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Agent trace

Basic recommender sol-high-03

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

Thinking text is unavailable for this run. The archive contains 63 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · high effort
Recorded conversation
20:43 · 69 steps
Private reward
0.032421 · Pass
Attempt cost
$2.31
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
System instructions

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

Personality

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

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

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

Writing style

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

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

Technical communication

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

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

Working with the user

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

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

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

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

Intermediate commentary

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

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

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

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

Final answer

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

Formatting rules

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

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

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

Good candidates include:

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

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

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

Rules for getting work done

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

File editing constraints

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

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

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

Autonomy and persistence

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

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

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

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

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

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

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

Destructive actions

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

Before taking a destructive action:

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

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

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

Using skills

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

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

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

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

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

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

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

02

Developer instructions

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Developer instructions

<skills_instructions>

Skills

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

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

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

<apps_instructions>

Apps (Connectors)

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

03

Developer instructions

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Developer instructions

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

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

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

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

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

They may be addressed as to=/root

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

All agents share the same directory. In detail:

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

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

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

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

04

Developer instructions

+00:01
Developer instructions

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

05

User context

+00:01
User context

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

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

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-24</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>

Harness context world state
{
  "type": "world_state",
  "full": true,
  "state": {
    "agents_md": {},
    "apps_instructions": true,
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-24",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}
Harness context turn context
{
  "type": "turn_context",
  "turn_id": "01a0d47e-669f-7b12-8df6-5b6d65fdd1a5",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-24",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "high",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "high",
  "summary": "auto"
}
06

Task instructions

+00:01
Task instructions

Build a music-discovery ranker

Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

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

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

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

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

The two public windows have different roles:

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

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

Additional files under /task/data/:

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

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

Discovery objective

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

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

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

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

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

Public tools

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

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

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

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, 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 builds the artifact and a concise /app/solution/README.md in /app/solution/. 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.
  • You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
07

exec command

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Model text

I’ll first profile the data, eligibility density, and public-window signal, then build a validation-safe ensemble, tune it against the provided evaluator, and refit the winning recipe on train+validation. I’ll keep a structurally valid fallback submission saved early, then overwrite it only after checks improve.

Tool callexecTool call Returned

Tool call · Input

const p = await tools.update_plan({plan:[
  {step:"Inspect data schemas, scale, time ranges, and candidate/eligibility structure","status":"in_progress"},
  {step:"Build a valid baseline and validation feature pipeline","status":"pending"},
  {step:"Tune ranking ensemble on the public temporal window","status":"pending"},
  {step:"Refit on train+validation and generate final test ranking","status":"pending"},
  {step:"Validate deliverables and document reproduction","status":"pending"}
]});
const r = await tools.exec_command({cmd:"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,220p' /task/requirements.txt",workdir:"/app",yield_time_ms:10000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

/app
/task/requirements.txt
/task/data/val_dislikes.parquet
/task/data/val_undislikes.parquet
/task/data/target_users.parquet
/task/data/val_unlikes.parquet
/task/data/train_dislikes.parquet
/task/data/train_undislikes.parquet
/task/data/test_preference_state.parquet
/task/data/validation_preference_state.parquet
/task/data/train_listens.parquet
/task/data/train_likes.parquet
/task/data/train_unlikes.parquet
/task/data/test_eligible_candidates.npz
/task/data/val_listens.parquet
/task/data/validation_eligible_candidates.npz
/task/data/candidate_catalog.parquet
/task/data/val_likes.parquet
/task/ranking_evaluation.py
/task/evaluate_public.py
/task/data/train_listens.parquet
/task/data/val_dislikes.parquet
/task/data/train_likes.parquet
/task/data/train_unlikes.parquet
/task/data/test_eligible_candidates.npz
/task/data/val_listens.parquet
/task/data/validation_eligible_candidates.npz
/task/data/candidate_catalog.parquet
/task/data/val_likes.parquet
/task/data/train_dislikes.parquet
/task/data/train_undislikes.parquet
/task/data/test_preference_state.parquet
/task/data/validation_preference_state.parquet
/task/data/val_unlikes.parquet
/task/data/target_users.parquet
/task/data/val_undislikes.parquet
# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
08

exec command

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"mkdir -p /app/solution && python - <<'PY'\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\nfor f in sorted(glob.glob('/task/data/*')):\n    print(os.path.basename(f), os.path.getsize(f)/1e6, 'MB')\n    if f.endswith('.parquet'):\n        pf=pq.ParquetFile(f)\n        print(' ',pf.metadata.num_rows, pf.schema_arrow)\nfor f in ['/task/data/validation_eligible_candidates.npz','/task/data/test_eligible_candidates.npz']:\n    z=np.load(f)\n    print(os.path.basename(f), {k:(z[k].shape,z[k].dtype) for k in z.files})\n    bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n    cnt=bits.sum(1)\n    print('eligible',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),cnt.mean(),'catalog same head',z['item_ids'][:10])\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'pandas'
09

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"which -a python python3; python --version; python3 --version; ls -la /task | sed -n '1,100p'; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | sed -n '1,40p'",workdir:"/app",yield_time_ms:10000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
/usr/bin/python3
/bin/python3
Python 3.12.13
Python 3.12.13
total 36
dr-xr-xr-x 1 root root 4096 Sep 24 17:34 .
drwxr-xr-x 1 root root 4096 Sep 24 17:37 ..
dr-xr-xr-x 1 root root 4096 Sep 24 17:30 data
-r--r--r-- 1 root root 5774 Sep 24 17:30 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 17:30 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 24 17:30 requirements.txt
10

exec command

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"python -m pip list | sed -n '1,160p'; env | sort | sed -n '1,160p'",workdir:"/app",yield_time_ms:10000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 0.8 seconds
Output:

Package Version
------- -------
pip     25.0.1
CODEX_CI=1
CODEX_HOME=/tmp/codex-home
CODEX_MANAGED_BY_NPM=1
CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex
CODEX_THREAD_ID=01a0d47e-668b-7612-a3fc-eff74959dc0c
COLORTERM=
GH_PAGER=cat
GIT_PAGER=cat
GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305
HOME=/home/agent
HOSTNAME=63ea770e5a15
LANG=C.UTF-8
LC_ALL=C.UTF-8
LC_CTYPE=C.UTF-8
NO_COLOR=1
NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin
NVM_CD_FLAGS=
NVM_DIR=/home/agent/.nvm
NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node
PAGER=cat
PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games
PWD=/app
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13
SHLVL=1
TERM=dumb
_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex
11

exec command

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"find / -type d \\( -name 'site-packages' -o -name 'dist-packages' \\) 2>/dev/null | sed -n '1,100p'; find / -type f -name 'pyarrow*.so' 2>/dev/null | sed -n '1,40p'",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 0.8 seconds
Output:

/usr/lib/python3/dist-packages
/usr/local/lib/python3.11/dist-packages
/usr/local/lib/python3.12/site-packages
/opt/task-venv/lib/python3.12/site-packages
12

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Model text

The default shell Python is intentionally bare; the task’s pinned environment is available separately, so I’m switching to that interpreter. No package installation or network access is needed.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport os, glob, pyarrow.parquet as pq, numpy as np\nfor f in sorted(glob.glob('/task/data/*')):\n    print(os.path.basename(f), round(os.path.getsize(f)/1e6,2), 'MB')\n    if f.endswith('.parquet'):\n        pf=pq.ParquetFile(f)\n        print(' ',pf.metadata.num_rows, pf.schema_arrow.names)\nfor f in ['/task/data/validation_eligible_candidates.npz','/task/data/test_eligible_candidates.npz']:\n    z=np.load(f)\n    print(os.path.basename(f), {k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n    bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n    cnt=bits.sum(1)\n    print('eligible',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),cnt.mean(),'catalog head',z['item_ids'][:10])\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 1.2 seconds
Output:

candidate_catalog.parquet 0.35 MB
  30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet 0.08 MB
  8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
test_eligible_candidates.npz 7.74 MB
test_preference_state.parquet 1.09 MB
  484776 ['uid', 'item_id', 'state']
train_dislikes.parquet 0.86 MB
  101075 ['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet 5.37 MB
  814831 ['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet 319.18 MB
  43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet 0.16 MB
  19960 ['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet 1.86 MB
  292665 ['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet 0.04 MB
  3563 ['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet 0.3 MB
  37795 ['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet 9.95 MB
  1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet 0.01 MB
  606 ['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet 0.07 MB
  9346 ['uid', 'timestamp', 'item_id', 'is_organic']
validation_eligible_candidates.npz 7.57 MB
validation_preference_state.parquet 1.06 MB
  469124 ['uid', 'item_id', 'state']
validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
eligible 24657 [28038.5 29242.  29852. ] 30000 29067.306291390727 catalog head [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749]
test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}
eligible 24552 [28001. 29220. 29835.] 30000 29041.3178807947 catalog head [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749]
13

exec command

+00:40
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"sed -n '1,260p' /task/evaluate_public.py && sed -n '1,320p' /task/ranking_evaluation.py",workdir:"/app",yield_time_ms:10000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

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

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

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

Usage::

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

import json
import sys
from pathlib import Path

import pandas as pd

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


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


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

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

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

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

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


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

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

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

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

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


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

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

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


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

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


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

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
14

exec command

+00:52
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_baseline.py\n+#!/usr/bin/env python3\n+\"\"\"Build cutoff-valid catalog-popularity rankings for both stages.\"\"\"\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+\n+\n+def rank_from_npz(name: str, output: str) -> None:\n+    with np.load(DATA / name, allow_pickle=False) as z:\n+        uids = z[\"uids\"].astype(np.int64)\n+        items = z[\"item_ids\"].astype(np.int64)\n+        packed = z[\"packed_eligible\"]\n+        rows = []\n+        for r, uid in enumerate(uids):\n+            eligible = np.unpackbits(\n+                packed[r], count=len(items), bitorder=\"little\"\n+            ).astype(bool)\n+            chosen = items[eligible][:100]\n+            rows.append(pd.DataFrame({\n+                \"uid\": uid,\n+                \"item_id\": chosen,\n+                \"rank\": np.arange(1, 101, dtype=np.int64),\n+            }))\n+    pd.concat(rows, ignore_index=True).to_parquet(OUT / output, index=False)\n+\n+\n+if __name__ == \"__main__\":\n+    OUT.mkdir(parents=True, exist_ok=True)\n+    rank_from_npz(\"validation_eligible_candidates.npz\", \"validation_baseline.parquet\")\n+    rank_from_npz(\"test_eligible_candidates.npz\", \"submission.parquet\")\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
15

exec command

+01:00
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/build_baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_baseline.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 6.2 seconds
Output:

