{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "sol-max-0-153-4-basic-r01-20260924T221413Z-retry01", "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f", "source": { "format": "codex-rollout", "sha256": "0ee9e51ddafddb4c84f741b397dedb4d2081bb359fcb1328fae3676de5d44817", "records": 720, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 111, "item_completed:AgentMessage": 5, "token_count": 105 } }, "sessionId": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-24T22:31:55.637Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:31:55.637Z", "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": "2026-09-24T22:31:57.143Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:31:57.143Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-24T22:31:57.143Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-24T22:31:57.143Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-24T22:31:57.143Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:31:57.143Z", "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": "2026-09-24T22:31:57.143Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:31:57.143Z", "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": "2026-09-24T22:31:57.143Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:31:57.143Z", "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": "2026-09-24T22:31:57.143Z", "text": "\n /app\n sh\n 2026-09-24\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-24T22:31:57.145Z", "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": "2026-09-24T22:31:57.146Z", "data": { "type": "turn_context", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "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": "2026-09-24T22:31:57.167Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T22:31:57.167Z", "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": "2026-09-24T22:31:59.718Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:31:59.718Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:32:00.759Z", "text": "I’ll inspect the data shapes, timelines, and available libraries first, then build a leakage-safe validation pipeline. I’ll keep a valid test submission saved early and iterate against the public NDCG evaluator, using validation events only as labels until the final refit." }, { "kind": "tool", "timestamp": "2026-09-24T22:32:02.128Z", "callId": "call_Irwo06QzQo6azh50LfY4v0Vh", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"pwd && ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && find /app -maxdepth 2 -type f -print\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:02.196Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 22:24 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 24 22:24 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:24 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:24 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:24 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:24 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:24 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:24 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 24 22:24 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:24 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:24 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:24 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 24 22:24 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:24 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:02.168Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4dd6a4087d2bf8b342beae5fefa", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 64, "total_tokens": 14545 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 64, "total_tokens": 14545 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 64, "total_tokens": 14545 } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:02.191Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-654cc740-c61b-4394-b9cb-2b2b3fdf7371", "process_id": "21206", "command": [ "/bin/sh", "-lc", "pwd && ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && find /app -maxdepth 2 -type f -print" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && find /app -maxdepth 2 -type f -print" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 22:24 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 24 22:24 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:24 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:24 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:24 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:24 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:24 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:24 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 24 22:24 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:24 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:24 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:24 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 24 22:24 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:24 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "/app\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 22:24 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 24 22:24 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:24 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:24 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:24 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:24 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:24 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:24 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 24 22:24 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:24 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:24 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:24 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 24 22:24 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:24 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4080 }, "formatted_output": "/app\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 24 22:24 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 24 22:24 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:24 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:24 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:24 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:24 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:24 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:24 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 24 22:24 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:24 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:24 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:24 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 24 22:24 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:24 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:25 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-24T22:32:05.508Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:05.508Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:32:10.101Z", "callId": "call_T4rDfCCF1FsdnNbSHRH5Gjqq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport pyarrow.parquet as pq, os, numpy as np, pandas as pd\\nbase='/task/data'\\nfor f in sorted(os.listdir(base)):\\n if f.endswith('.parquet'):\\n p=os.path.join(base,f); pf=pq.ParquetFile(p)\\n print(f, pf.metadata.num_rows, pf.schema_arrow)\\nfor st in ['validation','test']:\\n z=np.load(f'{base}/{st}_eligible_candidates.npz')\\n print(st, {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('eligible counts',np.min(counts),np.percentile(counts,[1,10,50,90,99]),np.max(counts), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:10.174Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:10.153Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73cf6c89-b453-48d9-b575-29e497324c8d", "process_id": "69648", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pyarrow.parquet as pq, os, numpy as np, pandas as pd\nbase='/task/data'\nfor f in sorted(os.listdir(base)):\n if f.endswith('.parquet'):\n p=os.path.join(base,f); pf=pq.ParquetFile(p)\n print(f, pf.metadata.num_rows, pf.schema_arrow)\nfor st in ['validation','test']:\n z=np.load(f'{base}/{st}_eligible_candidates.npz')\n print(st, {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('eligible counts',np.min(counts),np.percentile(counts,[1,10,50,90,99]),np.max(counts), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pyarrow.parquet as pq, os, numpy as np, pandas as pd\nbase='/task/data'\nfor f in sorted(os.listdir(base)):\n if f.endswith('.parquet'):\n p=os.path.join(base,f); pf=pq.ParquetFile(p)\n print(f, pf.metadata.num_rows, pf.schema_arrow)\nfor st in ['validation','test']:\n