate_submission.py <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\np=Path('/app/solution/submission.parquet');d=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));validate_ranking(d,targets);c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'));assert all(c.is_eligible(int(u),int(i)) for u,i in d[['uid','item_id']].itertuples(index=False,name=None));print(f'VALID: {len(d)} rows, {len(targets)} users, exactly 100 distinct eligible items each.')\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/validate_submission.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
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### Tool output · 2026-09-10T16:38:48.248Z
Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"5cd882","wall_time_seconds":1.000999941,"session_id":13487,"original_token_count":0,"output":""}
{"chunk_id":"11ea12","wall_time_seconds":1.001108637,"session_id":98470,"original_token_count":0,"output":""}

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      "cat > /app/solution/validate_submission.py <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nfrom pathlib import Path\nimport pandas as pd,numpy as np\np=Path('/app/solution/submission.parquet');d=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));validate_ranking(d,targets);c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'));assert all(c.is_eligible(int(u),int(i)) for u,i in d[['uid','item_id']].itertuples(index=False,name=None));print(f'VALID: {len(d)} rows, {len(targets)} users, exactly 100 distinct eligible items each.')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/validate_submission.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/validate_submission.py <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nfrom pathlib import Path\nimport pandas as pd,numpy as np\np=Path('/app/solution/submission.parquet');d=pd.read_parquet(p);targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));validate_ranking(d,targets);c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'));assert all(c.is_eligible(int(u),int(i)) for u,i in d[['uid','item_id']].itertuples(index=False,name=None));print(f'VALID: {len(d)} rows, {len(targets)} users, exactly 100 distinct eligible items each.')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/validate_submission.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n",
    "stderr": "",
    "aggregated_output": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 945155750
    },
    "formatted_output": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n"
  }
}

## Step 36 · agent · 2026-09-10T16:39:13.159Z

### Thinking unavailable — no text recorded


## Step 37 · agent · 2026-09-10T16:39:35.054Z

### Thinking unavailable — no text recorded


## Step 38 · agent · 2026-09-10T16:39:37.149Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:39:38.622Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal.log && tail -5 /app/solution/wide.log && tail -10 /app/solution/temporal_tune.log\",\"max_output_tokens\":2000}));\ntext(await tools.write_stdin({session_id:98470,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:39:38.764Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"7c283f","wall_time_seconds":0.00000478,"exit_code":0,"original_token_count":385,"output":"step 3300 loss 4.441898846626282 seconds 292.6\nstep 3400 loss 4.402866368293762 seconds 299.2\nstep 3500 loss 4.402282433509827 seconds 306.5\nVALIDATION 3500 0.028958840613774184\nstep 3600 loss 4.339553384780884 seconds 320.6\nstep 3700 loss 4.3163974523544315 seconds 327.4\n{'stage': 'validation', 'name': 'wide', 'steps': 3000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 4}\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Terminated\n{'history_power': 0.5, 'recent': 7, 'pop': -1.0, 'score': np.float64(0.0002454870260025464)}\n{'history_power': 0.5, 'recent': 7, 'pop': -0.5, 'score': np.float64(0.005252950645467541)}\n{'history_power': 0.5, 'recent': 7, 'pop': 0.0, 'score': np.float64(0.03167304168127979)}\n{'history_power': 0.5, 'recent': 7, 'pop': 0.5, 'score': np.float64(0.03314894139309265)}\n{'history_power': 0.5, 'recent': 7, 'pop': 1.0, 'score': np.float64(0.031093976524776547)}\n{'history_power': 0.5, 'recent': 3, 'pop': -1.0, 'score': np.float64(0.00021453665964069017)}\n{'history_power': 0.5, 'recent': 3, 'pop': -0.5, 'score': np.float64(0.005137591061226232)}\n{'history_power': 0.5, 'recent': 3, 'pop': 0.0, 'score': np.float64(0.029247049451383376)}\n{'history_power': 0.5, 'recent': 3, 'pop': 0.5, 'score': np.float64(0.032019930232883756)}\n{'history_power': 0.5, 'recent': 3, 'pop': 1.0, 'score': np.float64(0.030424860969715004)}\n"}
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    "stdout": "step 3300 loss 4.441898846626282 seconds 292.6\nstep 3400 loss 4.402866368293762 seconds 299.2\nstep 3500 loss 4.402282433509827 seconds 306.5\nVALIDATION 3500 0.028958840613774184\nstep 3600 loss 4.339553384780884 seconds 320.6\nstep 3700 loss 4.3163974523544315 seconds 327.4\n{'stage': 'validation', 'name': 'wide', 'steps': 3000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 4}\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Terminated\n{'history_power': 0.5, 'recent': 7, 'pop': -1.0, 'score': np.float64(0.0002454870260025464)}\n{'history_power': 0.5, 'recent': 7, 'pop': -0.5, 'score': np.float64(0.005252950645467541)}\n{'history_power': 0.5, 'recent': 7, 'pop': 0.0, 'score': np.float64(0.03167304168127979)}\n{'history_power': 0.5, 'recent': 7, 'pop': 0.5, 'score': np.float64(0.03314894139309265)}\n{'history_power': 0.5, 'recent': 7, 'pop': 1.0, 'score': np.float64(0.031093976524776547)}\n{'history_power': 0.5, 'recent': 3, 'pop': -1.0, 'score': np.float64(0.00021453665964069017)}\n{'history_power': 0.5, 'recent': 3, 'pop': -0.5, 'score': np.float64(0.005137591061226232)}\n{'history_power': 0.5, 'recent': 3, 'pop': 0.0, 'score': np.float64(0.029247049451383376)}\n{'history_power': 0.5, 'recent': 3, 'pop': 0.5, 'score': np.float64(0.032019930232883756)}\n{'history_power': 0.5, 'recent': 3, 'pop': 1.0, 'score': np.float64(0.030424860969715004)}\n",
    "stderr": "",
    "aggregated_output": "step 3300 loss 4.441898846626282 seconds 292.6\nstep 3400 loss 4.402866368293762 seconds 299.2\nstep 3500 loss 4.402282433509827 seconds 306.5\nVALIDATION 3500 0.028958840613774184\nstep 3600 loss 4.339553384780884 seconds 320.6\nstep 3700 loss 4.3163974523544315 seconds 327.4\n{'stage': 'validation', 'name': 'wide', 'steps': 3000, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 4}\n\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Terminated\n{'history_power': 0.5, 'recent': 7, 'pop': -1.0, 'score': np.float64(0.0002454870260025464)}\n{'history_power': 0.5, 'recent': 7, 'pop': -0.5, 'score': np.float64(0.005252950645467541)}\n{'history_power': 0.5, 'recent': 7, 'pop': 0.0, 'score': np.float64(0.03167304168127979)}\n{'history_power': 0.5, 'recent': 7, 'pop': 0.5, 'score': np.float64(0.03314894139309265)}\n{'history_power': 0.5, 'recent': 7, 'pop': 1.0, 'score': np.float64(0.031093976524776547)}\n{'history_power': 0.5, 'recent': 3, 'pop': -1.0, 'score': np.float64(0.00021453665964069017)}\n{'history_power': 0.5, 'recent': 3, 'pop': -0.5, 'score': np.float64(0.005137591061226232)}\n{'history_power': 0.5, 'recent': 3, 'pop': 0.0, 'score': np.float64(0.029247049451383376)}\n{'history_power': 0.5, 'recent': 3, 'pop': 0.5, 'score': np.float64(0.032019930232883756)}\n{'history_power': 0.5, 'recent': 3, 'pop': 1.0, 'score': np.float64(0.030424860969715004)}\n",
    "exit_code": 0,
    "duration": {
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      "nanos": 4780
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  }
}

## Step 39 · agent · 2026-09-10T16:39:42.268Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:39:46.542Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').iterdir():\\n if p.name.isdigit():\\n  try:\\n   c=(p/'cmdline').read_bytes().split(b'\\\\0')\\n   if len(c)>1 and c[1] in [b'/app/solution/train.py',b'/app/solution/tune.py']: print(p.name,b' '.join(c).decode(),(p/'status').read_text().split('VmRSS:')[1].split('\\\\n')[0])\\n  except: pass\\nPY\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:39:46.825Z
Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"831b41","wall_time_seconds":0.056532451,"exit_code":0,"original_token_count":112,"output":"532 /opt/task-venv/bin/python /app/solution/train.py --name temporal --temporal --resume /app/solution/base_best.pt --lr .0005 --steps 4000 --eval-every 500 --threads 3  \t 1502752 kB\n740 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4  \t 1113288 kB\n760 /opt/task-venv/bin/python /app/solution/tune.py temporal_early.pt --output temporal_tune  \t 2150504 kB\n"}

### Harness context
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      "-lc",
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      }
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    "status": "completed",
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  }
}

## Step 40 · agent · 2026-09-10T16:39:58.000Z

### Thinking unavailable — no text recorded


### Harness context
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      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name temporal --temporal --resume /app/solution/base_best.pt --lr .0005 --steps 4000 --eval-every 500 --threads 3 > /app/solution/temporal.log 2>&1"
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### exec · 2026-09-10T16:40:02.500Z
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### Tool output · 2026-09-10T16:40:02.625Z
Script completed
Wall time 0.1 seconds
Output:

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## Step 41 · agent · 2026-09-10T16:40:26.629Z

### Thinking unavailable — no text recorded


## Step 42 · agent · 2026-09-10T16:40:39.846Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:40:41.026Z
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}
### Tool output · 2026-09-10T16:40:41.107Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"2c1b83","wall_time_seconds":0.00000397,"exit_code":0,"original_token_count":209,"output":"step 3700 loss 4.3163974523544315 seconds 327.4\nstep 3800 loss 4.2977278566360475 seconds 334.9\nstep 3900 loss 4.260511755943298 seconds 341.9\nstep 4000 loss 4.253814301490784 seconds 348.7\nVALIDATION 4000 0.02928449190559451\ndone\nstep 100 loss 7.754246983528137 seconds 50.5\nstep 200 loss 7.11788414478302 seconds 83.8\nstep 300 loss 6.679131722450256 seconds 102.4\n\u0000\u0000Terminated\n{'history_power': 1.0, 'recent': 3, 'pop': -1.0, 'score': np.float64(0.0002413006089472053)}\n{'history_power': 1.0, 'recent': 3, 'pop': -0.5, 'score': np.float64(0.005276425698462579)}\n{'history_power': 1.0, 'recent': 3, 'pop': 0.0, 'score': np.float64(0.028902275465639556)}\n{'history_power': 1.0, 'recent': 3, 'pop': 0.5, 'score': np.float64(0.03197828316653057)}\n{'history_power': 1.0, 'recent': 3, 'pop': 1.0, 'score': np.float64(0.03051639973028708)}\n"}
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## Step 43 · agent · 2026-09-10T16:40:44.561Z

### Thinking unavailable — no text recorded


The first final submission is saved and passes the structural checks: 845,600 rows, covering all 8,456 users with exactly 100 distinct eligible tracks each. I’ll keep it in place while testing improvements. I also changed history pooling to PyTorch `embedding_bag` to reduce memory use without changing the model’s scoring form.


### exec · 2026-09-10T16:40:49.935Z
{
  "code": "text(await tools.exec_command({cmd:\"head -24 /app/solution/temporal_tune.json && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name masked --temporal --mask-history --resume /app/solution/base_best.pt --lr .0005 --steps 1200 --eval-every 300 --threads 3 > /app/solution/masked.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\n"
}
### Tool output · 2026-09-10T16:40:51.132Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"db442d","wall_time_seconds":1.001461111,"session_id":61769,"original_token_count":102,"output":"[\n  {\n    \"history_power\": 0.5,\n    \"recent\": 30,\n    \"pop\": 0.0,\n    \"score\": 0.03390204390730559\n  },\n  {\n    \"history_power\": 0.5,\n    \"recent\": 14,\n    \"pop\": 0.5,\n    \"score\": 0.03387726407410234\n  },\n  {\n    \"history_power\": 0.5,\n    \"recent\": 14,\n    \"pop\": 0.0,\n    \"score\": 0.03349477025285657\n  },\n  {\n    \"history_power\": 1.0,\n    \"recent\": 30,\n    \"pop\": 0.5,\n    \"score\": 0.03344860578613788\n"}

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## Step 44 · agent · 2026-09-10T16:41:13.926Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:41:37.895Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/trends.py <<'PY'\\n\\\"\\\"\\\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\\\"\\\"\\\"\\nimport argparse,json\\nimport numpy as np,pandas as pd,torch\\nfrom train import P,D,encode_users,truth,metric\\nfrom model import TwoTower\\n\\ndef priors(data,q):\\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\\n # Count recently first-adopted catalog items, with a bounded preference weight.\\n for days in [7,14,30]:\\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\\n return out\\n\\nif __name__=='__main__':\\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);pr=priors(data,ck['pop']);results=[];best=-1\\n with torch.no_grad():\\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\\n  for recent in [14,30,90]:\\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\\n   for st in range(0,len(users),256):\\n    s=uv[st:st+256]@iv.T\\n    for j,b in enumerate(bias):\\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\\n   for c,ts in zip(configs,tops):\\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\\n  for st in range(0,len(users),256):\\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\\nPY\",\"max_output_tokens\":1000}));\n"
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### Tool output · 2026-09-10T16:41:38.056Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"63537e","wall_time_seconds":0.00001537,"exit_code":0,"original_token_count":0,"output":""}

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      "cat > /app/solution/trends.py <<'PY'\n\"\"\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\"\"\"\nimport argparse,json\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,encode_users,truth,metric\nfrom model import TwoTower\n\ndef priors(data,q):\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\n # Count recently first-adopted catalog items, with a bounded preference weight.\n for days in [7,14,30]:\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);pr=priors(data,ck['pop']);results=[];best=-1\n with torch.no_grad():\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\n  for recent in [14,30,90]:\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\n   for st in range(0,len(users),256):\n    s=uv[st:st+256]@iv.T\n    for j,b in enumerate(bias):\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\n   for c,ts in zip(configs,tops):\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\n  for st in range(0,len(users),256):\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/trends.py <<'PY'\n\"\"\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\"\"\"\nimport argparse,json\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,encode_users,truth,metric\nfrom model import TwoTower\n\ndef priors(data,q):\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\n # Count recently first-adopted catalog items, with a bounded preference weight.\n for days in [7,14,30]:\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);pr=priors(data,ck['pop']);results=[];best=-1\n with torch.no_grad():\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\n  for recent in [14,30,90]:\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\n   for st in range(0,len(users),256):\n    s=uv[st:st+256]@iv.T\n    for j,b in enumerate(bias):\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\n   for c,ts in zip(configs,tops):\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\n  for st in range(0,len(users),256):\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\nPY"
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## Step 45 · agent · 2026-09-10T16:41:43.071Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:41:44.574Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/wide.log && tail -7 /app/solution/masked.log && cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:41:44.697Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"007223","wall_time_seconds":0.000026191,"exit_code":0,"original_token_count":163,"output":"step 300 loss 6.679131722450256 seconds 102.4\nstep 400 loss 6.413783297538758 seconds 121.0\nstep 500 loss 6.241750473976135 seconds 142.6\nVALIDATION 500 0.0187424650025185\nstep 600 loss 6.118262853622436 seconds 162.0\n{'stage': 'validation', 'name': 'masked', 'steps': 1200, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/base_best.pt', 'temporal': True, 'targets': 4, 'threads': 3, 'mask_history': True}\nstep 100 loss 6.477961859703064 seconds 10.6\nstep 200 loss 6.29188946723938 seconds 21.8\nstep 300 loss 6.1843799257278445 seconds 35.5\nVALIDATION 300 0.03107972610504186\n3638312960\n"}
{"current_time":"2026-09-10 16:41:44 UTC"}

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    "stdout": "step 300 loss 6.679131722450256 seconds 102.4\nstep 400 loss 6.413783297538758 seconds 121.0\nstep 500 loss 6.241750473976135 seconds 142.6\nVALIDATION 500 0.0187424650025185\nstep 600 loss 6.118262853622436 seconds 162.0\n{'stage': 'validation', 'name': 'masked', 'steps': 1200, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/base_best.pt', 'temporal': True, 'targets': 4, 'threads': 3, 'mask_history': True}\nstep 100 loss 6.477961859703064 seconds 10.6\nstep 200 loss 6.29188946723938 seconds 21.8\nstep 300 loss 6.1843799257278445 seconds 35.5\nVALIDATION 300 0.03107972610504186\n3638312960\n",
    "stderr": "",
    "aggregated_output": "step 300 loss 6.679131722450256 seconds 102.4\nstep 400 loss 6.413783297538758 seconds 121.0\nstep 500 loss 6.241750473976135 seconds 142.6\nVALIDATION 500 0.0187424650025185\nstep 600 loss 6.118262853622436 seconds 162.0\n{'stage': 'validation', 'name': 'masked', 'steps': 1200, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/base_best.pt', 'temporal': True, 'targets': 4, 'threads': 3, 'mask_history': True}\nstep 100 loss 6.477961859703064 seconds 10.6\nstep 200 loss 6.29188946723938 seconds 21.8\nstep 300 loss 6.1843799257278445 seconds 35.5\nVALIDATION 300 0.03107972610504186\n3638312960\n",
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### Harness context
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## Step 46 · agent · 2026-09-10T16:42:08.527Z

### Thinking unavailable — no text recorded


## Step 47 · agent · 2026-09-10T16:42:09.368Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:42:19.847Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/prepare_temporal.py';s=open(p).read().replace('def main(stage,weeks):','def main(stage,weeks,recent=0,history_power=1.):');s=s.replace(\\\"h=h.groupby(['u','i'],sort=True)[['w','like']].sum();hw=np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy());\\\", \\\"h=h.groupby(['u','i'],sort=True).agg(w=('w','sum'),like=('like','sum'),timestamp=('timestamp','max'));hw=(np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy()))**history_power;hw*=np.exp((h.timestamp.to_numpy(dtype=float)-start)/(recent*DAY)) if recent else 1.;\\\")\\ns=s.replace(\\\"np.savez(P/f'temporal_{stage}.npz'\\\", \\\"np.savez(P/f'temporal{\\\\\\\"_recent\\\\\\\" if recent else \\\\\\\"\\\\\\\"}_{stage}.npz'\\\")\\ns=s.replace(\\\"a=ap.parse_args();main(a.stage,a.weeks)\\\",\\\"ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);a=ap.parse_args();main(a.stage,a.weeks,a.recent,a.history_power)\\\");open(p,'w').write(s)\\np='/app/solution/train.py';s=open(p).read().replace(\\\"ap.add_argument('--mask-history',action='store_true');\\\", \\\"ap.add_argument('--mask-history',action='store_true');ap.add_argument('--pool-prefix',default='temporal');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);\\\")\\ns=s.replace(\\\"P/f'temporal_{args.stage}.npz'\\\", \\\"P/f'{args.pool_prefix}_{args.stage}.npz'\\\")\\ns=s.replace(\\\"P/f'{args.name}_validation.parquet');print('VALIDATION'\\\", \\\"P/f'{args.name}_validation.parquet',history_power=args.history_power,recent=args.recent);print('VALIDATION'\\\")\\ns=s.replace(\\\"rank(model,data,'test',P/'submission.parquet');\\\", \\\"rank(model,data,'test',P/'submission.parquet',history_power=args.history_power,recent=args.recent);\\\")\\nopen(p,'w').write(s)\\nPY\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:42:20.014Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"680509","wall_time_seconds":0.00000771,"exit_code":0,"original_token_count":0,"output":""}

### Harness context
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      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/prepare_temporal.py';s=open(p).read().replace('def main(stage,weeks):','def main(stage,weeks,recent=0,history_power=1.):');s=s.replace(\"h=h.groupby(['u','i'],sort=True)[['w','like']].sum();hw=np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy());\", \"h=h.groupby(['u','i'],sort=True).agg(w=('w','sum'),like=('like','sum'),timestamp=('timestamp','max'));hw=(np.sqrt(h.w.to_numpy())+2*np.sqrt(h.like.to_numpy()))**history_power;hw*=np.exp((h.timestamp.to_numpy(dtype=float)-start)/(recent*DAY)) if recent else 1.;\")\ns=s.replace(\"np.savez(P/f'temporal_{stage}.npz'\", \"np.savez(P/f'temporal{\\\"_recent\\\" if recent else \\\"\\\"}_{stage}.npz'\")\ns=s.replace(\"a=ap.parse_args();main(a.stage,a.weeks)\",\"ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);a=ap.parse_args();main(a.stage,a.weeks,a.recent,a.history_power)\");open(p,'w').write(s)\np='/app/solution/train.py';s=open(p).read().replace(\"ap.add_argument('--mask-history',action='store_true');\", \"ap.add_argument('--mask-history',action='store_true');ap.add_argument('--pool-prefix',default='temporal');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);\")\ns=s.replace(\"P/f'temporal_{args.stage}.npz'\", \"P/f'{args.pool_prefix}_{args.stage}.npz'\")\ns=s.replace(\"P/f'{args.name}_validation.parquet');print('VALIDATION'\", \"P/f'{args.name}_validation.parquet',history_power=args.history_power,recent=args.recent);print('VALIDATION'\")\ns=s.replace(\"rank(model,data,'test',P/'submission.parquet');\", \"rank(model,data,'test',P/'submission.parquet',history_power=args.history_power,recent=args.recent);\")\nopen(p,'w').write(s)\nPY"
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      }
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    "stderr": "",
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    "exit_code": 0,
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      "secs": 0,
      "nanos": 7710
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}

## Step 48 · agent · 2026-09-10T16:42:43.315Z

### Thinking unavailable — no text recorded


## Step 49 · agent · 2026-09-10T16:42:46.268Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:43:03.366Z
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}
### Tool output · 2026-09-10T16:43:03.565Z
Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"ccc5e1","wall_time_seconds":0.000014591,"exit_code":0,"original_token_count":108,"output":"VALIDATION 600 0.03130128168079242\nstep 700 loss 5.730468530654907 seconds 83.4\nstep 800 loss 5.64456431388855 seconds 94.0\nstep 900 loss 5.548845157623291 seconds 102.8\nVALIDATION 900 0.030778271516681728\nstep 1000 loss 5.4479723024368285 seconds 116.9\nstep 900 loss 5.891244940757751 seconds 210.1\nstep 1000 loss 5.8183108949661255 seconds 226.2\nVALIDATION 1000 0.017710371170475768\nstep 1100 loss 5.782671613693237 seconds 245.6\n"}

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## Step 50 · agent · 2026-09-10T16:43:18.040Z

### Thinking unavailable — no text recorded


The best public score is now 0.0339 NDCG@10, using temporal fine-tuning and a 30-day history decay. Excluding known-history tracks from sampled negatives has not improved it so far. Next I’m testing recent item-adoption trends as an item-only bias, which can stay inside the precomputed item vector.


### exec · 2026-09-10T16:43:21.557Z
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### Tool output · 2026-09-10T16:43:22.789Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"ca0be8","wall_time_seconds":1.001535187,"session_id":60711,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 16:43:22 UTC"}

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## Step 51 · agent · 2026-09-10T16:43:47.054Z

### Thinking unavailable — no text recorded


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### exec · 2026-09-10T16:43:47.892Z
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### Tool output · 2026-09-10T16:43:48.000Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"a7a2d8","wall_time_seconds":0.00001839,"exit_code":0,"original_token_count":83,"output":"step 1100 loss 5.398017716407776 seconds 127.4\nstep 1200 loss 5.305948562622071 seconds 137.8\nVALIDATION 1200 0.02970486394475962\ndone\nstep 1100 loss 5.782671613693237 seconds 245.6\nstep 1200 loss 5.755555238723755 seconds 261.8\nstep 1300 loss 5.710645275115967 seconds 276.8\nstep 1400 loss 5.670514235496521 seconds 291.9\nKilled\n"}

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## Step 52 · agent · 2026-09-10T16:44:01.286Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:44:04.157Z
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### Tool output · 2026-09-10T16:44:04.476Z
Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"1b4ab3","wall_time_seconds":0.091842765,"exit_code":0,"original_token_count":60,"output":"low 0\nhigh 0\nmax 2805\noom 6\noom_kill 2\noom_group_kill 0\nsock_throttled 0\n2081021952\n740 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4  \t 1862324 kB\n"}

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      "cat /sys/fs/cgroup/memory.events && cat /sys/fs/cgroup/memory.current && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').iterdir():\n if p.name.isdigit():\n  try:\n   c=(p/'cmdline').read_bytes().split(b'\\0')\n   if len(c)>1 and c[1].startswith(b'/app/solution/'): print(p.name,b' '.join(c).decode(),(p/'status').read_text().split('VmRSS:')[1].split('\\n')[0])\n  except:pass\nPY"
    ],
    "cwd": "file:///app",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "740 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4  \t 1862324 kB\n",
    "stderr": "",
    "aggregated_output": "740 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4  \t 1862324 kB\n",
    "exit_code": 0,
    "duration": {
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    "formatted_output": "740 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4  \t 1862324 kB\n"
  }
}

## Step 53 · agent · 2026-09-10T16:44:27.198Z

### Thinking unavailable — no text recorded


## Step 54 · agent · 2026-09-10T16:44:35.347Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:44:40.402Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/trends.py';s=open(p).read().replace('import argparse,json','import argparse,json,resource');s=s.replace(\\\"pr=priors(data,ck['pop']);results=[];best=-1\\\", \\\"print('loaded',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);pr=priors(data,ck['pop']);print('priors',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);results=[];best=-1\\\")\\ns=s.replace(\\\"    s=uv[st:st+256]@iv.T\\\", \\\"    if st%2048==0:print('batch',recent,st,'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\\\\n    s=uv[st:st+256]@iv.T\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend > /app/solution/trend.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:44:41.592Z
Script completed
Wall time 1.2 seconds
Output:

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## Step 55 · agent · 2026-09-10T16:44:45.484Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:44:47.098Z
{
  "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"tail -6 /app/solution/wide.log && cat /app/solution/trend.log\",\"max_output_tokens\":1500}));\n"
}
### Tool output · 2026-09-10T16:44:47.313Z
Script completed
Wall time 0.1 seconds
Output:

{"current_time":"2026-09-10 16:44:47 UTC"}
{"chunk_id":"d550a6","wall_time_seconds":0.00000484,"exit_code":0,"original_token_count":69,"output":"step 1500 loss 5.676119174957275 seconds 305.6\nVALIDATION 1500 0.020853297294464106\nstep 1600 loss 5.645335869789124 seconds 320.4\nstep 1700 loss 5.630647459030151 seconds 332.4\nstep 1800 loss 5.610941548347473 seconds 344.5\nstep 1900 loss 5.5728849601745605 seconds 357.0\n"}

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## Step 56 · agent · 2026-09-10T16:45:11.287Z

### Thinking unavailable — no text recorded


## Step 57 · agent · 2026-09-10T16:45:16.807Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:45:18.863Z
{
  "code": "text(await tools.exec_command({cmd:\"cat /app/solution/trend.log && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor n in ['train.py','tune.py','trends.py']:\\n p=Path('/app/solution')/n;s=p.read_text().replace(\\\"bitorder='little').astype(bool)\\\",\\\"bitorder='little').view(np.bool_)\\\");p.write_text(s)\\nPY\",\"max_output_tokens\":1500}));\n"
}
### Tool output · 2026-09-10T16:45:19.159Z
Script completed
Wall time 0.3 seconds
Output:

{"chunk_id":"2b03ce","wall_time_seconds":0.089779898,"exit_code":0,"original_token_count":28,"output":"loaded 955084\npriors 1058564\nbatch 14 0 rss 1058564\nbatch 14 2048 rss 3167080\nbatch 14 4096 rss 5409512\nKilled\n"}

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## Step 58 · agent · 2026-09-10T16:45:44.555Z

### Thinking unavailable — no text recorded


The wider model has not yet improved validation quality. The trend sweep also hit the memory limit; its memory use appears to grow during the batched ranking loop. I’m investigating that before rerunning it. The saved submission is intact.