total 16
drwxr-xr-x 1 agent agent 4096 Sep 24 17:38 .
drwxr-xr-x 1 agent agent 4096 Sep 24 17:34 ..
-rw-r--r-- 1 agent agent 1163 Sep 24 17:38 build_baseline.py
{
  "model": "/app/solution/validation_baseline.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
16

exec command

+01:19
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Model text

The first valid fallback is saved. Plain catalog popularity scores 0.010710 NDCG@10 over 4,263 scored users, which leaves substantial room. I’m now measuring three likely sources of lift separately: repeat affinity, recent item momentum, and collaborative similarity.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/analyze.py\n+#!/usr/bin/env python3\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+\n+D=Path('/task/data')\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n+catalog=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\n+tmap=pd.Index(targets)\n+imap=pd.Index(catalog)\n+\n+def ranges(path, col='timestamp'):\n+    pf=pq.ParquetFile(path)\n+    vals=[]\n+    for i in range(pf.num_row_groups):\n+        s=pf.metadata.row_group(i).column(pf.schema_arrow.get_field_index(col)).statistics\n+        vals += [s.min,s.max]\n+    return min(vals),max(vals)\n+\n+for stem in ['train_listens','train_likes','val_listens','val_likes']:\n+    print(stem, ranges(D/(stem+'.parquet')))\n+\n+# validation truth in compact integer form\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n+vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)]\n+vk=vk[vk.is_organic==1]\n+lab=pd.concat([vl[['uid','item_id']].assign(grade=1),vk[['uid','item_id']].assign(grade=3)])\n+lab=lab[lab.uid.isin(targets)&lab.item_id.isin(catalog)].groupby(['uid','item_id'],as_index=False).grade.max()\n+with np.load(D/'validation_eligible_candidates.npz') as z:\n+    ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}; p=z['packed_eligible']\n+ok=[bool((int(p[ur[int(u)],ic[int(i)]//8])>>(ic[int(i)]%8))&1) for u,i in lab[['uid','item_id']].itertuples(index=False,name=None)]\n+lab=lab[np.asarray(ok)]\n+print('labels',len(lab),'users',lab.uid.nunique(),'grade',lab.grade.value_counts().to_dict(),'items',lab.item_id.nunique())\n+print('label catalog ranks',np.quantile(imap.get_indexer(lab.item_id),[0,.1,.25,.5,.75,.9,.99,1]))\n+\n+# Target-user candidate positive history pairs, streamed.\n+pair_parts=[]; event_parts=[]\n+pf=pq.ParquetFile(D/'train_listens.parquet')\n+for bi,b in enumerate(pf.iter_batches(batch_size=1_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\n+    x=b.to_pandas()\n+    r=tmap.get_indexer(x.uid); c=imap.get_indexer(x.item_id)\n+    m=(r>=0)&(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\n+    if m.any():\n+        xx=pd.DataFrame({'r':r[m].astype(np.int32),'c':c[m].astype(np.int32),'ts':x.timestamp.to_numpy()[m],\n+                         'organic':x.is_organic.to_numpy()[m].astype(np.int8),'ratio':x.played_ratio_pct.to_numpy()[m].astype(np.float32)})\n+        pair_parts.append(xx.groupby(['r','c'],as_index=False).agg(n=('ts','size'),last=('ts','max'),organic=('organic','sum'),ratio=('ratio','max')))\n+    if bi%10==0: print('batch',bi,'parts rows',sum(map(len,pair_parts)))\n+hist=pd.concat(pair_parts).groupby(['r','c'],as_index=False).agg(n=('n','sum'),last=('last','max'),organic=('organic','sum'),ratio=('ratio','max'))\n+hist['uid']=targets[hist.r]; hist['item_id']=catalog[hist.c]\n+hist.to_parquet('/app/solution/target_history.parquet',index=False)\n+labkey=pd.MultiIndex.from_frame(lab[['uid','item_id']])\n+hkey=pd.MultiIndex.from_frame(hist[['uid','item_id']])\n+print('history pairs',len(hist),'events',hist.n.sum(),'label repeat',labkey.isin(hkey).mean())\n+matched=lab[labkey.isin(hkey)].merge(hist,on=['uid','item_id'])\n+print('repeat label stats',matched[['n','last','organic','ratio']].describe(percentiles=[.1,.5,.9,.99]))\n+print('labels/user',lab.groupby('uid').size().describe())\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/analyze.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 7.9 seconds
Output:

train_listens (0, 24786795)
train_likes (80, 24786790)
val_listens (24788600, 25393395)
val_likes (24788815, 25393390)
labels 37769 users 4263 grade {1: 33352, 3: 4417} items 14124
label catalog ranks [    0.    427.   1732.   5621.  13373.  21679.2 29100.  29999. ]
batch 0 parts rows 131932
batch 10 parts rows 1390687
batch 20 parts rows 2665765
batch 30 parts rows 3928994
batch 40 parts rows 5209799
history pairs 5514505 events 19959853 label repeat 0.0
repeat label stats          n  last  organic  ratio
count  0.0   0.0      0.0    0.0
mean   NaN   NaN      NaN    NaN
std    NaN   NaN      NaN    NaN
min    NaN   NaN      NaN    NaN
10%    NaN   NaN      NaN    NaN
50%    NaN   NaN      NaN    NaN
90%    NaN   NaN      NaN    NaN
99%    NaN   NaN      NaN    NaN
max    NaN   NaN      NaN    NaN
labels/user count    4263.000000
mean        8.859723
std        15.773444
min         1.000000
25%         2.000000
50%         4.000000
75%        10.000000
max       442.000000
dtype: float64
17

Agent message

+02:02
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18

exec command

+02:11
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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_global.py\n+#!/usr/bin/env python3\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\n+cat=pd.read_parquet(D/'candidate_catalog.parquet')\n+h=pd.read_parquet(O/'target_history.parquet')\n+g=h.groupby('c').agg(users=('r','size'),events=('n','sum'),organic=('organic','sum'),last=('last','max'))\n+for days in [3,7,14,30,60,90]:\n+    g[f'u{days}']=(h['last'] >= CUT-days*86400).groupby(h.c).sum()\n+g=g.reindex(np.arange(len(cat)),fill_value=0)\n+features={\n+ 'target_users':g.users.to_numpy(),\n+ 'target_events':g.events.to_numpy(),\n+ 'target_organic':g.organic.to_numpy(),\n+ 'recent3':g.u3.to_numpy(), 'recent7':g.u7.to_numpy(), 'recent14':g.u14.to_numpy(),\n+ 'recent30':g.u30.to_numpy(), 'recent60':g.u60.to_numpy(), 'recent90':g.u90.to_numpy(),\n+}\n+# popularity rank prior as small tie-breaker\n+for name,x in features.items():\n+    order=np.lexsort((np.arange(len(x)), -x))\n+    with np.load(D/'validation_eligible_candidates.npz') as z:\n+        uids=z['uids']; items=z['item_ids']; p=z['packed_eligible']\n+        out=[]\n+        for r,u in enumerate(uids):\n+            mask=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\n+            pick=order[mask[order]][:100]\n+            out.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\n+    pd.concat(out,ignore_index=True).to_parquet(O/f'validation_global_{name}.parquet',index=False)\n+    print(name, x.max(), np.count_nonzero(x))\n+g.to_parquet(O/'global_features.parquet')\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/tune_global.py >/app/solution/global_build.log && for f in /app/solution/validation_global_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/  */ /g'; done",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 13
Wall time 11.0 seconds
Output:
19

wait command

+02:36
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Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "13",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 17.3 seconds
Output:

{ "model": "/app/solution/validation_global_recent14.parquet", "scores": { "quality_ndcg@10": 0.019299, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent3.parquet", "scores": { "quality_ndcg@10": 0.024131, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent30.parquet", "scores": { "quality_ndcg@10": 0.016953, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent60.parquet", "scores": { "quality_ndcg@10": 0.015001, "quality_users": 4263 } } 
SESSION_ID=54524
20

exec command

+02:57
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The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:54524,chars:"",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);

Tool response

Script completed
Wall time 7.3 seconds
Output:

{ "model": "/app/solution/validation_global_recent7.parquet", "scores": { "quality_ndcg@10": 0.02466, "quality_users": 4263 } } { "model": "/app/solution/validation_global_recent90.parquet", "scores": { "quality_ndcg@10": 0.014074, "quality_users": 4263 } } { "model": "/app/solution/validation_global_target_events.parquet", "scores": { "quality_ndcg@10": 0.010769, "quality_users": 4263 } } { "model": "/app/solution/validation_global_target_organic.parquet", "scores": { "quality_ndcg@10": 0.010582, "quality_users": 4263 } } { "model": "/app/solution/validation_global_target_users.parquet", "scores": { "quality_ndcg@10": 0.011037, "quality_users": 4263 } } 
21

