{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "sol-max-0-153-4-basic-r02-20260924T221413Z", "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f", "source": { "format": "codex-rollout", "sha256": "f9e19dfa2f331647e0873a8a5cab2407095965baeb911d7e4b63b548b518c544", "records": 478, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 76, "item_completed:AgentMessage": 8, "token_count": 66 }, "missingToolResults": [ "call_rzqHPuGOLp8H4WlBhZGdJPTb" ] }, "sessionId": "01a0d59c-2da1-7c53-a992-58ab0686146d", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-24T22:49:37.476Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:49:37.476Z", "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": "2026-09-24T22:49:38.972Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:49:38.972Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-24T22:49:38.972Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-24T22:49:38.972Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-24T22:49:38.972Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:49:38.972Z", "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": "2026-09-24T22:49:38.972Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:49:38.972Z", "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": "2026-09-24T22:49:38.972Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:49:38.972Z", "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": "2026-09-24T22:49:38.972Z", "text": "\n /app\n sh\n 2026-09-24\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-24T22:49:38.974Z", "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": "2026-09-24", "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": "2026-09-24T22:49:38.975Z", "data": { "type": "turn_context", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-24", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-24T22:49:38.991Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:49:38.991Z", "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": "2026-09-24T22:49:42.417Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:42.417Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:49:43.666Z", "text": "I’ll first profile the event volumes, candidate masks, user/item coverage, and the public baseline. Then I’ll build a leakage-safe temporal ranker, tune it strictly on train→validation, preserve each improved valid artifact, and finally refit on train+validation for the hidden window." }, { "kind": "tool", "timestamp": "2026-09-24T22:49:46.025Z", "callId": "call_FJIpg0T8CfcMQLcIFvo0KZ6S", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,220p' /task/requirements.txt && printf '\\\\nEvaluator helper:\\\\n' && sed -n '1,260p' /task/ranking_evaluation.py && printf '\\\\nPublic evaluator:\\\\n' && sed -n '1,260p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:49:46.105Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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\nEvaluator helper:\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\nPublic evaluator:\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:46.047Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a903348487d2b323a5e559c7c86a", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 77, "total_tokens": 14618 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 77, "total_tokens": 14618 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 77, "total_tokens": 14618 } } }, { "kind": "context", "timestamp": "2026-09-24T22:49:46.098Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-686aad7a-6e08-42f7-a769-4a556982da9c", "process_id": "34791", "command": [ "/bin/sh", "-lc", "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,220p' /task/requirements.txt && printf '\\nEvaluator helper:\\n' && sed -n '1,260p' /task/ranking_evaluation.py && printf '\\nPublic evaluator:\\n' && sed -n '1,260p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,220p' /task/requirements.txt && printf '\\nEvaluator helper:\\n' && sed -n '1,260p' /task/ranking_evaluation.py && printf '\\nPublic evaluator:\\n' && sed -n '1,260p' /task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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\nEvaluator helper:\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\nPublic evaluator:\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", "stderr": "", "aggregated_output": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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\nEvaluator helper:\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\nPublic evaluator:\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", "exit_code": 0, "duration": { "secs": 0, "nanos": 4070 }, "formatted_output": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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\nEvaluator helper:\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\nPublic evaluator:\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" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-24T22:49:49.477Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:49.477Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:49:56.196Z", "callId": "call_nLfAdiVsxdDJ8mdKW5BoS4TD", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\np=Path('/task/data')\\nfor f in sorted(p.glob('*.parquet')):\\n d=pd.read_parquet(f)\\n print(f.name, d.shape, d.dtypes.to_dict())\\n print(' ', {c:(int(d[c].min()), int(d[c].max()), int(d[c].nunique())) for c in d.columns if c in ['uid','item_id','timestamp']})\\n if 'is_organic' in d: print(' organic',d.is_organic.value_counts().to_dict())\\nprint('\\\\nCatalog')\\nprint(pd.read_parquet(p/'candidate_catalog.parquet').head(10))\\nprint(pd.read_parquet(p/'target_users.parquet').head(), 'n=',len(pd.read_parquet(p/'target_users.parquet')))\\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n with np.load(p/fn) as z:\\n print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print('elig counts', np.quantile(counts,[0,.01,.1,.5,.9,.99,1]), 'mean',counts.mean(), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:49:56.322Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:56.258Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a90a636887d29106f96b5d7a9ff1", "usage": { "input_tokens": 18570, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 501, "reasoning_output_tokens": 119, "total_tokens": 19071 }, "turn_token_usage": { "input_tokens": 32907, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 782, "reasoning_output_tokens": 196, "total_tokens": 33689 }, "thread_token_usage": { "input_tokens": 32907, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 782, "reasoning_output_tokens": 196, "total_tokens": 33689 } } }, { "kind": "context", "timestamp": "2026-09-24T22:49:56.318Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8bbff56e-5460-44d8-81c2-54ab1392eed3", "process_id": "72664", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, d.shape, d.dtypes.to_dict())\n print(' ', {c:(int(d[c].min()), int(d[c].max()), int(d[c].nunique())) for c in d.columns if c in ['uid','item_id','timestamp']})\n if 'is_organic' in d: print(' organic',d.is_organic.value_counts().to_dict())\nprint('\\nCatalog')\nprint(pd.read_parquet(p/'candidate_catalog.parquet').head(10))\nprint(pd.read_parquet(p/'target_users.parquet').head(), 'n=',len(pd.read_parquet(p/'target_users.parquet')))\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n with np.load(p/fn) as z:\n print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts', np.quantile(counts,[0,.01,.1,.5,.9,.99,1]), 'mean',counts.mean(), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, d.shape, d.dtypes.to_dict())\n print(' ', {c:(int(d[c].min()), int(d[c].max()), int(d[c].nunique())) for c in d.columns if c in ['uid','item_id','timestamp']})\n if 'is_organic' in d: print(' organic',d.is_organic.value_counts().to_dict())\nprint('\\nCatalog')\nprint(pd.read_parquet(p/'candidate_catalog.parquet').head(10))\nprint(pd.read_parquet(p/'target_users.parquet').head(), 'n=',len(pd.read_parquet(p/'target_users.parquet')))\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n with np.load(p/fn) as z:\n print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts', np.quantile(counts,[0,.01,.1,.5,.9,.99,1]), 'mean',counts.mean(), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 2170 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-24T22:49:58.362Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:58.362Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:50:00.362Z", "callId": "call_7CKOe1pTk4H4a0P8cmpui2Ff", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"which -a python python3 || true\\nfind / -path '*/site-packages/pandas' -type d 2>/dev/null | head\\nfind / -type f -name 'python3.*' -perm -111 2>/dev/null | head -30\\nls -la /task | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:50:01.134Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n/opt/task-venv/lib/python3.12/site-packages/pandas\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:46 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:49 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:42 requirements.txt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:00.406Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a914ab7087d295d48ef400bc247f", "usage": { "input_tokens": 19127, "cached_input_tokens": 18432, "cache_write_input_tokens": 0, "output_tokens": 148, "reasoning_output_tokens": 28, "total_tokens": 19275 }, "turn_token_usage": { "input_tokens": 52034, "cached_input_tokens": 43776, "cache_write_input_tokens": 0, "output_tokens": 930, "reasoning_output_tokens": 224, "total_tokens": 52964 }, "thread_token_usage": { "input_tokens": 52034, "cached_input_tokens": 43776, "cache_write_input_tokens": 0, "output_tokens": 930, "reasoning_output_tokens": 224, "total_tokens": 52964 } } }, { "kind": "context", "timestamp": "2026-09-24T22:50:01.130Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-278ec2f2-ba70-44a5-887b-370e62ace17f", "process_id": "27970", "command": [ "/bin/sh", "-lc", "which -a python python3 || true\nfind / -path '*/site-packages/pandas' -type d 2>/dev/null | head\nfind / -type f -name 'python3.*' -perm -111 2>/dev/null | head -30\nls -la /task | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3 || true\nfind / -path '*/site-packages/pandas' -type d 2>/dev/null | head\nfind / -type f -name 'python3.*' -perm -111 2>/dev/null | head -30\nls -la /task | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/pandas\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:46 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:49 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:42 requirements.txt\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:46 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:49 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:42 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 605047323 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/var/lib/dpkg/info/python3.11-minimal.preinst\n/var/lib/dpkg/info/python3.11-minimal.postinst\n/var/lib/dpkg/info/python3.prerm\n/var/lib/dpkg/info/python3.postrm\n/var/lib/dpkg/info/python3.11.postinst\n/var/lib/dpkg/info/python3.preinst\n/var/lib/dpkg/info/python3.11-minimal.prerm\n/var/lib/dpkg/info/python3.11-minimal.postrm\n/var/lib/dpkg/info/python3.11.prerm\n/var/lib/dpkg/info/python3.postinst\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:46 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:49 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:42 requirements.txt\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-24T22:50:03.169Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:03.169Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:50:10.014Z", "callId": "call_KCSYAyT0JVV41sAbCnLdIo3b", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\np=Path('/task/data')\\nfor f in sorted(p.glob('*.parquet')):\\n d=pd.read_parquet(f)\\n print(f.name, d.shape, d.dtypes.to_dict())\\n print(' ', {c:(int(d[c].min()), int(d[c].max()), int(d[c].nunique())) for c in d.columns if c in ['uid','item_id','timestamp']})\\n if 'is_organic' in d: print(' organic',d.is_organic.value_counts().to_dict())\\nprint('\\\\nCatalog')\\nprint(pd.read_parquet(p/'candidate_catalog.parquet').head(10))\\nprint(pd.read_parquet(p/'target_users.parquet').head(), 'n=',len(pd.read_parquet(p/'target_users.parquet')))\\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n with np.load(p/fn) as z:\\n print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\\n print('elig counts', np.quantile(counts,[0,.01,.1,.5,.9,.99,1]), 'mean',counts.mean(), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:50:13.765Z", "content": "Script completed\nWall time 3.7 seconds\nOutput:\n\ncandidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n {'item_id': (206, 9390294, 30000)}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n {'uid': (100, 1000000, 8456)}\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7092), 'item_id': (206, 9390294, 28863)}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 5732), 'timestamp': (75, 24786580, 67779), 'item_id': (105, 9390541, 50876)}\n organic {1: 52278, 0: 48797}\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 8144), 'timestamp': (80, 24786790, 562774), 'item_id': (26, 9390522, 172303)}\n organic {1: 461839, 0: 352992}\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 9238), 'timestamp': (0, 24786795, 4806896), 'item_id': (22, 9390623, 845395)}\n organic {1: 22477622, 0: 21006537}\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 2769), 'timestamp': (15950, 24785395, 6956), 'item_id': (178, 9390240, 14725)}\n organic {1: 17751, 0: 2209}\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 6197), 'timestamp': (250, 24786570, 87635), 'item_id': (26, 9390601, 113072)}\n organic {1: 277065, 0: 15600}\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1600, 998800, 955), 'timestamp': (24789920, 25393280, 2386), 'item_id': (651, 9388630, 3135)}\n organic {1: 1835, 0: 1728}\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 4114), 'timestamp': (24788815, 25393390, 19790), 'item_id': (209, 9390461, 22902)}\n organic {1: 25181, 0: 12614}\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 8196), 'timestamp': (24788600, 25393395, 119851), 'item_id': (50, 9390541, 191747)}\n organic {1: 776486, 0: 725211}\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (900, 998800, 205), 'timestamp': (24810715, 25393035, 242), 'item_id': (15149, 9385279, 579)}\n organic {1: 527, 0: 79}\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1100, 998800, 1313), 'timestamp': (24789005, 25393385, 2941), 'item_id': (1928, 9390163, 8080)}\n organic {1: 8905, 0: 441}\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7038), 'item_id': (206, 9390294, 28814)}\n\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\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= 8456\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:10.098Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a91952d887d2a897e2bc22107188", "usage": { "input_tokens": 19629, "cached_input_tokens": 18944, "cache_write_input_tokens": 0, "output_tokens": 403, "reasoning_output_tokens": 14, "total_tokens": 20032 }, "turn_token_usage": { "input_tokens": 71663, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 1333, "reasoning_output_tokens": 238, "total_tokens": 72996 }, "thread_token_usage": { "input_tokens": 71663, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 1333, "reasoning_output_tokens": 238, "total_tokens": 72996 } } }, { "kind": "context", "timestamp": "2026-09-24T22:50:13.761Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4d1a1be0-8449-4d00-96a7-0a1604dbe99c", "process_id": "5727", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, d.shape, d.dtypes.to_dict())\n print(' ', {c:(int(d[c].min()), int(d[c].max()), int(d[c].nunique())) for c in d.columns if c in ['uid','item_id','timestamp']})\n if 'is_organic' in d: print(' organic',d.is_organic.value_counts().to_dict())\nprint('\\nCatalog')\nprint(pd.read_parquet(p/'candidate_catalog.parquet').head(10))\nprint(pd.read_parquet(p/'target_users.parquet').head(), 'n=',len(pd.read_parquet(p/'target_users.parquet')))\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n with np.load(p/fn) as z:\n print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts', np.quantile(counts,[0,.01,.1,.5,.9,.99,1]), 'mean',counts.mean(), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, d.shape, d.dtypes.to_dict())\n print(' ', {c:(int(d[c].min()), int(d[c].max()), int(d[c].nunique())) for c in d.columns if c in ['uid','item_id','timestamp']})\n if 'is_organic' in d: print(' organic',d.is_organic.value_counts().to_dict())\nprint('\\nCatalog')\nprint(pd.read_parquet(p/'candidate_catalog.parquet').head(10))\nprint(pd.read_parquet(p/'target_users.parquet').head(), 'n=',len(pd.read_parquet(p/'target_users.parquet')))\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n with np.load(p/fn) as z:\n print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts', np.quantile(counts,[0,.01,.1,.5,.9,.99,1]), 'mean',counts.mean(), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n {'item_id': (206, 9390294, 30000)}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n {'uid': (100, 1000000, 8456)}\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7092), 'item_id': (206, 9390294, 28863)}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 5732), 'timestamp': (75, 24786580, 67779), 'item_id': (105, 9390541, 50876)}\n organic {1: 52278, 0: 48797}\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 8144), 'timestamp': (80, 24786790, 562774), 'item_id': (26, 9390522, 172303)}\n organic {1: 461839, 0: 352992}\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 9238), 'timestamp': (0, 24786795, 4806896), 'item_id': (22, 9390623, 845395)}\n organic {1: 22477622, 0: 21006537}\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 2769), 'timestamp': (15950, 24785395, 6956), 'item_id': (178, 9390240, 14725)}\n organic {1: 17751, 0: 2209}\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 6197), 'timestamp': (250, 24786570, 87635), 'item_id': (26, 9390601, 113072)}\n organic {1: 277065, 0: 15600}\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1600, 998800, 955), 'timestamp': (24789920, 25393280, 2386), 'item_id': (651, 9388630, 3135)}\n organic {1: 1835, 0: 1728}\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 4114), 'timestamp': (24788815, 25393390, 19790), 'item_id': (209, 9390461, 22902)}\n organic {1: 25181, 0: 12614}\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 8196), 'timestamp': (24788600, 25393395, 119851), 'item_id': (50, 9390541, 191747)}\n organic {1: 776486, 0: 725211}\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (900, 998800, 205), 'timestamp': (24810715, 25393035, 242), 'item_id': (15149, 9385279, 579)}\n organic {1: 527, 0: 79}\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1100, 998800, 1313), 'timestamp': (24789005, 25393385, 2941), 'item_id': (1928, 9390163, 8080)}\n organic {1: 8905, 0: 441}\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7038), 'item_id': (206, 9390294, 28814)}\n\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\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= 8456\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n {'item_id': (206, 9390294, 30000)}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n {'uid': (100, 1000000, 8456)}\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7092), 'item_id': (206, 9390294, 28863)}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 5732), 'timestamp': (75, 24786580, 67779), 'item_id': (105, 9390541, 50876)}\n organic {1: 52278, 0: 48797}\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 8144), 'timestamp': (80, 24786790, 562774), 'item_id': (26, 9390522, 172303)}\n organic {1: 461839, 0: 352992}\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 9238), 'timestamp': (0, 24786795, 4806896), 'item_id': (22, 9390623, 845395)}\n organic {1: 22477622, 0: 21006537}\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 2769), 'timestamp': (15950, 24785395, 6956), 'item_id': (178, 9390240, 14725)}\n organic {1: 17751, 0: 2209}\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 6197), 'timestamp': (250, 24786570, 87635), 'item_id': (26, 9390601, 113072)}\n organic {1: 277065, 0: 15600}\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1600, 998800, 955), 'timestamp': (24789920, 25393280, 2386), 'item_id': (651, 9388630, 3135)}\n organic {1: 1835, 0: 1728}\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 4114), 'timestamp': (24788815, 25393390, 19790), 'item_id': (209, 9390461, 22902)}\n organic {1: 25181, 0: 12614}\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 8196), 'timestamp': (24788600, 25393395, 119851), 'item_id': (50, 9390541, 191747)}\n organic {1: 776486, 0: 725211}\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (900, 998800, 205), 'timestamp': (24810715, 25393035, 242), 'item_id': (15149, 9385279, 579)}\n organic {1: 527, 0: 79}\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1100, 998800, 1313), 'timestamp': (24789005, 25393385, 2941), 'item_id': (1928, 9390163, 8080)}\n organic {1: 8905, 0: 441}\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7038), 'item_id': (206, 9390294, 28814)}\n\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\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= 8456\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 583105218 }, "formatted_output": "candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n {'item_id': (206, 9390294, 30000)}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n {'uid': (100, 1000000, 8456)}\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7092), 'item_id': (206, 9390294, 28863)}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 5732), 'timestamp': (75, 24786580, 67779), 'item_id': (105, 9390541, 50876)}\n organic {1: 52278, 0: 48797}\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 8144), 'timestamp': (80, 24786790, 562774), 'item_id': (26, 9390522, 172303)}\n organic {1: 461839, 0: 352992}\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 9238), 'timestamp': (0, 24786795, 4806896), 'item_id': (22, 9390623, 845395)}\n organic {1: 22477622, 0: 21006537}\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 2769), 'timestamp': (15950, 24785395, 6956), 'item_id': (178, 9390240, 14725)}\n organic {1: 17751, 0: 2209}\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 1000000, 6197), 'timestamp': (250, 24786570, 87635), 'item_id': (26, 9390601, 113072)}\n organic {1: 277065, 0: 15600}\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1600, 998800, 955), 'timestamp': (24789920, 25393280, 2386), 'item_id': (651, 9388630, 3135)}\n organic {1: 1835, 0: 1728}\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (100, 999900, 4114), 'timestamp': (24788815, 25393390, 19790), 'item_id': (209, 9390461, 22902)}\n organic {1: 25181, 0: 12614}\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n {'uid': (100, 1000000, 8196), 'timestamp': (24788600, 25393395, 119851), 'item_id': (50, 9390541, 191747)}\n organic {1: 776486, 0: 725211}\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (900, 998800, 205), 'timestamp': (24810715, 25393035, 242), 'item_id': (15149, 9385279, 579)}\n organic {1: 527, 0: 79}\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n {'uid': (1100, 998800, 1313), 'timestamp': (24789005, 25393385, 2941), 'item_id': (1928, 9390163, 8080)}\n organic {1: 8905, 0: 441}\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n {'uid': (100, 1000000, 7038), 'item_id': (206, 9390294, 28814)}\n\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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\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= 8456\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-24T22:50:26.207Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:26.207Z", "text": "" } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-24T22:50:27.800Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:27.800Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:50:29.257Z", "text": "The data has 8,456 target users and 30,000 catalog tracks; eligibility is broad (about 29,000 tracks per user), so popularity alone will be weak and personalized collaborative structure should matter. I’m establishing a valid popularity fallback now, then I’ll measure progressively stronger temporal and collaborative variants against the exact public NDCG." }, { "kind": "tool", "timestamp": "2026-09-24T22:50:36.924Z", "callId": "call_qUnocd1I0zJ3T4wNO7cQtUGZ", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_fallback.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build a structurally valid popularity fallback for either cutoff.