z=np.load(f'{base}/{st}_eligible_candidates.npz')\n print(st, {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('eligible counts',np.min(counts),np.percentile(counts,[1,10,50,90,99]),np.max(counts), '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 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4880 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:10.172Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4e27a6887d2a3985d3f854f0e24", "usage": { "input_tokens": 15548, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 381, "reasoning_output_tokens": 116, "total_tokens": 15929 }, "turn_token_usage": { "input_tokens": 29885, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 589, "reasoning_output_tokens": 180, "total_tokens": 30474 }, "thread_token_usage": { "input_tokens": 29885, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 589, "reasoning_output_tokens": 180, "total_tokens": 30474 } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-24T22:32:11.895Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:11.895Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:32:13.376Z", "callId": "call_7GsnkyJyYG1yrSiqX8FdzBkQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"which python; python --version; ls -la /task | sed -n '1,120p'; find / -maxdepth 3 -type f -name python 2>/dev/null | head -30\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:13.436Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:28 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:31 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:25 data\n-r--r--r-- 1 root root 5774 Sep 24 22:24 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:24 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:24 requirements.txt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:13.429Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76d76b0d-7bb0-43ef-a112-9167a92d14d8", "process_id": "89288", "command": [ "/bin/sh", "-lc", "which python; python --version; ls -la /task | sed -n '1,120p'; find / -maxdepth 3 -type f -name python 2>/dev/null | head -30" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; python --version; ls -la /task | sed -n '1,120p'; find / -maxdepth 3 -type f -name python 2>/dev/null | head -30" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:28 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:31 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:25 data\n-r--r--r-- 1 root root 5774 Sep 24 22:24 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:24 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:24 requirements.txt\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:28 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:31 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:25 data\n-r--r--r-- 1 root root 5774 Sep 24 22:24 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:24 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:24 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4300 }, "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:28 .\ndrwxr-xr-x 1 root root 4096 Sep 24 22:31 ..\ndr-xr-xr-x 1 root root 4096 Sep 24 22:25 data\n-r--r--r-- 1 root root 5774 Sep 24 22:24 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:24 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 22:24 requirements.txt\n" } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:13.435Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4ea6ca887d2bc4f79794763fd2a", "usage": { "input_tokens": 15985, "cached_input_tokens": 15360, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 23, "total_tokens": 16099 }, "turn_token_usage": { "input_tokens": 45870, "cached_input_tokens": 40704, "cache_write_input_tokens": 0, "output_tokens": 703, "reasoning_output_tokens": 203, "total_tokens": 46573 }, "thread_token_usage": { "input_tokens": 45870, "cached_input_tokens": 40704, "cache_write_input_tokens": 0, "output_tokens": 703, "reasoning_output_tokens": 203, "total_tokens": 46573 } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-24T22:32:15.285Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:15.285Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:32:16.538Z", "callId": "call_eWzvk4V7otjKHjuHM63fQeTV", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python -m pip list --format=columns | sed -n '1,160p'; env | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:17.315Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\nPackage Version\n------- -------\npip 25.0.1\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCODEX_THREAD_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=9829679b44cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:16.603Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4edb0c887d282e444acb0c8bd1f", "usage": { "input_tokens": 16275, "cached_input_tokens": 15744, "cache_write_input_tokens": 0, "output_tokens": 95, "reasoning_output_tokens": 16, "total_tokens": 16370 }, "turn_token_usage": { "input_tokens": 62145, "cached_input_tokens": 56448, "cache_write_input_tokens": 0, "output_tokens": 798, "reasoning_output_tokens": 219, "total_tokens": 62943 }, "thread_token_usage": { "input_tokens": 62145, "cached_input_tokens": 56448, "cache_write_input_tokens": 0, "output_tokens": 798, "reasoning_output_tokens": 219, "total_tokens": 62943 } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:17.311Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-92840461-86cd-48b1-a413-ce978c24867f", "process_id": "35311", "command": [ "/bin/sh", "-lc", "python -m pip list --format=columns | sed -n '1,160p'; env | sort | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -m pip list --format=columns | sed -n '1,160p'; env | sort | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCODEX_THREAD_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=9829679b44cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCODEX_THREAD_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=9829679b44cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 608476912 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCODEX_THREAD_ID=01a0d58b-f9de-7830-99f5-a3cc9a1c7731\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=9829679b44cd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-24T22:32:19.368Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:19.368Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:32:20.271Z", "text": "The base Python executable is not exposing the task’s pinned libraries, despite the supplied inventory. I’m locating the task runtime before modeling; this is an environment-path issue, not a data issue." }, { "kind": "tool", "timestamp": "2026-09-24T22:32:22.983Z", "callId": "call_2of77vmB5U7D3NKXWYJtpJau", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"command -v uv || true; command -v python3; find /opt /usr/local /root /home -maxdepth 5 \\\\( -name 'pyarrow' -o -name 'site-packages' -o -name 'numpy' \\\\) 2>/dev/null | head -100; find / -maxdepth 4 -type f \\\\( -name 'activate' -o -name 'python3.11' -o -name 'python3.12' \\\\) 2>/dev/null | head -100\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:23.152Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n/usr/local/bin/python3\n/opt/task-venv/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages/numpy\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/usr/local/lib/python3.12/site-packages\n/usr/local/bin/python3.12\n/usr/share/binfmts/python3.11\n/usr/bin/python3.11\n/opt/task-venv/bin/activate\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:23.042Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4f181cc87d2b3b6a959c020e0b8", "usage": { "input_tokens": 16866, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 247, "reasoning_output_tokens": 42, "total_tokens": 17113 }, "turn_token_usage": { "input_tokens": 79011, "cached_input_tokens": 67584, "cache_write_input_tokens": 0, "output_tokens": 1045, "reasoning_output_tokens": 261, "total_tokens": 80056 }, "thread_token_usage": { "input_tokens": 79011, "cached_input_tokens": 67584, "cache_write_input_tokens": 0, "output_tokens": 1045, "reasoning_output_tokens": 261, "total_tokens": 80056 } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:23.148Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fac25caf-3a72-451e-81bd-78885b376f2c", "process_id": "71656", "command": [ "/bin/sh", "-lc", "command -v uv || true; command -v python3; find /opt /usr/local /root /home -maxdepth 5 \\( -name 'pyarrow' -o -name 'site-packages' -o -name 'numpy' \\) 2>/dev/null | head -100; find / -maxdepth 4 -type f \\( -name 'activate' -o -name 'python3.11' -o -name 'python3.12' \\) 2>/dev/null | head -100" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v