Agent message

+03:17
Thinking unavailable No text recorded

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22

exec command

+03:24
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Model text

A clear temporal pattern emerged: popularity among target users in only the last seven training days reaches 0.024660, more than doubling the catalog baseline; older/global popularity is much weaker. I’m using that seven-day momentum as the ensemble prior while fitting latent user–track affinity from 5.5 million cutoff-safe history pairs.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_svd.py\n+#!/usr/bin/env python3\n+\"\"\"Fit latent implicit-feedback models and emit validation blend variants.\"\"\"\n+from pathlib import Path\n+import argparse, time\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.decomposition import TruncatedSVD\n+\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\n+\n+def main():\n+    ap=argparse.ArgumentParser(); ap.add_argument('--mode',default='bm25'); ap.add_argument('--rank',type=int,default=96)\n+    a=ap.parse_args(); t=time.time()\n+    h=pd.read_parquet(O/'target_history.parquet')\n+    n_users=int(h.r.max())+1; n_items=30000\n+    rows=h.r.to_numpy(np.int32); cols=h.c.to_numpy(np.int32)\n+    age=(CUT-h['last'].to_numpy())/86400\n+    if a.mode=='binary':\n+        vals=np.ones(len(h),np.float32)\n+    elif a.mode=='count':\n+        vals=(1+np.log1p(h.n.to_numpy())).astype(np.float32)\n+    elif a.mode=='recent':\n+        vals=((1+np.log1p(h.n.to_numpy()))*(0.35+0.65*np.exp(-age/120))).astype(np.float32)\n+    elif a.mode=='organic':\n+        vals=(1+np.log1p(h.n.to_numpy())+0.5*np.log1p(h.organic.to_numpy())).astype(np.float32)\n+    else:\n+        vals=np.ones(len(h),np.float32)\n+    x=sparse.csr_matrix((vals,(rows,cols)),shape=(n_users,n_items),dtype=np.float32)\n+    df=np.diff(x.tocsc().indptr).astype(np.float32)\n+    if a.mode in ('bm25','tfidf'):\n+        idf=np.log((n_users+1)/(df+1)).astype(np.float32)\n+        x=x.multiply(idf).tocsr()\n+        norm=np.sqrt(np.asarray(x.multiply(x).sum(1)).ravel()).astype(np.float32)\n+        x=sparse.diags(1/np.maximum(norm,1e-6))@x\n+    print('matrix',x.shape,x.nnz,'build sec',time.time()-t,flush=True)\n+    svd=TruncatedSVD(n_components=a.rank,n_iter=5,random_state=2026)\n+    user=svd.fit_transform(x).astype(np.float32); comp=svd.components_.astype(np.float32)\n+    print('fit sec',time.time()-t,'explained',svd.explained_variance_ratio_.sum(),flush=True)\n+    gf=pd.read_parquet(O/'global_features.parquet')\n+    gp=np.log1p(gf.u7.to_numpy()).astype(np.float32)\n+    gp=(gp-gp.mean())/gp.std()\n+    alphas=[0,.03,.06,.1,.16,.25,.4,.65,1.0]\n+    outs={z:[] for z in alphas}\n+    with np.load(D/'validation_eligible_candidates.npz') as z:\n+        uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\n+        bs=128\n+        for st in range(0,n_users,bs):\n+            en=min(st+bs,n_users)\n+            s=user[st:en]@comp\n+            # Scale latent scores per listener so alpha is comparable.\n+            s=(s-s.mean(1,keepdims=True))/np.maximum(s.std(1,keepdims=True),1e-6)\n+            eligible=np.unpackbits(packed[st:en],axis=1,count=n_items,bitorder='little').astype(bool)\n+            for alpha in alphas:\n+                q=s+alpha*gp[None,:]\n+                q[~eligible]=-np.inf\n+                ix=np.argpartition(q,-100,axis=1)[:,-100:]\n+                vv=np.take_along_axis(q,ix,axis=1)\n+                ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\n+                for j in range(en-st):\n+                    outs[alpha].append(pd.DataFrame({'uid':uids[st+j],'item_id':items[ix[j]],'rank':np.arange(1,101)}))\n+            if st%1024==0: print('rank',st,flush=True)\n+    for alpha,parts in outs.items():\n+        pd.concat(parts,ignore_index=True).to_parquet(O/f'validation_svd_{a.mode}_r{a.rank}_a{alpha}.parquet',index=False)\n+    np.savez_compressed(O/f'svd_{a.mode}_r{a.rank}.npz',user=user,components=comp)\n+    print('total sec',time.time()-t,flush=True)\n+if __name__=='__main__': main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode tfidf --rank 96",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 16
Wall time 11.0 seconds
Output:
23

wait command

+04:04
Thinking unavailable No text recorded

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

Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "16",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 11.7 seconds
Output:

matrix (8456, 30000) 5514505 build sec 0.3065223693847656
fit sec 2.341540575027466 explained 0.18627316
rank 0
rank 1024
rank 2048
rank 3072
rank 4096
rank 5120
rank 6144
rank 7168
rank 8192
total sec 23.16431212425232
24

exec command

+04:20
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"find /app/solution -maxdepth 1 -name 'validation_svd_tfidf_r96_*.parquet' -print0 | xargs -0 -n1 -P4 /opt/task-venv/bin/python /task/evaluate_public.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 8.7 seconds
Output:

{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.16.parquet",
  "scores": {
    "quality_ndcg@10": 0.020148,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.06.parquet",
  "scores": {
    "quality_ndcg@10": 0.019912,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.25.parquet",
  "scores": {
    "quality_ndcg@10": 0.020425,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.65.parquet",
  "scores": {
    "quality_ndcg@10": 0.022179,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.1.parquet",
  "scores": {
    "quality_ndcg@10": 0.019989,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.03.parquet",
  "scores": {
    "quality_ndcg@10": 0.019833,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.parquet",
  "scores": {
    "quality_ndcg@10": 0.01962,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a0.4.parquet",
  "scores": {
    "quality_ndcg@10": 0.021004,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_tfidf_r96_a1.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.023927,
    "quality_users": 4263
  }
}
25

exec command

+04:32
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode binary --rank 128",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 18
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "18",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 12.5 seconds
Output:

matrix (8456, 30000) 5514505 build sec 0.22640752792358398
fit sec 2.741776943206787 explained 0.34042275
rank 0
rank 1024
rank 2048
rank 3072
rank 4096
rank 5120
rank 6144
rank 7168
rank 8192
total sec 23.73560380935669
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"find /app/solution -maxdepth 1 -name 'validation_svd_binary_r128_*.parquet' -print0 | xargs -0 -n1 -P4 /opt/task-venv/bin/python /task/evaluate_public.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 8.5 seconds
Output:

{
  "model": "/app/solution/validation_svd_binary_r128_a0.16.parquet",
  "scores": {
    "quality_ndcg@10": 0.020152,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a1.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.022085,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.25.parquet",
  "scores": {
    "quality_ndcg@10": 0.02018,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.06.parquet",
  "scores": {
    "quality_ndcg@10": 0.019771,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.1.parquet",
  "scores": {
    "quality_ndcg@10": 0.019923,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.65.parquet",
  "scores": {
    "quality_ndcg@10": 0.021227,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.parquet",
  "scores": {
    "quality_ndcg@10": 0.019398,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.4.parquet",
  "scores": {
    "quality_ndcg@10": 0.020411,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation_svd_binary_r128_a0.03.parquet",
  "scores": {
    "quality_ndcg@10": 0.019549,
    "quality_users": 4263
  }
}
26

exec command

+05:23
Thinking unavailable No text recorded

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

Model text

The latent model is useful but the unpersonalized seven-day trend still wins; TF-IDF SVD alone scores 0.019620, and its best tested blend reaches 0.023927. That suggests near-term discovery demand is dominated by item momentum. I’m tightening the prior to the exact objective—organic ≥80% listens plus organic likes—across shorter lookback windows before deciding how much personalization to retain.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_recent.py\n+#!/usr/bin/env python3\n+\"\"\"Tune cutoff-safe popularity features that mirror the discovery label.\"\"\"\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); imap=pd.Index(items)\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); tset=set(targets.tolist())\n+start=CUT-35*86400\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',start)],columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',start)],columns=['uid','timestamp','item_id','is_organic'])\n+li=li[li.item_id.isin(items)]; la=la[la.item_id.isin(items)]\n+li['day']=(CUT-li.timestamp)//86400; la['day']=(CUT-la.timestamp)//86400\n+print('recent rows',len(li),len(la),li.day.min(),li.day.max())\n+features={}\n+for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\n+    for scope,tm in [('t',li.uid.isin(tset)),('a',np.ones(len(li),bool))]:\n+      for typ,cm in [('o80',(li.is_organic==1)&(li.played_ratio_pct>=80)),\n+                     ('o50',(li.is_organic==1)&(li.played_ratio_pct>=50)),\n+                     ('x80',(li.played_ratio_pct>=80)),('x50',(li.played_ratio_pct>=50))]:\n+        q=li[(li.day<days)&tm&cm]\n+        x=q.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+        features[f'{scope}{typ}_{days}']=x\n+    q=la[(la.day<days)&(la.is_organic==1)&la.uid.isin(tset)]\n+    features[f'tlike_{days}']=q.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+\n+def write_rank(name,score):\n+    order=np.lexsort((np.arange(len(score)), -score))\n+    with np.load(D/'validation_eligible_candidates.npz') as z:\n+      out=[]\n+      for r,u in enumerate(z['uids']):\n+        m=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool)\n+        ix=order[m[order]][:100]\n+        out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][ix],'rank':np.arange(1,101)}))\n+    pd.concat(out,ignore_index=True).to_parquet(O/f'validation_recent_{name}.parquet',index=False)\n+\n+# Core candidates plus grade-weighted like blends.\n+for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\n+    for key in [f'to80_{days}',f'to50_{days}',f'tx80_{days}',f'ao80_{days}']:\n+        write_rank(key,features[key])\n+    base=features[f'to80_{days}']; lk=features[f'tlike_{days}']\n+    for w in [1,3,7]: write_rank(f'grade_{days}_w{w}',base+w*lk)\n+np.savez_compressed(O/'recent_features.npz',**features)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
27

exec command

+05:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_recent.py\n@@\n-def write_rank(name,score):\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n+truth=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),\n+                 vk[vk.is_organic==1][['uid','item_id']].assign(grade=3)])\n+truth=truth[truth.uid.isin(tset)&truth.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).grade.max()\n+with np.load(D/'validation_eligible_candidates.npz') as zz:\n+    euids=zz['uids'].copy(); ep=zz['packed_eligible'].copy(); eitems=zz['item_ids'].copy()\n+ur={int(u):r for r,u in enumerate(euids)}; ic={int(i):c for c,i in enumerate(eitems)}\n+gold={}\n+for (u,i),grade in truth.items():\n+    r=ur[int(u)]; c=ic[int(i)]\n+    if (int(ep[r,c//8])>>(c%8))&1: gold.setdefault(int(u),{})[int(i)]=int(grade)\n+\n+best=(-1,None,None)\n+def write_rank(name,score):\n+    global best\n     order=np.lexsort((np.arange(len(score)), -score))\n-    with np.load(D/'validation_eligible_candidates.npz') as z:\n-      out=[]\n-      for r,u in enumerate(z['uids']):\n-        m=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool)\n-        ix=order[m[order]][:100]\n-        out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][ix],'rank':np.arange(1,101)}))\n-    pd.concat(out,ignore_index=True).to_parquet(O/f'validation_recent_{name}.parquet',index=False)\n+    nd=[]\n+    disc=1/np.log2(np.arange(2,12))\n+    for u,g in gold.items():\n+        r=ur[u]\n+        chosen=[]\n+        for c in order:\n+            if (int(ep[r,c//8])>>(c%8))&1:\n+                chosen.append(int(eitems[c]))\n+                if len(chosen)==10: break\n+        gains=np.array([2**g.get(i,0)-1 for i in chosen])\n+        ideal=np.array(sorted((2**v-1 for v in g.values()),reverse=True)[:10])\n+        nd.append((gains*disc).sum()/(ideal*disc[:len(ideal)]).sum())\n+    val=float(np.mean(nd)); print(name,round(val,6),flush=True)\n+    if val>best[0]: best=(val,name,order.copy())\n@@\n np.savez_compressed(O/'recent_features.npz',**features)\n+print('BEST',best[0],best[1])\n+order=best[2]; out=[]\n+for r,u in enumerate(euids):\n+    m=np.unpackbits(ep[r],count=len(items),bitorder='little').astype(bool)\n+    ix=order[m[order]][:100]\n+    out.append(pd.DataFrame({'uid':u,'item_id':eitems[ix],'rank':np.arange(1,101)}))\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_recent_best.parquet',index=False)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
28