\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def build(candidate_file: str, output_file: str) -> None:\\n+ with np.load(DATA / candidate_file, allow_pickle=False) as archive:\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"packed_eligible\\\"]\\n+\\n+ rows = []\\n+ for row, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[row], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[np.flatnonzero(eligible)[:100]]\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": uid,\\n+ \\\"item_id\\\": chosen,\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ pd.concat(rows, ignore_index=True).to_parquet(OUT / output_file, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ build(\\\"validation_eligible_candidates.npz\\\", \\\"validation_popularity.parquet\\\")\\n+ build(\\\"test_eligible_candidates.npz\\\", \\\"submission.parquet\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T22:50:36.952Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:36.933Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-fe8e9b32-c62d-458e-a72b-f94108a863ea", "changes": { "/app/solution/build_fallback.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build a structurally valid popularity fallback for either cutoff.\"\"\"\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\n\n\ndef build(candidate_file: str, output_file: str) -> None:\n with np.load(DATA / candidate_file, allow_pickle=False) as archive:\n uids = archive[\"uids\"].astype(np.int64)\n items = archive[\"item_ids\"].astype(np.int64)\n packed = archive[\"packed_eligible\"]\n\n rows = []\n for row, uid in enumerate(uids):\n eligible = np.unpackbits(\n packed[row], count=len(items), bitorder=\"little\"\n ).astype(bool)\n chosen = items[np.flatnonzero(eligible)[:100]]\n rows.append(\n pd.DataFrame(\n {\n \"uid\": uid,\n \"item_id\": chosen,\n \"rank\": np.arange(1, 101, dtype=np.int64),\n }\n )\n )\n pd.concat(rows, ignore_index=True).to_parquet(OUT / output_file, index=False)\n\n\nif __name__ == \"__main__\":\n OUT.mkdir(parents=True, exist_ok=True)\n build(\"validation_eligible_candidates.npz\", \"validation_popularity.parquet\")\n build(\"test_eligible_candidates.npz\", \"submission.parquet\")\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_fallback.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:50:36.951Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a927491487d2acc08118d5998970", "usage": { "input_tokens": 22020, "cached_input_tokens": 19456, "cache_write_input_tokens": 0, "output_tokens": 1112, "reasoning_output_tokens": 603, "total_tokens": 23132 }, "turn_token_usage": { "input_tokens": 93683, "cached_input_tokens": 82176, "cache_write_input_tokens": 0, "output_tokens": 2445, "reasoning_output_tokens": 841, "total_tokens": 96128 }, "thread_token_usage": { "input_tokens": 93683, "cached_input_tokens": 82176, "cache_write_input_tokens": 0, "output_tokens": 2445, "reasoning_output_tokens": 841, "total_tokens": 96128 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-24T22:50:39.768Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:39.768Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:50:41.447Z", "callId": "call_xWp73bJe2VEOtP678MCJJk1a", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"mkdir -p /app/solution && /opt/task-venv/bin/python /app/solution/build_fallback.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution/submission.parquet && free -h\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:50:47.372Z", "content": "Script completed\nWall time 5.9 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 719K Sep 24 22:50 /app/solution/submission.parquet\n/bin/sh: 1: free: not found\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:41.560Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a93d407487d28100eb1896498a3b", "usage": { "input_tokens": 23156, "cached_input_tokens": 21888, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 17, "total_tokens": 23285 }, "turn_token_usage": { "input_tokens": 116839, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 2574, "reasoning_output_tokens": 858, "total_tokens": 119413 }, "thread_token_usage": { "input_tokens": 116839, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 2574, "reasoning_output_tokens": 858, "total_tokens": 119413 } } }, { "kind": "context", "timestamp": "2026-09-24T22:50:47.368Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a2ae67e4-3059-489f-a0e2-e005687e8a22", "process_id": "39188", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && /opt/task-venv/bin/python /app/solution/build_fallback.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution/submission.parquet && free -h" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && /opt/task-venv/bin/python /app/solution/build_fallback.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && ls -lh /app/solution/submission.parquet && free -h" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 719K Sep 24 22:50 /app/solution/submission.parquet\n/bin/sh: 1: free: not found\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 719K Sep 24 22:50 /app/solution/submission.parquet\n/bin/sh: 1: free: not found\n", "exit_code": 127, "duration": { "secs": 5, "nanos": 727911500 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 719K Sep 24 22:50 /app/solution/submission.parquet\n/bin/sh: 1: free: not found\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-24T22:50:58.765Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:58.765Z", "text": "" } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-24T22:51:10.085Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:51:10.085Z", "text": "" } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-24T22:51:13.672Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:51:13.672Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:51:16.725Z", "callId": "call_SSOUeU3WUOGaWSucWUTdDSkZ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"grep -E 'MemTotal|MemAvailable' /proc/meminfo && df -h /app /tmp && /opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\nfor fn in ['train_listens.parquet','val_listens.parquet']:\\n p=pq.ParquetFile('/task/data/'+fn)\\n print(fn,p.metadata.num_rows,p.num_row_groups,[(p.schema.names[i], p.schema_arrow.field(i).type) for i in range(len(p.schema.names))])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:51:17.119Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\nMemTotal: 15988964 kB\nMemAvailable: 14985440 kB\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 12G 85G 12% /\noverlay 96G 12G 85G 12% /\ntrain_listens.parquet 43484159 42 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\nval_listens.parquet 1501697 2 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:51:16.809Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a947bedc87d28cd8645f054f182f", "usage": { "input_tokens": 23395, "cached_input_tokens": 23040, "cache_write_input_tokens": 0, "output_tokens": 1356, "reasoning_output_tokens": 1193, "total_tokens": 24751 }, "turn_token_usage": { "input_tokens": 140234, "cached_input_tokens": 127104, "cache_write_input_tokens": 0, "output_tokens": 3930, "reasoning_output_tokens": 2051, "total_tokens": 144164 }, "thread_token_usage": { "input_tokens": 140234, "cached_input_tokens": 127104, "cache_write_input_tokens": 0, "output_tokens": 3930, "reasoning_output_tokens": 2051, "total_tokens": 144164 } } }, { "kind": "context", "timestamp": "2026-09-24T22:51:17.114Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-25a78768-8c73-445b-958f-e79eb49f168f", "process_id": "76929", "command": [ "/bin/sh", "-lc", "grep -E 'MemTotal|MemAvailable' /proc/meminfo && df -h /app /tmp && /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfor fn in ['train_listens.parquet','val_listens.parquet']:\n p=pq.ParquetFile('/task/data/'+fn)\n print(fn,p.metadata.num_rows,p.num_row_groups,[(p.schema.names[i], p.schema_arrow.field(i).type) for i in range(len(p.schema.names))])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "grep -E 'MemTotal|MemAvailable' /proc/meminfo && df -h /app /tmp && /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfor fn in ['train_listens.parquet','val_listens.parquet']:\n p=pq.ParquetFile('/task/data/'+fn)\n print(fn,p.metadata.num_rows,p.num_row_groups,[(p.schema.names[i], p.schema_arrow.field(i).type) for i in range(len(p.schema.names))])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens.parquet 43484159 42 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\nval_listens.parquet 1501697 2 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\n", "stderr": "", "aggregated_output": "train_listens.parquet 43484159 42 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\nval_listens.parquet 1501697 2 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 224878467 }, "formatted_output": "train_listens.parquet 43484159 42 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\nval_listens.parquet 1501697 2 [('uid', DataType(uint32)), ('timestamp', DataType(uint32)), ('item_id', DataType(uint32)), ('is_organic', DataType(uint8)), ('played_ratio_pct', DataType(uint16)), ('track_length_seconds', DataType(uint32))]\n" } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-24T22:51:25.481Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:51:25.481Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:00.940Z", "callId": "call_yLsxqPNo9u87Fnq2lCTuGmwE", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_global.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Tune leakage-safe catalog-popularity baselines on the public window.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import json\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+CUTOFF = 24_786_800\\n+\\n+\\n+def truth_and_discounts(items, uid_to_row, eligible):\\n+ item_to_col = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\\n+ targets = set(uid_to_row)\\n+ listens = pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ likes = pd.read_parquet(DATA / \\\"val_likes.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & listens.uid.isin(targets)][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n+ likes = likes[(likes.is_organic == 1) & likes.uid.isin(targets)][[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates()\\n+ gold = {}\\n+ for grade, frame in [(1, strong), (3, likes)]:\\n+ cols = item_to_col.reindex(frame.item_id).to_numpy()\\n+ for (uid, _), col in zip(frame.itertuples(index=False, name=None), cols):\\n+ if np.isnan(col):\\n+ continue\\n+ row, col = uid_to_row[int(uid)], int(col)\\n+ if eligible[row, col]:\\n+ gold.setdefault(int(uid), {})[col] = grade\\n+ return gold\\n+\\n+\\n+def evaluate(order, uids, eligible, gold, save=None):\\n+ discounts = 1 / np.log2(np.arange(2, 12))\\n+ vals, frames = [], []\\n+ for r, uid in enumerate(uids):\\n+ chosen = order[eligible[r, order]][:100]\\n+ if save is not None:\\n+ frames.append(pd.DataFrame({\\\"uid\\\": int(uid), \\\"item_id\\\": ITEMS[chosen], \\\"rank\\\": np.arange(1,101)}))\\n+ g = gold.get(int(uid))\\n+ if g:\\n+ grades = np.fromiter((g.get(int(c), 0) for c in chosen[:10]), dtype=np.int8)\\n+ dcg = ((2.0 ** grades - 1) * discounts).sum()\\n+ ideal = np.sort(np.fromiter(g.values(), dtype=np.int8))[::-1][:10]\\n+ vals.append(dcg / (((2.0 ** ideal - 1) * discounts[:len(ideal)]).sum()))\\n+ if save is not None:\\n+ pd.concat(frames, ignore_index=True).to_parquet(save, index=False)\\n+ return float(np.mean(vals))\\n+\\n+\\n+with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ UIDS = z[\\\"uids\\\"].astype(np.int64)\\n+ ITEMS = z[\\\"item_ids\\\"].astype(np.int64)\\n+ ELIG = np.unpackbits(z[\\\"packed_eligible\\\"], axis=1, count=len(ITEMS), bitorder=\\\"little\\\").astype(bool)\\n+uid_to_row = {int(x): i for i, x in enumerate(UIDS)}\\n+item_map = np.full(int(ITEMS.max()) + 1, -1, dtype=np.int32)\\n+item_map[ITEMS] = np.arange(len(ITEMS), dtype=np.int32)\\n+gold = truth_and_discounts(ITEMS, uid_to_row, ELIG)\\n+print(\\\"gold\\\", len(gold), \\\"events\\\", sum(map(len, gold.values())))\\n+\\n+features = {}\\n+for family, threshold in [(\\\"listen50\\\", 50), (\\\"strong80\\\", 80)]:\\n+ d = pd.read_parquet(DATA / \\\"train_listens.parquet\\\", columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ col = item_map[d.item_id.to_numpy()]\\n+ base = (col >= 0) & (d.played_ratio_pct.to_numpy() >= threshold)\\n+ ts = d.timestamp.to_numpy()\\n+ org = d.is_organic.to_numpy().astype(bool)\\n+ uid = d.uid.to_numpy()\\n+ for organic_name, organic_mask in [(\\\"all\\\", np.ones(len(d), bool)), (\\\"org\\\", org), (\\\"reco\\\", ~org)]:\\n+ for days in [1, 3, 7, 14, 30, 60, 90, 180, 1000]:\\n+ m = base & organic_mask & (ts >= CUTOFF - days*86400)\\n+ features[f\\\"{family}_{organic_name}_{days}d_events\\\"] = np.bincount(col[m], minlength=len(ITEMS)).astype(np.float64)\\n+ # Per-user occurrence/reach over full history and 30/90 days.\\n+ for days in [7, 30, 90, 1000]:\\n+ m = base & organic_mask & (ts >= CUTOFF - days*86400)\\n+ pairs = np.unique(uid[m].astype(np.int64) * len(ITEMS) + col[m])\\n+ features[f\\\"{family}_{organic_name}_{days}d_users\\\"] = np.bincount((pairs % len(ITEMS)).astype(np.int32), minlength=len(ITEMS)).astype(np.float64)\\n+ del d\\n+\\n+d = pd.read_parquet(DATA / \\\"train_likes.parquet\\\", columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+col = item_map[d.item_id.to_numpy()]\\n+ts = d.timestamp.to_numpy(); org=d.is_organic.to_numpy().astype(bool); uid=d.uid.to_numpy()\\n+for organic_name, organic_mask in [(\\\"all\\\", np.ones(len(d), bool)), (\\\"org\\\", org), (\\\"reco\\\", ~org)]:\\n+ for days in [7, 30, 90, 180, 1000]:\\n+ m=(col>=0)&organic_mask&(ts>=CUTOFF-days*86400)\\n+ features[f\\\"likes_{organic_name}_{days}d_events\\\"] = np.bincount(col[m], minlength=len(ITEMS)).astype(np.float64)\\n+ pairs=np.unique(uid[m].astype(np.int64)*len(ITEMS)+col[m])\\n+ features[f\\\"likes_{organic_name}_{days}d_users\\\"] = np.bincount((pairs%len(ITEMS)).astype(np.int32),minlength=len(ITEMS)).astype(np.float64)\\n+del d\\n+\\n+scores=[]\\n+for name, x in features.items():\\n+ score=evaluate(np.argsort(-x,kind=\\\"stable\\\"),UIDS,ELIG,gold)\\n+ scores.append((score,name))\\n+for score,name in sorted(scores, reverse=True)[:40]: print(f\\\"{score:.6f} {name}\\\")\\n+\\n+# Try simple rank blends among the best individual signals.\\n+best_names=[n for _,n in sorted(scores,reverse=True)[:15]]\\n+ranks={n:np.argsort(np.argsort(-features[n],kind=\\\"stable\\\"),kind=\\\"stable\\\").astype(float) for n in best_names}\\n+blend_results=[]\\n+for i,a in enumerate(best_names):\\n+ for b in best_names[i+1:]:\\n+ for w in [.2,.35,.5,.65,.8]:\\n+ order=np.argsort(w*ranks[a]+(1-w)*ranks[b],kind=\\\"stable\\\")\\n+ blend_results.append((evaluate(order,UIDS,ELIG,gold),a,b,w,order))\\n+for score,a,b,w,_ in sorted(blend_results,key=lambda x:x[0],reverse=True)[:20]: print(f\\\"BLEND {score:.6f} {w:.2f}*{a} + {1-w:.2f}*{b}\\\")\\n+best=max(blend_results,key=lambda x:x[0])\\n+evaluate(best[-1],UIDS,ELIG,gold,OUT/\\\"validation_global.parquet\\\")\\n+np.savez_compressed(OUT/\\\"global_features_train.npz\\\", **features)\\n+print(\\\"BEST\\\",best[:4])\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T22:52:00.997Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:00.950Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-efe1d21b-bcc7-4888-84fb-4e8a80f98b12", "changes": { "/app/solution/tune_global.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Tune leakage-safe catalog-popularity baselines on the public window.\"\"\"\nfrom pathlib import Path\nimport json\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nCUTOFF = 24_786_800\n\n\ndef truth_and_discounts(items, uid_to_row, eligible):\n item_to_col = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\n targets = set(uid_to_row)\n listens = pd.read_parquet(DATA / \"val_listens.parquet\", columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\n likes = pd.read_parquet(DATA / \"val_likes.parquet\", columns=[\"uid\", \"item_id\", \"is_organic\"])\n strong = listens[(listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & listens.uid.isin(targets)][[\"uid\", \"item_id\"]].drop_duplicates()\n likes = likes[(likes.is_organic == 1) & likes.uid.isin(targets)][[\"uid\", \"item_id\"]].drop_duplicates()\n gold = {}\n for grade, frame in [(1, strong), (3, likes)]:\n cols = item_to_col.reindex(frame.item_id).to_numpy()\n for (uid, _), col in zip(frame.itertuples(index=False, name=None), cols):\n if np.isnan(col):\n continue\n row, col = uid_to_row[int(uid)], int(col)\n if eligible[row, col]:\n gold.setdefault(int(uid), {})[col] = grade\n return gold\n\n\ndef evaluate(order, uids, eligible, gold, save=None):\n discounts = 1 / np.log2(np.arange(2, 12))\n vals, frames = [], []\n for r, uid in enumerate(uids):\n chosen = order[eligible[r, order]][:100]\n if save is not None:\n frames.append(pd.DataFrame({\"uid\": int(uid), \"item_id\": ITEMS[chosen], \"rank\": np.arange(1,101)}))\n g = gold.get(int(uid))\n if g:\n grades = np.fromiter((g.get(int(c), 0) for c in chosen[:10]), dtype=np.int8)\n dcg = ((2.0 ** grades - 1) * discounts).sum()\n ideal = np.sort(np.fromiter(g.values(), dtype=np.int8))[::-1][:10]\n vals.append(dcg / (((2.0 ** ideal - 1) * discounts[:len(ideal)]).sum()))\n if save is not None:\n pd.concat(frames, ignore_index=True).to_parquet(save, index=False)\n return float(np.mean(vals))\n\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as z:\n UIDS = z[\"uids\"].astype(np.int64)\n ITEMS = z[\"item_ids\"].astype(np.int64)\n ELIG = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(ITEMS), bitorder=\"little\").astype(bool)\nuid_to_row = {int(x): i for i, x in enumerate(UIDS)}\nitem_map = np.full(int(ITEMS.max()) + 1, -1, dtype=np.int32)\nitem_map[ITEMS] = np.arange(len(ITEMS), dtype=np.int32)\ngold = truth_and_discounts(ITEMS, uid_to_row, ELIG)\nprint(\"gold\", len(gold), \"events\", sum(map(len, gold.values())))\n\nfeatures = {}\nfor family, threshold in [(\"listen50\", 50), (\"strong80\", 80)]:\n d = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\n col = item_map[d.item_id.to_numpy()]\n base = (col >= 0) & (d.played_ratio_pct.to_numpy() >= threshold)\n ts = d.timestamp.to_numpy()\n org = d.is_organic.to_numpy().astype(bool)\n uid = d.uid.to_numpy()\n for organic_name, organic_mask in [(\"all\", np.ones(len(d), bool)), (\"org\", org), (\"reco\", ~org)]:\n for days in [1, 3, 7, 14, 30, 60, 90, 180, 1000]:\n m = base & organic_mask & (ts >= CUTOFF - days*86400)\n features[f\"{family}_{organic_name}_{days}d_events\"] = np.bincount(col[m], minlength=len(ITEMS)).astype(np.float64)\n # Per-user occurrence/reach over full history and 30/90 days.\n for days in [7, 30, 90, 1000]:\n m = base & organic_mask & (ts >= CUTOFF - days*86400)\n pairs = np.unique(uid[m].astype(np.int64) * len(ITEMS) + col[m])\n features[f\"{family}_{organic_name}_{days}d_users\"] = np.bincount((pairs % len(ITEMS)).astype(np.int32), minlength=len(ITEMS)).astype(np.float64)\n del d\n\nd = pd.read_parquet(DATA / \"train_likes.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\ncol = item_map[d.item_id.to_numpy()]\nts = d.timestamp.to_numpy(); org=d.is_organic.to_numpy().astype(bool); uid=d.uid.to_numpy()\nfor organic_name, organic_mask in [(\"all\", np.ones(len(d), bool)), (\"org\", org), (\"reco\", ~org)]:\n for days in [7, 30, 90, 180, 1000]:\n m=(col>=0)&organic_mask&(ts>=CUTOFF-days*86400)\n features[f\"likes_{organic_name}_{days}d_events\"] = np.bincount(col[m], minlength=len(ITEMS)).astype(np.float64)\n pairs=np.unique(uid[m].astype(np.int64)*len(ITEMS)+col[m])\n features[f\"likes_{organic_name}_{days}d_users\"] = np.bincount((pairs%len(ITEMS)).astype(np.int32),minlength=len(ITEMS)).astype(np.float64)\ndel d\n\nscores=[]\nfor name, x in features.items():\n score=evaluate(np.argsort(-x,kind=\"stable\"),UIDS,ELIG,gold)\n scores.append((score,name))\nfor score,name in sorted(scores, reverse=True)[:40]: print(f\"{score:.6f} {name}\")\n\n# Try simple rank blends among the best individual signals.\nbest_names=[n for _,n in sorted(scores,reverse=True)[:15]]\nranks={n:np.argsort(np.argsort(-features[n],kind=\"stable\"),kind=\"stable\").astype(float) for n in best_names}\nblend_results=[]\nfor i,a in enumerate(best_names):\n for b in best_names[i+1:]:\n for w in [.2,.35,.5,.65,.8]:\n order=np.argsort(w*ranks[a]+(1-w)*ranks[b],kind=\"stable\")\n blend_results.append((evaluate(order,UIDS,ELIG,gold),a,b,w,order))\nfor score,a,b,w,_ in sorted(blend_results,key=lambda x:x[0],reverse=True)[:20]: print(f\"BLEND {score:.6f} {w:.2f}*{a} + {1-w:.2f}*{b}\")\nbest=max(blend_results,key=lambda x:x[0])\nevaluate(best[-1],UIDS,ELIG,gold,OUT/\"validation_global.parquet\")\nnp.savez_compressed(OUT/\"global_features_train.npz\", **features)\nprint(\"BEST\",best[:4])\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_global.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:52:00.996Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a965497c87d2ad3ffc5a1a5d4737", "usage": { "input_tokens": 24976, "cached_input_tokens": 23168, "cache_write_input_tokens": 0, "output_tokens": 2377, "reasoning_output_tokens": 372, "total_tokens": 27353 }, "turn_token_usage": { "input_tokens": 165210, "cached_input_tokens": 150272, "cache_write_input_tokens": 0, "output_tokens": 6307, "reasoning_output_tokens": 2423, "total_tokens": 171517 }, "thread_token_usage": { "input_tokens": 165210, "cached_input_tokens": 150272, "cache_write_input_tokens": 0, "output_tokens": 6307, "reasoning_output_tokens": 2423, "total_tokens": 171517 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-24T22:52:04.615Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:52:04.615Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:05.090Z", "callId": "call_rOVeyakIiAhQ3BzSR5J36VbG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/tune_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:52:07.173Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\n{\"chunk_id\":\"c84f03\",\"wall_time_seconds\":1.916803757,\"exit_code\":1,\"original_token_count\":68,\"output\":\"Traceback (most recent call last):\\n File \\\"/app/solution/tune_global.py\\\", line 64, in \\ngold 4263 events 37769\\n col = item_map[d.item_id.to_numpy()]\\n ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\\nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:05.125Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a99166e487d2aa1830d40ab0ddeb", "usage": { "input_tokens": 27377, "cached_input_tokens": 24832, "cache_write_input_tokens": 0, "output_tokens": 167, "reasoning_output_tokens": 103, "total_tokens": 27544 }, "turn_token_usage": { "input_tokens": 192587, "cached_input_tokens": 175104, "cache_write_input_tokens": 0, "output_tokens": 6474, "reasoning_output_tokens": 2526, "total_tokens": 199061 }, "thread_token_usage": { "input_tokens": 192587, "cached_input_tokens": 175104, "cache_write_input_tokens": 0, "output_tokens": 6474, "reasoning_output_tokens": 2526, "total_tokens": 199061 } } }, { "kind": "context", "timestamp": "2026-09-24T22:52:07.169Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ab01a79-866d-4591-a28d-298ceab723a2", "process_id": "11935", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/tune_global.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/tune_global.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 64, in \ngold 4263 events 37769\n col = item_map[d.item_id.to_numpy()]\n ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 64, in \ngold 4263 events 37769\n col = item_map[d.item_id.to_numpy()]\n ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n", "exit_code": 1, "duration": { "secs": 1, "nanos": 916794986 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 64, in \ngold 4263 events 37769\n col = item_map[d.item_id.to_numpy()]\n ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^\nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n" } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-24T22:52:11.242Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:52:11.242Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:12.163Z", "callId": "call_NNuq1Ng9Y7HA4m6KipZ3j0EC", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_global.py\\n@@\\n-item_map = np.full(int(ITEMS.max()) + 1, -1, dtype=np.int32)\\n+item_map = np.full(10_000_001, -1, dtype=np.int32)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T22:52:12.235Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:12.173Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-312f365a-d781-4e4d-b74a-f736f3e8571f", "changes": { "/app/solution/tune_global.py": { "type": "update", "unified_diff": "@@ -55,3 +55,3 @@\n uid_to_row = {int(x): i for i, x in enumerate(UIDS)}\n-item_map = np.full(int(ITEMS.max()) + 1, -1, dtype=np.int32)\n+item_map = np.full(10_000_001, -1, dtype=np.int32)\n item_map[ITEMS] = np.arange(len(ITEMS), dtype=np.int32)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_global.