{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "base-sol-high-cli-0-153-4-r01-recovery-20260923T212844Z", "taskChecksum": "5593a6fe96897a82023e4dcff504f4feffe02a3a77165ed8866705da5c1a5ea1", "source": { "format": "codex-rollout", "sha256": "aabac3c880ac868196d3a73a25efb63edd68fcce1e5683c620a367409aa36523", "records": 581, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 74, "item_completed:AgentMessage": 7, "token_count": 88, "task_complete": 1 } }, "sessionId": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-23T21:41:27.935Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T21:41:27.935Z", "text": "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.\n\n# Personality\n\nAs 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.\n\nYou 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.\n\nConversations 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.\n\n## Writing style\n\nAvoid 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.\n\nIf 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.\n\n## Technical communication\n\nLead 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.\n\nYou 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.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in the `commentary` channel.\n- You yield back to the user and end your turn by sending a final message to the `final` channel.\n\nThe 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.\n\nWhen 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.\n\n## Intermediate commentary\n\nAs 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.\n\nIf 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.\n\nDo 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.\n\nNever praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like \"I will do rather than \", \"I will do , not \".\n\n## Final answer\n\nIn 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.\n\n### Formatting rules\n\nYour answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:\n\n- You may format with GitHub-flavored Markdown.\n- When referencing a real local file, prefer a clickable markdown link.\n * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n * If a file path has spaces, wrap the target in angle brackets: [My Report.md]().\n * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n * Do not use URIs like file://, vscode://, or https:// for file links.\n * Do not provide ranges of lines.\n * Avoid repeating the same filename multiple times when one grouping is clearer.\n\n### Visualizations\n\nUse 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.\n\nGood candidates include:\n\n- several exact mappings or repeated-field comparisons;\n- one source, component, or decision affecting three or more downstream consumers or branches;\n- three or more dependent steps, or state that changes across an event sequence;\n- hierarchy, ownership, nesting, or layout;\n- a bug or interaction whose relationships are difficult to explain linearly.\n\nPrefer 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.\n\nUsually 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.\n\n# Rules for getting work done\n\n- 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.\n- When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.\n- 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.\n- Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n\n## File editing constraints\n\nUse `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.\n\nYou 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.\n\nNever 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.\n\n## Autonomy and persistence\n\nAdapt accordingly based on the user’s request type. When asked to:\n\n- 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.\n- 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.\n- 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.\n- 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.\n\nYou avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances:\na) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope.\nb) 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).\n\nA 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.\n\nYou 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.\n\nWhen 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.\n\nIf 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.\n\n# Destructive actions\n\nBe cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.\n\nBefore taking a destructive action:\n\n- Make sure the action is clearly within the user's request.\n- Resolve the exact targets with read-only checks when necessary.\n- Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command.\n- When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.\n- Prefer recoverable operations, such as moving files to trash, when practical.\n- If the target or scope is unclear, stop and ask the user.\n\nNever run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.\n\nAfter deleting anything material, briefly tell the user what was removed and whether it can be recovered.\n\n# Using skills\n\nA skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.\n\n### How to use skills\n\n- Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill.\n- Trigger rules: If the user names an available skill (with `$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.\n- 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.\n- How to use a skill:\n 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.\n 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.\n 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.\n 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.\n 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist).\n- Coordination and sequencing:\n - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n - Announce which skills you're using and why. If you skip an obvious skill, say why.\n- Context hygiene:\n - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.\n - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked.\n - When variants exist, select only the relevant references and note the choice.\n- Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.\n\nWhen 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.\n\nExplicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work.\n\nWhen using a skill the user did not explicitly name, follow this procedure:\n\n- First, tell the user in the commentary channel **why** you are using the skill.\n- Then, use the skill as long as it stays within the scope of the task.\n- 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).\n\nIf 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.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-23T21:41:29.409Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn 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.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n" } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-23T21:41:29.409Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals.\n\nAt the start of your turn, you are the active agent.\nYou can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.\nAll 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.\n\nYou 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.\nChild agents can also spawn their own sub-agents.\nYou can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter.\n\nYou will receive messages in the analysis channel in the form:\n```\nMessage Type: MESSAGE | FINAL_ANSWER\nTask name: \nSender: \nPayload:\n\n```\nThey may be addressed as to=/root\n\nNote 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.\n\nAll agents share the same directory. In detail:\n- All agents have access to the same container and filesystem as you.\n- All agents use the same current working directory.\n- As a result, edits made by one agent are immediately visible to all other agents.\n\nWhen calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling.\n\nThere are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.\n\nFull-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." } ] }, { "id": 4, "role": "developer", "timestamp": "2026-09-23T21:41:29.409Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "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." } ] }, { "id": 5, "role": "user", "timestamp": "2026-09-23T21:41:29.409Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n- GitHub (github@openai-curated-remote)\n- Gmail (gmail@openai-curated-remote)\n- Google Calendar (google-calendar@openai-curated-remote)\n- Google Drive (google-drive@openai-curated-remote)\n- Linear (linear@openai-curated-remote)\n- Notion (notion@openai-curated-remote)\n- OpenAI Developers (openai-developers@openai-curated-remote)\n- Outlook Calendar (outlook-calendar@openai-curated-remote)\n- Outlook Email (outlook-email@openai-curated-remote)\n- SharePoint (sharepoint@openai-curated-remote)\n- Slack (slack@openai-curated-remote)\n- Teams (teams@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-09-23T21:41:29.409Z", "text": "\n /app\n sh\n 2026-09-23\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-23T21:41:29.411Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-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": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } }, { "kind": "context", "timestamp": "2026-09-23T21:41:29.412Z", "data": { "type": "turn_context", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-23", "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" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-23T21:41:29.428Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-23T21:41:29.428Z", "text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" } ] }, { "id": 7, "role": "agent", "timestamp": "2026-09-23T21:41:31.368Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:41:31.368Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T21:41:33.376Z", "text": "I’ll inspect the dataset shape, catalog coverage, and validation label density first, then build a strong baseline immediately so a valid submission is always preserved. I’ll use the public window only for scoring/tuning and keep its events out of validation features." }, { "kind": "tool", "timestamp": "2026-09-23T21:41:36.302Z", "callId": "call_oi4UvqdlDGrHWDkjWMlTpygc", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lah /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:41:36.367Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\ntotal 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:41:36.336Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44789a06087d2b439268f0f514e4c", "usage": { "input_tokens": 14337, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 182, "reasoning_output_tokens": 24, "total_tokens": 14519 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 182, "reasoning_output_tokens": 24, "total_tokens": 14519 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 182, "reasoning_output_tokens": 24, "total_tokens": 14519 } } }, { "kind": "context", "timestamp": "2026-09-23T21:41:36.361Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-63f67241-40ec-46b2-bc29-809ed0b81b9a", "process_id": "68868", "command": [ "/bin/sh", "-lc", "ls -lah /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lah /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,240p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "sed -n '1,200p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "sed -n '1,160p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4271 }, "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-23T21:41:42.588Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:41:42.588Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:41:51.719Z", "callId": "call_BTLfJvkXJH4CtBBPPU3GnTrr", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n t=pq.ParquetFile(p)\\n print(p.name, t.metadata.num_rows, t.schema.names)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\\nprint('catalog',cat.head(),cat.tail(),cat.dtypes)\\nprint('users',len(users),users.head(), users.dtypes)\\nfor nm in ['validation','test']:\\n z=np.load(D/f'{nm}_eligible_candidates.npz')\\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:41:51.776Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:41:51.769Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c5ac4cdd-de58-4897-be7e-273d055e59b0", "process_id": "79194", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\nprint('catalog',cat.head(),cat.tail(),cat.dtypes)\nprint('users',len(users),users.head(), users.dtypes)\nfor nm in ['validation','test']:\n z=np.load(D/f'{nm}_eligible_candidates.npz')\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\nprint('catalog',cat.head(),cat.tail(),cat.dtypes)\nprint('users',len(users),users.head(), users.dtypes)\nfor nm in ['validation','test']:\n z=np.load(D/f'{nm}_eligible_candidates.npz')\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4930 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:41:51.775Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44790b99c87d28f732603fe6043bd", "usage": { "input_tokens": 18781, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 450, "reasoning_output_tokens": 133, "total_tokens": 19231 }, "turn_token_usage": { "input_tokens": 33118, "cached_input_tokens": 28416, "cache_write_input_tokens": 0, "output_tokens": 632, "reasoning_output_tokens": 157, "total_tokens": 33750 }, "thread_token_usage": { "input_tokens": 33118, "cached_input_tokens": 28416, "cache_write_input_tokens": 0, "output_tokens": 632, "reasoning_output_tokens": 157, "total_tokens": 33750 } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-23T21:41:54.194Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:41:54.194Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:41:56.489Z", "callId": "call_SggCZxjepuyf54JV0IF41DhO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"which -a python python3; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:41:56.560Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:41:56.536Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c454307e-9f6b-49c1-bcc6-321b02a26e04", "process_id": "13263", "command": [ "/bin/sh", "-lc", "which -a python python3; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6440 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:39 solution\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:41:56.559Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447a0105087d29cf81ee9d2e2db74", "usage": { "input_tokens": 19287, "cached_input_tokens": 18560, "cache_write_input_tokens": 0, "output_tokens": 99, "reasoning_output_tokens": 17, "total_tokens": 19386 }, "turn_token_usage": { "input_tokens": 52405, "cached_input_tokens": 46976, "cache_write_input_tokens": 0, "output_tokens": 731, "reasoning_output_tokens": 174, "total_tokens": 53136 }, "thread_token_usage": { "input_tokens": 52405, "cached_input_tokens": 46976, "cache_write_input_tokens": 0, "output_tokens": 731, "reasoning_output_tokens": 174, "total_tokens": 53136 } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-23T21:41:58.243Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:41:58.243Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:42:01.381Z", "callId": "call_Y31IFJHaNaUzzIDJA6ahN8xP", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find / -maxdepth 4 -type d \\\\( -name site-packages -o -name dist-packages \\\\) 2>/dev/null; find /task -maxdepth 2 -type f | head -30; env | sort | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:42:01.451Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/data/val_likes.parquet\n/task/data/candidate_catalog.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/train_unlikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_preference_state.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/target_users.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCODEX_THREAD_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=761ae56a4bed\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:42:01.431Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447a4e2a087d289accd3b062c1b44", "usage": { "input_tokens": 19490, "cached_input_tokens": 19072, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 15, "total_tokens": 19613 }, "turn_token_usage": { "input_tokens": 71895, "cached_input_tokens": 66048, "cache_write_input_tokens": 0, "output_tokens": 854, "reasoning_output_tokens": 189, "total_tokens": 72749 }, "thread_token_usage": { "input_tokens": 71895, "cached_input_tokens": 66048, "cache_write_input_tokens": 0, "output_tokens": 854, "reasoning_output_tokens": 189, "total_tokens": 72749 } } }, { "kind": "context", "timestamp": "2026-09-23T21:42:01.447Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cfb9f686-3ddd-4b0f-a9f0-fa3cbde1803b", "process_id": "92690", "command": [ "/bin/sh", "-lc", "find / -maxdepth 4 -type d \\( -name site-packages -o -name dist-packages \\) 2>/dev/null; find /task -maxdepth 2 -type f | head -30; env | sort | sed -n '1,120p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -maxdepth 4 -type d \\( -name site-packages -o -name dist-packages \\) 2>/dev/null; find /task -maxdepth 2 -type f | head -30; env | sort | sed -n '1,120p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/data/val_likes.parquet\n/task/data/candidate_catalog.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/train_unlikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_preference_state.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/target_users.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCODEX_THREAD_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=761ae56a4bed\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "stderr": "", "aggregated_output": "/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/data/val_likes.parquet\n/task/data/candidate_catalog.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/train_unlikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_preference_state.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/target_users.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCODEX_THREAD_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=761ae56a4bed\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2020 }, "formatted_output": "/task/evaluate_public.py\n/task/ranking_evaluation.py\n/task/data/val_likes.parquet\n/task/data/candidate_catalog.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/train_unlikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_preference_state.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/target_users.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCODEX_THREAD_ID=01a0d037-6ae8-70e2-a781-e9e98c0008f2\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=761ae56a4bed\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-23T21:42:03.380Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:42:03.380Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:42:05.503Z", "callId": "call_c0EKQ3X6sNlXipH7fBPbYtlN", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python -m pip list; find /usr/local -maxdepth 5 -iname 'pandas*' 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:42:06.234Z", "content": "Script completed\nWall time 0.7 seconds\nOutput:\n\nPackage Version\n------- -------\npip 25.0.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:42:05.598Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447a9b6d887d288bd54279f41f8bf", "usage": { "input_tokens": 20103, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 24, "total_tokens": 20203 }, "turn_token_usage": { "input_tokens": 91998, "cached_input_tokens": 85376, "cache_write_input_tokens": 0, "output_tokens": 954, "reasoning_output_tokens": 213, "total_tokens": 92952 }, "thread_token_usage": { "input_tokens": 91998, "cached_input_tokens": 85376, "cache_write_input_tokens": 0, "output_tokens": 954, "reasoning_output_tokens": 213, "total_tokens": 92952 } } }, { "kind": "context", "timestamp": "2026-09-23T21:42:06.231Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da49243d-4ecf-4fba-8b96-b03411b77cba", "process_id": "57590", "command": [ "/bin/sh", "-lc", "python -m pip list; find /usr/local -maxdepth 5 -iname 'pandas*' 2>/dev/null | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -m pip list; find /usr/local -maxdepth 5 -iname 'pandas*' 2>/dev/null | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 564621861 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-23T21:42:09.066Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:42:09.066Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T21:42:10.532Z", "text": "The default Python executable is unexpectedly bare even though the task declares a pinned scientific stack. I’m locating the task runtime before modeling; I won’t install or modify packages." }, { "kind": "tool", "timestamp": "2026-09-23T21:42:14.624Z", "callId": "call_MuuDQvG8I5i9PgVCdJeIZQwD", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find /opt /usr /venv /root /home -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'libarrow*' 2>/dev/null | head -20; command -v uv parquet-tools duckdb || true; ls -la /opt /opt/codex-home /root/.local /home/agent/.local 2>/dev/null\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:42:14.916Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n/opt/task-venv/lib/python3.12/site-packages/pandas\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:42:14.689Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447ae6a9c87d2bfdfc643e0622ba9", "usage": { "input_tokens": 20240, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 221, "reasoning_output_tokens": 43, "total_tokens": 20461 }, "turn_token_usage": { "input_tokens": 112238, "cached_input_tokens": 105344, "cache_write_input_tokens": 0, "output_tokens": 1175, "reasoning_output_tokens": 256, "total_tokens": 113413 }, "thread_token_usage": { "input_tokens": 112238, "cached_input_tokens": 105344, "cache_write_input_tokens": 0, "output_tokens": 1175, "reasoning_output_tokens": 256, "total_tokens": 113413 } } }, { "kind": "context", "timestamp": "2026-09-23T21:42:14.912Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e773e807-09f2-4963-960d-b3b4d7502451", "process_id": "57270", "command": [ "/bin/sh", "-lc", "find /opt /usr /venv /root /home -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'libarrow*' 2>/dev/null | head -20; command -v uv parquet-tools duckdb || true; ls -la /opt /opt/codex-home /root/.local /home/agent/.local 2>/dev/null" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find /opt /usr /venv /root /home -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'libarrow*' 2>/dev/null | head -20; command -v uv parquet-tools duckdb || true; ls -la /opt /opt/codex-home /root/.local /home/agent/.local 2>/dev/null" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 124353057 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-23T21:42:16.583Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:42:16.583Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:42:27.036Z", "callId": "call_KIbl3W4XIEQ7PgOuFo7gda02", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n t=pq.ParquetFile(p)\\n print(p.name, t.metadata.num_rows, t.schema.names)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\\nprint('catalog\\\\n',cat.head(),'\\\\n',cat.tail(),'\\\\n',cat.dtypes)\\nprint('users',len(users),'\\\\n',users.head(),'\\\\n', users.dtypes)\\nfor nm in ['validation','test']:\\n z=np.load(D/f'{nm}_eligible_candidates.npz')\\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:42:28.569Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\ncandidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 \n item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nusers 8456 \n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 \n uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ] mean 29041.3178807947\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:42:27.085Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447b73e7487d2859e28afe02f1abd", "usage": { "input_tokens": 21141, "cached_input_tokens": 20096, "cache_write_input_tokens": 0, "output_tokens": 352, "reasoning_output_tokens": 14, "total_tokens": 21493 }, "turn_token_usage": { "input_tokens": 133379, "cached_input_tokens": 125440, "cache_write_input_tokens": 0, "output_tokens": 1527, "reasoning_output_tokens": 270, "total_tokens": 134906 }, "thread_token_usage": { "input_tokens": 133379, "cached_input_tokens": 125440, "cache_write_input_tokens": 0, "output_tokens": 1527, "reasoning_output_tokens": 270, "total_tokens": 134906 } } }, { "kind": "context", "timestamp": "2026-09-23T21:42:28.565Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ac5a9846-a61d-4df0-8f4e-fb393aa98171", "process_id": "13718", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\nprint('catalog\\n',cat.head(),'\\n',cat.tail(),'\\n',cat.dtypes)\nprint('users',len(users),'\\n',users.head(),'\\n', users.dtypes)\nfor nm in ['validation','test']:\n z=np.load(D/f'{nm}_eligible_candidates.npz')\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\nprint('catalog\\n',cat.head(),'\\n',cat.tail(),'\\n',cat.dtypes)\nprint('users',len(users),'\\n',users.head(),'\\n', users.dtypes)\nfor nm in ['validation','test']:\n z=np.load(D/f'{nm}_eligible_candidates.npz')\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 \n item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nusers 8456 \n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 \n uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ] mean 29041.3178807947\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 \n item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nusers 8456 \n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 \n uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ] mean 29041.3178807947\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 361815933 }, "formatted_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 \n item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nusers 8456 \n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 \n uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24657. 26724.2 28682.75 29242. 29644. 29979. 30000. ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749]\nelig counts [24552. 26672.7 28657. 29220. 29619.25 29975. 