exec command

+06:05
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_recent.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 22
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 12.4 seconds
Output:

recent rows 5037944 94894 0 35
to80_1 0.032906
to50_1 0.033452
tx80_1 0.027216
ao80_1 0.032991
grade_1_w1 0.033949
grade_1_w3 0.034619
grade_1_w7 0.034226
to80_2 0.032848
to50_2 0.03283
tx80_2 0.025839
ao80_2 0.032849
grade_2_w1 0.032905
grade_2_w3 0.032931
grade_2_w7 0.033914
to80_3 0.031497
to50_3 0.031293
tx80_3 0.023418
ao80_3 0.031654
grade_3_w1 0.031443
grade_3_w3 0.033033
grade_3_w7 0.033901
to80_4 0.031912
to50_4 0.031605
tx80_4 0.0223
ao80_4 0.031387
grade_4_w1 0.032051
grade_4_w3 0.033273
grade_4_w7 0.033985
to80_5 0.030616
to50_5 0.030861
tx80_5 0.023153
ao80_5 0.030652
grade_5_w1 0.031414
grade_5_w3 0.032279
grade_5_w7 0.033217
to80_6 0.030494
to50_6 0.030812
tx80_6 0.022576
ao80_6 0.030407
grade_6_w1 0.031421
grade_6_w3 0.032039
grade_6_w7 0.033831
to80_7 0.030288
to50_7 0.031196
tx80_7 0.022852
ao80_7 0.030286
grade_7_w1 0.031402
grade_7_w3 0.031467
grade_7_w7 0.031983
to80_8 0.029116
to50_8 0.029966
tx80_8 0.02185
ao80_8 0.02904
grade_8_w1 0.030797
grade_8_w3 0.031843
grade_8_w7 0.03242
to80_10 0.028041
to50_10 0.028148
tx80_10 0.020168
ao80_10 0.028248
grade_10_w1 0.029015
grade_10_w3 0.031529
grade_10_w7 0.032188
to80_12 0.027358
to50_12 0.027829
tx80_12 0.019644
ao80_12 0.027402
grade_12_w1 0.028556
grade_12_w3 0.029499
grade_12_w7 0.031744
to80_14 0.025493
to50_14 0.025983
tx80_14 0.019168
ao80_14 0.025481
grade_14_w1 0.026943
grade_14_w3 0.027997
grade_14_w7 0.029309
to80_18 0.022502
to50_18 0.02305
tx80_18 0.019326
ao80_18 0.022345
grade_18_w1 0.023931
grade_18_w3 0.026165
grade_18_w7 0.028175
to80_21 0.020376
to50_21 0.02067
tx80_21 0.017744
ao80_21 0.020384
grade_21_w1 0.021564
grade_21_w3 0.023368
grade_21_w7 0.024848
to80_28 0.019033
to50_28 0.018943
tx80_28 0.017123
ao80_28 0.019032
grade_28_w1 0.019478
grade_28_w3 0.020093
grade_28_w7 0.020607
to80_35 0.01867
to50_35 0.018652
tx80_35 0.016402
ao80_35 0.018672
grade_35_w1 0.019267
grade_35_w3 0.019516
grade_35_w7 0.020098
BEST 0.0346188748576378 grade_1_w3
29

exec command

+06:35
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recent_best.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 2.6 seconds
Output:

{
  "model": "/app/solution/validation_recent_best.parquet",
  "scores": {
    "quality_ndcg@10": 0.034619,
    "quality_users": 4263
  }
}
30

Agent message

+06:51
Thinking unavailable No text recorded

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

31

exec command

+06:54
Thinking unavailable No text recorded

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

Model text

Matching the target definition changed the picture sharply: one-day target-cohort organic activity, with organic likes weighted 3×, scores 0.034619—a 223% lift over the fallback and 40% over the earlier seven-day proxy. I’m now optimizing tie-breaking and decay around that signal; integer count ties are common, so secondary momentum can matter materially without disturbing the strongest primary ordering.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_blends.py\n+#!/usr/bin/env python3\n+from pathlib import Path\n+import numpy as np, pandas as pd\n+D=Path('/task/data'); O=Path('/app/solution')\n+with np.load(O/'recent_features.npz') as z: F={k:z[k] for k in z.files}\n+items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\n+targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\n+with np.load(D/'validation_eligible_candidates.npz') as z:\n+ uids=z['uids'].copy(); packed=z['packed_eligible'].copy(); eitems=z['item_ids'].copy()\n+ur={int(u):r for r,u in enumerate(uids)}; ic={int(i):c for c,i in enumerate(eitems)}\n+vl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\n+truth=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[vk.is_organic==1][['uid','item_id']].assign(g=3)])\n+truth=truth[truth.uid.isin(targets)&truth.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).g.max()\n+gold={}\n+for (u,i),g in truth.items():\n+ r=ur[int(u)]; c=ic[int(i)]\n+ if (int(packed[r,c//8])>>(c%8))&1: gold.setdefault(int(u),{})[int(i)]=int(g)\n+disc=1/np.log2(np.arange(2,12))\n+def evaluate(score):\n+ order=np.lexsort((np.arange(len(score)),-score)); vals=[]\n+ for u,g in gold.items():\n+  r=ur[u]; chosen=[]\n+  for c in order:\n+   if (int(packed[r,c//8])>>(c%8))&1:\n+    chosen.append(int(eitems[c]))\n+    if len(chosen)==10: break\n+  gain=np.array([2**g.get(i,0)-1 for i in chosen]); ideal=np.array(sorted((2**x-1 for x in g.values()),reverse=True)[:10])\n+  vals.append((gain*disc).sum()/(ideal*disc[:len(ideal)]).sum())\n+ return np.mean(vals),order\n+def scaled(x):\n+ x=np.log1p(x.astype(float)); return (x-x.min())/(x.max()-x.min()+1e-9)\n+best=(-1,'',None)\n+secondaries={\n+ 'cat':np.linspace(1,0,len(items)),\n+ 'to50_1':scaled(F['to50_1']), 'ao80_1':scaled(F['ao80_1']),\n+ 'g2':scaled(F['to80_2']+3*F['tlike_2']), 'g3':scaled(F['to80_3']+3*F['tlike_3']),\n+ 'g7':scaled(F['to80_7']+3*F['tlike_7']), 'o7':scaled(F['to80_7']),\n+}\n+for w in [1.5,2,2.5,3,3.5,4,5,6,7]:\n+ p=F['to80_1']+w*F['tlike_1']\n+ for sn,s in secondaries.items():\n+  for eps in [.01,.05,.15,.3,.49]:\n+   val,order=evaluate(p+eps*s)\n+   name=f'w{w}_{sn}_e{eps}'\n+   print(name,round(val,6),flush=True)\n+   if val>best[0]:best=(val,name,order.copy())\n+# multi-level lexical: day-1 primary, day-2 then catalog.\n+for w in [2,2.5,3,3.5,4,5]:\n+ p=F['to80_1']+w*F['tlike_1']\n+ for a in [.05,.15,.3,.49]:\n+  score=p+a*scaled(F['to80_2']+w*F['tlike_2'])+.01*secondaries['cat']\n+  val,order=evaluate(score); name=f'multi_w{w}_a{a}'; print(name,round(val,6),flush=True)\n+  if val>best[0]:best=(val,name,order.copy())\n+print('BEST',best[0],best[1])\n+out=[]\n+for r,u in enumerate(uids):\n+ m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); ix=best[2][m[best[2]]][:100]\n+ out.append(pd.DataFrame({'uid':u,'item_id':eitems[ix],'rank':np.arange(1,101)}))\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_blend_best.parquet',index=False)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
32

exec command

+07:20
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/tune_blends.py > /app/solution/blend_scores.txt && tail -n 25 /app/solution/blend_scores.txt && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_blend_best.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 25
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "25",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 14.8 seconds
Output:


SESSION_ID=7346
33

exec command

+07:54
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:7346,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output);

Tool response

Script completed
Wall time 2.4 seconds
Output:

multi_w2_a0.05 0.034175
multi_w2_a0.15 0.034114
multi_w2_a0.3 0.034222
multi_w2_a0.49 0.034222
multi_w2.5_a0.05 0.034609
multi_w2.5_a0.15 0.034588
multi_w2.5_a0.3 0.034588
multi_w2.5_a0.49 0.034588
multi_w3_a0.05 0.03456
multi_w3_a0.15 0.03457
multi_w3_a0.3 0.034605
multi_w3_a0.49 0.034589
multi_w3.5_a0.05 0.034731
multi_w3.5_a0.15 0.034735
multi_w3.5_a0.3 0.034735
multi_w3.5_a0.49 0.034735
multi_w4_a0.05 0.034497
multi_w4_a0.15 0.03449
multi_w4_a0.3 0.034472
multi_w4_a0.49 0.034476
multi_w5_a0.05 0.034474
multi_w5_a0.15 0.034479
multi_w5_a0.3 0.034457
multi_w5_a0.49 0.034457
BEST 0.03478709902101945 w3_g7_e0.01
{
  "model": "/app/solution/validation_blend_best.parquet",
  "scores": {
    "quality_ndcg@10": 0.034787,
    "quality_users": 4263
  }
}
34

Agent message

+08:08
Thinking unavailable No text recorded

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

35

Agent message

+08:18
Thinking unavailable No text recorded

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

36

exec command

+08:24
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"sort -k2,2nr /app/solution/blend_scores.txt | sed -n '1,25p'",workdir:"/app",yield_time_ms:10000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