### exec · 2026-09-10T16:45:47.298Z
{
  "code": "text(await tools.exec_command({cmd:\"sed -n '1,180p' /app/solution/trends.py && tail -6 /app/solution/wide.log\",\"max_output_tokens\":4000}));\n"
}
### Tool output · 2026-09-10T16:45:47.447Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"f6c68a","wall_time_seconds":0.00000714,"exit_code":0,"original_token_count":854,"output":"\"\"\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\"\"\"\nimport argparse,json,resource\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,encode_users,truth,metric\nfrom model import TwoTower\n\ndef priors(data,q):\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\n # Count recently first-adopted catalog items, with a bounded preference weight.\n for days in [7,14,30]:\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);print('loaded',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);pr=priors(data,ck['pop']);print('priors',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);results=[];best=-1\n with torch.no_grad():\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\n  for recent in [14,30,90]:\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\n   for st in range(0,len(users),256):\n    if st%2048==0:print('batch',recent,st,'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\n    s=uv[st:st+256]@iv.T\n    for j,b in enumerate(bias):\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\n   for c,ts in zip(configs,tops):\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\n  for st in range(0,len(users),256):\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\nstep 1900 loss 5.5728849601745605 seconds 357.0\nstep 2000 loss 5.552968859672546 seconds 371.9\nVALIDATION 2000 0.020329833948097377\nstep 2100 loss 5.540442333221436 seconds 387.6\nstep 2200 loss 5.532681984901428 seconds 399.7\nstep 2300 loss 5.51737428188324 seconds 411.8\n"}

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    "stdout": "\"\"\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\"\"\"\nimport argparse,json,resource\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,encode_users,truth,metric\nfrom model import TwoTower\n\ndef priors(data,q):\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\n # Count recently first-adopted catalog items, with a bounded preference weight.\n for days in [7,14,30]:\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);print('loaded',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);pr=priors(data,ck['pop']);print('priors',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);results=[];best=-1\n with torch.no_grad():\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\n  for recent in [14,30,90]:\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\n   for st in range(0,len(users),256):\n    if st%2048==0:print('batch',recent,st,'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\n    s=uv[st:st+256]@iv.T\n    for j,b in enumerate(bias):\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\n   for c,ts in zip(configs,tops):\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\n  for st in range(0,len(users),256):\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\nstep 1900 loss 5.5728849601745605 seconds 357.0\nstep 2000 loss 5.552968859672546 seconds 371.9\nVALIDATION 2000 0.020329833948097377\nstep 2100 loss 5.540442333221436 seconds 387.6\nstep 2200 loss 5.532681984901428 seconds 399.7\nstep 2300 loss 5.51737428188324 seconds 411.8\n",
    "stderr": "",
    "aggregated_output": "\"\"\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\"\"\"\nimport argparse,json,resource\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,encode_users,truth,metric\nfrom model import TwoTower\n\ndef priors(data,q):\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\n # Count recently first-adopted catalog items, with a bounded preference weight.\n for days in [7,14,30]:\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);print('loaded',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);pr=priors(data,ck['pop']);print('priors',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);results=[];best=-1\n with torch.no_grad():\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\n  for recent in [14,30,90]:\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\n   for st in range(0,len(users),256):\n    if st%2048==0:print('batch',recent,st,'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\n    s=uv[st:st+256]@iv.T\n    for j,b in enumerate(bias):\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\n   for c,ts in zip(configs,tops):\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\n  for st in range(0,len(users),256):\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\nstep 1900 loss 5.5728849601745605 seconds 357.0\nstep 2000 loss 5.552968859672546 seconds 371.9\nVALIDATION 2000 0.020329833948097377\nstep 2100 loss 5.540442333221436 seconds 387.6\nstep 2200 loss 5.532681984901428 seconds 399.7\nstep 2300 loss 5.51737428188324 seconds 411.8\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 7140
    },
    "formatted_output": "\"\"\"Calibrate cutoff-observable item adoption priors in the item-vector bias.\"\"\"\nimport argparse,json,resource\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,encode_users,truth,metric\nfrom model import TwoTower\n\ndef priors(data,q):\n q=q+1e-7;q=q/q.sum();out={};age=(int(data['cutoff'])-data['first'].astype(float))/86400\n # Count recently first-adopted catalog items, with a bounded preference weight.\n for days in [7,14,30]:\n  weight=np.minimum(data['y'],4.)*np.exp(-age/days)\n  sums=np.add.reduceat(weight,data['offset'][:-1]);weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\n  counts=np.bincount(data['i'],weights=weight,minlength=30000);counts/=counts.sum()\n  out[days]=np.log(.5*q+.5*counts)-np.log(q)\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('checkpoint');ap.add_argument('--output',default='trend');a=ap.parse_args();torch.set_num_threads(3);ck=torch.load(P/a.checkpoint,weights_only=False);data=dict(np.load(P/'data_validation.npz'));m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();ar=np.load(D/'validation_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users);print('loaded',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);pr=priors(data,ck['pop']);print('priors',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True);results=[];best=-1\n with torch.no_grad():\n  iv=m.item(torch.arange(30000));lp=np.log(ck['pop']+1e-7)\n  for recent in [14,30,90]:\n   uv=encode_users(m,data,.5,recent)[order];configs=[(days,strength,pop) for days in [7,14,30] for strength in [0.,.5,1.,2.] for pop in [0.,.25,.5]];tops=[[] for c in configs];bias=[torch.from_numpy(strength*pr[days]+pop*lp).float() for days,strength,pop in configs]\n   for st in range(0,len(users),256):\n    if st%2048==0:print('batch',recent,st,'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\n    s=uv[st:st+256]@iv.T\n    for j,b in enumerate(bias):\n     v=s+b;v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops[j].append(v.topk(10,1).indices.numpy())\n   for c,ts in zip(configs,tops):\n    score=metric(np.concatenate(ts),tt);row={'history_power':.5,'recent':recent,'trend_days':c[0],'trend_strength':c[1],'pop':c[2],'score':score};results.append(row)\n   print('recent',recent,'BEST',max(results,key=lambda x:x['score']),flush=True)\n  results.sort(key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}.json','w'),indent=2);b=results[0];uv=encode_users(m,data,b['history_power'],b['recent'])[order];iv[:,-1]+=torch.from_numpy(b['trend_strength']*pr[b['trend_days']]+b['pop']*lp).float();tops=[]\n  for st in range(0,len(users),256):\n   s=uv[st:st+256]@iv.T;s.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9);tops.append(s.topk(100,1).indices.numpy())\n  top=np.concatenate(tops);pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(P/f'{a.output}_validation.parquet',index=False)\nstep 1900 loss 5.5728849601745605 seconds 357.0\nstep 2000 loss 5.552968859672546 seconds 371.9\nVALIDATION 2000 0.020329833948097377\nstep 2100 loss 5.540442333221436 seconds 387.6\nstep 2200 loss 5.532681984901428 seconds 399.7\nstep 2300 loss 5.51737428188324 seconds 411.8\n"
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## Step 59 · agent · 2026-09-10T16:46:12.331Z

### Thinking unavailable — no text recorded


## Step 60 · agent · 2026-09-10T16:46:16.210Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:46:22.161Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/trends.py';s=open(p).read().replace(\\\"    s=uv[st:st+256]@iv.T\\\\n    for j,b\\\", \\\"    s=uv[st:st+256]@iv.T;mask=~torch.from_numpy(el[st:st+256])\\\\n    for j,b\\\").replace(\\\"v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9)\\\",\\\"v.masked_fill_(mask,-1e9)\\\");open(p,'w').write(s)\\np='/app/solution/tune.py';s=open(p).read().replace(\\\"    s=uv[st:st+256]@iv.T\\\\n    for pop\\\", \\\"    s=uv[st:st+256]@iv.T;mask=~torch.from_numpy(el[st:st+256])\\\\n    for pop\\\").replace(\\\"v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9)\\\",\\\"v.masked_fill_(mask,-1e9)\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend > /app/solution/trend.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:46:23.352Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"7a66a6","wall_time_seconds":1.001077404,"session_id":77112,"original_token_count":0,"output":""}

### Harness context
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### Harness context
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  "item": {
    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace(\"    s=uv[st:st+256]@iv.T\\n    for j,b\", \"    s=uv[st:st+256]@iv.T;mask=~torch.from_numpy(el[st:st+256])\\n    for j,b\").replace(\"v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9)\",\"v.masked_fill_(mask,-1e9)\");open(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace(\"    s=uv[st:st+256]@iv.T\\n    for pop\", \"    s=uv[st:st+256]@iv.T;mask=~torch.from_numpy(el[st:st+256])\\n    for pop\").replace(\"v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9)\",\"v.masked_fill_(mask,-1e9)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend > /app/solution/trend.log 2>&1"
    ],
    "cwd": "file:///app",
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace(\"    s=uv[st:st+256]@iv.T\\n    for j,b\", \"    s=uv[st:st+256]@iv.T;mask=~torch.from_numpy(el[st:st+256])\\n    for j,b\").replace(\"v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9)\",\"v.masked_fill_(mask,-1e9)\");open(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace(\"    s=uv[st:st+256]@iv.T\\n    for pop\", \"    s=uv[st:st+256]@iv.T;mask=~torch.from_numpy(el[st:st+256])\\n    for pop\").replace(\"v.masked_fill_(~torch.from_numpy(el[st:st+256]),-1e9)\",\"v.masked_fill_(mask,-1e9)\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend > /app/solution/trend.log 2>&1"
      }
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      "nanos": 258866633
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  }
}

## Step 61 · agent · 2026-09-10T16:46:52.607Z

### Thinking unavailable — no text recorded


### Harness context
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').iterdir():\n if not p.name.isdigit():continue\n try:c=(p/'cmdline').read_bytes().split(b'\\0')\n except OSError:continue\n if len(c)>4 and c[1]==b'/app/solution/train.py' and b'wide' in c:os.kill(int(p.name),signal.SIGTERM);print('stopped wide',p.name)\np=Path('/app/solution/model.py');s=p.read_text().replace(\"x=(self.history(ids)*weight.unsqueeze(-1)).sum(1)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\",\"x=F.embedding_bag(ids,self.history.weight,mode='sum',per_sample_weights=weight,padding_idx=self.history.padding_idx)/weight.sum(1,keepdim=True).clamp_min(1e-6);return self.project(x)\");p.write_text(s)\np=Path('/app/solution/train.py');s=p.read_text();start=s.index(' us=np.repeat');end=s.index('\\ndef truth',start);s=s[:start]+''' for st in range(0,n,256):\n  end=min(st+256,n);a=int(data['offset'][st]);b=int(data['offset'][end]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64));ww=torch.from_numpy(w[a:b]);offsets=torch.from_numpy((data['offset'][st:end]-a).astype(np.int64))\n  out[st:end]=torch.nn.functional.embedding_bag(ids,model.user.history.weight,offsets,mode='sum',per_sample_weights=ww)\n  mass[st:end]=torch.from_numpy(np.add.reduceat(w[a:b],data['offset'][st:end]-a))\n return model.user.project(out/mass[:,None].clamp_min(1e-8))\n''' + s[end:];p.write_text(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name wide --dim 512 --batch 128 --targets 8 --steps 3000 --eval-every 500 --threads 4 > /app/solution/wide.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "stopped wide 612\n",
    "stderr": "",
    "aggregated_output": "stopped wide 612\n",
    "exit_code": 0,
    "duration": {
      "secs": 519,
      "nanos": 50394479
    },
    "formatted_output": "stopped wide 612\n"
  }
}

## Step 62 · agent · 2026-09-10T16:47:15.911Z

### Thinking unavailable — no text recorded


## Step 63 · agent · 2026-09-10T16:47:20.106Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:47:21.221Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -12 /app/solution/trend.log && tail -6 /app/solution/wide.log && cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:47:21.305Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"47a472","wall_time_seconds":0.00000391,"exit_code":0,"original_token_count":75,"output":"loaded 876492\npriors 1054176\nbatch 14 0 rss 1054176\nKilled\nstep 2700 loss 5.468589515686035 seconds 462.8\nstep 2800 loss 5.46442542552948 seconds 476.0\nstep 2900 loss 5.444357724189758 seconds 488.0\nstep 3000 loss 5.4383259296417235 seconds 500.1\nVALIDATION 3000 0.019302590614703394\ndone\n190078976\n"}
{"current_time":"2026-09-10 16:47:21 UTC"}

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## Step 64 · agent · 2026-09-10T16:47:44.787Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:47:48.389Z
{
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}
### Tool output · 2026-09-10T16:47:49.567Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"c5e599","wall_time_seconds":1.001190893,"session_id":68258,"original_token_count":0,"output":""}

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## Step 65 · agent · 2026-09-10T16:47:52.053Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:47:53.067Z
{
  "code": "text(await tools.write_stdin({session_id:68258,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:47:53.728Z
Script completed
Wall time 0.7 seconds
Output:

{"chunk_id":"42236f","wall_time_seconds":0.641273151,"exit_code":0,"original_token_count":69,"output":"2.8.0+cpu\n0 286044 (256, 10) 20480\n10 586076 (256, 10) 20480\n20 676112 (256, 10) 20480\n30 796048 (256, 10) 20480\n40 946064 (256, 10) 20480\n50 1066128 (256, 10) 20480\n60 1245968 (256, 10) 20480\n70 1366032 (256, 10) 20480\n80 1456016 (256, 10) 20480\n90 1756048 (256, 10) 20480\n"}

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## Step 66 · agent · 2026-09-10T16:48:18.737Z

### Thinking unavailable — no text recorded


## Step 67 · agent · 2026-09-10T16:48:28.477Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:48:41.665Z
{
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}
### Tool output · 2026-09-10T16:48:42.842Z
Script completed
Wall time 1.2 seconds
Output:

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## Step 68 · agent · 2026-09-10T16:48:45.850Z

### Thinking unavailable — no text recorded


The memory issue is reproducible in repeated PyTorch ranking calls. I’ve changed the sweep to reuse score, mask, and top-k buffers, so it can evaluate the same rankings with bounded working memory. The wider model’s best score is about 0.0209; I’ll test it with temporal fine-tuning next.


### exec · 2026-09-10T16:48:54.403Z
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}
### Tool output · 2026-09-10T16:48:56.783Z
Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"469621","wall_time_seconds":1.001436774,"session_id":30570,"original_token_count":0,"output":""}
{"chunk_id":"e9c0a3","wall_time_seconds":1.004341629,"session_id":20705,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 16:48:56 UTC"}

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## Step 69 · agent · 2026-09-10T16:49:19.918Z

### Thinking unavailable — no text recorded


## Step 70 · agent · 2026-09-10T16:49:40.701Z

### Thinking unavailable — no text recorded


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      "cat > /app/solution/scoring.py <<'PY'\n\"\"\"Bounded-memory exact dot-product ranking; buffers are reused across grids.\"\"\"\nimport numpy as np,torch\n\n@torch.no_grad()\ndef rank_many(uv,iv,eligible,biases=None,k=10,batch=256):\n n=len(uv);ni=len(iv);biases=[torch.zeros(ni)] if biases is None else biases\n out=[np.empty((n,k),np.int32) for _ in biases]\n base=torch.empty((batch,ni));work=torch.empty_like(base);mask=torch.empty((batch,ni),dtype=torch.bool);values=torch.empty((batch,k));indices=torch.empty((batch,k),dtype=torch.int64)\n for st in range(0,n,batch):\n  nb=min(batch,n-st);torch.mm(uv[st:st+nb],iv.T,out=base[:nb]);torch.logical_not(torch.from_numpy(eligible[st:st+nb]),out=mask[:nb])\n  for j,bias in enumerate(biases):\n   work[:nb].copy_(base[:nb]).add_(bias);work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],k,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\n return out\nPY\n/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower\\nfrom scoring import rank_many');a=s.index('   for st in range(0,len(users),256):');b=s.index('   for c,ts',a);s=s[:a]+\"   tops=rank_many(uv,iv,el,bias,k=10)\\n\"+s[b:];s=s.replace('metric(np.concatenate(ts),tt)','metric(ts,tt)');a=s.index('  for st in range(0,len(users),256):');b=s.index(\"pd.DataFrame({'uid'\",a);s=s[:a]+\"  top=rank_many(uv,iv,el,k=100)[0];\"+s[b:];open(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower\\nfrom scoring import rank_many');a=s.index('   for st in range(0,len(users),256):');b=s.index('   for pop,top',a);s=s[:a]+\"   tops=dict(zip(tops,rank_many(uv,iv,el,[pop*lp for pop in tops],k=100)))\\n\"+s[b:];s=s.replace('top=np.concatenate(top);score=metric(top,tt)','score=metric(top,tt)');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend > /app/solution/trend.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/scoring.py <<'PY'\n\"\"\"Bounded-memory exact dot-product ranking; buffers are reused across grids.\"\"\"\nimport numpy as np,torch\n\n@torch.no_grad()\ndef rank_many(uv,iv,eligible,biases=None,k=10,batch=256):\n n=len(uv);ni=len(iv);biases=[torch.zeros(ni)] if biases is None else biases\n out=[np.empty((n,k),np.int32) for _ in biases]\n base=torch.empty((batch,ni));work=torch.empty_like(base);mask=torch.empty((batch,ni),dtype=torch.bool);values=torch.empty((batch,k));indices=torch.empty((batch,k),dtype=torch.int64)\n for st in range(0,n,batch):\n  nb=min(batch,n-st);torch.mm(uv[st:st+nb],iv.T,out=base[:nb]);torch.logical_not(torch.from_numpy(eligible[st:st+nb]),out=mask[:nb])\n  for j,bias in enumerate(biases):\n   work[:nb].copy_(base[:nb]).add_(bias);work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],k,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\n return out\nPY\n/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower\\nfrom scoring import rank_many');a=s.index('   for st in range(0,len(users),256):');b=s.index('   for c,ts',a);s=s[:a]+\"   tops=rank_many(uv,iv,el,bias,k=10)\\n\"+s[b:];s=s.replace('metric(np.concatenate(ts),tt)','metric(ts,tt)');a=s.index('  for st in range(0,len(users),256):');b=s.index(\"pd.DataFrame({'uid'\",a);s=s[:a]+\"  top=rank_many(uv,iv,el,k=100)[0];\"+s[b:];open(p,'w').write(s)\np='/app/solution/tune.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower\\nfrom scoring import rank_many');a=s.index('   for st in range(0,len(users),256):');b=s.index('   for pop,top',a);s=s[:a]+\"   tops=dict(zip(tops,rank_many(uv,iv,el,[pop*lp for pop in tops],k=100)))\\n\"+s[b:];s=s.replace('top=np.concatenate(top);score=metric(top,tt)','score=metric(top,tt)');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend > /app/solution/trend.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 61,
      "nanos": 928828616
    },
    "formatted_output": ""
  }
}

### Harness context
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  "item": {
    "type": "CommandExecution",
    "id": "exec-e04e9453-8c34-4474-8286-d1b1cf2c4ad8",
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    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare_temporal.py --recent 30 --history-power .5 > /app/solution/prepare_recent.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare_temporal.py --recent 30 --history-power .5 > /app/solution/prepare_recent.log 2>&1"
      }
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    "exit_code": 0,
    "duration": {
      "secs": 57,
      "nanos": 756816372
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}

### exec · 2026-09-10T16:49:59.918Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/popnn.py <<'PY'\\n\\\"\\\"\\\"Item-only neural next-week adoption prior, trained on historical weeks.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport numpy as np,torch,argparse,json,time\\nfrom torch import nn\\nfrom pathlib import Path\\nP=Path('/app/solution');DAY=86400\\nclass AdoptionNet(nn.Module):\\n def __init__(self):\\n  super().__init__();self.mlp=nn.Sequential(nn.Linear(8,32),nn.SiLU(),nn.Dropout(.1),nn.Linear(32,1));nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\\n def forward(self,x):return x[...,3]+self.mlp((x+5)/3).squeeze(-1)\\n\\ndef features(d,cutoff):\\n ok=d['first']<=cutoff;age=(cutoff-d['first'][ok].astype(float))/DAY;ids=d['i'][ok];parts=[]\\n for days in [1,3,7,14,30,60,120,365]:\\n  c=np.bincount(ids,weights=np.exp(-age/days),minlength=30000);parts.append(np.log(c+.05).astype(np.float32))\\n return np.stack(parts,axis=1)\\n\\ndef main(stage):\\n torch.set_num_threads(2);torch.manual_seed(485);d=dict(np.load(P/f'data_{stage}.npz'));t=np.load(P/f'temporal_{stage}.npz');cutoff=int(d['cutoff']);xs=[];ys=[]\\n for fold in range(12):\\n  x=features(d,cutoff-fold*7*DAY-7*DAY-1800);y=np.bincount(t['yp'][t['fold']==fold].reshape(-1),minlength=30000).astype(np.float32);y/=y.sum();xs.append(x);ys.append(y)\\n x=torch.from_numpy(np.array(xs));y=torch.from_numpy(np.array(ys));model=AdoptionNet();opt=torch.optim.AdamW(model.parameters(),lr=.003,weight_decay=.01);best=1e9;rng=np.random.default_rng(485);t0=time.time()\\n for step in range(1,601):\\n  model.train();fold=rng.choice(np.arange(2,12),size=2,replace=False);score=model(x[fold]);loss=-(y[fold]*score.log_softmax(-1)).sum(-1).mean();opt.zero_grad();loss.backward();opt.step()\\n  if step%50==0:\\n   model.eval()\\n   with torch.no_grad():valid=-(y[:2]*model(x[:2]).log_softmax(-1)).sum(-1).mean().item()\\n   print(step,loss.item(),valid,'seconds',time.time()-t0,flush=True)\\n   if valid<best:best=valid;beststep=step;beststate={k:v.detach().clone() for k,v in model.state_dict().items()}\\n # Refit selected training duration on all observed historical folds.\\n torch.manual_seed(485);model=AdoptionNet();opt=torch.optim.AdamW(model.parameters(),lr=.003,weight_decay=.01)\\n for step in range(beststep):\\n  fold=rng.choice(12,size=2,replace=False);loss=-(y[fold]*model(x[fold]).log_softmax(-1)).sum(-1).mean();opt.zero_grad();loss.backward();opt.step()\\n model.eval();now=features(d,cutoff)\\n with torch.no_grad():q=model(torch.from_numpy(now)).softmax(-1).numpy()\\n np.save(P/f'popnn_{stage}.npy',q);torch.save({'model':model.state_dict(),'steps':beststep,'features':now,'stage':stage},P/f'popnn_{stage}.pt');print('done',stage,beststep,best,flush=True)\\nif __name__=='__main__':\\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');a=ap.parse_args();main(a.stage)\\nPY\\n tail -5 /app/solution/trend.log && tail -3 /app/solution/prepare_recent.log && tail -4 /app/solution/wide_temporal.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:50:00.076Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"5a6fce","wall_time_seconds":0.000005121,"exit_code":0,"original_token_count":256,"output":"loaded 878320\npriors 1056268\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nvalidation fold 9 targets 38925 examples 43612 sec 44\nvalidation fold 10 targets 37630 examples 47405 sec 46\nvalidation fold 11 targets 37250 examples 51167 sec 49\n{'stage': 'validation', 'name': 'wide_temporal', 'steps': 1200, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/wide_best.pt', 'temporal': True, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': False, 'recent': 30.0, 'history_power': 0.5}\nstep 100 loss 6.566797523498535 seconds 30.3\n"}