uv || true; command -v python3; find /opt /usr/local /root /home -maxdepth 5 \\( -name 'pyarrow' -o -name 'site-packages' -o -name 'numpy' \\) 2>/dev/null | head -100; find / -maxdepth 4 -type f \\( -name 'activate' -o -name 'python3.11' -o -name 'python3.12' \\) 2>/dev/null | head -100" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python3\n/opt/task-venv/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages/numpy\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/usr/local/lib/python3.12/site-packages\n/usr/local/bin/python3.12\n/usr/share/binfmts/python3.11\n/usr/bin/python3.11\n/opt/task-venv/bin/activate\n", "stderr": "", "aggregated_output": "/usr/local/bin/python3\n/opt/task-venv/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages/numpy\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/usr/local/lib/python3.12/site-packages\n/usr/local/bin/python3.12\n/usr/share/binfmts/python3.11\n/usr/bin/python3.11\n/opt/task-venv/bin/activate\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2301 }, "formatted_output": "/usr/local/bin/python3\n/opt/task-venv/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages/numpy\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/usr/local/lib/python3.12/site-packages\n/usr/local/bin/python3.12\n/usr/share/binfmts/python3.11\n/usr/bin/python3.11\n/opt/task-venv/bin/activate\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-24T22:32:24.452Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:24.452Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:32:29.391Z", "callId": "call_qKX7FUvCFSxjygocdTx5oRC1", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, os, numpy as np, pandas as pd\\nbase='/task/data'\\nfor f in sorted(os.listdir(base)):\\n if f.endswith('.parquet'):\\n p=os.path.join(base,f); pf=pq.ParquetFile(p)\\n print('\\\\n',f, pf.metadata.num_rows)\\n print(pf.schema_arrow)\\nfor st in ['validation','test']:\\n z=np.load(f'{base}/{st}_eligible_candidates.npz')\\n print('\\\\n',st, {k:(z[k].shape,str(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('eligible counts',np.min(counts),np.percentile(counts,[1,10,50,90,99]),np.max(counts), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:30.935Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\n\n candidate_catalog.parquet 30000\nitem_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\n\n target_users.parquet 8456\nuid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\n\n test_preference_state.parquet 484776\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n train_dislikes.parquet 101075\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_likes.parquet 814831\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_listens.parquet 43484159\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n train_undislikes.parquet 19960\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_unlikes.parquet 292665\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_dislikes.parquet 3563\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_likes.parquet 37795\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_listens.parquet 1501697\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n val_undislikes.parquet 606\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_unlikes.parquet 9346\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n validation_preference_state.parquet 469124\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n validation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24657 [26724.2 28038.5 29242. 29852. 29979. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n\n test {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24552 [26672.7 28001. 29220. 29835. 29975. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:29.428Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4f751c087d2b0c5b2548606e42a", "usage": { "input_tokens": 17238, "cached_input_tokens": 16128, "cache_write_input_tokens": 0, "output_tokens": 299, "reasoning_output_tokens": 16, "total_tokens": 17537 }, "turn_token_usage": { "input_tokens": 96249, "cached_input_tokens": 83712, "cache_write_input_tokens": 0, "output_tokens": 1344, "reasoning_output_tokens": 277, "total_tokens": 97593 }, "thread_token_usage": { "input_tokens": 96249, "cached_input_tokens": 83712, "cache_write_input_tokens": 0, "output_tokens": 1344, "reasoning_output_tokens": 277, "total_tokens": 97593 } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:30.930Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-19ef0145-06c4-4e18-a635-e9ef63ae673c", "process_id": "89000", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, os, numpy as np, pandas as pd\nbase='/task/data'\nfor f in sorted(os.listdir(base)):\n if f.endswith('.parquet'):\n p=os.path.join(base,f); pf=pq.ParquetFile(p)\n print('\\n',f, pf.metadata.num_rows)\n print(pf.schema_arrow)\nfor st in ['validation','test']:\n z=np.load(f'{base}/{st}_eligible_candidates.npz')\n print('\\n',st, {k:(z[k].shape,str(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('eligible counts',np.min(counts),np.percentile(counts,[1,10,50,90,99]),np.max(counts), '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'\nimport pyarrow.parquet as pq, os, numpy as np, pandas as pd\nbase='/task/data'\nfor f in sorted(os.listdir(base)):\n if f.endswith('.parquet'):\n p=os.path.join(base,f); pf=pq.ParquetFile(p)\n print('\\n',f, pf.metadata.num_rows)\n print(pf.schema_arrow)\nfor st in ['validation','test']:\n z=np.load(f'{base}/{st}_eligible_candidates.npz')\n print('\\n',st, {k:(z[k].shape,str(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('eligible counts',np.min(counts),np.percentile(counts,[1,10,50,90,99]),np.max(counts), 'uids first',z['uids'][:5], 'items first',z['item_ids'][:5])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n candidate_catalog.parquet 30000\nitem_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\n\n target_users.parquet 8456\nuid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\n\n test_preference_state.parquet 484776\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n train_dislikes.parquet 101075\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_likes.parquet 814831\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_listens.parquet 43484159\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n train_undislikes.parquet 19960\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_unlikes.parquet 292665\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_dislikes.parquet 3563\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_likes.parquet 37795\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_listens.parquet 1501697\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n val_undislikes.parquet 606\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_unlikes.parquet 9346\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n validation_preference_state.parquet 469124\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n validation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24657 [26724.2 28038.5 29242. 29852. 29979. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n\n test {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24552 [26672.7 28001. 29220. 29835. 29975. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n", "stderr": "", "aggregated_output": "\n candidate_catalog.parquet 30000\nitem_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\n\n target_users.parquet 8456\nuid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\n\n test_preference_state.parquet 484776\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n train_dislikes.parquet 101075\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_likes.parquet 814831\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_listens.parquet 43484159\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n train_undislikes.parquet 19960\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_unlikes.parquet 292665\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_dislikes.parquet 3563\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_likes.parquet 37795\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_listens.parquet 1501697\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n val_undislikes.parquet 606\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_unlikes.parquet 9346\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n validation_preference_state.parquet 469124\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n validation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24657 [26724.2 28038.5 29242. 29852. 29979. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n\n test {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24552 [26672.7 28001. 