30000. ] mean 29041.3178807947\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-23T21:42:33.516Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:42:33.516Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:42:41.462Z", "callId": "call_xxJtHhYtl5NbfNCW7fBcnY0A", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; /opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor f in ['train_listens.parquet','val_listens.parquet','train_likes.parquet','val_likes.parquet']:\\n p=D/f; pf=pq.ParquetFile(p)\\n print(f,'rowgroups',pf.num_row_groups)\\n for c in ['uid','timestamp','item_id','is_organic','played_ratio_pct']:\\n if c in pf.schema.names:\\n i=pf.schema.names.index(c); stats=[]\\n for rg in range(pf.num_row_groups):\\n s=pf.metadata.row_group(rg).column(i).statistics\\n stats.append((s.min,s.max))\\n print(c,min(x[0] for x in stats),max(x[1] for x in stats))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:42:41.848Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\n/bin/sh: 1: free: not found\ntrain_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:42:41.579Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447c4d46487d29498fd09ff476082", "usage": { "input_tokens": 22499, "cached_input_tokens": 20992, "cache_write_input_tokens": 0, "output_tokens": 350, "reasoning_output_tokens": 99, "total_tokens": 22849 }, "turn_token_usage": { "input_tokens": 155878, "cached_input_tokens": 146432, "cache_write_input_tokens": 0, "output_tokens": 1877, "reasoning_output_tokens": 369, "total_tokens": 157755 }, "thread_token_usage": { "input_tokens": 155878, "cached_input_tokens": 146432, "cache_write_input_tokens": 0, "output_tokens": 1877, "reasoning_output_tokens": 369, "total_tokens": 157755 } } }, { "kind": "context", "timestamp": "2026-09-23T21:42:41.845Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e378b243-d9fc-4874-88e0-63404b6f321b", "process_id": "91310", "command": [ "/bin/sh", "-lc", "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor f in ['train_listens.parquet','val_listens.parquet','train_likes.parquet','val_likes.parquet']:\n p=D/f; pf=pq.ParquetFile(p)\n print(f,'rowgroups',pf.num_row_groups)\n for c in ['uid','timestamp','item_id','is_organic','played_ratio_pct']:\n if c in pf.schema.names:\n i=pf.schema.names.index(c); stats=[]\n for rg in range(pf.num_row_groups):\n s=pf.metadata.row_group(rg).column(i).statistics\n stats.append((s.min,s.max))\n print(c,min(x[0] for x in stats),max(x[1] for x in stats))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor f in ['train_listens.parquet','val_listens.parquet','train_likes.parquet','val_likes.parquet']:\n p=D/f; pf=pq.ParquetFile(p)\n print(f,'rowgroups',pf.num_row_groups)\n for c in ['uid','timestamp','item_id','is_organic','played_ratio_pct']:\n if c in pf.schema.names:\n i=pf.schema.names.index(c); stats=[]\n for rg in range(pf.num_row_groups):\n s=pf.metadata.row_group(rg).column(i).statistics\n stats.append((s.min,s.max))\n print(c,min(x[0] for x in stats),max(x[1] for x in stats))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n", "stderr": "", "aggregated_output": "train_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 220013655 }, "formatted_output": "train_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-23T21:42:48.805Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:42:48.805Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:42:57.796Z", "callId": "call_ZYc9XPFp0g2ToMmbLKTXi1lz", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nfor split in ['train','val']:\\n x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\\n print(split,'shape',x.shape,'memMB',x.memory_usage(deep=True).sum()/1e6,'users',x.uid.nunique(),'items',x.item_id.nunique(),'positive', (x.played_ratio_pct>=50).sum(),'strong organic',((x.played_ratio_pct>=80)&(x.is_organic==1)&x.uid.isin(target)).sum())\\n print('organic',x.is_organic.mean(),'ratio percentiles',np.percentile(x.played_ratio_pct,[0,25,50,75,80,90,99,100]))\\n del x\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:43:02.909Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\nMemTotal: 15988972 kB\nMemAvailable: 14986404 kB\ntrain shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [ 0. 7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [ 0. 9. 100. 100. 100. 100. 100. 159.]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:42:57.843Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447d20aa487d29df578c1951a7ad4", "usage": { "input_tokens": 23074, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 473, "reasoning_output_tokens": 182, "total_tokens": 23547 }, "turn_token_usage": { "input_tokens": 178952, "cached_input_tokens": 168704, "cache_write_input_tokens": 0, "output_tokens": 2350, "reasoning_output_tokens": 551, "total_tokens": 181302 }, "thread_token_usage": { "input_tokens": 178952, "cached_input_tokens": 168704, "cache_write_input_tokens": 0, "output_tokens": 2350, "reasoning_output_tokens": 551, "total_tokens": 181302 } } }, { "kind": "context", "timestamp": "2026-09-23T21:43:02.905Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0d0cec73-2f82-4e60-8e35-632c92d4a7c4", "process_id": "83201", "command": [ "/bin/sh", "-lc", "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nfor split in ['train','val']:\n x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\n print(split,'shape',x.shape,'memMB',x.memory_usage(deep=True).sum()/1e6,'users',x.uid.nunique(),'items',x.item_id.nunique(),'positive', (x.played_ratio_pct>=50).sum(),'strong organic',((x.played_ratio_pct>=80)&(x.is_organic==1)&x.uid.isin(target)).sum())\n print('organic',x.is_organic.mean(),'ratio percentiles',np.percentile(x.played_ratio_pct,[0,25,50,75,80,90,99,100]))\n del x\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nfor split in ['train','val']:\n x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\n print(split,'shape',x.shape,'memMB',x.memory_usage(deep=True).sum()/1e6,'users',x.uid.nunique(),'items',x.item_id.nunique(),'positive', (x.played_ratio_pct>=50).sum(),'strong organic',((x.played_ratio_pct>=80)&(x.is_organic==1)&x.uid.isin(target)).sum())\n print('organic',x.is_organic.mean(),'ratio percentiles',np.percentile(x.played_ratio_pct,[0,25,50,75,80,90,99,100]))\n del x\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [ 0. 7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [ 0. 9. 100. 100. 100. 100. 100. 159.]\n", "stderr": "", "aggregated_output": "train shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [ 0. 7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [ 0. 9. 100. 100. 100. 100. 100. 159.]\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 901599081 }, "formatted_output": "train shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [ 0. 7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [ 0. 9. 100. 100. 100. 100. 100. 159.]\n" } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-23T21:43:21.114Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:43:21.114Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T21:43:23.296Z", "text": "The data are tractable in memory: 43.5M training listens, 8,456 targets, and roughly 29K eligible tracks per user. I’m testing whether eligibility removes prior exposure and then comparing popularity, personalized recency, and collaborative latent-factor rankers on the public objective." }, { "kind": "tool", "timestamp": "2026-09-23T21:43:47.265Z", "callId": "call_WMcni8IWMfjorefcQ4SxqtO9", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D = Path('/task/data')\\n+cat = pd.read_parquet(D/'candidate_catalog.parquet')\\n+items = cat.item_id.to_numpy(np.int64)\\n+imap = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\\n+targets = pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+z=np.load(D/'validation_eligible_candidates.npz')\\n+elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\n+row={int(u):i for i,u in enumerate(z['uids'])}\\n+\\n+x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic','timestamp'])\\n+x=x[x.uid.isin(targets) & x.item_id.isin(items)]\\n+x['r']=x.item_id.map(imap)\\n+x['ur']=x.uid.map(row)\\n+pos=x[x.played_ratio_pct>=50]\\n+uniq=pos[['ur','r']].drop_duplicates().to_numpy()\\n+print('target candidate events',len(x),'pos',len(pos),'unique pos',len(uniq),'unique/user',len(uniq)/len(targets))\\n+print('historical positives eligible fraction',elig[uniq[:,0],uniq[:,1]].mean())\\n+anyx=x[['ur','r']].drop_duplicates().to_numpy()\\n+print('any listened eligible fraction',elig[anyx[:,0],anyx[:,1]].mean(),'unique any/user',len(anyx)/len(targets))\\n+\\n+# public truth basic properties\\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic'])\\n+vl=vl[vl.uid.isin(targets)&vl.item_id.isin(items)&(vl.played_ratio_pct>=80)&(vl.is_organic==1)]\\n+vk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+vk=vk[vk.uid.isin(targets)&vk.item_id.isin(items)&(vk.is_organic==1)]\\n+truth=pd.concat([vl.assign(g=1)[['uid','item_id','g']],vk.assign(g=3)[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\\n+truth['ur']=truth.uid.map(row); truth['r']=truth.item_id.map(imap)\\n+truth=truth[elig[truth.ur,truth.r]]\\n+print('truth rows/users/mean',len(truth),truth.uid.nunique(),len(truth)/truth.uid.nunique(),'grades',truth.g.value_counts().to_dict())\\n+hist=set(map(tuple,uniq.tolist()))\\n+print('truth seen positive fraction',np.mean([(int(a),int(b)) in hist for a,b in truth[['ur','r']].to_numpy()]))\\n+print('truth pop ranks',np.percentile(truth.r,[0,10,25,50,75,90,99,100]))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:43:47.329Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:43:47.281Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-cf608b89-616a-46fa-9e5b-a6a2a7a41610", "changes": { "/app/solution/explore.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\ncat = pd.read_parquet(D/'candidate_catalog.parquet')\nitems = cat.item_id.to_numpy(np.int64)\nimap = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\ntargets = pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nelig=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrow={int(u):i for i,u in enumerate(z['uids'])}\n\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic','timestamp'])\nx=x[x.uid.isin(targets) & x.item_id.isin(items)]\nx['r']=x.item_id.map(imap)\nx['ur']=x.uid.map(row)\npos=x[x.played_ratio_pct>=50]\nuniq=pos[['ur','r']].drop_duplicates().to_numpy()\nprint('target candidate events',len(x),'pos',len(pos),'unique pos',len(uniq),'unique/user',len(uniq)/len(targets))\nprint('historical positives eligible fraction',elig[uniq[:,0],uniq[:,1]].mean())\nanyx=x[['ur','r']].drop_duplicates().to_numpy()\nprint('any listened eligible fraction',elig[anyx[:,0],anyx[:,1]].mean(),'unique any/user',len(anyx)/len(targets))\n\n# public truth basic properties\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic'])\nvl=vl[vl.uid.isin(targets)&vl.item_id.isin(items)&(vl.played_ratio_pct>=80)&(vl.is_organic==1)]\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nvk=vk[vk.uid.isin(targets)&vk.item_id.isin(items)&(vk.is_organic==1)]\ntruth=pd.concat([vl.assign(g=1)[['uid','item_id','g']],vk.assign(g=3)[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\ntruth['ur']=truth.uid.map(row); truth['r']=truth.item_id.map(imap)\ntruth=truth[elig[truth.ur,truth.r]]\nprint('truth rows/users/mean',len(truth),truth.uid.nunique(),len(truth)/truth.uid.nunique(),'grades',truth.g.value_counts().to_dict())\nhist=set(map(tuple,uniq.tolist()))\nprint('truth seen positive fraction',np.mean([(int(a),int(b)) in hist for a,b in truth[['ur','r']].to_numpy()]))\nprint('truth pop ranks',np.percentile(truth.r,[0,10,25,50,75,90,99,100]))\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/explore.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:43:47.328Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab447e7397487d2a8c73ba389c6f323", "usage": { "input_tokens": 23743, "cached_input_tokens": 22912, "cache_write_input_tokens": 0, "output_tokens": 1340, "reasoning_output_tokens": 516, "total_tokens": 25083 }, "turn_token_usage": { "input_tokens": 202695, "cached_input_tokens": 191616, "cache_write_input_tokens": 0, "output_tokens": 3690, "reasoning_output_tokens": 1067, "total_tokens": 206385 }, "thread_token_usage": { "input_tokens": 202695, "cached_input_tokens": 191616, "cache_write_input_tokens": 0, "output_tokens": 3690, "reasoning_output_tokens": 1067, "total_tokens": 206385 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-23T21:43:49.258Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:43:49.258Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:43:51.291Z", "callId": "call_nRgbmKSa6J4GIeVgMRKx9zUS", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/explore.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T21:44:03.308Z", "content": "Script completed\nWall time 12.0 seconds\nOutput:\n\ntarget candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [ 0. 427. 1732. 5621. 13373. 21679.2 29100. 29999. ]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:43:51.355Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44813d94087d2b96d1beb3e83734f", "usage": { "input_tokens": 25107, "cached_input_tokens": 23552, "cache_write_input_tokens": 0, "output_tokens": 87, "reasoning_output_tokens": 14, "total_tokens": 25194 }, "turn_token_usage": { "input_tokens": 227802, "cached_input_tokens": 215168, "cache_write_input_tokens": 0, "output_tokens": 3777, "reasoning_output_tokens": 1081, "total_tokens": 231579 }, "thread_token_usage": { "input_tokens": 227802, "cached_input_tokens": 215168, "cache_write_input_tokens": 0, "output_tokens": 3777, "reasoning_output_tokens": 1081, "total_tokens": 231579 } } }, { "kind": "context", "timestamp": "2026-09-23T21:44:03.291Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c796dce0-0b6e-4ad7-aea7-198aec4bb006", "process_id": "44466", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/explore.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/explore.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [ 0. 427. 1732. 5621. 13373. 21679.2 29100. 29999. ]\n", "stderr": "", "aggregated_output": "target candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [ 0. 427. 1732. 5621. 13373. 21679.2 29100. 29999. ]\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 788090572 }, "formatted_output": "target candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [ 0. 427. 1732. 5621. 13373. 21679.2 29100. 29999. ]\n" } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-23T21:44:20.802Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:44:20.802Z", "text": "" } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-23T21:44:37.366Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:44:37.366Z", "text": "" } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-23T21:44:42.798Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:44:42.798Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:46:14.059Z", "callId": "call_9JRK2u5oRe18CC56YqAAUajN", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Build validation experiments or the final Made-for-You ranking.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse, json, time\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+from sklearn.neighbors import NearestNeighbors\\n+\\n+D = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+CUTOFF = {'validation': 24786800, 'test': 25395200}\\n+\\n+def timer(msg):\\n+ print(f'[{time.strftime(\\\"%H:%M:%S\\\")}] {msg}', flush=True)\\n+\\n+def data(stage):\\n+ cat = pd.read_parquet(D/'candidate_catalog.parquet')\\n+ items = cat.item_id.to_numpy(np.int64)\\n+ item_map = pd.Series(np.arange(len(items),dtype=np.int32),index=items)\\n+ targ = pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+ z=np.load(D/f'{\\\"validation\\\" if stage==\\\"validation\\\" else \\\"test\\\"}_eligible_candidates.npz')\\n+ elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\n+ assert np.array_equal(items,z['item_ids']) and np.array_equal(targ,z['uids'])\\n+ return cat,items,item_map,targ,elig\\n+\\n+def load_events(stage,item_map):\\n+ parts=[]\\n+ names=['train'] + (['val'] if stage=='test' else [])\\n+ for split in names:\\n+ timer(f'reading {split} listens')\\n+ x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ x=x[(x.played_ratio_pct>=50)&x.item_id.isin(item_map.index)]\\n+ x=x[['uid','timestamp','item_id','is_organic']]\\n+ parts.append(x)\\n+ x=pd.concat(parts,ignore_index=True)\\n+ x['col']=x.item_id.map(item_map).astype(np.int32)\\n+ x['row']=(x.uid//100).astype(np.int32)\\n+ # Positive events, with organic intent slightly upweighted and exponential recency.\\n+ age=(CUTOFF[stage]-x.timestamp.to_numpy())/86400.0\\n+ x['wlong']=(1.0+0.35*x.is_organic.to_numpy()) * (0.35+0.65*np.exp(-np.maximum(age,0)/120.0))\\n+ x['wrecent']=(1.0+0.50*x.is_organic.to_numpy()) * np.exp(-np.maximum(age,0)/35.0)\\n+ # Add explicit likes as high-confidence positives.\\n+ likes=[]\\n+ for split in names:\\n+ q=pd.read_parquet(D/f'{split}_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\\n+ q=q[q.item_id.isin(item_map.index)].copy()\\n+ q['col']=q.item_id.map(item_map).astype(np.int32); q['row']=(q.uid//100).astype(np.int32)\\n+ ageq=(CUTOFF[stage]-q.timestamp.to_numpy())/86400.0\\n+ q['wlong']=2.5*(1+.25*q.is_organic.to_numpy())*(.35+.65*np.exp(-np.maximum(ageq,0)/180.))\\n+ q['wrecent']=3.0*(1+.25*q.is_organic.to_numpy())*np.exp(-np.maximum(ageq,0)/60.)\\n+ likes.append(q[['row','col','wlong','wrecent']])\\n+ y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\\n+ del x,likes\\n+ shape=(10001,len(item_map))\\n+ mats={}\\n+ for col in ['wlong','wrecent']:\\n+ mats[col]=sparse.coo_matrix((y[col].to_numpy(np.float32),(y.row,y.col)),shape=shape).tocsr()\\n+ mats[col].sum_duplicates()\\n+ mats[col].data=np.log1p(mats[col].data).astype(np.float32)\\n+ timer(f'{col} matrix nnz={mats[col].nnz}')\\n+ return mats\\n+\\n+def bm25(X,K1=1.6,B=.75):\\n+ X=X.tocsr(copy=True)\\n+ rows=np.repeat(np.arange(X.shape[0]),np.diff(X.indptr))\\n+ rl=np.asarray(X.sum(axis=1)).ravel(); avg=max(rl.mean(),1e-6)\\n+ df=np.bincount(X.indices,minlength=X.shape[1])\\n+ idf=np.log(X.shape[0]/(1.0+df)).clip(0)\\n+ den=K1*((1-B)+B*rl[rows]/avg)\\n+ X.data *= idf[X.indices]*(K1+1)/(X.data+den)\\n+ return X\\n+\\n+def top_frame(scores,targ,items,elig,n=100):\\n+ rows=[]\\n+ for a in range(0,len(targ),256):\\n+ s=np.asarray(scores(a,min(a+256,len(targ))),dtype=np.float32)\\n+ s[~elig[a:a+len(s)]]=-np.inf\\n+ ix=np.argpartition(s,-n,axis=1)[:,-n:]\\n+ sv=np.take_along_axis(s,ix,axis=1)\\n+ order=np.argsort(-sv,axis=1)\\n+ ix=np.take_along_axis(ix,order,axis=1)\\n+ for j,u in enumerate(targ[a:a+len(s)]):\\n+ rows.append(pd.DataFrame({'uid':u,'item_id':items[ix[j]],'rank':np.arange(1,n+1)}))\\n+ return pd.concat(rows,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\\n+\\n+def public_score(frame,items,targ,elig):\\n+ im={int(v):i for i,v in enumerate(items)}; ur={int(v):i for i,v in enumerate(targ)}\\n+ vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+ vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\\n+ lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+ lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\\n+ g=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\\n+ g=g[[elig[ur[int(u)],im[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]]\\n+ gold={int(u):dict(zip(q.item_id.astype(int),q.g.astype(int))) for u,q in g.groupby('uid')}\\n+ dc=[]\\n+ for u,q in frame[frame['rank']<=10].sort_values(['uid','rank']).groupby('uid'):\\n+ if int(u) not in gold: continue\\n+ gg=gold[int(u)]; vals=np.array([gg.get(int(i),0) for i in q.item_id]); ideal=np.sort(list(gg.values()))[::-1][:10]\\n+ disc=1/np.log2(np.arange(2,12)); dc.append(((2**vals-1)*disc[:len(vals)]).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\\n+ return float(np.mean(dc)),len(dc)\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],default='validation'); ap.add_argument('--mode',choices=['all','best'],default='all'); args=ap.parse_args()\\n+ stage=args.stage\\n+ cat,items,imap,targ,elig=data(stage); mats=load_events(stage,imap)\\n+ rows=(targ//100).astype(int); pop=np.log1p(cat.positive_listens.to_numpy(np.float32))\\n+ outputs={}\\n+ outputs['pop']=top_frame(lambda a,b:np.broadcast_to(pop,(b-a,len(pop))).copy(),targ,items,elig)\\n+ timer('fitting BM25 SVD')\\n+ X=bm25(mats['wlong']); svd=TruncatedSVD(n_components=96,n_iter=5,random_state=42)\\n+ U=svd.fit_transform(X).astype(np.float32); V=svd.components_.astype(np.float32)\\n+ timer(f'SVD explained={svd.explained_variance_ratio_.sum():.3f}')\\n+ # Normalize latent preference score per user; blend with log popularity.\\n+ def latent(a,b):\\n+ s=U[rows[a:b]]@V\\n+ s=(s-s.mean(1,keepdims=True))/(s.std(1,keepdims=True)+1e-6)\\n+ return s\\n+ for alpha in ([0.0,0.08,0.16,0.28,0.45] if args.mode=='all' else [0.16]):\\n+ def score(a,b,al=alpha): return latent(a,b)+al*((pop-pop.mean())/pop.std())\\n+ outputs[f'svd_p{alpha}']=top_frame(score,targ,items,elig)\\n+ # Cosine KNN on a recency-aware profile, with similarity-weighted neighbors.\\n+ timer('fitting cosine neighbors')\\n+ R=bm25(mats['wrecent'],K1=1.2,B=.7)\\n+ nn=NearestNeighbors(n_neighbors=181,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\\n+ dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\\n+ # exclude self; CSR neighbor-to-user projection\\n+ mask=ind!=rows[:,None]\\n+ nr=np.repeat(np.arange(len(rows)),mask.sum(1)); nc=ind[mask]\\n+ for power in ([1.,2.,4.] if args.mode=='all' else [2.]):\\n+ nw=(sim[mask]**power).astype(np.float32)\\n+ S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\\n+ def knn(a,b):\\n+ z=(S[a:b]@mats['wlong']).toarray().astype(np.float32)\\n+ z=(z-z.mean(1,keepdims=True))/(z.std(1,keepdims=True)+1e-6)\\n+ return z\\n+ for alpha in ([0.,.12,.25] if args.mode=='all' else [.12]):\\n+ def score(a,b,al=alpha): return knn(a,b)+al*((pop-pop.mean())/pop.std())\\n+ outputs[f'knn{power}_p{alpha}']=top_frame(score,targ,items,elig)\\n+ OUT.mkdir(exist_ok=True)\\n+ report={}\\n+ for name,frame in outputs.items():\\n+ path=OUT/(('validation_'+name) if stage=='validation' else ('candidate_'+name))\\n+ frame.to_parquet(path.with_suffix('.parquet'),index=False)\\n+ if stage=='validation':\\n+ report[name]=public_score(frame,items,targ,elig); print(name,report[name],flush=True)\\n+ (OUT/f'{stage}_scores.json').write_text(json.dumps(report,indent=2))\\n+ timer('done')\\n+\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:46:14.100Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:46:14.067Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7792e802-e859-44c3-b6e5-d5844b185a25", "changes": { "/app/solution/build_ranker.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Build validation experiments or the final Made-for-You ranking.\"\"\"\nfrom __future__ import annotations\n\nimport argparse, json, time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.neighbors import NearestNeighbors\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\nCUTOFF = {'validation': 24786800, 'test': 25395200}\n\ndef timer(msg):\n print(f'[{time.strftime(\"%H:%M:%S\")}] {msg}', flush=True)\n\ndef data(stage):\n cat = pd.read_parquet(D/'candidate_catalog.parquet')\n items = cat.item_id.to_numpy(np.int64)\n item_map = pd.Series(np.arange(len(items),dtype=np.int32),index=items)\n targ = pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n z=np.load(D/f'{\"validation\" if stage==\"validation\" else \"test\"}_eligible_candidates.npz')\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n assert np.array_equal(items,z['item_ids']) and np.array_equal(targ,z['uids'])\n return cat,items,item_map,targ,elig\n\ndef load_events(stage,item_map):\n parts=[]\n names=['train'] + (['val'] if stage=='test' else [])\n for split in names:\n timer(f'reading {split} listens')\n x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n x=x[(x.played_ratio_pct>=50)&x.item_id.isin(item_map.index)]\n x=x[['uid','timestamp','item_id','is_organic']]\n parts.append(x)\n x=pd.concat(parts,ignore_index=True)\n x['col']=x.item_id.map(item_map).astype(np.int32)\n x['row']=(x.uid//100).astype(np.int32)\n # Positive events, with organic intent slightly upweighted and exponential recency.\n age=(CUTOFF[stage]-x.timestamp.to_numpy())/86400.0\n x['wlong']=(1.0+0.35*x.is_organic.to_numpy()) * (0.35+0.65*np.exp(-np.maximum(age,0)/120.0))\n x['wrecent']=(1.0+0.50*x.is_organic.to_numpy()) * np.exp(-np.maximum(age,0)/35.0)\n # Add explicit likes as high-confidence positives.\n likes=[]\n for split in names:\n q=pd.read_parquet(D/f'{split}_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\n q=q[q.item_id.isin(item_map.index)].copy()\n q['col']=q.item_id.map(item_map).astype(np.int32); q['row']=(q.uid//100).astype(np.int32)\n ageq=(CUTOFF[stage]-q.timestamp.to_numpy())/86400.0\n q['wlong']=2.5*(1+.25*q.is_organic.to_numpy())*(.35+.65*np.exp(-np.maximum(ageq,0)/180.))\n q['wrecent']=3.0*(1+.25*q.is_organic.to_numpy())*np.exp(-np.maximum(ageq,0)/60.)\n likes.append(q[['row','col','wlong','wrecent']])\n y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\n del x,likes\n shape=(10001,len(item_map))\n mats={}\n for col in ['wlong','wrecent']:\n mats[col]=sparse.coo_matrix((y[col].to_numpy(np.float32),(y.row,y.col)),shape=shape).tocsr()\n mats[col].sum_duplicates()\n mats[col].data=np.log1p(mats[col].data).astype(np.float32)\n timer(f'{col} matrix nnz={mats[col].nnz}')\n return mats\n\ndef bm25(X,K1=1.6,B=.75):\n X=X.tocsr(copy=True)\n rows=np.repeat(np.arange(X.shape[0]),np.diff(X.indptr))\n rl=np.asarray(X.sum(axis=1)).ravel(); avg=max(rl.mean(),1e-6)\n df=np.bincount(X.indices,minlength=X.shape[1])\n idf=np.log(X.shape[0]/(1.0+df)).clip(0)\n den=K1*((1-B)+B*rl[rows]/avg)\n X.data *= idf[X.indices]*(K1+1)/(X.data+den)\n return X\n\ndef top_frame(scores,targ,items,elig,n=100):\n rows=[]\n for a in range(0,len(targ),256):\n s=np.asarray(scores(a,min(a+256,len(targ))),dtype=np.float32)\n s[~elig[a:a+len(s)]]=-np.inf\n ix=np.argpartition(s,-n,axis=1)[:,-n:]\n sv=np.take_along_axis(s,ix,axis=1)\n order=np.argsort(-sv,axis=1)\n ix=np.take_along_axis(ix,order,axis=1)\n for j,u in enumerate(targ[a:a+len(s)]):\n rows.append(pd.DataFrame({'uid':u,'item_id':items[ix[j]],'rank':np.arange(1,n+1)}))\n return pd.concat(rows,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\n\ndef public_score(frame,items,targ,elig):\n im={int(v):i for i,v in enumerate(items)}; ur={int(v):i for i,v in enumerate(targ)}\n vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\n lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\n g=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\n g=g[[elig[ur[int(u)],im[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]]\n gold={int(u):dict(zip(q.item_id.astype(int),q.g.astype(int))) for u,q in g.groupby('uid')}\n dc=[]\n for u,q in frame[frame['rank']<=10].sort_values(['uid','rank']).groupby('uid'):\n if int(u) not in gold: continue\n gg=gold[int(u)]; vals=np.array([gg.get(int(i),0) for i in q.item_id]); ideal=np.sort(list(gg.values()))[::-1][:10]\n disc=1/np.log2(np.arange(2,12)); dc.append(((2**vals-1)*disc[:len(vals)]).