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:52:12.234Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a99759e887d2a0c00d5cb75b8072", "usage": { "input_tokens": 27686, "cached_input_tokens": 27136, "cache_write_input_tokens": 0, "output_tokens": 194, "reasoning_output_tokens": 101, "total_tokens": 27880 }, "turn_token_usage": { "input_tokens": 220273, "cached_input_tokens": 202240, "cache_write_input_tokens": 0, "output_tokens": 6668, "reasoning_output_tokens": 2627, "total_tokens": 226941 }, "thread_token_usage": { "input_tokens": 220273, "cached_input_tokens": 202240, "cache_write_input_tokens": 0, "output_tokens": 6668, "reasoning_output_tokens": 2627, "total_tokens": 226941 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:52:15.700Z", "callId": "call_c3AkUxqwnGAdQGeAFYexvkVk", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/tune_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:52:45.867Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"e6de29\",\"wall_time_seconds\":30.000674638,\"session_id\":83727,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:15.755Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a99c858087d2b79c9b8ca1d95f44", "usage": { "input_tokens": 27904, "cached_input_tokens": 27520, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 27966 }, "turn_token_usage": { "input_tokens": 248177, "cached_input_tokens": 229760, "cache_write_input_tokens": 0, "output_tokens": 6730, "reasoning_output_tokens": 2627, "total_tokens": 254907 }, "thread_token_usage": { "input_tokens": 248177, "cached_input_tokens": 229760, "cache_write_input_tokens": 0, "output_tokens": 6730, "reasoning_output_tokens": 2627, "total_tokens": 254907 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-24T22:52:47.708Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:52:47.708Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:48.417Z", "callId": "call_KKCoz781O9XPHjVjoi3B0VlX", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":83727,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:53:18.426Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"abaefe\",\"wall_time_seconds\":30.001478921,\"session_id\":83727,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:48.506Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5a9be155c87d2ac38e25d6551a9f9", "usage": { "input_tokens": 28024, "cached_input_tokens": 27776, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 18, "total_tokens": 28090 }, "turn_token_usage": { "input_tokens": 276201, "cached_input_tokens": 257536, "cache_write_input_tokens": 0, "output_tokens": 6796, "reasoning_output_tokens": 2645, "total_tokens": 282997 }, "thread_token_usage": { "input_tokens": 276201, "cached_input_tokens": 257536, "cache_write_input_tokens": 0, "output_tokens": 6796, "reasoning_output_tokens": 2645, "total_tokens": 282997 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-24T22:53:19.730Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:53:19.730Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:53:21.228Z", "text": "The fallback scores 0.010710 NDCG@10. I’ve confirmed that the public labels cover 4,263 users and 37,769 eligible relevant user-track pairs. A one-pass temporal popularity sweep is running now; after that I’ll use the strongest signal as the prior inside collaborative filtering rather than treating popularity as the final model." }, { "kind": "tool", "timestamp": "2026-09-24T22:53:21.915Z", "callId": "call_DdofhutBG5d09gsYibS4Ovwa", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":83727,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:53:51.924Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a7389c\",\"wall_time_seconds\":30.001029707,\"session_id\":83727,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:53:21.967Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": 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tools.write_stdin({\"session_id\":83727,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:55:05.897Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"87fb1f\",\"wall_time_seconds\":0.136841002,\"exit_code\":130,\"original_token_count\":409,\"output\":\"gold 4263 events 37769\\n0.031327 listen50_org_7d_users\\n0.030933 listen50_all_7d_events\\n0.030286 strong80_org_7d_users\\n0.030275 likes_all_7d_users\\n0.030187 likes_all_7d_events\\n0.030107 strong80_all_7d_events\\n0.029949 likes_org_7d_users\\n0.029297 likes_org_7d_events\\n0.028592 listen50_all_3d_events\\n0.028456 listen50_org_7d_events\\n0.028131 strong80_org_7d_events\\n0.026849 listen50_org_14d_events\\n0.026298 listen50_org_3d_events\\n0.025721 strong80_org_3d_events\\n0.025124 listen50_all_14d_events\\n0.024613 listen50_all_7d_users\\n0.023621 strong80_all_3d_events\\n0.022946 strong80_all_7d_users\\n0.022878 strong80_org_14d_events\\n0.022238 strong80_all_14d_events\\n0.019378 likes_org_30d_users\\n0.018967 listen50_org_30d_users\\n0.018928 strong80_org_30d_users\\n0.018429 likes_org_30d_events\\n0.018312 listen50_all_1d_events\\n0.018231 listen50_org_1d_events\\n0.017614 strong80_all_1d_events\\n0.017182 listen50_all_30d_events\\n0.017089 likes_all_30d_users\\n0.017017 listen50_all_30d_users\\n0.016986 strong80_all_30d_users\\n0.016953 strong80_all_60d_events\\n0.016892 listen50_all_60d_events\\n0.016812 likes_all_30d_events\\n0.016755 strong80_org_1d_events\\n0.016688 strong80_all_30d_events\\n0.016217 likes_org_90d_events\\n0.015980 strong80_all_90d_events\\n0.015977 likes_org_90d_users\\n0.015972 listen50_all_90d_events\\nTraceback (most recent call last):\\n File \\\"/app/solution/tune_global.py\\\", line 105, in \\n blend_results.append((evaluate(order,UIDS,ELIG,gold),a,b,w,order))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/tune_global.py\\\", line 40, in evaluate\\n g = gold.get(int(uid))\\n ^^^^^^^^\\nKeyboardInterrupt\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:05.804Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aa46aa0087d2bafed10499a5067a", "usage": { "input_tokens": 28710, "cached_input_tokens": 28416, "cache_write_input_tokens": 0, "output_tokens": 119, "reasoning_output_tokens": 66, "total_tokens": 28829 }, "turn_token_usage": { "input_tokens": 418450, "cached_input_tokens": 398208, "cache_write_input_tokens": 0, "output_tokens": 7270, "reasoning_output_tokens": 2772, "total_tokens": 425720 }, "thread_token_usage": { "input_tokens": 418450, "cached_input_tokens": 398208, "cache_write_input_tokens": 0, "output_tokens": 7270, "reasoning_output_tokens": 2772, "total_tokens": 425720 } } }, { "kind": "context", "timestamp": "2026-09-24T22:55:05.896Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3d6dfb7-04db-443f-a235-d7f09e2100c1", "process_id": "83727", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/tune_global.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/tune_global.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "gold 4263 events 37769\n0.031327 listen50_org_7d_users\n0.030933 listen50_all_7d_events\n0.030286 strong80_org_7d_users\n0.030275 likes_all_7d_users\n0.030187 likes_all_7d_events\n0.030107 strong80_all_7d_events\n0.029949 likes_org_7d_users\n0.029297 likes_org_7d_events\n0.028592 listen50_all_3d_events\n0.028456 listen50_org_7d_events\n0.028131 strong80_org_7d_events\n0.026849 listen50_org_14d_events\n0.026298 listen50_org_3d_events\n0.025721 strong80_org_3d_events\n0.025124 listen50_all_14d_events\n0.024613 listen50_all_7d_users\n0.023621 strong80_all_3d_events\n0.022946 strong80_all_7d_users\n0.022878 strong80_org_14d_events\n0.022238 strong80_all_14d_events\n0.019378 likes_org_30d_users\n0.018967 listen50_org_30d_users\n0.018928 strong80_org_30d_users\n0.018429 likes_org_30d_events\n0.018312 listen50_all_1d_events\n0.018231 listen50_org_1d_events\n0.017614 strong80_all_1d_events\n0.017182 listen50_all_30d_events\n0.017089 likes_all_30d_users\n0.017017 listen50_all_30d_users\n0.016986 strong80_all_30d_users\n0.016953 strong80_all_60d_events\n0.016892 listen50_all_60d_events\n0.016812 likes_all_30d_events\n0.016755 strong80_org_1d_events\n0.016688 strong80_all_30d_events\n0.016217 likes_org_90d_events\n0.015980 strong80_all_90d_events\n0.015977 likes_org_90d_users\n0.015972 listen50_all_90d_events\nTraceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 105, in \n blend_results.append((evaluate(order,UIDS,ELIG,gold),a,b,w,order))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_global.py\", line 40, in evaluate\n g = gold.get(int(uid))\n ^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "gold 4263 events 37769\n0.031327 listen50_org_7d_users\n0.030933 listen50_all_7d_events\n0.030286 strong80_org_7d_users\n0.030275 likes_all_7d_users\n0.030187 likes_all_7d_events\n0.030107 strong80_all_7d_events\n0.029949 likes_org_7d_users\n0.029297 likes_org_7d_events\n0.028592 listen50_all_3d_events\n0.028456 listen50_org_7d_events\n0.028131 strong80_org_7d_events\n0.026849 listen50_org_14d_events\n0.026298 listen50_org_3d_events\n0.025721 strong80_org_3d_events\n0.025124 listen50_all_14d_events\n0.024613 listen50_all_7d_users\n0.023621 strong80_all_3d_events\n0.022946 strong80_all_7d_users\n0.022878 strong80_org_14d_events\n0.022238 strong80_all_14d_events\n0.019378 likes_org_30d_users\n0.018967 listen50_org_30d_users\n0.018928 strong80_org_30d_users\n0.018429 likes_org_30d_events\n0.018312 listen50_all_1d_events\n0.018231 listen50_org_1d_events\n0.017614 strong80_all_1d_events\n0.017182 listen50_all_30d_events\n0.017089 likes_all_30d_users\n0.017017 listen50_all_30d_users\n0.016986 strong80_all_30d_users\n0.016953 strong80_all_60d_events\n0.016892 listen50_all_60d_events\n0.016812 likes_all_30d_events\n0.016755 strong80_org_1d_events\n0.016688 strong80_all_30d_events\n0.016217 likes_org_90d_events\n0.015980 strong80_all_90d_events\n0.015977 likes_org_90d_users\n0.015972 listen50_all_90d_events\nTraceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 105, in \n blend_results.append((evaluate(order,UIDS,ELIG,gold),a,b,w,order))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_global.py\", line 40, in evaluate\n g = gold.get(int(uid))\n ^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 170, "nanos": 29410573 }, "formatted_output": "gold 4263 events 37769\n0.031327 listen50_org_7d_users\n0.030933 listen50_all_7d_events\n0.030286 strong80_org_7d_users\n0.030275 likes_all_7d_users\n0.030187 likes_all_7d_events\n0.030107 strong80_all_7d_events\n0.029949 likes_org_7d_users\n0.029297 likes_org_7d_events\n0.028592 listen50_all_3d_events\n0.028456 listen50_org_7d_events\n0.028131 strong80_org_7d_events\n0.026849 listen50_org_14d_events\n0.026298 listen50_org_3d_events\n0.025721 strong80_org_3d_events\n0.025124 listen50_all_14d_events\n0.024613 listen50_all_7d_users\n0.023621 strong80_all_3d_events\n0.022946 strong80_all_7d_users\n0.022878 strong80_org_14d_events\n0.022238 strong80_all_14d_events\n0.019378 likes_org_30d_users\n0.018967 listen50_org_30d_users\n0.018928 strong80_org_30d_users\n0.018429 likes_org_30d_events\n0.018312 listen50_all_1d_events\n0.018231 listen50_org_1d_events\n0.017614 strong80_all_1d_events\n0.017182 listen50_all_30d_events\n0.017089 likes_all_30d_users\n0.017017 listen50_all_30d_users\n0.016986 strong80_all_30d_users\n0.016953 strong80_all_60d_events\n0.016892 listen50_all_60d_events\n0.016812 likes_all_30d_events\n0.016755 strong80_org_1d_events\n0.016688 strong80_all_30d_events\n0.016217 likes_org_90d_events\n0.015980 strong80_all_90d_events\n0.015977 likes_org_90d_users\n0.015972 listen50_all_90d_events\nTraceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 105, in \n blend_results.append((evaluate(order,UIDS,ELIG,gold),a,b,w,order))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_global.py\", line 40, in evaluate\n g = gold.get(int(uid))\n ^^^^^^^^\nKeyboardInterrupt\n" } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-24T22:55:12.215Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:55:12.215Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:16.781Z", "callId": "call_zdpiaCIpL9m000pGw0ofbJnk", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_global.py\\n@@\\n def evaluate(order, uids, eligible, gold, save=None):\\n discounts = 1 / np.log2(np.arange(2, 12))\\n vals, frames = [], []\\n- for r, uid in enumerate(uids):\\n- chosen = order[eligible[r, order]][:100]\\n+ iterator = enumerate(uids) if save is not None else ((uid_to_row[uid], uid) for uid in gold)\\n+ head = order[:2000 if save is not None else 256]\\n+ for r, uid in iterator:\\n+ chosen = head[eligible[r, head]][:100 if save is not None else 10]\\n@@\\n for score,name in sorted(scores, reverse=True)[:40]: print(f\\\"{score:.6f} {name}\\\")\\n+np.savez_compressed(OUT/\\\"global_features_train.npz\\\", **features)\\n@@\\n-np.savez_compressed(OUT/\\\"global_features_train.npz\\\", **features)\\n print(\\\"BEST\\\",best[:4])\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T22:55:16.830Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:16.790Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ce246dce-ee21-467d-8bc8-902e16836761", "changes": { "/app/solution/tune_global.py": { "type": "update", "unified_diff": "@@ -35,4 +35,6 @@\n vals, frames = [], []\n- for r, uid in enumerate(uids):\n- chosen = order[eligible[r, order]][:100]\n+ iterator = enumerate(uids) if save is not None else ((uid_to_row[uid], uid) for uid in gold)\n+ head = order[:2000 if save is not None else 256]\n+ for r, uid in iterator:\n+ chosen = head[eligible[r, head]][:100 if save is not None else 10]\n if save is not None:\n@@ -95,2 +97,3 @@\n for score,name in sorted(scores, reverse=True)[:40]: print(f\"{score:.6f} {name}\")\n+np.savez_compressed(OUT/\"global_features_train.npz\", **features)\n \n@@ -108,3 +111,2 @@\n evaluate(best[-1],UIDS,ELIG,gold,OUT/\"validation_global.parquet\")\n-np.savez_compressed(OUT/\"global_features_train.npz\", **features)\n print(\"BEST\",best[:4])\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_global.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:55:16.828Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aa4a1c3887d29dd98350d882d9f6", "usage": { "input_tokens": 29465, "cached_input_tokens": 28544, "cache_write_input_tokens": 0, "output_tokens": 527, "reasoning_output_tokens": 244, "total_tokens": 29992 }, "turn_token_usage": { "input_tokens": 447915, "cached_input_tokens": 426752, "cache_write_input_tokens": 0, "output_tokens": 7797, "reasoning_output_tokens": 3016, "total_tokens": 455712 }, "thread_token_usage": { "input_tokens": 447915, "cached_input_tokens": 426752, "cache_write_input_tokens": 0, "output_tokens": 7797, "reasoning_output_tokens": 3016, "total_tokens": 455712 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-24T22:55:18.312Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:55:18.312Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:19.344Z", "callId": "call_WPnSenUifVcsVYvVHokPbW0L", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:55:32.961Z", "content": "Script completed\nWall time 13.6 seconds\nOutput:\n\n{\"chunk_id\":\"8c57c9\",\"wall_time_seconds\":13.412885416,\"exit_code\":1,\"original_token_count\":119,\"output\":\"gold 4263 events 37769\\nTraceback (most recent call last):\\n File \\\"/app/solution/tune_global.py\\\", line 95, in \\n score=evaluate(np.argsort(-x,kind=\\\"stable\\\"),UIDS,ELIG,gold)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/tune_global.py\\\", line 45, in evaluate\\n dcg = ((2.0 ** grades - 1) * discounts).sum()\\n ~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\\nValueError: operands could not be broadcast together with shapes (9,) (10,) \\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:19.417Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aa551d5887d281f1cfd93f3e90bb", "usage": { "input_tokens": 30016, "cached_input_tokens": 29312, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 8, "total_tokens": 30090 }, "turn_token_usage": { "input_tokens": 477931, "cached_input_tokens": 456064, "cache_write_input_tokens": 0, "output_tokens": 7871, "reasoning_output_tokens": 3024, "total_tokens": 485802 }, "thread_token_usage": { "input_tokens": 477931, "cached_input_tokens": 456064, "cache_write_input_tokens": 0, "output_tokens": 7871, "reasoning_output_tokens": 3024, "total_tokens": 485802 } } }, { "kind": "context", "timestamp": "2026-09-24T22:55:32.953Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ed3a7b53-9d5f-4b90-8fcd-5dff202c8aa5", "process_id": "57087", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/tune_global.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/tune_global.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "gold 4263 events 37769\nTraceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 95, in \n score=evaluate(np.argsort(-x,kind=\"stable\"),UIDS,ELIG,gold)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_global.py\", line 45, in evaluate\n dcg = ((2.0 ** grades - 1) * discounts).sum()\n ~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: operands could not be broadcast together with shapes (9,) (10,) \n", "stderr": "", "aggregated_output": "gold 4263 events 37769\nTraceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 95, in \n score=evaluate(np.argsort(-x,kind=\"stable\"),UIDS,ELIG,gold)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_global.py\", line 45, in evaluate\n dcg = ((2.0 ** grades - 1) * discounts).sum()\n ~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: operands could not be broadcast together with shapes (9,) (10,) \n", "exit_code": 1, "duration": { "secs": 13, "nanos": 412884316 }, "formatted_output": "gold 4263 events 37769\nTraceback (most recent call last):\n File \"/app/solution/tune_global.py\", line 95, in \n score=evaluate(np.argsort(-x,kind=\"stable\"),UIDS,ELIG,gold)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_global.py\", line 45, in evaluate\n dcg = ((2.0 ** grades - 1) * discounts).sum()\n ~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: operands could not be broadcast together with shapes (9,) (10,) \n" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-24T22:55:34.794Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:55:34.794Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:37.076Z", "callId": "call_EpOzMVyah9FKcwR5dhXd8sfm", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_global.py\\n@@\\n- dcg = ((2.0 ** grades - 1) * discounts).sum()\\n+ dcg = ((2.0 ** grades - 1) * discounts[:len(grades)]).sum()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T22:55:37.126Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:37.084Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-71fa39c8-9486-41c2-99b1-27556a0bf856", "changes": { "/app/solution/tune_global.py": { "type": "update", "unified_diff": "@@ -44,3 +44,3 @@\n grades = np.fromiter((g.get(int(c), 0) for c in chosen[:10]), dtype=np.int8)\n- dcg = ((2.0 ** grades - 1) * discounts).sum()\n+ dcg = ((2.0 ** grades - 1) * discounts[:len(grades)]).sum()\n ideal = np.sort(np.fromiter(g.values(), dtype=np.int8))[::-1][:10]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_global.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:55:37.125Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aa65289087d2a61c847e77a848a3", "usage": { "input_tokens": 30289, "cached_input_tokens": 29824, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 39, "total_tokens": 30422 }, "turn_token_usage": { "input_tokens": 508220, "cached_input_tokens": 485888, "cache_write_input_tokens": 0, "output_tokens": 8004, "reasoning_output_tokens": 3063, "total_tokens": 516224 }, "thread_token_usage": { "input_tokens": 508220, "cached_input_tokens": 485888, "cache_write_input_tokens": 0, "output_tokens": 8004, "reasoning_output_tokens": 3063, "total_tokens": 516224 } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-24T22:55:39.725Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:55:39.725Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:40.651Z", "callId": "call_AlicL6Csh32AEjsvNYgAwdFR", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_global.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:56:10.843Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"397249\",\"wall_time_seconds\":30.001774903,\"session_id\":99027,\"original_token_count\":320,\"output\":\"gold 4263 events 37769\\n0.031327 listen50_org_7d_users\\n0.030933 listen50_all_7d_events\\n0.030286 strong80_org_7d_users\\n0.030275 likes_all_7d_users\\n0.030187 likes_all_7d_events\\n0.030107 strong80_all_7d_events\\n0.029949 likes_org_7d_users\\n0.029297 likes_org_7d_events\\n0.028592 listen50_all_3d_events\\n0.028456 listen50_org_7d_events\\n0.028131 strong80_org_7d_events\\n0.026849 listen50_org_14d_events\\n0.026298 listen50_org_3d_events\\n0.025721 strong80_org_3d_events\\n0.025124 listen50_all_14d_events\\n0.024613 listen50_all_7d_users\\n0.023621 strong80_all_3d_events\\n0.022946 strong80_all_7d_users\\n0.022878 strong80_org_14d_events\\n0.022238 strong80_all_14d_events\\n0.019378 likes_org_30d_users\\n0.018967 listen50_org_30d_users\\n0.018928 strong80_org_30d_users\\n0.018429 likes_org_30d_events\\n0.018312 listen50_all_1d_events\\n0.018231 listen50_org_1d_events\\n0.017614 strong80_all_1d_events\\n0.017182 listen50_all_30d_events\\n0.017089 likes_all_30d_users\\n0.017017 listen50_all_30d_users\\n0.016986 strong80_all_30d_users\\n0.016953 strong80_all_60d_events\\n0.016892 listen50_all_60d_events\\n0.016812 likes_all_30d_events\\n0.016755 strong80_org_1d_events\\n0.016688 strong80_all_30d_events\\n0.016217 likes_org_90d_events\\n0.015980 strong80_all_90d_events\\n0.015977 likes_org_90d_users\\n0.015972 listen50_all_90d_events\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:40.716Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aa69677887d2bac831f1ba1f0a2e", "usage": { "input_tokens": 30446, "cached_input_tokens": 30080, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 15, "total_tokens": 30527 }, "turn_token_usage": { "input_tokens": 538666, "cached_input_tokens": 515968, "cache_write_input_tokens": 0, "output_tokens": 8085, "reasoning_output_tokens": 3078, "total_tokens": 546751 }, "thread_token_usage": { "input_tokens": 538666, "cached_input_tokens": 515968, "cache_write_input_tokens": 0, "output_tokens": 8085, "reasoning_output_tokens": 3078, "total_tokens": 546751 } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-24T22:56:12.681Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:12.681Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:56:13.399Z", "callId": "call_ETAUDoc1DehQWqGxC6nHSLEH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":99027,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:56:43.408Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"bbc98d\",\"wall_time_seconds\":30.000938584,\"session_id\":99027,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:56:13.482Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aa8b2af487d2a7ab7d8ef4d04555", "usage": { "input_tokens": 31061, "cached_input_tokens": 30208, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 17, "total_tokens": 31126 }, "turn_token_usage": { "input_tokens": 569727, "cached_input_tokens": 546176, "cache_write_input_tokens": 0, "output_tokens": 8150, "reasoning_output_tokens": 3095, "total_tokens": 577877 }, "thread_token_usage": { "input_tokens": 569727, "cached_input_tokens": 546176, "cache_write_input_tokens": 0, "output_tokens": 8150, "reasoning_output_tokens": 3095, "total_tokens": 577877 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-24T22:56:45.299Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:45.299Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:56:45.988Z", "callId": "call_J7aVqey0MV0OTYcC4xx9fuFl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":99027,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T22:57:08.549Z", "content": "Script completed\nWall time 22.6 seconds\nOutput:\n\n{\"chunk_id\":\"48b8bc\",\"wall_time_seconds\":22.551085726,\"exit_code\":0,\"original_token_count\":374,\"output\":\"BLEND 0.034854 0.20*listen50_org_7d_users + 0.80*listen50_all_3d_events\\nBLEND 0.034804 0.20*strong80_org_7d_users + 0.80*listen50_all_3d_events\\nBLEND 0.034744 0.35*listen50_org_7d_users + 0.65*listen50_all_3d_events\\nBLEND 0.034553 0.35*strong80_org_7d_users + 0.65*listen50_all_3d_events\\nBLEND 0.034542 0.50*listen50_org_7d_users + 0.50*listen50_all_3d_events\\nBLEND 0.034484 0.65*listen50_org_7d_users + 0.35*listen50_all_3d_events\\nBLEND 0.034112 0.35*likes_org_7d_events + 0.65*listen50_all_3d_events\\nBLEND 0.034076 0.35*likes_org_7d_users + 0.65*listen50_all_3d_events\\nBLEND 0.033854 0.50*strong80_org_7d_users + 0.50*listen50_all_3d_events\\nBLEND 0.033806 0.20*listen50_org_7d_users + 0.80*listen50_all_7d_events\\nBLEND 0.033777 0.50*listen50_org_7d_users + 0.50*likes_all_7d_users\\nBLEND 0.033765 0.50*strong80_org_7d_users + 0.50*likes_all_7d_events\\nBLEND 0.033743 0.50*strong80_org_7d_users + 0.50*likes_all_7d_users\\nBLEND 0.033733 0.80*listen50_all_7d_events + 0.20*strong80_org_7d_users\\nBLEND 0.033669 0.50*likes_all_7d_events + 0.50*listen50_all_3d_events\\nBLEND 0.033625 0.20*likes_org_7d_events + 0.80*listen50_all_3d_events\\nBLEND 0.033606 0.65*listen50_org_7d_users + 0.35*likes_all_7d_users\\nBLEND 0.033602 0.65*strong80_org_7d_users + 0.35*listen50_all_3d_events\\nBLEND 0.033580 0.20*likes_org_7d_users + 0.80*listen50_all_3d_events\\nBLEND 0.033577 0.65*strong80_all_7d_events + 0.35*likes_org_7d_events\\nBEST (0.0348541542251852, 'listen50_org_7d_users', 'listen50_all_3d_events', 0.2)\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:56:46.083Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aaabbbc487d29a14d5f8168e94b9", "usage": { "input_tokens": 31183, "cached_input_tokens": 30848, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 18, "total_tokens": 31249 }, "turn_token_usage": { "input_tokens": 600910, "cached_input_tokens": 577024, "cache_write_input_tokens": 0, "output_tokens": 8216, "reasoning_output_tokens": 3113, "total_tokens": 609126 }, "thread_token_usage": { "input_tokens": 600910, "cached_input_tokens": 577024, "cache_write_input_tokens": 0, "output_tokens": 8216, "reasoning_output_tokens": 3113, "total_tokens": 609126 } } }, { "kind": "context", "timestamp": "2026-09-24T22:57:08.547Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf76ae8d-c070-4bd3-b1c7-89c253e592da", "process_id": "99027", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/tune_global.