29220. 29835. 29975. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 375612754 }, "formatted_output": "\n candidate_catalog.parquet 30000\nitem_id: uint32\npositive_listens: int64\norganic_positive_listens: uint64\npopularity_rank: int32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 591\n\n target_users.parquet 8456\nuid: uint32\nrecent_positive_listens: double\nhistory_positive_listens: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 472\n\n test_preference_state.parquet 484776\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n train_dislikes.parquet 101075\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_likes.parquet 814831\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_listens.parquet 43484159\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n train_undislikes.parquet 19960\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n train_unlikes.parquet 292665\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_dislikes.parquet 3563\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_likes.parquet 37795\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_listens.parquet 1501697\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\n\n val_undislikes.parquet 606\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n val_unlikes.parquet 9346\nuid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 527\n\n validation_preference_state.parquet 469124\nuid: uint32\nitem_id: uint32\nstate: string\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 403\n\n validation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24657 [26724.2 28038.5 29242. 29852. 29979. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n\n test {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts 24552 [26672.7 28001. 29220. 29835. 29975. ] 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983]\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-24T22:32:33.631Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:33.631Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:32:34.895Z", "callId": "call_dXKDBzXx9DociChnDqsh2oCE", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,260p' /task/evaluate_public.py; sed -n '1,300p' /task/ranking_evaluation.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:34.966Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:34.934Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0f4aada1-51ad-4211-b1a0-42e2b47d3fda", "process_id": "47187", "command": [ "/bin/sh", "-lc", "sed -n '1,260p' /task/evaluate_public.py; sed -n '1,300p' /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1,260p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "sed -n '1,300p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \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\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \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\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3801 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \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\"\"\"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" } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:34.965Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a4ff223487d29adbe83343fd71d1", "usage": { "input_tokens": 18641, "cached_input_tokens": 17024, "cache_write_input_tokens": 0, "output_tokens": 159, "reasoning_output_tokens": 80, "total_tokens": 18800 }, "turn_token_usage": { "input_tokens": 114890, "cached_input_tokens": 100736, "cache_write_input_tokens": 0, "output_tokens": 1503, "reasoning_output_tokens": 357, "total_tokens": 116393 }, "thread_token_usage": { "input_tokens": 114890, "cached_input_tokens": 100736, "cache_write_input_tokens": 0, "output_tokens": 1503, "reasoning_output_tokens": 357, "total_tokens": 116393 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-24T22:32:36.793Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:36.793Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:32:42.005Z", "callId": "call_WiWmq8TUxR8c8Xd5JjvQX73J", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; nproc; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nB='/task/data/'\\nfor f in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n d=pd.read_parquet(B+f)\\n print('\\\\n',f, d.shape)\\n print(d.head())\\n print(d.describe(include='all'))\\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n for s in ['train','val']:\\n d=pd.read_parquet(B+f'{s}_{fam}.parquet',columns=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if fam=='listens' else []))\\n print(s,fam,'rows',len(d),'uid',d.uid.nunique(),'item',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),'organic',d.is_organic.mean(), 'targets rows',d.uid.isin(pd.read_parquet(B+'target_users.parquet').uid).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:32:44.236Z", "content": "Script completed\nWall time 2.2 seconds\nOutput:\n\n/bin/sh: 1: free: not found\n8\n\n target_users.parquet (8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n\n candidate_catalog.parquet (30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\n\n validation_preference_state.parquet (469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 469124.000000 4.691240e+05 469124\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 418435\nmean 504385.907138 4.713535e+06 NaN\nstd 288319.228881 2.700276e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251100.000000 2.395349e+06 NaN\n50% 505400.000000 4.721761e+06 NaN\n75% 756900.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\n\n test_preference_state.parquet (484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 484776.000000 4.847760e+05 484776\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 432659\nmean 505610.804165 4.713084e+06 NaN\nstd 288538.868491 2.700333e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251525.000000 2.394162e+06 NaN\n50% 508000.000000 4.721571e+06 NaN\n75% 761300.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\ntrain listens rows 43484159 uid 9238 item 845395 time 0 24786795 organic 0.5169151828370419 targets rows 43222297\nval listens rows 1501697 uid 8196 item 191747 time 24788600 25393395 organic 0.517072352145606 targets rows 1482553\ntrain likes rows 814831 uid 8144 item 172303 time 80 24786790 organic 0.5667911505576003 targets rows 690968\nval likes rows 37795 uid 4114 item 22902 time 24788815 25393390 organic 0.6662521497552586 targets rows 33177\ntrain dislikes rows 101075 uid 5732 item 50876 time 75 24786580 organic 0.5172198862231017 targets rows 84266\nval dislikes rows 3563 uid 955 item 3135 time 24789920 25393280 organic 0.5150154364299747 targets rows 2664\ntrain unlikes rows 292665 uid 6197 item 113072 time 250 24786570 organic 0.9466967351750295 targets rows 245808\nval unlikes rows 9346 uid 1313 item 8080 time 24789005 25393385 organic 0.9528140380911619 targets rows 6297\ntrain undislikes rows 19960 uid 2769 item 14725 time 15950 24785395 organic 0.8893286573146293 targets rows 16819\nval undislikes rows 606 uid 205 item 579 time 24810715 25393035 organic 0.8696369636963697 targets rows 466\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:32:42.063Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a503310087d283a29284b587d095", "usage": { "input_tokens": 22038, "cached_input_tokens": 18432, "cache_write_input_tokens": 0, "output_tokens": 342, "reasoning_output_tokens": 44, "total_tokens": 22380 }, "turn_token_usage": { "input_tokens": 136928, "cached_input_tokens": 119168, "cache_write_input_tokens": 0, "output_tokens": 1845, "reasoning_output_tokens": 401, "total_tokens": 138773 }, "thread_token_usage": { "input_tokens": 136928, "cached_input_tokens": 119168, "cache_write_input_tokens": 0, "output_tokens": 1845, "reasoning_output_tokens": 401, "total_tokens": 138773 } } }, { "kind": "context", "timestamp": "2026-09-24T22:32:44.232Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c744a0bf-61fa-488a-95d4-52f072f09255", "process_id": "72610", "command": [ "/bin/sh", "-lc", "free -h; nproc; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nB='/task/data/'\nfor f in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n d=pd.read_parquet(B+f)\n print('\\n',f, d.shape)\n print(d.head())\n print(d.describe(include='all'))\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for s in ['train','val']:\n d=pd.read_parquet(B+f'{s}_{fam}.parquet',columns=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if fam=='listens' else []))\n print(s,fam,'rows',len(d),'uid',d.uid.nunique(),'item',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),'organic',d.is_organic.mean(), 'targets