### Harness context
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-54a62032-80f8-4c0a-ae1f-244d142217fa",
    "process_id": "91238",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/popnn.py <<'PY'\n\"\"\"Item-only neural next-week adoption prior, trained on historical weeks.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport numpy as np,torch,argparse,json,time\nfrom torch import nn\nfrom pathlib import Path\nP=Path('/app/solution');DAY=86400\nclass AdoptionNet(nn.Module):\n def __init__(self):\n  super().__init__();self.mlp=nn.Sequential(nn.Linear(8,32),nn.SiLU(),nn.Dropout(.1),nn.Linear(32,1));nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def forward(self,x):return x[...,3]+self.mlp((x+5)/3).squeeze(-1)\n\ndef features(d,cutoff):\n ok=d['first']<=cutoff;age=(cutoff-d['first'][ok].astype(float))/DAY;ids=d['i'][ok];parts=[]\n for days in [1,3,7,14,30,60,120,365]:\n  c=np.bincount(ids,weights=np.exp(-age/days),minlength=30000);parts.append(np.log(c+.05).astype(np.float32))\n return np.stack(parts,axis=1)\n\ndef main(stage):\n torch.set_num_threads(2);torch.manual_seed(485);d=dict(np.load(P/f'data_{stage}.npz'));t=np.load(P/f'temporal_{stage}.npz');cutoff=int(d['cutoff']);xs=[];ys=[]\n for fold in range(12):\n  x=features(d,cutoff-fold*7*DAY-7*DAY-1800);y=np.bincount(t['yp'][t['fold']==fold].reshape(-1),minlength=30000).astype(np.float32);y/=y.sum();xs.append(x);ys.append(y)\n x=torch.from_numpy(np.array(xs));y=torch.from_numpy(np.array(ys));model=AdoptionNet();opt=torch.optim.AdamW(model.parameters(),lr=.003,weight_decay=.01);best=1e9;rng=np.random.default_rng(485);t0=time.time()\n for step in range(1,601):\n  model.train();fold=rng.choice(np.arange(2,12),size=2,replace=False);score=model(x[fold]);loss=-(y[fold]*score.log_softmax(-1)).sum(-1).mean();opt.zero_grad();loss.backward();opt.step()\n  if step%50==0:\n   model.eval()\n   with torch.no_grad():valid=-(y[:2]*model(x[:2]).log_softmax(-1)).sum(-1).mean().item()\n   print(step,loss.item(),valid,'seconds',time.time()-t0,flush=True)\n   if valid<best:best=valid;beststep=step;beststate={k:v.detach().clone() for k,v in model.state_dict().items()}\n # Refit selected training duration on all observed historical folds.\n torch.manual_seed(485);model=AdoptionNet();opt=torch.optim.AdamW(model.parameters(),lr=.003,weight_decay=.01)\n for step in range(beststep):\n  fold=rng.choice(12,size=2,replace=False);loss=-(y[fold]*model(x[fold]).log_softmax(-1)).sum(-1).mean();opt.zero_grad();loss.backward();opt.step()\n model.eval();now=features(d,cutoff)\n with torch.no_grad():q=model(torch.from_numpy(now)).softmax(-1).numpy()\n np.save(P/f'popnn_{stage}.npy',q);torch.save({'model':model.state_dict(),'steps':beststep,'features':now,'stage':stage},P/f'popnn_{stage}.pt');print('done',stage,beststep,best,flush=True)\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');a=ap.parse_args();main(a.stage)\nPY\n tail -5 /app/solution/trend.log && tail -3 /app/solution/prepare_recent.log && tail -4 /app/solution/wide_temporal.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/popnn.py <<'PY'\n\"\"\"Item-only neural next-week adoption prior, trained on historical weeks.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport numpy as np,torch,argparse,json,time\nfrom torch import nn\nfrom pathlib import Path\nP=Path('/app/solution');DAY=86400\nclass AdoptionNet(nn.Module):\n def __init__(self):\n  super().__init__();self.mlp=nn.Sequential(nn.Linear(8,32),nn.SiLU(),nn.Dropout(.1),nn.Linear(32,1));nn.init.zeros_(self.mlp[-1].weight);nn.init.zeros_(self.mlp[-1].bias)\n def forward(self,x):return x[...,3]+self.mlp((x+5)/3).squeeze(-1)\n\ndef features(d,cutoff):\n ok=d['first']<=cutoff;age=(cutoff-d['first'][ok].astype(float))/DAY;ids=d['i'][ok];parts=[]\n for days in [1,3,7,14,30,60,120,365]:\n  c=np.bincount(ids,weights=np.exp(-age/days),minlength=30000);parts.append(np.log(c+.05).astype(np.float32))\n return np.stack(parts,axis=1)\n\ndef main(stage):\n torch.set_num_threads(2);torch.manual_seed(485);d=dict(np.load(P/f'data_{stage}.npz'));t=np.load(P/f'temporal_{stage}.npz');cutoff=int(d['cutoff']);xs=[];ys=[]\n for fold in range(12):\n  x=features(d,cutoff-fold*7*DAY-7*DAY-1800);y=np.bincount(t['yp'][t['fold']==fold].reshape(-1),minlength=30000).astype(np.float32);y/=y.sum();xs.append(x);ys.append(y)\n x=torch.from_numpy(np.array(xs));y=torch.from_numpy(np.array(ys));model=AdoptionNet();opt=torch.optim.AdamW(model.parameters(),lr=.003,weight_decay=.01);best=1e9;rng=np.random.default_rng(485);t0=time.time()\n for step in range(1,601):\n  model.train();fold=rng.choice(np.arange(2,12),size=2,replace=False);score=model(x[fold]);loss=-(y[fold]*score.log_softmax(-1)).sum(-1).mean();opt.zero_grad();loss.backward();opt.step()\n  if step%50==0:\n   model.eval()\n   with torch.no_grad():valid=-(y[:2]*model(x[:2]).log_softmax(-1)).sum(-1).mean().item()\n   print(step,loss.item(),valid,'seconds',time.time()-t0,flush=True)\n   if valid<best:best=valid;beststep=step;beststate={k:v.detach().clone() for k,v in model.state_dict().items()}\n # Refit selected training duration on all observed historical folds.\n torch.manual_seed(485);model=AdoptionNet();opt=torch.optim.AdamW(model.parameters(),lr=.003,weight_decay=.01)\n for step in range(beststep):\n  fold=rng.choice(12,size=2,replace=False);loss=-(y[fold]*model(x[fold]).log_softmax(-1)).sum(-1).mean();opt.zero_grad();loss.backward();opt.step()\n model.eval();now=features(d,cutoff)\n with torch.no_grad():q=model(torch.from_numpy(now)).softmax(-1).numpy()\n np.save(P/f'popnn_{stage}.npy',q);torch.save({'model':model.state_dict(),'steps':beststep,'features':now,'stage':stage},P/f'popnn_{stage}.pt');print('done',stage,beststep,best,flush=True)\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');a=ap.parse_args();main(a.stage)\nPY\n tail -5 /app/solution/trend.log && tail -3 /app/solution/prepare_recent.log && tail -4 /app/solution/wide_temporal.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded 878320\npriors 1056268\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nvalidation fold 9 targets 38925 examples 43612 sec 44\nvalidation fold 10 targets 37630 examples 47405 sec 46\nvalidation fold 11 targets 37250 examples 51167 sec 49\n{'stage': 'validation', 'name': 'wide_temporal', 'steps': 1200, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/wide_best.pt', 'temporal': True, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': False, 'recent': 30.0, 'history_power': 0.5}\nstep 100 loss 6.566797523498535 seconds 30.3\n",
    "stderr": "",
    "aggregated_output": "loaded 878320\npriors 1056268\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nvalidation fold 9 targets 38925 examples 43612 sec 44\nvalidation fold 10 targets 37630 examples 47405 sec 46\nvalidation fold 11 targets 37250 examples 51167 sec 49\n{'stage': 'validation', 'name': 'wide_temporal', 'steps': 1200, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/wide_best.pt', 'temporal': True, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': False, 'recent': 30.0, 'history_power': 0.5}\nstep 100 loss 6.566797523498535 seconds 30.3\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5121
    },
    "formatted_output": "loaded 878320\npriors 1056268\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nvalidation fold 9 targets 38925 examples 43612 sec 44\nvalidation fold 10 targets 37630 examples 47405 sec 46\nvalidation fold 11 targets 37250 examples 51167 sec 49\n{'stage': 'validation', 'name': 'wide_temporal', 'steps': 1200, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.0005, 'eval_every': 300, 'seed': 42, 'resume': '/app/solution/wide_best.pt', 'temporal': True, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': False, 'recent': 30.0, 'history_power': 0.5}\nstep 100 loss 6.566797523498535 seconds 30.3\n"
  }
}

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## Step 71 · agent · 2026-09-10T16:50:16.608Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:50:19.778Z
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### Tool output · 2026-09-10T16:50:22.140Z
Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"899967","wall_time_seconds":1.001423274,"session_id":49822,"original_token_count":0,"output":""}
{"chunk_id":"436dc4","wall_time_seconds":1.001126406,"session_id":64050,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 16:50:22 UTC"}

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## Step 72 · agent · 2026-09-10T16:50:42.380Z

### Thinking unavailable — no text recorded


## Step 73 · agent · 2026-09-10T16:51:02.625Z

### Thinking unavailable — no text recorded


Recent item-adoption trends raised public NDCG@10 to **0.0416**. The adjustment fits into the item vector’s bias coordinate, so serving still requires one user vector and a dot-product search. I’m testing whether a small neural trend predictor improves that calibration, alongside the remaining temporal-model variants.


### exec · 2026-09-10T16:51:05.967Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/popnn.log && tail -5 /app/solution/recent.log && tail -5 /app/solution/wide_temporal.log && head -12 /app/solution/trend.json\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:51:06.133Z
Script completed
Wall time 0.1 seconds
Output:

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## Step 74 · agent · 2026-09-10T16:51:19.320Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:51:24.702Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/trends.py';s=open(p).read().replace(\\\" return out\\\\n\\\", \\\" stage='validation' if int(data['cutoff'])==24786800 else 'test'\\\\n if (P/f'popnn_{stage}.npy').exists():\\\\n  forecast=np.load(P/f'popnn_{stage}.npy');out[0]=np.log(.5*q+.5*forecast)-np.log(q)\\\\n return out\\\\n\\\")\\ns=s.replace(\\\"for days in [7,14,30] for strength\\\", \\\"for days in pr for strength\\\")\\nopen(p,'w').write(s)\\nPY\\n tail -2 /app/solution/popnn.log\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend_nn > /app/solution/trend_nn.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
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### Tool output · 2026-09-10T16:51:25.934Z
Script completed
Wall time 1.2 seconds
Output:

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## Step 75 · agent · 2026-09-10T16:51:35.843Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:51:36.981Z
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}
### Tool output · 2026-09-10T16:51:37.188Z
Script completed
Wall time 0.2 seconds
Output:

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## Step 76 · agent · 2026-09-10T16:51:57.968Z

### Thinking unavailable — no text recorded


## Step 77 · agent · 2026-09-10T16:52:20.447Z

### Thinking unavailable — no text recorded


## Step 78 · agent · 2026-09-10T16:52:36.898Z

### Thinking unavailable — no text recorded


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### exec · 2026-09-10T16:53:07.932Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/submit.py <<'PY'\\n\\\"\\\"\\\"Export independent neural towers and rank by their exact dot product.\\\"\\\"\\\"\\nimport argparse,json\\nimport numpy as np,pandas as pd,torch\\nfrom torch import nn\\nfrom model import TwoTower\\nfrom train import P,D,encode_users,truth,metric\\nfrom trends import priors\\nfrom scoring import rank_many\\n\\nclass RetrievalUserTower(nn.Module):\\n def __init__(self,towers,weights):\\n  super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('scales',torch.tensor(weights).sqrt())\\n def forward(self,histories,history_weights):\\n  return torch.cat([tower(ids,w)*scale for tower,ids,w,scale in zip(self.towers,histories,history_weights,self.scales)],dim=-1)\\n\\nclass RetrievalItemTower(nn.Module):\\n def __init__(self,towers,weights,calibration):\\n  super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('scales',torch.tensor(weights).sqrt());self.register_buffer('calibration',torch.stack(calibration))\\n def forward(self,ids):\\n  vectors=[]\\n  for j,(tower,scale) in enumerate(zip(self.towers,self.scales)):\\n   vector=tower(ids);vector=torch.cat([vector[:,:-1],vector[:,-1:]+self.calibration[j,ids,None]],dim=-1);vectors.append(vector*scale)\\n  return torch.cat(vectors,dim=-1)\\n\\ndef history_weights(data,c):\\n w=data['w'].copy()**c.get('history_power',1.)\\n if c.get('recent',0):w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(c['recent']*86400))\\n return w\\n\\n@torch.no_grad()\\ndef run(config,output=None,export=False):\\n torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\\n models=[];weights=[];calibration=[];uvs=[]\\n for c in config['components']:\\n  ck=torch.load(P/c['checkpoint'],weights_only=False);m=TwoTower(len(items),ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();models.append(m);weights.append(float(c.get('weight',1.)));bias=c.get('pop',0)*np.log(ck['pop']+1e-7)\\n  if c.get('trend_strength',0):bias=bias+c['trend_strength']*priors(data,ck['pop'])[c['trend_days']]\\n  calibration.append(torch.from_numpy(np.asarray(bias,dtype=np.float32)));uvs.append(encode_users(m,data,c.get('history_power',1.),c.get('recent',0))[order]*np.sqrt(weights[-1]))\\n user_tower=RetrievalUserTower([m.user for m in models],weights).eval();item_tower=RetrievalItemTower([m.item for m in models],weights,calibration).eval();uv=torch.cat(uvs,dim=1);iv=torch.cat([item_tower(torch.arange(st,min(st+1024,len(items)))) for st in range(0,len(items),1024)])\\n # Independently exercise the request-time user forward and its score contract.\\n for r in [0,len(users)//2,len(users)-1]:\\n  u=order[r];a=int(data['offset'][u]);b=int(data['offset'][u+1]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64))[None,:];hw=[torch.from_numpy(history_weights(data,c)[a:b])[None,:] for c in config['components']];direct=user_tower([ids]*len(hw),hw)[0]\\n  torch.testing.assert_close(direct,uv[r],rtol=2e-4,atol=2e-4);torch.testing.assert_close(direct@item_tower(torch.arange(100)).T,uv[r]@iv[:100].T,rtol=2e-4,atol=2e-4)\\n top=rank_many(uv,iv,eligible,k=100)[0];assert eligible[np.arange(len(users))[:,None],top].all();out=P/(output or ('submission.parquet' if stage=='test' else 'validation.parquet'));temp=out.with_suffix('.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temp,index=False);temp.replace(out)\\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\\n if export:\\n  np.save(P/'item_vectors.npy',iv.numpy());np.save(P/'item_ids.npy',items);torch.save({'config':config,'user_tower':user_tower.state_dict(),'item_tower':item_tower.state_dict(),'dimensions':[m.user.history.embedding_dim for m in models]},P/'retrieval_towers.pt')\\n print(json.dumps({'output':str(out),'users':len(users),'rows':len(users)*100,'dimension':iv.shape[1],'validation_ndcg':score}),flush=True);return score\\n\\nif __name__=='__main__':\\n ap=argparse.ArgumentParser();ap.add_argument('--config',default=str(P/'final_config.json'));ap.add_argument('--output');ap.add_argument('--export',action='store_true');a=ap.parse_args();run(json.load(open(a.config)),a.output,a.export)\\nPY\\n tail -6 /app/solution/recent.log && tail -5 /app/solution/wide_temporal.log && tail -4 /app/solution/trend_nn.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:53:08.392Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"0794ac","wall_time_seconds":0.0000057,"exit_code":0,"original_token_count":221,"output":"VALIDATION 900 0.03185758698770279\nstep 1000 loss 5.469254789352417 seconds 101.4\nstep 1100 loss 5.398198032379151 seconds 113.0\nstep 1200 loss 5.293139762878418 seconds 125.7\nVALIDATION 1200 0.031723690095286736\ndone\nVALIDATION 600 0.03317549444316282\nstep 700 loss 5.862479157447815 seconds 191.9\nstep 800 loss 5.755891885757446 seconds 214.5\nstep 900 loss 5.656712942123413 seconds 232.3\nVALIDATION 900 0.035548026051817075\npriors 1059072\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\n"}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f54ef1ed-7546-4bdd-a651-5b5eb1911e87",
    "process_id": "77523",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/submit.py <<'PY'\n\"\"\"Export independent neural towers and rank by their exact dot product.\"\"\"\nimport argparse,json\nimport numpy as np,pandas as pd,torch\nfrom torch import nn\nfrom model import TwoTower\nfrom train import P,D,encode_users,truth,metric\nfrom trends import priors\nfrom scoring import rank_many\n\nclass RetrievalUserTower(nn.Module):\n def __init__(self,towers,weights):\n  super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('scales',torch.tensor(weights).sqrt())\n def forward(self,histories,history_weights):\n  return torch.cat([tower(ids,w)*scale for tower,ids,w,scale in zip(self.towers,histories,history_weights,self.scales)],dim=-1)\n\nclass RetrievalItemTower(nn.Module):\n def __init__(self,towers,weights,calibration):\n  super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('scales',torch.tensor(weights).sqrt());self.register_buffer('calibration',torch.stack(calibration))\n def forward(self,ids):\n  vectors=[]\n  for j,(tower,scale) in enumerate(zip(self.towers,self.scales)):\n   vector=tower(ids);vector=torch.cat([vector[:,:-1],vector[:,-1:]+self.calibration[j,ids,None]],dim=-1);vectors.append(vector*scale)\n  return torch.cat(vectors,dim=-1)\n\ndef history_weights(data,c):\n w=data['w'].copy()**c.get('history_power',1.)\n if c.get('recent',0):w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(c['recent']*86400))\n return w\n\n@torch.no_grad()\ndef run(config,output=None,export=False):\n torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\n models=[];weights=[];calibration=[];uvs=[]\n for c in config['components']:\n  ck=torch.load(P/c['checkpoint'],weights_only=False);m=TwoTower(len(items),ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();models.append(m);weights.append(float(c.get('weight',1.)));bias=c.get('pop',0)*np.log(ck['pop']+1e-7)\n  if c.get('trend_strength',0):bias=bias+c['trend_strength']*priors(data,ck['pop'])[c['trend_days']]\n  calibration.append(torch.from_numpy(np.asarray(bias,dtype=np.float32)));uvs.append(encode_users(m,data,c.get('history_power',1.),c.get('recent',0))[order]*np.sqrt(weights[-1]))\n user_tower=RetrievalUserTower([m.user for m in models],weights).eval();item_tower=RetrievalItemTower([m.item for m in models],weights,calibration).eval();uv=torch.cat(uvs,dim=1);iv=torch.cat([item_tower(torch.arange(st,min(st+1024,len(items)))) for st in range(0,len(items),1024)])\n # Independently exercise the request-time user forward and its score contract.\n for r in [0,len(users)//2,len(users)-1]:\n  u=order[r];a=int(data['offset'][u]);b=int(data['offset'][u+1]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64))[None,:];hw=[torch.from_numpy(history_weights(data,c)[a:b])[None,:] for c in config['components']];direct=user_tower([ids]*len(hw),hw)[0]\n  torch.testing.assert_close(direct,uv[r],rtol=2e-4,atol=2e-4);torch.testing.assert_close(direct@item_tower(torch.arange(100)).T,uv[r]@iv[:100].T,rtol=2e-4,atol=2e-4)\n top=rank_many(uv,iv,eligible,k=100)[0];assert eligible[np.arange(len(users))[:,None],top].all();out=P/(output or ('submission.parquet' if stage=='test' else 'validation.parquet'));temp=out.with_suffix('.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temp,index=False);temp.replace(out)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n if export:\n  np.save(P/'item_vectors.npy',iv.numpy());np.save(P/'item_ids.npy',items);torch.save({'config':config,'user_tower':user_tower.state_dict(),'item_tower':item_tower.state_dict(),'dimensions':[m.user.history.embedding_dim for m in models]},P/'retrieval_towers.pt')\n print(json.dumps({'output':str(out),'users':len(users),'rows':len(users)*100,'dimension':iv.shape[1],'validation_ndcg':score}),flush=True);return score\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--config',default=str(P/'final_config.json'));ap.add_argument('--output');ap.add_argument('--export',action='store_true');a=ap.parse_args();run(json.load(open(a.config)),a.output,a.export)\nPY\n tail -6 /app/solution/recent.log && tail -5 /app/solution/wide_temporal.log && tail -4 /app/solution/trend_nn.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/submit.py <<'PY'\n\"\"\"Export independent neural towers and rank by their exact dot product.\"\"\"\nimport argparse,json\nimport numpy as np,pandas as pd,torch\nfrom torch import nn\nfrom model import TwoTower\nfrom train import P,D,encode_users,truth,metric\nfrom trends import priors\nfrom scoring import rank_many\n\nclass RetrievalUserTower(nn.Module):\n def __init__(self,towers,weights):\n  super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('scales',torch.tensor(weights).sqrt())\n def forward(self,histories,history_weights):\n  return torch.cat([tower(ids,w)*scale for tower,ids,w,scale in zip(self.towers,histories,history_weights,self.scales)],dim=-1)\n\nclass RetrievalItemTower(nn.Module):\n def __init__(self,towers,weights,calibration):\n  super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('scales',torch.tensor(weights).sqrt());self.register_buffer('calibration',torch.stack(calibration))\n def forward(self,ids):\n  vectors=[]\n  for j,(tower,scale) in enumerate(zip(self.towers,self.scales)):\n   vector=tower(ids);vector=torch.cat([vector[:,:-1],vector[:,-1:]+self.calibration[j,ids,None]],dim=-1);vectors.append(vector*scale)\n  return torch.cat(vectors,dim=-1)\n\ndef history_weights(data,c):\n w=data['w'].copy()**c.get('history_power',1.)\n if c.get('recent',0):w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(c['recent']*86400))\n return w\n\n@torch.no_grad()\ndef run(config,output=None,export=False):\n torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\n models=[];weights=[];calibration=[];uvs=[]\n for c in config['components']:\n  ck=torch.load(P/c['checkpoint'],weights_only=False);m=TwoTower(len(items),ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();models.append(m);weights.append(float(c.get('weight',1.)));bias=c.get('pop',0)*np.log(ck['pop']+1e-7)\n  if c.get('trend_strength',0):bias=bias+c['trend_strength']*priors(data,ck['pop'])[c['trend_days']]\n  calibration.append(torch.from_numpy(np.asarray(bias,dtype=np.float32)));uvs.append(encode_users(m,data,c.get('history_power',1.),c.get('recent',0))[order]*np.sqrt(weights[-1]))\n user_tower=RetrievalUserTower([m.user for m in models],weights).eval();item_tower=RetrievalItemTower([m.item for m in models],weights,calibration).eval();uv=torch.cat(uvs,dim=1);iv=torch.cat([item_tower(torch.arange(st,min(st+1024,len(items)))) for st in range(0,len(items),1024)])\n # Independently exercise the request-time user forward and its score contract.\n for r in [0,len(users)//2,len(users)-1]:\n  u=order[r];a=int(data['offset'][u]);b=int(data['offset'][u+1]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64))[None,:];hw=[torch.from_numpy(history_weights(data,c)[a:b])[None,:] for c in config['components']];direct=user_tower([ids]*len(hw),hw)[0]\n  torch.testing.assert_close(direct,uv[r],rtol=2e-4,atol=2e-4);torch.testing.assert_close(direct@item_tower(torch.arange(100)).T,uv[r]@iv[:100].T,rtol=2e-4,atol=2e-4)\n top=rank_many(uv,iv,eligible,k=100)[0];assert eligible[np.arange(len(users))[:,None],top].all();out=P/(output or ('submission.parquet' if stage=='test' else 'validation.parquet'));temp=out.with_suffix('.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temp,index=False);temp.replace(out)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n if export:\n  np.save(P/'item_vectors.npy',iv.numpy());np.save(P/'item_ids.npy',items);torch.save({'config':config,'user_tower':user_tower.state_dict(),'item_tower':item_tower.state_dict(),'dimensions':[m.user.history.embedding_dim for m in models]},P/'retrieval_towers.pt')\n print(json.dumps({'output':str(out),'users':len(users),'rows':len(users)*100,'dimension':iv.shape[1],'validation_ndcg':score}),flush=True);return score\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--config',default=str(P/'final_config.json'));ap.add_argument('--output');ap.add_argument('--export',action='store_true');a=ap.parse_args();run(json.load(open(a.config)),a.output,a.export)\nPY\n tail -6 /app/solution/recent.log && tail -5 /app/solution/wide_temporal.log && tail -4 /app/solution/trend_nn.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALIDATION 900 0.03185758698770279\nstep 1000 loss 5.469254789352417 seconds 101.4\nstep 1100 loss 5.398198032379151 seconds 113.0\nstep 1200 loss 5.293139762878418 seconds 125.7\nVALIDATION 1200 0.031723690095286736\ndone\nVALIDATION 600 0.03317549444316282\nstep 700 loss 5.862479157447815 seconds 191.9\nstep 800 loss 5.755891885757446 seconds 214.5\nstep 900 loss 5.656712942123413 seconds 232.3\nVALIDATION 900 0.035548026051817075\npriors 1059072\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\n",
    "stderr": "",
    "aggregated_output": "VALIDATION 900 0.03185758698770279\nstep 1000 loss 5.469254789352417 seconds 101.4\nstep 1100 loss 5.398198032379151 seconds 113.0\nstep 1200 loss 5.293139762878418 seconds 125.7\nVALIDATION 1200 0.031723690095286736\ndone\nVALIDATION 600 0.03317549444316282\nstep 700 loss 5.862479157447815 seconds 191.9\nstep 800 loss 5.755891885757446 seconds 214.5\nstep 900 loss 5.656712942123413 seconds 232.3\nVALIDATION 900 0.035548026051817075\npriors 1059072\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5700
    },
    "formatted_output": "VALIDATION 900 0.03185758698770279\nstep 1000 loss 5.469254789352417 seconds 101.4\nstep 1100 loss 5.398198032379151 seconds 113.0\nstep 1200 loss 5.293139762878418 seconds 125.7\nVALIDATION 1200 0.031723690095286736\ndone\nVALIDATION 600 0.03317549444316282\nstep 700 loss 5.862479157447815 seconds 191.9\nstep 800 loss 5.755891885757446 seconds 214.5\nstep 900 loss 5.656712942123413 seconds 232.3\nVALIDATION 900 0.035548026051817075\npriors 1059072\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.041140026213600064)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.25, 'score': np.float64(0.04164356877061926)}\n"
  }
}

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### Harness context
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  "item": {
    "type": "CommandExecution",
    "id": "exec-a83d7e6d-a8c2-4b57-a692-8b0e095f9d24",
    "process_id": "60913",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace(\" return out\\n\", \" stage='validation' if int(data['cutoff'])==24786800 else 'test'\\n if (P/f'popnn_{stage}.npy').exists():\\n  forecast=np.load(P/f'popnn_{stage}.npy');out[0]=np.log(.5*q+.5*forecast)-np.log(q)\\n return out\\n\")\ns=s.replace(\"for days in [7,14,30] for strength\", \"for days in pr for strength\")\nopen(p,'w').write(s)\nPY\n tail -2 /app/solution/popnn.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend_nn > /app/solution/trend_nn.log 2>&1"
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      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace(\" return out\\n\", \" stage='validation' if int(data['cutoff'])==24786800 else 'test'\\n if (P/f'popnn_{stage}.npy').exists():\\n  forecast=np.load(P/f'popnn_{stage}.npy');out[0]=np.log(.5*q+.5*forecast)-np.log(q)\\n return out\\n\")\ns=s.replace(\"for days in [7,14,30] for strength\", \"for days in pr for strength\")\nopen(p,'w').write(s)\nPY\n tail -2 /app/solution/popnn.log\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py temporal_early.pt --output trend_nn > /app/solution/trend_nn.log 2>&1"
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  }
}

## Step 79 · agent · 2026-09-10T16:53:30.109Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:53:35.170Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\np='/app/solution/'\\njson.dump({'stage':'test','components':[{'checkpoint':'initial_final_last.pt','weight':1.,'history_power':1.,'recent':0,'pop':0}]},open(p+'initial_config.json','w'),indent=2)\\nr=json.load(open(p+'trend_nn.json'));print('neural in grid',sorted(set(x['trend_days'] for x in r))); print('best NN',next((x for x in r if x['trend_days']==0),None))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare_temporal.py --stage test > /app/solution/prepare_temporal_test.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name base2 --steps 1800 --eval-every 600 --seed 125 --threads 3 > /app/solution/base2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:53:37.538Z
Script completed
Wall time 2.4 seconds
Output:

{"chunk_id":"0ce2aa","wall_time_seconds":1.001637809,"session_id":58472,"original_token_count":40,"output":"neural in grid [0, 7, 14, 30]\nbest NN {'history_power': 0.5, 'recent': 14, 'trend_days': 0, 'trend_strength': 1.0, 'pop': 0.5, 'score': 0.04034401160418666}\n"}
{"chunk_id":"778db4","wall_time_seconds":1.001393971,"session_id":61379,"original_token_count":0,"output":""}
{"current_time":"2026-09-10 16:53:37 UTC"}

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## Step 80 · agent · 2026-09-10T16:54:00.978Z

### Thinking unavailable — no text recorded


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## Step 81 · agent · 2026-09-10T16:54:22.188Z

### Thinking unavailable — no text recorded


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## Step 82 · agent · 2026-09-10T16:54:30.078Z

### Thinking unavailable — no text recorded


The learned trend predictor did not beat the simpler adoption-trend correction, which remains best at 0.0416 NDCG@10. Temporal fine-tuning improved the wider model to 0.0355 before calibration, so I’m checking that combination next. I’m also preparing train-plus-validation examples for the final refit.