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/tune_global.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "gold 4263 events 37769\n0.031327 listen50_org_7d_users\n0.030933 listen50_all_7d_events\n0.030286 strong80_org_7d_users\n0.030275 likes_all_7d_users\n0.030187 likes_all_7d_events\n0.030107 strong80_all_7d_events\n0.029949 likes_org_7d_users\n0.029297 likes_org_7d_events\n0.028592 listen50_all_3d_events\n0.028456 listen50_org_7d_events\n0.028131 strong80_org_7d_events\n0.026849 listen50_org_14d_events\n0.026298 listen50_org_3d_events\n0.025721 strong80_org_3d_events\n0.025124 listen50_all_14d_events\n0.024613 listen50_all_7d_users\n0.023621 strong80_all_3d_events\n0.022946 strong80_all_7d_users\n0.022878 strong80_org_14d_events\n0.022238 strong80_all_14d_events\n0.019378 likes_org_30d_users\n0.018967 listen50_org_30d_users\n0.018928 strong80_org_30d_users\n0.018429 likes_org_30d_events\n0.018312 listen50_all_1d_events\n0.018231 listen50_org_1d_events\n0.017614 strong80_all_1d_events\n0.017182 listen50_all_30d_events\n0.017089 likes_all_30d_users\n0.017017 listen50_all_30d_users\n0.016986 strong80_all_30d_users\n0.016953 strong80_all_60d_events\n0.016892 listen50_all_60d_events\n0.016812 likes_all_30d_events\n0.016755 strong80_org_1d_events\n0.016688 strong80_all_30d_events\n0.016217 likes_org_90d_events\n0.015980 strong80_all_90d_events\n0.015977 likes_org_90d_users\n0.015972 listen50_all_90d_events\nBLEND 0.034854 0.20*listen50_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.034804 0.20*strong80_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.034744 0.35*listen50_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.034553 0.35*strong80_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.034542 0.50*listen50_org_7d_users + 0.50*listen50_all_3d_events\nBLEND 0.034484 0.65*listen50_org_7d_users + 0.35*listen50_all_3d_events\nBLEND 0.034112 0.35*likes_org_7d_events + 0.65*listen50_all_3d_events\nBLEND 0.034076 0.35*likes_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.033854 0.50*strong80_org_7d_users + 0.50*listen50_all_3d_events\nBLEND 0.033806 0.20*listen50_org_7d_users + 0.80*listen50_all_7d_events\nBLEND 0.033777 0.50*listen50_org_7d_users + 0.50*likes_all_7d_users\nBLEND 0.033765 0.50*strong80_org_7d_users + 0.50*likes_all_7d_events\nBLEND 0.033743 0.50*strong80_org_7d_users + 0.50*likes_all_7d_users\nBLEND 0.033733 0.80*listen50_all_7d_events + 0.20*strong80_org_7d_users\nBLEND 0.033669 0.50*likes_all_7d_events + 0.50*listen50_all_3d_events\nBLEND 0.033625 0.20*likes_org_7d_events + 0.80*listen50_all_3d_events\nBLEND 0.033606 0.65*listen50_org_7d_users + 0.35*likes_all_7d_users\nBLEND 0.033602 0.65*strong80_org_7d_users + 0.35*listen50_all_3d_events\nBLEND 0.033580 0.20*likes_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.033577 0.65*strong80_all_7d_events + 0.35*likes_org_7d_events\nBEST (0.0348541542251852, 'listen50_org_7d_users', 'listen50_all_3d_events', 0.2)\n", "stderr": "", "aggregated_output": "gold 4263 events 37769\n0.031327 listen50_org_7d_users\n0.030933 listen50_all_7d_events\n0.030286 strong80_org_7d_users\n0.030275 likes_all_7d_users\n0.030187 likes_all_7d_events\n0.030107 strong80_all_7d_events\n0.029949 likes_org_7d_users\n0.029297 likes_org_7d_events\n0.028592 listen50_all_3d_events\n0.028456 listen50_org_7d_events\n0.028131 strong80_org_7d_events\n0.026849 listen50_org_14d_events\n0.026298 listen50_org_3d_events\n0.025721 strong80_org_3d_events\n0.025124 listen50_all_14d_events\n0.024613 listen50_all_7d_users\n0.023621 strong80_all_3d_events\n0.022946 strong80_all_7d_users\n0.022878 strong80_org_14d_events\n0.022238 strong80_all_14d_events\n0.019378 likes_org_30d_users\n0.018967 listen50_org_30d_users\n0.018928 strong80_org_30d_users\n0.018429 likes_org_30d_events\n0.018312 listen50_all_1d_events\n0.018231 listen50_org_1d_events\n0.017614 strong80_all_1d_events\n0.017182 listen50_all_30d_events\n0.017089 likes_all_30d_users\n0.017017 listen50_all_30d_users\n0.016986 strong80_all_30d_users\n0.016953 strong80_all_60d_events\n0.016892 listen50_all_60d_events\n0.016812 likes_all_30d_events\n0.016755 strong80_org_1d_events\n0.016688 strong80_all_30d_events\n0.016217 likes_org_90d_events\n0.015980 strong80_all_90d_events\n0.015977 likes_org_90d_users\n0.015972 listen50_all_90d_events\nBLEND 0.034854 0.20*listen50_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.034804 0.20*strong80_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.034744 0.35*listen50_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.034553 0.35*strong80_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.034542 0.50*listen50_org_7d_users + 0.50*listen50_all_3d_events\nBLEND 0.034484 0.65*listen50_org_7d_users + 0.35*listen50_all_3d_events\nBLEND 0.034112 0.35*likes_org_7d_events + 0.65*listen50_all_3d_events\nBLEND 0.034076 0.35*likes_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.033854 0.50*strong80_org_7d_users + 0.50*listen50_all_3d_events\nBLEND 0.033806 0.20*listen50_org_7d_users + 0.80*listen50_all_7d_events\nBLEND 0.033777 0.50*listen50_org_7d_users + 0.50*likes_all_7d_users\nBLEND 0.033765 0.50*strong80_org_7d_users + 0.50*likes_all_7d_events\nBLEND 0.033743 0.50*strong80_org_7d_users + 0.50*likes_all_7d_users\nBLEND 0.033733 0.80*listen50_all_7d_events + 0.20*strong80_org_7d_users\nBLEND 0.033669 0.50*likes_all_7d_events + 0.50*listen50_all_3d_events\nBLEND 0.033625 0.20*likes_org_7d_events + 0.80*listen50_all_3d_events\nBLEND 0.033606 0.65*listen50_org_7d_users + 0.35*likes_all_7d_users\nBLEND 0.033602 0.65*strong80_org_7d_users + 0.35*listen50_all_3d_events\nBLEND 0.033580 0.20*likes_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.033577 0.65*strong80_all_7d_events + 0.35*likes_org_7d_events\nBEST (0.0348541542251852, 'listen50_org_7d_users', 'listen50_all_3d_events', 0.2)\n", "exit_code": 0, "duration": { "secs": 87, "nanos": 705909316 }, "formatted_output": "gold 4263 events 37769\n0.031327 listen50_org_7d_users\n0.030933 listen50_all_7d_events\n0.030286 strong80_org_7d_users\n0.030275 likes_all_7d_users\n0.030187 likes_all_7d_events\n0.030107 strong80_all_7d_events\n0.029949 likes_org_7d_users\n0.029297 likes_org_7d_events\n0.028592 listen50_all_3d_events\n0.028456 listen50_org_7d_events\n0.028131 strong80_org_7d_events\n0.026849 listen50_org_14d_events\n0.026298 listen50_org_3d_events\n0.025721 strong80_org_3d_events\n0.025124 listen50_all_14d_events\n0.024613 listen50_all_7d_users\n0.023621 strong80_all_3d_events\n0.022946 strong80_all_7d_users\n0.022878 strong80_org_14d_events\n0.022238 strong80_all_14d_events\n0.019378 likes_org_30d_users\n0.018967 listen50_org_30d_users\n0.018928 strong80_org_30d_users\n0.018429 likes_org_30d_events\n0.018312 listen50_all_1d_events\n0.018231 listen50_org_1d_events\n0.017614 strong80_all_1d_events\n0.017182 listen50_all_30d_events\n0.017089 likes_all_30d_users\n0.017017 listen50_all_30d_users\n0.016986 strong80_all_30d_users\n0.016953 strong80_all_60d_events\n0.016892 listen50_all_60d_events\n0.016812 likes_all_30d_events\n0.016755 strong80_org_1d_events\n0.016688 strong80_all_30d_events\n0.016217 likes_org_90d_events\n0.015980 strong80_all_90d_events\n0.015977 likes_org_90d_users\n0.015972 listen50_all_90d_events\nBLEND 0.034854 0.20*listen50_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.034804 0.20*strong80_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.034744 0.35*listen50_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.034553 0.35*strong80_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.034542 0.50*listen50_org_7d_users + 0.50*listen50_all_3d_events\nBLEND 0.034484 0.65*listen50_org_7d_users + 0.35*listen50_all_3d_events\nBLEND 0.034112 0.35*likes_org_7d_events + 0.65*listen50_all_3d_events\nBLEND 0.034076 0.35*likes_org_7d_users + 0.65*listen50_all_3d_events\nBLEND 0.033854 0.50*strong80_org_7d_users + 0.50*listen50_all_3d_events\nBLEND 0.033806 0.20*listen50_org_7d_users + 0.80*listen50_all_7d_events\nBLEND 0.033777 0.50*listen50_org_7d_users + 0.50*likes_all_7d_users\nBLEND 0.033765 0.50*strong80_org_7d_users + 0.50*likes_all_7d_events\nBLEND 0.033743 0.50*strong80_org_7d_users + 0.50*likes_all_7d_users\nBLEND 0.033733 0.80*listen50_all_7d_events + 0.20*strong80_org_7d_users\nBLEND 0.033669 0.50*likes_all_7d_events + 0.50*listen50_all_3d_events\nBLEND 0.033625 0.20*likes_org_7d_events + 0.80*listen50_all_3d_events\nBLEND 0.033606 0.65*listen50_org_7d_users + 0.35*likes_all_7d_users\nBLEND 0.033602 0.65*strong80_org_7d_users + 0.35*listen50_all_3d_events\nBLEND 0.033580 0.20*likes_org_7d_users + 0.80*listen50_all_3d_events\nBLEND 0.033577 0.65*strong80_all_7d_events + 0.35*likes_org_7d_events\nBEST (0.0348541542251852, 'listen50_org_7d_users', 'listen50_all_3d_events', 0.2)\n" } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-24T22:57:11.688Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:11.688Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:57:13.027Z", "callId": "call_y9uzsSOhAWqc3eW382MMsA1s", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_global.parquet && ls -lh /app/solution/global_features_train.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T22:57:15.574Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_global.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.034854,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 3.6M Sep 24 22:56 /app/solution/global_features_train.npz\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:57:13.130Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aac4cc5487d2b1ead00b953a16a5", "usage": { "input_tokens": 31990, "cached_input_tokens": 30976, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 17, "total_tokens": 32090 }, "turn_token_usage": { "input_tokens": 632900, "cached_input_tokens": 608000, "cache_write_input_tokens": 0, "output_tokens": 8316, "reasoning_output_tokens": 3130, "total_tokens": 641216 }, "thread_token_usage": { "input_tokens": 632900, "cached_input_tokens": 608000, "cache_write_input_tokens": 0, "output_tokens": 8316, "reasoning_output_tokens": 3130, "total_tokens": 641216 } } }, { "kind": "context", "timestamp": "2026-09-24T22:57:15.570Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-85af6beb-3838-431b-b65c-fef46b4ae221", "process_id": "11532", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_global.parquet && ls -lh /app/solution/global_features_train.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_global.parquet && ls -lh /app/solution/global_features_train.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_global.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.034854,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 3.6M Sep 24 22:56 /app/solution/global_features_train.npz\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_global.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.034854,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 3.6M Sep 24 22:56 /app/solution/global_features_train.npz\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 379315488 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_global.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.034854,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 3.6M Sep 24 22:56 /app/solution/global_features_train.npz\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-24T22:57:26.545Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:26.545Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-24T22:57:35.777Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:35.777Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-24T22:57:45.453Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:45.453Z", "text": "" } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-24T22:57:54.780Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:54.780Z", "text": "" } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-24T22:58:05.721Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:58:05.721Z", "text": "" } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-24T22:58:09.223Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:58:09.223Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:58:10.659Z", "text": "The temporal sweep raised validation NDCG from 0.010710 to 0.034854; the strongest prior combines three-day positive-listen momentum with seven-day organic listener reach. I’m now building user-to-user collaborative neighborhoods from the last 90 days of positive history (with inverse-frequency weighting), then testing which neighbor window and popularity blend best predicts organic discovery." }, { "kind": "tool", "timestamp": "2026-09-24T22:59:01.271Z", "callId": "call_Mg7SmQjoYb8oiAgZK2URVLiN", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_knn.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Tune a user-neighborhood discovery ranker on train -> validation.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import gc\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.preprocessing import normalize\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+CUTOFF = 24_786_800\\n+DAY = 86_400\\n+\\n+\\n+def make_truth(uids, items, elig):\\n+ ur = {int(v): i for i, v in enumerate(uids)}\\n+ im = np.full(10_000_001, -1, np.int32); im[items] = np.arange(len(items), dtype=np.int32)\\n+ listens = pd.read_parquet(DATA/\\\"val_listens.parquet\\\", columns=[\\\"uid\\\",\\\"item_id\\\",\\\"is_organic\\\",\\\"played_ratio_pct\\\"])\\n+ likes = pd.read_parquet(DATA/\\\"val_likes.parquet\\\", columns=[\\\"uid\\\",\\\"item_id\\\",\\\"is_organic\\\"])\\n+ frames = [(1, listens[(listens.is_organic==1)&(listens.played_ratio_pct>=80)]),\\n+ (3, likes[likes.is_organic==1])]\\n+ gold = {}\\n+ for grade,d in frames:\\n+ for uid,item in d[[\\\"uid\\\",\\\"item_id\\\"]].drop_duplicates().itertuples(index=False,name=None):\\n+ r=ur.get(int(uid)); c=im[int(item)]\\n+ if r is not None and c>=0 and elig[r,c]: gold.setdefault(int(uid),{})[int(c)]=grade\\n+ return gold, ur, im\\n+\\n+\\n+def binary_matrix(rows, cols, shape, values=None):\\n+ if values is None: values=np.ones(len(rows),np.float32)\\n+ x=sparse.coo_matrix((values,(rows,cols)),shape=shape,dtype=np.float32).tocsr()\\n+ x.sum_duplicates()\\n+ x.data[:] = 1\\n+ x.eliminate_zeros()\\n+ return x\\n+\\n+\\n+def top_neighbors(x, query_rows, kmax=200, batch=256):\\n+ all_ind=np.empty((len(query_rows),kmax),np.int32)\\n+ all_val=np.empty((len(query_rows),kmax),np.float32)\\n+ xt=x.T.tocsc()\\n+ for lo in range(0,len(query_rows),batch):\\n+ qr=query_rows[lo:lo+batch]\\n+ sim=(x[qr]@xt).toarray()\\n+ sim[np.arange(len(qr)),qr]=-1\\n+ ix=np.argpartition(sim,-kmax,axis=1)[:,-kmax:]\\n+ vv=np.take_along_axis(sim,ix,axis=1)\\n+ order=np.argsort(-vv,axis=1)\\n+ all_ind[lo:lo+len(qr)]=np.take_along_axis(ix,order,axis=1)\\n+ all_val[lo:lo+len(qr)]=np.take_along_axis(vv,order,axis=1)\\n+ if lo%1024==0: print(\\\" neighbors\\\",lo,\\\"/\\\",len(query_rows),flush=True)\\n+ return all_ind,all_val\\n+\\n+\\n+def sim_sparse(inds, vals, k, nusers):\\n+ ii=inds[:,:k].ravel(); vv=vals[:,:k].copy()\\n+ sums=vv.sum(1,keepdims=True); vv/=np.maximum(sums,1e-8)\\n+ rr=np.repeat(np.arange(len(inds),dtype=np.int32),k)\\n+ return sparse.csr_matrix((vv.ravel(),(rr,ii)),shape=(len(inds),nusers))\\n+\\n+\\n+def score_product(pred, uids, ur, eligible, gold, global_value, alphas):\\n+ \\\"\\\"\\\"Evaluate additive neighbor-probability + global-rate blends.\\\"\\\"\\\"\\n+ disc=1/np.log2(np.arange(2,12))\\n+ out={a:[] for a in alphas}\\n+ global_head=np.argsort(-global_value,kind=\\\"stable\\\")[:1000]\\n+ for uid,g in gold.items():\\n+ r=ur[uid]; start,end=pred.indptr[r],pred.indptr[r+1]\\n+ ci=pred.indices[start:end]; cv=pred.data[start:end]\\n+ if len(ci)>1000:\\n+ take=np.argpartition(cv,-1000)[-1000:]; ci=ci[take]; cv=cv[take]\\n+ cand=np.unique(np.concatenate([ci,global_head]))\\n+ cand=cand[eligible[r,cand]]\\n+ cf=np.zeros(len(cand),np.float32)\\n+ # pred indices within each CSR row are sorted.\\n+ pos=np.searchsorted(ci,cand); ok=(pos10:\\n+ top=np.argpartition(sc,-10)[-10:]; top=top[np.argsort(-sc[top],kind=\\\"stable\\\")]\\n+ else: top=np.argsort(-sc)\\n+ grades=np.fromiter((g.get(int(cand[j]),0) for j in top),dtype=np.int8)\\n+ out[a].append(float(((2.**grades-1)*disc[:len(grades)]).sum()/idcg))\\n+ return {a:float(np.mean(v)) for a,v in out.items()}\\n+\\n+\\n+def write_ranking(pred,uids,items,eligible,global_value,alpha,path):\\n+ gh=np.argsort(-global_value,kind=\\\"stable\\\")[:3000]\\n+ frames=[]\\n+ for r,uid in enumerate(uids):\\n+ st,en=pred.indptr[r],pred.indptr[r+1]; ci=pred.indices[st:en]; cv=pred.data[st:en]\\n+ if len(ci)>3000:\\n+ take=np.argpartition(cv,-3000)[-3000:];ci=ci[take];cv=cv[take]\\n+ cand=np.unique(np.concatenate([ci,gh]));cand=cand[eligible[r,cand]]\\n+ cf=np.zeros(len(cand),np.float32); pos=np.searchsorted(ci,cand);ok=pos=CUTOFF-90*DAY)&(d.played_ratio_pct.to_numpy()>=50)\\n+x=binary_matrix(uid_map[d.uid.to_numpy()[m]],d.item_id.to_numpy()[m],(len(ALL_UIDS),10_000_001))\\n+df=np.bincount(x.indices,minlength=x.shape[1]).astype(np.float32)\\n+idf=np.log((len(ALL_UIDS)+1)/(df+1)); x.data*=idf[x.indices]\\n+normalize(x,norm=\\\"l2\\\",axis=1,copy=False)\\n+print(\\\"profile\\\",x.shape,x.nnz,\\\"built\\\",time.time()-t0,flush=True)\\n+nind,nval=top_neighbors(x,qrows,kmax=200)\\n+np.savez_compressed(OUT/\\\"neighbors_train90.npz\\\",indices=nind,values=nval,all_uids=ALL_UIDS)\\n+del x,df,idf;gc.collect()\\n+print(\\\"neighbors done\\\",time.time()-t0,flush=True)\\n+\\n+# Candidate item histories for neighbors.\\n+cidx=item_map[d.item_id.to_numpy()]\\n+urow=uid_map[d.uid.to_numpy()]\\n+ts=d.timestamp.to_numpy(); ratio=d.played_ratio_pct.to_numpy(); org=d.is_organic.to_numpy().astype(bool)\\n+bases={}\\n+for days in [3,7,14,30,90]:\\n+ for name,behavior in [(\\\"all50\\\",ratio>=50),(\\\"org50\\\",(ratio>=50)&org),(\\\"org80\\\",(ratio>=80)&org)]:\\n+ mm=(cidx>=0)&(ts>=CUTOFF-days*DAY)&behavior\\n+ bases[f\\\"{name}_{days}d\\\"]=binary_matrix(urow[mm],cidx[mm],(len(ALL_UIDS),len(ITEMS)))\\n+ print(\\\"matrix\\\",name,days,bases[f\\\"{name}_{days}d\\\"].nnz,flush=True)\\n+del d;gc.collect()\\n+\\n+g=np.load(OUT/\\\"global_features_train.npz\\\")\\n+# Exact selected validation global blend converted to a calibrated user rate.\\n+ga=g[\\\"listen50_org_7d_users\\\"]; gb=g[\\\"listen50_all_3d_events\\\"]\\n+ra=np.argsort(np.argsort(-ga,kind=\\\"stable\\\"),kind=\\\"stable\\\").astype(float)\\n+rb=np.argsort(np.argsort(-gb,kind=\\\"stable\\\"),kind=\\\"stable\\\").astype(float)\\n+global_order=np.argsort(.2*ra+.8*rb,kind=\\\"stable\\\")\\n+# Rate-like prior with rank ordering from the tuned blend.\\n+global_value=np.empty(len(ITEMS),np.float32)\\n+global_value[global_order]=np.maximum(1e-5,1-np.arange(len(ITEMS))/len(ITEMS))**8\\n+\\n+alphas=[0,.002,.005,.01,.02,.05,.1,.2,.5]\\n+results=[]\\n+best=None\\n+for k in [10,25,50,100,200]:\\n+ s=sim_sparse(nind,nval,k,len(ALL_UIDS))\\n+ for name,b in bases.items():\\n+ pred=(s@b).tocsr();pred.sort_indices()\\n+ scores=score_product(pred,UIDS,target_row,ELIG,gold,global_value,alphas)\\n+ for a,v in scores.items():results.append((v,k,name,a))\\n+ local=max((v,a) for a,v in scores.items())\\n+ print(\\\"RESULT\\\",k,name,local,flush=True)\\n+ if best is None or local[0]>best[0]:\\n+ best=(local[0],k,name,local[1]);write_ranking(pred,UIDS,ITEMS,ELIG,global_value,local[1],OUT/\\\"validation_knn.parquet\\\")\\n+ del pred\\n+ del s;gc.collect()\\n+print(\\\"TOP\\\")\\n+for z in sorted(results,reverse=True)[:30]:print(z)\\n+print(\\\"BEST\\\",best,\\\"elapsed\\\",time.time()-t0)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T22:59:01.320Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:59:01.280Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-24e03fb4-dce6-4c48-a6ec-bda44b9bb363", "changes": { "/app/solution/tune_knn.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Tune a user-neighborhood discovery ranker on train -> validation.\"\"\"\nfrom pathlib import Path\nimport gc\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nCUTOFF = 24_786_800\nDAY = 86_400\n\n\ndef make_truth(uids, items, elig):\n ur = {int(v): i for i, v in enumerate(uids)}\n im = np.full(10_000_001, -1, np.int32); im[items] = np.arange(len(items), dtype=np.int32)\n listens = pd.read_parquet(DATA/\"val_listens.parquet\", columns=[\"uid\",\"item_id\",\"is_organic\",\"played_ratio_pct\"])\n likes = pd.read_parquet(DATA/\"val_likes.parquet\", columns=[\"uid\",\"item_id\",\"is_organic\"])\n frames = [(1, listens[(listens.is_organic==1)&(listens.played_ratio_pct>=80)]),\n (3, likes[likes.is_organic==1])]\n gold = {}\n for grade,d in frames:\n for uid,item in d[[\"uid\",\"item_id\"]].drop_duplicates().itertuples(index=False,name=None):\n r=ur.get(int(uid)); c=im[int(item)]\n if r is not None and c>=0 and elig[r,c]: gold.setdefault(int(uid),{})[int(c)]=grade\n return gold, ur, im\n\n\ndef binary_matrix(rows, cols, shape, values=None):\n if values is None: values=np.ones(len(rows),np.float32)\n x=sparse.coo_matrix((values,(rows,cols)),shape=shape,dtype=np.float32).tocsr()\n x.sum_duplicates()\n x.data[:] = 1\n x.eliminate_zeros()\n return x\n\n\ndef top_neighbors(x, query_rows, kmax=200, batch=256):\n all_ind=np.empty((len(query_rows),kmax),np.int32)\n all_val=np.empty((len(query_rows),kmax),np.float32)\n xt=x.T.tocsc()\n for lo in range(0,len(query_rows),batch):\n qr=query_rows[lo:lo+batch]\n sim=(x[qr]@xt).toarray()\n sim[np.arange(len(qr)),qr]=-1\n ix=np.argpartition(sim,-kmax,axis=1)[:,-kmax:]\n vv=np.take_along_axis(sim,ix,axis=1)\n order=np.argsort(-vv,axis=1)\n all_ind[lo:lo+len(qr)]=np.take_along_axis(ix,order,axis=1)\n all_val[lo:lo+len(qr)]=np.take_along_axis(vv,order,axis=1)\n if lo%1024==0: print(\" neighbors\",lo,\"/\",len(query_rows),flush=True)\n return all_ind,all_val\n\n\ndef sim_sparse(inds, vals, k, nusers):\n ii=inds[:,:k].ravel(); vv=vals[:,:k].copy()\n sums=vv.sum(1,keepdims=True); vv/=np.maximum(sums,1e-8)\n rr=np.repeat(np.arange(len(inds),dtype=np.int32),k)\n return sparse.csr_matrix((vv.ravel(),(rr,ii)),shape=(len(inds),nusers))\n\n\ndef score_product(pred, uids, ur, eligible, gold, global_value, alphas):\n \"\"\"Evaluate additive neighbor-probability + global-rate blends.\"\"\"\n disc=1/np.log2(np.arange(2,12))\n out={a:[] for a in alphas}\n global_head=np.argsort(-global_value,kind=\"stable\")[:1000]\n for uid,g in gold.items():\n r=ur[uid]; start,end=pred.indptr[r],pred.indptr[r+1]\n ci=pred.indices[start:end]; cv=pred.data[start:end]\n if len(ci)>1000:\n take=np.argpartition(cv,-1000)[-1000:]; ci=ci[take]; cv=cv[take]\n cand=np.unique(np.concatenate([ci,global_head]))\n cand=cand[eligible[r,cand]]\n cf=np.zeros(len(cand),np.float32)\n # pred indices within each CSR row are sorted.\n pos=np.searchsorted(ci,cand); ok=(pos10:\n top=np.argpartition(sc,-10)[-10:]; top=top[np.argsort(-sc[top],kind=\"stable\")]\n else: top=np.argsort(-sc)\n grades=np.fromiter((g.get(int(cand[j]),0) for j in top),dtype=np.int8)\n out[a].append(float(((2.