rows',d.uid.isin(pd.read_parquet(B+'target_users.parquet').uid).sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; nproc; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nB='/task/data/'\nfor f in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n d=pd.read_parquet(B+f)\n print('\\n',f, d.shape)\n print(d.head())\n print(d.describe(include='all'))\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for s in ['train','val']:\n d=pd.read_parquet(B+f'{s}_{fam}.parquet',columns=['uid','timestamp','item_id','is_organic'] + (['played_ratio_pct'] if fam=='listens' else []))\n print(s,fam,'rows',len(d),'uid',d.uid.nunique(),'item',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),'organic',d.is_organic.mean(), 'targets rows',d.uid.isin(pd.read_parquet(B+'target_users.parquet').uid).sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n target_users.parquet (8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n\n candidate_catalog.parquet (30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\n\n validation_preference_state.parquet (469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 469124.000000 4.691240e+05 469124\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 418435\nmean 504385.907138 4.713535e+06 NaN\nstd 288319.228881 2.700276e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251100.000000 2.395349e+06 NaN\n50% 505400.000000 4.721761e+06 NaN\n75% 756900.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\n\n test_preference_state.parquet (484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 484776.000000 4.847760e+05 484776\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 432659\nmean 505610.804165 4.713084e+06 NaN\nstd 288538.868491 2.700333e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251525.000000 2.394162e+06 NaN\n50% 508000.000000 4.721571e+06 NaN\n75% 761300.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\ntrain listens rows 43484159 uid 9238 item 845395 time 0 24786795 organic 0.5169151828370419 targets rows 43222297\nval listens rows 1501697 uid 8196 item 191747 time 24788600 25393395 organic 0.517072352145606 targets rows 1482553\ntrain likes rows 814831 uid 8144 item 172303 time 80 24786790 organic 0.5667911505576003 targets rows 690968\nval likes rows 37795 uid 4114 item 22902 time 24788815 25393390 organic 0.6662521497552586 targets rows 33177\ntrain dislikes rows 101075 uid 5732 item 50876 time 75 24786580 organic 0.5172198862231017 targets rows 84266\nval dislikes rows 3563 uid 955 item 3135 time 24789920 25393280 organic 0.5150154364299747 targets rows 2664\ntrain unlikes rows 292665 uid 6197 item 113072 time 250 24786570 organic 0.9466967351750295 targets rows 245808\nval unlikes rows 9346 uid 1313 item 8080 time 24789005 25393385 organic 0.9528140380911619 targets rows 6297\ntrain undislikes rows 19960 uid 2769 item 14725 time 15950 24785395 organic 0.8893286573146293 targets rows 16819\nval undislikes rows 606 uid 205 item 579 time 24810715 25393035 organic 0.8696369636963697 targets rows 466\n", "stderr": "", "aggregated_output": "\n target_users.parquet (8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n\n candidate_catalog.parquet (30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\n\n validation_preference_state.parquet (469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 469124.000000 4.691240e+05 469124\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 418435\nmean 504385.907138 4.713535e+06 NaN\nstd 288319.228881 2.700276e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251100.000000 2.395349e+06 NaN\n50% 505400.000000 4.721761e+06 NaN\n75% 756900.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\n\n test_preference_state.parquet (484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 484776.000000 4.847760e+05 484776\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 432659\nmean 505610.804165 4.713084e+06 NaN\nstd 288538.868491 2.700333e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251525.000000 2.394162e+06 NaN\n50% 508000.000000 4.721571e+06 NaN\n75% 761300.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\ntrain listens rows 43484159 uid 9238 item 845395 time 0 24786795 organic 0.5169151828370419 targets rows 43222297\nval listens rows 1501697 uid 8196 item 191747 time 24788600 25393395 organic 0.517072352145606 targets rows 1482553\ntrain likes rows 814831 uid 8144 item 172303 time 80 24786790 organic 0.5667911505576003 targets rows 690968\nval likes rows 37795 uid 4114 item 22902 time 24788815 25393390 organic 0.6662521497552586 targets rows 33177\ntrain dislikes rows 101075 uid 5732 item 50876 time 75 24786580 organic 0.5172198862231017 targets rows 84266\nval dislikes rows 3563 uid 955 item 3135 time 24789920 25393280 organic 0.5150154364299747 targets rows 2664\ntrain unlikes rows 292665 uid 6197 item 113072 time 250 24786570 organic 0.9466967351750295 targets rows 245808\nval unlikes rows 9346 uid 1313 item 8080 time 24789005 25393385 organic 0.9528140380911619 targets rows 6297\ntrain undislikes rows 19960 uid 2769 item 14725 time 15950 24785395 organic 0.8893286573146293 targets rows 16819\nval undislikes rows 606 uid 205 item 579 time 24810715 25393035 organic 0.8696369636963697 targets rows 466\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 61830056 }, "formatted_output": "\n target_users.parquet (8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n\n candidate_catalog.parquet (30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\n\n validation_preference_state.parquet (469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 469124.000000 4.691240e+05 469124\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 418435\nmean 504385.907138 4.713535e+06 NaN\nstd 288319.228881 2.700276e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251100.000000 2.395349e+06 NaN\n50% 505400.000000 4.721761e+06 NaN\n75% 756900.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\n\n test_preference_state.parquet (484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n uid item_id state\ncount 484776.000000 4.847760e+05 484776\nunique NaN NaN 2\ntop NaN NaN liked\nfreq NaN NaN 432659\nmean 505610.804165 4.713084e+06 NaN\nstd 288538.868491 2.700333e+06 NaN\nmin 100.000000 2.060000e+02 NaN\n25% 251525.000000 2.394162e+06 NaN\n50% 508000.000000 4.721571e+06 NaN\n75% 761300.000000 7.037358e+06 NaN\nmax 1000000.000000 9.390294e+06 NaN\ntrain listens rows 43484159 uid 9238 item 845395 time 0 24786795 organic 0.5169151828370419 targets rows 43222297\nval listens rows 1501697 uid 8196 item 191747 time 24788600 25393395 organic 0.517072352145606 targets rows 1482553\ntrain likes rows 814831 uid 8144 item 172303 time 80 24786790 organic 0.5667911505576003 targets rows 690968\nval likes rows 37795 uid 4114 item 22902 time 24788815 25393390 organic 0.6662521497552586 targets rows 33177\ntrain dislikes rows 101075 uid 5732 item 50876 time 75 24786580 organic 0.5172198862231017 targets rows 84266\nval dislikes rows 3563 uid 955 item 3135 time 24789920 25393280 organic 0.5150154364299747 targets rows 2664\ntrain unlikes rows 292665 uid 6197 item 113072 time 250 24786570 organic 0.9466967351750295 targets rows 245808\nval unlikes rows 9346 uid 1313 item 8080 time 24789005 25393385 organic 0.9528140380911619 targets rows 6297\ntrain undislikes rows 19960 uid 2769 item 14725 time 15950 24785395 organic 0.8893286573146293 targets rows 16819\nval undislikes rows 606 uid 205 item 579 time 24810715 25393035 organic 0.8696369636963697 targets rows 466\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-24T22:32:53.879Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:32:53.879Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:33:11.413Z", "callId": "call_YD56XeKk43T1MDZ0FZyr19MJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom collections import defaultdict\\nB='/task/data/'\\ntarget=pd.read_parquet(B+'target_users.parquet'); tus=set(target.uid.astype(int))\\ncat=pd.read_parquet(B+'candidate_catalog.parquet'); cset=set(cat.item_id.astype(int)); crank=dict(zip(cat.item_id,cat.popularity_rank))\\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ui={int(x):i for i,x in enumerate(uids)}; ii={int(x):i for i,x in enumerate(items)}\\ndef elig(u,i):\\n c=ii.get(int(i)); r=ui.get(int(u)); return c is not None and bool((int(packed[r,c//8])>>(c%8))&1)\\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\\n# history candidate positive / any info\\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\\n# Eligible status for train pairs, prefs\\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\\nfor state in ['liked','disliked']:\\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:33:41.579Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:33:11.450Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a50c851087d28ac2ba81514559ce", "usage": { "input_tokens": 23966, "cached_input_tokens": 21888, "cache_write_input_tokens": 0, "output_tokens": 1449, "reasoning_output_tokens": 466, "total_tokens": 25415 }, "turn_token_usage": { "input_tokens": 160894, "cached_input_tokens": 141056, "cache_write_input_tokens": 0, "output_tokens": 3294, "reasoning_output_tokens": 867, "total_tokens": 164188 }, "thread_token_usage": { "input_tokens": 160894, "cached_input_tokens": 141056, "cache_write_input_tokens": 0, "output_tokens": 3294, "reasoning_output_tokens": 867, "total_tokens": 164188 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-24T22:33:45.226Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:33:45.226Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:33:56.101Z", "callId": "call_TpASgOZwSdkGj9wKJSkNmLer", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np\\nB='/task/data/'\\ntarget=pd.read_parquet(B+'target_users.parquet'); tus=set(target.uid.astype(int))\\ncat=pd.read_parquet(B+'candidate_catalog.parquet'); crank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ui={int(x):i for i,x in enumerate(uids)}; ii={int(x):i for i,x in enumerate(items)}\\ndef filterelig(df):\\n rr=np.array([ui.