### exec · 2026-09-10T16:54:34.335Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/wide_temporal.log && tail -3 /app/solution/prepare_temporal_test.log && tail -3 /app/solution/base2.log\\ncp /app/solution/wide_temporal_best.pt /app/solution/wide_temporal_selected.pt\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py wide_temporal_selected.pt --output wide_trend > /app/solution/wide_trend.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:54:35.530Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"980e70","wall_time_seconds":1.000554747,"session_id":43495,"original_token_count":104,"output":"step 1100 loss 5.483668885231018 seconds 276.3\nstep 1200 loss 5.434225368499756 seconds 300.4\nVALIDATION 1200 0.0324066664974535\ndone\ntest fold 9 targets 38412 examples 44800 sec 40\ntest fold 10 targets 38928 examples 48682 sec 43\ntest fold 11 targets 37622 examples 52476 sec 45\nstep 300 loss 6.769835176467896 seconds 26.0\nstep 400 loss 6.469158310890197 seconds 32.4\nstep 500 loss 6.27032579421997 seconds 38.5\n"}
{"current_time":"2026-09-10 16:54:35 UTC"}

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## Step 83 · agent · 2026-09-10T16:54:39.211Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:54:52.872Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_base --steps 1500 --eval-every 1500 --threads 3 --no-submit > /app/solution/final_base.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/compare.py <<'PY'\\nimport json,argparse\\nimport torch,numpy as np,pandas as pd\\nfrom train import P,D,encode_users,truth,metric\\nfrom model import TwoTower\\nfrom trends import priors\\nfrom scoring import rank_many\\nap=argparse.ArgumentParser();ap.add_argument('checkpoints',nargs='+');a=ap.parse_args();torch.set_num_threads(2);data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');items=ar['item_ids'];users=ar['uids'];el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);tt=truth(items,users,el);order=pd.Index(data['users']).get_indexer(users)\\nwith torch.no_grad():\\n for path in a.checkpoints:\\n  ck=torch.load(P/path,weights_only=False);m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();uv=encode_users(m,data,.5,30)[order];iv=m.item(torch.arange(30000));bias=torch.from_numpy(priors(data,ck['pop'])[7]+.25*np.log(ck['pop']+1e-7)).float();iv[:,-1]+=bias\\n  top=rank_many(uv,iv,el,k=10)[0];print(path,ck['step'],metric(top,tt),flush=True);np.savez(P/f'cache_{path[:-3]}.npz',uv=uv.numpy(),iv=iv.numpy())\\nPY\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:54:54.162Z
Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"d28422","wall_time_seconds":1.001334274,"session_id":7600,"original_token_count":0,"output":""}
{"chunk_id":"188f40","wall_time_seconds":0.0000075,"exit_code":0,"original_token_count":0,"output":""}

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## Step 84 · agent · 2026-09-10T16:55:22.031Z

### Thinking unavailable — no text recorded


## Step 85 · agent · 2026-09-10T16:55:39.999Z

### Thinking unavailable — no text recorded


## Step 86 · agent · 2026-09-10T16:55:49.061Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:55:51.375Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/compare.py base_best.pt temporal_early.pt temporal_last.pt recent_best.pt masked_best.pt > /app/solution/compare.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/base2.log && tail -4 /app/solution/final_base.log && cat /app/solution/wide_trend.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:55:52.723Z
Script completed
Wall time 1.3 seconds
Output:

{"chunk_id":"d42f2c","wall_time_seconds":1.001242907,"session_id":23751,"original_token_count":0,"output":""}
{"chunk_id":"6c8ef9","wall_time_seconds":0.00000929,"exit_code":0,"original_token_count":236,"output":"VALIDATION 1200 0.02014481888648327\nstep 1300 loss 5.6998852396011355 seconds 97.2\nstep 1400 loss 5.655211753845215 seconds 107.1\nstep 1500 loss 5.6230434131622316 seconds 117.2\n{'stage': 'test', 'name': 'final_base', 'steps': 1500, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 1500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 4, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 0, 'history_power': 1.0}\nstep 100 loss 7.965823488235474 seconds 11.8\nstep 200 loss 7.271602005958557 seconds 24.1\nstep 300 loss 6.805093240737915 seconds 35.9\nloaded 1065720\npriors 1243604\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04256719327785488)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04361581025230603)}\n"}
{"current_time":"2026-09-10 16:55:52 UTC"}

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    "aggregated_output": "VALIDATION 1200 0.02014481888648327\nstep 1300 loss 5.6998852396011355 seconds 97.2\nstep 1400 loss 5.655211753845215 seconds 107.1\nstep 1500 loss 5.6230434131622316 seconds 117.2\n{'stage': 'test', 'name': 'final_base', 'steps': 1500, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 1500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 4, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 0, 'history_power': 1.0}\nstep 100 loss 7.965823488235474 seconds 11.8\nstep 200 loss 7.271602005958557 seconds 24.1\nstep 300 loss 6.805093240737915 seconds 35.9\nloaded 1065720\npriors 1243604\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04256719327785488)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04361581025230603)}\n",
    "exit_code": 0,
    "duration": {
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    "formatted_output": "VALIDATION 1200 0.02014481888648327\nstep 1300 loss 5.6998852396011355 seconds 97.2\nstep 1400 loss 5.655211753845215 seconds 107.1\nstep 1500 loss 5.6230434131622316 seconds 117.2\n{'stage': 'test', 'name': 'final_base', 'steps': 1500, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.002, 'eval_every': 1500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 4, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 0, 'history_power': 1.0}\nstep 100 loss 7.965823488235474 seconds 11.8\nstep 200 loss 7.271602005958557 seconds 24.1\nstep 300 loss 6.805093240737915 seconds 35.9\nloaded 1065720\npriors 1243604\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04256719327785488)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04361581025230603)}\n"
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## Step 87 · agent · 2026-09-10T16:56:18.630Z

### Thinking unavailable — no text recorded


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      "-lc",
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## Step 88 · agent · 2026-09-10T16:56:40.139Z

### Thinking unavailable — no text recorded


## Step 89 · agent · 2026-09-10T16:57:02.642Z

### Thinking unavailable — no text recorded


## Step 90 · agent · 2026-09-10T16:57:21.137Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:57:22.850Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/base2.log && tail -4 /app/solution/final_base.log && tail -5 /app/solution/wide_trend.log && cat /app/solution/compare.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T16:57:23.111Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"e839f3","wall_time_seconds":0.000005871,"exit_code":0,"original_token_count":249,"output":"step 1600 loss 5.600442476272583 seconds 129.7\nstep 1700 loss 5.574978971481324 seconds 143.8\nstep 1800 loss 5.56102991104126 seconds 153.8\nVALIDATION 1800 0.020575278317227403\ndone\nstep 1300 loss 5.721963171958923 seconds 119.3\nstep 1400 loss 5.709104752540588 seconds 125.1\nstep 1500 loss 5.653708600997925 seconds 130.9\ndone\nloaded 1065720\npriors 1243604\nrecent 14 BEST {'history_power': 0.5, 'recent': 14, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04256719327785488)}\nrecent 30 BEST {'history_power': 0.5, 'recent': 30, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.04361581025230603)}\nrecent 90 BEST {'history_power': 0.5, 'recent': 90, 'trend_days': 7, 'trend_strength': 1.0, 'pop': 0.5, 'score': np.float64(0.0439805407989549)}\nbase_best.pt 1500 0.03946298667791115\ntemporal_early.pt 500 0.04164356877061926\ntemporal_last.pt 4000 0.03812342955345053\nrecent_best.pt 300 0.04133652084927401\nmasked_best.pt 600 0.040600850851132284\n"}
{"current_time":"2026-09-10 16:57:22 UTC"}

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## Step 91 · agent · 2026-09-10T16:57:34.764Z

### Thinking unavailable — no text recorded


The calibrated wider model reached **0.0440 NDCG@10**. The calibrated 128-dimensional model is close at 0.0416, so I’ll test whether combining their vectors improves quality enough to justify the extra dimensions. I’m refitting both on train plus validation; any chosen combination will still produce one concatenated user vector for one inner-product search.


### exec · 2026-09-10T16:57:45.359Z
{
  "code": "text(await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_temporal --temporal --resume /app/solution/final_base_last.pt --lr .0005 --steps 500 --eval-every 500 --threads 2 --no-submit > /app/solution/final_temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_wide_base --dim 512 --batch 128 --targets 8 --steps 1500 --eval-every 1500 --threads 3 --no-submit > /app/solution/final_wide_base.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name temporal2 --temporal --resume /app/solution/base2_best.pt --lr .0005 --steps 800 --eval-every 200 --threads 3 --seed 125 --recent 30 --history-power .5 > /app/solution/temporal2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]));\n"
}
### Tool output · 2026-09-10T16:57:46.538Z
Script completed
Wall time 1.2 seconds
Output:

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## Step 92 · agent · 2026-09-10T16:58:08.235Z

### Thinking unavailable — no text recorded


## Step 93 · agent · 2026-09-10T16:58:19.053Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:58:49.091Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/ensemble.py <<'PY'\\n\\\"\\\"\\\"Select neural tower concatenations using only public validation outcomes.\\\"\\\"\\\"\\nimport json,argparse,itertools,hashlib\\nimport torch,numpy as np,pandas as pd\\nfrom model import TwoTower\\nfrom train import P,D,encode_users,truth,metric\\nfrom trends import priors\\nfrom submit import run\\n\\n@torch.no_grad()\\ndef vectors(c,data,order):\\n key=hashlib.sha1(json.dumps(c,sort_keys=True).encode()).hexdigest()[:12];path=P/f'vectors_{key}.npz'\\n if path.exists():\\n  z=np.load(path);return torch.from_numpy(z['uv']),torch.from_numpy(z['iv'])\\n ck=torch.load(P/c['checkpoint'],weights_only=False);m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();uv=encode_users(m,data,c.get('history_power',1),c.get('recent',0))[order];iv=torch.cat([m.item(torch.arange(st,min(st+2048,30000))) for st in range(0,30000,2048)]);bias=c.get('pop',0)*np.log(ck['pop']+1e-7)\\n if c.get('trend_strength',0):bias+=c['trend_strength']*priors(data,ck['pop'])[c['trend_days']]\\n iv[:,-1]+=torch.from_numpy(bias).float();np.savez(path,uv=uv.numpy(),iv=iv.numpy());return uv,iv\\n\\n@torch.no_grad()\\ndef rank_combinations(uvs,ivs,eligible,combinations,k=10):\\n n=len(uvs[0]);ni=len(ivs[0]);batch=256;out=[np.empty((n,k),np.int32) for _ in combinations];base=[torch.empty((batch,ni)) for _ in uvs];work=torch.empty_like(base[0]);mask=torch.empty((batch,ni),dtype=torch.bool);values=torch.empty((batch,k));indices=torch.empty((batch,k),dtype=torch.int64)\\n for st in range(0,n,batch):\\n  nb=min(batch,n-st)\\n  for uv,iv,b in zip(uvs,ivs,base):torch.mm(uv[st:st+nb],iv.T,out=b[:nb])\\n  torch.logical_not(torch.from_numpy(eligible[st:st+nb]),out=mask[:nb])\\n  for j,weights in enumerate(combinations):\\n   work[:nb].zero_()\\n   for weight,b in zip(weights,base):\\n    if weight:work[:nb].add_(b[:nb],alpha=weight)\\n   work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],k,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\\n return out\\n\\nif __name__=='__main__':\\n ap=argparse.ArgumentParser();ap.add_argument('--configs',required=True);ap.add_argument('--output',default='ensemble');ap.add_argument('--denominator',type=int,default=4);a=ap.parse_args();torch.set_num_threads(3);cs=json.load(open(a.configs));data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];combinations=[tuple(w/a.denominator for w in ws) for ws in itertools.product(range(a.denominator+1),repeat=len(cs)) if sum(ws)==a.denominator and sum(w>0 for w in ws)<=3];tops=rank_combinations([v[0] for v in vs],[v[1] for v in vs],el,combinations);results=sorted([{'weights':w,'score':metric(top,tt)} for w,top in zip(combinations,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);print(results[:10],flush=True);config={'stage':'validation','components':[dict(c,weight=w) for c,w in zip(cs,results[0]['weights']) if w]};json.dump(config,open(P/f'{a.output}_config.json','w'),indent=2);run(config,f'{a.output}_validation.parquet')\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport json\\np='/app/solution/'\\ncs=[]\\nfor name,recent,pop in [('temporal_early.pt',30,.25),('wide_temporal_selected.pt',90,.5),('recent_best.pt',30,.25)]:cs.append({'checkpoint':name,'history_power':.5,'recent':recent,'pop':pop,'trend_days':7,'trend_strength':1.})\\njson.dump(cs,open(p+'ensemble_candidates.json','w'),indent=2)\\nPY\\n tail -4 /app/solution/final_temporal.log && tail -4 /app/solution/final_wide_base.log && tail -4 /app/solution/temporal2.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T16:58:49.349Z
Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"662aa4","wall_time_seconds":0.00000733,"exit_code":0,"original_token_count":177,"output":"step 100 loss 6.623866791725159 seconds 10.3\nstep 200 loss 6.457741475105285 seconds 21.8\nstep 300 loss 6.33023540019989 seconds 34.4\nstep 400 loss 6.20713559627533 seconds 46.2\n{'stage': 'test', 'name': 'final_wide_base', 'steps': 1500, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 1500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 0, 'history_power': 1.0}\nstep 100 loss 7.7392862510681155 seconds 31.8\nstep 400 loss 6.156851363182068 seconds 35.9\nVALIDATION 400 0.03419722442493865\nstep 500 loss 6.053585867881775 seconds 46.4\nstep 600 loss 5.959062561988831 seconds 54.5\n"}

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    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/ensemble.py <<'PY'\n\"\"\"Select neural tower concatenations using only public validation outcomes.\"\"\"\nimport json,argparse,itertools,hashlib\nimport torch,numpy as np,pandas as pd\nfrom model import TwoTower\nfrom train import P,D,encode_users,truth,metric\nfrom trends import priors\nfrom submit import run\n\n@torch.no_grad()\ndef vectors(c,data,order):\n key=hashlib.sha1(json.dumps(c,sort_keys=True).encode()).hexdigest()[:12];path=P/f'vectors_{key}.npz'\n if path.exists():\n  z=np.load(path);return torch.from_numpy(z['uv']),torch.from_numpy(z['iv'])\n ck=torch.load(P/c['checkpoint'],weights_only=False);m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();uv=encode_users(m,data,c.get('history_power',1),c.get('recent',0))[order];iv=torch.cat([m.item(torch.arange(st,min(st+2048,30000))) for st in range(0,30000,2048)]);bias=c.get('pop',0)*np.log(ck['pop']+1e-7)\n if c.get('trend_strength',0):bias+=c['trend_strength']*priors(data,ck['pop'])[c['trend_days']]\n iv[:,-1]+=torch.from_numpy(bias).float();np.savez(path,uv=uv.numpy(),iv=iv.numpy());return uv,iv\n\n@torch.no_grad()\ndef rank_combinations(uvs,ivs,eligible,combinations,k=10):\n n=len(uvs[0]);ni=len(ivs[0]);batch=256;out=[np.empty((n,k),np.int32) for _ in combinations];base=[torch.empty((batch,ni)) for _ in uvs];work=torch.empty_like(base[0]);mask=torch.empty((batch,ni),dtype=torch.bool);values=torch.empty((batch,k));indices=torch.empty((batch,k),dtype=torch.int64)\n for st in range(0,n,batch):\n  nb=min(batch,n-st)\n  for uv,iv,b in zip(uvs,ivs,base):torch.mm(uv[st:st+nb],iv.T,out=b[:nb])\n  torch.logical_not(torch.from_numpy(eligible[st:st+nb]),out=mask[:nb])\n  for j,weights in enumerate(combinations):\n   work[:nb].zero_()\n   for weight,b in zip(weights,base):\n    if weight:work[:nb].add_(b[:nb],alpha=weight)\n   work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],k,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--configs',required=True);ap.add_argument('--output',default='ensemble');ap.add_argument('--denominator',type=int,default=4);a=ap.parse_args();torch.set_num_threads(3);cs=json.load(open(a.configs));data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];combinations=[tuple(w/a.denominator for w in ws) for ws in itertools.product(range(a.denominator+1),repeat=len(cs)) if sum(ws)==a.denominator and sum(w>0 for w in ws)<=3];tops=rank_combinations([v[0] for v in vs],[v[1] for v in vs],el,combinations);results=sorted([{'weights':w,'score':metric(top,tt)} for w,top in zip(combinations,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);print(results[:10],flush=True);config={'stage':'validation','components':[dict(c,weight=w) for c,w in zip(cs,results[0]['weights']) if w]};json.dump(config,open(P/f'{a.output}_config.json','w'),indent=2);run(config,f'{a.output}_validation.parquet')\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport json\np='/app/solution/'\ncs=[]\nfor name,recent,pop in [('temporal_early.pt',30,.25),('wide_temporal_selected.pt',90,.5),('recent_best.pt',30,.25)]:cs.append({'checkpoint':name,'history_power':.5,'recent':recent,'pop':pop,'trend_days':7,'trend_strength':1.})\njson.dump(cs,open(p+'ensemble_candidates.json','w'),indent=2)\nPY\n tail -4 /app/solution/final_temporal.log && tail -4 /app/solution/final_wide_base.log && tail -4 /app/solution/temporal2.log"
    ],
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    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/ensemble.py <<'PY'\n\"\"\"Select neural tower concatenations using only public validation outcomes.\"\"\"\nimport json,argparse,itertools,hashlib\nimport torch,numpy as np,pandas as pd\nfrom model import TwoTower\nfrom train import P,D,encode_users,truth,metric\nfrom trends import priors\nfrom submit import run\n\n@torch.no_grad()\ndef vectors(c,data,order):\n key=hashlib.sha1(json.dumps(c,sort_keys=True).encode()).hexdigest()[:12];path=P/f'vectors_{key}.npz'\n if path.exists():\n  z=np.load(path);return torch.from_numpy(z['uv']),torch.from_numpy(z['iv'])\n ck=torch.load(P/c['checkpoint'],weights_only=False);m=TwoTower(30000,ck['args']['dim']);m.load_state_dict(ck['model']);m.eval();uv=encode_users(m,data,c.get('history_power',1),c.get('recent',0))[order];iv=torch.cat([m.item(torch.arange(st,min(st+2048,30000))) for st in range(0,30000,2048)]);bias=c.get('pop',0)*np.log(ck['pop']+1e-7)\n if c.get('trend_strength',0):bias+=c['trend_strength']*priors(data,ck['pop'])[c['trend_days']]\n iv[:,-1]+=torch.from_numpy(bias).float();np.savez(path,uv=uv.numpy(),iv=iv.numpy());return uv,iv\n\n@torch.no_grad()\ndef rank_combinations(uvs,ivs,eligible,combinations,k=10):\n n=len(uvs[0]);ni=len(ivs[0]);batch=256;out=[np.empty((n,k),np.int32) for _ in combinations];base=[torch.empty((batch,ni)) for _ in uvs];work=torch.empty_like(base[0]);mask=torch.empty((batch,ni),dtype=torch.bool);values=torch.empty((batch,k));indices=torch.empty((batch,k),dtype=torch.int64)\n for st in range(0,n,batch):\n  nb=min(batch,n-st)\n  for uv,iv,b in zip(uvs,ivs,base):torch.mm(uv[st:st+nb],iv.T,out=b[:nb])\n  torch.logical_not(torch.from_numpy(eligible[st:st+nb]),out=mask[:nb])\n  for j,weights in enumerate(combinations):\n   work[:nb].zero_()\n   for weight,b in zip(weights,base):\n    if weight:work[:nb].add_(b[:nb],alpha=weight)\n   work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],k,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\n return out\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--configs',required=True);ap.add_argument('--output',default='ensemble');ap.add_argument('--denominator',type=int,default=4);a=ap.parse_args();torch.set_num_threads(3);cs=json.load(open(a.configs));data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];combinations=[tuple(w/a.denominator for w in ws) for ws in itertools.product(range(a.denominator+1),repeat=len(cs)) if sum(ws)==a.denominator and sum(w>0 for w in ws)<=3];tops=rank_combinations([v[0] for v in vs],[v[1] for v in vs],el,combinations);results=sorted([{'weights':w,'score':metric(top,tt)} for w,top in zip(combinations,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);print(results[:10],flush=True);config={'stage':'validation','components':[dict(c,weight=w) for c,w in zip(cs,results[0]['weights']) if w]};json.dump(config,open(P/f'{a.output}_config.json','w'),indent=2);run(config,f'{a.output}_validation.parquet')\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport json\np='/app/solution/'\ncs=[]\nfor name,recent,pop in [('temporal_early.pt',30,.25),('wide_temporal_selected.pt',90,.5),('recent_best.pt',30,.25)]:cs.append({'checkpoint':name,'history_power':.5,'recent':recent,'pop':pop,'trend_days':7,'trend_strength':1.})\njson.dump(cs,open(p+'ensemble_candidates.json','w'),indent=2)\nPY\n tail -4 /app/solution/final_temporal.log && tail -4 /app/solution/final_wide_base.log && tail -4 /app/solution/temporal2.log"
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    "stderr": "",
    "aggregated_output": "step 100 loss 6.623866791725159 seconds 10.3\nstep 200 loss 6.457741475105285 seconds 21.8\nstep 300 loss 6.33023540019989 seconds 34.4\nstep 400 loss 6.20713559627533 seconds 46.2\n{'stage': 'test', 'name': 'final_wide_base', 'steps': 1500, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.002, 'eval_every': 1500, 'seed': 42, 'resume': None, 'temporal': False, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 0, 'history_power': 1.0}\nstep 100 loss 7.7392862510681155 seconds 31.8\nstep 400 loss 6.156851363182068 seconds 35.9\nVALIDATION 400 0.03419722442493865\nstep 500 loss 6.053585867881775 seconds 46.4\nstep 600 loss 5.959062561988831 seconds 54.5\n",
    "exit_code": 0,
    "duration": {
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## Step 94 · agent · 2026-09-10T16:58:52.942Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T16:58:54.519Z
{
  "code": "text(await tools.clock__curr_time({}));\ntext(await tools.exec_command({cmd:\"tail -3 /app/solution/final_temporal.log && tail -3 /app/solution/temporal2.log\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:58:54.708Z
Script completed
Wall time 0.1 seconds
Output:

{"current_time":"2026-09-10 16:58:54 UTC"}
{"chunk_id":"d74560","wall_time_seconds":0.000007561,"exit_code":0,"original_token_count":55,"output":"step 400 loss 6.20713559627533 seconds 46.2\nstep 500 loss 6.113993792533875 seconds 58.2\ndone\nstep 500 loss 6.053585867881775 seconds 46.4\nstep 600 loss 5.959062561988831 seconds 54.5\nVALIDATION 600 0.03451275143219014\n"}

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## Step 95 · agent · 2026-09-10T16:59:03.855Z

### Thinking unavailable — no text recorded


### Harness context
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    "process_id": "7103",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name temporal2 --temporal --resume /app/solution/base2_best.pt --lr .0005 --steps 800 --eval-every 200 --threads 3 --seed 125 --recent 30 --history-power .5 > /app/solution/temporal2.log 2>&1"
    ],
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      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name temporal2 --temporal --resume /app/solution/base2_best.pt --lr .0005 --steps 800 --eval-every 200 --threads 3 --seed 125 --recent 30 --history-power .5 > /app/solution/temporal2.log 2>&1"
      }
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### exec · 2026-09-10T16:59:09.309Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\np='/app/solution/'\\nc={'stage':'test','components':[{'checkpoint':'final_temporal_last.pt','weight':1.,'history_power':.5,'recent':30,'trend_days':7,'trend_strength':1.,'pop':.25}]};json.dump(c,open(p+'final_config.json','w'),indent=2)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --export > /app/solution/export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py --configs /app/solution/ensemble_candidates.json --output ensemble > /app/solution/ensemble.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T16:59:11.650Z
Script completed
Wall time 2.3 seconds
Output:

{"chunk_id":"56810c","wall_time_seconds":1.001278396,"session_id":93748,"original_token_count":0,"output":""}
{"chunk_id":"a7e610","wall_time_seconds":1.00176501,"session_id":16191,"original_token_count":0,"output":""}

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    ],
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## Step 96 · agent · 2026-09-10T16:59:39.988Z

### Thinking unavailable — no text recorded


## Step 97 · agent · 2026-09-10T17:00:01.641Z

### Thinking unavailable — no text recorded


## Step 98 · agent · 2026-09-10T17:00:23.968Z

### Thinking unavailable — no text recorded


## Step 99 · agent · 2026-09-10T17:00:45.298Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:00:46.481Z
{
  "code": "text(await tools.exec_command({cmd:\"cat /app/solution/export.log && tail -3 /app/solution/ensemble.log && tail -4 /app/solution/temporal2.log && tail -3 /app/solution/final_wide_base.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:00:46.621Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"f51428","wall_time_seconds":0.00000583,"exit_code":0,"original_token_count":683,"output":"Traceback (most recent call last):\n  File \"/app/solution/submit.py\", line 50, in <module>\n    ap=argparse.ArgumentParser();ap.add_argument('--config',default=str(P/'final_config.json'));ap.add_argument('--output');ap.add_argument('--export',action='store_true');a=ap.parse_args();run(json.load(open(a.config)),a.output,a.export)\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/submit.py\", line 32, in run\n    torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\n                                                                                                                                                                                                                                                                                                                                                                                                         ^^^^^^^^^^^^^^^^\nAssertionError\n    torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\n                                                                                                                                                                                                                                                                                                                                                                                                         ^^^^^^^^^^^^^^^^\nAssertionError\nstep 700 loss 5.897401299476623 seconds 63.5\nstep 800 loss 5.79326907157898 seconds 70.0\nVALIDATION 800 0.03434474532293453\ndone\nstep 400 loss 6.413711247444152 seconds 109.1\nstep 500 loss 6.262359633445739 seconds 130.3\nstep 600 loss 6.120621757507324 seconds 151.5\n"}
{"current_time":"2026-09-10 17:00:46 UTC"}

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    "stdout": "Traceback (most recent call last):\n  File \"/app/solution/submit.py\", line 50, in <module>\n    ap=argparse.ArgumentParser();ap.add_argument('--config',default=str(P/'final_config.json'));ap.add_argument('--output');ap.add_argument('--export',action='store_true');a=ap.parse_args();run(json.load(open(a.config)),a.output,a.export)\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/submit.py\", line 32, in run\n    torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\n                                                                                                                                                                                                                                                                                                                                                                                                         ^^^^^^^^^^^^^^^^\nAssertionError\n    torch.set_num_threads(3);stage=config.get('stage','test');data=dict(np.load(P/f'data_{stage}.npz'));ar=np.load(D/f'{stage}_eligible_candidates.npz');users=ar['uids'];items=ar['item_ids'];eligible=np.unpackbits(ar['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_);assert np.array_equal(items,data['items']);order=pd.Index(data['users']).get_indexer(users);assert (order>=0).all()\n                                                                                                                                                                                                                                                                                                                                                                                                         ^^^^^^^^^^^^^^^^\nAssertionError\nstep 700 loss 5.897401299476623 seconds 63.5\nstep 800 loss 5.79326907157898 seconds 70.0\nVALIDATION 800 0.03434474532293453\ndone\nstep 400 loss 6.413711247444152 seconds 109.1\nstep 500 loss 6.262359633445739 seconds 130.3\nstep 600 loss 6.120621757507324 seconds 151.5\n",
    "stderr": "",
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    "exit_code": 0,
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## Step 100 · agent · 2026-09-10T17:01:12.650Z

### Thinking unavailable — no text recorded


## Step 101 · agent · 2026-09-10T17:01:13.861Z

### Thinking unavailable — no text recorded


The refitted model is ready, but the export check caught an inference bug: some target users have no usable history within the retrieval catalog. I’m adding an explicit empty-history path for those users and rechecking validation before replacing the saved submission.