BEST 0.03478709902101945 w3_g7_e0.01
w3_g7_e0.01 0.034787
w3_g7_e0.05 0.034787
w3_g7_e0.15 0.034787
w3_g7_e0.3 0.034787
w3_g7_e0.49 0.034787
w3_to50_1_e0.01 0.034744
w3_to50_1_e0.05 0.034744
w3_to50_1_e0.15 0.034744
w3_to50_1_e0.3 0.034744
w3_to50_1_e0.49 0.034744
w3.5_g7_e0.01 0.034739
w3.5_g7_e0.05 0.034739
w3.5_g7_e0.15 0.034739
w3.5_g7_e0.3 0.034739
w3.5_g7_e0.49 0.034739
w3.5_g2_e0.01 0.034736
w3.5_g2_e0.05 0.034736
w3.5_g2_e0.15 0.034736
w3.5_g2_e0.3 0.034736
w3.5_g2_e0.49 0.034736
multi_w3.5_a0.15 0.034735
multi_w3.5_a0.3 0.034735
multi_w3.5_a0.49 0.034735
w3.5_g3_e0.01 0.034735
37

exec command

+08:30
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_collab.py\n+#!/usr/bin/env python3\n+\"\"\"Personalize trending candidates by similarity to their recent adopters.\"\"\"\n+from pathlib import Path\n+import numpy as np, pandas as pd\n+from scipy import sparse\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\n+items=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64); imap=pd.Index(items)\n+uids=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); umap=pd.Index(uids); uset=set(uids.tolist())\n+# Objective-matched recent adopter matrix.\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic','played_ratio_pct'])\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic'])\n+li=li[(li.is_organic==1)&(li.played_ratio_pct>=80)&li.uid.isin(uset)&li.item_id.isin(items)][['uid','item_id']].assign(w=1.)\n+la=la[(la.is_organic==1)&la.uid.isin(uset)&la.item_id.isin(items)][['uid','item_id']].assign(w=3.)\n+a=pd.concat([li,la]).groupby(['uid','item_id'],as_index=False).w.max()\n+rr=umap.get_indexer(a.uid); cc=imap.get_indexer(a.item_id)\n+A=sparse.csr_matrix((a.w.to_numpy(np.float32),(rr,cc)),shape=(len(uids),len(items)))\n+with np.load(O/'svd_tfidf_r96.npz') as z: U=z['user'].astype(np.float32)\n+U/=np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\n+P=(A.T@U).astype(np.float32); P/=np.maximum(np.linalg.norm(P,axis=1,keepdims=True),1e-7)\n+with np.load(O/'recent_features.npz') as z:\n+ base=(z['to80_1']+3*z['tlike_1']).astype(np.float32); tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\n+tie=np.log1p(tie); tie=(tie-tie.min())/(tie.max()-tie.min())\n+# Truth and denominator.\n+vl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\n+tr=pd.concat([vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),vk[vk.is_organic==1][['uid','item_id']].assign(g=3)])\n+tr=tr[tr.uid.isin(uset)&tr.item_id.isin(set(items.tolist()))].groupby(['uid','item_id']).g.max()\n+with np.load(D/'validation_eligible_candidates.npz') as z: eu=z['uids'].copy(); ep=z['packed_eligible'].copy(); ei=z['item_ids'].copy()\n+eur={int(u):r for r,u in enumerate(eu)}; eic={int(i):c for c,i in enumerate(ei)}; gold={}\n+for (u,i),g in tr.items():\n+ r=eur[int(u)]; c=eic[int(i)]\n+ if (int(ep[r,c//8])>>(c%8))&1:gold.setdefault(int(u),{})[int(i)]=int(g)\n+disc=1/np.log2(np.arange(2,12)); denom={u:(np.array(sorted((2**x-1 for x in g.values()),reverse=True)[:10])*disc[:min(10,len(g))]).sum() for u,g in gold.items()}\n+variants=[]\n+for eps in [-.49,-.2,-.05,0,.02,.05,.1,.2,.35,.49,.75,1.0]: variants.append(('raw',eps))\n+for beta in [.02,.05,.1,.2,.3,.5,1.0]:variants.append(('log',beta))\n+tot={v:[] for v in variants}; bs=128\n+for st in range(0,len(uids),bs):\n+ en=min(st+bs,len(uids)); aff=U[st:en]@P.T\n+ elig=np.unpackbits(ep[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\n+ az=(aff-aff.mean(1,keepdims=True))/np.maximum(aff.std(1,keepdims=True),1e-6)\n+ bz=np.log1p(base); bz=(bz-bz.mean())/bz.std()\n+ for v in variants:\n+  typ,x=v\n+  q=(base[None,:]+.01*tie[None,:]+x*aff) if typ=='raw' else (bz[None,:]+x*az)\n+  q=np.where(elig,q,-np.inf)\n+  ix=np.argpartition(q,-10,axis=1)[:,-10:]; vv=np.take_along_axis(q,ix,axis=1); ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\n+  for j in range(en-st):\n+   u=int(eu[st+j]); g=gold.get(u)\n+   if g:tot[v].append(sum((2**g.get(int(ei[c]),0)-1)*disc[k] for k,c in enumerate(ix[j]))/denom[u])\n+for v in variants:print(v,round(float(np.mean(tot[v])),6))\n+best=max(variants,key=lambda v:np.mean(tot[v])); print('BEST',best,np.mean(tot[best]))\n+# Generate the selected full ranking.\n+out=[]\n+for st in range(0,len(uids),bs):\n+ en=min(st+bs,len(uids)); aff=U[st:en]@P.T; elig=np.unpackbits(ep[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\n+ az=(aff-aff.mean(1,keepdims=True))/np.maximum(aff.std(1,keepdims=True),1e-6); bz=np.log1p(base); bz=(bz-bz.mean())/bz.std()\n+ typ,x=best; q=(base[None,:]+.01*tie[None,:]+x*aff) if typ=='raw' else (bz[None,:]+x*az); q=np.where(elig,q,-np.inf)\n+ ix=np.argpartition(q,-100,axis=1)[:,-100:]; vv=np.take_along_axis(q,ix,axis=1); ix=np.take_along_axis(ix,np.argsort(-vv,axis=1),axis=1)\n+ for j in range(en-st):out.append(pd.DataFrame({'uid':eu[st+j],'item_id':ei[ix[j]],'rank':np.arange(1,101)}))\n+pd.concat(out,ignore_index=True).to_parquet(O/'validation_collab_best.parquet',index=False)\n+np.savez_compressed(O/'collab_model.npz',U=U,P=P)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
38

exec command

+09:04
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_best.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 29
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "29",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 13.0 seconds
Output:

('raw', -0.49) 0.034522
('raw', -0.2) 0.034522
('raw', -0.05) 0.034494
('raw', 0) 0.034787
('raw', 0.02) 0.03485
('raw', 0.05) 0.034848
('raw', 0.1) 0.034831
('raw', 0.2) 0.034835
('raw', 0.35) 0.034835
('raw', 0.49) 0.034835
('raw', 0.75) 0.034835
('raw', 1.0) 0.034835
('log', 0.02) 0.034966
('log', 0.05) 0.035576
('log', 0.1) 0.036818
('log', 0.2) 0.039314
('log', 0.3) 0.04129
('log', 0.5) 0.043651
('log', 1.0) 0.043156
BEST ('log', 0.5) 0.04365137646605114
{
  "model": "/app/solution/validation_collab_best.parquet",
  "scores": {
    "quality_ndcg@10": 0.04365,
    "quality_users": 4263
  }
}
39

exec command

+09:40
Thinking unavailable No text recorded

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

Model text

Collaborative momentum produced the largest gain: tracks are represented by the latent profiles of their most recent organic adopters, then matched to each listener. Blending that affinity with objective-matched momentum scores 0.043650 publicly, about 4× the catalog baseline. I’m narrowing the blend coefficient and then will refit the same cutoff-safe recipe using validation as observed history for the hidden window.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_collab.py\n@@\n-for beta in [.02,.05,.1,.2,.3,.5,1.0]:variants.append(('log',beta))\n+for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0]:variants.append(('log',beta))\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py | tee /app/solution/collab_scores.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 31
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "31",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 17.2 seconds
Output:

('raw', -0.49) 0.034522
('raw', -0.2) 0.034522
('raw', -0.05) 0.034494
('raw', 0) 0.034787
('raw', 0.02) 0.03485
('raw', 0.05) 0.034848
('raw', 0.1) 0.034831
('raw', 0.2) 0.034835
('raw', 0.35) 0.034835
('raw', 0.49) 0.034835
('raw', 0.75) 0.034835
('raw', 1.0) 0.034835
('log', 0.02) 0.034966
('log', 0.05) 0.035576
('log', 0.1) 0.036818
('log', 0.2) 0.039314
('log', 0.3) 0.04129
('log', 0.4) 0.042445
('log', 0.45) 0.043012
('log', 0.5) 0.043651
('log', 0.55) 0.043845
('log', 0.6) 0.044065
('log', 0.65) 0.044083
('log', 0.7) 0.044247
('log', 0.8) 0.044413
('log', 0.9) 0.044056
('log', 1.0) 0.043156
BEST ('log', 0.8) 0.0444126936936653
40

exec command

+10:21
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_collab.py\n@@\n-from pathlib import Path\n-import numpy as np, pandas as pd\n+from pathlib import Path\n+import argparse\n+import numpy as np, pandas as pd\n@@\n D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); args=ap.parse_args()\n@@\n-with np.load(O/'svd_tfidf_r96.npz') as z: U=z['user'].astype(np.float32)\n+with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32)\n@@\n-pd.concat(out,ignore_index=True).to_parquet(O/'validation_collab_best.parquet',index=False)\n-np.savez_compressed(O/'collab_model.npz',U=U,P=P)\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}.parquet',index=False)\n+np.savez_compressed(O/f'collab_model_{args.latent}.npz',U=U,P=P)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
41

exec command

+10:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 | tee /app/solution/collab_binary_scores.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 33
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "33",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 17.3 seconds
Output:


SESSION_ID=7200
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:7200,chars:"",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

('raw', -0.49) 0.034478
('raw', -0.2) 0.034478
('raw', -0.05) 0.034476
('raw', 0) 0.034787
('raw', 0.02) 0.034851
('raw', 0.05) 0.034856
('raw', 0.1) 0.034853
('raw', 0.2) 0.034853
('raw', 0.35) 0.034853
('raw', 0.49) 0.034853
('raw', 0.75) 0.034853
('raw', 1.0) 0.034853
('log', 0.02) 0.034932
('log', 0.05) 0.035388
('log', 0.1) 0.036414
('log', 0.2) 0.038163
('log', 0.3) 0.039865
('log', 0.4) 0.041577
('log', 0.45) 0.041996
('log', 0.5) 0.042468
('log', 0.55) 0.042735
('log', 0.6) 0.043161
('log', 0.65) 0.043585
('log', 0.7) 0.044399
('log', 0.8) 0.044531
('log', 0.9) 0.044584
('log', 1.0) 0.044654
BEST ('log', 1.0) 0.04465410532862228
42

exec command

+11:08
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_collab.py\n@@\n-for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0]:variants.append(('log',beta))\n+for beta in [.02,.05,.1,.2,.3,.4,.45,.5,.55,.6,.65,.7,.8,.9,1.0,1.1,1.2,1.35,1.5,1.75,2.0]:variants.append(('log',beta))\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 | tee /app/solution/collab_binary_scores2.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 36
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "36",
  "yield_time_ms": 30000,
  "max_tokens": 20000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:


SESSION_ID=68238
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:68238,chars:"",yield_time_ms:30000,max_output_tokens:20000}); text(r.output);

Tool response

Script completed
Wall time 4.6 seconds
Output:

('raw', -0.49) 0.034478
('raw', -0.2) 0.034478
('raw', -0.05) 0.034476
('raw', 0) 0.034787
('raw', 0.02) 0.034851
('raw', 0.05) 0.034856
('raw', 0.1) 0.034853
('raw', 0.2) 0.034853
('raw', 0.35) 0.034853
('raw', 0.49) 0.034853
('raw', 0.75) 0.034853
('raw', 1.0) 0.034853
('log', 0.02) 0.034932
('log', 0.05) 0.035388
('log', 0.1) 0.036414
('log', 0.2) 0.038163
('log', 0.3) 0.039865
('log', 0.4) 0.041577
('log', 0.45) 0.041996
('log', 0.5) 0.042468
('log', 0.55) 0.042735
('log', 0.6) 0.043161
('log', 0.65) 0.043585
('log', 0.7) 0.044399
('log', 0.8) 0.044531
('log', 0.9) 0.044584
('log', 1.0) 0.044654
('log', 1.1) 0.044023
('log', 1.2) 0.043852
('log', 1.35) 0.043083
('log', 1.5) 0.041362
('log', 1.75) 0.039758
('log', 2.0) 0.037727
BEST ('log', 1.0) 0.04465410532862228
43

exec command

+11:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 2.6 seconds
Output:

{
  "model": "/app/solution/validation_collab_binary_r128.parquet",
  "scores": {
    "quality_ndcg@10": 0.044668,
    "quality_users": 4263
  }
}
44

Agent message

+12:08
Thinking unavailable No text recorded

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

45

exec command

+12:14
Thinking unavailable No text recorded

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

Model text

The best validated configuration is now binary latent profiles (128 dimensions) plus recent-adopter affinity at a 1:1 standardized blend, scoring 0.044668 publicly. I’m doing one final capacity check, then the production build will reconstruct profiles from train+validation, use only the last observed day for momentum/adopters, and apply test-cutoff eligibility.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_svd.py --mode binary --rank 256",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 39
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "39",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 15.4 seconds
Output:

matrix (8456, 30000) 5514505 build sec 0.22344207763671875
fit sec 4.9727489948272705 explained 0.40143767
rank 0
rank 1024
rank 2048
rank 3072
rank 4096
rank 5120
rank 6144
rank 7168
rank 8192
total sec 26.54409885406494
46

exec command

+12:50
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r256 | tee /app/solution/collab_binary256_scores.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 40
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "40",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 17.3 seconds
Output:


SESSION_ID=36561
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:36561,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);

Tool response

Script completed
Wall time 4.9 seconds
Output:

('raw', -0.49) 0.034477
('raw', -0.2) 0.034477
('raw', -0.05) 0.034476
('raw', 0) 0.034787
('raw', 0.02) 0.034907
('raw', 0.05) 0.034855
('raw', 0.1) 0.034853
('raw', 0.2) 0.034852
('raw', 0.35) 0.034852
('raw', 0.49) 0.034852
('raw', 0.75) 0.034852
('raw', 1.0) 0.034852
('log', 0.02) 0.034944
('log', 0.05) 0.035385
('log', 0.1) 0.036576
('log', 0.2) 0.03836
('log', 0.3) 0.04024
('log', 0.4) 0.041658
('log', 0.45) 0.042193
('log', 0.5) 0.042493
('log', 0.55) 0.042893
('log', 0.6) 0.043472
('log', 0.65) 0.043955
('log', 0.7) 0.044345
('log', 0.8) 0.04456
('log', 0.9) 0.04432
('log', 1.0) 0.044548
('log', 1.1) 0.043749
('log', 1.2) 0.043567
('log', 1.35) 0.042365
('log', 1.5) 0.041035
('log', 1.75) 0.039094
('log', 2.0) 0.037621
BEST ('log', 0.8) 0.044559889656155954
47

exec command

+13:38
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_final.py\n+#!/usr/bin/env python3\n+\"\"\"Refit the selected discovery ranker on train+validation and rank test candidates.\"\"\"\n+from pathlib import Path\n+import time\n+\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.decomposition import TruncatedSVD\n+\n+DATA = Path('/task/data')\n+OUT = Path('/app/solution')\n+FINAL_CUTOFF = 25_393_400  # five seconds after the last validation event\n+LATENT_DIM = 128\n+BLEND = 1.0\n+\n+\n+def main() -> None:\n+    started = time.time()\n+    items = pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\n+    users = pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64)\n+    imap, umap = pd.Index(items), pd.Index(users)\n+    user_set, item_set = set(users.tolist()), set(items.tolist())\n+\n+    # The streamed train aggregation is produced by analyze.py and retained as\n+    # a reproducible intermediate. Add validation positive listens, then refit.\n+    train = pd.read_parquet(OUT/'target_history.parquet', columns=['r','c'])\n+    val = pd.read_parquet(DATA/'val_listens.parquet', columns=['uid','item_id','played_ratio_pct'])\n+    val = val[(val.played_ratio_pct >= 50) & val.uid.isin(user_set) & val.item_id.isin(item_set)]\n+    vr, vc = umap.get_indexer(val.uid), imap.get_indexer(val.item_id)\n+    pairs = pd.concat([\n+        train.rename(columns={'r':'r','c':'c'})[['r','c']],\n+        pd.DataFrame({'r':vr.astype(np.int32), 'c':vc.astype(np.int32)}),\n+    ]).drop_duplicates()\n+    X = sparse.csr_matrix(\n+        (np.ones(len(pairs),np.float32),(pairs.r.to_numpy(),pairs.c.to_numpy())),\n+        shape=(len(users),len(items)), dtype=np.float32,\n+    )\n+    svd = TruncatedSVD(n_components=LATENT_DIM,n_iter=5,random_state=2026)\n+    U = svd.fit_transform(X).astype(np.float32)\n+    U /= np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\n+    print('latent fit',X.nnz,round(time.time()-started,2),'sec',flush=True)\n+\n+    # The selected momentum window is the final observed 24 hours. Organic\n+    # strong listens and likes mirror the evaluation labels exactly.\n+    li = pd.read_parquet(DATA/'val_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+    la = pd.read_parquet(DATA/'val_likes.parquet', columns=['uid','timestamp','item_id','is_organic'])\n+    common_li = li.uid.isin(user_set) & li.item_id.isin(item_set) & (li.is_organic == 1)\n+    common_la = la.uid.isin(user_set) & la.item_id.isin(item_set) & (la.is_organic == 1)\n+\n+    def recent_pairs(hours: int) -> tuple[pd.DataFrame,pd.DataFrame]:\n+        floor = FINAL_CUTOFF-hours*3600\n+        l = li[common_li & (li.timestamp >= floor) & (li.played_ratio_pct >= 80)][['uid','item_id']].drop_duplicates()\n+        k = la[common_la & (la.timestamp >= floor)][['uid','item_id']].drop_duplicates()\n+        return l,k\n+\n+    l1,k1 = recent_pairs(24)\n+    l7,k7 = recent_pairs(24*7)\n+    base = (\n+        l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+        + 3*l1.iloc[:0].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    )\n+    # Written separately to keep the like channel's predictive weight explicit.\n+    base = l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    base += 3*k1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    tie = l7.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    tie += 3*k7.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    tie = np.log1p(tie); tie = (tie-tie.min())/max(float(tie.max()-tie.min()),1e-7)\n+\n+    adopters = pd.concat([l1.assign(w=1.),k1.assign(w=3.)]).groupby(['uid','item_id'],as_index=False).w.max()\n+    ar, ac = umap.get_indexer(adopters.uid), imap.get_indexer(adopters.item_id)\n+    A = sparse.csr_matrix((adopters.w.to_numpy(np.float32),(ar,ac)),shape=X.shape)\n+    P = (A.T@U).astype(np.float32)\n+    P /= np.maximum(np.linalg.norm(P,axis=1,keepdims=True),1e-7)\n+    bz = np.log1p(base); bz = (bz-bz.mean())/max(float(bz.std()),1e-7)\n+\n+    with np.load(DATA/'test_eligible_candidates.npz',allow_pickle=False) as z:\n+        eu, ei, packed = z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\n+    assert np.array_equal(eu.astype(np.int64),users)\n+    assert np.array_equal(ei.astype(np.int64),items)\n+    out=[]; batch=128\n+    for st in range(0,len(users),batch):\n+        en=min(st+batch,len(users))\n+        affinity=U[st:en]@P.T\n+        az=(affinity-affinity.mean(1,keepdims=True))/np.maximum(affinity.std(1,keepdims=True),1e-7)\n+        score=bz[None,:]+BLEND*az+1e-6*tie[None,:]\n+        eligible=np.unpackbits(packed[st:en],axis=1,count=len(items),bitorder='little').astype(bool)\n+        score=np.where(eligible,score,-np.inf)\n+        ix=np.argpartition(score,-100,axis=1)[:,-100:]\n+        vv=np.take_along_axis(score,ix,axis=1)\n+        ix=np.take_along_axis(ix,np.argsort(-vv,axis=1,kind='stable'),axis=1)\n+        for j in range(en-st):\n+            out.append(pd.DataFrame({'uid':eu[st+j],'item_id':ei[ix[j]],'rank':np.arange(1,101,dtype=np.int64)}))\n+    submission=pd.concat(out,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\n+    submission.to_parquet(OUT/'submission.parquet',index=False)\n+    print('wrote',len(submission),'rows in',round(time.time()-started,2),'sec',flush=True)\n+\n+\n+if __name__ == '__main__':\n+    main()\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
48

exec command

+14:13
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/build_final.py\n@@\n-    base = (\n-        l1.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n-        + 3*l1.iloc[:0].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n-    )\n-    # Written separately to keep the like channel's predictive weight explicit.\n+    # Written separately to keep the like channel's predictive weight explicit.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
49

exec command

+14:18
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 8.7 seconds
Output:

latent fit 5669201 3.75 sec
wrote 845600 rows in 7.17 sec
50

exec command

+14:39
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_recent.py\n@@\n for days in [1,2,3,4,5,6,7,8,10,12,14,18,21,28,35]:\n@@\n     base=features[f'to80_{days}']; lk=features[f'tlike_{days}']\n     for w in [1,3,7]: write_rank(f'grade_{days}_w{w}',base+w*lk)\n+# Finer search around the winning one-day boundary.\n+for hours in [6,9,12,15,18,21,24,27,30,36,42,48,60]:\n+    ml=(li.timestamp >= CUT-hours*3600)&li.uid.isin(tset)&(li.is_organic==1)&(li.played_ratio_pct>=80)\n+    mk=(la.timestamp >= CUT-hours*3600)&la.uid.isin(tset)&(la.is_organic==1)\n+    bl=li[ml].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    lk=la[mk].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+    for w in [2,2.5,3,3.5,4,5]: write_rank(f'hours_{hours}_w{w}',bl+w*lk)\n np.savez_compressed(O/'recent_features.npz',**features)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
51

exec command

+14:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_recent.py > /app/solution/recent_scores_hours.txt && grep '^hours_' /app/solution/recent_scores_hours.txt | sort -k2,2nr | sed -n '1,20p' && tail -n 1 /app/solution/recent_scores_hours.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 46
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "46",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}