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([ii.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); q=np.flatnonzero(ok); out[q]=((packed[rr[q],cc[q]//8]>>(cc[q]%8))&1).astype(bool); return df[out]\\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nstrong=filterelig(vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()); del vl\\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nlikes=filterelig(vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()); del vg\\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict(),flush=True)\\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]),flush=True)\\ngold['rank']=gold.item_id.map(crank)\\nprint('catalog rank relevant\\\\n',gold.groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]),flush=True)\\nprint('not catalog?',gold['rank'].isna().sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:33:57.382Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\nstrong eligible 35597 likes 4417 gold 37769 users 4263 grade {1: 33352, 3: 4417}\nper-user gold count 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n25% 2.000000\n50% 4.000000\n75% 10.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\ncatalog rank relevant\n count mean std min ... 75% 90% 99% max\ng ... \n1 33352.0 8406.287659 8124.885100 1.0 ... 13274.0 21656.0 29102.0 30000.0\n3 4417.0 8891.975323 8053.409263 2.0 ... 14181.0 21801.8 28812.6 29989.0\n\n[2 rows x 11 columns]\nnot catalog? 0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:33:56.190Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a545d54887d2ab2f9815f101e89d", "usage": { "input_tokens": 25438, "cached_input_tokens": 23808, "cache_write_input_tokens": 0, "output_tokens": 752, "reasoning_output_tokens": 138, "total_tokens": 26190 }, "turn_token_usage": { "input_tokens": 186332, "cached_input_tokens": 164864, "cache_write_input_tokens": 0, "output_tokens": 4046, "reasoning_output_tokens": 1005, "total_tokens": 190378 }, "thread_token_usage": { "input_tokens": 186332, "cached_input_tokens": 164864, "cache_write_input_tokens": 0, "output_tokens": 4046, "reasoning_output_tokens": 1005, "total_tokens": 190378 } } }, { "kind": "context", "timestamp": "2026-09-24T22:33:57.378Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2ae48ab6-f395-46a8-8302-d9ffbd043320", "process_id": "1920", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nB='/task/data/'\ntarget=pd.read_parquet(B+'target_users.parquet'); tus=set(target.uid.astype(int))\ncat=pd.read_parquet(B+'candidate_catalog.parquet'); crank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ui={int(x):i for i,x in enumerate(uids)}; ii={int(x):i for i,x in enumerate(items)}\ndef filterelig(df):\n rr=np.array([ui.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([ii.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); q=np.flatnonzero(ok); out[q]=((packed[rr[q],cc[q]//8]>>(cc[q]%8))&1).astype(bool); return df[out]\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=filterelig(vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()); del vl\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=filterelig(vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()); del vg\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict(),flush=True)\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]),flush=True)\ngold['rank']=gold.item_id.map(crank)\nprint('catalog rank relevant\\n',gold.groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]),flush=True)\nprint('not catalog?',gold['rank'].isna().sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nB='/task/data/'\ntarget=pd.read_parquet(B+'target_users.parquet'); tus=set(target.uid.astype(int))\ncat=pd.read_parquet(B+'candidate_catalog.parquet'); crank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ui={int(x):i for i,x in enumerate(uids)}; ii={int(x):i for i,x in enumerate(items)}\ndef filterelig(df):\n rr=np.array([ui.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([ii.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); q=np.flatnonzero(ok); out[q]=((packed[rr[q],cc[q]//8]>>(cc[q]%8))&1).astype(bool); return df[out]\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=filterelig(vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()); del vl\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=filterelig(vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()); del vg\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict(),flush=True)\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]),flush=True)\ngold['rank']=gold.item_id.map(crank)\nprint('catalog rank relevant\\n',gold.groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]),flush=True)\nprint('not catalog?',gold['rank'].isna().sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "strong eligible 35597 likes 4417 gold 37769 users 4263 grade {1: 33352, 3: 4417}\nper-user gold count 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n25% 2.000000\n50% 4.000000\n75% 10.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\ncatalog rank relevant\n count mean std min ... 75% 90% 99% max\ng ... \n1 33352.0 8406.287659 8124.885100 1.0 ... 13274.0 21656.0 29102.0 30000.0\n3 4417.0 8891.975323 8053.409263 2.0 ... 14181.0 21801.8 28812.6 29989.0\n\n[2 rows x 11 columns]\nnot catalog? 0\n", "stderr": "", "aggregated_output": "strong eligible 35597 likes 4417 gold 37769 users 4263 grade {1: 33352, 3: 4417}\nper-user gold count 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n25% 2.000000\n50% 4.000000\n75% 10.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\ncatalog rank relevant\n count mean std min ... 75% 90% 99% max\ng ... \n1 33352.0 8406.287659 8124.885100 1.0 ... 13274.0 21656.0 29102.0 30000.0\n3 4417.0 8891.975323 8053.409263 2.0 ... 14181.0 21801.8 28812.6 29989.0\n\n[2 rows x 11 columns]\nnot catalog? 0\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 111979250 }, "formatted_output": "strong eligible 35597 likes 4417 gold 37769 users 4263 grade {1: 33352, 3: 4417}\nper-user gold count 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n25% 2.000000\n50% 4.000000\n75% 10.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\ncatalog rank relevant\n count mean std min ... 75% 90% 99% max\ng ... \n1 33352.0 8406.287659 8124.885100 1.0 ... 13274.0 21656.0 29102.0 30000.0\n3 4417.0 8891.975323 8053.409263 2.0 ... 14181.0 21801.8 28812.6 29989.0\n\n[2 rows x 11 columns]\nnot catalog? 0\n" } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-24T22:34:01.822Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:01.822Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:02.459Z", "callId": "call_7YtRscGMoQ5y9tno9bHTtFM6", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/.gitkeep\\n+\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:34:02.526Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:02.473Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-73de2bf8-dc9c-4154-901f-1a2449b49a8b", "changes": { "/app/solution/.gitkeep": { "type": "add", "content": "\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/.gitkeep\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:02.524Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a555961487d2859b2d3ff1115d94", "usage": { "input_tokens": 26501, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 141, "reasoning_output_tokens": 96, "total_tokens": 26642 }, "turn_token_usage": { "input_tokens": 212833, "cached_input_tokens": 190080, "cache_write_input_tokens": 0, "output_tokens": 4187, "reasoning_output_tokens": 1101, "total_tokens": 217020 }, "thread_token_usage": { "input_tokens": 212833, "cached_input_tokens": 190080, "cache_write_input_tokens": 0, "output_tokens": 4187, "reasoning_output_tokens": 1101, "total_tokens": 217020 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-24T22:34:03.848Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:03.848Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:08.144Z", "callId": "call_6DlwmuQqtd97l2tFqWHNywIb", "name": "exec", "input": { "code": "let{output,...rest}=await tools.exec_command({\"cmd\":\"ls -la /app/solution || mkdir -p /app/solution; /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nz=np.load('/task/data/validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\\nrows=[]\\nfor r,uid in enumerate(u):\\n ids=it[np.flatnonzero(m[r])[:100]]\\n rows.append(pd.DataFrame({'uid':uid,'item_id':ids,'rank':np.arange(1,101)}))\\npd.concat(rows,ignore_index=True).to_parquet('/app/solution/validation_pop.