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\n return float(np.mean(dc)),len(dc)\n\ndef main():\n ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],default='validation'); ap.add_argument('--mode',choices=['all','best'],default='all'); args=ap.parse_args()\n stage=args.stage\n cat,items,imap,targ,elig=data(stage); mats=load_events(stage,imap)\n rows=(targ//100).astype(int); pop=np.log1p(cat.positive_listens.to_numpy(np.float32))\n outputs={}\n outputs['pop']=top_frame(lambda a,b:np.broadcast_to(pop,(b-a,len(pop))).copy(),targ,items,elig)\n timer('fitting BM25 SVD')\n X=bm25(mats['wlong']); svd=TruncatedSVD(n_components=96,n_iter=5,random_state=42)\n U=svd.fit_transform(X).astype(np.float32); V=svd.components_.astype(np.float32)\n timer(f'SVD explained={svd.explained_variance_ratio_.sum():.3f}')\n # Normalize latent preference score per user; blend with log popularity.\n def latent(a,b):\n s=U[rows[a:b]]@V\n s=(s-s.mean(1,keepdims=True))/(s.std(1,keepdims=True)+1e-6)\n return s\n for alpha in ([0.0,0.08,0.16,0.28,0.45] if args.mode=='all' else [0.16]):\n def score(a,b,al=alpha): return latent(a,b)+al*((pop-pop.mean())/pop.std())\n outputs[f'svd_p{alpha}']=top_frame(score,targ,items,elig)\n # Cosine KNN on a recency-aware profile, with similarity-weighted neighbors.\n timer('fitting cosine neighbors')\n R=bm25(mats['wrecent'],K1=1.2,B=.7)\n nn=NearestNeighbors(n_neighbors=181,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\n dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\n # exclude self; CSR neighbor-to-user projection\n mask=ind!=rows[:,None]\n nr=np.repeat(np.arange(len(rows)),mask.sum(1)); nc=ind[mask]\n for power in ([1.,2.,4.] if args.mode=='all' else [2.]):\n nw=(sim[mask]**power).astype(np.float32)\n S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\n def knn(a,b):\n z=(S[a:b]@mats['wlong']).toarray().astype(np.float32)\n z=(z-z.mean(1,keepdims=True))/(z.std(1,keepdims=True)+1e-6)\n return z\n for alpha in ([0.,.12,.25] if args.mode=='all' else [.12]):\n def score(a,b,al=alpha): return knn(a,b)+al*((pop-pop.mean())/pop.std())\n outputs[f'knn{power}_p{alpha}']=top_frame(score,targ,items,elig)\n OUT.mkdir(exist_ok=True)\n report={}\n for name,frame in outputs.items():\n path=OUT/(('validation_'+name) if stage=='validation' else ('candidate_'+name))\n frame.to_parquet(path.with_suffix('.parquet'),index=False)\n if stage=='validation':\n report[name]=public_score(frame,items,targ,elig); print(name,report[name],flush=True)\n (OUT/f'{stage}_scores.json').write_text(json.dumps(report,indent=2))\n timer('done')\n\nif __name__=='__main__': main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:46:14.099Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab4482399e087d28f79d64d91c36a04", "usage": { "input_tokens": 25362, "cached_input_tokens": 24960, "cache_write_input_tokens": 0, "output_tokens": 4059, "reasoning_output_tokens": 1198, "total_tokens": 29421 }, "turn_token_usage": { "input_tokens": 253164, "cached_input_tokens": 240128, "cache_write_input_tokens": 0, "output_tokens": 7836, "reasoning_output_tokens": 2279, "total_tokens": 261000 }, "thread_token_usage": { "input_tokens": 253164, "cached_input_tokens": 240128, "cache_write_input_tokens": 0, "output_tokens": 7836, "reasoning_output_tokens": 2279, "total_tokens": 261000 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-23T21:46:16.161Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:46:16.161Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:46:18.393Z", "callId": "call_hs2M54V3mbTdaJ4mx4F0g6dx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode all\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T21:46:19.559Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=97791", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:46:18.457Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab448a6660487d2be58962df66266a0", "usage": { "input_tokens": 29445, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 91, "reasoning_output_tokens": 11, "total_tokens": 29536 }, "turn_token_usage": { "input_tokens": 282609, "cached_input_tokens": 265344, "cache_write_input_tokens": 0, "output_tokens": 7927, "reasoning_output_tokens": 2290, "total_tokens": 290536 }, "thread_token_usage": { "input_tokens": 282609, "cached_input_tokens": 265344, "cache_write_input_tokens": 0, "output_tokens": 7927, "reasoning_output_tokens": 2290, "total_tokens": 290536 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-23T21:46:21.091Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:46:21.091Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:46:22.320Z", "callId": "call_8yqcygWAtP5BxU1Dy7xh2c8o", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97791,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:46:52.341Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:46:22.388Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab448abdffc87d2a11d0c7a930b3c59", "usage": { "input_tokens": 29564, "cached_input_tokens": 29312, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 29618 }, "turn_token_usage": { "input_tokens": 312173, "cached_input_tokens": 294656, "cache_write_input_tokens": 0, "output_tokens": 7981, "reasoning_output_tokens": 2297, "total_tokens": 320154 }, "thread_token_usage": { "input_tokens": 312173, "cached_input_tokens": 294656, "cache_write_input_tokens": 0, "output_tokens": 7981, "reasoning_output_tokens": 2297, "total_tokens": 320154 } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-23T21:46:54.380Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:46:54.380Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:46:55.570Z", "callId": "call_t5ho82v7a7Q4DvZzclVLCIoB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97791,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:47:25.580Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n[21:46:52] fitting cosine neighbors\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:46:56.843Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab448cc954487d2895aa51b0d04d56e", "usage": { "input_tokens": 29714, "cached_input_tokens": 29440, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 15, "total_tokens": 29776 }, "turn_token_usage": { "input_tokens": 341887, "cached_input_tokens": 324096, "cache_write_input_tokens": 0, "output_tokens": 8043, "reasoning_output_tokens": 2312, "total_tokens": 349930 }, "thread_token_usage": { "input_tokens": 341887, "cached_input_tokens": 324096, "cache_write_input_tokens": 0, "output_tokens": 8043, "reasoning_output_tokens": 2312, "total_tokens": 349930 } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-23T21:47:27.155Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:47:27.155Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:47:28.359Z", "callId": "call_wFkXLwIWdMbAXjswuKXlRYMz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97791,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:47:58.368Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:47:28.418Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab448edd52c87d29ced9943306f5142", "usage": { "input_tokens": 29810, "cached_input_tokens": 29568, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 29864 }, "turn_token_usage": { "input_tokens": 371697, "cached_input_tokens": 353664, "cache_write_input_tokens": 0, "output_tokens": 8097, "reasoning_output_tokens": 2319, "total_tokens": 379794 }, "thread_token_usage": { "input_tokens": 371697, "cached_input_tokens": 353664, "cache_write_input_tokens": 0, "output_tokens": 8097, "reasoning_output_tokens": 2319, "total_tokens": 379794 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-23T21:48:00.073Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:48:00.073Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:48:01.319Z", "callId": "call_7L7wTznz3mbktf3LLR1nVRVb", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97791,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:48:31.328Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:48:01.385Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab4490e9bfc87d2a21198bca4279226", "usage": { "input_tokens": 29887, "cached_input_tokens": 29568, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 14, "total_tokens": 29948 }, "turn_token_usage": { "input_tokens": 401584, "cached_input_tokens": 383232, "cache_write_input_tokens": 0, "output_tokens": 8158, "reasoning_output_tokens": 2333, "total_tokens": 409742 }, "thread_token_usage": { "input_tokens": 401584, "cached_input_tokens": 383232, "cache_write_input_tokens": 0, "output_tokens": 8158, "reasoning_output_tokens": 2333, "total_tokens": 409742 } } }, { "kind": "context", "timestamp": "2026-09-23T21:48:34.111Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e2a54aa2-26aa-463e-8624-9e4b7ec29638", "process_id": "97791", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode all" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode all" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n[21:46:52] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n", "stderr": "", "aggregated_output": "[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n[21:46:52] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n", "exit_code": 0, "duration": { "secs": 135, "nanos": 554149196 }, "formatted_output": "[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n[21:46:52] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n" } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-23T21:48:36.932Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:48:36.932Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:48:36.939Z", "callId": "call_8XksmsKzlLFfcGVYJKka7QKg", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97791,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:48:36.981Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:48:36.980Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab4492f952087d2831f3f19f0256d5c", "usage": { "input_tokens": 30224, "cached_input_tokens": 29696, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 30281 }, "turn_token_usage": { "input_tokens": 431808, "cached_input_tokens": 412928, "cache_write_input_tokens": 0, "output_tokens": 8215, "reasoning_output_tokens": 2343, "total_tokens": 440023 }, "thread_token_usage": { "input_tokens": 431808, "cached_input_tokens": 412928, "cache_write_input_tokens": 0, "output_tokens": 8215, "reasoning_output_tokens": 2343, "total_tokens": 440023 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-23T21:48:39.435Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:48:39.435Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T21:48:42.125Z", "text": "The first full backtest is valid and already beats popularity by over 2×: popularity scores 0.0107 NDCG@10, while collaborative models reach 0.0229. The best so far is a sharply weighted nearest-listener model; I’m now tuning temporal horizons, neighbor count, and hybridization, which are the highest-leverage choices for this data." } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-23T21:48:58.726Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:48:58.726Z", "text": "" } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-23T21:49:00.018Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:49:00.018Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:49:04.741Z", "callId": "call_fb9eC7jY6ZseH4b82HcsBzWo", "name": "exec", "input": { "code": "let{output,...rest}=await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfor s in ['validation','test']:\\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\\n print(s,x.state.value_counts(),x.groupby('uid').size().describe(),x.groupby(['uid','item_id']).size().value_counts().head())\\nPY\\npython /task/evaluate_public.py solution/validation_knn4.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});text(rest);text(output);\n" }, "result": { "timestamp": "2026-09-23T21:49:05.733Z", "content": "Script completed\nWall time 1.0 seconds\nOutput:\n\n{\"chunk_id\":\"27d94b\",\"wall_time_seconds\":0.825254369,\"exit_code\":0,\"original_token_count\":156}\nvalidation state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64 1 465704\n2 1710\nName: count, dtype: int64\ntest state\nliked 432659\ndisliked 52117\nName: count, dtype: int64 count 7092.000000\nmean 68.355330\nstd 112.302043\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 83.000000\nmax 1861.000000\ndtype: float64 1 481224\n2 1776\nName: count, dtype: int64\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:49:04.786Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449356c8087d2939cd0f2a5f8ba19", "usage": { "input_tokens": 30382, "cached_input_tokens": 30080, "cache_write_input_tokens": 0, "output_tokens": 823, "reasoning_output_tokens": 581, "total_tokens": 31205 }, "turn_token_usage": { "input_tokens": 462190, "cached_input_tokens": 443008, "cache_write_input_tokens": 0, "output_tokens": 9038, "reasoning_output_tokens": 2924, "total_tokens": 471228 }, "thread_token_usage": { "input_tokens": 462190, "cached_input_tokens": 443008, "cache_write_input_tokens": 0, "output_tokens": 9038, "reasoning_output_tokens": 2924, "total_tokens": 471228 } } }, { "kind": "context", "timestamp": "2026-09-23T21:49:05.729Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-96b87e69-c1c4-4fa0-93ea-45a77037f31e", "process_id": "24334", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor s in ['validation','test']:\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\n print(s,x.state.value_counts(),x.groupby('uid').size().describe(),x.groupby(['uid','item_id']).size().value_counts().head())\nPY\npython /task/evaluate_public.py solution/validation_knn4.parquet 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor s in ['validation','test']:\n x=pd.read_parquet('/task/data/'+s+'_preference_state.parquet')\n print(s,x.state.value_counts(),x.groupby('uid').size().describe(),x.groupby(['uid','item_id']).size().value_counts().head())\nPY\npython /task/evaluate_public.py solution/validation_knn4.parquet 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64 1 465704\n2 1710\nName: count, dtype: int64\ntest state\nliked 432659\ndisliked 52117\nName: count, dtype: int64 count 7092.000000\nmean 68.355330\nstd 112.302043\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 83.000000\nmax 1861.000000\ndtype: float64 1 481224\n2 1776\nName: count, dtype: int64\n", "stderr": "", "aggregated_output": "validation state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64 1 465704\n2 1710\nName: count, dtype: int64\ntest state\nliked 432659\ndisliked 52117\nName: count, dtype: int64 count 7092.000000\nmean 68.355330\nstd 112.302043\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 83.000000\nmax 1861.000000\ndtype: float64 1 481224\n2 1776\nName: count, dtype: int64\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 825250859 }, "formatted_output": "validation state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 count 7038.000000\nmean 66.655868\nstd 108.686351\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 81.000000\nmax 1847.000000\ndtype: float64 1 465704\n2 1710\nName: count, dtype: int64\ntest state\nliked 432659\ndisliked 52117\nName: count, dtype: int64 count 7092.000000\nmean 68.355330\nstd 112.302043\nmin 1.000000\n25% 7.000000\n50% 30.000000\n75% 83.000000\nmax 1861.000000\ndtype: float64 1 481224\n2 1776\nName: count, dtype: int64\n" } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-23T21:49:23.241Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:49:23.241Z", "text": "" } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-23T21:49:27.859Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:49:27.859Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:50:08.981Z", "callId": "call_VW0B1qThhIx38nLTWldUVJ9C", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_ranker.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Validation-only hyperparameter search; never used as validation features.\\\"\\\"\\\"\\n+import numpy as np, pandas as pd\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from sklearn.decomposition import TruncatedSVD\\n+from build_ranker import data, load_events, bm25, top_frame, public_score, timer, OUT, D\\n+\\n+cat,items,imap,targ,elig=data('validation')\\n+mats=load_events('validation',imap); rows=(targ//100).astype(int)\\n+\\n+# Add cutoff-correct active likes as profile evidence (and compare independently).\\n+state=pd.read_parquet(D/'validation_preference_state.parquet')\\n+state=state[state.item_id.isin(items)]\\n+sm=[]\\n+for name,sign in [('liked',1.),('disliked',-1.)]:\\n+ q=state[state.state==name]\\n+ sm.append(sparse.coo_matrix((np.full(len(q),sign,np.float32),((q.uid//100).astype(int),q.item_id.map(imap).astype(int))),shape=mats['wlong'].shape).tocsr())\\n+liked,disliked=sm\\n+\\n+def gold_data():\\n+ ur={int(v):i for i,v in enumerate(targ)}; im={int(v):i for i,v in enumerate(items)}\\n+ vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+ vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\\n+ lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+ lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\\n+ q=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\\n+ q=q[[elig[ur[int(u)],im[int(i)]] for u,i in q[['uid','item_id']].itertuples(index=False,name=None)]]\\n+ return {ur[int(u)]:{im[int(i)]:int(g) for i,g in zip(z.item_id,z.g)} for u,z in q.groupby('uid')}\\n+gold=gold_data(); disc=1/np.log2(np.arange(2,12))\\n+\\n+def metric(scorefn):\\n+ vals=[]\\n+ for a in range(0,len(targ),256):\\n+ b=min(a+256,len(targ)); s=scorefn(a,b); s[~elig[a:b]]=-np.inf\\n+ ix=np.argpartition(s,-10,axis=1)[:,-10:]\\n+ ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\\n+ for j in range(b-a):\\n+ gg=gold.get(a+j)\\n+ if not gg: continue\\n+ grades=np.array([gg.get(int(i),0) for i in ix[j]])\\n+ ideal=np.sort(list(gg.values()))[::-1][:10]\\n+ vals.append(((2**grades-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\\n+ return np.mean(vals)\\n+\\n+# Broad-neighbor retrieval, then tune effective neighborhood and popularity shrinkage.\\n+for profile_name,R0 in [('recent',mats['wrecent']),('long',mats['wlong']),('active',mats['wrecent']+3*liked)]:\\n+ timer('neighbors '+profile_name)\\n+ R=bm25(R0,K1=1.2,B=.7)\\n+ nn=NearestNeighbors(n_neighbors=401,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\\n+ dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\\n+ df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\\n+ for k in [40,80,180,400]:\\n+ ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\\n+ nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\\n+ for power in [2.,4.,7.]:\\n+ nw=(ss[keep]**power).astype(np.float32)\\n+ S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\\n+ for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32)),('recent',mats['wrecent'])]:\\n+ for gamma in [0.,.15,.3,.5]:\\n+ scale=np.power(df+3,gamma)\\n+ val=metric(lambda a,b,S=S,M=M,scale=scale:(S[a:b]@M).toarray()/scale)\\n+ print('RESULT',profile_name,k,power,predname,gamma,val,flush=True)\\n+\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:50:09.046Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:50:08.989Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d3c3db22-fbcd-4b31-9a34-f7adbf722e42", "changes": { "/app/solution/tune_ranker.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Validation-only hyperparameter search; never used as validation features.\"\"\"\nimport numpy as np, pandas as pd\nfrom scipy import sparse\nfrom sklearn.neighbors import NearestNeighbors\nfrom sklearn.decomposition import TruncatedSVD\nfrom build_ranker import data, load_events, bm25, top_frame, public_score, timer, OUT, D\n\ncat,items,imap,targ,elig=data('validation')\nmats=load_events('validation',imap); rows=(targ//100).astype(int)\n\n# Add cutoff-correct active likes as profile evidence (and compare independently).\nstate=pd.read_parquet(D/'validation_preference_state.parquet')\nstate=state[state.item_id.isin(items)]\nsm=[]\nfor name,sign in [('liked',1.),('disliked',-1.)]:\n q=state[state.state==name]\n sm.append(sparse.coo_matrix((np.full(len(q),sign,np.float32),((q.uid//100).astype(int),q.item_id.map(imap).astype(int))),shape=mats['wlong'].shape).tocsr())\nliked,disliked=sm\n\ndef gold_data():\n ur={int(v):i for i,v in enumerate(targ)}; im={int(v):i for i,v in enumerate(items)}\n vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\n lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\n q=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\n q=q[[elig[ur[int(u)],im[int(i)]] for u,i in q[['uid','item_id']].itertuples(index=False,name=None)]]\n return {ur[int(u)]:{im[int(i)]:int(g) for i,g in zip(z.item_id,z.g)} for u,z in q.groupby('uid')}\ngold=gold_data(); disc=1/np.log2(np.arange(2,12))\n\ndef metric(scorefn):\n vals=[]\n for a in range(0,len(targ),256):\n b=min(a+256,len(targ)); s=scorefn(a,b); s[~elig[a:b]]=-np.inf\n ix=np.argpartition(s,-10,axis=1)[:,-10:]\n ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n for j in range(b-a):\n gg=gold.get(a+j)\n if not gg: continue\n grades=np.array([gg.get(int(i),0) for i in ix[j]])\n ideal=np.sort(list(gg.values()))[::-1][:10]\n vals.append(((2**grades-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\n return np.mean(vals)\n\n# Broad-neighbor retrieval, then tune effective neighborhood and popularity shrinkage.\nfor profile_name,R0 in [('recent',mats['wrecent']),('long',mats['wlong']),('active',mats['wrecent']+3*liked)]:\n timer('neighbors '+profile_name)\n R=bm25(R0,K1=1.2,B=.7)\n nn=NearestNeighbors(n_neighbors=401,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\n dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\n df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\n for k in [40,80,180,400]:\n ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\n nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\n for power in [2.,4.,7.]:\n nw=(ss[keep]**power).astype(np.float32)\n S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\n for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32)),('recent',mats['wrecent'])]:\n for gamma in [0.,.15,.3,.5]:\n scale=np.power(df+3,gamma)\n val=metric(lambda a,b,S=S,M=M,scale=scale:(S[a:b]@M).toarray()/scale)\n print('RESULT',profile_name,k,power,predname,gamma,val,flush=True)\n\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:50:09.045Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab4495205d087d2ad6b8fde816ec531", "usage": { "input_tokens": 31496, "cached_input_tokens": 30208, "cache_write_input_tokens": 0, "output_tokens": 1948, "reasoning_output_tokens": 654, "total_tokens": 33444 }, "turn_token_usage": { "input_tokens": 493686, "cached_input_tokens": 473216, "cache_write_input_tokens": 0, "output_tokens": 10986, "reasoning_output_tokens": 3578, "total_tokens": 504672 }, "thread_token_usage": { "input_tokens": 493686, "cached_input_tokens": 473216, "cache_write_input_tokens": 0, "output_tokens": 10986, "reasoning_output_tokens": 3578, "total_tokens": 504672 } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-23T21:50:17.165Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:50:17.165Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:50:26.525Z", "callId": "call_IJI8hjIS6tR83GCVaXRij0PH", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_ranker.py\\n@@\\n-for profile_name,R0 in [('recent',mats['wrecent']),('long',mats['wlong']),('active',mats['wrecent']+3*liked)]:\\n+for profile_name,R0 in [('recent',mats['wrecent']),('active',mats['wrecent']+3*liked)]:\\n@@\\n- for k in [40,80,180,400]:\\n+ for k in [40,100,250]:\\n@@\\n- for power in [2.,4.,7.]:\\n+ for power in [4.,7.]:\\n@@\\n- for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32)),('recent',mats['wrecent'])]:\\n- for gamma in [0.,.15,.3,.5]:\\n+ for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32))]:\\n+ for gamma in [0.,.15,.3]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:50:26.564Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:50:26.533Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-cafecb96-33b6-402b-8b61-d88b341f68ad", "changes": { "/app/solution/tune_ranker.py": { "type": "update", "unified_diff": "@@ -46,3 +46,3 @@\n # Broad-neighbor retrieval, then tune effective neighborhood and popularity shrinkage.