Tool response

Script completed
Wall time 16.5 seconds
Output:


SESSION_ID=15742
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:15742,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(r.output);

Tool response

Script completed
Wall time 5.1 seconds
Output:

hours_21_w3 0.035042
hours_18_w5 0.035011
hours_18_w4 0.035007
hours_21_w3.5 0.034919
hours_15_w4 0.034879
hours_18_w3.5 0.03487
hours_21_w4 0.03485
hours_15_w5 0.034839
hours_15_w3 0.034747
hours_15_w3.5 0.034709
hours_21_w2.5 0.034636
hours_24_w3 0.034619
hours_24_w3.5 0.034602
hours_18_w3 0.034556
hours_21_w5 0.034552
hours_24_w4 0.034538
hours_18_w2.5 0.034511
hours_27_w5 0.034502
hours_24_w2.5 0.034482
hours_27_w4 0.034451
BEST 0.035042412582765914 hours_21_w3
52

exec command

+15:30
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_collab.py\n@@\n-ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); args=ap.parse_args()\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); args=ap.parse_args()\n@@\n-li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic','played_ratio_pct'])\n-la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-86400)],columns=['uid','item_id','is_organic'])\n+li=pd.read_parquet(D/'train_listens.parquet',filters=[('timestamp','>=',CUT-args.hours*3600)],columns=['uid','item_id','is_organic','played_ratio_pct'])\n+la=pd.read_parquet(D/'train_likes.parquet',filters=[('timestamp','>=',CUT-args.hours*3600)],columns=['uid','item_id','is_organic'])\n@@\n with np.load(O/'recent_features.npz') as z:\n- base=(z['to80_1']+3*z['tlike_1']).astype(np.float32); tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\n+ tie=(z['to80_7']+3*z['tlike_7']).astype(np.float32)\n+base=li.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n+base+=3*la.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy(np.float32)\n@@\n-pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}.parquet',index=False)\n-np.savez_compressed(O/f'collab_model_{args.latent}.npz',U=U,P=P)\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}_h{args.hours}.parquet',index=False)\n+np.savez_compressed(O/f'collab_model_{args.latent}_h{args.hours}.npz',U=U,P=P)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
53

exec command

+15:42
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 | tee /app/solution/collab_binary128_h21_scores.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 49
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "49",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 17.4 seconds
Output:


SESSION_ID=13544
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:13544,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);

Tool response

Script completed
Wall time 2.5 seconds
Output:

('raw', -0.49) 0.034871
('raw', -0.2) 0.034871
('raw', -0.05) 0.034884
('raw', 0) 0.035079
('raw', 0.02) 0.03514
('raw', 0.05) 0.035111
('raw', 0.1) 0.03511
('raw', 0.2) 0.03511
('raw', 0.35) 0.03511
('raw', 0.49) 0.03511
('raw', 0.75) 0.03511
('raw', 1.0) 0.03511
('log', 0.02) 0.03514
('log', 0.05) 0.035496
('log', 0.1) 0.036223
('log', 0.2) 0.038207
('log', 0.3) 0.039512
('log', 0.4) 0.040944
('log', 0.45) 0.041581
('log', 0.5) 0.04231
('log', 0.55) 0.042683
('log', 0.6) 0.04302
('log', 0.65) 0.043466
('log', 0.7) 0.044103
('log', 0.8) 0.044612
('log', 0.9) 0.044429
('log', 1.0) 0.044704
('log', 1.1) 0.044046
('log', 1.2) 0.043482
('log', 1.35) 0.042559
('log', 1.5) 0.041448
('log', 1.75) 0.039684
('log', 2.0) 0.037831
BEST ('log', 1.0) 0.044704257604108524
54

exec command

+16:22
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128_h21.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 2.6 seconds
Output:

{
  "model": "/app/solution/validation_collab_binary_r128_h21.parquet",
  "scores": {
    "quality_ndcg@10": 0.044685,
    "quality_users": 4263
  }
}
55

exec command

+16:31
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/build_final.py\n@@\n-    # The selected momentum window is the final observed 24 hours. Organic\n+    # The selected momentum window is the final observed 21 hours. Organic\n@@\n-    l1,k1 = recent_pairs(24)\n+    l1,k1 = recent_pairs(21)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
56

exec command

+16:34
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 8.7 seconds
Output:

latent fit 5669201 3.77 sec
wrote 845600 rows in 7.26 sec
57

exec command

+16:51
Thinking unavailable No text recorded

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

Model text

The finer search settled on a 21-hour momentum/adopter window, with a small public improvement to 0.044685. The final submission has now been refit on 5,669,201 distinct train+validation user–track positives and rebuilt against test eligibility. I’m running contract-level validation and documenting the exact rebuild path.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/README.md\n+# Music discovery ranker\n+\n+The final model is an implicit collaborative momentum ranker. It uses binary\n+target-user/catalog interactions with at least 50% completion, refits a\n+128-dimensional randomized SVD on `train + validation`, and represents each\n+currently trending track by the normalized latent centroid of target users who\n+organically discovered it in the last 21 observed hours. The final score is an\n+equal standardized blend of:\n+\n+- 21-hour organic discovery momentum (organic >=80% listens plus 3x organic\n+  like counts), and\n+- listener similarity to the track's recent organic adopters.\n+\n+A seven-day momentum value is retained only as a deterministic tie-breaker.\n+All ranking happens after applying the supplied per-user cutoff eligibility.\n+\n+Public temporal validation NDCG@10: **0.044685** over 4,263 scored users. The\n+plain catalog-popularity fallback scored 0.010710.\n+\n+## Rebuild\n+\n+Use the task's pinned Python environment:\n+\n+```sh\n+cd /app\n+OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/analyze.py\n+OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/build_final.py\n+```\n+\n+The first command streams the large training listen log and writes the compact\n+`target_history.parquet` intermediate. The second adds validation history,\n+refits the selected model, applies `test_eligible_candidates.npz`, and writes\n+`submission.parquet`. Random seeds and cutoff constants are fixed in code.\n+\n+`submission.parquet` contains exactly the required columns `uid`, `item_id`,\n+and `rank`, with 100 cutoff-eligible distinct tracks per target user.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(S)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nn=validate_ranking(f,targets)\nc=CandidateSet(D/'test_eligible_candidates.npz')\neligible=all(c.is_eligible(u,i) for u,i in n[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(n),'users':n.uid.nunique(),'columns':list(f.columns),'dtypes':{k:str(v) for k,v in f.dtypes.items()},'all_test_eligible':eligible,'bytes':S.stat().st_size})\nPY\nls -lh /app/solution/submission.parquet /app/solution/README.md /app/solution/build_final.py /app/solution/target_history.parquet\ndu -sh /app/solution",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 1.9 seconds
Output:

{'rows': 845600, 'users': 8456, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'all_test_eligible': True, 'bytes': 1640967}
-rw-r--r-- 1 agent agent 1.6K Sep 24 17:54 /app/solution/README.md
-rw-r--r-- 1 agent agent 5.1K Sep 24 17:54 /app/solution/build_final.py
-rw-r--r-- 1 agent agent 1.6M Sep 24 17:54 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent  54M Sep 24 17:39 /app/solution/target_history.parquet
232M	/app/solution
58

Agent message

+17:23
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59

exec command

+17:29
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The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data/'\nt=pd.read_parquet(D+'target_users.parquet').uid\nc=set(pd.read_parquet(D+'candidate_catalog.parquet').item_id.astype(int))\nfor stage in ['validation','test']:\n p=pd.read_parquet(D+stage+'_preference_state.parquet')\n q=p[p.uid.isin(t)&p.item_id.isin(c)]\n print(stage,len(p),len(q),q.state.value_counts().to_dict(),q.groupby('uid').size().describe().to_dict())\nPY",workdir:"/app",yield_time_ms:10000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 0.9 seconds
Output:

validation 469124 469124 {'liked': 418435, 'disliked': 50689} {'count': 7038.0, 'mean': 66.65586814435919, 'std': 108.6863506794777, 'min': 1.0, '25%': 7.0, '50%': 30.0, '75%': 81.0, 'max': 1847.0}
test 484776 484776 {'liked': 432659, 'disliked': 52117} {'count': 7092.0, 'mean': 68.35532994923858, 'std': 112.3020426159094, 'min': 1.0, '25%': 7.0, '50%': 30.0, '75%': 83.0, 'max': 1861.0}
60

exec command

+17:37
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nD='/task/data/'; O='/app/solution/'\nuids=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); items=pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy(); um=pd.Index(uids); im=pd.Index(items)\nwith np.load(O+'svd_binary_r128.npz') as z: U=z['user']; C=z['components']\np=pd.read_parquet(D+'validation_preference_state.parquet'); r=um.get_indexer(p.uid); c=im.get_indexer(p.item_id)\nfor st in ['liked','disliked']:\n m=p.state.eq(st).to_numpy(); A=sparse.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(uids),len(items)))\n [email protected]\n print(st,'base norm median',np.median(np.linalg.norm(U,axis=1)),'adj norm',np.quantile(np.linalg.norm(V,axis=1),[.25,.5,.75,.9]))\nPY",workdir:"/app",yield_time_ms:10000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 1.1 seconds
Output:

liked base norm median 11.444354 adj norm [0.11191944 0.61916333 1.82857478 3.62718737]
disliked base norm median 11.444354 adj norm [0.         0.03020553 0.2039081  0.43307543]
61

exec command

+17:46
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_collab.py\n@@\n-ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); args=ap.parse_args()\n+ap=argparse.ArgumentParser(); ap.add_argument('--latent',default='tfidf_r96'); ap.add_argument('--hours',type=int,default=24); ap.add_argument('--like-profile',type=float,default=0); ap.add_argument('--dislike-profile',type=float,default=0); args=ap.parse_args()\n@@\n-with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32)\n+with np.load(O/f'svd_{args.latent}.npz') as z: U=z['user'].astype(np.float32); C=z['components'].astype(np.float32)\n+if args.like_profile or args.dislike_profile:\n+ pref=pd.read_parquet(D/'validation_preference_state.parquet')\n+ pr=umap.get_indexer(pref.uid); pc=imap.get_indexer(pref.item_id)\n+ for state,weight in [('liked',args.like_profile),('disliked',-args.dislike_profile)]:\n+  m=pref.state.eq(state).to_numpy()\n+  M=sparse.csr_matrix((np.ones(m.sum(),np.float32),(pr[m],pc[m])),shape=(len(uids),len(items)))\n+  U += weight*([email protected])\n U/=np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\n@@\n-pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{args.latent}_h{args.hours}.parquet',index=False)\n-np.savez_compressed(O/f'collab_model_{args.latent}_h{args.hours}.npz',U=U,P=P)\n+tag=f'{args.latent}_h{args.hours}_lp{args.like_profile}_dp{args.dislike_profile}'\n+pd.concat(out,ignore_index=True).to_parquet(O/f'validation_collab_{tag}.parquet',index=False)\n+np.savez_compressed(O/f'collab_model_{tag}.npz',U=U,P=P)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
62

exec command

+17:57
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 3 --dislike-profile 10 | tee /app/solution/collab_pref_scores.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 59
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "59",
  "yield_time_ms": 30000,
  "max_tokens": 12000
}