### exec · 2026-09-10T17:01:17.925Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nfor s in ['validation','test']:\\n d=np.load('/app/solution/data_'+s+'.npz');a=np.load('/task/data/'+s+'_eligible_candidates.npz');o=pd.Index(d['users']).get_indexer(a['uids']);print(s,'missing',int((o<0).sum()),a['uids'][o<0])\\nPY\\ncat /app/solution/ensemble_results.json | head -28\",\"max_output_tokens\":1500}));\n"
}
### Tool output · 2026-09-10T17:01:20.173Z
Script completed
Wall time 2.2 seconds
Output:

{"chunk_id":"e506a5","wall_time_seconds":2.056642607,"exit_code":0,"original_token_count":116,"output":"validation missing 7 [121300 122000 213900 319800 523800 772200 862800]\ntest missing 6 [121300 122000 213900 319800 772200 862800]\n[\n  {\n    \"weights\": [\n      0.25,\n      0.5,\n      0.25\n    ],\n    \"score\": 0.04665704229007788\n  },\n  {\n    \"weights\": [\n      0.0,\n      0.5,\n      0.5\n    ],\n    \"score\": 0.04651112484753336\n  },\n  {\n    \"weights\": [\n      0.5,\n      0.5,\n      0.0\n    ],\n    \"score\": 0.04629031123258433\n  },\n  {\n    \"weights\": [\n      0.0,\n"}

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    "stdout": "validation missing 7 [121300 122000 213900 319800 523800 772200 862800]\ntest missing 6 [121300 122000 213900 319800 772200 862800]\n[\n  {\n    \"weights\": [\n      0.25,\n      0.5,\n      0.25\n    ],\n    \"score\": 0.04665704229007788\n  },\n  {\n    \"weights\": [\n      0.0,\n      0.5,\n      0.5\n    ],\n    \"score\": 0.04651112484753336\n  },\n  {\n    \"weights\": [\n      0.5,\n      0.5,\n      0.0\n    ],\n    \"score\": 0.04629031123258433\n  },\n  {\n    \"weights\": [\n      0.0,\n",
    "stderr": "",
    "aggregated_output": "validation missing 7 [121300 122000 213900 319800 523800 772200 862800]\ntest missing 6 [121300 122000 213900 319800 772200 862800]\n[\n  {\n    \"weights\": [\n      0.25,\n      0.5,\n      0.25\n    ],\n    \"score\": 0.04665704229007788\n  },\n  {\n    \"weights\": [\n      0.0,\n      0.5,\n      0.5\n    ],\n    \"score\": 0.04651112484753336\n  },\n  {\n    \"weights\": [\n      0.5,\n      0.5,\n      0.0\n    ],\n    \"score\": 0.04629031123258433\n  },\n  {\n    \"weights\": [\n      0.0,\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 56629466
    },
    "formatted_output": "validation missing 7 [121300 122000 213900 319800 523800 772200 862800]\ntest missing 6 [121300 122000 213900 319800 772200 862800]\n[\n  {\n    \"weights\": [\n      0.25,\n      0.5,\n      0.25\n    ],\n    \"score\": 0.04665704229007788\n  },\n  {\n    \"weights\": [\n      0.0,\n      0.5,\n      0.5\n    ],\n    \"score\": 0.04651112484753336\n  },\n  {\n    \"weights\": [\n      0.5,\n      0.5,\n      0.0\n    ],\n    \"score\": 0.04629031123258433\n  },\n  {\n    \"weights\": [\n      0.0,\n"
  }
}

## Step 102 · agent · 2026-09-10T17:01:39.901Z

### Thinking unavailable — no text recorded


## Step 103 · agent · 2026-09-10T17:02:04.076Z

### Thinking unavailable — no text recorded


## Step 104 · agent · 2026-09-10T17:02:11.764Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:02:29.771Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/model.py');s=p.read_text().replace('def project(self,x):','def project(self,x,has_history=None):').replace(\\\"  return torch.cat([x,torch.ones_like(x[:,:1])],1)\\\",\\\"  if has_history is not None:x=x*has_history.to(x.dtype)\\\\n  return torch.cat([x,torch.ones_like(x[:,:1])],1)\\\");s=s.replace('return self.project(x)\\\\n','return self.project(x,weight.sum(1,keepdim=True)>0)\\\\n');p.write_text(s)\\np=Path('/app/solution/train.py');s=p.read_text().replace(\\\"return model.user.project(out/mass[:,None].clamp_min(1e-8))\\\", \\\"return model.user.project(out/mass[:,None].clamp_min(1e-8),mass[:,None]>0)\\\");pos=s.index('\\\\ndef truth');s=s[:pos]+'''\\\\n@torch.no_grad()\\ndef align_users(model,vectors,order):\\n out=vectors[np.maximum(order,0)].clone();missing=order<0\\n if missing.any():out[missing]=model.user(torch.full((int(missing.sum()),1),model.user.history.padding_idx,dtype=torch.int64),torch.zeros((int(missing.sum()),1)))\\n return out\\n''' + s[pos:];s=s.replace(\\\"uv=encode_users(model,data,history_power,recent)[order]\\\", \\\"uv=align_users(model,encode_users(model,data,history_power,recent),order)\\\");p.write_text(s)\\nfor name in ['tune.py','trends.py','compare.py','ensemble.py','submit.py']:\\n p=Path('/app/solution')/name;s=p.read_text();s=s.replace('encode_users,truth','encode_users,align_users,truth');s=s.replace(\\\"assert (order>=0).all()\\\", \\\"assert len(order)==len(users)\\\")\\n pairs=[(\\\"encode_users(m,data,hp,recent)[order]\\\",\\\"align_users(m,encode_users(m,data,hp,recent),order)\\\"),(\\\"encode_users(m,data,.5,recent)[order]\\\",\\\"align_users(m,encode_users(m,data,.5,recent),order)\\\"),(\\\"encode_users(m,data,.5,30)[order]\\\",\\\"align_users(m,encode_users(m,data,.5,30),order)\\\"),(\\\"encode_users(m,data,b['history_power'],b['recent'])[order]\\\",\\\"align_users(m,encode_users(m,data,b['history_power'],b['recent']),order)\\\"),(\\\"encode_users(m,data,c.get('history_power',1),c.get('recent',0))[order]\\\",\\\"align_users(m,encode_users(m,data,c.get('history_power',1),c.get('recent',0)),order)\\\"),(\\\"encode_users(m,data,c.get('history_power',1.),c.get('recent',0))[order]\\\",\\\"align_users(m,encode_users(m,data,c.get('history_power',1.),c.get('recent',0)),order)\\\")]\\n for a,b in pairs:s=s.replace(a,b)\\n if name=='ensemble.py':s=s.replace(\\\"json.dumps(c,sort_keys=True).encode()\\\", \\\"('coldfix'+json.dumps(c,sort_keys=True)).encode()\\\")\\n if name=='submit.py':\\n  s=s.replace(\\\"for r in [0,len(users)//2,len(users)-1]:\\\", \\\"for r in [0,len(users)//2,len(users)-1]+np.flatnonzero(order<0)[:1].tolist():\\\")\\n  start=s.index(\\\"  u=order[r];\\\");end=s.index(\\\"direct=user_tower\\\",start)\\n  s=s[:start]+'''  u=order[r]\\n  if u<0:\\n   ids=torch.full((1,1),len(items),dtype=torch.int64);hw=[torch.zeros((1,1)) for c in config['components']]\\n  else:\\n   a=int(data['offset'][u]);b=int(data['offset'][u+1]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64))[None,:];hw=[torch.from_numpy(history_weights(data,c)[a:b])[None,:] for c in config['components']]\\n  '''+s[end:]\\n p.write_text(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --export > /app/solution/export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T17:02:30.953Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"69b86b","wall_time_seconds":1.001190973,"session_id":76828,"original_token_count":0,"output":""}

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## Step 105 · agent · 2026-09-10T17:02:33.591Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:02:35.529Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_wide_base.log && tail -2 /app/solution/export.log && cat /app/solution/temporal2_metrics.jsonl\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:02:35.707Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"21aac4","wall_time_seconds":0.000005391,"exit_code":0,"original_token_count":91,"output":"step 800 loss 5.978299379348755 seconds 193.9\nstep 900 loss 5.921048188209534 seconds 215.2\nstep 1000 loss 5.8539347648620605 seconds 236.4\nstep 1100 loss 5.822439866065979 seconds 257.6\n{\"step\": 200, \"score\": 0.035731319414061824}\n{\"step\": 400, \"score\": 0.03419722442493865}\n{\"step\": 600, \"score\": 0.03451275143219014}\n{\"step\": 800, \"score\": 0.03434474532293453}\n"}
{"current_time":"2026-09-10 17:02:35 UTC"}

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}

## Step 106 · agent · 2026-09-10T17:03:01.056Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:03:05.070Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\np='/app/solution/';cs=json.load(open(p+'ensemble_candidates.json'));cs.append({'checkpoint':'temporal2_best.pt','history_power':.5,'recent':30,'pop':.25,'trend_days':7,'trend_strength':1.});json.dump(cs,open(p+'ensemble2_candidates.json','w'),indent=2)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py --configs /app/solution/ensemble2_candidates.json --output ensemble2 > /app/solution/ensemble2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/export.log && ls -lh /app/solution/submission.parquet /app/solution/retrieval_towers.pt\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T17:03:06.295Z
Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d886b1","wall_time_seconds":1.001598002,"session_id":92821,"original_token_count":0,"output":""}
{"chunk_id":"3f6714","wall_time_seconds":0.00000557,"exit_code":0,"original_token_count":69,"output":"{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 129, \"validation_ndcg\": null}\n-rw-r--r-- 1 agent agent  31M Sep 10 17:02 /app/solution/retrieval_towers.pt\n-rw-r--r-- 1 agent agent 1.7M Sep 10 17:02 /app/solution/submission.parquet\n"}

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    "process_id": "41380",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/export.log && ls -lh /app/solution/submission.parquet /app/solution/retrieval_towers.pt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /app/solution/export.log",
        "name": "export.log",
        "path": "/app/solution/export.log"
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        "type": "list_files",
        "cmd": "ls -lh /app/solution/submission.parquet /app/solution/retrieval_towers.pt",
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    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 129, \"validation_ndcg\": null}\n-rw-r--r-- 1 agent agent  31M Sep 10 17:02 /app/solution/retrieval_towers.pt\n-rw-r--r-- 1 agent agent 1.7M Sep 10 17:02 /app/solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 129, \"validation_ndcg\": null}\n-rw-r--r-- 1 agent agent  31M Sep 10 17:02 /app/solution/retrieval_towers.pt\n-rw-r--r-- 1 agent agent 1.7M Sep 10 17:02 /app/solution/submission.parquet\n",
    "exit_code": 0,
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  }
}

## Step 107 · agent · 2026-09-10T17:03:30.856Z

### Thinking unavailable — no text recorded


### Harness context
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    "type": "CommandExecution",
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    "process_id": "92821",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport json\np='/app/solution/';cs=json.load(open(p+'ensemble_candidates.json'));cs.append({'checkpoint':'temporal2_best.pt','history_power':.5,'recent':30,'pop':.25,'trend_days':7,'trend_strength':1.});json.dump(cs,open(p+'ensemble2_candidates.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py --configs /app/solution/ensemble2_candidates.json --output ensemble2 > /app/solution/ensemble2.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\np='/app/solution/';cs=json.load(open(p+'ensemble_candidates.json'));cs.append({'checkpoint':'temporal2_best.pt','history_power':.5,'recent':30,'pop':.25,'trend_days':7,'trend_strength':1.});json.dump(cs,open(p+'ensemble2_candidates.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble.py --configs /app/solution/ensemble2_candidates.json --output ensemble2 > /app/solution/ensemble2.log 2>&1"
      }
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### Harness context
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  "item": {
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    "process_id": "5819",
    "command": [
      "/bin/sh",
      "-lc",
      "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_wide_base --dim 512 --batch 128 --targets 8 --steps 1500 --eval-every 1500 --threads 3 --no-submit > /app/solution/final_wide_base.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_wide_base --dim 512 --batch 128 --targets 8 --steps 1500 --eval-every 1500 --threads 3 --no-submit > /app/solution/final_wide_base.log 2>&1"
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}

## Step 108 · agent · 2026-09-10T17:03:54.466Z

### Thinking unavailable — no text recorded


## Step 109 · agent · 2026-09-10T17:03:56.113Z

### Thinking unavailable — no text recorded


The empty-history path is fixed, and the refitted 129-dimensional model has replaced the initial submission. Its export checks pass, including the empty-history case and exact dot-product scoring. A combined model is scoring about 0.0467 on validation; I’m comparing one more independently initialized model before selecting the final combination.


### exec · 2026-09-10T17:04:21.365Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/user_calibration.py <<'PY'\\n\\\"\\\"\\\"History-derived query calibration remains an exact vector inner product.\\\"\\\"\\\"\\nimport argparse,json\\nimport numpy as np,pandas as pd,torch\\nfrom train import P,D,truth,metric\\nfrom ensemble import vectors\\n\\n# These scalar history summaries contain no user-ID parameters or outcomes.\\ndef statistics(data,order,q,normalization=None):\\n lp=np.log(q+1e-7);w=data['w'].copy()**.5;w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(30*86400));offs=data['offset'][:-1];mass=np.add.reduceat(w,offs);mean=np.add.reduceat(w*lp[data['i']],offs)/(mass+1e-9);eff=mass**2/(np.add.reduceat(w*w,offs)+1e-9);valid=order>=0;idx=np.maximum(order,0);mean=mean[idx];loge=np.log(eff[idx]+1)\\n if normalization is None:normalization={'pop_center':float(np.median(mean[valid])),'pop_std':float(np.std(mean[valid])),'eff_center':float(np.median(loge[valid]))}\\n z=np.clip((mean-normalization['pop_center'])/max(normalization['pop_std'],.1),-3,3);e=np.clip(loge-normalization['eff_center'],-2,2);z[~valid]=0;e[~valid]=0\\n return np.stack([z,e],axis=1).astype(np.float32),normalization,lp.astype(np.float32)\\n\\nif __name__=='__main__':\\n ap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='user_calibration');a=ap.parse_args();torch.set_num_threads(3);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop'];stat,norm,lp=statistics(data,order,q);stat=torch.from_numpy(stat);lp=torch.from_numpy(lp-lp.mean());vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);item_bias=sum(c['weight']*v[:,-1] for (u,v),c in zip(vs,cs));configs=[(power,beta) for power in [-.25,0.,.25,.5] for beta in [-.5,-.25,0.,.25,.5,1.]];out=[np.empty((len(uv),10),np.int32) for _ in configs];base=torch.empty((256,30000));work=torch.empty_like(base);popterm=torch.empty_like(base);mask=torch.empty((256,30000),dtype=torch.bool);values=torch.empty((256,10));indices=torch.empty((256,10),dtype=torch.int64)\\n with torch.no_grad():\\n  for st in range(0,len(uv),256):\\n   nb=min(256,len(uv)-st);torch.mm(uv[st:st+nb],iv.T,out=base[:nb]);base[:nb].sub_(item_bias);torch.mul(stat[st:st+nb,:1],lp[None,:],out=popterm[:nb]);torch.logical_not(torch.from_numpy(el[st:st+nb]),out=mask[:nb])\\n   for j,(power,beta) in enumerate(configs):\\n    gate=(stat[st:st+nb,1:2]*power).exp().clamp(.5,1.5);work[:nb].copy_(base[:nb]).mul_(gate).add_(item_bias).add_(popterm[:nb],alpha=beta);work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],10,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\\n results=sorted([{'confidence_power':p,'pop_weight':b,'score':metric(top,tt)} for (p,b),top in zip(configs,out)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);cfg['user_calibration']=dict(results[0],normalization=norm);json.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True)\\nPY\\n tail -4 /app/solution/ensemble2.log && tail -4 /app/solution/final_wide_base.log\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T17:04:21.937Z
Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"d0740d","wall_time_seconds":0.00000591,"exit_code":0,"original_token_count":270,"output":"[{'weights': (0.25, 0.5, 0.0, 0.25), 'score': np.float64(0.04742659137852173)}, {'weights': (0.0, 0.5, 0.25, 0.25), 'score': np.float64(0.04737509471971666)}, {'weights': (0.25, 0.25, 0.0, 0.5), 'score': np.float64(0.04714911602949057)}, {'weights': (0.0, 0.25, 0.25, 0.5), 'score': np.float64(0.04694173837668528)}, {'weights': (0.0, 0.25, 0.5, 0.25), 'score': np.float64(0.046767669926076824)}, {'weights': (0.25, 0.5, 0.25, 0.0), 'score': np.float64(0.04665704229007788)}, {'weights': (0.0, 0.5, 0.0, 0.5), 'score': np.float64(0.046627754548530444)}, {'weights': (0.0, 0.5, 0.5, 0.0), 'score': np.float64(0.04651112484753336)}, {'weights': (0.5, 0.25, 0.0, 0.25), 'score': np.float64(0.046419713219950344)}, {'weights': (0.5, 0.5, 0.0, 0.0), 'score': np.float64(0.04629031123258433)}]\n{\"output\": \"/app/solution/ensemble2_validation.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": 0.04742659137852173}\nstep 1300 loss 5.751217670440674 seconds 300.4\nstep 1400 loss 5.726674475669861 seconds 324.2\nstep 1500 loss 5.686846084594727 seconds 351.4\ndone\n"}

### Harness context
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  "item": {
    "type": "CommandExecution",
    "id": "exec-5b753701-35c6-489d-b25e-83dfdffd11e9",
    "process_id": "99064",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/user_calibration.py <<'PY'\n\"\"\"History-derived query calibration remains an exact vector inner product.\"\"\"\nimport argparse,json\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,truth,metric\nfrom ensemble import vectors\n\n# These scalar history summaries contain no user-ID parameters or outcomes.\ndef statistics(data,order,q,normalization=None):\n lp=np.log(q+1e-7);w=data['w'].copy()**.5;w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(30*86400));offs=data['offset'][:-1];mass=np.add.reduceat(w,offs);mean=np.add.reduceat(w*lp[data['i']],offs)/(mass+1e-9);eff=mass**2/(np.add.reduceat(w*w,offs)+1e-9);valid=order>=0;idx=np.maximum(order,0);mean=mean[idx];loge=np.log(eff[idx]+1)\n if normalization is None:normalization={'pop_center':float(np.median(mean[valid])),'pop_std':float(np.std(mean[valid])),'eff_center':float(np.median(loge[valid]))}\n z=np.clip((mean-normalization['pop_center'])/max(normalization['pop_std'],.1),-3,3);e=np.clip(loge-normalization['eff_center'],-2,2);z[~valid]=0;e[~valid]=0\n return np.stack([z,e],axis=1).astype(np.float32),normalization,lp.astype(np.float32)\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='user_calibration');a=ap.parse_args();torch.set_num_threads(3);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop'];stat,norm,lp=statistics(data,order,q);stat=torch.from_numpy(stat);lp=torch.from_numpy(lp-lp.mean());vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);item_bias=sum(c['weight']*v[:,-1] for (u,v),c in zip(vs,cs));configs=[(power,beta) for power in [-.25,0.,.25,.5] for beta in [-.5,-.25,0.,.25,.5,1.]];out=[np.empty((len(uv),10),np.int32) for _ in configs];base=torch.empty((256,30000));work=torch.empty_like(base);popterm=torch.empty_like(base);mask=torch.empty((256,30000),dtype=torch.bool);values=torch.empty((256,10));indices=torch.empty((256,10),dtype=torch.int64)\n with torch.no_grad():\n  for st in range(0,len(uv),256):\n   nb=min(256,len(uv)-st);torch.mm(uv[st:st+nb],iv.T,out=base[:nb]);base[:nb].sub_(item_bias);torch.mul(stat[st:st+nb,:1],lp[None,:],out=popterm[:nb]);torch.logical_not(torch.from_numpy(el[st:st+nb]),out=mask[:nb])\n   for j,(power,beta) in enumerate(configs):\n    gate=(stat[st:st+nb,1:2]*power).exp().clamp(.5,1.5);work[:nb].copy_(base[:nb]).mul_(gate).add_(item_bias).add_(popterm[:nb],alpha=beta);work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],10,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\n results=sorted([{'confidence_power':p,'pop_weight':b,'score':metric(top,tt)} for (p,b),top in zip(configs,out)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);cfg['user_calibration']=dict(results[0],normalization=norm);json.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True)\nPY\n tail -4 /app/solution/ensemble2.log && tail -4 /app/solution/final_wide_base.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/user_calibration.py <<'PY'\n\"\"\"History-derived query calibration remains an exact vector inner product.\"\"\"\nimport argparse,json\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,truth,metric\nfrom ensemble import vectors\n\n# These scalar history summaries contain no user-ID parameters or outcomes.\ndef statistics(data,order,q,normalization=None):\n lp=np.log(q+1e-7);w=data['w'].copy()**.5;w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(30*86400));offs=data['offset'][:-1];mass=np.add.reduceat(w,offs);mean=np.add.reduceat(w*lp[data['i']],offs)/(mass+1e-9);eff=mass**2/(np.add.reduceat(w*w,offs)+1e-9);valid=order>=0;idx=np.maximum(order,0);mean=mean[idx];loge=np.log(eff[idx]+1)\n if normalization is None:normalization={'pop_center':float(np.median(mean[valid])),'pop_std':float(np.std(mean[valid])),'eff_center':float(np.median(loge[valid]))}\n z=np.clip((mean-normalization['pop_center'])/max(normalization['pop_std'],.1),-3,3);e=np.clip(loge-normalization['eff_center'],-2,2);z[~valid]=0;e[~valid]=0\n return np.stack([z,e],axis=1).astype(np.float32),normalization,lp.astype(np.float32)\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='user_calibration');a=ap.parse_args();torch.set_num_threads(3);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop'];stat,norm,lp=statistics(data,order,q);stat=torch.from_numpy(stat);lp=torch.from_numpy(lp-lp.mean());vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);item_bias=sum(c['weight']*v[:,-1] for (u,v),c in zip(vs,cs));configs=[(power,beta) for power in [-.25,0.,.25,.5] for beta in [-.5,-.25,0.,.25,.5,1.]];out=[np.empty((len(uv),10),np.int32) for _ in configs];base=torch.empty((256,30000));work=torch.empty_like(base);popterm=torch.empty_like(base);mask=torch.empty((256,30000),dtype=torch.bool);values=torch.empty((256,10));indices=torch.empty((256,10),dtype=torch.int64)\n with torch.no_grad():\n  for st in range(0,len(uv),256):\n   nb=min(256,len(uv)-st);torch.mm(uv[st:st+nb],iv.T,out=base[:nb]);base[:nb].sub_(item_bias);torch.mul(stat[st:st+nb,:1],lp[None,:],out=popterm[:nb]);torch.logical_not(torch.from_numpy(el[st:st+nb]),out=mask[:nb])\n   for j,(power,beta) in enumerate(configs):\n    gate=(stat[st:st+nb,1:2]*power).exp().clamp(.5,1.5);work[:nb].copy_(base[:nb]).mul_(gate).add_(item_bias).add_(popterm[:nb],alpha=beta);work[:nb].masked_fill_(mask[:nb],-1e9);torch.topk(work[:nb],10,1,out=(values[:nb],indices[:nb]));out[j][st:st+nb]=indices[:nb].numpy()\n results=sorted([{'confidence_power':p,'pop_weight':b,'score':metric(top,tt)} for (p,b),top in zip(configs,out)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);cfg['user_calibration']=dict(results[0],normalization=norm);json.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True)\nPY\n tail -4 /app/solution/ensemble2.log && tail -4 /app/solution/final_wide_base.log"
      }
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## Step 110 · agent · 2026-09-10T17:04:44.509Z