\n-for profile_name,R0 in [('recent',mats['wrecent']),('long',mats['wlong']),('active',mats['wrecent']+3*liked)]:\n+for profile_name,R0 in [('recent',mats['wrecent']),('active',mats['wrecent']+3*liked)]:\n timer('neighbors '+profile_name)\n@@ -52,10 +52,10 @@\n df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\n- for k in [40,80,180,400]:\n+ for k in [40,100,250]:\n ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\n nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\n- for power in [2.,4.,7.]:\n+ for power in [4.,7.]:\n nw=(ss[keep]**power).astype(np.float32)\n S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\n- for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32)),('recent',mats['wrecent'])]:\n- for gamma in [0.,.15,.3,.5]:\n+ for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32))]:\n+ for gamma in [0.,.15,.3]:\n scale=np.power(df+3,gamma)\n@@ -63,2 +63 @@\n print('RESULT',profile_name,k,power,predname,gamma,val,flush=True)\n-\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:50:26.563Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab4499152f087d2b5637c456b4957be", "usage": { "input_tokens": 33468, "cached_input_tokens": 31360, "cache_write_input_tokens": 0, "output_tokens": 520, "reasoning_output_tokens": 218, "total_tokens": 33988 }, "turn_token_usage": { "input_tokens": 527154, "cached_input_tokens": 504576, "cache_write_input_tokens": 0, "output_tokens": 11506, "reasoning_output_tokens": 3796, "total_tokens": 538660 }, "thread_token_usage": { "input_tokens": 527154, "cached_input_tokens": 504576, "cache_write_input_tokens": 0, "output_tokens": 11506, "reasoning_output_tokens": 3796, "total_tokens": 538660 } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-23T21:50:29.031Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:50:29.031Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:50:31.415Z", "callId": "call_mCDHj2wnY7JENgT3jSwyHMCS", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/tune_ranker.py > solution/tune.log 2>&1\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T21:50:32.605Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=45398", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:50:31.519Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449a2e9c487d29267ebea390cd5a2", "usage": { "input_tokens": 34012, "cached_input_tokens": 33280, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 11, "total_tokens": 34107 }, "turn_token_usage": { "input_tokens": 561166, "cached_input_tokens": 537856, "cache_write_input_tokens": 0, "output_tokens": 11601, "reasoning_output_tokens": 3807, "total_tokens": 572767 }, "thread_token_usage": { "input_tokens": 561166, "cached_input_tokens": 537856, "cache_write_input_tokens": 0, "output_tokens": 11601, "reasoning_output_tokens": 3807, "total_tokens": 572767 } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-23T21:50:34.780Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:50:34.780Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:50:35.628Z", "callId": "call_KdH3QPnptHR4MfD1ujuSS5dp", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -20 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:50:35.712Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:50:32] reading train listens\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:50:35.664Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a19fe17f-8a86-4583-b02f-4ad289b87759", "process_id": "48487", "command": [ "/bin/sh", "-lc", "tail -20 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -20 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:50:32] reading train listens\n", "stderr": "", "aggregated_output": "[21:50:32] reading train listens\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 1911 }, "formatted_output": "[21:50:32] reading train listens\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:50:35.711Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449a8d9e487d2a6c71710a43c7f4c", "usage": { "input_tokens": 34135, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 8, "total_tokens": 34197 }, "turn_token_usage": { "input_tokens": 595301, "cached_input_tokens": 571648, "cache_write_input_tokens": 0, "output_tokens": 11663, "reasoning_output_tokens": 3815, "total_tokens": 606964 }, "thread_token_usage": { "input_tokens": 595301, "cached_input_tokens": 571648, "cache_write_input_tokens": 0, "output_tokens": 11663, "reasoning_output_tokens": 3815, "total_tokens": 606964 } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-23T21:50:37.484Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:50:37.484Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:50:38.625Z", "callId": "call_XdEehCWlrOzN6Zh1nosODFpV", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:51:08.636Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:50:38.709Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449ac24e887d2accde441ccd9c590", "usage": { "input_tokens": 34231, "cached_input_tokens": 33920, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 34284 }, "turn_token_usage": { "input_tokens": 629532, "cached_input_tokens": 605568, "cache_write_input_tokens": 0, "output_tokens": 11716, "reasoning_output_tokens": 3821, "total_tokens": 641248 }, "thread_token_usage": { "input_tokens": 629532, "cached_input_tokens": 605568, "cache_write_input_tokens": 0, "output_tokens": 11716, "reasoning_output_tokens": 3821, "total_tokens": 641248 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:51:11.138Z", "callId": "call_QngWGvyzqCYNcgdU4ymKKSUt", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -15 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:51:11.225Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:51:11.174Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-95e67118-61ca-4810-befd-92ef14787738", "process_id": "1439", "command": [ "/bin/sh", "-lc", "tail -15 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\n", "stderr": "", "aggregated_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5020 }, "formatted_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:51:11.223Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449cce29c87d283bcc75b62a8972b", "usage": { "input_tokens": 34307, "cached_input_tokens": 34048, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 34359 }, "turn_token_usage": { "input_tokens": 663839, "cached_input_tokens": 639616, "cache_write_input_tokens": 0, "output_tokens": 11768, "reasoning_output_tokens": 3821, "total_tokens": 675607 }, "thread_token_usage": { "input_tokens": 663839, "cached_input_tokens": 639616, "cache_write_input_tokens": 0, "output_tokens": 11768, "reasoning_output_tokens": 3821, "total_tokens": 675607 } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-23T21:51:13.914Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:51:13.914Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:51:15.104Z", "callId": "call_4bFyKSzJSs5PdzZc2jvdaUET", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:51:45.112Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:51:15.192Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449cfb05c87d286b22e59f82a5fd9", "usage": { "input_tokens": 34575, "cached_input_tokens": 34176, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 34631 }, "turn_token_usage": { "input_tokens": 698414, "cached_input_tokens": 673792, "cache_write_input_tokens": 0, "output_tokens": 11824, "reasoning_output_tokens": 3830, "total_tokens": 710238 }, "thread_token_usage": { "input_tokens": 698414, "cached_input_tokens": 673792, "cache_write_input_tokens": 0, "output_tokens": 11824, "reasoning_output_tokens": 3830, "total_tokens": 710238 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:51:47.914Z", "callId": "call_yRVxHfu9v6eUVh64AdxcvPIK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -25 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:51:47.997Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:51:47.950Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f82d98f8-ade2-4c9d-867e-d4b69199a827", "process_id": "2148", "command": [ "/bin/sh", "-lc", "tail -25 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -25 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\n", "stderr": "", "aggregated_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4131 }, "formatted_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:51:47.996Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449f1856887d29d8c6d56ff128ae4", "usage": { "input_tokens": 34654, "cached_input_tokens": 34432, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 34706 }, "turn_token_usage": { "input_tokens": 733068, "cached_input_tokens": 708224, "cache_write_input_tokens": 0, "output_tokens": 11876, "reasoning_output_tokens": 3830, "total_tokens": 744944 }, "thread_token_usage": { "input_tokens": 733068, "cached_input_tokens": 708224, "cache_write_input_tokens": 0, "output_tokens": 11876, "reasoning_output_tokens": 3830, "total_tokens": 744944 } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-23T21:51:50.081Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:51:50.081Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:51:51.285Z", "callId": "call_40MhNPNFa9TNypXACh1UL0Yc", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:52:21.293Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:51:51.375Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab449f43c9887d2bf154363aa6e5025", "usage": { "input_tokens": 35083, "cached_input_tokens": 34432, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 21, "total_tokens": 35151 }, "turn_token_usage": { "input_tokens": 768151, "cached_input_tokens": 742656, "cache_write_input_tokens": 0, "output_tokens": 11944, "reasoning_output_tokens": 3851, "total_tokens": 780095 }, "thread_token_usage": { "input_tokens": 768151, "cached_input_tokens": 742656, "cache_write_input_tokens": 0, "output_tokens": 11944, "reasoning_output_tokens": 3851, "total_tokens": 780095 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:52:24.213Z", "callId": "call_ajcSBDDYYbrW1HnKjJij5kuK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -25 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:52:24.305Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:52:24.249Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1d79b775-3b47-4f21-9553-b29c17ce0bc5", "process_id": "9537", "command": [ "/bin/sh", "-lc", "tail -25 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -25 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\n", "stderr": "", "aggregated_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4170 }, "formatted_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:52:24.304Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a15847c87d2b1ad2f6aaa3278b5", "usage": { "input_tokens": 35174, "cached_input_tokens": 34944, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 35226 }, "turn_token_usage": { "input_tokens": 803325, "cached_input_tokens": 777600, "cache_write_input_tokens": 0, "output_tokens": 11996, "reasoning_output_tokens": 3851, "total_tokens": 815321 }, "thread_token_usage": { "input_tokens": 803325, "cached_input_tokens": 777600, "cache_write_input_tokens": 0, "output_tokens": 11996, "reasoning_output_tokens": 3851, "total_tokens": 815321 } } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-23T21:52:26.551Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:52:26.551Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:52:27.781Z", "callId": "call_mat0Dh3iY0VjswMh0ebAGiLB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:52:57.789Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:52:27.874Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a18ba2087d297695b57f9a98179", "usage": { "input_tokens": 35741, "cached_input_tokens": 34944, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 15, "total_tokens": 35803 }, "turn_token_usage": { "input_tokens": 839066, "cached_input_tokens": 812544, "cache_write_input_tokens": 0, "output_tokens": 12058, "reasoning_output_tokens": 3866, "total_tokens": 851124 }, "thread_token_usage": { "input_tokens": 839066, "cached_input_tokens": 812544, "cache_write_input_tokens": 0, "output_tokens": 12058, "reasoning_output_tokens": 3866, "total_tokens": 851124 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:53:00.467Z", "callId": "call_2uaw6jg2Zzs9WnnpidNAtfgR", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -30 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:53:00.575Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:53:00.504Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ebdb08e4-ab52-4137-8c3c-4c0c5530d662", "process_id": "84030", "command": [ "/bin/sh", "-lc", "tail -30 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -30 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\n", "stderr": "", "aggregated_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3761 }, "formatted_output": "[21:50:32] reading train listens\n[21:50:36] wlong matrix nnz=5687487\n[21:50:37] wrecent matrix nnz=5687487\n[21:50:37] neighbors recent\nRESULT recent 40 4.0 long 0.0 0.022311272758873454\nRESULT recent 40 4.0 long 0.15 0.021321659191147497\nRESULT recent 40 4.0 long 0.3 0.018456725244912358\nRESULT recent 40 4.0 binary 0.0 0.020344948122429554\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:53:00.574Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a39fdd887d29fd51a7067f127a6", "usage": { "input_tokens": 35826, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 35878 }, "turn_token_usage": { "input_tokens": 874892, "cached_input_tokens": 848128, "cache_write_input_tokens": 0, "output_tokens": 12110, "reasoning_output_tokens": 3866, "total_tokens": 887002 }, "thread_token_usage": { "input_tokens": 874892, "cached_input_tokens": 848128, "cache_write_input_tokens": 0, "output_tokens": 12110, "reasoning_output_tokens": 3866, "total_tokens": 887002 } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-23T21:53:03.350Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:53:03.350Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:53:04.540Z", "callId": "call_Nlfa1QdF90qu0IRmJz9HfblH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:53:34.548Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:53:04.639Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a3d095487d29b1153e8642da573", "usage": { "input_tokens": 36531, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 13, "total_tokens": 36591 }, "turn_token_usage": { "input_tokens": 911423, "cached_input_tokens": 883712, "cache_write_input_tokens": 0, "output_tokens": 12170, "reasoning_output_tokens": 3879, "total_tokens": 923593 }, "thread_token_usage": { "input_tokens": 911423, "cached_input_tokens": 883712, "cache_write_input_tokens": 0, "output_tokens": 12170, "reasoning_output_tokens": 3879, "total_tokens": 923593 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:53:37.230Z", "callId": "call_Dcie1uc8zJxIrqOv8uAXWbtG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -25 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:53:37.334Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nRESULT recent 40 4.0 binary 0.15 0.02054663915036692\nRESULT recent 40 4.0 binary 0.3 0.01812363727834161\nRESULT recent 40 7.0 long 0.0 0.01898376739495104\nRESULT recent 40 7.0 long 0.15 0.01734622632411481\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\nRESULT recent 250 4.0 long 0.15 0.023551356916060827\nRESULT recent 250 4.0 long 0.3 0.02227602208108781\nRESULT recent 250 4.0 binary 0.0 0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:53:37.266Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0e789847-e9d1-4d55-b93e-d9268c0c7b7b", "process_id": "98330", "command": [ "/bin/sh", "-lc", "tail -25 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -25 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0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\nRESULT recent 250 4.0 long 0.15 0.023551356916060827\nRESULT recent 250 4.0 long 0.3 0.02227602208108781\nRESULT recent 250 4.0 binary 0.0 0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:53:37.332Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a5ec51c87d2b0722b052c6c1846", "usage": { "input_tokens": 36614, "cached_input_tokens": 36352, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 36666 }, "turn_token_usage": { "input_tokens": 948037, "cached_input_tokens": 920064, "cache_write_input_tokens": 0, "output_tokens": 12222, "reasoning_output_tokens": 3879, "total_tokens": 960259 }, "thread_token_usage": { "input_tokens": 948037, "cached_input_tokens": 920064, "cache_write_input_tokens": 0, "output_tokens": 12222, "reasoning_output_tokens": 3879, "total_tokens": 960259 } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-23T21:53:39.160Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:53:39.160Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:53:40.380Z", "callId": "call_50043sXfZEIub7jle7JSXY2Y", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:54:10.388Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:53:40.465Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a61b55087d2aafcb4b5947592b3", "usage": { "input_tokens": 37264, "cached_input_tokens": 36480, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 37322 }, "turn_token_usage": { "input_tokens": 985301, "cached_input_tokens": 956544, "cache_write_input_tokens": 0, "output_tokens": 12280, "reasoning_output_tokens": 3890, "total_tokens": 997581 }, "thread_token_usage": { "input_tokens": 985301, "cached_input_tokens": 956544, "cache_write_input_tokens": 0, "output_tokens": 12280, "reasoning_output_tokens": 3890, "total_tokens": 997581 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:54:13.083Z", "callId": "call_WypSAcMeqbwd0YYY9VoFK7ye", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -25 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:54:13.190Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nRESULT recent 40 7.0 long 0.3 0.014046235393760148\nRESULT recent 40 7.0 binary 0.0 0.018651243141382324\nRESULT recent 40 7.0 binary 0.15 0.016816779299697746\nRESULT recent 40 7.0 binary 0.3 0.013383691065744541\nRESULT recent 100 4.0 long 0.0 0.022835137440614973\nRESULT recent 100 4.0 long 0.15 0.022929850239060495\nRESULT recent 100 4.0 long 0.3 0.020747680268171448\nRESULT recent 100 4.0 binary 0.0 0.020580789575862117\nRESULT recent 100 4.0 binary 0.15 0.02186057385632178\nRESULT recent 100 4.0 binary 0.3 0.020129057890859076\nRESULT recent 100 7.0 long 0.0 0.01989755647359011\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 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0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:54:13.189Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a82a38087d283ebb1986120b58d", "usage": { "input_tokens": 37345, "cached_input_tokens": 37120, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 37397 }, "turn_token_usage": { "input_tokens": 1022646, "cached_input_tokens": 993664, "cache_write_input_tokens": 0, "output_tokens": 12332, "reasoning_output_tokens": 3890, "total_tokens": 1034978 }, "thread_token_usage": { "input_tokens": 1022646, "cached_input_tokens": 993664, "cache_write_input_tokens": 0, "output_tokens": 12332, "reasoning_output_tokens": 3890, "total_tokens": 1034978 } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-23T21:54:15.076Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:54:15.076Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:54:16.265Z", "callId": "call_j5RcxKf5XkNhIHijNPz5qLWC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:54:46.272Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:54:16.357Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44a85848887d2a8583a9c04479a12", "usage": { "input_tokens": 37995, "cached_input_tokens": 37120, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 38051 }, "turn_token_usage": { "input_tokens": 1060641, "cached_input_tokens": 1030784, "cache_write_input_tokens": 0, "output_tokens": 12388, "reasoning_output_tokens": 3899, "total_tokens": 1073029 }, "thread_token_usage": { "input_tokens": 1060641, "cached_input_tokens": 1030784, "cache_write_input_tokens": 0, "output_tokens": 12388, "reasoning_output_tokens": 3899, "total_tokens": 1073029 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:54:49.848Z", "callId": "call_1Xe8kOz74Kkbu2dbTUgO3Oiq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -20 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:54:49.962Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nRESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\nRESULT recent 250 4.0 long 0.15 0.023551356916060827\nRESULT recent 250 4.0 long 0.3 0.02227602208108781\nRESULT recent 250 4.0 binary 0.0 0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:54:49.884Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f88e6a00-b795-40a8-bafd-128109d6b178", "process_id": "43645", "command": [ "/bin/sh", "-lc", "tail -20 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -20 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "RESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\nRESULT recent 250 4.0 long 0.15 0.023551356916060827\nRESULT recent 250 4.0 long 0.3 0.02227602208108781\nRESULT recent 250 4.0 binary 0.0 0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\n", "stderr": "", "aggregated_output": "RESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\nRESULT recent 250 4.0 long 0.15 0.023551356916060827\nRESULT recent 250 4.0 long 0.3 0.02227602208108781\nRESULT recent 250 4.0 binary 0.0 0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3570 }, "formatted_output": "RESULT recent 100 7.0 long 0.15 0.018481547759148932\nRESULT recent 100 7.0 long 0.3 0.014758901548445509\nRESULT recent 100 7.0 binary 0.0 0.019060139371666923\nRESULT recent 100 7.0 binary 0.15 0.01770961264070054\nRESULT recent 100 7.0 binary 0.3 0.01419990090070805\nRESULT recent 250 4.0 long 0.0 0.022426506181846103\nRESULT recent 250 4.0 long 0.15 0.023551356916060827\nRESULT recent 250 4.0 long 0.3 0.02227602208108781\nRESULT recent 250 4.0 binary 0.0 0.020591858353666954\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:54:49.960Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44aa68ab087d2aadb669cc9f61f5c", "usage": { "input_tokens": 38074, "cached_input_tokens": 37760, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 38126 }, "turn_token_usage": { "input_tokens": 1098715, "cached_input_tokens": 1068544, "cache_write_input_tokens": 0, "output_tokens": 12440, "reasoning_output_tokens": 3899, "total_tokens": 1111155 }, "thread_token_usage": { "input_tokens": 1098715, "cached_input_tokens": 1068544, "cache_write_input_tokens": 0, "output_tokens": 12440, "reasoning_output_tokens": 3899, "total_tokens": 1111155 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-23T21:54:59.249Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:54:59.249Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:55:00.340Z", "callId": "call_FqmP3EUTWVOB73zWjVmpN0VE", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:55:30.347Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:55:00.402Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44aaa64b087d2b3d79c13ad08fb8a", "usage": { "input_tokens": 38596, "cached_input_tokens": 37888, "cache_write_input_tokens": 0, "output_tokens": 271, "reasoning_output_tokens": 224, "total_tokens": 38867 }, "turn_token_usage": { "input_tokens": 1137311, "cached_input_tokens": 1106432, "cache_write_input_tokens": 0, "output_tokens": 12711, "reasoning_output_tokens": 4123, "total_tokens": 1150022 }, "thread_token_usage": { "input_tokens": 1137311, "cached_input_tokens": 1106432, "cache_write_input_tokens": 0, "output_tokens": 12711, "reasoning_output_tokens": 4123, "total_tokens": 1150022 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:55:33.042Z", "callId": "call_U6VrVBBsCvo515U296oSh0jX", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -20 solution/tune.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:55:33.138Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nRESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\nRESULT active 40 4.0 long 0.3 0.01854174140454525\nRESULT active 40 4.0 binary 0.0 0.020110542552463122\nRESULT active 40 4.0 binary 0.15 0.020863365665483278\nRESULT active 40 4.0 binary 0.3 0.017939687714838764\nRESULT active 40 7.0 long 0.0 0.019516408672719553\nRESULT active 40 7.0 long 0.15 0.018034528977421825\nRESULT active 40 7.0 long 0.3 0.01440588408366143\nRESULT active 40 7.0 binary 0.0 0.018822879499314937\nRESULT active 40 7.0 binary 0.15 0.01691269504955608\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:55:33.076Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-426ffd53-b07d-414a-8542-ea2d191c78db", "process_id": "68547", "command": [ "/bin/sh", "-lc", "tail -20 solution/tune.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -20 solution/tune.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "RESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\nRESULT active 40 4.0 long 0.3 0.01854174140454525\nRESULT active 40 4.0 binary 0.0 0.020110542552463122\nRESULT active 40 4.0 binary 0.15 0.020863365665483278\nRESULT active 40 4.0 binary 0.3 0.017939687714838764\nRESULT active 40 7.0 long 0.0 0.019516408672719553\nRESULT active 40 7.0 long 0.15 0.018034528977421825\nRESULT active 40 7.0 long 0.3 0.01440588408366143\nRESULT active 40 7.0 binary 0.0 0.018822879499314937\nRESULT active 40 7.0 binary 0.15 0.01691269504955608\n", "stderr": "", "aggregated_output": "RESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\nRESULT active 40 4.0 long 0.3 0.01854174140454525\nRESULT active 40 4.0 binary 0.0 0.020110542552463122\nRESULT active 40 4.0 binary 0.15 0.020863365665483278\nRESULT active 40 4.0 binary 0.3 0.017939687714838764\nRESULT active 40 7.0 long 0.0 0.019516408672719553\nRESULT active 40 7.0 long 0.15 0.018034528977421825\nRESULT active 40 7.0 long 0.3 0.01440588408366143\nRESULT active 40 7.0 binary 0.0 0.018822879499314937\nRESULT active 40 7.0 binary 0.15 0.01691269504955608\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3550 }, "formatted_output": "RESULT recent 250 4.0 binary 0.15 0.022318962777160143\nRESULT recent 250 4.0 binary 0.3 0.021869597797091033\nRESULT recent 250 7.0 long 0.0 0.0198037391940496\nRESULT recent 250 7.0 long 0.15 0.018715042179824774\nRESULT recent 250 7.0 long 0.3 0.015202845289084185\nRESULT recent 250 7.0 binary 0.0 0.019330934920472553\nRESULT recent 250 7.0 binary 0.15 0.017991104672778695\nRESULT recent 250 7.0 binary 0.3 0.014492146274288019\n[21:54:35] neighbors active\nRESULT active 40 4.0 long 0.0 0.02217389562577427\nRESULT active 40 4.0 long 0.15 0.021176833672565006\nRESULT active 40 4.0 long 0.3 0.01854174140454525\nRESULT active 40 4.0 binary 0.0 0.020110542552463122\nRESULT active 40 4.0 binary 0.15 0.020863365665483278\nRESULT active 40 4.0 binary 0.3 0.017939687714838764\nRESULT active 40 7.0 long 0.0 0.019516408672719553\nRESULT active 40 7.0 long 0.15 0.018034528977421825\nRESULT active 40 7.0 long 0.3 0.01440588408366143\nRESULT active 40 7.0 binary 0.0 0.018822879499314937\nRESULT active 40 7.0 binary 0.15 0.01691269504955608\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:55:33.136Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44ad2947087d2ae592137fb32d3c6", "usage": { "input_tokens": 38890, "cached_input_tokens": 38400, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 38942 }, "turn_token_usage": { "input_tokens": 1176201, "cached_input_tokens": 1144832, "cache_write_input_tokens": 0, "output_tokens": 12763, "reasoning_output_tokens": 4123, "total_tokens": 1188964 }, "thread_token_usage": { "input_tokens": 1176201, "cached_input_tokens": 1144832, "cache_write_input_tokens": 0, "output_tokens": 12763, "reasoning_output_tokens": 4123, "total_tokens": 1188964 } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-23T21:55:35.005Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:55:35.005Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:55:36.373Z", "callId": "call_4QWctPAoBKtFfX8ysjtgeR8f", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":45398,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:55:36.616Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:55:36.485Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44ad592c887d291ab50ceb3ded734", "usage": { "input_tokens": 39412, "cached_input_tokens": 38656, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 16, "total_tokens": 39480 }, "turn_token_usage": { "input_tokens": 1215613, "cached_input_tokens": 1183488, "cache_write_input_tokens": 0, "output_tokens": 12831, "reasoning_output_tokens": 4139, "total_tokens": 1228444 }, "thread_token_usage": { "input_tokens": 1215613, "cached_input_tokens": 1183488, "cache_write_input_tokens": 0, "output_tokens": 12831, "reasoning_output_tokens": 4139, "total_tokens": 1228444 } } }, { "kind": "context", "timestamp": "2026-09-23T21:55:36.614Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-30ccc0df-1b48-4e71-a26b-13135f7ae5e0", "process_id": "45398", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/tune_ranker.py > solution/tune.