**grades-1)*disc[:len(grades)]).sum()/idcg))\n return {a:float(np.mean(v)) for a,v in out.items()}\n\n\ndef write_ranking(pred,uids,items,eligible,global_value,alpha,path):\n gh=np.argsort(-global_value,kind=\"stable\")[:3000]\n frames=[]\n for r,uid in enumerate(uids):\n st,en=pred.indptr[r],pred.indptr[r+1]; ci=pred.indices[st:en]; cv=pred.data[st:en]\n if len(ci)>3000:\n take=np.argpartition(cv,-3000)[-3000:];ci=ci[take];cv=cv[take]\n cand=np.unique(np.concatenate([ci,gh]));cand=cand[eligible[r,cand]]\n cf=np.zeros(len(cand),np.float32); pos=np.searchsorted(ci,cand);ok=pos=CUTOFF-90*DAY)&(d.played_ratio_pct.to_numpy()>=50)\nx=binary_matrix(uid_map[d.uid.to_numpy()[m]],d.item_id.to_numpy()[m],(len(ALL_UIDS),10_000_001))\ndf=np.bincount(x.indices,minlength=x.shape[1]).astype(np.float32)\nidf=np.log((len(ALL_UIDS)+1)/(df+1)); x.data*=idf[x.indices]\nnormalize(x,norm=\"l2\",axis=1,copy=False)\nprint(\"profile\",x.shape,x.nnz,\"built\",time.time()-t0,flush=True)\nnind,nval=top_neighbors(x,qrows,kmax=200)\nnp.savez_compressed(OUT/\"neighbors_train90.npz\",indices=nind,values=nval,all_uids=ALL_UIDS)\ndel x,df,idf;gc.collect()\nprint(\"neighbors done\",time.time()-t0,flush=True)\n\n# Candidate item histories for neighbors.\ncidx=item_map[d.item_id.to_numpy()]\nurow=uid_map[d.uid.to_numpy()]\nts=d.timestamp.to_numpy(); ratio=d.played_ratio_pct.to_numpy(); org=d.is_organic.to_numpy().astype(bool)\nbases={}\nfor days in [3,7,14,30,90]:\n for name,behavior in [(\"all50\",ratio>=50),(\"org50\",(ratio>=50)&org),(\"org80\",(ratio>=80)&org)]:\n mm=(cidx>=0)&(ts>=CUTOFF-days*DAY)&behavior\n bases[f\"{name}_{days}d\"]=binary_matrix(urow[mm],cidx[mm],(len(ALL_UIDS),len(ITEMS)))\n print(\"matrix\",name,days,bases[f\"{name}_{days}d\"].nnz,flush=True)\ndel d;gc.collect()\n\ng=np.load(OUT/\"global_features_train.npz\")\n# Exact selected validation global blend converted to a calibrated user rate.\nga=g[\"listen50_org_7d_users\"]; gb=g[\"listen50_all_3d_events\"]\nra=np.argsort(np.argsort(-ga,kind=\"stable\"),kind=\"stable\").astype(float)\nrb=np.argsort(np.argsort(-gb,kind=\"stable\"),kind=\"stable\").astype(float)\nglobal_order=np.argsort(.2*ra+.8*rb,kind=\"stable\")\n# Rate-like prior with rank ordering from the tuned blend.\nglobal_value=np.empty(len(ITEMS),np.float32)\nglobal_value[global_order]=np.maximum(1e-5,1-np.arange(len(ITEMS))/len(ITEMS))**8\n\nalphas=[0,.002,.005,.01,.02,.05,.1,.2,.5]\nresults=[]\nbest=None\nfor k in [10,25,50,100,200]:\n s=sim_sparse(nind,nval,k,len(ALL_UIDS))\n for name,b in bases.items():\n pred=(s@b).tocsr();pred.sort_indices()\n scores=score_product(pred,UIDS,target_row,ELIG,gold,global_value,alphas)\n for a,v in scores.items():results.append((v,k,name,a))\n local=max((v,a) for a,v in scores.items())\n print(\"RESULT\",k,name,local,flush=True)\n if best is None or local[0]>best[0]:\n best=(local[0],k,name,local[1]);write_ranking(pred,UIDS,ITEMS,ELIG,global_value,local[1],OUT/\"validation_knn.parquet\")\n del pred\n del s;gc.collect()\nprint(\"TOP\")\nfor z in sorted(results,reverse=True)[:30]:print(z)\nprint(\"BEST\",best,\"elapsed\",time.time()-t0)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_knn.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:59:01.319Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aacbc2f887d2b4158cf03ceb6882", "usage": { "input_tokens": 32191, "cached_input_tokens": 31872, "cache_write_input_tokens": 0, "output_tokens": 5789, "reasoning_output_tokens": 2777, "total_tokens": 37980 }, "turn_token_usage": { "input_tokens": 665091, "cached_input_tokens": 639872, "cache_write_input_tokens": 0, "output_tokens": 14105, "reasoning_output_tokens": 5907, "total_tokens": 679196 }, "thread_token_usage": { "input_tokens": 665091, "cached_input_tokens": 639872, "cache_write_input_tokens": 0, "output_tokens": 14105, "reasoning_output_tokens": 5907, "total_tokens": 679196 } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-24T22:59:07.789Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:59:07.789Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:59:15.521Z", "callId": "call_sH7Ri1B41gx4RFkh7u8nwisd", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_knn.py\\n@@\\n- if len(ci)>1000:\\n- take=np.argpartition(cv,-1000)[-1000:]; ci=ci[take]; cv=cv[take]\\n+ if len(ci)>1000:\\n+ take=np.argpartition(cv,-1000)[-1000:]; ci=ci[take]; cv=cv[take]\\n+ so=np.argsort(ci); ci=ci[so]; cv=cv[so]\\n@@\\n- pos=np.searchsorted(ci,cand); ok=(pos3000:\\n- take=np.argpartition(cv,-3000)[-3000:];ci=ci[take];cv=cv[take]\\n+ if len(ci)>3000:\\n+ take=np.argpartition(cv,-3000)[-3000:];ci=ci[take];cv=cv[take]\\n+ so=np.argsort(ci);ci=ci[so];cv=cv[so]\\n@@\\n- cf=np.zeros(len(cand),np.float32); pos=np.searchsorted(ci,cand);ok=pos=80)]),(3,lk[lk.is_organic==1])]:\\n+ for u,it in d[['uid','item_id']].drop_duplicates().itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=im[int(it)]\\n+ if r is not None and c>=0 and elig[r,c]:g.setdefault(int(u),{})[int(c)]=grade\\n+ return g,ur,im\\n+\\n+def evaluate(p,uids,ur,elig,gold,gv,alphas):\\n+ disc=1/np.log2(np.arange(2,12));gh=np.argsort(-gv)[:500];out={a:[] for a in alphas}\\n+ for uid,g in gold.items():\\n+ r=ur[uid];st,en=p.indptr[r],p.indptr[r+1];ci=p.indices[st:en];cv=p.data[st:en]\\n+ if len(ci)>1500:\\n+ z=np.argpartition(cv,-1500)[-1500:];ci=ci[z];cv=cv[z];z=np.argsort(ci);ci=ci[z];cv=cv[z]\\n+ ca=np.unique(np.r_[ci,gh]);ca=ca[elig[r,ca]];cf=np.zeros(len(ca),np.float32)\\n+ pos=np.searchsorted(ci,ca);v=np.flatnonzero(pos=CUTOFF-90*DAY)&(rat>=50);x=binmat(rr[m],d.item_id.to_numpy()[m],(len(AU),10000001));df=np.bincount(x.indices,minlength=10000001);x.data*=np.log((len(AU)+1)/(df[x.indices]+1));normalize(x,copy=False)\\n+NI,NV=neighbors(x,Q);np.savez_compressed(OUT/'neighbors_train90_1600.npz',indices=NI,values=NV,all_uids=AU);del x,df;gc.collect();print('neighbors',time.time()-t,flush=True)\\n+\\n+B={}\\n+for days in [3,7]:\\n+ for name,beh in [('all50',(rat>=50)),('org50',(rat>=50)&org)]:\\n+ m=(ci>=0)&(ts>=CUTOFF-days*DAY)&beh;B[f'{name}_{days}']=binmat(rr[m],ci[m],(len(AU),len(I)))\\n+likes=pd.read_parquet(DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']);lc=IM[likes.item_id.to_numpy()];lr=UM[likes.uid.to_numpy()];lt=likes.timestamp.to_numpy();lo=likes.is_organic.to_numpy().astype(bool)\\n+for name,beh in [('likeall',np.ones(len(likes),bool)),('likeorg',lo)]:\\n+ m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\\n+# Weighted recommender-event mixtures. Keep matrices float-valued.\\n+for w in [1,2,4,8]: B[f'org7_likeall{w}']=B['org50_7']+w*B['likeall']\\n+for w in [.25,.5,1]: B[f'org7_all7_{w}']=B['org50_7']+w*B['all50_7']\\n+for w in [.5,1,2]: B[f'org7_org3_{w}']=B['org50_7']+w*B['org50_3']\\n+del d,likes;gc.collect()\\n+g=np.load(OUT/'global_features_train.npz');ga=g['listen50_org_7d_users'];gb=g['listen50_all_3d_events'];ra=np.argsort(np.argsort(-ga,kind='stable'),kind='stable');rb=np.argsort(np.argsort(-gb,kind='stable'),kind='stable');go=np.argsort(.2*ra+.8*rb,kind='stable');GV=np.empty(len(I),np.float32);GV[go]=np.maximum(1e-5,1-np.arange(len(I))/len(I))**8\\n+best=(.0447416,None);alphas=[0,.002,.005,.01,.02,.05,.1,.2,.5,1,2,5]\\n+for k in [200,400,800,1600]:\\n+ S=smat(NI,NV,k,len(AU))\\n+ for name,b in B.items():\\n+ p=(S@b).tocsr();p.sort_indices();scores=evaluate(p,U,UR,E,G,GV,alphas);v,a=max((v,a) for a,v in scores.items());print('R',k,name,v,a,flush=True)\\n+ if v>best[0]:best=(v,(k,name,a));write(p,U,I,E,GV,a,OUT/'validation_knn_more.parquet')\\n+ del p\\n+ del S;gc.collect()\\n+print('BEST',best,'elapsed',time.time()-t)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:06:12.434Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:06:12.385Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-13c67eb6-1ff2-4666-8220-87c6f9518131", "changes": { "/app/solution/tune_knn_more.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Broader-neighborhood and explicit-like experiments.\"\"\"\nfrom pathlib import Path\nimport gc,time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\nDATA=Path('/task/data'); OUT=Path('/app/solution'); CUTOFF=24786800; DAY=86400\n\ndef binmat(r,c,shape):\n x=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=shape).tocsr();x.sum_duplicates();x.data[:]=1;return x\n\ndef neighbors(x,q,k=1600):\n ind=np.empty((len(q),k),np.int32);val=np.empty((len(q),k),np.float32);xt=x.T.tocsc()\n for lo in range(0,len(q),256):\n qr=q[lo:lo+256];a=(x[qr]@xt).toarray();a[np.arange(len(qr)),qr]=-1\n ii=np.argpartition(a,-k,axis=1)[:,-k:];vv=np.take_along_axis(a,ii,axis=1);o=np.argsort(-vv,axis=1)\n ind[lo:lo+len(qr)]=np.take_along_axis(ii,o,axis=1);val[lo:lo+len(qr)]=np.take_along_axis(vv,o,axis=1)\n return ind,val\n\ndef smat(ind,val,k,n):\n v=val[:,:k].copy();v/=np.maximum(v.sum(1,keepdims=True),1e-9)\n return sparse.csr_matrix((v.ravel(),(np.repeat(np.arange(len(ind)),k),ind[:,:k].ravel())),shape=(len(ind),n))\n\ndef truth(uids,items,elig):\n ur={int(x):i for i,x in enumerate(uids)};im=np.full(10000001,-1,np.int32);im[items]=np.arange(len(items))\n ls=pd.read_parquet(DATA/'val_listens.parquet');lk=pd.read_parquet(DATA/'val_likes.parquet');g={}\n for grade,d in [(1,ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)]),(3,lk[lk.is_organic==1])]:\n for u,it in d[['uid','item_id']].drop_duplicates().itertuples(index=False,name=None):\n r=ur.get(int(u));c=im[int(it)]\n if r is not None and c>=0 and elig[r,c]:g.setdefault(int(u),{})[int(c)]=grade\n return g,ur,im\n\ndef evaluate(p,uids,ur,elig,gold,gv,alphas):\n disc=1/np.log2(np.arange(2,12));gh=np.argsort(-gv)[:500];out={a:[] for a in alphas}\n for uid,g in gold.items():\n r=ur[uid];st,en=p.indptr[r],p.indptr[r+1];ci=p.indices[st:en];cv=p.data[st:en]\n if len(ci)>1500:\n z=np.argpartition(cv,-1500)[-1500:];ci=ci[z];cv=cv[z];z=np.argsort(ci);ci=ci[z];cv=cv[z]\n ca=np.unique(np.r_[ci,gh]);ca=ca[elig[r,ca]];cf=np.zeros(len(ca),np.float32)\n pos=np.searchsorted(ci,ca);v=np.flatnonzero(pos=CUTOFF-90*DAY)&(rat>=50);x=binmat(rr[m],d.item_id.to_numpy()[m],(len(AU),10000001));df=np.bincount(x.indices,minlength=10000001);x.data*=np.log((len(AU)+1)/(df[x.indices]+1));normalize(x,copy=False)\nNI,NV=neighbors(x,Q);np.savez_compressed(OUT/'neighbors_train90_1600.npz',indices=NI,values=NV,all_uids=AU);del x,df;gc.collect();print('neighbors',time.time()-t,flush=True)\n\nB={}\nfor days in [3,7]:\n for name,beh in [('all50',(rat>=50)),('org50',(rat>=50)&org)]:\n m=(ci>=0)&(ts>=CUTOFF-days*DAY)&beh;B[f'{name}_{days}']=binmat(rr[m],ci[m],(len(AU),len(I)))\nlikes=pd.read_parquet(DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']);lc=IM[likes.item_id.to_numpy()];lr=UM[likes.uid.to_numpy()];lt=likes.timestamp.to_numpy();lo=likes.is_organic.to_numpy().astype(bool)\nfor name,beh in [('likeall',np.ones(len(likes),bool)),('likeorg',lo)]:\n m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\n# Weighted recommender-event mixtures. Keep matrices float-valued.\nfor w in [1,2,4,8]: B[f'org7_likeall{w}']=B['org50_7']+w*B['likeall']\nfor w in [.25,.5,1]: B[f'org7_all7_{w}']=B['org50_7']+w*B['all50_7']\nfor w in [.5,1,2]: B[f'org7_org3_{w}']=B['org50_7']+w*B['org50_3']\ndel d,likes;gc.collect()\ng=np.load(OUT/'global_features_train.npz');ga=g['listen50_org_7d_users'];gb=g['listen50_all_3d_events'];ra=np.argsort(np.argsort(-ga,kind='stable'),kind='stable');rb=np.argsort(np.argsort(-gb,kind='stable'),kind='stable');go=np.argsort(.2*ra+.8*rb,kind='stable');GV=np.empty(len(I),np.float32);GV[go]=np.maximum(1e-5,1-np.arange(len(I))/len(I))**8\nbest=(.0447416,None);alphas=[0,.002,.005,.01,.02,.05,.1,.2,.5,1,2,5]\nfor k in [200,400,800,1600]:\n S=smat(NI,NV,k,len(AU))\n for name,b in B.items():\n p=(S@b).tocsr();p.sort_indices();scores=evaluate(p,U,UR,E,G,GV,alphas);v,a=max((v,a) for a,v in scores.items());print('R',k,name,v,a,flush=True)\n if v>best[0]:best=(v,(k,name,a));write(p,U,I,E,GV,a,OUT/'validation_knn_more.parquet')\n del p\n del S;gc.collect()\nprint('BEST',best,'elapsed',time.time()-t)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_knn_more.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:06:12.432Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5acaf7f3087d281f556e5657eae22", "usage": { "input_tokens": 44266, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 2838, "reasoning_output_tokens": 486, "total_tokens": 47104 }, "turn_token_usage": { "input_tokens": 1197238, "cached_input_tokens": 1136768, "cache_write_input_tokens": 0, "output_tokens": 19585, "reasoning_output_tokens": 7749, "total_tokens": 1216823 }, "thread_token_usage": { "input_tokens": 1197238, "cached_input_tokens": 1136768, "cache_write_input_tokens": 0, "output_tokens": 19585, "reasoning_output_tokens": 7749, "total_tokens": 1216823 } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-24T23:06:16.978Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:16.978Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:06:18.078Z", "callId": "call_khY1OvnRrZjwTGRrPYiJzLl3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_knn_more.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:06:36.488Z", "content": "Script completed\nWall time 18.4 seconds\nOutput:\n\n{\"chunk_id\":\"0bbb76\",\"wall_time_seconds\":18.241706687,\"exit_code\":1,\"original_token_count\":213,\"output\":\"neighbors 16.515608310699463\\nTraceback (most recent call last):\\n File \\\"/app/solution/tune_knn_more.py\\\", line 76, in \\n m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/tune_knn_more.py\\\", line 13, in binmat\\n x=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=shape).tocsr();x.sum_duplicates();x.data[:]=1;return x\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\\\", line 101, in __init__\\n self._check()\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\\\", line 225, in _check\\n raise ValueError(f'negative axis {i} index: {idx.min()}')\\nValueError: negative axis 0 index: -1\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:06:18.176Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ace4b8e087d284aee18adb8b3518", "usage": { "input_tokens": 47128, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 6, "total_tokens": 47202 }, "turn_token_usage": { "input_tokens": 1244366, "cached_input_tokens": 1147904, "cache_write_input_tokens": 0, "output_tokens": 19659, "reasoning_output_tokens": 7755, "total_tokens": 1264025 }, "thread_token_usage": { "input_tokens": 1244366, "cached_input_tokens": 1147904, "cache_write_input_tokens": 0, "output_tokens": 19659, "reasoning_output_tokens": 7755, "total_tokens": 1264025 } } }, { "kind": "context", "timestamp": "2026-09-24T23:06:36.484Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dc222b79-64fe-4781-a1dc-e8fa8d975d86", "process_id": "45588", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/tune_knn_more.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/tune_knn_more.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "neighbors 16.515608310699463\nTraceback (most recent call last):\n File \"/app/solution/tune_knn_more.py\", line 76, in \n m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_knn_more.py\", line 13, in binmat\n x=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=shape).tocsr();x.sum_duplicates();x.data[:]=1;return x\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 101, in __init__\n self._check()\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 225, in _check\n raise ValueError(f'negative axis {i} index: {idx.min()}')\nValueError: negative axis 0 index: -1\n", "stderr": "", "aggregated_output": "neighbors 16.515608310699463\nTraceback (most recent call last):\n File \"/app/solution/tune_knn_more.py\", line 76, in \n m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_knn_more.py\", line 13, in binmat\n x=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=shape).tocsr();x.sum_duplicates();x.data[:]=1;return x\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 101, in __init__\n self._check()\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 225, in _check\n raise ValueError(f'negative axis {i} index: {idx.min()}')\nValueError: negative axis 0 index: -1\n", "exit_code": 1, "duration": { "secs": 18, "nanos": 241699606 }, "formatted_output": "neighbors 16.515608310699463\nTraceback (most recent call last):\n File \"/app/solution/tune_knn_more.py\", line 76, in \n m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_knn_more.py\", line 13, in binmat\n x=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=shape).tocsr();x.sum_duplicates();x.data[:]=1;return x\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 101, in __init__\n self._check()\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 225, in _check\n raise ValueError(f'negative axis {i} index: {idx.min()}')\nValueError: negative axis 0 index: -1\n" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-24T23:06:41.687Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:41.687Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:06:50.045Z", "callId": "call_23ehwua4RHFaCU4UYEEWRj16", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_knn_more.py\\n@@\\n-m=(ts>=CUTOFF-90*DAY)&(rat>=50);x=binmat(rr[m],d.item_id.to_numpy()[m],(len(AU),10000001));df=np.bincount(x.indices,minlength=10000001);x.data*=np.log((len(AU)+1)/(df[x.indices]+1));normalize(x,copy=False)\\n-NI,NV=neighbors(x,Q);np.savez_compressed(OUT/'neighbors_train90_1600.npz',indices=NI,values=NV,all_uids=AU);del x,df;gc.collect();print('neighbors',time.time()-t,flush=True)\\n+neighbor_path=OUT/'neighbors_train90_1600.npz'\\n+if neighbor_path.exists():\\n+ zz=np.load(neighbor_path);NI=zz['indices'];NV=zz['values']\\n+else:\\n+ m=(ts>=CUTOFF-90*DAY)&(rat>=50);x=binmat(rr[m],d.item_id.to_numpy()[m],(len(AU),10000001));df=np.bincount(x.indices,minlength=10000001);x.data*=np.log((len(AU)+1)/(df[x.indices]+1));normalize(x,copy=False)\\n+ NI,NV=neighbors(x,Q);np.savez_compressed(neighbor_path,indices=NI,values=NV,all_uids=AU);del x,df;gc.collect()\\n+print('neighbors',time.time()-t,flush=True)\\n@@\\n- m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\\n+ m=(lr>=0)&(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:06:50.139Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:06:50.053Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a9e1bde9-db65-4e01-b4df-5d7141aa37b0", "changes": { "/app/solution/tune_knn_more.py": { "type": "update", "unified_diff": "@@ -66,4 +66,9 @@\n ts=d.timestamp.to_numpy();rat=d.played_ratio_pct.to_numpy();org=d.is_organic.to_numpy().astype(bool);rr=UM[d.uid.to_numpy()];ci=IM[d.item_id.to_numpy()]\n-m=(ts>=CUTOFF-90*DAY)&(rat>=50);x=binmat(rr[m],d.item_id.to_numpy()[m],(len(AU),10000001));df=np.bincount(x.indices,minlength=10000001);x.data*=np.log((len(AU)+1)/(df[x.indices]+1));normalize(x,copy=False)\n-NI,NV=neighbors(x,Q);np.savez_compressed(OUT/'neighbors_train90_1600.npz',indices=NI,values=NV,all_uids=AU);del x,df;gc.collect();print('neighbors',time.time()-t,flush=True)\n+neighbor_path=OUT/'neighbors_train90_1600.npz'\n+if neighbor_path.exists():\n+ zz=np.load(neighbor_path);NI=zz['indices'];NV=zz['values']\n+else:\n+ m=(ts>=CUTOFF-90*DAY)&(rat>=50);x=binmat(rr[m],d.item_id.to_numpy()[m],(len(AU),10000001));df=np.bincount(x.indices,minlength=10000001);x.data*=np.log((len(AU)+1)/(df[x.indices]+1));normalize(x,copy=False)\n+ NI,NV=neighbors(x,Q);np.savez_compressed(neighbor_path,indices=NI,values=NV,all_uids=AU);del x,df;gc.collect()\n+print('neighbors',time.time()-t,flush=True)\n \n@@ -75,3 +80,3 @@\n for name,beh in [('likeall',np.ones(len(likes),bool)),('likeorg',lo)]:\n- m=(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\n+ m=(lr>=0)&(lc>=0)&(lt>=CUTOFF-7*DAY)&beh;B[name]=binmat(lr[m],lc[m],(len(AU),len(I)))\n # Weighted recommender-event mixtures. Keep matrices float-valued.\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_knn_more.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:06:50.138Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5acfcbd6087d29a6ccd897c6c416f", "usage": { "input_tokens": 47534, "cached_input_tokens": 46976, "cache_write_input_tokens": 0, "output_tokens": 559, "reasoning_output_tokens": 86, "total_tokens": 48093 }, "turn_token_usage": { "input_tokens": 1291900, "cached_input_tokens": 1194880, "cache_write_input_tokens": 0, "output_tokens": 20218, "reasoning_output_tokens": 7841, "total_tokens": 1312118 }, "thread_token_usage": { "input_tokens": 1291900, "cached_input_tokens": 1194880, "cache_write_input_tokens": 0, "output_tokens": 20218, "reasoning_output_tokens": 7841, "total_tokens": 1312118 } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-24T23:06:52.529Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:52.529Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:06:53.596Z", "callId": "call_WVK2FRnop3AhJlQm7MweiBtU", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_knn_more.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:07:23.762Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"e46451\",\"wall_time_seconds\":30.00174701,\"session_id\":61765,\"original_token_count\":63,\"output\":\"neighbors 1.7161102294921875\\nR 200 all50_3 0.04211195474561581 2\\nR 200 org50_3 0.04379937434860784 1\\nR 200 all50_7 0.043337714201823835 0.2\\nR 200 org50_7 0.044741601996178125 0.05\\nR 200 likeall 0.03825838922042122 1\\nR 200 likeorg 0.03766856271242539 1\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:06:53.737Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ad0af82087d28f292c6f31eb4f87", "usage": { "input_tokens": 48117, "cached_input_tokens": 47360, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 6, "total_tokens": 48191 }, "turn_token_usage": { "input_tokens": 1340017, "cached_input_tokens": 1242240, "cache_write_input_tokens": 0, "output_tokens": 20292, "reasoning_output_tokens": 7847, "total_tokens": 1360309 }, "thread_token_usage": { "input_tokens": 1340017, "cached_input_tokens": 1242240, "cache_write_input_tokens": 0, "output_tokens": 20292, "reasoning_output_tokens": 7847, "total_tokens": 1360309 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-24T23:07:25.739Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:25.739Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:07:26.461Z", "callId": "call_xzOoxweBxAurdoTJl9M1fFDr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:07:56.469Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"ec623a\",\"wall_time_seconds\":30.000215836,\"session_id\":61765,\"original_token_count\":55,\"output\":\"R 200 org7_likeall1 0.04646457852874448 0.05\\nR 200 org7_likeall2 0.046934028719854685 0.05\\nR 200 org7_likeall4 0.04545877160539661 0.2\\nR 200 org7_likeall8 0.04338937533397394 5\\nR 200 org7_all7_0.25 0.04627651488897812 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:07:26.607Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ad2befb887d294b77f4c9e9f2f75", "usage": { "input_tokens": 48372, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 27, "total_tokens": 48447 }, "turn_token_usage": { "input_tokens": 1388389, "cached_input_tokens": 1290240, "cache_write_input_tokens": 0, "output_tokens": 20367, "reasoning_output_tokens": 7874, "total_tokens": 1408756 }, "thread_token_usage": { "input_tokens": 1388389, "cached_input_tokens": 1290240, "cache_write_input_tokens": 0, "output_tokens": 20367, "reasoning_output_tokens": 7874, "total_tokens": 1408756 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-24T23:07:58.943Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:58.943Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:07:59.657Z", "callId": "call_kF0GPLb0vST7ZJi0ge0vjLvx", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:08:29.664Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a4edda\",\"wall_time_seconds\":30.000555518,\"session_id\":61765,\"original_token_count\":75,\"output\":\"R 200 org7_all7_0.5 0.046393006451947445 0.05\\nR 200 org7_all7_1 0.045965647366170925 0.2\\nR 200 org7_org3_0.5 0.045462224875451096 0.01\\nR 200 org7_org3_1 0.04510422144651707 0.05\\nR 200 org7_org3_2 0.04501331807185135 0.1\\nR 400 all50_3 0.04253871542190134 0.2\\nR 400 org50_3 0.04607301296793772 0.005\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:07:59.759Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ad4caba887d2b26ddff04255f183", "usage": { "input_tokens": 48612, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 13, "total_tokens": 48673 }, "turn_token_usage": { "input_tokens": 1437001, "cached_input_tokens": 1338496, "cache_write_input_tokens": 0, "output_tokens": 20428, "reasoning_output_tokens": 7887, "total_tokens": 1457429 }, "thread_token_usage": { "input_tokens": 1437001, "cached_input_tokens": 1338496, "cache_write_input_tokens": 0, "output_tokens": 20428, "reasoning_output_tokens": 7887, "total_tokens": 1457429 } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-24T23:08:31.883Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:08:31.883Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:08:33.290Z", "text": "Increasing neighborhood breadth and adding neighbor likes are helping: the best validation score is now 0.046934, with organic seven-day neighbor listens plus a modest explicit-like boost. I’m continuing the sweep through broader neighborhoods and mixed interaction channels, while saving every new best validation