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});text(rest);text(output);" }, "result": { "timestamp": "2026-09-24T22:34:13.413Z", "content": "Script completed\nWall time 5.3 seconds\nOutput:\n\n{\"chunk_id\":\"a538c6\",\"wall_time_seconds\":5.053615055,\"exit_code\":0,\"original_token_count\":71}\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 22:34 .\ndrwxr-xr-x 1 agent agent 4096 Sep 24 22:28 ..\n-rw-r--r-- 1 agent agent 1 Sep 24 22:34 .gitkeep\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:08.196Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a55abcb487d287e1917e84448d20", "usage": { "input_tokens": 26666, "cached_input_tokens": 26368, "cache_write_input_tokens": 0, "output_tokens": 264, "reasoning_output_tokens": 16, "total_tokens": 26930 }, "turn_token_usage": { "input_tokens": 239499, "cached_input_tokens": 216448, "cache_write_input_tokens": 0, "output_tokens": 4451, "reasoning_output_tokens": 1117, "total_tokens": 243950 }, "thread_token_usage": { "input_tokens": 239499, "cached_input_tokens": 216448, "cache_write_input_tokens": 0, "output_tokens": 4451, "reasoning_output_tokens": 1117, "total_tokens": 243950 } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:13.406Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4b86da19-df86-451d-92be-7a5542579860", "process_id": "34206", "command": [ "/bin/sh", "-lc", "ls -la /app/solution || mkdir -p /app/solution; /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nz=np.load('/task/data/validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nrows=[]\nfor r,uid in enumerate(u):\n ids=it[np.flatnonzero(m[r])[:100]]\n rows.append(pd.DataFrame({'uid':uid,'item_id':ids,'rank':np.arange(1,101)}))\npd.concat(rows,ignore_index=True).to_parquet('/app/solution/validation_pop.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /app/solution || mkdir -p /app/solution; /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nz=np.load('/task/data/validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nrows=[]\nfor r,uid in enumerate(u):\n ids=it[np.flatnonzero(m[r])[:100]]\n rows.append(pd.DataFrame({'uid':uid,'item_id':ids,'rank':np.arange(1,101)}))\npd.concat(rows,ignore_index=True).to_parquet('/app/solution/validation_pop.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 53581074 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-24T22:34:19.012Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:19.012Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:20.399Z", "callId": "call_AkyJ9xxUhTTGQDlFpLm8xRkT", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"cat /sys/fs/cgroup/memory.max 2>/dev/null || true; cat /sys/fs/cgroup/memory.current 2>/dev/null || true; ulimit -a\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:34:20.491Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n8589934592\n5748178944\ntime(seconds) unlimited\nfile(blocks) unlimited\ndata(kbytes) unlimited\nstack(kbytes) 8192\ncoredump(blocks) unlimited\nmemory(kbytes) unlimited\nlocked memory(kbytes) 8192\nprocess unlimited\nnofiles 524288\nvmemory(kbytes) unlimited\nlocks unlimited\nrtprio 0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:20.436Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-413ebe0c-221d-4d70-add6-4fa21a8dba62", "process_id": "20239", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.max 2>/dev/null || true; cat /sys/fs/cgroup/memory.current 2>/dev/null || true; ulimit -a" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.max 2>/dev/null || true; cat /sys/fs/cgroup/memory.current 2>/dev/null || true; ulimit -a" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "8589934592\n5748178944\ntime(seconds) unlimited\nfile(blocks) unlimited\ndata(kbytes) unlimited\nstack(kbytes) 8192\ncoredump(blocks) unlimited\nmemory(kbytes) unlimited\nlocked memory(kbytes) 8192\nprocess unlimited\nnofiles 524288\nvmemory(kbytes) unlimited\nlocks unlimited\nrtprio 0\n", "stderr": "", "aggregated_output": "8589934592\n5748178944\ntime(seconds) unlimited\nfile(blocks) unlimited\ndata(kbytes) unlimited\nstack(kbytes) 8192\ncoredump(blocks) unlimited\nmemory(kbytes) unlimited\nlocked memory(kbytes) 8192\nprocess unlimited\nnofiles 524288\nvmemory(kbytes) unlimited\nlocks unlimited\nrtprio 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3800 }, "formatted_output": "8589934592\n5748178944\ntime(seconds) unlimited\nfile(blocks) unlimited\ndata(kbytes) unlimited\nstack(kbytes) 8192\ncoredump(blocks) unlimited\nmemory(kbytes) unlimited\nlocked memory(kbytes) 8192\nprocess unlimited\nnofiles 524288\nvmemory(kbytes) unlimited\nlocks unlimited\nrtprio 0\n" } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:20.489Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a565a1a887d29e383476cb24adfc", "usage": { "input_tokens": 27099, "cached_input_tokens": 26496, "cache_write_input_tokens": 0, "output_tokens": 342, "reasoning_output_tokens": 257, "total_tokens": 27441 }, "turn_token_usage": { "input_tokens": 266598, "cached_input_tokens": 242944, "cache_write_input_tokens": 0, "output_tokens": 4793, "reasoning_output_tokens": 1374, "total_tokens": 271391 }, "thread_token_usage": { "input_tokens": 266598, "cached_input_tokens": 242944, "cache_write_input_tokens": 0, "output_tokens": 4793, "reasoning_output_tokens": 1374, "total_tokens": 271391 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-24T22:34:22.048Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:22.048Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:22.909Z", "callId": "call_oSSGB4Fqiu2rT9QglS1Kfzy3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ps aux --sort=-rss | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:34:22.966Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/bin/sh: 1: ps: not found\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:22.946Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-90df6f48-8af8-4bbe-872a-b719e42f5943", "process_id": "12427", "command": [ "/bin/sh", "-lc", "ps aux --sort=-rss | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps aux --sort=-rss | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4991 }, "formatted_output": "/bin/sh: 1: ps: not found\n" } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:22.964Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a56cc2ac87d28bf6aeeeaa72d084", "usage": { "input_tokens": 27557, "cached_input_tokens": 26880, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 24, "total_tokens": 27638 }, "turn_token_usage": { "input_tokens": 294155, "cached_input_tokens": 269824, "cache_write_input_tokens": 0, "output_tokens": 4874, "reasoning_output_tokens": 1398, "total_tokens": 299029 }, "thread_token_usage": { "input_tokens": 294155, "cached_input_tokens": 269824, "cache_write_input_tokens": 0, "output_tokens": 4874, "reasoning_output_tokens": 1398, "total_tokens": 299029 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-24T22:34:24.187Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:24.187Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:26.339Z", "callId": "call_EijnSVdKMQ5Cxq01yExFvE8a", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for p in /proc/[0-9]*; do [ -r \\\"$p/cmdline\\\" ] || continue; c=$(tr '\\\\0' ' ' < \\\"$p/cmdline\\\"); case \\\"$c\\\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \\\"$p/status\\\" 2>/dev/null; echo \\\"$p $c\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:34:26.411Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/proc/8774/status VmRSS:\t 7612 kB\n/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 >(c%8))&1)\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# history candidate positive / any info\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\n# Eligible