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/tune_ranker.py > solution/tune.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 130, "duration": { "secs": 305, "nanos": 10005232 }, "formatted_output": "" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-23T21:55:42.824Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:55:42.824Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:56:02.392Z", "callId": "call_dbvgqNlDCVR5aOqXKRBVJn6S", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_allitems.py\\n+#!/opt/task-venv/bin/python\\n+import numpy as np, pandas as pd\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from build_ranker import data,load_events,bm25,D,timer\\n+from tune_ranker import metric\\n+\\n+cat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\\n+timer('reading all positive listens for neighbor profiles')\\n+x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+x=x[x.played_ratio_pct>=50]\\n+age=(24786800-x.timestamp.to_numpy())/86400.\\n+w=(1+.5*x.is_organic.to_numpy())*np.exp(-np.maximum(age,0)/35.)\\n+R=sparse.coo_matrix((w.astype(np.float32),((x.uid//100).astype(np.int32),x.item_id.astype(np.int32))),shape=(10001,int(x.item_id.max())+1)).tocsr()\\n+R.sum_duplicates(); R.data=np.log1p(R.data)\\n+del x,w\\n+timer(f'all item profile nnz={R.nnz} shape={R.shape}')\\n+R=bm25(R,K1=1.2,B=.7)\\n+nn=NearestNeighbors(n_neighbors=401,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\\n+dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\\n+df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\\n+for k in [100,250,400]:\\n+ ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\\n+ nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\\n+ for power in [2.,4.]:\\n+ S=sparse.csr_matrix(((ss[keep]**power).astype(np.float32),(nr,nc)),shape=(len(rows),R.shape[0]))\\n+ for gamma in [0.,.1,.2]:\\n+ scale=np.power(df+3,gamma)\\n+ val=metric(lambda a,b,S=S,scale=scale:(S[a:b]@mats['wlong']).toarray()/scale)\\n+ print('ALLRESULT',k,power,gamma,val,flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:56:02.506Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:56:02.399Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-681d2858-34b8-45b5-a548-1827d0a92772", "changes": { "/app/solution/tune_allitems.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\nimport numpy as np, pandas as pd\nfrom scipy import sparse\nfrom sklearn.neighbors import NearestNeighbors\nfrom build_ranker import data,load_events,bm25,D,timer\nfrom tune_ranker import metric\n\ncat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\ntimer('reading all positive listens for neighbor profiles')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nx=x[x.played_ratio_pct>=50]\nage=(24786800-x.timestamp.to_numpy())/86400.\nw=(1+.5*x.is_organic.to_numpy())*np.exp(-np.maximum(age,0)/35.)\nR=sparse.coo_matrix((w.astype(np.float32),((x.uid//100).astype(np.int32),x.item_id.astype(np.int32))),shape=(10001,int(x.item_id.max())+1)).tocsr()\nR.sum_duplicates(); R.data=np.log1p(R.data)\ndel x,w\ntimer(f'all item profile nnz={R.nnz} shape={R.shape}')\nR=bm25(R,K1=1.2,B=.7)\nnn=NearestNeighbors(n_neighbors=401,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\ndist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\ndf=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\nfor k in [100,250,400]:\n ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\n nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\n for power in [2.,4.]:\n S=sparse.csr_matrix(((ss[keep]**power).astype(np.float32),(nr,nc)),shape=(len(rows),R.shape[0]))\n for gamma in [0.,.1,.2]:\n scale=np.power(df+3,gamma)\n val=metric(lambda a,b,S=S,scale=scale:(S[a:b]@mats['wlong']).toarray()/scale)\n print('ALLRESULT',k,power,gamma,val,flush=True)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_allitems.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:56:02.505Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44ad8dda887d29ce3205ef01d6bc2", "usage": { "input_tokens": 39503, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 730, "reasoning_output_tokens": 110, "total_tokens": 40233 }, "turn_token_usage": { "input_tokens": 1255116, "cached_input_tokens": 1222784, "cache_write_input_tokens": 0, "output_tokens": 13561, "reasoning_output_tokens": 4249, "total_tokens": 1268677 }, "thread_token_usage": { "input_tokens": 1255116, "cached_input_tokens": 1222784, "cache_write_input_tokens": 0, "output_tokens": 13561, "reasoning_output_tokens": 4249, "total_tokens": 1268677 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-23T21:56:06.795Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:56:06.795Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:56:25.641Z", "callId": "call_JZVQBGnwuW7shP9rdsamtQZw", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_allitems.py\\n@@\\n-from tune_ranker import metric\\n-\\n cat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\\n+ur={int(v):i for i,v in enumerate(targ)}; im={int(v):i for i,v in enumerate(items)}\\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\\n+lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\n+lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\\n+q=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\\n+q=q[[elig[ur[int(u)],im[int(i)]] for u,i in q[['uid','item_id']].itertuples(index=False,name=None)]]\\n+gold={ur[int(u)]:{im[int(i)]:int(g) for i,g in zip(z.item_id,z.g)} for u,z in q.groupby('uid')}; disc=1/np.log2(np.arange(2,12))\\n+def metric(scorefn):\\n+ vals=[]\\n+ for a in range(0,len(targ),256):\\n+ b=min(a+256,len(targ)); s=scorefn(a,b); s[~elig[a:b]]=-np.inf\\n+ ix=np.argpartition(s,-10,axis=1)[:,-10:]; ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\\n+ for j in range(b-a):\\n+ gg=gold.get(a+j)\\n+ if not gg: continue\\n+ gr=np.array([gg.get(int(i),0) for i in ix[j]]); ideal=np.sort(list(gg.values()))[::-1][:10]\\n+ vals.append(((2**gr-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\\n+ return np.mean(vals)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:56:25.716Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:56:25.649Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ae07553d-6b85-4020-9eea-5d8515716af6", "changes": { "/app/solution/tune_allitems.py": { "type": "update", "unified_diff": "@@ -5,5 +5,22 @@\n from build_ranker import data,load_events,bm25,D,timer\n-from tune_ranker import metric\n-\n cat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\n+ur={int(v):i for i,v in enumerate(targ)}; im={int(v):i for i,v in enumerate(items)}\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\n+lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n+lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\n+q=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\n+q=q[[elig[ur[int(u)],im[int(i)]] for u,i in q[['uid','item_id']].itertuples(index=False,name=None)]]\n+gold={ur[int(u)]:{im[int(i)]:int(g) for i,g in zip(z.item_id,z.g)} for u,z in q.groupby('uid')}; disc=1/np.log2(np.arange(2,12))\n+def metric(scorefn):\n+ vals=[]\n+ for a in range(0,len(targ),256):\n+ b=min(a+256,len(targ)); s=scorefn(a,b); s[~elig[a:b]]=-np.inf\n+ ix=np.argpartition(s,-10,axis=1)[:,-10:]; ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n+ for j in range(b-a):\n+ gg=gold.get(a+j)\n+ if not gg: continue\n+ gr=np.array([gg.get(int(i),0) for i in ix[j]]); ideal=np.sort(list(gg.values()))[::-1][:10]\n+ vals.append(((2**gr-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\n+ return np.mean(vals)\n timer('reading all positive listens for neighbor profiles')\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_allitems.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:56:25.715Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44af2dc6887d2af8e0bc6e1a290c1", "usage": { "input_tokens": 40257, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 660, "reasoning_output_tokens": 64, "total_tokens": 40917 }, "turn_token_usage": { "input_tokens": 1295373, "cached_input_tokens": 1262080, "cache_write_input_tokens": 0, "output_tokens": 14221, "reasoning_output_tokens": 4313, "total_tokens": 1309594 }, "thread_token_usage": { "input_tokens": 1295373, "cached_input_tokens": 1262080, "cache_write_input_tokens": 0, "output_tokens": 14221, "reasoning_output_tokens": 4313, "total_tokens": 1309594 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-23T21:56:27.356Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:56:27.356Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:56:29.834Z", "callId": "call_HGaK641GK5KsHCu5MOt7Lsve", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/tune_allitems.py > solution/tune_allitems.log 2>&1\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T21:56:31.007Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=85155", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:56:29.947Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b0a359c87d2bc98c867a3574099", "usage": { "input_tokens": 40941, "cached_input_tokens": 40064, "cache_write_input_tokens": 0, "output_tokens": 92, "reasoning_output_tokens": 6, "total_tokens": 41033 }, "turn_token_usage": { "input_tokens": 1336314, "cached_input_tokens": 1302144, "cache_write_input_tokens": 0, "output_tokens": 14313, "reasoning_output_tokens": 4319, "total_tokens": 1350627 }, "thread_token_usage": { "input_tokens": 1336314, "cached_input_tokens": 1302144, "cache_write_input_tokens": 0, "output_tokens": 14313, "reasoning_output_tokens": 4319, "total_tokens": 1350627 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-23T21:56:48.628Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:56:48.628Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:56:50.570Z", "callId": "call_Qj7R6wMEWlYAObgxhNEZOviw", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -10 solution/tune_allitems.log; awk '/MemAvailable/ {print}' /proc/meminfo\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:56:50.682Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nMemAvailable: 12605640 kB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:56:50.607Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9bfe9a4a-c1c3-41b3-800b-0c764589cbf8", "process_id": "66307", "command": [ "/bin/sh", "-lc", "tail -10 solution/tune_allitems.log; awk '/MemAvailable/ {print}' /proc/meminfo" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 solution/tune_allitems.log; awk '/MemAvailable/ {print}' /proc/meminfo" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nMemAvailable: 12605640 kB\n", "stderr": "", "aggregated_output": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nMemAvailable: 12605640 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4591 }, "formatted_output": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nMemAvailable: 12605640 kB\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:56:50.680Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b0f3b0487d2bfea53be0439a00e", "usage": { "input_tokens": 41061, "cached_input_tokens": 40704, "cache_write_input_tokens": 0, "output_tokens": 543, "reasoning_output_tokens": 473, "total_tokens": 41604 }, "turn_token_usage": { "input_tokens": 1377375, "cached_input_tokens": 1342848, "cache_write_input_tokens": 0, "output_tokens": 14856, "reasoning_output_tokens": 4792, "total_tokens": 1392231 }, "thread_token_usage": { "input_tokens": 1377375, "cached_input_tokens": 1342848, "cache_write_input_tokens": 0, "output_tokens": 14856, "reasoning_output_tokens": 4792, "total_tokens": 1392231 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-23T21:56:53.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:56:53.152Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:56:54.380Z", "callId": "call_HdqwsD8gDMeIoAyqDcQIaOTA", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85155,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:57:24.388Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:56:54.490Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b230b3087d2948d8aef4be27b42", "usage": { "input_tokens": 41746, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 27, "total_tokens": 41820 }, "turn_token_usage": { "input_tokens": 1419121, "cached_input_tokens": 1383680, "cache_write_input_tokens": 0, "output_tokens": 14930, "reasoning_output_tokens": 4819, "total_tokens": 1434051 }, "thread_token_usage": { "input_tokens": 1419121, "cached_input_tokens": 1383680, "cache_write_input_tokens": 0, "output_tokens": 14930, "reasoning_output_tokens": 4819, "total_tokens": 1434051 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:57:27.727Z", "callId": "call_1wPnWG1mwGym1FoJAHtyXMrW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -20 solution/tune_allitems.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:57:27.843Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:57:27.764Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-611727f0-b8b4-4f5d-888c-0deef1f35b28", "process_id": "45340", "command": [ "/bin/sh", "-lc", "tail -20 solution/tune_allitems.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -20 solution/tune_allitems.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\n", "stderr": "", "aggregated_output": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5130 }, "formatted_output": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:57:27.841Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b44afe087d29edc88aabdd42169", "usage": { "input_tokens": 41843, "cached_input_tokens": 41600, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 41897 }, "turn_token_usage": { "input_tokens": 1460964, "cached_input_tokens": 1425280, "cache_write_input_tokens": 0, "output_tokens": 14984, "reasoning_output_tokens": 4819, "total_tokens": 1475948 }, "thread_token_usage": { "input_tokens": 1460964, "cached_input_tokens": 1425280, "cache_write_input_tokens": 0, "output_tokens": 14984, "reasoning_output_tokens": 4819, "total_tokens": 1475948 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-23T21:57:30.043Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:57:30.043Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:57:31.311Z", "callId": "call_ZrVicP8B4ow1vzansLCZWynU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85155,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:58:01.319Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:57:31.368Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b482f5487d289d1190cf6780db8", "usage": { "input_tokens": 42138, "cached_input_tokens": 41728, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 25, "total_tokens": 42210 }, "turn_token_usage": { "input_tokens": 1503102, "cached_input_tokens": 1467008, "cache_write_input_tokens": 0, "output_tokens": 15056, "reasoning_output_tokens": 4844, "total_tokens": 1518158 }, "thread_token_usage": { "input_tokens": 1503102, "cached_input_tokens": 1467008, "cache_write_input_tokens": 0, "output_tokens": 15056, "reasoning_output_tokens": 4844, "total_tokens": 1518158 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:58:06.136Z", "callId": "call_DqbQeh7fokGwYzLgTw63uDic", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -20 solution/tune_allitems.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:58:06.240Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:58:06.172Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1c70174c-4c63-48e3-bdc8-3e0d5a22fd49", "process_id": "71662", "command": [ "/bin/sh", "-lc", "tail -20 solution/tune_allitems.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -20 solution/tune_allitems.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\n", "stderr": "", "aggregated_output": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4130 }, "formatted_output": "[21:56:31] reading train listens\n[21:56:34] wlong matrix nnz=5687487\n[21:56:35] wrecent matrix nnz=5687487\n[21:56:36] reading all positive listens for neighbor profiles\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:58:06.239Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b69895c87d2a2489cb86a058297", "usage": { "input_tokens": 42233, "cached_input_tokens": 41984, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 42287 }, "turn_token_usage": { "input_tokens": 1545335, "cached_input_tokens": 1508992, "cache_write_input_tokens": 0, "output_tokens": 15110, "reasoning_output_tokens": 4844, "total_tokens": 1560445 }, "thread_token_usage": { "input_tokens": 1545335, "cached_input_tokens": 1508992, "cache_write_input_tokens": 0, "output_tokens": 15110, "reasoning_output_tokens": 4844, "total_tokens": 1560445 } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-23T21:58:11.841Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:58:11.841Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:58:13.069Z", "callId": "call_HbCJ84mMqzzFFC888MHYSmoB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85155,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:58:43.077Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:58:13.162Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b6ebe1887d290f8a335f30eef44", "usage": { "input_tokens": 42638, "cached_input_tokens": 42112, "cache_write_input_tokens": 0, "output_tokens": 170, "reasoning_output_tokens": 123, "total_tokens": 42808 }, "turn_token_usage": { "input_tokens": 1587973, "cached_input_tokens": 1551104, "cache_write_input_tokens": 0, "output_tokens": 15280, "reasoning_output_tokens": 4967, "total_tokens": 1603253 }, "thread_token_usage": { "input_tokens": 1587973, "cached_input_tokens": 1551104, "cache_write_input_tokens": 0, "output_tokens": 15280, "reasoning_output_tokens": 4967, "total_tokens": 1603253 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:58:46.381Z", "callId": "call_fzr0FkBHo5OqX4NzltvBVk7c", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -15 solution/tune_allitems.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:58:46.486Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\nALLRESULT 250 4.0 0.2 0.020950930703289836\nALLRESULT 400 2.0 0.0 0.01822958536902243\nALLRESULT 400 2.0 0.1 0.020290484780381916\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:58:46.417Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6a52ea03-19c5-40dc-81a2-8ff62af22ea5", "process_id": "87717", "command": [ "/bin/sh", "-lc", "tail -15 solution/tune_allitems.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 solution/tune_allitems.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\nALLRESULT 250 4.0 0.2 0.020950930703289836\nALLRESULT 400 2.0 0.0 0.01822958536902243\nALLRESULT 400 2.0 0.1 0.020290484780381916\n", "stderr": "", "aggregated_output": "[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\nALLRESULT 250 4.0 0.2 0.020950930703289836\nALLRESULT 400 2.0 0.0 0.01822958536902243\nALLRESULT 400 2.0 0.1 0.020290484780381916\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3730 }, "formatted_output": "[21:56:38] all item profile nnz=8471966 shape=(10001, 9390602)\nALLRESULT 100 2.0 0.0 0.020129368850585718\nALLRESULT 100 2.0 0.1 0.022242303244863905\nALLRESULT 100 2.0 0.2 0.02317603331956884\nALLRESULT 100 4.0 0.0 0.02041036785880678\nALLRESULT 100 4.0 0.1 0.021416718932840882\nALLRESULT 100 4.0 0.2 0.020243273686518973\nALLRESULT 250 2.0 0.0 0.019151238286538284\nALLRESULT 250 2.0 0.1 0.02103898311706801\nALLRESULT 250 2.0 0.2 0.023071577754172394\nALLRESULT 250 4.0 0.0 0.020251780513414357\nALLRESULT 250 4.0 0.1 0.02131986149121726\nALLRESULT 250 4.0 0.2 0.020950930703289836\nALLRESULT 400 2.0 0.0 0.01822958536902243\nALLRESULT 400 2.0 0.1 0.020290484780381916\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:58:46.484Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b93543887d2b4b26da4ed5ba989", "usage": { "input_tokens": 42831, "cached_input_tokens": 42496, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 42885 }, "turn_token_usage": { "input_tokens": 1630804, "cached_input_tokens": 1593600, "cache_write_input_tokens": 0, "output_tokens": 15334, "reasoning_output_tokens": 4967, "total_tokens": 1646138 }, "thread_token_usage": { "input_tokens": 1630804, "cached_input_tokens": 1593600, "cache_write_input_tokens": 0, "output_tokens": 15334, "reasoning_output_tokens": 4967, "total_tokens": 1646138 } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-23T21:58:48.369Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:58:48.369Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:58:49.631Z", "callId": "call_DN9RXXV86AkOLAi3ppRF6SB5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85155,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:59:19.640Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:58:49.744Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44b96c71c87d2a9f18bd1a93cd12b", "usage": { "input_tokens": 43242, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 43296 }, "turn_token_usage": { "input_tokens": 1674046, "cached_input_tokens": 1636224, "cache_write_input_tokens": 0, "output_tokens": 15388, "reasoning_output_tokens": 4974, "total_tokens": 1689434 }, "thread_token_usage": { "input_tokens": 1674046, "cached_input_tokens": 1636224, "cache_write_input_tokens": 0, "output_tokens": 15388, "reasoning_output_tokens": 4974, "total_tokens": 1689434 } } }, { "kind": "context", "timestamp": "2026-09-23T21:59:21.784Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1222e4f4-5b29-4feb-bfe1-e6f764dc759f", "process_id": "85155", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/tune_allitems.py > solution/tune_allitems.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/tune_allitems.py > solution/tune_allitems.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 171, "nanos": 779134637 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T21:59:22.035Z", "callId": "call_L4Ty2eWm3u1HYJJ9jPHkpb62", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":85155,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:59:22.138Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:59:22.137Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44bb7d83c87d2adaeded5ad3ddd81", "usage": { "input_tokens": 43319, "cached_input_tokens": 43008, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 43364 }, "turn_token_usage": { "input_tokens": 1717365, "cached_input_tokens": 1679232, "cache_write_input_tokens": 0, "output_tokens": 15433, "reasoning_output_tokens": 4974, "total_tokens": 1732798 }, "thread_token_usage": { "input_tokens": 1717365, "cached_input_tokens": 1679232, "cache_write_input_tokens": 0, "output_tokens": 15433, "reasoning_output_tokens": 4974, "total_tokens": 1732798 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-23T21:59:33.097Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:59:33.097Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T21:59:36.100Z", "text": "Tuning found a modest additional gain from a broader 250-neighbor pool plus light item-frequency shrinkage (0.02355 versus 0.02287). Whole-catalog neighbor matching added complementary signal but was slightly weaker alone. I’m testing a compact ensemble, then I’ll refit the winning configuration on train + validation and validate every submission invariant." }, { "kind": "tool", "timestamp": "2026-09-23T21:59:48.959Z", "callId": "call_EwvyKTZ5GBPkqUde8vrhzZDs", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_ensemble.py\\n+#!/opt/task-venv/bin/python\\n+import numpy as np,pandas as pd\\n+from build_ranker import data,public_score,OUT\\n+cat,items,imap,targ,elig=data('validation')\\n+models={\\n+ 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\\n+ 'svd':pd.read_parquet(OUT/'validation_svd_p0.45.parquet'),\\n+ 'knn2':pd.read_parquet(OUT/'validation_knn2.0_p0.0.parquet'),\\n+ 'pop':pd.read_parquet(OUT/'validation_pop.parquet')}\\n+for c in [0,10,30,60,100]:\\n+ for w in [.1,.2,.35,.5,.7]:\\n+ parts=[]\\n+ for name,m in models.items():\\n+ ww=1 if name=='knn' else (w if name=='svd' else 0)\\n+ if ww: parts.append(m.assign(score=ww/(c+m['rank']))[['uid','item_id','score']])\\n+ q=pd.concat(parts).groupby(['uid','item_id'],as_index=False).score.sum()\\n+ q['rank']=q.groupby('uid').score.rank(method='first',ascending=False).astype(int)\\n+ q=q[q['rank']<=100][['uid','item_id','rank']]\\n+ # backfill if union somehow short (not expected)\\n+ if len(q)!=len(targ)*100: continue\\n+ print('ENS',c,w,public_score(q,items,targ,elig),flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T21:59:49.091Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:59:48.967Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7c193dcc-bd83-40e5-8d67-e317119550b8", "changes": { "/app/solution/tune_ensemble.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\nimport numpy as np,pandas as pd\nfrom build_ranker import data,public_score,OUT\ncat,items,imap,targ,elig=data('validation')\nmodels={\n 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\n 'svd':pd.read_parquet(OUT/'validation_svd_p0.45.parquet'),\n 'knn2':pd.read_parquet(OUT/'validation_knn2.0_p0.0.parquet'),\n 'pop':pd.read_parquet(OUT/'validation_pop.parquet')}\nfor c in [0,10,30,60,100]:\n for w in [.1,.2,.35,.5,.7]:\n parts=[]\n for name,m in models.items():\n ww=1 if name=='knn' else (w if name=='svd' else 0)\n if ww: parts.append(m.assign(score=ww/(c+m['rank']))[['uid','item_id','score']])\n q=pd.concat(parts).groupby(['uid','item_id'],as_index=False).score.sum()\n q['rank']=q.groupby('uid').score.rank(method='first',ascending=False).astype(int)\n q=q[q['rank']<=100][['uid','item_id','rank']]\n # backfill if union somehow short (not expected)\n if len(q)!=len(targ)*100: continue\n print('ENS',c,w,public_score(q,items,targ,elig),flush=True)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_ensemble.