### Thinking unavailable — no text recorded


## Step 111 · agent · 2026-09-10T17:05:06.089Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:05:11.172Z
{
  "code": "text(await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_wide_temporal --dim 512 --batch 128 --targets 8 --temporal --resume /app/solution/final_wide_base_last.pt --lr .0005 --steps 900 --eval-every 900 --recent 90 --history-power .5 --threads 3 --no-submit > /app/solution/final_wide_temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_base2 --steps 1800 --eval-every 1800 --threads 3 --seed 125 --no-submit > /app/solution/final_base2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/user_calibration.py --config /app/solution/ensemble2_config.json > /app/solution/user_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:05:12.355Z
Script completed
Wall time 1.2 seconds
Output:

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{"current_time":"2026-09-10 17:05:12 UTC"}

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## Step 112 · agent · 2026-09-10T17:05:36.831Z

### Thinking unavailable — no text recorded


### Harness context
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## Step 113 · agent · 2026-09-10T17:05:56.266Z

### Thinking unavailable — no text recorded


## Step 114 · agent · 2026-09-10T17:06:15.758Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:06:27.636Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/calibrate_ensemble.py <<'PY'\\n\\\"\\\"\\\"Calibrate shared item priors without changing retrieval decomposition.\\\"\\\"\\\"\\nimport json,argparse\\nimport torch,numpy as np,pandas as pd\\nfrom train import P,D,truth,metric\\nfrom trends import priors\\nfrom ensemble import vectors\\nfrom scoring import rank_many\\nfrom submit import run\\nap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='calibrated');a=ap.parse_args();torch.set_num_threads(3);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop']\\nfor c in cs[1:]:assert np.allclose(q,torch.load(P/c['checkpoint'],weights_only=False)['pop'])\\ntrend=priors(data,q)[7];lp=np.log(q+1e-7);configs=[(ds,dp) for ds in [-.5,-.25,0,.25,.5,.75,1.] for dp in [-.25,0,.25,.5,.75]];bias=[torch.from_numpy(ds*trend+dp*lp).float() for ds,dp in configs];tops=rank_many(uv,iv,el,bias,k=10);results=sorted([{'trend_delta':ds,'pop_delta':dp,'score':metric(top,tt)} for (ds,dp),top in zip(configs,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);b=results[0]\\nfor c in cs:c['pop']+=b['pop_delta'];c['trend_strength']+=b['trend_delta']\\njson.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True);run(cfg,f'{a.output}_validation.parquet')\\nPY\\n tail -3 /app/solution/user_calibration.log && tail -3 /app/solution/final_wide_temporal.log && tail -3 /app/solution/final_base2.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:06:27.850Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"428dac","wall_time_seconds":0.00000714,"exit_code":0,"original_token_count":370,"output":"[{'confidence_power': 0.0, 'pop_weight': 0.0, 'score': np.float64(0.04742659137852173)}, {'confidence_power': -0.25, 'pop_weight': 0.0, 'score': np.float64(0.04737489597591366)}, {'confidence_power': 0.25, 'pop_weight': 0.0, 'score': np.float64(0.046942344526043414)}, {'confidence_power': 0.5, 'pop_weight': 0.0, 'score': np.float64(0.04685273859084746)}, {'confidence_power': -0.25, 'pop_weight': -0.25, 'score': np.float64(0.04620951203165368)}, {'confidence_power': 0.0, 'pop_weight': -0.25, 'score': np.float64(0.04613769829818196)}, {'confidence_power': 0.0, 'pop_weight': 0.25, 'score': np.float64(0.04583352142915961)}, {'confidence_power': 0.25, 'pop_weight': 0.25, 'score': np.float64(0.045173512194612894)}, {'confidence_power': -0.25, 'pop_weight': 0.25, 'score': np.float64(0.044749119121212905)}, {'confidence_power': 0.5, 'pop_weight': 0.25, 'score': np.float64(0.04456076795496236)}]\n{'stage': 'test', 'name': 'final_wide_temporal', 'steps': 900, 'dim': 512, 'batch': 128, 'history': 96, 'lr': 0.0005, 'eval_every': 900, 'seed': 42, 'resume': '/app/solution/final_wide_base_last.pt', 'temporal': True, 'targets': 8, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 90.0, 'history_power': 0.5}\nstep 100 loss 6.5746458768844604 seconds 33.0\nstep 200 loss 6.411447730064392 seconds 59.0\nstep 500 loss 6.326228857040405 seconds 45.0\nstep 600 loss 6.196667938232422 seconds 53.2\nstep 700 loss 6.088356337547302 seconds 61.1\n"}
{"current_time":"2026-09-10 17:06:27 UTC"}

### Harness context
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    "command": [
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      "-lc",
      "cat > /app/solution/calibrate_ensemble.py <<'PY'\n\"\"\"Calibrate shared item priors without changing retrieval decomposition.\"\"\"\nimport json,argparse\nimport torch,numpy as np,pandas as pd\nfrom train import P,D,truth,metric\nfrom trends import priors\nfrom ensemble import vectors\nfrom scoring import rank_many\nfrom submit import run\nap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='calibrated');a=ap.parse_args();torch.set_num_threads(3);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop']\nfor c in cs[1:]:assert np.allclose(q,torch.load(P/c['checkpoint'],weights_only=False)['pop'])\ntrend=priors(data,q)[7];lp=np.log(q+1e-7);configs=[(ds,dp) for ds in [-.5,-.25,0,.25,.5,.75,1.] for dp in [-.25,0,.25,.5,.75]];bias=[torch.from_numpy(ds*trend+dp*lp).float() for ds,dp in configs];tops=rank_many(uv,iv,el,bias,k=10);results=sorted([{'trend_delta':ds,'pop_delta':dp,'score':metric(top,tt)} for (ds,dp),top in zip(configs,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);b=results[0]\nfor c in cs:c['pop']+=b['pop_delta'];c['trend_strength']+=b['trend_delta']\njson.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True);run(cfg,f'{a.output}_validation.parquet')\nPY\n tail -3 /app/solution/user_calibration.log && tail -3 /app/solution/final_wide_temporal.log && tail -3 /app/solution/final_base2.log"
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## Step 115 · agent · 2026-09-10T17:06:54.341Z

### Thinking unavailable — no text recorded


## Step 116 · agent · 2026-09-10T17:07:18.822Z

### Thinking unavailable — no text recorded


## Step 117 · agent · 2026-09-10T17:07:24.776Z

### Thinking unavailable — no text recorded


The best combination now scores **0.0474 NDCG@10** using three trained components in a single 771-dimensional vector. History-based popularity and confidence adjustments did not help, so I’m leaving them out. The last two final refits are running; I’m checking the shared item-prior calibration while they finish.


### exec · 2026-09-10T17:07:28.723Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate_ensemble.py --config /app/solution/ensemble2_config.json > /app/solution/calibrated.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:07:29.941Z
Script completed
Wall time 1.2 seconds
Output:

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{"current_time":"2026-09-10 17:07:29 UTC"}

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## Step 118 · agent · 2026-09-10T17:07:53.317Z

### Thinking unavailable — no text recorded


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## Step 119 · agent · 2026-09-10T17:08:15.527Z

### Thinking unavailable — no text recorded


## Step 120 · agent · 2026-09-10T17:08:36.276Z

### Thinking unavailable — no text recorded


## Step 121 · agent · 2026-09-10T17:09:01.720Z

### Thinking unavailable — no text recorded


### Harness context
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      "-lc",
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## Step 122 · agent · 2026-09-10T17:09:26.708Z

### Thinking unavailable — no text recorded


## Step 123 · agent · 2026-09-10T17:09:49.780Z

### Thinking unavailable — no text recorded


## Step 124 · agent · 2026-09-10T17:09:52.294Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:09:53.489Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/calibrated.log && tail -3 /app/solution/final_wide_temporal.log && tail -3 /app/solution/final_base2.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:09:53.640Z
Script completed
Wall time 0.0 seconds
Output:

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{"current_time":"2026-09-10 17:09:53 UTC"}

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## Step 125 · agent · 2026-09-10T17:10:19.258Z

### Thinking unavailable — no text recorded


## Step 126 · agent · 2026-09-10T17:10:41.394Z

### Thinking unavailable — no text recorded


## Step 127 · agent · 2026-09-10T17:11:00.684Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:11:20.987Z
{
  "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_temporal2 --temporal --resume /app/solution/final_base2_last.pt --lr .0005 --steps 200 --eval-every 200 --threads 3 --seed 125 --recent 30 --history-power .5 --no-submit > /app/solution/final_temporal2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/trends.py';s=open(p).read().replace('def priors(data,q):','def priors(data,q,normalization=1.,mix=.5):').replace('for days in [7,14,30]:','for days in [3,7,14,30]:').replace(\\\"weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\\\", \\\"weight/=np.repeat(sums+1e-9,np.diff(data['offset']))**normalization\\\").replace('np.log(.5*q+.5*counts)-np.log(q)','np.log((1-mix)*q+mix*counts)-np.log(q)');open(p,'w').write(s)\\nfor p in ['/app/solution/submit.py','/app/solution/ensemble.py']:\\n s=open(p).read().replace(\\\"priors(data,ck['pop'])[c['trend_days']]\\\", \\\"priors(data,ck['pop'],c.get('trend_normalization',1.),c.get('trend_mix',.5))[c['trend_days']]\\\")\\n open(p,'w').write(s)\\nPY\\ncat > /app/solution/alternative_trends.py <<'PY'\\nimport json,argparse\\nimport numpy as np,pandas as pd,torch\\nfrom train import P,D,truth,metric\\nfrom trends import priors\\nfrom ensemble import vectors\\nfrom scoring import rank_many\\nfrom submit import run\\nap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='alternative');a=ap.parse_args();torch.set_num_threads(4);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop'];qs=q+1e-7;qs/=qs.sum();lp=np.log(q+1e-7);old=priors(data,q)[7]*sum(c['weight']*c['trend_strength'] for c in cs);configs=[];bias=[]\\nfor power in [0.,.5,1.]:\\n ps=priors(data,q,power,.5)\\n for days in [3,7,14]:\\n  counts=np.maximum(0,qs*(2*np.exp(ps[days])-1));counts/=counts.sum()\\n  for mix in [.25,.5,.75]:\\n   ratio=np.log((1-mix)*qs+mix*counts)-np.log(qs)\\n   for strength in [1.,1.5,2.]:\\n    for dp in [0.,.25]:\\n     configs.append({'trend_normalization':power,'trend_mix':mix,'trend_days':days,'trend_strength':strength,'pop_delta':dp});bias.append(torch.from_numpy(strength*ratio-old+dp*lp).float())\\nprint('configurations',len(configs),flush=True);tops=rank_many(uv,iv,el,bias,k=10);results=sorted([dict(c,score=metric(top,tt)) for c,top in zip(configs,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);b=results[0]\\nfor c in cs:\\n for k in ['trend_normalization','trend_mix','trend_days','trend_strength']:c[k]=b[k]\\n c['pop']+=b['pop_delta']\\njson.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True);run(cfg,f'{a.output}_validation.parquet')\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/alternative_trends.py --config /app/solution/calibrated_config.json > /app/solution/alternative.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:11:23.321Z
Script completed
Wall time 2.3 seconds
Output:

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{"current_time":"2026-09-10 17:11:23 UTC"}

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## Step 128 · agent · 2026-09-10T17:11:26.173Z

### Thinking unavailable — no text recorded


The shared prior calibration raised validation NDCG@10 to **0.0483**. The final refits are nearly complete. I’m checking one last set of prior definitions; after that I’ll export the chosen towers and verify the final ranking’s schema, eligibility, and dot-product contract.


## Step 129 · agent · 2026-09-10T17:11:31.950Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:11:33.463Z
{
  "code": "text(await tools.exec_command({cmd:\"cat /app/solution/final_temporal2.log && tail -2 /app/solution/alternative.log && du -sh /app/solution\",\"max_output_tokens\":1200}));\n"
}
### Tool output · 2026-09-10T17:11:33.626Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"9a1b70","wall_time_seconds":0.000006711,"exit_code":0,"original_token_count":96,"output":"{'stage': 'test', 'name': 'final_temporal2', 'steps': 200, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.0005, 'eval_every': 200, 'seed': 125, 'resume': '/app/solution/final_base2_last.pt', 'temporal': True, 'targets': 4, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 30.0, 'history_power': 0.5}\nconfigurations 162\n3.0G\t/app/solution\n"}

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## Step 130 · agent · 2026-09-10T17:11:56.470Z

### Thinking unavailable — no text recorded


## Step 131 · agent · 2026-09-10T17:12:14.581Z

### Thinking unavailable — no text recorded


### Harness context
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      "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace('def priors(data,q):','def priors(data,q,normalization=1.,mix=.5):').replace('for days in [7,14,30]:','for days in [3,7,14,30]:').replace(\"weight/=np.repeat(sums+1e-9,np.diff(data['offset']))\", \"weight/=np.repeat(sums+1e-9,np.diff(data['offset']))**normalization\").replace('np.log(.5*q+.5*counts)-np.log(q)','np.log((1-mix)*q+mix*counts)-np.log(q)');open(p,'w').write(s)\nfor p in ['/app/solution/submit.py','/app/solution/ensemble.py']:\n s=open(p).read().replace(\"priors(data,ck['pop'])[c['trend_days']]\", \"priors(data,ck['pop'],c.get('trend_normalization',1.),c.get('trend_mix',.5))[c['trend_days']]\")\n open(p,'w').write(s)\nPY\ncat > /app/solution/alternative_trends.py <<'PY'\nimport json,argparse\nimport numpy as np,pandas as pd,torch\nfrom train import P,D,truth,metric\nfrom trends import priors\nfrom ensemble import vectors\nfrom scoring import rank_many\nfrom submit import run\nap=argparse.ArgumentParser();ap.add_argument('--config',required=True);ap.add_argument('--output',default='alternative');a=ap.parse_args();torch.set_num_threads(4);cfg=json.load(open(a.config));cs=cfg['components'];data=dict(np.load(P/'data_validation.npz'));ar=np.load(D/'validation_eligible_candidates.npz');el=np.unpackbits(ar['packed_eligible'],axis=1,count=30000,bitorder='little').view(np.bool_);order=pd.Index(data['users']).get_indexer(ar['uids']);tt=truth(ar['item_ids'],ar['uids'],el);vs=[vectors(c,data,order) for c in cs];uv=torch.cat([u*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);iv=torch.cat([v*np.sqrt(c['weight']) for (u,v),c in zip(vs,cs)],1);q=torch.load(P/cs[0]['checkpoint'],weights_only=False)['pop'];qs=q+1e-7;qs/=qs.sum();lp=np.log(q+1e-7);old=priors(data,q)[7]*sum(c['weight']*c['trend_strength'] for c in cs);configs=[];bias=[]\nfor power in [0.,.5,1.]:\n ps=priors(data,q,power,.5)\n for days in [3,7,14]:\n  counts=np.maximum(0,qs*(2*np.exp(ps[days])-1));counts/=counts.sum()\n  for mix in [.25,.5,.75]:\n   ratio=np.log((1-mix)*qs+mix*counts)-np.log(qs)\n   for strength in [1.,1.5,2.]:\n    for dp in [0.,.25]:\n     configs.append({'trend_normalization':power,'trend_mix':mix,'trend_days':days,'trend_strength':strength,'pop_delta':dp});bias.append(torch.from_numpy(strength*ratio-old+dp*lp).float())\nprint('configurations',len(configs),flush=True);tops=rank_many(uv,iv,el,bias,k=10);results=sorted([dict(c,score=metric(top,tt)) for c,top in zip(configs,tops)],key=lambda x:-x['score']);json.dump(results,open(P/f'{a.output}_results.json','w'),indent=2);b=results[0]\nfor c in cs:\n for k in ['trend_normalization','trend_mix','trend_days','trend_strength']:c[k]=b[k]\n c['pop']+=b['pop_delta']\njson.dump(cfg,open(P/f'{a.output}_config.json','w'),indent=2);print(results[:10],flush=True);run(cfg,f'{a.output}_validation.parquet')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/alternative_trends.py --config /app/solution/calibrated_config.json > /app/solution/alternative.log 2>&1"
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## Step 132 · agent · 2026-09-10T17:12:38.777Z

### Thinking unavailable — no text recorded


## Step 133 · agent · 2026-09-10T17:12:52.714Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:12:53.683Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_temporal2.log && tail -3 /app/solution/alternative.log && cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:12:53.840Z
Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"fcab9a","wall_time_seconds":0.000003991,"exit_code":0,"original_token_count":529,"output":"{'stage': 'test', 'name': 'final_temporal2', 'steps': 200, 'dim': 128, 'batch': 256, 'history': 96, 'lr': 0.0005, 'eval_every': 200, 'seed': 125, 'resume': '/app/solution/final_base2_last.pt', 'temporal': True, 'targets': 4, 'threads': 3, 'mask_history': False, 'pool_prefix': 'temporal', 'no_submit': True, 'recent': 30.0, 'history_power': 0.5}\nstep 100 loss 6.5766522455215455 seconds 7.8\nstep 200 loss 6.4046986961364745 seconds 16.1\ndone\nconfigurations 162\n[{'trend_normalization': 0.5, 'trend_mix': 0.75, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.25, 'score': np.float64(0.05179399043498042)}, {'trend_normalization': 0.5, 'trend_mix': 0.75, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.05156438606148637)}, {'trend_normalization': 0.0, 'trend_mix': 0.75, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.051455159166842714)}, {'trend_normalization': 0.0, 'trend_mix': 0.75, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.25, 'score': np.float64(0.051409257143445644)}, {'trend_normalization': 0.5, 'trend_mix': 0.5, 'trend_days': 3, 'trend_strength': 2.0, 'pop_delta': 0.25, 'score': np.float64(0.05126933605790391)}, {'trend_normalization': 0.0, 'trend_mix': 0.5, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.051199280216160846)}, {'trend_normalization': 0.5, 'trend_mix': 0.75, 'trend_days': 7, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.05115999571644635)}, {'trend_normalization': 1.0, 'trend_mix': 0.75, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.05114854680818217)}, {'trend_normalization': 0.0, 'trend_mix': 0.75, 'trend_days': 7, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.05109979379888926)}, {'trend_normalization': 0.5, 'trend_mix': 0.5, 'trend_days': 3, 'trend_strength': 1.5, 'pop_delta': 0.0, 'score': np.float64(0.05108855481579537)}]\n{\"output\": \"/app/solution/alternative_validation.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": 0.05179399043498042}\n3167305728\n"}
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## Step 134 · agent · 2026-09-10T17:13:18.967Z

### Thinking unavailable — no text recorded


## Step 135 · agent · 2026-09-10T17:13:42.804Z

### Thinking unavailable — no text recorded


## Step 136 · agent · 2026-09-10T17:14:00.737Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:14:06.762Z
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### Tool output · 2026-09-10T17:14:09.093Z
Script completed
Wall time 2.3 seconds
Output:

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{"chunk_id":"10128a","wall_time_seconds":1.001208702,"session_id":61907,"original_token_count":0,"output":""}

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## Step 137 · agent · 2026-09-10T17:14:28.562Z

### Thinking unavailable — no text recorded


## Step 138 · agent · 2026-09-10T17:14:48.831Z

### Thinking unavailable — no text recorded


The latest prior definition raised public validation NDCG@10 to **0.0518**, and all final neural refits are complete. I’m exporting that model and making a final, smaller check of nearby calibration settings and mixture weights. I’ll then finish the reproduction commands and artifact checks.


### exec · 2026-09-10T17:15:04.315Z
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}
### Tool output · 2026-09-10T17:15:05.624Z
Script completed
Wall time 1.3 seconds
Output:

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{"current_time":"2026-09-10 17:15:05 UTC"}

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## Step 139 · agent · 2026-09-10T17:15:29.628Z

### Thinking unavailable — no text recorded


## Step 140 · agent · 2026-09-10T17:15:49.199Z

### Thinking unavailable — no text recorded


### Harness context
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## Step 141 · agent · 2026-09-10T17:16:10.495Z

### Thinking unavailable — no text recorded


## Step 142 · agent · 2026-09-10T17:16:32.997Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:16:47.787Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/refit.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nSOLUTION_ROOT=/app/solution\\nTASK_PYTHON=/opt/task-venv/bin/python\\nexport OPENBLAS_NUM_THREADS=1\\nexport PYTHONDONTWRITEBYTECODE=1\\nexport TMPDIR=\\\"$SOLUTION_ROOT/tmp\\\"\\nmkdir -p \\\"$TMPDIR\\\"\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/prepare.py\\\" test\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/prepare_temporal.py\\\" --stage test\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/train.py\\\" --stage test --name final_base --steps 1500 --eval-every 1500 --threads 6 --no-submit\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/train.py\\\" --stage test --name final_wide_base --dim 512 --batch 128 --targets 8 --steps 1500 --eval-every 1500 --threads 6 --no-submit\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/train.py\\\" --stage test --name final_base2 --steps 1800 --eval-every 1800 --threads 6 --seed 125 --no-submit\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/train.py\\\" --stage test --name final_temporal --temporal --resume \\\"$SOLUTION_ROOT/final_base_last.pt\\\" --lr .0005 --steps 500 --eval-every 500 --threads 6 --no-submit\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/train.py\\\" --stage test --name final_wide_temporal --dim 512 --batch 128 --targets 8 --temporal --resume \\\"$SOLUTION_ROOT/final_wide_base_last.pt\\\" --lr .0005 --steps 900 --eval-every 900 --recent 90 --history-power .5 --threads 6 --no-submit\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/train.py\\\" --stage test --name final_temporal2 --temporal --resume \\\"$SOLUTION_ROOT/final_base2_last.pt\\\" --lr .0005 --steps 200 --eval-every 200 --threads 6 --seed 125 --recent 30 --history-power .5 --no-submit\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/submit.py\\\" --config \\\"$SOLUTION_ROOT/final_config.json\\\" --export\\n\\\"$TASK_PYTHON\\\" \\\"$SOLUTION_ROOT/validate_submission.py\\\"\\nSH\\nchmod +x /app/solution/refit.sh\\n/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/submit.py';s=open(p).read();idx=s.index('\\\\ndef history_weights');s=s[:idx]+'''\\\\ndef load_retrieval_towers(path=P/'retrieval_towers.pt'):\\n \\\"\\\"\\\"Load the exported serving networks without training checkpoints.\\\"\\\"\\\"\\n bundle=torch.load(path,weights_only=False);config=bundle['config'];weights=[c.get('weight',1.) for c in config['components']];nitems=bundle['item_tower']['towers.0.embedding.weight'].shape[0];models=[TwoTower(nitems,d) for d in bundle['dimensions']]\\n user=RetrievalUserTower([m.user for m in models],weights);item=RetrievalItemTower([m.item for m in models],weights,[torch.zeros(nitems) for _ in models]);user.load_state_dict(bundle['user_tower']);item.load_state_dict(bundle['item_tower']);return user.eval(),item.eval(),config\\n''' +s[idx:];open(p,'w').write(s)\\nPY\\ncat /app/solution/public_evaluation.json && tail -2 /app/solution/export.log && tail -2 /app/solution/fine_weights.log && tail -2 /app/solution/fine_priors.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:16:48.038Z
Script completed
Wall time 0.1 seconds
Output:

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{"current_time":"2026-09-10 17:16:47 UTC"}

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## Step 143 · agent · 2026-09-10T17:17:11.645Z

### Thinking unavailable — no text recorded


## Step 144 · agent · 2026-09-10T17:17:33.410Z

### Thinking unavailable — no text recorded


## Step 145 · agent · 2026-09-10T17:17:57.215Z

### Thinking unavailable — no text recorded


## Step 146 · agent · 2026-09-10T17:18:20.358Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:18:21.716Z
{
  "code": "text(await tools.exec_command({cmd:\"tail -2 /app/solution/fine_priors.log && tail -1 /app/solution/fine_weights.log && cat /app/solution/final_config.json\",\"max_output_tokens\":2200}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:18:21.926Z
Script completed
Wall time 0.0 seconds
Output:

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## Step 147 · agent · 2026-09-10T17:18:38.863Z

### Thinking unavailable — no text recorded


The final tuning checks reached **0.05265 NDCG@10**. I’m checking whether the best mixture weights and prior settings also work well together, then I’ll export the winner and complete the final validation. No further model training is needed.