Tool response

Script completed
Wall time 17.3 seconds
Output:


SESSION_ID=89873
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:89873,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);

Tool response

Script completed
Wall time 4.6 seconds
Output:

('raw', -0.49) 0.034843
('raw', -0.2) 0.034843
('raw', -0.05) 0.034845
('raw', 0) 0.035079
('raw', 0.02) 0.035133
('raw', 0.05) 0.035129
('raw', 0.1) 0.035129
('raw', 0.2) 0.035136
('raw', 0.35) 0.035137
('raw', 0.49) 0.035137
('raw', 0.75) 0.035137
('raw', 1.0) 0.035137
('log', 0.02) 0.035184
('log', 0.05) 0.035463
('log', 0.1) 0.036307
('log', 0.2) 0.038141
('log', 0.3) 0.039579
('log', 0.4) 0.041144
('log', 0.45) 0.041627
('log', 0.5) 0.042281
('log', 0.55) 0.042781
('log', 0.6) 0.043116
('log', 0.65) 0.043422
('log', 0.7) 0.043989
('log', 0.8) 0.044089
('log', 0.9) 0.044274
('log', 1.0) 0.044146
('log', 1.1) 0.043748
('log', 1.2) 0.043323
('log', 1.35) 0.042081
('log', 1.5) 0.041016
('log', 1.75) 0.038737
('log', 2.0) 0.036627
BEST ('log', 0.9) 0.04427390575522779
63

exec command

+18:37
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 1 --dislike-profile 0 | tee /app/solution/collab_like1_scores.txt",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 61
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

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

Tool response

Script completed
Wall time 17.3 seconds
Output:


SESSION_ID=55452
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:55452,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);

Tool response

Script completed
Wall time 3.5 seconds
Output:

('raw', -0.49) 0.034867
('raw', -0.2) 0.034867
('raw', -0.05) 0.034881
('raw', 0) 0.035079
('raw', 0.02) 0.035164
('raw', 0.05) 0.035135
('raw', 0.1) 0.035115
('raw', 0.2) 0.035115
('raw', 0.35) 0.035115
('raw', 0.49) 0.035115
('raw', 0.75) 0.035115
('raw', 1.0) 0.035099
('log', 0.02) 0.035129
('log', 0.05) 0.035489
('log', 0.1) 0.036285
('log', 0.2) 0.038261
('log', 0.3) 0.039663
('log', 0.4) 0.040966
('log', 0.45) 0.041715
('log', 0.5) 0.042273
('log', 0.55) 0.042729
('log', 0.6) 0.043287
('log', 0.65) 0.043553
('log', 0.7) 0.044097
('log', 0.8) 0.044629
('log', 0.9) 0.044919
('log', 1.0) 0.044986
('log', 1.1) 0.044737
('log', 1.2) 0.044121
('log', 1.35) 0.042842
('log', 1.5) 0.042229
('log', 1.75) 0.039953
('log', 2.0) 0.037889
BEST ('log', 1.0) 0.04498647700478426
64

exec command

+19:17
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_collab_binary_r128_h21_lp1.0_dp0.0.parquet",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 2.6 seconds
Output:

{
  "model": "/app/solution/validation_collab_binary_r128_h21_lp1.0_dp0.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.04496,
    "quality_users": 4263
  }
}
65

exec command

+19:24
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/build_final.py\n@@\n     svd = TruncatedSVD(n_components=LATENT_DIM,n_iter=5,random_state=2026)\n     U = svd.fit_transform(X).astype(np.float32)\n+    # Active likes sharpen the listener profile in the fitted item basis. A\n+    # unit positive adjustment was selected on the temporal validation cut.\n+    pref = pd.read_parquet(DATA/'test_preference_state.parquet')\n+    pref = pref[pref.state.eq('liked')]\n+    pr, pc = umap.get_indexer(pref.uid), imap.get_indexer(pref.item_id)\n+    L = sparse.csr_matrix((np.ones(len(pref),np.float32),(pr,pc)),shape=X.shape)\n+    U += L @ svd.components_.T\n     U /= np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\n*** Update File: /app/solution/README.md\n@@\n-128-dimensional randomized SVD on `train + validation`, and represents each\n+128-dimensional randomized SVD on `train + validation`, sharpens each listener\n+profile with the cutoff-specific active-like state, and represents each\n@@\n-Public temporal validation NDCG@10: **0.044685** over 4,263 scored users. The\n+Public temporal validation NDCG@10: **0.044960** over 4,263 scored users. The\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
66

exec command

+19:33
Thinking unavailable No text recorded

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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/build_final.py && PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nc=CandidateSet(D/'test_eligible_candidates.npz')\nassert all(c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint('VALID',len(f),f.uid.nunique(),p.stat().st_size)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 10.7 seconds
Output:

latent fit 5669201 3.82 sec
wrote 845600 rows in 7.34 sec
VALID 845600 8456 1640803
67

exec command

+19:51
Thinking unavailable No text recorded

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

Model text

Active likes provided a modest but repeatable gain; the selected validation score is now 0.044960. I’ve incorporated the cutoff-correct test preference state into the refit and revalidated all 845,600 rows. I’m checking whether a conservative negative-state adjustment helps before freezing the artifact; the current valid submission remains saved throughout.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/tune_collab.py --latent binary_r128 --hours 21 --like-profile 1 --dislike-profile 3 | tail -n 23",workdir:"/app",yield_time_ms:30000,max_output_tokens:5000,tty:true});
text(r.output);
if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script running with cell ID 66
Wall time 11.0 seconds
Output:
Tool callwaitTool call Returned

Tool call · Input

{
  "cell_id": "66",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}

Tool response

Script completed
Wall time 17.3 seconds
Output:


SESSION_ID=50595
Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:50595,chars:"",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);

Tool response

Script completed
Wall time 2.2 seconds
Output:

('raw', 1.0) 0.035115
('log', 0.02) 0.035146
('log', 0.05) 0.035544
('log', 0.1) 0.036319
('log', 0.2) 0.038336
('log', 0.3) 0.039712
('log', 0.4) 0.04107
('log', 0.45) 0.042032
('log', 0.5) 0.042556
('log', 0.55) 0.043119
('log', 0.6) 0.043483
('log', 0.65) 0.043615
('log', 0.7) 0.044135
('log', 0.8) 0.044454
('log', 0.9) 0.044667
('log', 1.0) 0.044876
('log', 1.1) 0.044271
('log', 1.2) 0.043594
('log', 1.35) 0.042784
('log', 1.5) 0.041997
('log', 1.75) 0.03955
('log', 2.0) 0.037688
BEST ('log', 1.0) 0.044875728761976125
68

exec command

+20:37
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/analyze.py /app/solution/build_final.py /app/solution/build_baseline.py && sha256sum /app/solution/submission.parquet && sed -n '1,220p' /app/solution/README.md",workdir:"/app",yield_time_ms:10000,max_output_tokens:10000});
text(r.output);
text(await tools.update_plan({explanation:"Selected and refit the strongest public-validated collaborative momentum model; final artifact passes schema and cutoff eligibility checks.","plan":[{"step":"Inspect data schemas, scale, time ranges, and candidate/eligibility structure","status":"completed"},{"step":"Build a valid baseline and validation feature pipeline","status":"completed"},{"step":"Tune ranking ensemble on the public temporal window","status":"completed"},{"step":"Refit on train+validation and generate final test ranking","status":"completed"},{"step":"Validate deliverables and document reproduction","status":"completed"}]}));

Tool response

Script completed
Wall time 0.2 seconds
Output:

1e7d088b56b8c273776eb9177a65bb9aaab1a96c639d2340a510c94d5174fc57  /app/solution/submission.parquet
# Music discovery ranker

The final model is an implicit collaborative momentum ranker. It uses binary
target-user/catalog interactions with at least 50% completion, refits a
128-dimensional randomized SVD on `train + validation`, sharpens each listener
profile with the cutoff-specific active-like state, and represents each
currently trending track by the normalized latent centroid of target users who
organically discovered it in the last 21 observed hours. The final score is an
equal standardized blend of:

- 21-hour organic discovery momentum (organic >=80% listens plus 3x organic
  like counts), and
- listener similarity to the track's recent organic adopters.

A seven-day momentum value is retained only as a deterministic tie-breaker.
All ranking happens after applying the supplied per-user cutoff eligibility.

Public temporal validation NDCG@10: **0.044960** over 4,263 scored users. The
plain catalog-popularity fallback scored 0.010710.

## Rebuild

Use the task's pinned Python environment:

```sh
cd /app
OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/analyze.py
OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python solution/build_final.py
```

The first command streams the large training listen log and writes the compact
`target_history.parquet` intermediate. The second adds validation history,
refits the selected model, applies `test_eligible_candidates.npz`, and writes
`submission.parquet`. Random seeds and cutoff constants are fixed in code.

`submission.parquet` contains exactly the required columns `uid`, `item_id`,
and `rank`, with 100 cutoff-eligible distinct tracks per target user.

{}
69

Final response

+20:43
Thinking unavailable No text recorded

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

Model text

Completed the music-discovery ranker.

  • Final artifact: submission.parquet
  • Rebuild code: build_final.py
  • Documentation: README.md
  • Public validation NDCG@10: 0.044960
  • Verified: 845,600 rows, 8,456 users, ranks 1–100, unique items, and all candidates test-eligible.
About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt
sol-high-0.147.0-exact-r03-2026-09-24
Recorded model
gpt-5.6-sol
Harness version
0.147.0
Session
01a0d47e-668b-7612-a3fc-eff74959dc0c
Source SHA-256
bd20338415c646a2d89641d4cbe38853b9d9b7d58ec071802f4636ddc1709e8b

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