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T21:59:49.090Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44bba789887d28fd1d88b0ad0a04e", "usage": { "input_tokens": 43387, "cached_input_tokens": 43136, "cache_write_input_tokens": 0, "output_tokens": 788, "reasoning_output_tokens": 289, "total_tokens": 44175 }, "turn_token_usage": { "input_tokens": 1760752, "cached_input_tokens": 1722368, "cache_write_input_tokens": 0, "output_tokens": 16221, "reasoning_output_tokens": 5263, "total_tokens": 1776973 }, "thread_token_usage": { "input_tokens": 1760752, "cached_input_tokens": 1722368, "cache_write_input_tokens": 0, "output_tokens": 16221, "reasoning_output_tokens": 5263, "total_tokens": 1776973 } } }, { "kind": "tool", "timestamp": "2026-09-23T21:59:53.326Z", "callId": "call_n9e29spX9StrdExmpjtW8NLa", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/tune_ensemble.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T21:59:55.015Z", "content": "Script completed\nWall time 1.7 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"/app/solution/tune_ensemble.py\", line 6, in \n 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_knn4.0_p0.0.parquet'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:59:53.410Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44bd578c487d2b6318a27166050ad", "usage": { "input_tokens": 44199, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 0, "total_tokens": 44272 }, "turn_token_usage": { "input_tokens": 1804951, "cached_input_tokens": 1765632, "cache_write_input_tokens": 0, "output_tokens": 16294, "reasoning_output_tokens": 5263, "total_tokens": 1821245 }, "thread_token_usage": { "input_tokens": 1804951, "cached_input_tokens": 1765632, "cache_write_input_tokens": 0, "output_tokens": 16294, "reasoning_output_tokens": 5263, "total_tokens": 1821245 } } }, { "kind": "context", "timestamp": "2026-09-23T21:59:55.012Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fa5d7eca-8964-4416-bd15-4c0b0525889b", "process_id": "15509", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/tune_ensemble.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/tune_ensemble.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/tune_ensemble.py\", line 6, in \n 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_knn4.0_p0.0.parquet'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/tune_ensemble.py\", line 6, in \n 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_knn4.0_p0.0.parquet'\n", "exit_code": 1, "duration": { "secs": 1, "nanos": 508630439 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/tune_ensemble.py\", line 6, in \n 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_knn4.0_p0.0.parquet'\n" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-23T21:59:57.666Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T21:59:57.666Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T21:59:59.394Z", "callId": "call_0C58JP7aso8fHZagZWBlwsBO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls solution/*.parquet | sed -n '1,40p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T21:59:59.490Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nsolution/validation_knn1.0_p0.parquet\nsolution/validation_knn2.0_p0.parquet\nsolution/validation_knn4.0_p0.parquet\nsolution/validation_pop.parquet\nsolution/validation_svd_p0.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T21:59:59.438Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-65b6c755-b82a-413e-ac0f-d02f34ee55d2", "process_id": "4266", "command": [ "/bin/sh", "-lc", "ls solution/*.parquet | sed -n '1,40p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls solution/*.parquet | sed -n '1,40p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "solution/validation_knn1.0_p0.parquet\nsolution/validation_knn2.0_p0.parquet\nsolution/validation_knn4.0_p0.parquet\nsolution/validation_pop.parquet\nsolution/validation_svd_p0.parquet\n", "stderr": "", "aggregated_output": "solution/validation_knn1.0_p0.parquet\nsolution/validation_knn2.0_p0.parquet\nsolution/validation_knn4.0_p0.parquet\nsolution/validation_pop.parquet\nsolution/validation_svd_p0.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6390 }, "formatted_output": "solution/validation_knn1.0_p0.parquet\nsolution/validation_knn2.0_p0.parquet\nsolution/validation_knn4.0_p0.parquet\nsolution/validation_pop.parquet\nsolution/validation_svd_p0.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-23T21:59:59.489Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44bdb389487d2a10c39b1fc3899e6", "usage": { "input_tokens": 44578, "cached_input_tokens": 44032, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 7, "total_tokens": 44647 }, "turn_token_usage": { "input_tokens": 1849529, "cached_input_tokens": 1809664, "cache_write_input_tokens": 0, "output_tokens": 16363, "reasoning_output_tokens": 5270, "total_tokens": 1865892 }, "thread_token_usage": { "input_tokens": 1849529, "cached_input_tokens": 1809664, "cache_write_input_tokens": 0, "output_tokens": 16363, "reasoning_output_tokens": 5270, "total_tokens": 1865892 } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-23T22:00:08.638Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:00:08.638Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:00:22.339Z", "callId": "call_jCps2PSCAyhvqA4urdGAfKjt", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- nn=NearestNeighbors(n_neighbors=181,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\\n+ nn=NearestNeighbors(n_neighbors=(251 if args.mode=='best' else 181),metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\\n@@\\n- for power in ([1.,2.,4.] if args.mode=='all' else [2.]):\\n+ df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\\n+ for power in ([1.,2.,4.] if args.mode=='all' else [4.]):\\n@@\\n- for alpha in ([0.,.12,.25] if args.mode=='all' else [.12]):\\n- def score(a,b,al=alpha): return knn(a,b)+al*((pop-pop.mean())/pop.std())\\n- outputs[f'knn{power}_p{alpha}']=top_frame(score,targ,items,elig)\\n+ for gamma in ([0.,.12,.25] if args.mode=='all' else [.15]):\\n+ def score(a,b,ga=gamma): return knn(a,b)/np.power(df+3,ga)\\n+ outputs[f'knn{power}_g{gamma}']=top_frame(score,targ,items,elig)\\n@@\\n for name,frame in outputs.items():\\n@@\\n report[name]=public_score(frame,items,targ,elig); print(name,report[name],flush=True)\\n+ if stage=='test':\\n+ outputs['knn4.0_g0.15'].to_parquet(OUT/'submission.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:00:22.496Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:00:22.347Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c146817a-8bbf-4c7c-9496-d0f0224c359a", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -127,3 +127,3 @@\n R=bm25(mats['wrecent'],K1=1.2,B=.7)\n- nn=NearestNeighbors(n_neighbors=181,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\n+ nn=NearestNeighbors(n_neighbors=(251 if args.mode=='best' else 181),metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\n dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\n@@ -132,3 +132,4 @@\n nr=np.repeat(np.arange(len(rows)),mask.sum(1)); nc=ind[mask]\n- for power in ([1.,2.,4.] if args.mode=='all' else [2.]):\n+ df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\n+ for power in ([1.,2.,4.] if args.mode=='all' else [4.]):\n nw=(sim[mask]**power).astype(np.float32)\n@@ -139,5 +140,5 @@\n return z\n- for alpha in ([0.,.12,.25] if args.mode=='all' else [.12]):\n- def score(a,b,al=alpha): return knn(a,b)+al*((pop-pop.mean())/pop.std())\n- outputs[f'knn{power}_p{alpha}']=top_frame(score,targ,items,elig)\n+ for gamma in ([0.,.12,.25] if args.mode=='all' else [.15]):\n+ def score(a,b,ga=gamma): return knn(a,b)/np.power(df+3,ga)\n+ outputs[f'knn{power}_g{gamma}']=top_frame(score,targ,items,elig)\n OUT.mkdir(exist_ok=True)\n@@ -149,2 +150,4 @@\n report[name]=public_score(frame,items,targ,elig); print(name,report[name],flush=True)\n+ if stage=='test':\n+ outputs['knn4.0_g0.15'].to_parquet(OUT/'submission.parquet',index=False)\n (OUT/f'{stage}_scores.json').write_text(json.dumps(report,indent=2))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:00:22.494Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44bdfc8d487d28c186dc0b77da5e1", "usage": { "input_tokens": 44726, "cached_input_tokens": 44416, "cache_write_input_tokens": 0, "output_tokens": 657, "reasoning_output_tokens": 221, "total_tokens": 45383 }, "turn_token_usage": { "input_tokens": 1894255, "cached_input_tokens": 1854080, "cache_write_input_tokens": 0, "output_tokens": 17020, "reasoning_output_tokens": 5491, "total_tokens": 1911275 }, "thread_token_usage": { "input_tokens": 1894255, "cached_input_tokens": 1854080, "cache_write_input_tokens": 0, "output_tokens": 17020, "reasoning_output_tokens": 5491, "total_tokens": 1911275 } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-23T22:00:27.363Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:00:27.363Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:00:32.319Z", "callId": "call_zrvaCtLCLUwYrhbtMKBqEkiv", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\\n def knn(a,b):\\n- z=(S[a:b]@mats['wlong']).toarray().astype(np.float32)\\n- z=(z-z.mean(1,keepdims=True))/(z.std(1,keepdims=True)+1e-6)\\n- return z\\n+ return (S[a:b]@mats['wlong']).toarray().astype(np.float32)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:00:32.419Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:00:32.327Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-00eb00dd-8f45-49b5-befd-b622834b264f", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -137,5 +137,3 @@\n def knn(a,b):\n- z=(S[a:b]@mats['wlong']).toarray().astype(np.float32)\n- z=(z-z.mean(1,keepdims=True))/(z.std(1,keepdims=True)+1e-6)\n- return z\n+ return (S[a:b]@mats['wlong']).toarray().astype(np.float32)\n for gamma in ([0.,.12,.25] if args.mode=='all' else [.15]):\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:00:32.418Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44bf70f5c87d2b3e8c2cf06021437", "usage": { "input_tokens": 45407, "cached_input_tokens": 44544, "cache_write_input_tokens": 0, "output_tokens": 249, "reasoning_output_tokens": 85, "total_tokens": 45656 }, "turn_token_usage": { "input_tokens": 1939662, "cached_input_tokens": 1898624, "cache_write_input_tokens": 0, "output_tokens": 17269, "reasoning_output_tokens": 5576, "total_tokens": 1956931 }, "thread_token_usage": { "input_tokens": 1939662, "cached_input_tokens": 1898624, "cache_write_input_tokens": 0, "output_tokens": 17269, "reasoning_output_tokens": 5576, "total_tokens": 1956931 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-23T22:00:36.377Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:00:36.377Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:00:38.625Z", "callId": "call_cbHnks3uFg1Fz1KzPlqa6YDp", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode best\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T22:00:39.791Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=7941", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:00:38.734Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c00bca487d29c19bfa757ee9456", "usage": { "input_tokens": 45680, "cached_input_tokens": 45184, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 21, "total_tokens": 45781 }, "turn_token_usage": { "input_tokens": 1985342, "cached_input_tokens": 1943808, "cache_write_input_tokens": 0, "output_tokens": 17370, "reasoning_output_tokens": 5597, "total_tokens": 2002712 }, "thread_token_usage": { "input_tokens": 1985342, "cached_input_tokens": 1943808, "cache_write_input_tokens": 0, "output_tokens": 17370, "reasoning_output_tokens": 5597, "total_tokens": 2002712 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-23T22:00:44.673Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:00:44.673Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:00:45.756Z", "callId": "call_YkbS2GMYCnkNbx0F4Learm6f", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7941,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:01:10.301Z", "content": "Script completed\nWall time 24.5 seconds\nOutput:\n\n[22:00:40] reading train listens\n[22:00:43] wlong matrix nnz=5687487\n[22:00:44] wrecent matrix nnz=5687487\n[22:00:46] fitting BM25 SVD\n[22:00:49] SVD explained=0.213\n[22:00:53] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.023551356916060827, 4263)\n[22:01:10] done\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:00:45.868Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c08195887d29ded272653181d3e", "usage": { "input_tokens": 45809, "cached_input_tokens": 45440, "cache_write_input_tokens": 0, "output_tokens": 125, "reasoning_output_tokens": 78, "total_tokens": 45934 }, "turn_token_usage": { "input_tokens": 2031151, "cached_input_tokens": 1989248, "cache_write_input_tokens": 0, "output_tokens": 17495, "reasoning_output_tokens": 5675, "total_tokens": 2048646 }, "thread_token_usage": { "input_tokens": 2031151, "cached_input_tokens": 1989248, "cache_write_input_tokens": 0, "output_tokens": 17495, "reasoning_output_tokens": 5675, "total_tokens": 2048646 } } }, { "kind": "context", "timestamp": "2026-09-23T22:01:10.299Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-37ebc02f-5832-48eb-b08e-d12fe5aac1f9", "process_id": "7941", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode best" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode best" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:00:40] reading train listens\n[22:00:43] wlong matrix nnz=5687487\n[22:00:44] wrecent matrix nnz=5687487\n[22:00:46] fitting BM25 SVD\n[22:00:49] SVD explained=0.213\n[22:00:53] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.023551356916060827, 4263)\n[22:01:10] done\n", "stderr": "", "aggregated_output": "[22:00:40] reading train listens\n[22:00:43] wlong matrix nnz=5687487\n[22:00:44] wrecent matrix nnz=5687487\n[22:00:46] fitting BM25 SVD\n[22:00:49] SVD explained=0.213\n[22:00:53] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.023551356916060827, 4263)\n[22:01:10] done\n", "exit_code": 0, "duration": { "secs": 31, "nanos": 510484714 }, "formatted_output": "[22:00:40] reading train listens\n[22:00:43] wlong matrix nnz=5687487\n[22:00:44] wrecent matrix nnz=5687487\n[22:00:46] fitting BM25 SVD\n[22:00:49] SVD explained=0.213\n[22:00:53] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.023551356916060827, 4263)\n[22:01:10] done\n" } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-23T22:01:13.500Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:01:13.500Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:01:15.560Z", "callId": "call_vxdsnmlULWOXQOrTvmruU3cw", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_knn4.0_g0.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:01:18.148Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\n{\n \"model\": \"solution/validation_knn4.0_g0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023551,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:01:15.720Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c26940487d2bdc1df1d252b9d10", "usage": { "input_tokens": 46108, "cached_input_tokens": 45568, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 10, "total_tokens": 46191 }, "turn_token_usage": { "input_tokens": 2077259, "cached_input_tokens": 2034816, "cache_write_input_tokens": 0, "output_tokens": 17578, "reasoning_output_tokens": 5685, "total_tokens": 2094837 }, "thread_token_usage": { "input_tokens": 2077259, "cached_input_tokens": 2034816, "cache_write_input_tokens": 0, "output_tokens": 17578, "reasoning_output_tokens": 5685, "total_tokens": 2094837 } } }, { "kind": "context", "timestamp": "2026-09-23T22:01:18.144Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09a7e59f-6944-475d-8686-baf390fd4a3b", "process_id": "33235", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_knn4.0_g0.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_knn4.0_g0.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_knn4.0_g0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023551,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_knn4.0_g0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023551,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 401830951 }, "formatted_output": "{\n \"model\": \"solution/validation_knn4.0_g0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023551,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-23T22:01:37.158Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:01:37.158Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:01:59.845Z", "callId": "call_BUGUkAgWKQyWZVqRRo0oDi0m", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_objective.py\\n+#!/opt/task-venv/bin/python\\n+import numpy as np,pandas as pd\\n+from scipy import sparse\\n+from sklearn.neighbors import NearestNeighbors\\n+from build_ranker import data,load_events,bm25,top_frame,public_score,D,timer\\n+\\n+cat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\\n+R=bm25(mats['wrecent'],K1=1.2,B=.7)\\n+nn=NearestNeighbors(n_neighbors=251,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\\n+dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32); keep=ind!=rows[:,None]\\n+S=sparse.csr_matrix(((sim[keep]**4).astype(np.float32),(np.repeat(np.arange(len(rows)),keep.sum(1)),ind[keep])),shape=(len(rows),R.shape[0]))\\n+\\n+timer('building objective-aligned bonus matrices')\\n+x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\n+x=x[x.item_id.isin(items)]; x['col']=x.item_id.map(imap).astype(np.int32); x['row']=(x.uid//100).astype(np.int32)\\n+shape=mats['wlong'].shape\\n+def mat(mask):\\n+ q=x[mask]; m=sparse.coo_matrix((np.ones(len(q),np.float32),(q.row,q.col)),shape=shape).tocsr();m.sum_duplicates();m.data=np.log1p(m.data);return m\\n+features={'org50':mat((x.is_organic==1)&(x.played_ratio_pct>=50)),'org80':mat((x.is_organic==1)&(x.played_ratio_pct>=80)),'all80':mat(x.played_ratio_pct>=80)}\\n+del x\\n+q=pd.read_parquet(D/'train_likes.parquet'); q=q[q.item_id.isin(items)];\\n+features['likes']=sparse.coo_matrix((np.ones(len(q),np.float32),((q.uid//100).astype(int),q.item_id.map(imap).astype(int))),shape=shape).tocsr();features['likes'].sum_duplicates();features['likes'].data=np.log1p(features['likes'].data)\\n+for name,F in features.items():\\n+ for beta in [.5,1.,2.,4.]:\\n+ M=mats['wlong']+beta*F; df=np.asarray((M>0).sum(axis=0)).ravel().astype(np.float32); scale=np.power(df+3,.15)\\n+ frame=top_frame(lambda a,b,M=M,scale=scale:(S[a:b]@M).toarray()/scale,targ,items,elig)\\n+ print('OBJ',name,beta,public_score(frame,items,targ,elig),flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:01:59.963Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:01:59.853Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d0bb92c5-c286-405f-8f75-045f13f92d55", "changes": { "/app/solution/tune_objective.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nfrom sklearn.neighbors import NearestNeighbors\nfrom build_ranker import data,load_events,bm25,top_frame,public_score,D,timer\n\ncat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\nR=bm25(mats['wrecent'],K1=1.2,B=.7)\nnn=NearestNeighbors(n_neighbors=251,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\ndist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32); keep=ind!=rows[:,None]\nS=sparse.csr_matrix(((sim[keep]**4).astype(np.float32),(np.repeat(np.arange(len(rows)),keep.sum(1)),ind[keep])),shape=(len(rows),R.shape[0]))\n\ntimer('building objective-aligned bonus matrices')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nx=x[x.item_id.isin(items)]; x['col']=x.item_id.map(imap).astype(np.int32); x['row']=(x.uid//100).astype(np.int32)\nshape=mats['wlong'].shape\ndef mat(mask):\n q=x[mask]; m=sparse.coo_matrix((np.ones(len(q),np.float32),(q.row,q.col)),shape=shape).tocsr();m.sum_duplicates();m.data=np.log1p(m.data);return m\nfeatures={'org50':mat((x.is_organic==1)&(x.played_ratio_pct>=50)),'org80':mat((x.is_organic==1)&(x.played_ratio_pct>=80)),'all80':mat(x.played_ratio_pct>=80)}\ndel x\nq=pd.read_parquet(D/'train_likes.parquet'); q=q[q.item_id.isin(items)];\nfeatures['likes']=sparse.coo_matrix((np.ones(len(q),np.float32),((q.uid//100).astype(int),q.item_id.map(imap).astype(int))),shape=shape).tocsr();features['likes'].sum_duplicates();features['likes'].data=np.log1p(features['likes'].data)\nfor name,F in features.items():\n for beta in [.5,1.,2.,4.]:\n M=mats['wlong']+beta*F; df=np.asarray((M>0).sum(axis=0)).ravel().astype(np.float32); scale=np.power(df+3,.15)\n frame=top_frame(lambda a,b,M=M,scale=scale:(S[a:b]@M).toarray()/scale,targ,items,elig)\n print('OBJ',name,beta,public_score(frame,items,targ,elig),flush=True)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_objective.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:01:59.962Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c2e618487d2965f6fef64405be8", "usage": { "input_tokens": 46264, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 1198, "reasoning_output_tokens": 479, "total_tokens": 47462 }, "turn_token_usage": { "input_tokens": 2123523, "cached_input_tokens": 2080768, "cache_write_input_tokens": 0, "output_tokens": 18776, "reasoning_output_tokens": 6164, "total_tokens": 2142299 }, "thread_token_usage": { "input_tokens": 2123523, "cached_input_tokens": 2080768, "cache_write_input_tokens": 0, "output_tokens": 18776, "reasoning_output_tokens": 6164, "total_tokens": 2142299 } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-23T22:02:03.430Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:02:03.430Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:02:05.918Z", "callId": "call_Yd1KtN1xpAYGKGWwzWxCvoAD", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/tune_objective.py > solution/tune_objective.log 2>&1\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T22:02:07.107Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=33085", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:02:06.110Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c586ab487d2b20306ba1cbca09d", "usage": { "input_tokens": 47486, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 15, "total_tokens": 47587 }, "turn_token_usage": { "input_tokens": 2171009, "cached_input_tokens": 2126848, "cache_write_input_tokens": 0, "output_tokens": 18877, "reasoning_output_tokens": 6179, "total_tokens": 2189886 }, "thread_token_usage": { "input_tokens": 2171009, "cached_input_tokens": 2126848, "cache_write_input_tokens": 0, "output_tokens": 18877, "reasoning_output_tokens": 6179, "total_tokens": 2189886 } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-23T22:02:10.230Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:02:10.230Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:02:11.487Z", "callId": "call_nQHXGcVouka1Ly7FMmZ9i1CF", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:02:41.494Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:02:11.599Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c5f5cec87d2ad0432ad3a7577ba", "usage": { "input_tokens": 47615, "cached_input_tokens": 47360, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 47668 }, "turn_token_usage": { "input_tokens": 2218624, "cached_input_tokens": 2174208, "cache_write_input_tokens": 0, "output_tokens": 18930, "reasoning_output_tokens": 6185, "total_tokens": 2237554 }, "thread_token_usage": { "input_tokens": 2218624, "cached_input_tokens": 2174208, "cache_write_input_tokens": 0, "output_tokens": 18930, "reasoning_output_tokens": 6185, "total_tokens": 2237554 } } }, { "kind": "tool", "timestamp": "2026-09-23T22:02:46.165Z", "callId": "call_UPzcHUxLbULTUOXDYgujW6Z0", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -15 solution/tune_objective.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:02:46.322Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:02:46.201Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-924c95db-cea7-44e1-8812-d17bf9d14854", "process_id": "95204", "command": [ "/bin/sh", "-lc", "tail -15 solution/tune_objective.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 solution/tune_objective.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\n", "stderr": "", "aggregated_output": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3460 }, "formatted_output": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\n" } } }, { "kind": "context", "timestamp": "2026-09-23T22:02:46.320Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c81b46087d28dba6e0134318c2f", "usage": { "input_tokens": 47691, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 47745 }, "turn_token_usage": { "input_tokens": 2266315, "cached_input_tokens": 2221696, "cache_write_input_tokens": 0, "output_tokens": 18984, "reasoning_output_tokens": 6185, "total_tokens": 2285299 }, "thread_token_usage": { "input_tokens": 2266315, "cached_input_tokens": 2221696, "cache_write_input_tokens": 0, "output_tokens": 18984, "reasoning_output_tokens": 6185, "total_tokens": 2285299 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-23T22:02:50.003Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:02:50.003Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:02:51.212Z", "callId": "call_KJdqISfxZ7zkwKL0jMioNur6", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:03:21.220Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:02:51.331Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44c869b8c87d28887e468344c7f15", "usage": { "input_tokens": 47869, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 47925 }, "turn_token_usage": { "input_tokens": 2314184, "cached_input_tokens": 2269184, "cache_write_input_tokens": 0, "output_tokens": 19040, "reasoning_output_tokens": 6194, "total_tokens": 2333224 }, "thread_token_usage": { "input_tokens": 2314184, "cached_input_tokens": 2269184, "cache_write_input_tokens": 0, "output_tokens": 19040, "reasoning_output_tokens": 6194, "total_tokens": 2333224 } } }, { "kind": "tool", "timestamp": "2026-09-23T22:03:24.448Z", "callId": "call_AsoIqZkwPnfftIw76vn56bjB", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -15 solution/tune_objective.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:03:24.592Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:03:24.484Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7f415b48-ff39-4cc4-bb4d-b4c07f244a5f", "process_id": "29015", "command": [ "/bin/sh", "-lc", "tail -15 solution/tune_objective.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 solution/tune_objective.