### exec · 2026-09-10T17:19:00.929Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\np='/app/solution/';c=json.load(open(p+'fine_priors_config.json'));w=json.load(open(p+'fine_weights_config.json'))['components'];wm={x['checkpoint']:x['weight'] for x in w}\\nfor x in c['components']:x['weight']=wm[x['checkpoint']]\\njson.dump(c,open(p+'combined_config.json','w'),indent=2)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/combined_config.json --output combined_validation.parquet > /app/solution/combined.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/benchmark.py <<'PY'\\n\\\"\\\"\\\"Check exported towers against cached vectors and time CPU user inference.\\\"\\\"\\\"\\nimport time,json\\nimport numpy as np,pandas as pd,torch\\nfrom submit import load_retrieval_towers,history_weights\\nfrom train import P,D\\n\\ntorch.set_num_threads(1);user,item,cfg=load_retrieval_towers();data=dict(np.load(P/'data_test.npz'));uids=pd.read_parquet(D/'target_users.parquet').uid.to_numpy();order=pd.Index(data['users']).get_indexer(uids);sizes=np.where(order>=0,np.diff(data['offset'])[np.maximum(order,0)],0);all_weights=[history_weights(data,c) for c in cfg['components']];cached=np.load(P/'item_vectors.npy',mmap_mode='r');reports=[]\\nwith torch.no_grad():\\n ids=torch.tensor([0,17,521,4001,29999]);torch.testing.assert_close(item(ids),torch.from_numpy(np.array(cached[ids.numpy()])),rtol=1e-5,atol=1e-5)\\n for label,quantile in [('median',.5),('p95_history',.95)]:\\n  wanted=np.quantile(sizes,quantile);r=int(np.argmin(abs(sizes-wanted)));u=order[r];a=int(data['offset'][u]);b=int(data['offset'][u+1]);hist=torch.from_numpy(data['i'][a:b].astype(np.int64))[None,:];weights=[torch.from_numpy(w[a:b])[None,:] for w in all_weights]\\n  for _ in range(10):v=user([hist]*len(weights),weights)\\n  times=[]\\n  for _ in range(100):\\n   start=time.perf_counter();v=user([hist]*len(weights),weights);times.append((time.perf_counter()-start)*1000)\\n  assert torch.isfinite(v).all();reports.append({'case':label,'history_items':b-a,'median_ms':float(np.median(times)),'p95_ms':float(np.quantile(times,.95))})\\nresult={'device':'CPU','threads':1,'dimension':cached.shape[1],'cached_item_vectors_match_exported_network':True,'user_forward_only':reports};json.dump(result,open(P/'latency.json','w'),indent=2);print(json.dumps(result,indent=2))\\nPY\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T17:19:02.134Z
Script completed
Wall time 1.2 seconds
Output:

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      {
        "type": "unknown",
        "cmd": "cat > /app/solution/benchmark.py <<'PY'\n\"\"\"Check exported towers against cached vectors and time CPU user inference.\"\"\"\nimport time,json\nimport numpy as np,pandas as pd,torch\nfrom submit import load_retrieval_towers,history_weights\nfrom train import P,D\n\ntorch.set_num_threads(1);user,item,cfg=load_retrieval_towers();data=dict(np.load(P/'data_test.npz'));uids=pd.read_parquet(D/'target_users.parquet').uid.to_numpy();order=pd.Index(data['users']).get_indexer(uids);sizes=np.where(order>=0,np.diff(data['offset'])[np.maximum(order,0)],0);all_weights=[history_weights(data,c) for c in cfg['components']];cached=np.load(P/'item_vectors.npy',mmap_mode='r');reports=[]\nwith torch.no_grad():\n ids=torch.tensor([0,17,521,4001,29999]);torch.testing.assert_close(item(ids),torch.from_numpy(np.array(cached[ids.numpy()])),rtol=1e-5,atol=1e-5)\n for label,quantile in [('median',.5),('p95_history',.95)]:\n  wanted=np.quantile(sizes,quantile);r=int(np.argmin(abs(sizes-wanted)));u=order[r];a=int(data['offset'][u]);b=int(data['offset'][u+1]);hist=torch.from_numpy(data['i'][a:b].astype(np.int64))[None,:];weights=[torch.from_numpy(w[a:b])[None,:] for w in all_weights]\n  for _ in range(10):v=user([hist]*len(weights),weights)\n  times=[]\n  for _ in range(100):\n   start=time.perf_counter();v=user([hist]*len(weights),weights);times.append((time.perf_counter()-start)*1000)\n  assert torch.isfinite(v).all();reports.append({'case':label,'history_items':b-a,'median_ms':float(np.median(times)),'p95_ms':float(np.quantile(times,.95))})\nresult={'device':'CPU','threads':1,'dimension':cached.shape[1],'cached_item_vectors_match_exported_network':True,'user_forward_only':reports};json.dump(result,open(P/'latency.json','w'),indent=2);print(json.dumps(result,indent=2))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3670
    },
    "formatted_output": ""
  }
}

## Step 148 · agent · 2026-09-10T17:19:08.419Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:19:10.096Z
{
  "code": "text(await tools.exec_command({cmd:\"sed -n '1,145p' /app/solution/train.py && cat /app/solution/combined.log\",\"max_output_tokens\":6000}));\n"
}
### Tool output · 2026-09-10T17:19:10.304Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"57a484","wall_time_seconds":0.00000369,"exit_code":0,"original_token_count":1994,"output":"import os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom model import TwoTower\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\n\ndef pools(data,seed=42,poolsize=2048):\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\n return ip,yp,q/q.sum()\n\n@torch.no_grad()\ndef encode_users(model,data,history_power=1.,recent=0):\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\n w=data['w'].copy()**history_power\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\n for st in range(0,n,256):\n  end=min(st+256,n);a=int(data['offset'][st]);b=int(data['offset'][end]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64));ww=torch.from_numpy(w[a:b]);offsets=torch.from_numpy((data['offset'][st:end]-a).astype(np.int64))\n  out[st:end]=torch.nn.functional.embedding_bag(ids,model.user.history.weight,offsets,mode='sum',per_sample_weights=ww)\n  mass[st:end]=torch.from_numpy(np.add.reduceat(w[a:b],data['offset'][st:end]-a))\n return model.user.project(out/mass[:,None].clamp_min(1e-8),mass[:,None]>0)\n\n@torch.no_grad()\ndef align_users(model,vectors,order):\n out=vectors[np.maximum(order,0)].clone();missing=order<0\n if missing.any():out[missing]=model.user(torch.full((int(missing.sum()),1),model.user.history.padding_idx,dtype=torch.int64),torch.zeros((int(missing.sum()),1)))\n return out\n\ndef truth(items,users,eligible):\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\n for fam in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\n for u in np.unique(keys//len(items)):\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\n return keys,gains,idcg\n\ndef metric(top,t):\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\n\n@torch.no_grad()\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_)\n assert np.array_equal(items,data['items'])\n order=pd.Index(data['users']).get_indexer(users);uv=align_users(model,encode_users(model,data,history_power,recent),order);iv=model.item(torch.arange(len(items)));top=[];scores=[]\n for st in range(0,len(users),256):\n  s=uv[st:st+256]@iv.T\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\n  if return_scores:scores.append(s.numpy())\n top=np.concatenate(top)\n if out:\n  temporary=Path(str(out)+'.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temporary,index=False);temporary.replace(out)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n return score,(np.concatenate(scores) if return_scores else top)\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');ap.add_argument('--temporal',action='store_true');ap.add_argument('--targets',type=int,default=4);ap.add_argument('--threads',type=int,default=6);ap.add_argument('--mask-history',action='store_true');ap.add_argument('--pool-prefix',default='temporal');ap.add_argument('--no-submit',action='store_true');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);args=ap.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\n print(vars(args),flush=True)\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,args.targets))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*args.targets)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\n  if args.mask_history:\n   known=torch.from_numpy(np.sort(ip[u].astype(np.int64),axis=1));pos=torch.searchsorted(known,ids[None,:].expand(args.batch,-1).contiguous()).clamp_max(known.shape[1]-1);mask=known.gather(1,pos)==ids[None,:];positive=(ids[None,:,None]==torch.from_numpy(targets.astype(np.int64))[:,None,:]).any(-1);scores=scores.masked_fill(mask&~positive,-1e9)\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*args.targets+torch.arange(args.targets)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\n   if args.stage=='validation':\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet',history_power=args.history_power,recent=args.recent);print('VALIDATION',step,score,flush=True)\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\n')\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\n   elif not args.no_submit:rank(model,data,'test',P/'submission.parquet',history_power=args.history_power,recent=args.recent);print('saved submission',step,flush=True)\n print('done',flush=True)\nif __name__=='__main__':main()\n"}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fb42693c-77b2-49cb-90ca-8b7c735b4a5d",
    "process_id": "46210",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,145p' /app/solution/train.py && cat /app/solution/combined.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1,145p' /app/solution/train.py",
        "name": "train.py",
        "path": "/app/solution/train.py"
      },
      {
        "type": "read",
        "cmd": "cat /app/solution/combined.log",
        "name": "combined.log",
        "path": "/app/solution/combined.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "import os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom model import TwoTower\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\n\ndef pools(data,seed=42,poolsize=2048):\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\n return ip,yp,q/q.sum()\n\n@torch.no_grad()\ndef encode_users(model,data,history_power=1.,recent=0):\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\n w=data['w'].copy()**history_power\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\n for st in range(0,n,256):\n  end=min(st+256,n);a=int(data['offset'][st]);b=int(data['offset'][end]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64));ww=torch.from_numpy(w[a:b]);offsets=torch.from_numpy((data['offset'][st:end]-a).astype(np.int64))\n  out[st:end]=torch.nn.functional.embedding_bag(ids,model.user.history.weight,offsets,mode='sum',per_sample_weights=ww)\n  mass[st:end]=torch.from_numpy(np.add.reduceat(w[a:b],data['offset'][st:end]-a))\n return model.user.project(out/mass[:,None].clamp_min(1e-8),mass[:,None]>0)\n\n@torch.no_grad()\ndef align_users(model,vectors,order):\n out=vectors[np.maximum(order,0)].clone();missing=order<0\n if missing.any():out[missing]=model.user(torch.full((int(missing.sum()),1),model.user.history.padding_idx,dtype=torch.int64),torch.zeros((int(missing.sum()),1)))\n return out\n\ndef truth(items,users,eligible):\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\n for fam in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\n for u in np.unique(keys//len(items)):\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\n return keys,gains,idcg\n\ndef metric(top,t):\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\n\n@torch.no_grad()\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_)\n assert np.array_equal(items,data['items'])\n order=pd.Index(data['users']).get_indexer(users);uv=align_users(model,encode_users(model,data,history_power,recent),order);iv=model.item(torch.arange(len(items)));top=[];scores=[]\n for st in range(0,len(users),256):\n  s=uv[st:st+256]@iv.T\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\n  if return_scores:scores.append(s.numpy())\n top=np.concatenate(top)\n if out:\n  temporary=Path(str(out)+'.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temporary,index=False);temporary.replace(out)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n return score,(np.concatenate(scores) if return_scores else top)\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');ap.add_argument('--temporal',action='store_true');ap.add_argument('--targets',type=int,default=4);ap.add_argument('--threads',type=int,default=6);ap.add_argument('--mask-history',action='store_true');ap.add_argument('--pool-prefix',default='temporal');ap.add_argument('--no-submit',action='store_true');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);args=ap.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\n print(vars(args),flush=True)\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,args.targets))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*args.targets)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\n  if args.mask_history:\n   known=torch.from_numpy(np.sort(ip[u].astype(np.int64),axis=1));pos=torch.searchsorted(known,ids[None,:].expand(args.batch,-1).contiguous()).clamp_max(known.shape[1]-1);mask=known.gather(1,pos)==ids[None,:];positive=(ids[None,:,None]==torch.from_numpy(targets.astype(np.int64))[:,None,:]).any(-1);scores=scores.masked_fill(mask&~positive,-1e9)\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*args.targets+torch.arange(args.targets)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\n   if args.stage=='validation':\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet',history_power=args.history_power,recent=args.recent);print('VALIDATION',step,score,flush=True)\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\n')\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\n   elif not args.no_submit:rank(model,data,'test',P/'submission.parquet',history_power=args.history_power,recent=args.recent);print('saved submission',step,flush=True)\n print('done',flush=True)\nif __name__=='__main__':main()\n",
    "stderr": "",
    "aggregated_output": "import os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom model import TwoTower\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\n\ndef pools(data,seed=42,poolsize=2048):\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\n return ip,yp,q/q.sum()\n\n@torch.no_grad()\ndef encode_users(model,data,history_power=1.,recent=0):\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\n w=data['w'].copy()**history_power\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\n for st in range(0,n,256):\n  end=min(st+256,n);a=int(data['offset'][st]);b=int(data['offset'][end]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64));ww=torch.from_numpy(w[a:b]);offsets=torch.from_numpy((data['offset'][st:end]-a).astype(np.int64))\n  out[st:end]=torch.nn.functional.embedding_bag(ids,model.user.history.weight,offsets,mode='sum',per_sample_weights=ww)\n  mass[st:end]=torch.from_numpy(np.add.reduceat(w[a:b],data['offset'][st:end]-a))\n return model.user.project(out/mass[:,None].clamp_min(1e-8),mass[:,None]>0)\n\n@torch.no_grad()\ndef align_users(model,vectors,order):\n out=vectors[np.maximum(order,0)].clone();missing=order<0\n if missing.any():out[missing]=model.user(torch.full((int(missing.sum()),1),model.user.history.padding_idx,dtype=torch.int64),torch.zeros((int(missing.sum()),1)))\n return out\n\ndef truth(items,users,eligible):\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\n for fam in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\n for u in np.unique(keys//len(items)):\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\n return keys,gains,idcg\n\ndef metric(top,t):\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\n\n@torch.no_grad()\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_)\n assert np.array_equal(items,data['items'])\n order=pd.Index(data['users']).get_indexer(users);uv=align_users(model,encode_users(model,data,history_power,recent),order);iv=model.item(torch.arange(len(items)));top=[];scores=[]\n for st in range(0,len(users),256):\n  s=uv[st:st+256]@iv.T\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\n  if return_scores:scores.append(s.numpy())\n top=np.concatenate(top)\n if out:\n  temporary=Path(str(out)+'.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temporary,index=False);temporary.replace(out)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n return score,(np.concatenate(scores) if return_scores else top)\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');ap.add_argument('--temporal',action='store_true');ap.add_argument('--targets',type=int,default=4);ap.add_argument('--threads',type=int,default=6);ap.add_argument('--mask-history',action='store_true');ap.add_argument('--pool-prefix',default='temporal');ap.add_argument('--no-submit',action='store_true');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);args=ap.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\n print(vars(args),flush=True)\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,args.targets))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*args.targets)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\n  if args.mask_history:\n   known=torch.from_numpy(np.sort(ip[u].astype(np.int64),axis=1));pos=torch.searchsorted(known,ids[None,:].expand(args.batch,-1).contiguous()).clamp_max(known.shape[1]-1);mask=known.gather(1,pos)==ids[None,:];positive=(ids[None,:,None]==torch.from_numpy(targets.astype(np.int64))[:,None,:]).any(-1);scores=scores.masked_fill(mask&~positive,-1e9)\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*args.targets+torch.arange(args.targets)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\n   if args.stage=='validation':\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet',history_power=args.history_power,recent=args.recent);print('VALIDATION',step,score,flush=True)\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\n')\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\n   elif not args.no_submit:rank(model,data,'test',P/'submission.parquet',history_power=args.history_power,recent=args.recent);print('saved submission',step,flush=True)\n print('done',flush=True)\nif __name__=='__main__':main()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3690
    },
    "formatted_output": "import os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json,sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd,torch\nfrom model import TwoTower\nP=Path('/app/solution');D=Path('/task/data');torch.set_num_threads(6)\n\ndef pools(data,seed=42,poolsize=2048):\n rng=np.random.default_rng(seed);n=len(data['users']);ip=np.empty((n,poolsize),np.int32);yp=np.empty_like(ip);q=np.zeros(len(data['items']),np.float64)\n for u,(a,b) in enumerate(zip(data['offset'][:-1],data['offset'][1:])):\n  ids=data['i'][a:b];w=data['w'][a:b].copy()+1e-5;y=data['y'][a:b].copy()+1e-5;w/=w.sum();y/=y.sum();ip[u]=rng.choice(ids,poolsize,p=w);yp[u]=rng.choice(ids,poolsize,p=y);np.add.at(q,ids,y)\n return ip,yp,q/q.sum()\n\n@torch.no_grad()\ndef encode_users(model,data,history_power=1.,recent=0):\n n=len(data['users']);dim=model.user.history.embedding_dim;out=torch.zeros((n,dim));mass=torch.zeros(n)\n w=data['w'].copy()**history_power\n if recent:w*=np.exp((data['last'].astype(float)-int(data['cutoff']))/(recent*86400))\n for st in range(0,n,256):\n  end=min(st+256,n);a=int(data['offset'][st]);b=int(data['offset'][end]);ids=torch.from_numpy(data['i'][a:b].astype(np.int64));ww=torch.from_numpy(w[a:b]);offsets=torch.from_numpy((data['offset'][st:end]-a).astype(np.int64))\n  out[st:end]=torch.nn.functional.embedding_bag(ids,model.user.history.weight,offsets,mode='sum',per_sample_weights=ww)\n  mass[st:end]=torch.from_numpy(np.add.reduceat(w[a:b],data['offset'][st:end]-a))\n return model.user.project(out/mass[:,None].clamp_min(1e-8),mass[:,None]>0)\n\n@torch.no_grad()\ndef align_users(model,vectors,order):\n out=vectors[np.maximum(order,0)].clone();missing=order<0\n if missing.any():out[missing]=model.user(torch.full((int(missing.sum()),1),model.user.history.padding_idx,dtype=torch.int64),torch.zeros((int(missing.sum()),1)))\n return out\n\ndef truth(items,users,eligible):\n um=pd.Series(np.arange(len(users)),index=users);im=pd.Series(np.arange(len(items)),index=items);parts=[]\n for fam in ['listens','likes']:\n  d=pd.read_parquet(D/f'val_{fam}.parquet');d=d[d.is_organic==1]\n  if fam=='listens':d=d[d.played_ratio_pct>=80]\n  d=d[['uid','item_id']].drop_duplicates();d['u']=d.uid.map(um);d['i']=d.item_id.map(im);d=d.dropna();u=d.u.to_numpy(int);i=d.i.to_numpy(int);ok=eligible[u,i];parts.append(pd.DataFrame({'key':u[ok]*len(items)+i[ok],'gain':1 if fam=='listens' else 7}))\n g=pd.concat(parts).groupby('key').gain.max();keys=g.index.to_numpy();gains=g.to_numpy();idcg=np.zeros(len(users));disc=1/np.log2(np.arange(2,12))\n for u in np.unique(keys//len(items)):\n  v=np.sort(gains[keys//len(items)==u])[::-1][:10];idcg[u]=(v*disc[:len(v)]).sum()\n return keys,gains,idcg\n\ndef metric(top,t):\n keys,gains,idcg=t;query=(np.arange(len(top))[:,None]*30000+top[:,:10]);pos=np.searchsorted(keys,query);ok=(pos<len(keys));pos=np.minimum(pos,len(keys)-1);ok&=keys[pos]==query;dcg=(gains[pos]*ok/np.log2(np.arange(2,12))).sum(1);return (dcg[idcg>0]/idcg[idcg>0]).mean()\n\n@torch.no_grad()\ndef rank(model,data,stage,out=None,pop=0.,history_power=1.,recent=0,return_scores=False):\n model.eval();a=np.load(D/f'{stage}_eligible_candidates.npz');users=a['uids'];items=a['item_ids'];eligible=np.unpackbits(a['packed_eligible'],axis=1,count=len(items),bitorder='little').view(np.bool_)\n assert np.array_equal(items,data['items'])\n order=pd.Index(data['users']).get_indexer(users);uv=align_users(model,encode_users(model,data,history_power,recent),order);iv=model.item(torch.arange(len(items)));top=[];scores=[]\n for st in range(0,len(users),256):\n  s=uv[st:st+256]@iv.T\n  if pop:s+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()[None,:]\n  s.masked_fill_(~torch.from_numpy(eligible[st:st+256]),-1e9);top.append(s.topk(100,dim=1).indices.numpy())\n  if return_scores:scores.append(s.numpy())\n top=np.concatenate(top)\n if out:\n  temporary=Path(str(out)+'.tmp.parquet');pd.DataFrame({'uid':np.repeat(users,100),'item_id':items[top.reshape(-1)],'rank':np.tile(np.arange(1,101),len(users))}).to_parquet(temporary,index=False);temporary.replace(out)\n score=metric(top,truth(items,users,eligible)) if stage=='validation' else None\n return score,(np.concatenate(scores) if return_scores else top)\n\ndef main():\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--name',default='base');ap.add_argument('--steps',type=int,default=3000);ap.add_argument('--dim',type=int,default=128);ap.add_argument('--batch',type=int,default=256);ap.add_argument('--history',type=int,default=96);ap.add_argument('--lr',type=float,default=.002);ap.add_argument('--eval-every',type=int,default=500);ap.add_argument('--seed',type=int,default=42);ap.add_argument('--resume');ap.add_argument('--temporal',action='store_true');ap.add_argument('--targets',type=int,default=4);ap.add_argument('--threads',type=int,default=6);ap.add_argument('--mask-history',action='store_true');ap.add_argument('--pool-prefix',default='temporal');ap.add_argument('--no-submit',action='store_true');ap.add_argument('--recent',type=float,default=0);ap.add_argument('--history-power',type=float,default=1.);args=ap.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed)\n data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\n if args.resume:model.load_state_dict(torch.load(args.resume,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=args.lr,weight_decay=.005);qmix=.5*q+.5/nitems;logq=torch.from_numpy(np.log(qmix)).float();t0=time.time();losses=[];best=-1\n print(vars(args),flush=True)\n for step in range(1,args.steps+1):\n  model.train();u=rng.integers(len(ip),size=args.batch);hi=ip[u[:,None],rng.integers(ip.shape[1],size=(args.batch,args.history))];targets=yp[u[:,None],rng.integers(yp.shape[1],size=(args.batch,args.targets))];weight=(hi[:,:,None]!=targets[:,None,:]).all(2).astype(np.float32);weight*=rng.random(weight.shape)>.1\n  candidates=np.r_[targets.reshape(-1),rng.integers(nitems,size=args.batch*args.targets)];ids=torch.from_numpy(candidates.astype(np.int64));scores=model(torch.from_numpy(hi.astype(np.int64)),torch.from_numpy(weight),ids)-logq[ids][None,:]\n  if args.mask_history:\n   known=torch.from_numpy(np.sort(ip[u].astype(np.int64),axis=1));pos=torch.searchsorted(known,ids[None,:].expand(args.batch,-1).contiguous()).clamp_max(known.shape[1]-1);mask=known.gather(1,pos)==ids[None,:];positive=(ids[None,:,None]==torch.from_numpy(targets.astype(np.int64))[:,None,:]).any(-1);scores=scores.masked_fill(mask&~positive,-1e9)\n  logp=torch.log_softmax(scores,1);positions=torch.arange(args.batch)[:,None]*args.targets+torch.arange(args.targets)[None,:];loss=-logp.gather(1,positions).mean();opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step();losses.append(loss.item())\n  if step%100==0:print('step',step,'loss',np.mean(losses[-100:]),'seconds',round(time.time()-t0,1),flush=True)\n  if step%args.eval_every==0 or step==args.steps:\n   ck={'model':model.state_dict(),'args':vars(args),'step':step,'pop':q};torch.save(ck,P/f'{args.name}_last.pt')\n   if args.stage=='validation':\n    score,_=rank(model,data,'validation',P/f'{args.name}_validation.parquet',history_power=args.history_power,recent=args.recent);print('VALIDATION',step,score,flush=True)\n    with open(P/f'{args.name}_metrics.jsonl','a') as f:f.write(json.dumps({'step':step,'score':score})+'\\n')\n    if score>best:best=score;torch.save(ck,P/f'{args.name}_best.pt');(P/'validation.parquet').write_bytes((P/f'{args.name}_validation.parquet').read_bytes())\n   elif not args.no_submit:rank(model,data,'test',P/'submission.parquet',history_power=args.history_power,recent=args.recent);print('saved submission',step,flush=True)\n print('done',flush=True)\nif __name__=='__main__':main()\n"
  }
}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
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  "usage": {
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    "cached_input_tokens": 126592,
    "cache_write_input_tokens": 0,
    "output_tokens": 94,
    "reasoning_output_tokens": 42,
    "total_tokens": 128168
  },
  "turn_token_usage": {
    "input_tokens": 5639366,
    "cached_input_tokens": 5489536,
    "cache_write_input_tokens": 0,
    "output_tokens": 80717,
    "reasoning_output_tokens": 50947,
    "total_tokens": 5720083
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  "thread_token_usage": {
    "input_tokens": 5639366,
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    "cache_write_input_tokens": 0,
    "output_tokens": 80717,
    "reasoning_output_tokens": 50947,
    "total_tokens": 5720083
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}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c65e08e4-8819-404f-8230-cfb9d65144b1",
    "process_id": "25514",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport json\np='/app/solution/';c=json.load(open(p+'fine_priors_config.json'));w=json.load(open(p+'fine_weights_config.json'))['components'];wm={x['checkpoint']:x['weight'] for x in w}\nfor x in c['components']:x['weight']=wm[x['checkpoint']]\njson.dump(c,open(p+'combined_config.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/combined_config.json --output combined_validation.parquet > /app/solution/combined.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\np='/app/solution/';c=json.load(open(p+'fine_priors_config.json'));w=json.load(open(p+'fine_weights_config.json'))['components'];wm={x['checkpoint']:x['weight'] for x in w}\nfor x in c['components']:x['weight']=wm[x['checkpoint']]\njson.dump(c,open(p+'combined_config.json','w'),indent=2)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/combined_config.json --output combined_validation.parquet > /app/solution/combined.log 2>&1"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 679410596
    },
    "formatted_output": ""
  }
}

## Step 149 · agent · 2026-09-10T17:19:34.575Z

### Thinking unavailable — no text recorded


## Step 150 · agent · 2026-09-10T17:19:46.052Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:19:55.512Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train.py');s=p.read_text().replace('from model import TwoTower','from model import TwoTower\\\\nfrom scoring import rank_many').replace('recent=0,return_scores=False):','recent=0):');start=s.index(\\\" order=pd.Index(data['users']).get_indexer(users);uv=\\\",s.index('def rank'));end=s.index(' if out:',start);s=s[:start]+''' order=pd.Index(data['users']).get_indexer(users)\\n uv=align_users(model,encode_users(model,data,history_power,recent),order)\\n iv=torch.cat([model.item(torch.arange(st,min(st+2048,len(items)))) for st in range(0,len(items),2048)])\\n if pop:iv[:,-1]+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()\\n top=rank_many(uv,iv,eligible,k=100)[0]\\n''' +s[end:];s=s.replace('return score,(np.concatenate(scores) if return_scores else top)','return score,top');old=\\\" data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\\\";new=\\\"\\\"\\\" data=dict(np.load(P/f'data_{args.stage}.npz'))\\n if args.temporal:\\n  with np.load(P/f'{args.pool_prefix}_{args.stage}.npz') as pool:ip,yp,q=pool['ip'],pool['yp'],pool['q']\\n else:ip,yp,q=pools(data,args.seed)\\n data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\\\"\\\"\\\";assert old in s;s=s.replace(old,new);p.write_text(s)\\nPY\\ncat /app/solution/combined.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:19:55.651Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"565cd2","wall_time_seconds":0.00000457,"exit_code":0,"original_token_count":37,"output":"{\"output\": \"/app/solution/combined_validation.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": 0.052233899744463956}\n"}
{"current_time":"2026-09-10 17:19:55 UTC"}