ranking." }, { "kind": "tool", "timestamp": "2026-09-24T23:08:34.144Z", "callId": "call_9F2wCDNIkYAr7OKVgRHdDegQ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:09:04.151Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"7a7c72\",\"wall_time_seconds\":30.000163471,\"session_id\":61765,\"original_token_count\":61,\"output\":\"R 400 all50_7 0.043193474889065435 0.05\\nR 400 org50_7 0.0461250986635217 0\\nR 400 likeall 0.04056810882293019 0.5\\nR 400 likeorg 0.03926995403748139 1\\nR 400 org7_likeall1 0.04783425889951014 0.002\\nR 400 org7_likeall2 0.04906428845319923 0.002\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:08:34.237Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ad6dd59487d288a3f64a03fbea6a", "usage": { "input_tokens": 48891, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 118, "reasoning_output_tokens": 7, "total_tokens": 49009 }, "turn_token_usage": { "input_tokens": 1485892, "cached_input_tokens": 1386880, "cache_write_input_tokens": 0, "output_tokens": 20546, "reasoning_output_tokens": 7894, "total_tokens": 1506438 }, "thread_token_usage": { "input_tokens": 1485892, "cached_input_tokens": 1386880, "cache_write_input_tokens": 0, "output_tokens": 20546, "reasoning_output_tokens": 7894, "total_tokens": 1506438 } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-24T23:09:08.912Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:09:08.912Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:09:09.137Z", "callId": "call_doU8YVjpIbP40qSV07f0OlzA", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:09:39.144Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"3aabe8\",\"wall_time_seconds\":30.000644446,\"session_id\":61765,\"original_token_count\":55,\"output\":\"R 400 org7_likeall4 0.04855540513727686 0.01\\nR 400 org7_likeall8 0.04636127482998606 0.5\\nR 400 org7_all7_0.25 0.04713537913824123 0\\nR 400 org7_all7_0.5 0.04705075983591456 0.01\\nR 400 org7_all7_1 0.04618793083095365 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:09:09.222Z", "data": { "type": 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"representation": "summary", "timestamp": "2026-09-24T23:09:41.257Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:09:41.966Z", "callId": "call_XF5ml0kyDP3ioyHAISj99gL2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:10:11.979Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"02dbc4\",\"wall_time_seconds\":30.001434117,\"session_id\":61765,\"original_token_count\":52,\"output\":\"R 400 org7_org3_0.5 0.04696890297283842 0.002\\nR 400 org7_org3_1 0.046854999892751305 0.02\\nR 400 org7_org3_2 0.04724006625629877 0.002\\nR 800 all50_3 0.04193834467446781 0.1\\nR 800 org50_3 0.04705650761312603 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:09:42.074Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5adb35b5087d2a20b41ea24811287", "usage": { "input_tokens": 49415, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 49469 }, "turn_token_usage": { "input_tokens": 1584494, "cached_input_tokens": 1484672, "cache_write_input_tokens": 0, "output_tokens": 20658, "reasoning_output_tokens": 7910, "total_tokens": 1605152 }, "thread_token_usage": { "input_tokens": 1584494, "cached_input_tokens": 1484672, "cache_write_input_tokens": 0, "output_tokens": 20658, "reasoning_output_tokens": 7910, "total_tokens": 1605152 } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-24T23:10:14.232Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:10:14.232Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:10:15.141Z", "callId": "call_TQyGqKcvjFJWyf9rjxTRZsCe", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:10:45.148Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"0327bc\",\"wall_time_seconds\":30.00033879,\"session_id\":61765,\"original_token_count\":60,\"output\":\"R 800 all50_7 0.04249102351836053 0.1\\nR 800 org50_7 0.046385097886822685 0\\nR 800 likeall 0.04199712397840711 0.5\\nR 800 likeorg 0.04021401311946421 0.2\\nR 800 org7_likeall1 0.048473573968794446 0.002\\nR 800 org7_likeall2 0.04901006118331083 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:10:15.202Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5add45a4c87d2af468c484b03a76a", "usage": { "input_tokens": 49636, "cached_input_tokens": 49280, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 10, "total_tokens": 49694 }, "turn_token_usage": { "input_tokens": 1634130, "cached_input_tokens": 1533952, "cache_write_input_tokens": 0, "output_tokens": 20716, "reasoning_output_tokens": 7920, "total_tokens": 1654846 }, "thread_token_usage": { "input_tokens": 1634130, "cached_input_tokens": 1533952, "cache_write_input_tokens": 0, "output_tokens": 20716, "reasoning_output_tokens": 7920, "total_tokens": 1654846 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-24T23:10:49.751Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:10:49.751Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:10:49.755Z", "callId": "call_3zXhQvAflFUCcohMPrNMrFHK", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:11:19.762Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"76e8be\",\"wall_time_seconds\":30.000711683,\"session_id\":61765,\"original_token_count\":34,\"output\":\"R 800 org7_likeall4 0.049405397159863355 0\\nR 800 org7_likeall8 0.047969950571473054 0.005\\nR 800 org7_all7_0.25 0.046586640130775334 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:10:49.837Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": 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"call_jzUFwGeafxK2Xm1GO52obRyQ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:11:51.862Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"23b976\",\"wall_time_seconds\":30.001269885,\"session_id\":61765,\"original_token_count\":42,\"output\":\"R 800 org7_all7_0.5 0.04717563844467856 0\\nR 800 org7_all7_1 0.04573447287716431 0.05\\nR 800 org7_org3_0.5 0.04732075375019509 0\\nR 800 org7_org3_1 0.04734209325489621 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:21.958Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": 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tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:12:25.274Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"e6d1e0\",\"wall_time_seconds\":30.001352837,\"session_id\":61765,\"original_token_count\":31,\"output\":\"R 800 org7_org3_2 0.047485950561902614 0.005\\nR 1600 all50_3 0.04087218470798334 0.005\\nR 1600 org50_3 0.04702772352347042 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:55.339Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ae381be887d2bfe1379f13e5a207", "usage": { "input_tokens": 50260, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 50314 }, "turn_token_usage": { "input_tokens": 1784310, "cached_input_tokens": 1682944, "cache_write_input_tokens": 0, "output_tokens": 20892, "reasoning_output_tokens": 7952, "total_tokens": 1805202 }, "thread_token_usage": { "input_tokens": 1784310, "cached_input_tokens": 1682944, "cache_write_input_tokens": 0, "output_tokens": 20892, "reasoning_output_tokens": 7952, "total_tokens": 1805202 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-24T23:12:27.709Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:12:27.709Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:12:28.476Z", "callId": "call_VeXpJJFRdOBZVEZXAYBr42pN", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:12:58.485Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"2a265e\",\"wall_time_seconds\":30.001166547,\"session_id\":61765,\"original_token_count\":39,\"output\":\"R 1600 all50_7 0.040736441507714855 0\\nR 1600 org50_7 0.04578455449682251 0.2\\nR 1600 likeall 0.04280234570879443 0.2\\nR 1600 likeorg 0.04104890838473826 0.1\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:12:28.555Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ae597f6c87d2b34068c6b07000c4", "usage": { "input_tokens": 50438, "cached_input_tokens": 50048, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 50492 }, "turn_token_usage": { "input_tokens": 1834748, "cached_input_tokens": 1732992, "cache_write_input_tokens": 0, "output_tokens": 20946, "reasoning_output_tokens": 7958, "total_tokens": 1855694 }, "thread_token_usage": { "input_tokens": 1834748, "cached_input_tokens": 1732992, "cache_write_input_tokens": 0, "output_tokens": 20946, "reasoning_output_tokens": 7958, "total_tokens": 1855694 } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-24T23:13:02.825Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:13:02.825Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:13:03.556Z", "callId": "call_4wF5XnDR9g7U3QgJc8jGm8XL", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:13:33.568Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"fac77f\",\"wall_time_seconds\":30.003447973,\"session_id\":61765,\"original_token_count\":34,\"output\":\"R 1600 org7_likeall1 0.047530785860631065 0\\nR 1600 org7_likeall2 0.04864556922365739 0.002\\nR 1600 org7_likeall4 0.049730724128032755 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:13:03.607Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ae7ab3a087d284b4a18146e02bd3", "usage": { "input_tokens": 50631, "cached_input_tokens": 50304, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 50686 }, "turn_token_usage": { "input_tokens": 1885379, "cached_input_tokens": 1783296, "cache_write_input_tokens": 0, "output_tokens": 21001, "reasoning_output_tokens": 7965, "total_tokens": 1906380 }, "thread_token_usage": { "input_tokens": 1885379, "cached_input_tokens": 1783296, "cache_write_input_tokens": 0, "output_tokens": 21001, "reasoning_output_tokens": 7965, "total_tokens": 1906380 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-24T23:13:35.008Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:13:35.008Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:13:35.752Z", "callId": "call_2ULqatyclpzK0sbEVU7Vehye", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:14:05.760Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"0db2a2\",\"wall_time_seconds\":30.001287264,\"session_id\":61765,\"original_token_count\":23,\"output\":\"R 1600 org7_likeall8 0.04915501724005263 0.005\\nR 1600 org7_all7_0.25 0.04564241071485929 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:13:35.860Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5ae9dc3ac87d282f638a4741bab27", "usage": { "input_tokens": 50807, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 22, "total_tokens": 50877 }, "turn_token_usage": { "input_tokens": 1936186, "cached_input_tokens": 1833728, "cache_write_input_tokens": 0, "output_tokens": 21071, "reasoning_output_tokens": 7987, "total_tokens": 1957257 }, "thread_token_usage": { "input_tokens": 1936186, "cached_input_tokens": 1833728, "cache_write_input_tokens": 0, "output_tokens": 21071, "reasoning_output_tokens": 7987, "total_tokens": 1957257 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-24T23:14:07.706Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:07.706Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:14:08.469Z", "callId": "call_Y3ViSC5TCZ5jvsULpHvzuirj", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:14:38.478Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a5f6ab\",\"wall_time_seconds\":30.001623346,\"session_id\":61765,\"original_token_count\":34,\"output\":\"R 1600 org7_all7_0.5 0.04530115983936477 0.002\\nR 1600 org7_all7_1 0.04405751611008598 0.02\\nR 1600 org7_org3_0.5 0.0465855924955309 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:08.565Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aebe139c87d29f4b46d29b8b583e", "usage": { "input_tokens": 50982, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 51036 }, "turn_token_usage": { "input_tokens": 1987168, "cached_input_tokens": 1884416, "cache_write_input_tokens": 0, "output_tokens": 21125, "reasoning_output_tokens": 7993, "total_tokens": 2008293 }, "thread_token_usage": { "input_tokens": 1987168, "cached_input_tokens": 1884416, "cache_write_input_tokens": 0, "output_tokens": 21125, "reasoning_output_tokens": 7993, "total_tokens": 2008293 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-24T23:14:41.239Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:41.239Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:14:41.934Z", "callId": "call_gdUmPgtdLTidFpqzK6cvWel4", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61765,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:14:55.448Z", "content": "Script completed\nWall time 13.5 seconds\nOutput:\n\n{\"chunk_id\":\"c1d955\",\"wall_time_seconds\":13.504572204,\"exit_code\":0,\"original_token_count\":45,\"output\":\"R 1600 org7_org3_1 0.04658786225031045 0\\nR 1600 org7_org3_2 0.046766990767014224 0.02\\nBEST (np.float64(0.049730724128032755), (1600, 'org7_likeall4', 0)) elapsed 480.3168683052063\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:42.054Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aedeab6887d2a5c8a2e38a16b280", "usage": { "input_tokens": 51168, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 51222 }, "turn_token_usage": { "input_tokens": 2038336, "cached_input_tokens": 1935232, "cache_write_input_tokens": 0, "output_tokens": 21179, "reasoning_output_tokens": 7999, "total_tokens": 2059515 }, "thread_token_usage": { "input_tokens": 2038336, "cached_input_tokens": 1935232, "cache_write_input_tokens": 0, "output_tokens": 21179, "reasoning_output_tokens": 7999, "total_tokens": 2059515 } } }, { "kind": "context", "timestamp": "2026-09-24T23:14:55.446Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8d0d8ce3-5414-4920-9503-3f29b3265243", "process_id": "61765", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/tune_knn_more.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/tune_knn_more.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "neighbors 1.7161102294921875\nR 200 all50_3 0.04211195474561581 2\nR 200 org50_3 0.04379937434860784 1\nR 200 all50_7 0.043337714201823835 0.2\nR 200 org50_7 0.044741601996178125 0.05\nR 200 likeall 0.03825838922042122 1\nR 200 likeorg 0.03766856271242539 1\nR 200 org7_likeall1 0.04646457852874448 0.05\nR 200 org7_likeall2 0.046934028719854685 0.05\nR 200 org7_likeall4 0.04545877160539661 0.2\nR 200 org7_likeall8 0.04338937533397394 5\nR 200 org7_all7_0.25 0.04627651488897812 0\nR 200 org7_all7_0.5 0.046393006451947445 0.05\nR 200 org7_all7_1 0.045965647366170925 0.2\nR 200 org7_org3_0.5 0.045462224875451096 0.01\nR 200 org7_org3_1 0.04510422144651707 0.05\nR 200 org7_org3_2 0.04501331807185135 0.1\nR 400 all50_3 0.04253871542190134 0.2\nR 400 org50_3 0.04607301296793772 0.005\nR 400 all50_7 0.043193474889065435 0.05\nR 400 org50_7 0.0461250986635217 0\nR 400 likeall 0.04056810882293019 0.5\nR 400 likeorg 0.03926995403748139 1\nR 400 org7_likeall1 0.04783425889951014 0.002\nR 400 org7_likeall2 0.04906428845319923 0.002\nR 400 org7_likeall4 0.04855540513727686 0.01\nR 400 org7_likeall8 0.04636127482998606 0.5\nR 400 org7_all7_0.25 0.04713537913824123 0\nR 400 org7_all7_0.5 0.04705075983591456 0.01\nR 400 org7_all7_1 0.04618793083095365 0\nR 400 org7_org3_0.5 0.04696890297283842 0.002\nR 400 org7_org3_1 0.046854999892751305 0.02\nR 400 org7_org3_2 0.04724006625629877 0.002\nR 800 all50_3 0.04193834467446781 0.1\nR 800 org50_3 0.04705650761312603 0\nR 800 all50_7 0.04249102351836053 0.1\nR 800 org50_7 0.046385097886822685 0\nR 800 likeall 0.04199712397840711 0.5\nR 800 likeorg 0.04021401311946421 0.2\nR 800 org7_likeall1 0.048473573968794446 0.002\nR 800 org7_likeall2 0.04901006118331083 0\nR 800 org7_likeall4 0.049405397159863355 0\nR 800 org7_likeall8 0.047969950571473054 0.005\nR 800 org7_all7_0.25 0.046586640130775334 0\nR 800 org7_all7_0.5 0.04717563844467856 0\nR 800 org7_all7_1 0.04573447287716431 0.05\nR 800 org7_org3_0.5 0.04732075375019509 0\nR 800 org7_org3_1 0.04734209325489621 0\nR 800 org7_org3_2 0.047485950561902614 0.005\nR 1600 all50_3 0.04087218470798334 0.005\nR 1600 org50_3 0.04702772352347042 0\nR 1600 all50_7 0.040736441507714855 0\nR 1600 org50_7 0.04578455449682251 0.2\nR 1600 likeall 0.04280234570879443 0.2\nR 1600 likeorg 0.04104890838473826 0.1\nR 1600 org7_likeall1 0.047530785860631065 0\nR 1600 org7_likeall2 0.04864556922365739 0.002\nR 1600 org7_likeall4 0.049730724128032755 0\nR 1600 org7_likeall8 0.04915501724005263 0.005\nR 1600 org7_all7_0.25 0.04564241071485929 0\nR 1600 org7_all7_0.5 0.04530115983936477 0.002\nR 1600 org7_all7_1 0.04405751611008598 0.02\nR 1600 org7_org3_0.5 0.0465855924955309 0\nR 1600 org7_org3_1 0.04658786225031045 0\nR 1600 org7_org3_2 0.046766990767014224 0.02\nBEST (np.float64(0.049730724128032755), (1600, 'org7_likeall4', 0)) elapsed 480.3168683052063\n", "stderr": "", "aggregated_output": "neighbors 1.7161102294921875\nR 200 all50_3 0.04211195474561581 2\nR 200 org50_3 0.04379937434860784 1\nR 200 all50_7 0.043337714201823835 0.2\nR 200 org50_7 0.044741601996178125 0.05\nR 200 likeall 0.03825838922042122 1\nR 200 likeorg 0.03766856271242539 1\nR 200 org7_likeall1 0.04646457852874448 0.05\nR 200 org7_likeall2 0.046934028719854685 0.05\nR 200 org7_likeall4 0.04545877160539661 0.2\nR 200 org7_likeall8 0.04338937533397394 5\nR 200 org7_all7_0.25 0.04627651488897812 0\nR 200 org7_all7_0.5 0.046393006451947445 0.05\nR 200 org7_all7_1 0.045965647366170925 0.2\nR 200 org7_org3_0.5 0.045462224875451096 0.01\nR 200 org7_org3_1 0.04510422144651707 0.05\nR 200 org7_org3_2 0.04501331807185135 0.1\nR 400 all50_3 0.04253871542190134 0.2\nR 400 org50_3 0.04607301296793772 0.005\nR 400 all50_7 0.043193474889065435 0.05\nR 400 org50_7 0.0461250986635217 0\nR 400 likeall 0.04056810882293019 0.5\nR 400 likeorg 0.03926995403748139 1\nR 400 org7_likeall1 0.04783425889951014 0.002\nR 400 org7_likeall2 0.04906428845319923 0.002\nR 400 org7_likeall4 0.04855540513727686 0.01\nR 400 org7_likeall8 0.04636127482998606 0.5\nR 400 org7_all7_0.25 0.04713537913824123 0\nR 400 org7_all7_0.5 0.04705075983591456 0.01\nR 400 org7_all7_1 0.04618793083095365 0\nR 400 org7_org3_0.5 0.04696890297283842 0.002\nR 400 org7_org3_1 0.046854999892751305 0.02\nR 400 org7_org3_2 0.04724006625629877 0.002\nR 800 all50_3 0.04193834467446781 0.1\nR 800 org50_3 0.04705650761312603 0\nR 800 all50_7 0.04249102351836053 0.1\nR 800 org50_7 0.046385097886822685 0\nR 800 likeall 0.04199712397840711 0.5\nR 800 likeorg 0.04021401311946421 0.2\nR 800 org7_likeall1 0.048473573968794446 0.002\nR 800 org7_likeall2 0.04901006118331083 0\nR 800 org7_likeall4 0.049405397159863355 0\nR 800 org7_likeall8 0.047969950571473054 0.005\nR 800 org7_all7_0.25 0.046586640130775334 0\nR 800 org7_all7_0.5 0.04717563844467856 0\nR 800 org7_all7_1 0.04573447287716431 0.05\nR 800 org7_org3_0.5 0.04732075375019509 0\nR 800 org7_org3_1 0.04734209325489621 0\nR 800 org7_org3_2 0.047485950561902614 0.005\nR 1600 all50_3 0.04087218470798334 0.005\nR 1600 org50_3 0.04702772352347042 0\nR 1600 all50_7 0.040736441507714855 0\nR 1600 org50_7 0.04578455449682251 0.2\nR 1600 likeall 0.04280234570879443 0.2\nR 1600 likeorg 0.04104890838473826 0.1\nR 1600 org7_likeall1 0.047530785860631065 0\nR 1600 org7_likeall2 0.04864556922365739 0.002\nR 1600 org7_likeall4 0.049730724128032755 0\nR 1600 org7_likeall8 0.04915501724005263 0.005\nR 1600 org7_all7_0.25 0.04564241071485929 0\nR 1600 org7_all7_0.5 0.04530115983936477 0.002\nR 1600 org7_all7_1 0.04405751611008598 0.02\nR 1600 org7_org3_0.5 0.0465855924955309 0\nR 1600 org7_org3_1 0.04658786225031045 0\nR 1600 org7_org3_2 0.046766990767014224 0.02\nBEST (np.float64(0.049730724128032755), (1600, 'org7_likeall4', 0)) elapsed 480.3168683052063\n", "exit_code": 0, "duration": { "secs": 481, "nanos": 686428684 }, "formatted_output": "neighbors 1.7161102294921875\nR 200 all50_3 0.04211195474561581 2\nR 200 org50_3 0.04379937434860784 1\nR 200 all50_7 0.043337714201823835 0.2\nR 200 org50_7 0.044741601996178125 0.05\nR 200 likeall 0.03825838922042122 1\nR 200 likeorg 0.03766856271242539 1\nR 200 org7_likeall1 0.04646457852874448 0.05\nR 200 org7_likeall2 0.046934028719854685 0.05\nR 200 org7_likeall4 0.04545877160539661 0.2\nR 200 org7_likeall8 0.04338937533397394 5\nR 200 org7_all7_0.25 0.04627651488897812 0\nR 200 org7_all7_0.5 0.046393006451947445 0.05\nR 200 org7_all7_1 0.045965647366170925 0.2\nR 200 org7_org3_0.5 0.045462224875451096 0.01\nR 200 org7_org3_1 0.04510422144651707 0.05\nR 200 org7_org3_2 0.04501331807185135 0.1\nR 400 all50_3 0.04253871542190134 0.2\nR 400 org50_3 0.04607301296793772 0.005\nR 400 all50_7 0.043193474889065435 0.05\nR 400 org50_7 0.0461250986635217 0\nR 400 likeall 0.04056810882293019 0.5\nR 400 likeorg 0.03926995403748139 1\nR 400 org7_likeall1 0.04783425889951014 0.002\nR 400 org7_likeall2 0.04906428845319923 0.002\nR 400 org7_likeall4 0.04855540513727686 0.01\nR 400 org7_likeall8 0.04636127482998606 0.5\nR 400 org7_all7_0.25 0.04713537913824123 0\nR 400 org7_all7_0.5 0.04705075983591456 0.01\nR 400 org7_all7_1 0.04618793083095365 0\nR 400 org7_org3_0.5 0.04696890297283842 0.002\nR 400 org7_org3_1 0.046854999892751305 0.02\nR 400 org7_org3_2 0.04724006625629877 0.002\nR 800 all50_3 0.04193834467446781 0.1\nR 800 org50_3 0.04705650761312603 0\nR 800 all50_7 0.04249102351836053 0.1\nR 800 org50_7 0.046385097886822685 0\nR 800 likeall 0.04199712397840711 0.5\nR 800 likeorg 0.04021401311946421 0.2\nR 800 org7_likeall1 0.048473573968794446 0.002\nR 800 org7_likeall2 0.04901006118331083 0\nR 800 org7_likeall4 0.049405397159863355 0\nR 800 org7_likeall8 0.047969950571473054 0.005\nR 800 org7_all7_0.25 0.046586640130775334 0\nR 800 org7_all7_0.5 0.04717563844467856 0\nR 800 org7_all7_1 0.04573447287716431 0.05\nR 800 org7_org3_0.5 0.04732075375019509 0\nR 800 org7_org3_1 0.04734209325489621 0\nR 800 org7_org3_2 0.047485950561902614 0.005\nR 1600 all50_3 0.04087218470798334 0.005\nR 1600 org50_3 0.04702772352347042 0\nR 1600 all50_7 0.040736441507714855 0\nR 1600 org50_7 0.04578455449682251 0.2\nR 1600 likeall 0.04280234570879443 0.2\nR 1600 likeorg 0.04104890838473826 0.1\nR 1600 org7_likeall1 0.047530785860631065 0\nR 1600 org7_likeall2 0.04864556922365739 0.002\nR 1600 org7_likeall4 0.049730724128032755 0\nR 1600 org7_likeall8 0.04915501724005263 0.005\nR 1600 org7_all7_0.25 0.04564241071485929 0\nR 1600 org7_all7_0.5 0.04530115983936477 0.002\nR 1600 org7_all7_1 0.04405751611008598 0.02\nR 1600 org7_org3_0.5 0.0465855924955309 0\nR 1600 org7_org3_1 0.04658786225031045 0\nR 1600 org7_org3_2 0.046766990767014224 0.02\nBEST (np.float64(0.049730724128032755), (1600, 'org7_likeall4', 0)) elapsed 480.3168683052063\n" } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-24T23:14:57.672Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:57.672Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:14:59.158Z", "callId": "call_8a635KZAzYEaMatE4lS9rMoG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn_more.parquet && ls -lh /app/solution/validation_knn_more.parquet /app/solution/neighbors_train90_1600.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:15:01.704Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_knn_more.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.049731,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 74M Sep 24 23:06 /app/solution/neighbors_train90_1600.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:13 /app/solution/validation_knn_more.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:59.230Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aeefa42087d2b2978e8b1509a62b", "usage": { "input_tokens": 51363, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 120, "reasoning_output_tokens": 20, "total_tokens": 51483 }, "turn_token_usage": { "input_tokens": 2089699, "cached_input_tokens": 1986176, "cache_write_input_tokens": 0, "output_tokens": 21299, "reasoning_output_tokens": 8019, "total_tokens": 2110998 }, "thread_token_usage": { "input_tokens": 2089699, "cached_input_tokens": 1986176, "cache_write_input_tokens": 0, "output_tokens": 21299, "reasoning_output_tokens": 8019, "total_tokens": 2110998 } } }, { "kind": "context", "timestamp": "2026-09-24T23:15:01.700Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c976b789-2ccd-4d0b-8060-1f40d11bee0e", "process_id": "80055", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn_more.parquet && ls -lh /app/solution/validation_knn_more.parquet /app/solution/neighbors_train90_1600.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn_more.parquet && ls -lh /app/solution/validation_knn_more.parquet /app/solution/neighbors_train90_1600.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_knn_more.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.049731,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 74M Sep 24 23:06 /app/solution/neighbors_train90_1600.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:13 /app/solution/validation_knn_more.parquet\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_knn_more.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.049731,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 74M Sep 24 23:06 /app/solution/neighbors_train90_1600.