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\n", "stderr": "", "aggregated_output": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3690 }, "formatted_output": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\n" } } }, { "kind": "context", "timestamp": "2026-09-23T22:03:24.591Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44ca979bc87d28b54eb21a406224a", "usage": { "input_tokens": 47948, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 48002 }, "turn_token_usage": { "input_tokens": 2362132, "cached_input_tokens": 2316928, "cache_write_input_tokens": 0, "output_tokens": 19094, "reasoning_output_tokens": 6194, "total_tokens": 2381226 }, "thread_token_usage": { "input_tokens": 2362132, "cached_input_tokens": 2316928, "cache_write_input_tokens": 0, "output_tokens": 19094, "reasoning_output_tokens": 6194, "total_tokens": 2381226 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-23T22:03:27.489Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:03:27.489Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:03:28.708Z", "callId": "call_XoqdNq7LAkewPRpOYn17Qdb0", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:03:58.716Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:03:28.802Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44cacf36c87d2862dcd1294b0d8d3", "usage": { "input_tokens": 48189, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 48247 }, "turn_token_usage": { "input_tokens": 2410321, "cached_input_tokens": 2364672, "cache_write_input_tokens": 0, "output_tokens": 19152, "reasoning_output_tokens": 6205, "total_tokens": 2429473 }, "thread_token_usage": { "input_tokens": 2410321, "cached_input_tokens": 2364672, "cache_write_input_tokens": 0, "output_tokens": 19152, "reasoning_output_tokens": 6205, "total_tokens": 2429473 } } }, { "kind": "tool", "timestamp": "2026-09-23T22:04:02.600Z", "callId": "call_0mlq9DPnlG9KCjdIIlaPxWny", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -15 solution/tune_objective.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:04:02.712Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:04:02.635Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-40fdbec0-34e6-4adc-9498-0452e3bc315c", "process_id": "27067", "command": [ "/bin/sh", "-lc", "tail -15 solution/tune_objective.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 solution/tune_objective.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\n", "stderr": "", "aggregated_output": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4000 }, "formatted_output": "[22:02:07] reading train listens\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\n" } } }, { "kind": "context", "timestamp": "2026-09-23T22:04:02.710Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44ccef07c87d28ffc808dafad76be", "usage": { "input_tokens": 48270, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 48324 }, "turn_token_usage": { "input_tokens": 2458591, "cached_input_tokens": 2412672, "cache_write_input_tokens": 0, "output_tokens": 19206, "reasoning_output_tokens": 6205, "total_tokens": 2477797 }, "thread_token_usage": { "input_tokens": 2458591, "cached_input_tokens": 2412672, "cache_write_input_tokens": 0, "output_tokens": 19206, "reasoning_output_tokens": 6205, "total_tokens": 2477797 } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-23T22:04:06.115Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:04:06.115Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:04:07.390Z", "callId": "call_mdk0aGLpIJdC4l7HgtOGRAgZ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:04:37.399Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:04:07.471Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44cd3288487d2a3a675d21affb8a0", "usage": { "input_tokens": 48595, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 16, "total_tokens": 48658 }, "turn_token_usage": { "input_tokens": 2507186, "cached_input_tokens": 2460800, "cache_write_input_tokens": 0, "output_tokens": 19269, "reasoning_output_tokens": 6221, "total_tokens": 2526455 }, "thread_token_usage": { "input_tokens": 2507186, "cached_input_tokens": 2460800, "cache_write_input_tokens": 0, "output_tokens": 19269, "reasoning_output_tokens": 6221, "total_tokens": 2526455 } } }, { "kind": "tool", "timestamp": "2026-09-23T22:04:41.013Z", "callId": "call_Izli8ou6CVoKbHZBESGWEJod", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -15 solution/tune_objective.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:04:41.132Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:04:41.048Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a714720b-4666-40ea-a783-3cb8e40c1b9d", "process_id": "8550", "command": [ "/bin/sh", "-lc", "tail -15 solution/tune_objective.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 solution/tune_objective.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\n", "stderr": "", "aggregated_output": "[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4530 }, "formatted_output": "[22:02:10] wlong matrix nnz=5687487\n[22:02:11] wrecent matrix nnz=5687487\n[22:02:14] building objective-aligned bonus matrices\nOBJ org50 0.5 (0.024241907413717446, 4263)\nOBJ org50 1.0 (0.024664004091426703, 4263)\nOBJ org50 2.0 (0.024189773608623292, 4263)\nOBJ org50 4.0 (0.02395971036830549, 4263)\nOBJ org80 0.5 (0.024163148214815643, 4263)\nOBJ org80 1.0 (0.024402044269641616, 4263)\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\n" } } }, { "kind": "context", "timestamp": "2026-09-23T22:04:41.130Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44cf60c3c87d281d6a1c00dc48f3d", "usage": { "input_tokens": 48681, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 48735 }, "turn_token_usage": { "input_tokens": 2555867, "cached_input_tokens": 2509184, "cache_write_input_tokens": 0, "output_tokens": 19323, "reasoning_output_tokens": 6221, "total_tokens": 2575190 }, "thread_token_usage": { "input_tokens": 2555867, "cached_input_tokens": 2509184, "cache_write_input_tokens": 0, "output_tokens": 19323, "reasoning_output_tokens": 6221, "total_tokens": 2575190 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-23T22:04:43.534Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:04:43.534Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:04:44.773Z", "callId": "call_JnEeyAJnakCnRMQWfRU9PRz2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33085,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:05:14.756Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:04:44.873Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44cf9b09487d2849e1cbb92ba365b", "usage": { "input_tokens": 49058, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 22, "total_tokens": 49127 }, "turn_token_usage": { "input_tokens": 2604925, "cached_input_tokens": 2557696, "cache_write_input_tokens": 0, "output_tokens": 19392, "reasoning_output_tokens": 6243, "total_tokens": 2624317 }, "thread_token_usage": { "input_tokens": 2604925, "cached_input_tokens": 2557696, "cache_write_input_tokens": 0, "output_tokens": 19392, "reasoning_output_tokens": 6243, "total_tokens": 2624317 } } }, { "kind": "context", "timestamp": "2026-09-23T22:05:14.754Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b50f7e1-2431-4be0-b9f4-231098d46879", "process_id": "33085", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/tune_objective.py > solution/tune_objective.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/tune_objective.py > solution/tune_objective.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 188, "nanos": 649314354 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-23T22:05:17.471Z", "callId": "call_dki5IyouCE3a0GO61K4mcNfp", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"tail -10 solution/tune_objective.log\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:05:17.581Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nOBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\nOBJ likes 0.5 (0.023481755431357644, 4263)\nOBJ likes 1.0 (0.023601555882565475, 4263)\nOBJ likes 2.0 (0.023243495721101372, 4263)\nOBJ likes 4.0 (0.022418683579532375, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:05:17.506Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-36a1ed5f-2680-4f1a-89aa-4eacfd9838dd", "process_id": "99229", "command": [ "/bin/sh", "-lc", "tail -10 solution/tune_objective.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 solution/tune_objective.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "OBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\nOBJ likes 0.5 (0.023481755431357644, 4263)\nOBJ likes 1.0 (0.023601555882565475, 4263)\nOBJ likes 2.0 (0.023243495721101372, 4263)\nOBJ likes 4.0 (0.022418683579532375, 4263)\n", "stderr": "", "aggregated_output": "OBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\nOBJ likes 0.5 (0.023481755431357644, 4263)\nOBJ likes 1.0 (0.023601555882565475, 4263)\nOBJ likes 2.0 (0.023243495721101372, 4263)\nOBJ likes 4.0 (0.022418683579532375, 4263)\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8091 }, "formatted_output": "OBJ org80 2.0 (0.024430194727971543, 4263)\nOBJ org80 4.0 (0.02388713729874625, 4263)\nOBJ all80 0.5 (0.02191888443372886, 4263)\nOBJ all80 1.0 (0.021140922813357867, 4263)\nOBJ all80 2.0 (0.020740108952095812, 4263)\nOBJ all80 4.0 (0.019945707410319865, 4263)\nOBJ likes 0.5 (0.023481755431357644, 4263)\nOBJ likes 1.0 (0.023601555882565475, 4263)\nOBJ likes 2.0 (0.023243495721101372, 4263)\nOBJ likes 4.0 (0.022418683579532375, 4263)\n" } } }, { "kind": "context", "timestamp": "2026-09-23T22:05:17.579Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d1af4b487d2a7fee44360983a87", "usage": { "input_tokens": 49150, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 49204 }, "turn_token_usage": { "input_tokens": 2654075, "cached_input_tokens": 2606592, "cache_write_input_tokens": 0, "output_tokens": 19446, "reasoning_output_tokens": 6243, "total_tokens": 2673521 }, "thread_token_usage": { "input_tokens": 2654075, "cached_input_tokens": 2606592, "cache_write_input_tokens": 0, "output_tokens": 19446, "reasoning_output_tokens": 6243, "total_tokens": 2673521 } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-23T22:05:19.303Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:05:19.303Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:05:20.532Z", "callId": "call_PZCirtUeewkrr8UhYm0YdURI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33085,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:05:20.621Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\nwrite_stdin failed: Unknown process id 33085", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:05:20.620Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d1dfea087d291d3e2540b110179", "usage": { "input_tokens": 49433, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 49488 }, "turn_token_usage": { "input_tokens": 2703508, "cached_input_tokens": 2655616, "cache_write_input_tokens": 0, "output_tokens": 19501, "reasoning_output_tokens": 6251, "total_tokens": 2723009 }, "thread_token_usage": { "input_tokens": 2703508, "cached_input_tokens": 2655616, "cache_write_input_tokens": 0, "output_tokens": 19501, "reasoning_output_tokens": 6251, "total_tokens": 2723009 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-23T22:05:28.382Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:05:28.382Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:05:41.014Z", "callId": "call_oyV13zIKB68WAl87gn3iYiKR", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\\n- del x,likes\\n shape=(10001,len(item_map))\\n+ org=x[x.is_organic==1]\\n+ orgmat=sparse.coo_matrix((np.ones(len(org),np.float32),(org.row,org.col)),shape=shape).tocsr()\\n+ orgmat.sum_duplicates(); orgmat.data=np.log1p(orgmat.data).astype(np.float32)\\n+ y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\\n+ del x,likes,org\\n@@\\n timer(f'{col} matrix nnz={mats[col].nnz}')\\n+ mats['org50']=orgmat\\n return mats\\n@@\\n- df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\\n+ predmat=mats['wlong'] + (mats['org50'] if args.mode=='best' else 0)\\n+ df=np.asarray((predmat>0).sum(axis=0)).ravel().astype(np.float32)\\n@@\\n S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\\n def knn(a,b):\\n- return (S[a:b]@mats['wlong']).toarray().astype(np.float32)\\n+ return (S[a:b]@predmat).toarray().astype(np.float32)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:05:41.126Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:05:41.023Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-90768f72-05ec-49a1-8c07-f66cf1fb699c", "changes": { "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -55,5 +55,8 @@\n likes.append(q[['row','col','wlong','wrecent']])\n- y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\n- del x,likes\n shape=(10001,len(item_map))\n+ org=x[x.is_organic==1]\n+ orgmat=sparse.coo_matrix((np.ones(len(org),np.float32),(org.row,org.col)),shape=shape).tocsr()\n+ orgmat.sum_duplicates(); orgmat.data=np.log1p(orgmat.data).astype(np.float32)\n+ y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\n+ del x,likes,org\n mats={}\n@@ -64,2 +67,3 @@\n timer(f'{col} matrix nnz={mats[col].nnz}')\n+ mats['org50']=orgmat\n return mats\n@@ -132,3 +136,4 @@\n nr=np.repeat(np.arange(len(rows)),mask.sum(1)); nc=ind[mask]\n- df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\n+ predmat=mats['wlong'] + (mats['org50'] if args.mode=='best' else 0)\n+ df=np.asarray((predmat>0).sum(axis=0)).ravel().astype(np.float32)\n for power in ([1.,2.,4.] if args.mode=='all' else [4.]):\n@@ -137,3 +142,3 @@\n def knn(a,b):\n- return (S[a:b]@mats['wlong']).toarray().astype(np.float32)\n+ return (S[a:b]@predmat).toarray().astype(np.float32)\n for gamma in ([0.,.12,.25] if args.mode=='all' else [.15]):\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:05:41.124Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d21179487d281c2276ba9f386b3", "usage": { "input_tokens": 49525, "cached_input_tokens": 49280, "cache_write_input_tokens": 0, "output_tokens": 589, "reasoning_output_tokens": 186, "total_tokens": 50114 }, "turn_token_usage": { "input_tokens": 2753033, "cached_input_tokens": 2704896, "cache_write_input_tokens": 0, "output_tokens": 20090, "reasoning_output_tokens": 6437, "total_tokens": 2773123 }, "thread_token_usage": { "input_tokens": 2753033, "cached_input_tokens": 2704896, "cache_write_input_tokens": 0, "output_tokens": 20090, "reasoning_output_tokens": 6437, "total_tokens": 2773123 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-23T22:05:44.172Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:05:44.172Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:05:46.590Z", "callId": "call_U2V4OLdNHivzifBY1BJPzZgK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T22:05:47.756Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=20518", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:05:46.641Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d35710087d284758f738d6e69b9", "usage": { "input_tokens": 50138, "cached_input_tokens": 49408, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 25, "total_tokens": 50243 }, "turn_token_usage": { "input_tokens": 2803171, "cached_input_tokens": 2754304, "cache_write_input_tokens": 0, "output_tokens": 20195, "reasoning_output_tokens": 6462, "total_tokens": 2823366 }, "thread_token_usage": { "input_tokens": 2803171, "cached_input_tokens": 2754304, "cache_write_input_tokens": 0, "output_tokens": 20195, "reasoning_output_tokens": 6462, "total_tokens": 2823366 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-23T22:05:50.252Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:05:50.252Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:06:01.621Z", "callId": "call_z7fPb2QiQfZPMOskSniZoL0K", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made for You ranker\\n+\\n+`submission.parquet` contains the final 100-track ranking for every target\\n+user. It was produced by `build_ranker.py`, using only events available at the\\n+test cutoff:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --mode best\\n+```\\n+\\n+The selected model is an implicit collaborative nearest-listener ranker. User\\n+profiles combine positive listens, explicit likes, organic intent, and\\n+exponential recency weighting. BM25 weighting controls activity/popularity\\n+bias before cosine neighbor retrieval. Candidate scores use 250 neighbors,\\n+favor organic historical positives to match the discovery target, and apply a\\n+small listener-frequency shrinkage. Scores are masked by the supplied\\n+per-user cutoff-correct eligibility set before ranking.\\n+\\n+Hyperparameters were selected on the temporal validation window with no\\n+validation events used as validation features. The chosen validation model\\n+scored 0.024664 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\\n+popularity baseline. The final artifact refits the same approach using the\\n+training and validation events as observed history and uses the test preference\\n+and eligibility cutoff.\\n+\\n+`explore.py` and the `tune_*.py` files document the development experiments;\\n+only `build_ranker.py` is needed to reproduce the final artifact.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:06:01.813Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:06:01.710Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4057549d-3070-499c-a45f-c22c2feffbff", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You ranker\n\n`submission.parquet` contains the final 100-track ranking for every target\nuser. It was produced by `build_ranker.py`, using only events available at the\ntest cutoff:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --mode best\n```\n\nThe selected model is an implicit collaborative nearest-listener ranker. User\nprofiles combine positive listens, explicit likes, organic intent, and\nexponential recency weighting. BM25 weighting controls activity/popularity\nbias before cosine neighbor retrieval. Candidate scores use 250 neighbors,\nfavor organic historical positives to match the discovery target, and apply a\nsmall listener-frequency shrinkage. Scores are masked by the supplied\nper-user cutoff-correct eligibility set before ranking.\n\nHyperparameters were selected on the temporal validation window with no\nvalidation events used as validation features. The chosen validation model\nscored 0.024664 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\npopularity baseline. The final artifact refits the same approach using the\ntraining and validation events as observed history and uses the test preference\nand eligibility cutoff.\n\n`explore.py` and the `tune_*.py` files document the development experiments;\nonly `build_ranker.py` is needed to reproduce the final artifact.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:06:01.748Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d3bf39887d2b9ec2a18d8fe9e64", "usage": { "input_tokens": 50271, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 398, "reasoning_output_tokens": 33, "total_tokens": 50669 }, "turn_token_usage": { "input_tokens": 2853442, "cached_input_tokens": 2804224, "cache_write_input_tokens": 0, "output_tokens": 20593, "reasoning_output_tokens": 6495, "total_tokens": 2874035 }, "thread_token_usage": { "input_tokens": 2853442, "cached_input_tokens": 2804224, "cache_write_input_tokens": 0, "output_tokens": 20593, "reasoning_output_tokens": 6495, "total_tokens": 2874035 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-23T22:06:05.893Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:06:05.893Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:06:07.126Z", "callId": "call_iBccsFkCCZQgWiWRyCWqOrJ2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":20518,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:06:15.999Z", "content": "Script completed\nWall time 8.9 seconds\nOutput:\n\n[22:05:48] reading train listens\n[22:05:49] reading val listens\n[22:05:52] wlong matrix nnz=5850135\n[22:05:53] wrecent matrix nnz=5850135\n[22:05:55] fitting BM25 SVD\n[22:05:57] SVD explained=0.215\n[22:06:02] fitting cosine neighbors\n[22:06:15] done\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:06:07.255Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d4a25c087d297e1704d1d24acf0", "usage": { "input_tokens": 50693, "cached_input_tokens": 50048, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 70, "total_tokens": 50810 }, "turn_token_usage": { "input_tokens": 2904135, "cached_input_tokens": 2854272, "cache_write_input_tokens": 0, "output_tokens": 20710, "reasoning_output_tokens": 6565, "total_tokens": 2924845 }, "thread_token_usage": { "input_tokens": 2904135, "cached_input_tokens": 2854272, "cache_write_input_tokens": 0, "output_tokens": 20710, "reasoning_output_tokens": 6565, "total_tokens": 2924845 } } }, { "kind": "context", "timestamp": "2026-09-23T22:06:15.997Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-89122f35-3959-4d91-aa42-2798888d682c", "process_id": "20518", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:05:48] reading train listens\n[22:05:49] reading val listens\n[22:05:52] wlong matrix nnz=5850135\n[22:05:53] wrecent matrix nnz=5850135\n[22:05:55] fitting BM25 SVD\n[22:05:57] SVD explained=0.215\n[22:06:02] fitting cosine neighbors\n[22:06:15] done\n", "stderr": "", "aggregated_output": "[22:05:48] reading train listens\n[22:05:49] reading val listens\n[22:05:52] wlong matrix nnz=5850135\n[22:05:53] wrecent matrix nnz=5850135\n[22:05:55] fitting BM25 SVD\n[22:05:57] SVD explained=0.215\n[22:06:02] fitting cosine neighbors\n[22:06:15] done\n", "exit_code": 0, "duration": { "secs": 29, "nanos": 242803584 }, "formatted_output": "[22:05:48] reading train listens\n[22:05:49] reading val listens\n[22:05:52] wlong matrix nnz=5850135\n[22:05:53] wrecent matrix nnz=5850135\n[22:05:55] fitting BM25 SVD\n[22:05:57] SVD explained=0.215\n[22:06:02] fitting cosine neighbors\n[22:06:15] done\n" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-23T22:06:18.790Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:06:18.790Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:06:27.532Z", "callId": "call_iRGTjJNgMmYJpcMTPmj6ij1S", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\nf=pd.read_parquet(p); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets)\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not c.is_eligible(u,i)]\\nprint({'path':str(p),'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':len(bad),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'bytes':p.stat().st_size})\\nPY\\nls -lah solution/submission.parquet solution/build_ranker.py solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:06:29.393Z", "content": "Script completed\nWall time 1.8 seconds\nOutput:\n\n{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1834771}\n-rw-r--r-- 1 agent agent 1.4K Sep 23 22:06 solution/README.md\n-rw-r--r-- 1 agent agent 8.4K Sep 23 22:05 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 22:06 solution/submission.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:06:27.668Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d58451487d2b57ee1ca6e10dc9a", "usage": { "input_tokens": 50937, "cached_input_tokens": 50560, "cache_write_input_tokens": 0, "output_tokens": 321, "reasoning_output_tokens": 40, "total_tokens": 51258 }, "turn_token_usage": { "input_tokens": 2955072, "cached_input_tokens": 2904832, "cache_write_input_tokens": 0, "output_tokens": 21031, "reasoning_output_tokens": 6605, "total_tokens": 2976103 }, "thread_token_usage": { "input_tokens": 2955072, "cached_input_tokens": 2904832, "cache_write_input_tokens": 0, "output_tokens": 21031, "reasoning_output_tokens": 6605, "total_tokens": 2976103 } } }, { "kind": "context", "timestamp": "2026-09-23T22:06:29.385Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-714f1448-7697-4307-abf3-a0302b103f9e", "process_id": "97187", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(p); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets)\nc=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not c.is_eligible(u,i)]\nprint({'path':str(p),'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':len(bad),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'bytes':p.stat().st_size})\nPY\nls -lah solution/submission.parquet solution/build_ranker.py solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nf=pd.read_parquet(p); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets)\nc=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not c.is_eligible(u,i)]\nprint({'path':str(p),'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':len(bad),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'bytes':p.stat().st_size})\nPY\nls -lah solution/submission.parquet solution/build_ranker.py solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1834771}\n-rw-r--r-- 1 agent agent 1.4K Sep 23 22:06 solution/README.md\n-rw-r--r-- 1 agent agent 8.4K Sep 23 22:05 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 22:06 solution/submission.parquet\n", "stderr": "", "aggregated_output": "{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1834771}\n-rw-r--r-- 1 agent agent 1.4K Sep 23 22:06 solution/README.md\n-rw-r--r-- 1 agent agent 8.4K Sep 23 22:05 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 22:06 solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 675485890 }, "formatted_output": "{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1834771}\n-rw-r--r-- 1 agent agent 1.4K Sep 23 22:06 solution/README.md\n-rw-r--r-- 1 agent agent 8.4K Sep 23 22:05 solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.8M Sep 23 22:06 solution/submission.parquet\n" } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-23T22:06:34.989Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:06:34.989Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T22:06:37.764Z", "text": "The final refit is complete and structurally valid: 845,600 rows, all 8,456 users, ranks 1–100, no duplicate items, and zero test-eligibility violations. A valid best artifact is safely saved. I’m using the remaining time for one low-risk ensemble check; I’ll only replace it if the temporal backtest improves." }, { "kind": "tool", "timestamp": "2026-09-23T22:06:40.074Z", "callId": "call_O2lOpHEIcHBvnJ4kvA5Y37lB", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode best\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T22:06:41.239Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=87098", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:06:40.166Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d65998087d2aceeaeef283d1a84", "usage": { "input_tokens": 51421, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 275, "reasoning_output_tokens": 113, "total_tokens": 51696 }, "turn_token_usage": { "input_tokens": 3006493, "cached_input_tokens": 2955648, "cache_write_input_tokens": 0, "output_tokens": 21306, "reasoning_output_tokens": 6718, "total_tokens": 3027799 }, "thread_token_usage": { "input_tokens": 3006493, "cached_input_tokens": 2955648, "cache_write_input_tokens": 