### Harness context
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### Harness context
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    "command": [
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      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train.py');s=p.read_text().replace('from model import TwoTower','from model import TwoTower\\nfrom scoring import rank_many').replace('recent=0,return_scores=False):','recent=0):');start=s.index(\" order=pd.Index(data['users']).get_indexer(users);uv=\",s.index('def rank'));end=s.index(' if out:',start);s=s[:start]+''' order=pd.Index(data['users']).get_indexer(users)\n uv=align_users(model,encode_users(model,data,history_power,recent),order)\n iv=torch.cat([model.item(torch.arange(st,min(st+2048,len(items)))) for st in range(0,len(items),2048)])\n if pop:iv[:,-1]+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()\n top=rank_many(uv,iv,eligible,k=100)[0]\n''' +s[end:];s=s.replace('return score,(np.concatenate(scores) if return_scores else top)','return score,top');old=\" data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\";new=\"\"\" data=dict(np.load(P/f'data_{args.stage}.npz'))\n if args.temporal:\n  with np.load(P/f'{args.pool_prefix}_{args.stage}.npz') as pool:ip,yp,q=pool['ip'],pool['yp'],pool['q']\n else:ip,yp,q=pools(data,args.seed)\n data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\"\"\";assert old in s;s=s.replace(old,new);p.write_text(s)\nPY\ncat /app/solution/combined.log"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train.py');s=p.read_text().replace('from model import TwoTower','from model import TwoTower\\nfrom scoring import rank_many').replace('recent=0,return_scores=False):','recent=0):');start=s.index(\" order=pd.Index(data['users']).get_indexer(users);uv=\",s.index('def rank'));end=s.index(' if out:',start);s=s[:start]+''' order=pd.Index(data['users']).get_indexer(users)\n uv=align_users(model,encode_users(model,data,history_power,recent),order)\n iv=torch.cat([model.item(torch.arange(st,min(st+2048,len(items)))) for st in range(0,len(items),2048)])\n if pop:iv[:,-1]+=pop*torch.from_numpy(np.log(data['pop']+1e-9)).float()\n top=rank_many(uv,iv,eligible,k=100)[0]\n''' +s[end:];s=s.replace('return score,(np.concatenate(scores) if return_scores else top)','return score,top');old=\" data=dict(np.load(P/f'data_{args.stage}.npz'));ip,yp,q=pools(data,args.seed) if not args.temporal else tuple(dict(np.load(P/f'{args.pool_prefix}_{args.stage}.npz'))[k] for k in ['ip','yp','q']);data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\";new=\"\"\" data=dict(np.load(P/f'data_{args.stage}.npz'))\n if args.temporal:\n  with np.load(P/f'{args.pool_prefix}_{args.stage}.npz') as pool:ip,yp,q=pool['ip'],pool['yp'],pool['q']\n else:ip,yp,q=pools(data,args.seed)\n data['pop']=q;nitems=len(q);model=TwoTower(nitems,args.dim)\"\"\";assert old in s;s=s.replace(old,new);p.write_text(s)\nPY\ncat /app/solution/combined.log"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\"output\": \"/app/solution/combined_validation.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": 0.052233899744463956}\n",
    "stderr": "",
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  }
}

## Step 151 · agent · 2026-09-10T17:20:17.577Z

### Thinking unavailable — no text recorded


## Step 152 · agent · 2026-09-10T17:20:39.063Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:20:45.820Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json,shutil\\nfrom pathlib import Path\\np=Path('/app/solution');c=json.load(open(p/'fine_priors_config.json'));json.dump(c,open(p/'validation_config.json','w'),indent=2);shutil.copyfile(p/'fine_priors_validation.parquet',p/'validation.parquet');mapping={'temporal_early.pt':'final_temporal_last.pt','wide_temporal_selected.pt':'final_wide_temporal_last.pt','temporal2_best.pt':'final_temporal2_last.pt'};c['stage']='test'\\nfor x in c['components']:x['checkpoint']=mapping[x['checkpoint']]\\njson.dump(c,open(p/'final_config.json','w'),indent=2)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --export > /app/solution/export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(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_evaluation.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train.py /app/solution/prepare.py /app/solution/prepare_temporal.py /app/solution/submit.py /app/solution/scoring.py /app/solution/trends.py /app/solution/benchmark.py && sh -n /app/solution/refit.sh\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T17:20:48.328Z
Script completed
Wall time 2.5 seconds
Output:

{"chunk_id":"8c9296","wall_time_seconds":1.002115207,"session_id":79274,"original_token_count":0,"output":""}
{"chunk_id":"8cd081","wall_time_seconds":1.001270463,"session_id":35748,"original_token_count":0,"output":""}
{"chunk_id":"3a6e61","wall_time_seconds":0.00000419,"exit_code":0,"original_token_count":0,"output":""}

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### Harness context
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### Harness context
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### Harness context
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}

## Step 153 · agent · 2026-09-10T17:21:11.599Z

### Thinking unavailable — no text recorded


## Step 154 · agent · 2026-09-10T17:21:34.792Z

### Thinking unavailable — no text recorded


## Step 155 · agent · 2026-09-10T17:21:56.019Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:22:18.032Z
{
  "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\nThe final ranker achieves **0.052649 graded NDCG@10** on the public validation window, scored over 4,263 users by `/task/evaluate_public.py`. The final model was refitted on **train + validation** history. `submission.parquet` contains 100 recommendations for each of the 8,456 target users.\\n\\nThe two serving networks are `RetrievalUserTower` and `RetrievalItemTower` in `submit.py`; their trainable neural components are defined in `model.py`. Three independently trained components have latent widths 128, 512, and 128. Each user component pools weighted history-item embeddings with PyTorch `embedding_bag`, then applies layer normalization, a residual GELU MLP, and normalization with a learned scale. Each item component uses its own item embedding and residual GELU MLP. User IDs only group event histories; there is no learned user-ID lookup. Empty catalog histories produce a zero latent vector and the constant bias coordinate.\\n\\nEach component adds one coordinate for item bias. Validation-selected popularity and recent-adoption priors are incorporated into that item coordinate **before** offline embedding export. Weighted concatenation, with weights 0.25 / 0.50 / 0.25, produces **771-dimensional** user and item vectors. The final relevance score is exactly their dot product. `item_vectors.npy`, `item_ids.npy`, and `retrieval_towers.pt` are the serving artifacts. `load_retrieval_towers()` loads both exported networks without training checkpoints. Item vectors are independently precomputable; the small benchmark uses batched exact inner-product retrieval, while production can index the same vectors for maximum-inner-product search.\\n\\nTraining uses CPU AdamW and importance-corrected sampled softmax with 2,048 candidate columns per minibatch. History denoising is followed by temporal fine-tuning on 12 historical weekly windows: inputs precede the cutoff and targets are first-time subsequent organic engagement. Organic likes have gain 7; organic listens completed to at least 80% have gain 1. The three components use 1,500 / 1,500 / 1,800 denoising steps and 500 / 900 / 200 temporal steps. Historical likes strengthen history features, and cutoff-active dislikes damp corresponding history weights. Aggregation uses event tables, ragged histories, and bounded sampled sequences; **no global user-item interaction matrix is constructed or decomposed**. These operations can be sharded by user.\\n\\nValidation features and item priors use training history through timestamp 24786800. Final features use train plus validation through 25393400 and the test preference snapshot. Rankings use the corresponding platform eligibility masks. All selected settings are recorded in `validation_config.json` and `final_config.json`. The reported score measures observational offline discovery quality.\\n\\nGenerate the final submission and exported serving artifacts from the retained refitted checkpoints:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/final_config.json --export\\n/opt/task-venv/bin/python /app/solution/validate_submission.py\\n```\\n\\nRefit the complete selected pipeline from the supplied events:\\n\\n```sh\\nsh /app/solution/refit.sh\\n```\\n\\nReproduce the public ranking and score from the retained validation checkpoints:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/validation_config.json --output validation.parquet\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n```\\n\\n`public_evaluation.json` contains the official score. Export checks compare direct user-network outputs with batched history encoding, including empty histories. `benchmark.py` checks cached item vectors against the exported item network and records single-thread CPU user-forward timings in `latency.json`; these timings exclude feature aggregation and index retrieval.\\nMD\\ncat /app/solution/export.log && cat /app/solution/public_evaluation.json\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:22:18.196Z
Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"41780a","wall_time_seconds":0.00000489,"exit_code":0,"original_token_count":63,"output":"{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": null}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.052649,\n    \"quality_users\": 4263\n  }\n}\n"}
{"current_time":"2026-09-10 17:22:18 UTC"}

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      "/bin/sh",
      "-lc",
      "cat > /app/solution/README.md <<'MD'\nThe final ranker achieves **0.052649 graded NDCG@10** on the public validation window, scored over 4,263 users by `/task/evaluate_public.py`. The final model was refitted on **train + validation** history. `submission.parquet` contains 100 recommendations for each of the 8,456 target users.\n\nThe two serving networks are `RetrievalUserTower` and `RetrievalItemTower` in `submit.py`; their trainable neural components are defined in `model.py`. Three independently trained components have latent widths 128, 512, and 128. Each user component pools weighted history-item embeddings with PyTorch `embedding_bag`, then applies layer normalization, a residual GELU MLP, and normalization with a learned scale. Each item component uses its own item embedding and residual GELU MLP. User IDs only group event histories; there is no learned user-ID lookup. Empty catalog histories produce a zero latent vector and the constant bias coordinate.\n\nEach component adds one coordinate for item bias. Validation-selected popularity and recent-adoption priors are incorporated into that item coordinate **before** offline embedding export. Weighted concatenation, with weights 0.25 / 0.50 / 0.25, produces **771-dimensional** user and item vectors. The final relevance score is exactly their dot product. `item_vectors.npy`, `item_ids.npy`, and `retrieval_towers.pt` are the serving artifacts. `load_retrieval_towers()` loads both exported networks without training checkpoints. Item vectors are independently precomputable; the small benchmark uses batched exact inner-product retrieval, while production can index the same vectors for maximum-inner-product search.\n\nTraining uses CPU AdamW and importance-corrected sampled softmax with 2,048 candidate columns per minibatch. History denoising is followed by temporal fine-tuning on 12 historical weekly windows: inputs precede the cutoff and targets are first-time subsequent organic engagement. Organic likes have gain 7; organic listens completed to at least 80% have gain 1. The three components use 1,500 / 1,500 / 1,800 denoising steps and 500 / 900 / 200 temporal steps. Historical likes strengthen history features, and cutoff-active dislikes damp corresponding history weights. Aggregation uses event tables, ragged histories, and bounded sampled sequences; **no global user-item interaction matrix is constructed or decomposed**. These operations can be sharded by user.\n\nValidation features and item priors use training history through timestamp 24786800. Final features use train plus validation through 25393400 and the test preference snapshot. Rankings use the corresponding platform eligibility masks. All selected settings are recorded in `validation_config.json` and `final_config.json`. The reported score measures observational offline discovery quality.\n\nGenerate the final submission and exported serving artifacts from the retained refitted checkpoints:\n\n```sh\n/opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/final_config.json --export\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\nRefit the complete selected pipeline from the supplied events:\n\n```sh\nsh /app/solution/refit.sh\n```\n\nReproduce the public ranking and score from the retained validation checkpoints:\n\n```sh\n/opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/validation_config.json --output validation.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n`public_evaluation.json` contains the official score. Export checks compare direct user-network outputs with batched history encoding, including empty histories. `benchmark.py` checks cached item vectors against the exported item network and records single-thread CPU user-forward timings in `latency.json`; these timings exclude feature aggregation and index retrieval.\nMD\ncat /app/solution/export.log && cat /app/solution/public_evaluation.json"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/README.md <<'MD'\nThe final ranker achieves **0.052649 graded NDCG@10** on the public validation window, scored over 4,263 users by `/task/evaluate_public.py`. The final model was refitted on **train + validation** history. `submission.parquet` contains 100 recommendations for each of the 8,456 target users.\n\nThe two serving networks are `RetrievalUserTower` and `RetrievalItemTower` in `submit.py`; their trainable neural components are defined in `model.py`. Three independently trained components have latent widths 128, 512, and 128. Each user component pools weighted history-item embeddings with PyTorch `embedding_bag`, then applies layer normalization, a residual GELU MLP, and normalization with a learned scale. Each item component uses its own item embedding and residual GELU MLP. User IDs only group event histories; there is no learned user-ID lookup. Empty catalog histories produce a zero latent vector and the constant bias coordinate.\n\nEach component adds one coordinate for item bias. Validation-selected popularity and recent-adoption priors are incorporated into that item coordinate **before** offline embedding export. Weighted concatenation, with weights 0.25 / 0.50 / 0.25, produces **771-dimensional** user and item vectors. The final relevance score is exactly their dot product. `item_vectors.npy`, `item_ids.npy`, and `retrieval_towers.pt` are the serving artifacts. `load_retrieval_towers()` loads both exported networks without training checkpoints. Item vectors are independently precomputable; the small benchmark uses batched exact inner-product retrieval, while production can index the same vectors for maximum-inner-product search.\n\nTraining uses CPU AdamW and importance-corrected sampled softmax with 2,048 candidate columns per minibatch. History denoising is followed by temporal fine-tuning on 12 historical weekly windows: inputs precede the cutoff and targets are first-time subsequent organic engagement. Organic likes have gain 7; organic listens completed to at least 80% have gain 1. The three components use 1,500 / 1,500 / 1,800 denoising steps and 500 / 900 / 200 temporal steps. Historical likes strengthen history features, and cutoff-active dislikes damp corresponding history weights. Aggregation uses event tables, ragged histories, and bounded sampled sequences; **no global user-item interaction matrix is constructed or decomposed**. These operations can be sharded by user.\n\nValidation features and item priors use training history through timestamp 24786800. Final features use train plus validation through 25393400 and the test preference snapshot. Rankings use the corresponding platform eligibility masks. All selected settings are recorded in `validation_config.json` and `final_config.json`. The reported score measures observational offline discovery quality.\n\nGenerate the final submission and exported serving artifacts from the retained refitted checkpoints:\n\n```sh\n/opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/final_config.json --export\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\nRefit the complete selected pipeline from the supplied events:\n\n```sh\nsh /app/solution/refit.sh\n```\n\nReproduce the public ranking and score from the retained validation checkpoints:\n\n```sh\n/opt/task-venv/bin/python /app/solution/submit.py --config /app/solution/validation_config.json --output validation.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n`public_evaluation.json` contains the official score. Export checks compare direct user-network outputs with batched history encoding, including empty histories. `benchmark.py` checks cached item vectors against the exported item network and records single-thread CPU user-forward timings in `latency.json`; these timings exclude feature aggregation and index retrieval.\nMD\ncat /app/solution/export.log && cat /app/solution/public_evaluation.json"
      }
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    "status": "completed",
    "stdout": "{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": null}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.052649,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": null}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.052649,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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    },
    "formatted_output": "{\"output\": \"/app/solution/submission.parquet\", \"users\": 8456, \"rows\": 845600, \"dimension\": 771, \"validation_ndcg\": null}\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.052649,\n    \"quality_users\": 4263\n  }\n}\n"
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## Step 156 · agent · 2026-09-10T17:22:26.035Z

### Thinking unavailable — no text recorded


The selected model’s official public score is **0.052649 NDCG@10**. The final submission and 771-dimensional serving vectors are exported, and the README and refit script are complete. I’m running the final eligibility check and verifying the cached vectors against the exported item network.


### exec · 2026-09-10T17:22:32.270Z
{
  "code": "text(await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/validate_submission.py > /app/solution/submission_validation.txt\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/benchmark.py > /app/solution/benchmark.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]));\ntext(await tools.exec_command({cmd:\"ls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/item_vectors.npy /app/solution/item_ids.npy /app/solution/retrieval_towers.pt /app/solution/README.md && cat /app/solution/final_config.json\",\"max_output_tokens\":2000}));\n"
}
### Tool output · 2026-09-10T17:22:33.489Z
Script completed
Wall time 1.2 seconds
Output:

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## Step 157 · agent · 2026-09-10T17:22:58.115Z

### Thinking unavailable — no text recorded


## Step 158 · agent · 2026-09-10T17:23:19.830Z

### Thinking unavailable — no text recorded


## Step 159 · agent · 2026-09-10T17:23:35.941Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:23:39.738Z
{
  "code": "text(await tools.exec_command({cmd:\"cat /app/solution/submission_validation.txt && cat /app/solution/benchmark.log && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/README.md');s=p.read_text().replace('importance-corrected sampled softmax','sampled softmax corrected for the negative-sampling distribution');p.write_text(s)\\nroot=Path('/app/solution');files=list(root.glob('vectors_*.npz'))+list(root.glob('cache_*.npz'));size=sum(p.stat().st_size for p in files)\\nfor p in files:p.unlink()\\nprint('Removed',len(files),'recomputable vector-cache files,',round(size/1024**2),'MiB')\\nPY\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n"
}
### Tool output · 2026-09-10T17:23:39.958Z
Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"d3216b","wall_time_seconds":0.00000714,"exit_code":0,"original_token_count":138,"output":"VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n{\n  \"device\": \"CPU\",\n  \"threads\": 1,\n  \"dimension\": 771,\n  \"cached_item_vectors_match_exported_network\": true,\n  \"user_forward_only\": [\n    {\n      \"case\": \"median\",\n      \"history_items\": 512,\n      \"median_ms\": 1.2121400000069116,\n      \"p95_ms\": 1.4115545001004648\n    },\n    {\n      \"case\": \"p95_history\",\n      \"history_items\": 1890,\n      \"median_ms\": 2.555883500008349,\n      \"p95_ms\": 2.9134915499071212\n    }\n  ]\n}\nRemoved 21 recomputable vector-cache files, 679 MiB\n"}
{"current_time":"2026-09-10 17:23:39 UTC"}

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      "/bin/sh",
      "-lc",
      "cat /app/solution/submission_validation.txt && cat /app/solution/benchmark.log && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/README.md');s=p.read_text().replace('importance-corrected sampled softmax','sampled softmax corrected for the negative-sampling distribution');p.write_text(s)\nroot=Path('/app/solution');files=list(root.glob('vectors_*.npz'))+list(root.glob('cache_*.npz'));size=sum(p.stat().st_size for p in files)\nfor p in files:p.unlink()\nprint('Removed',len(files),'recomputable vector-cache files,',round(size/1024**2),'MiB')\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /app/solution/submission_validation.txt && cat /app/solution/benchmark.log && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/README.md');s=p.read_text().replace('importance-corrected sampled softmax','sampled softmax corrected for the negative-sampling distribution');p.write_text(s)\nroot=Path('/app/solution');files=list(root.glob('vectors_*.npz'))+list(root.glob('cache_*.npz'));size=sum(p.stat().st_size for p in files)\nfor p in files:p.unlink()\nprint('Removed',len(files),'recomputable vector-cache files,',round(size/1024**2),'MiB')\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n{\n  \"device\": \"CPU\",\n  \"threads\": 1,\n  \"dimension\": 771,\n  \"cached_item_vectors_match_exported_network\": true,\n  \"user_forward_only\": [\n    {\n      \"case\": \"median\",\n      \"history_items\": 512,\n      \"median_ms\": 1.2121400000069116,\n      \"p95_ms\": 1.4115545001004648\n    },\n    {\n      \"case\": \"p95_history\",\n      \"history_items\": 1890,\n      \"median_ms\": 2.555883500008349,\n      \"p95_ms\": 2.9134915499071212\n    }\n  ]\n}\nRemoved 21 recomputable vector-cache files, 679 MiB\n",
    "stderr": "",
    "aggregated_output": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n{\n  \"device\": \"CPU\",\n  \"threads\": 1,\n  \"dimension\": 771,\n  \"cached_item_vectors_match_exported_network\": true,\n  \"user_forward_only\": [\n    {\n      \"case\": \"median\",\n      \"history_items\": 512,\n      \"median_ms\": 1.2121400000069116,\n      \"p95_ms\": 1.4115545001004648\n    },\n    {\n      \"case\": \"p95_history\",\n      \"history_items\": 1890,\n      \"median_ms\": 2.555883500008349,\n      \"p95_ms\": 2.9134915499071212\n    }\n  ]\n}\nRemoved 21 recomputable vector-cache files, 679 MiB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 7140
    },
    "formatted_output": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\n{\n  \"device\": \"CPU\",\n  \"threads\": 1,\n  \"dimension\": 771,\n  \"cached_item_vectors_match_exported_network\": true,\n  \"user_forward_only\": [\n    {\n      \"case\": \"median\",\n      \"history_items\": 512,\n      \"median_ms\": 1.2121400000069116,\n      \"p95_ms\": 1.4115545001004648\n    },\n    {\n      \"case\": \"p95_history\",\n      \"history_items\": 1890,\n      \"median_ms\": 2.555883500008349,\n      \"p95_ms\": 2.9134915499071212\n    }\n  ]\n}\nRemoved 21 recomputable vector-cache files, 679 MiB\n"
  }
}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2e75a5cac87d1ace2a1c77298ba63",
  "usage": {
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 87852,
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  },
  "thread_token_usage": {
    "input_tokens": 6309155,
    "cached_input_tokens": 6149120,
    "cache_write_input_tokens": 0,
    "output_tokens": 87852,
    "reasoning_output_tokens": 55826,
    "total_tokens": 6397007
  }
}

## Step 160 · agent · 2026-09-10T17:24:02.971Z

### Thinking unavailable — no text recorded


## Step 161 · agent · 2026-09-10T17:24:30.904Z

### Thinking unavailable — no text recorded


## Step 162 · agent · 2026-09-10T17:24:55.510Z

### Thinking unavailable — no text recorded


## Step 163 · agent · 2026-09-10T17:25:16.763Z

### Thinking unavailable — no text recorded


## Step 164 · agent · 2026-09-10T17:25:23.611Z

### Thinking unavailable — no text recorded


### exec · 2026-09-10T17:25:27.816Z
{
  "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport json\\np=Path('/app/solution');r=json.load(open(p/'latency.json'));s=p/'README.md';text=s.read_text();text+='\\\\nOn the supplied CPU with one PyTorch thread, median user-forward time was **1.21 ms** for a 512-item history and **2.56 ms** for a 1,890-item history. All 845,600 submitted rows passed the shared schema checks and test-cutoff eligibility checks.\\\\n';s.write_text(text)\\nprint((p/'submission_validation.txt').read_text().strip())\\nprint('Official public NDCG@10:',json.load(open(p/'public_evaluation.json'))['scores']['quality_ndcg@10'])\\nprint('Final artifacts and reproduction commands are ready.')\\nPY\",\"max_output_tokens\":1000}));\n"
}
### Tool output · 2026-09-10T17:25:27.966Z
Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"f75852","wall_time_seconds":0.00000527,"exit_code":0,"original_token_count":41,"output":"VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\nOfficial public NDCG@10: 0.052649\nFinal artifacts and reproduction commands are ready.\n"}

### Harness context
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1dd4dca4-d4f9-44ac-9084-dc75654fb08a",
    "process_id": "17413",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport json\np=Path('/app/solution');r=json.load(open(p/'latency.json'));s=p/'README.md';text=s.read_text();text+='\\nOn the supplied CPU with one PyTorch thread, median user-forward time was **1.21 ms** for a 512-item history and **2.56 ms** for a 1,890-item history. All 845,600 submitted rows passed the shared schema checks and test-cutoff eligibility checks.\\n';s.write_text(text)\nprint((p/'submission_validation.txt').read_text().strip())\nprint('Official public NDCG@10:',json.load(open(p/'public_evaluation.json'))['scores']['quality_ndcg@10'])\nprint('Final artifacts and reproduction commands are ready.')\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport json\np=Path('/app/solution');r=json.load(open(p/'latency.json'));s=p/'README.md';text=s.read_text();text+='\\nOn the supplied CPU with one PyTorch thread, median user-forward time was **1.21 ms** for a 512-item history and **2.56 ms** for a 1,890-item history. All 845,600 submitted rows passed the shared schema checks and test-cutoff eligibility checks.\\n';s.write_text(text)\nprint((p/'submission_validation.txt').read_text().strip())\nprint('Official public NDCG@10:',json.load(open(p/'public_evaluation.json'))['scores']['quality_ndcg@10'])\nprint('Final artifacts and reproduction commands are ready.')\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\nOfficial public NDCG@10: 0.052649\nFinal artifacts and reproduction commands are ready.\n",
    "stderr": "",
    "aggregated_output": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\nOfficial public NDCG@10: 0.052649\nFinal artifacts and reproduction commands are ready.\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5270
    },
    "formatted_output": "VALID: 845600 rows, 8456 users, exactly 100 distinct eligible items each.\nOfficial public NDCG@10: 0.052649\nFinal artifacts and reproduction commands are ready.\n"
  }
}

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "response_id": "resp_0f46634900f4e9db016aa2e79c5bf887d1b840949bce32fddf",
  "usage": {
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    "cache_write_input_tokens": 0,
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 58076,
    "total_tokens": 6538579
  },
  "thread_token_usage": {
    "input_tokens": 6448264,
    "cached_input_tokens": 6286208,
    "cache_write_input_tokens": 0,
    "output_tokens": 90315,
    "reasoning_output_tokens": 58076,
    "total_tokens": 6538579
  }
}

## Step 165 · agent · 2026-09-10T17:25:33.111Z

Built and refitted on train + validation.

- [Submission](/app/solution/submission.parquet): **845,600 rows**, validated for all 8,456 users and test eligibility.
- Public NDCG@10: **0.052649**.
- Separate CPU PyTorch towers preserve exact dot-product scoring.

Code, exported embeddings, and reproduction commands are documented in [README.md](/app/solution/README.md).

### Harness context
{
  "type": "token_usage_record",
  "thread_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
  "session_id": "01a08c24-e92e-7ca0-ac3f-88d73fe11ee5",
  "root_turn_id": "01a08c24-e93d-7c93-acea-21d3cb0a1e25",
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  "usage": {
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}