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:13 /app/solution/validation_knn_more.parquet\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 359618409 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_knn_more.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.049731,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 74M Sep 24 23:06 /app/solution/neighbors_train90_1600.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:13 /app/solution/validation_knn_more.parquet\n" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-24T23:15:12.185Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:12.185Z", "text": "" } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-24T23:15:20.985Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:20.985Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:15:22.576Z", "text": "The best public score is now 0.049731, verified by the official evaluator. Its useful pattern is stable and interpretable: broad taste neighbors from 90-day positive history, followed by recent organic discoveries from those neighbors, with likes weighted more heavily. I’m refitting that exact architecture on train+validation now and writing the hidden-test submission before doing any remaining optional refinements." }, { "kind": "tool", "timestamp": "2026-09-24T23:16:00.489Z", "callId": "call_1cgMo1PpP77YtKfnYseRjqEn", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Refit the selected discovery ranker and build the hidden-test artifact.\\n+\\n+The model uses 90-day TF-IDF/cosine user neighborhoods, then recommends the\\n+catalog tracks those neighbors organically completed or explicitly liked in\\n+the most recent seven days. It only ranks cutoff-correct eligible candidates.\\n+\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.preprocessing import normalize\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+DAY = 86_400\\n+FINAL_CUTOFF = 25_393_400\\n+PROFILE_DAYS = 90\\n+ACTION_DAYS = 7\\n+N_NEIGHBORS = 1_600\\n+LIKE_WEIGHT = 4.0\\n+\\n+\\n+def binary_matrix(rows, cols, shape):\\n+ matrix = sparse.coo_matrix(\\n+ (np.ones(len(rows), dtype=np.float32), (rows, cols)), shape=shape\\n+ ).tocsr()\\n+ matrix.sum_duplicates()\\n+ matrix.data[:] = 1.0\\n+ return matrix\\n+\\n+\\n+def nearest_users(profile, query_rows, k):\\n+ indices = np.empty((len(query_rows), k), dtype=np.int32)\\n+ values = np.empty((len(query_rows), k), dtype=np.float32)\\n+ transpose = profile.T.tocsc()\\n+ for lo in range(0, len(query_rows), 256):\\n+ rows = query_rows[lo : lo + 256]\\n+ similarities = (profile[rows] @ transpose).toarray()\\n+ similarities[np.arange(len(rows)), rows] = -1.0\\n+ chosen = np.argpartition(similarities, -k, axis=1)[:, -k:]\\n+ chosen_values = np.take_along_axis(similarities, chosen, axis=1)\\n+ order = np.argsort(-chosen_values, axis=1)\\n+ indices[lo : lo + len(rows)] = np.take_along_axis(chosen, order, axis=1)\\n+ values[lo : lo + len(rows)] = np.take_along_axis(\\n+ chosen_values, order, axis=1\\n+ )\\n+ return indices, values\\n+\\n+\\n+def main():\\n+ OUT.mkdir(parents=True, exist_ok=True)\\n+ with np.load(DATA / \\\"test_eligible_candidates.npz\\\") as archive:\\n+ target_uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ catalog_items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ eligible = np.unpackbits(\\n+ archive[\\\"packed_eligible\\\"],\\n+ axis=1,\\n+ count=len(catalog_items),\\n+ bitorder=\\\"little\\\",\\n+ ).astype(bool)\\n+\\n+ listen_columns = [\\n+ \\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"\\n+ ]\\n+ listens = pd.concat(\\n+ [\\n+ pd.read_parquet(DATA / \\\"train_listens.parquet\\\", columns=listen_columns),\\n+ pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=listen_columns),\\n+ ],\\n+ ignore_index=True,\\n+ )\\n+ all_uids = np.sort(listens.uid.unique()).astype(np.int64)\\n+ uid_map = np.full(1_000_001, -1, dtype=np.int32)\\n+ uid_map[all_uids] = np.arange(len(all_uids), dtype=np.int32)\\n+ item_map = np.full(10_000_001, -1, dtype=np.int32)\\n+ item_map[catalog_items] = np.arange(len(catalog_items), dtype=np.int32)\\n+\\n+ user_rows = uid_map[listens.uid.to_numpy()]\\n+ timestamps = listens.timestamp.to_numpy()\\n+ ratios = listens.played_ratio_pct.to_numpy()\\n+ organic = listens.is_organic.to_numpy().astype(bool)\\n+ catalog_columns = item_map[listens.item_id.to_numpy()]\\n+\\n+ # Long enough to capture stable taste, while excluding stale interests.\\n+ profile_mask = (\\n+ (timestamps >= FINAL_CUTOFF - PROFILE_DAYS * DAY) & (ratios >= 50)\\n+ )\\n+ profile = binary_matrix(\\n+ user_rows[profile_mask],\\n+ listens.item_id.to_numpy()[profile_mask],\\n+ (len(all_uids), 10_000_001),\\n+ )\\n+ document_frequency = np.bincount(\\n+ profile.indices, minlength=profile.shape[1]\\n+ )\\n+ profile.data *= np.log(\\n+ (len(all_uids) + 1) / (document_frequency[profile.indices] + 1)\\n+ )\\n+ normalize(profile, norm=\\\"l2\\\", axis=1, copy=False)\\n+\\n+ query_rows = uid_map[target_uids]\\n+ neighbor_indices, neighbor_values = nearest_users(\\n+ profile, query_rows, N_NEIGHBORS\\n+ )\\n+ neighbor_values /= np.maximum(\\n+ neighbor_values.sum(axis=1, keepdims=True), 1e-9\\n+ )\\n+ neighbor_matrix = sparse.csr_matrix(\\n+ (\\n+ neighbor_values.ravel(),\\n+ (\\n+ np.repeat(np.arange(len(target_uids)), N_NEIGHBORS),\\n+ neighbor_indices.ravel(),\\n+ ),\\n+ ),\\n+ shape=(len(target_uids), len(all_uids)),\\n+ )\\n+\\n+ recent = timestamps >= FINAL_CUTOFF - ACTION_DAYS * DAY\\n+ organic_positive = (\\n+ recent & organic & (ratios >= 50) & (catalog_columns >= 0)\\n+ )\\n+ neighbor_actions = binary_matrix(\\n+ user_rows[organic_positive],\\n+ catalog_columns[organic_positive],\\n+ (len(all_uids), len(catalog_items)),\\n+ )\\n+\\n+ like_columns = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ likes = pd.concat(\\n+ [\\n+ pd.read_parquet(DATA / \\\"train_likes.parquet\\\", columns=like_columns),\\n+ pd.read_parquet(DATA / \\\"val_likes.parquet\\\", columns=like_columns),\\n+ ],\\n+ ignore_index=True,\\n+ )\\n+ like_rows = uid_map[likes.uid.to_numpy()]\\n+ like_catalog = item_map[likes.item_id.to_numpy()]\\n+ like_mask = (\\n+ (like_rows >= 0)\\n+ & (like_catalog >= 0)\\n+ & (likes.timestamp.to_numpy() >= FINAL_CUTOFF - ACTION_DAYS * DAY)\\n+ )\\n+ neighbor_likes = binary_matrix(\\n+ like_rows[like_mask],\\n+ like_catalog[like_mask],\\n+ (len(all_uids), len(catalog_items)),\\n+ )\\n+ neighbor_actions = neighbor_actions + LIKE_WEIGHT * neighbor_likes\\n+ predictions = (neighbor_matrix @ neighbor_actions).tocsr()\\n+\\n+ # Recent organic reach is used only as a deterministic fallback beyond the\\n+ # collaborative support (normally well outside the evaluated top ten).\\n+ recent_reach = np.asarray((neighbor_actions > 0).sum(axis=0)).ravel()\\n+ fallback = np.argsort(-recent_reach, kind=\\\"stable\\\")\\n+ output = []\\n+ for row, uid in enumerate(target_uids):\\n+ start, end = predictions.indptr[row], predictions.indptr[row + 1]\\n+ candidates = predictions.indices[start:end]\\n+ scores = predictions.data[start:end]\\n+ valid = eligible[row, candidates]\\n+ candidates, scores = candidates[valid], scores[valid]\\n+ if len(candidates) >= 100:\\n+ top = np.argpartition(scores, -100)[-100:]\\n+ top = top[np.argsort(-scores[top], kind=\\\"stable\\\")]\\n+ chosen = candidates[top]\\n+ else:\\n+ chosen = candidates[np.argsort(-scores, kind=\\\"stable\\\")]\\n+ fill = fallback[eligible[row, fallback]]\\n+ fill = fill[~np.isin(fill, chosen, assume_unique=False)]\\n+ chosen = np.concatenate([chosen, fill[: 100 - len(chosen)]])\\n+ output.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": int(uid),\\n+ \\\"item_id\\\": catalog_items[chosen],\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ submission = pd.concat(output, ignore_index=True)\\n+ submission.to_parquet(OUT / \\\"submission.parquet\\\", index=False)\\n+ print(\\n+ f\\\"wrote {len(submission):,} rows for \\\"\\n+ f\\\"{submission.uid.nunique():,} users\\\"\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:16:00.543Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:00.497Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a0edce26-7dc3-4370-aa5d-2a1c9ba50bd3", "changes": { "/app/solution/build_submission.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Refit the selected discovery ranker and build the hidden-test artifact.\n\nThe model uses 90-day TF-IDF/cosine user neighborhoods, then recommends the\ncatalog tracks those neighbors organically completed or explicitly liked in\nthe most recent seven days. It only ranks cutoff-correct eligible candidates.\n\"\"\"\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nDAY = 86_400\nFINAL_CUTOFF = 25_393_400\nPROFILE_DAYS = 90\nACTION_DAYS = 7\nN_NEIGHBORS = 1_600\nLIKE_WEIGHT = 4.0\n\n\ndef binary_matrix(rows, cols, shape):\n matrix = sparse.coo_matrix(\n (np.ones(len(rows), dtype=np.float32), (rows, cols)), shape=shape\n ).tocsr()\n matrix.sum_duplicates()\n matrix.data[:] = 1.0\n return matrix\n\n\ndef nearest_users(profile, query_rows, k):\n indices = np.empty((len(query_rows), k), dtype=np.int32)\n values = np.empty((len(query_rows), k), dtype=np.float32)\n transpose = profile.T.tocsc()\n for lo in range(0, len(query_rows), 256):\n rows = query_rows[lo : lo + 256]\n similarities = (profile[rows] @ transpose).toarray()\n similarities[np.arange(len(rows)), rows] = -1.0\n chosen = np.argpartition(similarities, -k, axis=1)[:, -k:]\n chosen_values = np.take_along_axis(similarities, chosen, axis=1)\n order = np.argsort(-chosen_values, axis=1)\n indices[lo : lo + len(rows)] = np.take_along_axis(chosen, order, axis=1)\n values[lo : lo + len(rows)] = np.take_along_axis(\n chosen_values, order, axis=1\n )\n return indices, values\n\n\ndef main():\n OUT.mkdir(parents=True, exist_ok=True)\n with np.load(DATA / \"test_eligible_candidates.npz\") as archive:\n target_uids = archive[\"uids\"].astype(np.int64)\n catalog_items = archive[\"item_ids\"].astype(np.int64)\n eligible = np.unpackbits(\n archive[\"packed_eligible\"],\n axis=1,\n count=len(catalog_items),\n bitorder=\"little\",\n ).astype(bool)\n\n listen_columns = [\n \"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"\n ]\n listens = pd.concat(\n [\n pd.read_parquet(DATA / \"train_listens.parquet\", columns=listen_columns),\n pd.read_parquet(DATA / \"val_listens.parquet\", columns=listen_columns),\n ],\n ignore_index=True,\n )\n all_uids = np.sort(listens.uid.unique()).astype(np.int64)\n uid_map = np.full(1_000_001, -1, dtype=np.int32)\n uid_map[all_uids] = np.arange(len(all_uids), dtype=np.int32)\n item_map = np.full(10_000_001, -1, dtype=np.int32)\n item_map[catalog_items] = np.arange(len(catalog_items), dtype=np.int32)\n\n user_rows = uid_map[listens.uid.to_numpy()]\n timestamps = listens.timestamp.to_numpy()\n ratios = listens.played_ratio_pct.to_numpy()\n organic = listens.is_organic.to_numpy().astype(bool)\n catalog_columns = item_map[listens.item_id.to_numpy()]\n\n # Long enough to capture stable taste, while excluding stale interests.\n profile_mask = (\n (timestamps >= FINAL_CUTOFF - PROFILE_DAYS * DAY) & (ratios >= 50)\n )\n profile = binary_matrix(\n user_rows[profile_mask],\n listens.item_id.to_numpy()[profile_mask],\n (len(all_uids), 10_000_001),\n )\n document_frequency = np.bincount(\n profile.indices, minlength=profile.shape[1]\n )\n profile.data *= np.log(\n (len(all_uids) + 1) / (document_frequency[profile.indices] + 1)\n )\n normalize(profile, norm=\"l2\", axis=1, copy=False)\n\n query_rows = uid_map[target_uids]\n neighbor_indices, neighbor_values = nearest_users(\n profile, query_rows, N_NEIGHBORS\n )\n neighbor_values /= np.maximum(\n neighbor_values.sum(axis=1, keepdims=True), 1e-9\n )\n neighbor_matrix = sparse.csr_matrix(\n (\n neighbor_values.ravel(),\n (\n np.repeat(np.arange(len(target_uids)), N_NEIGHBORS),\n neighbor_indices.ravel(),\n ),\n ),\n shape=(len(target_uids), len(all_uids)),\n )\n\n recent = timestamps >= FINAL_CUTOFF - ACTION_DAYS * DAY\n organic_positive = (\n recent & organic & (ratios >= 50) & (catalog_columns >= 0)\n )\n neighbor_actions = binary_matrix(\n user_rows[organic_positive],\n catalog_columns[organic_positive],\n (len(all_uids), len(catalog_items)),\n )\n\n like_columns = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n likes = pd.concat(\n [\n pd.read_parquet(DATA / \"train_likes.parquet\", columns=like_columns),\n pd.read_parquet(DATA / \"val_likes.parquet\", columns=like_columns),\n ],\n ignore_index=True,\n )\n like_rows = uid_map[likes.uid.to_numpy()]\n like_catalog = item_map[likes.item_id.to_numpy()]\n like_mask = (\n (like_rows >= 0)\n & (like_catalog >= 0)\n & (likes.timestamp.to_numpy() >= FINAL_CUTOFF - ACTION_DAYS * DAY)\n )\n neighbor_likes = binary_matrix(\n like_rows[like_mask],\n like_catalog[like_mask],\n (len(all_uids), len(catalog_items)),\n )\n neighbor_actions = neighbor_actions + LIKE_WEIGHT * neighbor_likes\n predictions = (neighbor_matrix @ neighbor_actions).tocsr()\n\n # Recent organic reach is used only as a deterministic fallback beyond the\n # collaborative support (normally well outside the evaluated top ten).\n recent_reach = np.asarray((neighbor_actions > 0).sum(axis=0)).ravel()\n fallback = np.argsort(-recent_reach, kind=\"stable\")\n output = []\n for row, uid in enumerate(target_uids):\n start, end = predictions.indptr[row], predictions.indptr[row + 1]\n candidates = predictions.indices[start:end]\n scores = predictions.data[start:end]\n valid = eligible[row, candidates]\n candidates, scores = candidates[valid], scores[valid]\n if len(candidates) >= 100:\n top = np.argpartition(scores, -100)[-100:]\n top = top[np.argsort(-scores[top], kind=\"stable\")]\n chosen = candidates[top]\n else:\n chosen = candidates[np.argsort(-scores, kind=\"stable\")]\n fill = fallback[eligible[row, fallback]]\n fill = fill[~np.isin(fill, chosen, assume_unique=False)]\n chosen = np.concatenate([chosen, fill[: 100 - len(chosen)]])\n output.append(\n pd.DataFrame(\n {\n \"uid\": int(uid),\n \"item_id\": catalog_items[chosen],\n \"rank\": np.arange(1, 101, dtype=np.int64),\n }\n )\n )\n submission = pd.concat(output, ignore_index=True)\n submission.to_parquet(OUT / \"submission.parquet\", index=False)\n print(\n f\"wrote {len(submission):,} rows for \"\n f\"{submission.uid.nunique():,} users\"\n )\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_submission.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:16:00.542Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5aef5e05087d2b758ce47ff67c819", "usage": { "input_tokens": 51623, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 3201, "reasoning_output_tokens": 919, "total_tokens": 54824 }, "turn_token_usage": { "input_tokens": 2141322, "cached_input_tokens": 2037376, "cache_write_input_tokens": 0, "output_tokens": 24500, "reasoning_output_tokens": 8938, "total_tokens": 2165822 }, "thread_token_usage": { "input_tokens": 2141322, "cached_input_tokens": 2037376, "cache_write_input_tokens": 0, "output_tokens": 24500, "reasoning_output_tokens": 8938, "total_tokens": 2165822 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-24T23:16:03.128Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:03.128Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:16:04.138Z", "callId": "call_USo3lE04MdYYFRfPmzP4DyQd", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:16:20.552Z", "content": "Script completed\nWall time 16.4 seconds\nOutput:\n\n{\"chunk_id\":\"ee1a03\",\"wall_time_seconds\":16.203377919,\"exit_code\":0,\"original_token_count\":9,\"output\":\"wrote 845,600 rows for 8,456 users\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:04.272Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5af30ce2c87d2a8ec50356ab81f4a", "usage": { "input_tokens": 54848, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 7, "total_tokens": 54920 }, "turn_token_usage": { "input_tokens": 2196170, "cached_input_tokens": 2088832, "cache_write_input_tokens": 0, "output_tokens": 24572, "reasoning_output_tokens": 8945, "total_tokens": 2220742 }, "thread_token_usage": { "input_tokens": 2196170, "cached_input_tokens": 2088832, "cache_write_input_tokens": 0, "output_tokens": 24572, "reasoning_output_tokens": 8945, "total_tokens": 2220742 } } }, { "kind": "context", "timestamp": "2026-09-24T23:16:20.548Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b0343e1c-209e-429b-98af-40f8e5c8c2d3", "process_id": "26211", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/build_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/build_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 rows for 8,456 users\n", "stderr": "", "aggregated_output": "wrote 845,600 rows for 8,456 users\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 203371948 }, "formatted_output": "wrote 845,600 rows for 8,456 users\n" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-24T23:16:23.833Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:23.833Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:16:29.672Z", "callId": "call_IWoUaseXJRlEEeCFg2Q5hIYs", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made for You discovery ranker\\n+\\n+`submission.parquet` is the final hidden-test ranking. It contains 100 eligible\\n+tracks for each of the 8,456 target users.\\n+\\n+The selected model was tuned strictly as a train-history to validation-outcome\\n+backtest. It represents each listener by positive listens from the previous 90\\n+days, applies track inverse-frequency weighting, and finds cosine-similar\\n+listeners. Candidate scores aggregate the 1,600 nearest listeners' organic\\n+positive listens from the prior seven days, with explicit likes weighted 4x.\\n+The public validation ranking scored **0.049731 NDCG@10** with the supplied\\n+evaluator (versus 0.010710 for catalog popularity and 0.034854 for the tuned\\n+temporal-popularity model).\\n+\\n+For the deliverable, `build_submission.py` refits the same architecture on\\n+`train_* + val_*`, uses `test_eligible_candidates.npz`, and writes\\n+`submission.parquet`. Run it with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_submission.py\\n+```\\n+\\n+The experimental validation scripts and best validation artifacts are retained\\n+for reproducibility. No validation-window event is used as a validation-stage\\n+feature.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:16:29.772Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:29.680Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-0fdb15a1-b8a1-4aa7-b456-4be0b92ca9f8", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You discovery ranker\n\n`submission.parquet` is the final hidden-test ranking. It contains 100 eligible\ntracks for each of the 8,456 target users.\n\nThe selected model was tuned strictly as a train-history to validation-outcome\nbacktest. It represents each listener by positive listens from the previous 90\ndays, applies track inverse-frequency weighting, and finds cosine-similar\nlisteners. Candidate scores aggregate the 1,600 nearest listeners' organic\npositive listens from the prior seven days, with explicit likes weighted 4x.\nThe public validation ranking scored **0.049731 NDCG@10** with the supplied\nevaluator (versus 0.010710 for catalog popularity and 0.034854 for the tuned\ntemporal-popularity model).\n\nFor the deliverable, `build_submission.py` refits the same architecture on\n`train_* + val_*`, uses `test_eligible_candidates.npz`, and writes\n`submission.parquet`. Run it with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_submission.py\n```\n\nThe experimental validation scripts and best validation artifacts are retained\nfor reproducibility. No validation-window event is used as a validation-stage\nfeature.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:16:29.771Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5af44b77087d28a82301d5a09eb91", "usage": { "input_tokens": 54990, "cached_input_tokens": 54656, "cache_write_input_tokens": 0, "output_tokens": 405, "reasoning_output_tokens": 70, "total_tokens": 55395 }, "turn_token_usage": { "input_tokens": 2251160, "cached_input_tokens": 2143488, "cache_write_input_tokens": 0, "output_tokens": 24977, "reasoning_output_tokens": 9015, "total_tokens": 2276137 }, "thread_token_usage": { "input_tokens": 2251160, "cached_input_tokens": 2143488, "cache_write_input_tokens": 0, "output_tokens": 24977, "reasoning_output_tokens": 9015, "total_tokens": 2276137 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:16:35.428Z", "callId": "call_dBqReFqFsLYeTJRPqhLDw0yf", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport sys,numpy as np,pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking,CandidateSet\\np=Path('/app/solution/submission.parquet');d=pd.read_parquet(p)\\nt=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid))\\nx=validate_ranking(d,t)\\nc=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\\nbad=sum(not c.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'duplicates':int(x.duplicated(['uid','item_id']).sum()),'size_bytes':p.stat().st_size})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:16:37.342Z", "content": "Script completed\nWall time 1.9 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'size_bytes': 1525007}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:35.505Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5af4dfb3c87d2b1f697569737cc13", "usage": { "input_tokens": 55419, "cached_input_tokens": 54784, "cache_write_input_tokens": 0, "output_tokens": 244, "reasoning_output_tokens": 0, "total_tokens": 55663 }, "turn_token_usage": { "input_tokens": 2306579, "cached_input_tokens": 2198272, "cache_write_input_tokens": 0, "output_tokens": 25221, "reasoning_output_tokens": 9015, "total_tokens": 2331800 }, "thread_token_usage": { "input_tokens": 2306579, "cached_input_tokens": 2198272, "cache_write_input_tokens": 0, "output_tokens": 25221, "reasoning_output_tokens": 9015, "total_tokens": 2331800 } } }, { "kind": "context", "timestamp": "2026-09-24T23:16:37.338Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-455c50d0-6e2d-4031-a6cd-0b8e9008f6d1", "process_id": "40878", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,CandidateSet\np=Path('/app/solution/submission.parquet');d=pd.read_parquet(p)\nt=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid))\nx=validate_ranking(d,t)\nc=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nbad=sum(not c.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'duplicates':int(x.duplicated(['uid','item_id']).sum()),'size_bytes':p.stat().st_size})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,CandidateSet\np=Path('/app/solution/submission.parquet');d=pd.read_parquet(p)\nt=set(map(int,pd.read_parquet('/task/data/target_users.parquet').uid))\nx=validate_ranking(d,t)\nc=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nbad=sum(not c.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'duplicates':int(x.duplicated(['uid','item_id']).sum()),'size_bytes':p.stat().st_size})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'size_bytes': 1525007}\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'size_bytes': 1525007}\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 747898697 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'size_bytes': 1525007}\n" } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-24T23:16:50.738Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:50.738Z", "text": "" } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-24T23:16:57.035Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:57.035Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:16:58.285Z", "text": "The refit is complete and the submission has passed the full structural and per-user eligibility audit: 845,600 rows, 8,456 users, no duplicates, and zero ineligible tracks. With that safe artifact preserved, I’m using the remaining time for a focused sensitivity check on profile horizon and organic-vs-recommended history; I’ll only replace the final model if the exact public backtest improves." }, { "kind": "tool", "timestamp": "2026-09-24T23:17:22.525Z", "callId": "call_kOxHGD11BIebcilChuA90ALZ", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_profiles.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Focused sensitivity test for the neighbor-profile construction.