0, "output_tokens": 21306, "reasoning_output_tokens": 6718, "total_tokens": 3027799 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-23T22:06:48.202Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:06:48.202Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:06:49.447Z", "callId": "call_d1FThowBYq8VOXRtdgivxMMJ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":87098,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:07:12.269Z", "content": "Script completed\nWall time 22.7 seconds\nOutput:\n\n[22:06:41] reading train listens\n[22:06:45] wlong matrix nnz=5687487\n[22:06:46] wrecent matrix nnz=5687487\n[22:06:48] fitting BM25 SVD\n[22:06:51] SVD explained=0.213\n[22:06:55] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.024664004091426703, 4263)\n[22:07:12] done\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:06:49.585Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d71795c87d2b90f92736dba541e", "usage": { "input_tokens": 51724, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 157, "total_tokens": 51928 }, "turn_token_usage": { "input_tokens": 3058217, "cached_input_tokens": 3006848, "cache_write_input_tokens": 0, "output_tokens": 21510, "reasoning_output_tokens": 6875, "total_tokens": 3079727 }, "thread_token_usage": { "input_tokens": 3058217, "cached_input_tokens": 3006848, "cache_write_input_tokens": 0, "output_tokens": 21510, "reasoning_output_tokens": 6875, "total_tokens": 3079727 } } }, { "kind": "context", "timestamp": "2026-09-23T22:07:12.268Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0ec398ad-b606-4f49-a567-39236ed4369f", "process_id": "87098", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode best" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/build_ranker.py --stage validation --mode best" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:06:41] reading train listens\n[22:06:45] wlong matrix nnz=5687487\n[22:06:46] wrecent matrix nnz=5687487\n[22:06:48] fitting BM25 SVD\n[22:06:51] SVD explained=0.213\n[22:06:55] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.024664004091426703, 4263)\n[22:07:12] done\n", "stderr": "", "aggregated_output": "[22:06:41] reading train listens\n[22:06:45] wlong matrix nnz=5687487\n[22:06:46] wrecent matrix nnz=5687487\n[22:06:48] fitting BM25 SVD\n[22:06:51] SVD explained=0.213\n[22:06:55] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.024664004091426703, 4263)\n[22:07:12] done\n", "exit_code": 0, "duration": { "secs": 32, "nanos": 30025221 }, "formatted_output": "[22:06:41] reading train listens\n[22:06:45] wlong matrix nnz=5687487\n[22:06:46] wrecent matrix nnz=5687487\n[22:06:48] fitting BM25 SVD\n[22:06:51] SVD explained=0.213\n[22:06:55] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.024664004091426703, 4263)\n[22:07:12] done\n" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-23T22:07:16.357Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:07:16.357Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:07:22.492Z", "callId": "call_Eiv7JxSWLmvbr5wswbi1grr6", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_ensemble.py\\n@@\\n- 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\\n- 'svd':pd.read_parquet(OUT/'validation_svd_p0.45.parquet'),\\n- 'knn2':pd.read_parquet(OUT/'validation_knn2.0_p0.0.parquet'),\\n+ 'knn':pd.read_parquet(OUT/'validation_knn4.0_g0.parquet'),\\n+ 'svd':pd.read_parquet(OUT/'validation_svd_p0.parquet'),\\n+ 'knn2':pd.read_parquet(OUT/'validation_knn2.0_p0.parquet'),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:07:22.603Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:07:22.500Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-dbaea4c1-f644-4889-a201-1935e87a5ae2", "changes": { "/app/solution/tune_ensemble.py": { "type": "update", "unified_diff": "@@ -5,5 +5,5 @@\n models={\n- 'knn':pd.read_parquet(OUT/'validation_knn4.0_p0.0.parquet'),\n- 'svd':pd.read_parquet(OUT/'validation_svd_p0.45.parquet'),\n- 'knn2':pd.read_parquet(OUT/'validation_knn2.0_p0.0.parquet'),\n+ 'knn':pd.read_parquet(OUT/'validation_knn4.0_g0.parquet'),\n+ 'svd':pd.read_parquet(OUT/'validation_svd_p0.parquet'),\n+ 'knn2':pd.read_parquet(OUT/'validation_knn2.0_p0.parquet'),\n 'pop':pd.read_parquet(OUT/'validation_pop.parquet')}\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_ensemble.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:07:22.602Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d908b6887d2804cc0101051da98", "usage": { "input_tokens": 52102, "cached_input_tokens": 51584, "cache_write_input_tokens": 0, "output_tokens": 238, "reasoning_output_tokens": 38, "total_tokens": 52340 }, "turn_token_usage": { "input_tokens": 3110319, "cached_input_tokens": 3058432, "cache_write_input_tokens": 0, "output_tokens": 21748, "reasoning_output_tokens": 6913, "total_tokens": 3132067 }, "thread_token_usage": { "input_tokens": 3110319, "cached_input_tokens": 3058432, "cache_write_input_tokens": 0, "output_tokens": 21748, "reasoning_output_tokens": 6913, "total_tokens": 3132067 } } }, { "kind": "tool", "timestamp": "2026-09-23T22:07:26.397Z", "callId": "call_ml2xIbC5sGJljT7YJ9vp9Byx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/tune_ensemble.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:07:56.608Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nENS 0 0.1 (0.02492553726247394, 4263)\nENS 0 0.2 (0.0252111625428567, 4263)\nENS 0 0.35 (0.02544145425701465, 4263)\nENS 0 0.5 (0.025647975632913343, 4263)\nENS 0 0.7 (0.025428994061897112, 4263)\nENS 10 0.1 (0.02474748873688913, 4263)\nENS 10 0.2 (0.024764483097568027, 4263)\nENS 10 0.35 (0.025016425401718244, 4263)\nENS 10 0.5 (0.02536528015450063, 4263)\nENS 10 0.7 (0.025976567397094807, 4263)\nENS 30 0.1 (0.02471147835666406, 4263)\nENS 30 0.2 (0.025008075853128735, 4263)\nENS 30 0.35 (0.025364435969060194, 4263)\nENS 30 0.5 (0.025076506849182185, 4263)\nENS 30 0.7 (0.024946977844438396, 4263)\nENS 60 0.1 (0.024922734023879477, 4263)\nENS 60 0.2 (0.025157270216076486, 4263)\nENS 60 0.35 (0.024721420593551, 4263)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:07:26.549Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44d9b1dcc87d2bb8ca656a146519a", "usage": { "input_tokens": 52364, "cached_input_tokens": 51968, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 0, "total_tokens": 52423 }, "turn_token_usage": { "input_tokens": 3162683, "cached_input_tokens": 3110400, "cache_write_input_tokens": 0, "output_tokens": 21807, "reasoning_output_tokens": 6913, "total_tokens": 3184490 }, "thread_token_usage": { "input_tokens": 3162683, "cached_input_tokens": 3110400, "cache_write_input_tokens": 0, "output_tokens": 21807, "reasoning_output_tokens": 6913, "total_tokens": 3184490 } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-23T22:08:07.236Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:08:07.236Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-23T22:08:07.400Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6db457da-8ee3-47ca-a987-46e45e2e4ab3", "process_id": "4899", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/tune_ensemble.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/tune_ensemble.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ENS 0 0.1 (0.02492553726247394, 4263)\nENS 0 0.2 (0.0252111625428567, 4263)\nENS 0 0.35 (0.02544145425701465, 4263)\nENS 0 0.5 (0.025647975632913343, 4263)\nENS 0 0.7 (0.025428994061897112, 4263)\nENS 10 0.1 (0.02474748873688913, 4263)\nENS 10 0.2 (0.024764483097568027, 4263)\nENS 10 0.35 (0.025016425401718244, 4263)\nENS 10 0.5 (0.02536528015450063, 4263)\nENS 10 0.7 (0.025976567397094807, 4263)\nENS 30 0.1 (0.02471147835666406, 4263)\nENS 30 0.2 (0.025008075853128735, 4263)\nENS 30 0.35 (0.025364435969060194, 4263)\nENS 30 0.5 (0.025076506849182185, 4263)\nENS 30 0.7 (0.024946977844438396, 4263)\nENS 60 0.1 (0.024922734023879477, 4263)\nENS 60 0.2 (0.025157270216076486, 4263)\nENS 60 0.35 (0.024721420593551, 4263)\nENS 60 0.5 (0.02464480122319216, 4263)\nENS 60 0.7 (0.024241923382386135, 4263)\nENS 100 0.1 (0.02473221450554234, 4263)\nENS 100 0.2 (0.02445955622467808, 4263)\nENS 100 0.35 (0.024537040970283044, 4263)\nENS 100 0.5 (0.024096504547943248, 4263)\nENS 100 0.7 (0.023734298497818117, 4263)\n", "stderr": "", "aggregated_output": "ENS 0 0.1 (0.02492553726247394, 4263)\nENS 0 0.2 (0.0252111625428567, 4263)\nENS 0 0.35 (0.02544145425701465, 4263)\nENS 0 0.5 (0.025647975632913343, 4263)\nENS 0 0.7 (0.025428994061897112, 4263)\nENS 10 0.1 (0.02474748873688913, 4263)\nENS 10 0.2 (0.024764483097568027, 4263)\nENS 10 0.35 (0.025016425401718244, 4263)\nENS 10 0.5 (0.02536528015450063, 4263)\nENS 10 0.7 (0.025976567397094807, 4263)\nENS 30 0.1 (0.02471147835666406, 4263)\nENS 30 0.2 (0.025008075853128735, 4263)\nENS 30 0.35 (0.025364435969060194, 4263)\nENS 30 0.5 (0.025076506849182185, 4263)\nENS 30 0.7 (0.024946977844438396, 4263)\nENS 60 0.1 (0.024922734023879477, 4263)\nENS 60 0.2 (0.025157270216076486, 4263)\nENS 60 0.35 (0.024721420593551, 4263)\nENS 60 0.5 (0.02464480122319216, 4263)\nENS 60 0.7 (0.024241923382386135, 4263)\nENS 100 0.1 (0.02473221450554234, 4263)\nENS 100 0.2 (0.02445955622467808, 4263)\nENS 100 0.35 (0.024537040970283044, 4263)\nENS 100 0.5 (0.024096504547943248, 4263)\nENS 100 0.7 (0.023734298497818117, 4263)\n", "exit_code": 0, "duration": { "secs": 40, "nanos": 794319460 }, "formatted_output": "ENS 0 0.1 (0.02492553726247394, 4263)\nENS 0 0.2 (0.0252111625428567, 4263)\nENS 0 0.35 (0.02544145425701465, 4263)\nENS 0 0.5 (0.025647975632913343, 4263)\nENS 0 0.7 (0.025428994061897112, 4263)\nENS 10 0.1 (0.02474748873688913, 4263)\nENS 10 0.2 (0.024764483097568027, 4263)\nENS 10 0.35 (0.025016425401718244, 4263)\nENS 10 0.5 (0.02536528015450063, 4263)\nENS 10 0.7 (0.025976567397094807, 4263)\nENS 30 0.1 (0.02471147835666406, 4263)\nENS 30 0.2 (0.025008075853128735, 4263)\nENS 30 0.35 (0.025364435969060194, 4263)\nENS 30 0.5 (0.025076506849182185, 4263)\nENS 30 0.7 (0.024946977844438396, 4263)\nENS 60 0.1 (0.024922734023879477, 4263)\nENS 60 0.2 (0.025157270216076486, 4263)\nENS 60 0.35 (0.024721420593551, 4263)\nENS 60 0.5 (0.02464480122319216, 4263)\nENS 60 0.7 (0.024241923382386135, 4263)\nENS 100 0.1 (0.02473221450554234, 4263)\nENS 100 0.2 (0.02445955622467808, 4263)\nENS 100 0.35 (0.024537040970283044, 4263)\nENS 100 0.5 (0.024096504547943248, 4263)\nENS 100 0.7 (0.023734298497818117, 4263)\n" } } }, { "kind": "tool", "timestamp": "2026-09-23T22:08:23.119Z", "callId": "call_cu7lx3PTK72T0npj12UAphuq", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n for name,frame in outputs.items():\\n@@\\n report[name]=public_score(frame,items,targ,elig); print(name,report[name],flush=True)\\n if stage=='test':\\n- outputs['knn4.0_g0.15'].to_parquet(OUT/'submission.parquet',index=False)\\n+ # Validation-selected reciprocal-rank fusion adds complementary global\\n+ # latent structure while retaining the stronger local-neighbor model.\\n+ k=outputs['knn4.0_g0.15'].assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\\n+ v=outputs['svd_p0.16'].assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\\n+ final=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\\n+ final['rank']=final.groupby('uid').score.rank(method='first',ascending=False).astype(np.int64)\\n+ final=final[final['rank']<=100][['uid','item_id','rank']].astype(np.int64)\\n+ final.to_parquet(OUT/'submission.parquet',index=False)\\n*** Update File: /app/solution/README.md\\n@@\\n-small listener-frequency shrinkage. Scores are masked by the supplied\\n-per-user cutoff-correct eligibility set before ranking.\\n+small listener-frequency shrinkage. A validation-selected reciprocal-rank\\n+fusion adds a complementary latent-SVD ranking. Scores are masked by the\\n+supplied per-user cutoff-correct eligibility set before ranking.\\n@@\\n-scored 0.024664 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\\n+scored 0.025977 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\\n@@\\n-training and validation events as observed history and uses the test preference\\n-and eligibility cutoff.\\n+training and validation events as observed history and uses the test\\n+eligibility cutoff.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-23T22:08:23.249Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:08:23.128Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2ef7165a-431e-4564-8166-9c7e39898017", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -15,4 +15,5 @@\n favor organic historical positives to match the discovery target, and apply a\n-small listener-frequency shrinkage. Scores are masked by the supplied\n-per-user cutoff-correct eligibility set before ranking.\n+small listener-frequency shrinkage. A validation-selected reciprocal-rank\n+fusion adds a complementary latent-SVD ranking. Scores are masked by the\n+supplied per-user cutoff-correct eligibility set before ranking.\n \n@@ -20,6 +21,6 @@\n validation events used as validation features. The chosen validation model\n-scored 0.024664 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\n+scored 0.025977 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\n popularity baseline. The final artifact refits the same approach using the\n-training and validation events as observed history and uses the test preference\n-and eligibility cutoff.\n+training and validation events as observed history and uses the test\n+eligibility cutoff.\n \n", "move_path": null }, "/app/solution/build_ranker.py": { "type": "update", "unified_diff": "@@ -154,3 +154,10 @@\n if stage=='test':\n- outputs['knn4.0_g0.15'].to_parquet(OUT/'submission.parquet',index=False)\n+ # Validation-selected reciprocal-rank fusion adds complementary global\n+ # latent structure while retaining the stronger local-neighbor model.\n+ k=outputs['knn4.0_g0.15'].assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\n+ v=outputs['svd_p0.16'].assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\n+ final=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\n+ final['rank']=final.groupby('uid').score.rank(method='first',ascending=False).astype(np.int64)\n+ final=final[final['rank']<=100][['uid','item_id','rank']].astype(np.int64)\n+ final.to_parquet(OUT/'submission.parquet',index=False)\n (OUT/f'{stage}_scores.json').write_text(json.dumps(report,indent=2))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\nM /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-23T22:08:23.248Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44dbcebac87d28d26573a05a6fd4d", "usage": { "input_tokens": 52823, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 788, "reasoning_output_tokens": 283, "total_tokens": 53611 }, "turn_token_usage": { "input_tokens": 3215506, "cached_input_tokens": 3162624, "cache_write_input_tokens": 0, "output_tokens": 22595, "reasoning_output_tokens": 7196, "total_tokens": 3238101 }, "thread_token_usage": { "input_tokens": 3215506, "cached_input_tokens": 3162624, "cache_write_input_tokens": 0, "output_tokens": 22595, "reasoning_output_tokens": 7196, "total_tokens": 3238101 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-23T22:08:27.499Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:08:27.499Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:08:29.747Z", "callId": "call_9MZrcZBgjWtpLd5732e9TFRa", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-23T22:08:30.912Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=36219", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:08:29.864Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44dd78ee087d2b9738537722d41b2", "usage": { "input_tokens": 53635, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 63, "total_tokens": 53778 }, "turn_token_usage": { "input_tokens": 3269141, "cached_input_tokens": 3215232, "cache_write_input_tokens": 0, "output_tokens": 22738, "reasoning_output_tokens": 7259, "total_tokens": 3291879 }, "thread_token_usage": { "input_tokens": 3269141, "cached_input_tokens": 3215232, "cache_write_input_tokens": 0, "output_tokens": 22738, "reasoning_output_tokens": 7259, "total_tokens": 3291879 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-23T22:08:39.624Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:08:39.624Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:08:47.295Z", "callId": "call_dltpdVb1iSMHLiKm7XHE4Pj9", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfrom pathlib import Path\\np=Path('/app/solution')\\nk=pd.read_parquet(p/'validation_knn4.0_g0.parquet').assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\\nv=pd.read_parquet(p/'validation_svd_p0.parquet').assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\\nf=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\\nf['rank']=f.groupby('uid').score.rank(method='first',ascending=False).astype('int64')\\nf[f['rank']<=100][['uid','item_id','rank']].to_parquet(p/'validation.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:08:52.057Z", "content": "Script completed\nWall time 4.8 seconds\nOutput:\n\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025977,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:08:47.463Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44ddf1ea087d2b81fbb8d1d2de3ed", "usage": { "input_tokens": 53806, "cached_input_tokens": 53504, "cache_write_input_tokens": 0, "output_tokens": 480, "reasoning_output_tokens": 226, "total_tokens": 54286 }, "turn_token_usage": { "input_tokens": 3322947, "cached_input_tokens": 3268736, "cache_write_input_tokens": 0, "output_tokens": 23218, "reasoning_output_tokens": 7485, "total_tokens": 3346165 }, "thread_token_usage": { "input_tokens": 3322947, "cached_input_tokens": 3268736, "cache_write_input_tokens": 0, "output_tokens": 23218, "reasoning_output_tokens": 7485, "total_tokens": 3346165 } } }, { "kind": "context", "timestamp": "2026-09-23T22:08:52.052Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8cebfd67-9076-4d8b-99dc-a6e099bee5ca", "process_id": "91580", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/app/solution')\nk=pd.read_parquet(p/'validation_knn4.0_g0.parquet').assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\nv=pd.read_parquet(p/'validation_svd_p0.parquet').assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\nf=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\nf['rank']=f.groupby('uid').score.rank(method='first',ascending=False).astype('int64')\nf[f['rank']<=100][['uid','item_id','rank']].to_parquet(p/'validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/app/solution')\nk=pd.read_parquet(p/'validation_knn4.0_g0.parquet').assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\nv=pd.read_parquet(p/'validation_svd_p0.parquet').assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\nf=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\nf['rank']=f.groupby('uid').score.rank(method='first',ascending=False).astype('int64')\nf[f['rank']<=100][['uid','item_id','rank']].to_parquet(p/'validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025977,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025977,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 521089720 }, "formatted_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025977,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-23T22:08:53.645Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:08:53.645Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:08:54.983Z", "callId": "call_H0TykbWXLuxzCQOEl2EvWKXk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":36219,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:09:00.269Z", "content": "Script completed\nWall time 5.3 seconds\nOutput:\n\n[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:08:55.038Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44df44a6c87d288a80d4b5a44aff9", "usage": { "input_tokens": 54352, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 54406 }, "turn_token_usage": { "input_tokens": 3377299, "cached_input_tokens": 3322368, "cache_write_input_tokens": 0, "output_tokens": 23272, "reasoning_output_tokens": 7492, "total_tokens": 3400571 }, "thread_token_usage": { "input_tokens": 3377299, "cached_input_tokens": 3322368, "cache_write_input_tokens": 0, "output_tokens": 23272, "reasoning_output_tokens": 7492, "total_tokens": 3400571 } } }, { "kind": "context", "timestamp": "2026-09-23T22:09:00.268Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3600d11b-b533-4ea3-bc91-75ec63decfc2", "process_id": "36219", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n", "stderr": "", "aggregated_output": "[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n", "exit_code": 0, "duration": { "secs": 30, "nanos": 356828683 }, "formatted_output": "[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n" } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-23T22:09:05.838Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:09:05.838Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-23T22:09:15.072Z", "callId": "call_THaoOS3EDOrl06r9nXoB8KXZ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\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')\\nbad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint('rows',len(f),'users',f.uid.nunique(),'duplicate_pairs',f.duplicated(['uid','item_id']).sum(),'bad_eligibility',bad,'rank_counts',f.groupby('uid').size().value_counts().to_dict())\\nprint(f.head(12).to_string(index=False))\\nPY\\nsha256sum solution/submission.parquet\\nfind solution -maxdepth 1 -type f -printf '%f %k KB\\\\n' | sort | tail -20\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-23T22:09:17.058Z", "content": "Script completed\nWall time 2.0 seconds\nOutput:\n\nrows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid item_id rank\n 100 75946 23\n 100 196692 78\n 100 237151 52\n 100 388527 27\n 100 489014 81\n 100 507759 47\n 100 517293 8\n 100 549737 43\n 100 841419 84\n 100 994326 38\n 100 1525699 40\n 100 1558292 89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-23T22:09:15.173Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44dfc80c487d28112182a60900427", "usage": { "input_tokens": 54533, "cached_input_tokens": 54144, "cache_write_input_tokens": 0, "output_tokens": 391, "reasoning_output_tokens": 95, "total_tokens": 54924 }, "turn_token_usage": { "input_tokens": 3431832, "cached_input_tokens": 3376512, "cache_write_input_tokens": 0, "output_tokens": 23663, "reasoning_output_tokens": 7587, "total_tokens": 3455495 }, "thread_token_usage": { "input_tokens": 3431832, "cached_input_tokens": 3376512, "cache_write_input_tokens": 0, "output_tokens": 23663, "reasoning_output_tokens": 7587, "total_tokens": 3455495 } } }, { "kind": "context", "timestamp": "2026-09-23T22:09:17.054Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-10effb5e-06fa-4751-8104-f36c52390e8e", "process_id": "1457", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\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')\nbad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint('rows',len(f),'users',f.uid.nunique(),'duplicate_pairs',f.duplicated(['uid','item_id']).sum(),'bad_eligibility',bad,'rank_counts',f.groupby('uid').size().value_counts().to_dict())\nprint(f.head(12).to_string(index=False))\nPY\nsha256sum solution/submission.parquet\nfind solution -maxdepth 1 -type f -printf '%f %k KB\\n' | sort | tail -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\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')\nbad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint('rows',len(f),'users',f.uid.nunique(),'duplicate_pairs',f.duplicated(['uid','item_id']).sum(),'bad_eligibility',bad,'rank_counts',f.groupby('uid').size().value_counts().to_dict())\nprint(f.head(12).to_string(index=False))\nPY\nsha256sum solution/submission.parquet\nfind solution -maxdepth 1 -type f -printf '%f %k KB\\n' | sort | tail -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid item_id rank\n 100 75946 23\n 100 196692 78\n 100 237151 52\n 100 388527 27\n 100 489014 81\n 100 507759 47\n 100 517293 8\n 100 549737 43\n 100 841419 84\n 100 994326 38\n 100 1525699 40\n 100 1558292 89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n", "stderr": "", "aggregated_output": "rows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid item_id rank\n 100 75946 23\n 100 196692 78\n 100 237151 52\n 100 388527 27\n 100 489014 81\n 100 507759 47\n 100 517293 8\n 100 549737 43\n 100 841419 84\n 100 994326 38\n 100 1525699 40\n 100 1558292 89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 818031459 }, "formatted_output": "rows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid item_id rank\n 100 75946 23\n 100 196692 78\n 100 237151 52\n 100 388527 27\n 100 489014 81\n 100 507759 47\n 100 517293 8\n 100 549737 43\n 100 841419 84\n 100 994326 38\n 100 1525699 40\n 100 1558292 89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n" } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-23T22:09:22.786Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-23T22:09:22.786Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-23T22:09:28.698Z", "text": "Built and validated the final music-discovery ranker.\n\n- Submission: [submission.parquet](/app/solution/submission.parquet)\n- Rebuild code: [build_ranker.py](/app/solution/build_ranker.py)\n- Documentation: [README.md](/app/solution/README.md)\n- Public validation ranking: [validation.parquet](/app/solution/validation.parquet)\n\nResults:\n\n- Public NDCG@10: **0.025977**\n- 845,600 rows across 8,456 users\n- Exactly 100 unique tracks per user\n- Valid ranks 1–100\n- Zero test-eligibility violations\n- Submission SHA-256: `7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef`" }, { "kind": "context", "timestamp": "2026-09-23T22:09:28.766Z", "data": { "type": "token_usage_record", "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "session_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2", "root_turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8", "response_id": "resp_03c6e9ed541b0d0c016ab44e0d449c87d295bd710fbe43722a", "usage": { "input_tokens": 55345, "cached_input_tokens": 54400, "cache_write_input_tokens": 0, "output_tokens": 304, "reasoning_output_tokens": 117, "total_tokens": 55649 }, "turn_token_usage": { "input_tokens": 3487177, "cached_input_tokens": 3430912, "cache_write_input_tokens": 0, "output_tokens": 23967, "reasoning_output_tokens": 7704, "total_tokens": 3511144 }, "thread_token_usage": { "input_tokens": 3487177, "cached_input_tokens": 3430912, "cache_write_input_tokens": 0, "output_tokens": 23967, "reasoning_output_tokens": 7704, "total_tokens": 3511144 } } } ] } ] }