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import gc,time\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.preprocessing import normalize\\n+\\n+D=Path('/task/data');O=Path('/app/solution');CUT=24786800;DAY=86400;K=1600\\n+\\n+def bm(r,c,shape):\\n+ x=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=shape).tocsr();x.sum_duplicates();x.data[:]=1;return x\\n+def neigh(x,q):\\n+ ii=np.empty((len(q),K),np.int32);vv=np.empty((len(q),K),np.float32);xt=x.T.tocsc()\\n+ for lo in range(0,len(q),256):\\n+ qr=q[lo:lo+256];a=(x[qr]@xt).toarray();a[np.arange(len(qr)),qr]=-1;z=np.argpartition(a,-K,axis=1)[:,-K:];v=np.take_along_axis(a,z,axis=1);o=np.argsort(-v,axis=1);ii[lo:lo+len(qr)]=np.take_along_axis(z,o,axis=1);vv[lo:lo+len(qr)]=np.take_along_axis(v,o,axis=1)\\n+ return ii,vv\\n+def gold(U,I,E):\\n+ ur={int(x):i for i,x in enumerate(U)};im=np.full(10000001,-1,np.int32);im[I]=np.arange(len(I));g={}\\n+ for grade,fn in [(1,'val_listens.parquet'),(3,'val_likes.parquet')]:\\n+ x=pd.read_parquet(D/fn);x=x[(x.is_organic==1)&((x.played_ratio_pct>=80) if grade==1 else True)]\\n+ for u,it in x[['uid','item_id']].drop_duplicates().itertuples(index=False,name=None):\\n+ r=ur.get(int(u));c=im[int(it)]\\n+ if r is not None and c>=0 and E[r,c]:g.setdefault(int(u),{})[int(c)]=grade\\n+ return g,ur,im\\n+def evalp(p,UR,E,G):\\n+ ds=1/np.log2(np.arange(2,12));vals=[]\\n+ for u,g in G.items():\\n+ r=UR[u];st,en=p.indptr[r],p.indptr[r+1];ci=p.indices[st:en];sc=p.data[st:en];v=E[r,ci];ci=ci[v];sc=sc[v]\\n+ z=np.argpartition(sc,-10)[-10:];z=z[np.argsort(-sc[z])];gr=np.array([g.get(int(ci[j]),0) for j in z]);ideal=np.sort(list(g.values()))[::-1][:10];vals.append(((2**gr-1)*ds).sum()/(((2**np.array(ideal)-1)*ds[:len(ideal)]).sum()))\\n+ return np.mean(vals)\\n+\\n+t=time.time()\\n+with np.load(D/'validation_eligible_candidates.npz') as z:\\n+ U=z['uids'].astype(np.int64);I=z['item_ids'].astype(np.int64);E=np.unpackbits(z['packed_eligible'],axis=1,count=len(I),bitorder='little').astype(bool)\\n+G,UR,IM=gold(U,I,E)\\n+x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);AU=np.sort(x.uid.unique()).astype(np.int64);UM=np.full(1000001,-1,np.int32);UM[AU]=np.arange(len(AU));Q=UM[U]\\n+r=UM[x.uid.to_numpy()];ts=x.timestamp.to_numpy();item=x.item_id.to_numpy();ratio=x.played_ratio_pct.to_numpy();org=x.is_organic.to_numpy().astype(bool);ci=IM[item]\\n+m=(ci>=0)&(ts>=CUT-7*DAY)&org&(ratio>=50);B=bm(r[m],ci[m],(len(AU),len(I)))\\nl=pd.read_parquet(D/'train_likes.parquet');lc=IM[l.item_id.to_numpy()];lr=UM[l.uid.to_numpy()];m=(lr>=0)&(lc>=0)&(l.timestamp.to_numpy()>=CUT-7*DAY);B=B+4*bm(lr[m],lc[m],(len(AU),len(I)));del l\\n+variants=[('all30',30,50,None),('all60',60,50,None),('all180',180,50,None),('all_full',10000,50,None),('organic90',90,50,True),('organic180',180,50,True),('strong90',90,80,None),('strong180',180,80,None)]\\n+best=(.049730724,'all90')\\n+for name,days,thr,onlyorg in variants:\\n+ m=(ts>=CUT-days*DAY)&(ratio>=thr)\\n+ if onlyorg:m&=org\\n+ X=bm(r[m],item[m],(len(AU),10000001));df=np.bincount(X.indices,minlength=10000001);X.data*=np.log((len(AU)+1)/(df[X.indices]+1));normalize(X,copy=False)\\n+ ni,nv=neigh(X,Q);nv/=np.maximum(nv.sum(1,keepdims=True),1e-9);S=sparse.csr_matrix((nv.ravel(),(np.repeat(np.arange(len(U)),K),ni.ravel())),shape=(len(U),len(AU)));P=(S@B).tocsr();score=evalp(P,UR,E,G);print(name,score,'nnz',X.nnz,'elapsed',time.time()-t,flush=True)\\n+ if score>best[0]:best=(score,name);np.savez_compressed(O/'neighbors_best_profile.npz',indices=ni,values=nv,all_uids=AU,name=name)\\n+ del X,df,ni,nv,S,P;gc.collect()\\n+print('BEST',best)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:17:22.645Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\napply_patch verification failed: invalid hunk at line 43, 'l=pd.read_parquet(D/'train_likes.parquet');lc=IM[l.item_id.to_numpy()];lr=UM[l.uid.to_numpy()];m=(lr>=0)&(lc>=0)&(l.timestamp.to_numpy()>=CUT-7*DAY);B=B+4*bm(lr[m],lc[m],(len(AU),len(I)));del l' is not a valid hunk header. Valid hunk headers: '*** Add File: {path}', '*** Delete File: {path}', '*** Update File: {path}'", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:17:22.644Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5af5585bc87d2a5d28e60fcaf3dee", "usage": { "input_tokens": 55724, "cached_input_tokens": 55296, "cache_write_input_tokens": 0, "output_tokens": 2372, "reasoning_output_tokens": 834, "total_tokens": 58096 }, "turn_token_usage": { "input_tokens": 2362303, "cached_input_tokens": 2253568, "cache_write_input_tokens": 0, "output_tokens": 27593, "reasoning_output_tokens": 9849, "total_tokens": 2389896 }, "thread_token_usage": { "input_tokens": 2362303, "cached_input_tokens": 2253568, "cache_write_input_tokens": 0, "output_tokens": 27593, "reasoning_output_tokens": 9849, "total_tokens": 2389896 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-24T23:17:26.308Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:26.308Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:02.413Z", "callId": "call_Skt0s7oxi4CmHungjPVGwCgs", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_profiles.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Focused sensitivity test for the neighbor-profile construction.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import gc\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.preprocessing import normalize\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+CUT = 24_786_800\\n+DAY = 86_400\\n+K = 1_600\\n+\\n+\\n+def bm(rows, cols, shape):\\n+ x = sparse.coo_matrix(\\n+ (np.ones(len(rows), np.float32), (rows, cols)), shape=shape\\n+ ).tocsr()\\n+ x.sum_duplicates()\\n+ x.data[:] = 1\\n+ return x\\n+\\n+\\n+def neigh(x, query):\\n+ ii = np.empty((len(query), K), np.int32)\\n+ vv = np.empty((len(query), K), np.float32)\\n+ xt = x.T.tocsc()\\n+ for lo in range(0, len(query), 256):\\n+ qr = query[lo : lo + 256]\\n+ a = (x[qr] @ xt).toarray()\\n+ a[np.arange(len(qr)), qr] = -1\\n+ z = np.argpartition(a, -K, axis=1)[:, -K:]\\n+ v = np.take_along_axis(a, z, axis=1)\\n+ order = np.argsort(-v, axis=1)\\n+ ii[lo : lo + len(qr)] = np.take_along_axis(z, order, axis=1)\\n+ vv[lo : lo + len(qr)] = np.take_along_axis(v, order, axis=1)\\n+ return ii, vv\\n+\\n+\\n+def gold(uids, items, eligible):\\n+ ur = {int(x): i for i, x in enumerate(uids)}\\n+ im = np.full(10_000_001, -1, np.int32)\\n+ im[items] = np.arange(len(items))\\n+ result = {}\\n+ for grade, fn in [(1, \\\"val_listens.parquet\\\"), (3, \\\"val_likes.parquet\\\")]:\\n+ x = pd.read_parquet(D / fn)\\n+ keep = x.is_organic == 1\\n+ if grade == 1:\\n+ keep &= x.played_ratio_pct >= 80\\n+ x = x[keep]\\n+ for uid, item in x[[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().itertuples(\\n+ index=False, name=None\\n+ ):\\n+ row = ur.get(int(uid))\\n+ col = im[int(item)]\\n+ if row is not None and col >= 0 and eligible[row, col]:\\n+ result.setdefault(int(uid), {})[int(col)] = grade\\n+ return result, ur, im\\n+\\n+\\n+def evalp(prediction, user_rows, eligible, labels):\\n+ discounts = 1 / np.log2(np.arange(2, 12))\\n+ values = []\\n+ for uid, truth in labels.items():\\n+ row = user_rows[uid]\\n+ start, end = prediction.indptr[row], prediction.indptr[row + 1]\\n+ candidates = prediction.indices[start:end]\\n+ scores = prediction.data[start:end]\\n+ valid = eligible[row, candidates]\\n+ candidates, scores = candidates[valid], scores[valid]\\n+ top = np.argpartition(scores, -10)[-10:]\\n+ top = top[np.argsort(-scores[top])]\\n+ grades = np.array([truth.get(int(candidates[j]), 0) for j in top])\\n+ ideal = np.sort(list(truth.values()))[::-1][:10]\\n+ idcg = ((2 ** np.array(ideal) - 1) * discounts[: len(ideal)]).sum()\\n+ values.append(((2**grades - 1) * discounts).sum() / idcg)\\n+ return np.mean(values)\\n+\\n+\\n+t = time.time()\\n+with np.load(D / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ U = z[\\\"uids\\\"].astype(np.int64)\\n+ I = z[\\\"item_ids\\\"].astype(np.int64)\\n+ E = np.unpackbits(\\n+ z[\\\"packed_eligible\\\"], axis=1, count=len(I), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+G, UR, IM = gold(U, I, E)\\n+x = pd.read_parquet(\\n+ D / \\\"train_listens.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+)\\n+AU = np.sort(x.uid.unique()).astype(np.int64)\\n+UM = np.full(1_000_001, -1, np.int32)\\n+UM[AU] = np.arange(len(AU))\\n+Q = UM[U]\\n+r = UM[x.uid.to_numpy()]\\n+ts = x.timestamp.to_numpy()\\n+item = x.item_id.to_numpy()\\n+ratio = x.played_ratio_pct.to_numpy()\\n+org = x.is_organic.to_numpy().astype(bool)\\n+ci = IM[item]\\n+m = (ci >= 0) & (ts >= CUT - 7 * DAY) & org & (ratio >= 50)\\n+B = bm(r[m], ci[m], (len(AU), len(I)))\\n+likes = pd.read_parquet(D / \\\"train_likes.parquet\\\")\\n+lc = IM[likes.item_id.to_numpy()]\\n+lr = UM[likes.uid.to_numpy()]\\n+m = (\\n+ (lr >= 0)\\n+ & (lc >= 0)\\n+ & (likes.timestamp.to_numpy() >= CUT - 7 * DAY)\\n+)\\n+B = B + 4 * bm(lr[m], lc[m], (len(AU), len(I)))\\n+del likes\\n+\\n+variants = [\\n+ (\\\"all30\\\", 30, 50, False),\\n+ (\\\"all60\\\", 60, 50, False),\\n+ (\\\"all180\\\", 180, 50, False),\\n+ (\\\"all_full\\\", 10_000, 50, False),\\n+ (\\\"organic90\\\", 90, 50, True),\\n+ (\\\"organic180\\\", 180, 50, True),\\n+ (\\\"strong90\\\", 90, 80, False),\\n+ (\\\"strong180\\\", 180, 80, False),\\n+]\\n+best = (0.049730724, \\\"all90\\\")\\n+for name, days, threshold, only_organic in variants:\\n+ m = (ts >= CUT - days * DAY) & (ratio >= threshold)\\n+ if only_organic:\\n+ m &= org\\n+ profile = bm(r[m], item[m], (len(AU), 10_000_001))\\n+ df = np.bincount(profile.indices, minlength=10_000_001)\\n+ profile.data *= np.log((len(AU) + 1) / (df[profile.indices] + 1))\\n+ normalize(profile, copy=False)\\n+ ni, nv = neigh(profile, Q)\\n+ nv /= np.maximum(nv.sum(1, keepdims=True), 1e-9)\\n+ sim = sparse.csr_matrix(\\n+ (\\n+ nv.ravel(),\\n+ (np.repeat(np.arange(len(U)), K), ni.ravel()),\\n+ ),\\n+ shape=(len(U), len(AU)),\\n+ )\\n+ pred = (sim @ B).tocsr()\\n+ score = evalp(pred, UR, E, G)\\n+ print(name, score, \\\"nnz\\\", profile.nnz, \\\"elapsed\\\", time.time() - t, flush=True)\\n+ if score > best[0]:\\n+ best = (score, name)\\n+ np.savez_compressed(\\n+ O / \\\"neighbors_best_profile.npz\\\",\\n+ indices=ni,\\n+ values=nv,\\n+ all_uids=AU,\\n+ name=name,\\n+ )\\n+ del profile, df, ni, nv, sim, pred\\n+ gc.collect()\\n+print(\\\"BEST\\\", best)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:18:02.483Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:02.421Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f55f533e-165d-4b95-8503-adae4c002457", "changes": { "/app/solution/tune_profiles.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Focused sensitivity test for the neighbor-profile construction.\"\"\"\nfrom pathlib import Path\nimport gc\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\nD = Path(\"/task/data\")\nO = Path(\"/app/solution\")\nCUT = 24_786_800\nDAY = 86_400\nK = 1_600\n\n\ndef bm(rows, cols, shape):\n x = sparse.coo_matrix(\n (np.ones(len(rows), np.float32), (rows, cols)), shape=shape\n ).tocsr()\n x.sum_duplicates()\n x.data[:] = 1\n return x\n\n\ndef neigh(x, query):\n ii = np.empty((len(query), K), np.int32)\n vv = np.empty((len(query), K), np.float32)\n xt = x.T.tocsc()\n for lo in range(0, len(query), 256):\n qr = query[lo : lo + 256]\n a = (x[qr] @ xt).toarray()\n a[np.arange(len(qr)), qr] = -1\n z = np.argpartition(a, -K, axis=1)[:, -K:]\n v = np.take_along_axis(a, z, axis=1)\n order = np.argsort(-v, axis=1)\n ii[lo : lo + len(qr)] = np.take_along_axis(z, order, axis=1)\n vv[lo : lo + len(qr)] = np.take_along_axis(v, order, axis=1)\n return ii, vv\n\n\ndef gold(uids, items, eligible):\n ur = {int(x): i for i, x in enumerate(uids)}\n im = np.full(10_000_001, -1, np.int32)\n im[items] = np.arange(len(items))\n result = {}\n for grade, fn in [(1, \"val_listens.parquet\"), (3, \"val_likes.parquet\")]:\n x = pd.read_parquet(D / fn)\n keep = x.is_organic == 1\n if grade == 1:\n keep &= x.played_ratio_pct >= 80\n x = x[keep]\n for uid, item in x[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(\n index=False, name=None\n ):\n row = ur.get(int(uid))\n col = im[int(item)]\n if row is not None and col >= 0 and eligible[row, col]:\n result.setdefault(int(uid), {})[int(col)] = grade\n return result, ur, im\n\n\ndef evalp(prediction, user_rows, eligible, labels):\n discounts = 1 / np.log2(np.arange(2, 12))\n values = []\n for uid, truth in labels.items():\n row = user_rows[uid]\n start, end = prediction.indptr[row], prediction.indptr[row + 1]\n candidates = prediction.indices[start:end]\n scores = prediction.data[start:end]\n valid = eligible[row, candidates]\n candidates, scores = candidates[valid], scores[valid]\n top = np.argpartition(scores, -10)[-10:]\n top = top[np.argsort(-scores[top])]\n grades = np.array([truth.get(int(candidates[j]), 0) for j in top])\n ideal = np.sort(list(truth.values()))[::-1][:10]\n idcg = ((2 ** np.array(ideal) - 1) * discounts[: len(ideal)]).sum()\n values.append(((2**grades - 1) * discounts).sum() / idcg)\n return np.mean(values)\n\n\nt = time.time()\nwith np.load(D / \"validation_eligible_candidates.npz\") as z:\n U = z[\"uids\"].astype(np.int64)\n I = z[\"item_ids\"].astype(np.int64)\n E = np.unpackbits(\n z[\"packed_eligible\"], axis=1, count=len(I), bitorder=\"little\"\n ).astype(bool)\nG, UR, IM = gold(U, I, E)\nx = pd.read_parquet(\n D / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n)\nAU = np.sort(x.uid.unique()).astype(np.int64)\nUM = np.full(1_000_001, -1, np.int32)\nUM[AU] = np.arange(len(AU))\nQ = UM[U]\nr = UM[x.uid.to_numpy()]\nts = x.timestamp.to_numpy()\nitem = x.item_id.to_numpy()\nratio = x.played_ratio_pct.to_numpy()\norg = x.is_organic.to_numpy().astype(bool)\nci = IM[item]\nm = (ci >= 0) & (ts >= CUT - 7 * DAY) & org & (ratio >= 50)\nB = bm(r[m], ci[m], (len(AU), len(I)))\nlikes = pd.read_parquet(D / \"train_likes.parquet\")\nlc = IM[likes.item_id.to_numpy()]\nlr = UM[likes.uid.to_numpy()]\nm = (\n (lr >= 0)\n & (lc >= 0)\n & (likes.timestamp.to_numpy() >= CUT - 7 * DAY)\n)\nB = B + 4 * bm(lr[m], lc[m], (len(AU), len(I)))\ndel likes\n\nvariants = [\n (\"all30\", 30, 50, False),\n (\"all60\", 60, 50, False),\n (\"all180\", 180, 50, False),\n (\"all_full\", 10_000, 50, False),\n (\"organic90\", 90, 50, True),\n (\"organic180\", 180, 50, True),\n (\"strong90\", 90, 80, False),\n (\"strong180\", 180, 80, False),\n]\nbest = (0.049730724, \"all90\")\nfor name, days, threshold, only_organic in variants:\n m = (ts >= CUT - days * DAY) & (ratio >= threshold)\n if only_organic:\n m &= org\n profile = bm(r[m], item[m], (len(AU), 10_000_001))\n df = np.bincount(profile.indices, minlength=10_000_001)\n profile.data *= np.log((len(AU) + 1) / (df[profile.indices] + 1))\n normalize(profile, copy=False)\n ni, nv = neigh(profile, Q)\n nv /= np.maximum(nv.sum(1, keepdims=True), 1e-9)\n sim = sparse.csr_matrix(\n (\n nv.ravel(),\n (np.repeat(np.arange(len(U)), K), ni.ravel()),\n ),\n shape=(len(U), len(AU)),\n )\n pred = (sim @ B).tocsr()\n score = evalp(pred, UR, E, G)\n print(name, score, \"nnz\", profile.nnz, \"elapsed\", time.time() - t, flush=True)\n if score > best[0]:\n best = (score, name)\n np.savez_compressed(\n O / \"neighbors_best_profile.npz\",\n indices=ni,\n values=nv,\n all_uids=AU,\n name=name,\n )\n del profile, df, ni, nv, sim, pred\n gc.collect()\nprint(\"BEST\", best)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_profiles.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:18:02.482Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5af82f13087d2b9b28b7162176b4e", "usage": { "input_tokens": 58253, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 2034, "reasoning_output_tokens": 27, "total_tokens": 60287 }, "turn_token_usage": { "input_tokens": 2420556, "cached_input_tokens": 2309120, "cache_write_input_tokens": 0, "output_tokens": 29627, "reasoning_output_tokens": 9876, "total_tokens": 2450183 }, "thread_token_usage": { "input_tokens": 2420556, "cached_input_tokens": 2309120, "cache_write_input_tokens": 0, "output_tokens": 29627, "reasoning_output_tokens": 9876, "total_tokens": 2450183 } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-24T23:18:05.622Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:05.622Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:06.663Z", "callId": "call_cndmAAx29yUHdkuoMtY204CI", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_profiles.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:18:17.428Z", "content": "Script completed\nWall time 10.8 seconds\nOutput:\n\n{\"chunk_id\":\"783919\",\"wall_time_seconds\":10.580102867,\"exit_code\":1,\"original_token_count\":103,\"output\":\"Traceback (most recent call last):\\n File \\\"/app/solution/tune_profiles.py\\\", line 148, in \\n score = evalp(pred, UR, E, G)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/tune_profiles.py\\\", line 80, in evalp\\n values.append(((2**grades - 1) * discounts).sum() / idcg)\\n ~~~~~~~~~~~~~~~~^~~~~~~~~~~\\nValueError: operands could not be broadcast together with shapes (0,) (10,) \\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:06.790Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5afaac41c87d2bf7aa1b422c312d3", "usage": { "input_tokens": 60311, "cached_input_tokens": 58112, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 17, "total_tokens": 60394 }, "turn_token_usage": { "input_tokens": 2480867, "cached_input_tokens": 2367232, "cache_write_input_tokens": 0, "output_tokens": 29710, "reasoning_output_tokens": 9893, "total_tokens": 2510577 }, "thread_token_usage": { "input_tokens": 2480867, "cached_input_tokens": 2367232, "cache_write_input_tokens": 0, "output_tokens": 29710, "reasoning_output_tokens": 9893, "total_tokens": 2510577 } } }, { "kind": "context", "timestamp": "2026-09-24T23:18:17.424Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-48f414e1-139f-45ea-8a9f-543a407590a1", "process_id": "55572", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/tune_profiles.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/tune_profiles.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/tune_profiles.py\", line 148, in \n score = evalp(pred, UR, E, G)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_profiles.py\", line 80, in evalp\n values.append(((2**grades - 1) * discounts).sum() / idcg)\n ~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: operands could not be broadcast together with shapes (0,) (10,) \n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/tune_profiles.py\", line 148, in \n score = evalp(pred, UR, E, G)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_profiles.py\", line 80, in evalp\n values.append(((2**grades - 1) * discounts).sum() / idcg)\n ~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: operands could not be broadcast together with shapes (0,) (10,) \n", "exit_code": 1, "duration": { "secs": 10, "nanos": 580100157 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/tune_profiles.py\", line 148, in \n score = evalp(pred, UR, E, G)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/tune_profiles.py\", line 80, in evalp\n values.append(((2**grades - 1) * discounts).sum() / idcg)\n ~~~~~~~~~~~~~~~~^~~~~~~~~~~\nValueError: operands could not be broadcast together with shapes (0,) (10,) \n" } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-24T23:18:22.355Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:22.355Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:25.417Z", "callId": "call_2nbcRP8YqGqxaSaflm9CylSP", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_profiles.py\\n@@\\n- top = np.argpartition(scores, -10)[-10:]\\n- top = top[np.argsort(-scores[top])]\\n+ take = min(10, len(scores))\\n+ if take:\\n+ top = np.argpartition(scores, -take)[-take:]\\n+ top = top[np.argsort(-scores[top])]\\n+ else:\\n+ top = np.empty(0, dtype=np.int64)\\n@@\\n- values.append(((2**grades - 1) * discounts).sum() / idcg)\\n+ values.append(((2**grades - 1) * discounts[:take]).sum() / idcg)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:18:25.483Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:25.426Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-518ee210-3b50-44fb-993a-c885777196f7", "changes": { "/app/solution/tune_profiles.py": { "type": "update", "unified_diff": "@@ -74,4 +74,8 @@\n candidates, scores = candidates[valid], scores[valid]\n- top = np.argpartition(scores, -10)[-10:]\n- top = top[np.argsort(-scores[top])]\n+ take = min(10, len(scores))\n+ if take:\n+ top = np.argpartition(scores, -take)[-take:]\n+ top = top[np.argsort(-scores[top])]\n+ else:\n+ top = np.empty(0, dtype=np.int64)\n grades = np.array([truth.get(int(candidates[j]), 0) for j in top])\n@@ -79,3 +83,3 @@\n idcg = ((2 ** np.array(ideal) - 1) * discounts[: len(ideal)]).sum()\n- values.append(((2**grades - 1) * discounts).sum() / idcg)\n+ values.append(((2**grades - 1) * discounts[:take]).sum() / idcg)\n return np.mean(values)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_profiles.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:18:25.482Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5afb9a27887d28e500b81e21f4f6e", "usage": { "input_tokens": 60570, "cached_input_tokens": 60160, "cache_write_input_tokens": 0, "output_tokens": 364, "reasoning_output_tokens": 167, "total_tokens": 60934 }, "turn_token_usage": { "input_tokens": 2541437, "cached_input_tokens": 2427392, "cache_write_input_tokens": 0, "output_tokens": 30074, "reasoning_output_tokens": 10060, "total_tokens": 2571511 }, "thread_token_usage": { "input_tokens": 2541437, "cached_input_tokens": 2427392, "cache_write_input_tokens": 0, "output_tokens": 30074, "reasoning_output_tokens": 10060, "total_tokens": 2571511 } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-24T23:18:26.772Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:26.772Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:29.900Z", "callId": "call_VXlDCWt2ZM6Vl5qcFmIEBGMW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_profiles.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:19:00.066Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"9837eb\",\"wall_time_seconds\":30.001153088,\"session_id\":24699,\"original_token_count\":32,\"output\":\"all30 0.047774936661974 nnz 2152142 elapsed 9.829466342926025\\nall60 0.04940383164461992 nnz 3448914 elapsed 19.990835905075073\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:30.006Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5afc1bf0087d28fd9b70490c0471a", "usage": { "input_tokens": 60958, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 7, "total_tokens": 61031 }, "turn_token_usage": { "input_tokens": 2602395, "cached_input_tokens": 2487808, "cache_write_input_tokens": 0, "output_tokens": 30147, "reasoning_output_tokens": 10067, "total_tokens": 2632542 }, "thread_token_usage": { "input_tokens": 2602395, "cached_input_tokens": 2487808, "cache_write_input_tokens": 0, "output_tokens": 30147, "reasoning_output_tokens": 10067, "total_tokens": 2632542 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-24T23:19:02.527Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:19:02.527Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:19:03.236Z", "callId": "call_AxDX5AKMSprJHdS1RHUe1Sos", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":24699,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-24T23:19:33.245Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"aed648\",\"wall_time_seconds\":30.000998942,\"session_id\":24699,\"original_token_count\":34,\"output\":\"all180 0.04947102690348059 nnz 6501529 elapsed 35.579099893569946\\nall_full 0.04886827249138639 nnz 8471966 elapsed 54.858768701553345\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:19:03.372Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5afe440ac87d2a8451dd6ffa41918", "usage": { "input_tokens": 61140, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 11, "total_tokens": 61199 }, "turn_token_usage": { "input_tokens": 2663535, "cached_input_tokens": 2548608, "cache_write_input_tokens": 0, "output_tokens": 30206, "reasoning_output_tokens": 10078, "total_tokens": 2693741 }, "thread_token_usage": { "input_tokens": 2663535, "cached_input_tokens": 2548608, "cache_write_input_tokens": 0, "output_tokens": 30206, "reasoning_output_tokens": 10078, "total_tokens": 2693741 } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-24T23:19:35.156Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:19:35.156Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:19:35.883Z", "callId": "call_rzqHPuGOLp8H4WlBhZGdJPTb", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":24699,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r));\n" }, "result": null }, { "kind": "context", "timestamp": "2026-09-24T23:19:36.004Z", "data": { "type": "token_usage_record", "thread_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "session_id": "01a0d59c-2da1-7c53-a992-58ab0686146d", "root_turn_id": "01a0d59c-2db0-7923-94ee-1266e8ea733a", "response_id": "resp_00b3a4d175c564b0016ab5b0057b2487d2801ed072f6c54a59", "usage": { "input_tokens": 61308, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 61363 }, "turn_token_usage": { "input_tokens": 2724843, "cached_input_tokens": 2609536, "cache_write_input_tokens": 0, "output_tokens": 30261, "reasoning_output_tokens": 10085, "total_tokens": 2755104 }, "thread_token_usage": { "input_tokens": 2724843, "cached_input_tokens": 2609536, "cache_write_input_tokens": 0, "output_tokens": 30261, "reasoning_output_tokens": 10085, "total_tokens": 2755104 } } } ] } ] }