status for train pairs, prefs\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\nfor state in ['liked','disliked']:\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\nPY \n/proc/9464/status VmRSS:\t 3763224 kB\n/proc/9464 /opt/task-venv/bin/python - \n/proc/9564/status VmRSS:\t 1660 kB\n/proc/9564 /bin/sh -c for p in /proc/[0-9]*; do [ -r \"$p/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$p/cmdline\"); case \"$c\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \"$p/status\" 2>/dev/null; echo \"$p $c\";; esac; done \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:26.393Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3cef30ea-a47d-413a-b607-8958cca2c4f0", "process_id": "28957", "command": [ "/bin/sh", "-lc", "for p in /proc/[0-9]*; do [ -r \"$p/cmdline\" ] || continue; c=$(tr '\\0' ' ' < \"$p/cmdline\"); case \"$c\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \"$p/status\" 2>/dev/null; echo \"$p $c\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in /proc/[0-9]*; do [ -r \"$p/cmdline\" ] || continue; c=$(tr '\\0' ' ' < \"$p/cmdline\"); case \"$c\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \"$p/status\" 2>/dev/null; echo \"$p $c\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/8774/status VmRSS:\t 7612 kB\n/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 >(c%8))&1)\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# history candidate positive / any info\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\n# Eligible status for train pairs, prefs\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\nfor state in ['liked','disliked']:\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\nPY \n/proc/9464/status VmRSS:\t 3763224 kB\n/proc/9464 /opt/task-venv/bin/python - \n/proc/9564/status VmRSS:\t 1660 kB\n/proc/9564 /bin/sh -c for p in /proc/[0-9]*; do [ -r \"$p/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$p/cmdline\"); case \"$c\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \"$p/status\" 2>/dev/null; echo \"$p $c\";; esac; done \n", "stderr": "", "aggregated_output": "/proc/8774/status VmRSS:\t 7612 kB\n/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 >(c%8))&1)\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# history candidate positive / any info\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\n# Eligible status for train pairs, prefs\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\nfor state in ['liked','disliked']:\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\nPY \n/proc/9464/status VmRSS:\t 3763224 kB\n/proc/9464 /opt/task-venv/bin/python - \n/proc/9564/status VmRSS:\t 1660 kB\n/proc/9564 /bin/sh -c for p in /proc/[0-9]*; do [ -r \"$p/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$p/cmdline\"); case \"$c\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \"$p/status\" 2>/dev/null; echo \"$p $c\";; esac; done \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 24920 }, "formatted_output": "/proc/8774/status VmRSS:\t 7612 kB\n/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 >(c%8))&1)\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# history candidate positive / any info\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\n# Eligible status for train pairs, prefs\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\nfor state in ['liked','disliked']:\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\nPY \n/proc/9464/status VmRSS:\t 3763224 kB\n/proc/9464 /opt/task-venv/bin/python - \n/proc/9564/status VmRSS:\t 1660 kB\n/proc/9564 /bin/sh -c for p in /proc/[0-9]*; do [ -r \"$p/cmdline\" ] || continue; c=$(tr '\u0000' ' ' < \"$p/cmdline\"); case \"$c\" in *python*) awk '/VmRSS/{print FILENAME,$0}' \"$p/status\" 2>/dev/null; echo \"$p $c\";; esac; done \n" } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:26.409Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a56f2b7487d291da8ffa4b91de82", "usage": { "input_tokens": 27672, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 140, "reasoning_output_tokens": 12, "total_tokens": 27812 }, "turn_token_usage": { "input_tokens": 321827, "cached_input_tokens": 297216, "cache_write_input_tokens": 0, "output_tokens": 5014, "reasoning_output_tokens": 1410, "total_tokens": 326841 }, "thread_token_usage": { "input_tokens": 321827, "cached_input_tokens": 297216, "cache_write_input_tokens": 0, "output_tokens": 5014, "reasoning_output_tokens": 1410, "total_tokens": 326841 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-24T22:34:28.256Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:28.256Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:29.446Z", "callId": "call_KQstgav6B8257gbQsVD2FdZ4", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"kill 9464 9463 2>/dev/null || true; sleep 1; cat /sys/fs/cgroup/memory.current\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:34:30.469Z", "content": "Script completed\nWall time 1.0 seconds\nOutput:\n\n1961242624\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:29.484Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a572a8a087d2922512fdeef58a86", "usage": { "input_tokens": 33232, "cached_input_tokens": 27520, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 46, "total_tokens": 33353 }, "turn_token_usage": { "input_tokens": 355059, "cached_input_tokens": 324736, "cache_write_input_tokens": 0, "output_tokens": 5135, "reasoning_output_tokens": 1456, "total_tokens": 360194 }, "thread_token_usage": { "input_tokens": 355059, "cached_input_tokens": 324736, "cache_write_input_tokens": 0, "output_tokens": 5135, "reasoning_output_tokens": 1456, "total_tokens": 360194 } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:29.554Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a427b36c-f8f8-470c-8abd-1bacfbd8c9d1", "process_id": "89157", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom collections import defaultdict\nB='/task/data/'\ntarget=pd.read_parquet(B+'target_users.parquet'); tus=set(target.uid.astype(int))\ncat=pd.read_parquet(B+'candidate_catalog.parquet'); cset=set(cat.item_id.astype(int)); crank=dict(zip(cat.item_id,cat.popularity_rank))\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ui={int(x):i for i,x in enumerate(uids)}; ii={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n c=ii.get(int(i)); r=ui.get(int(u)); return c is not None and bool((int(packed[r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# history candidate positive / any info\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\n# Eligible status for train pairs, prefs\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\nfor state in ['liked','disliked']:\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom collections import defaultdict\nB='/task/data/'\ntarget=pd.read_parquet(B+'target_users.parquet'); tus=set(target.uid.astype(int))\ncat=pd.read_parquet(B+'candidate_catalog.parquet'); cset=set(cat.item_id.astype(int)); crank=dict(zip(cat.item_id,cat.popularity_rank))\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']; ui={int(x):i for i,x in enumerate(uids)}; ii={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n c=ii.get(int(i)); r=ui.get(int(u)); return c is not None and bool((int(packed[r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(B+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvg=pd.read_parquet(B+'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nstrong=vl[(vl.uid.isin(tus))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vg[(vg.uid.isin(tus))&(vg.is_organic==1)][['uid','item_id']].drop_duplicates()\nstrong=strong[[elig(u,i) for u,i in strong.itertuples(index=False,name=None)]]\nlikes=likes[[elig(u,i) for u,i in likes.itertuples(index=False,name=None)]]\ngold=pd.concat([strong.assign(g=1), likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong eligible',len(strong),'likes',len(likes),'gold',len(gold),'users',gold.uid.nunique(),'grade',gold.g.value_counts().to_dict())\nprint('per-user gold',gold.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.99]))\nprint('catalog rank relevant',gold.assign(rank=gold.item_id.map(crank)).groupby('g')['rank'].describe(percentiles=[.1,.25,.5,.75,.9,.99]))\n# history candidate positive / any info\ntr=pd.read_parquet(B+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\ntr=tr[tr.uid.isin(tus)&tr.item_id.isin(cset)]\nprint('train target catalog rows',len(tr),'pairs',len(tr.drop_duplicates(['uid','item_id'])))\npairs=tr.groupby(['uid','item_id']).agg(n=('timestamp','size'), last=('timestamp','max'), org=('is_organic','sum'), strong=('played_ratio_pct',lambda x:(x>=80).sum()), meanr=('played_ratio_pct','mean')).reset_index()\ng=gold.merge(pairs,on=['uid','item_id'],how='left')\nprint('relevant prior pair fraction',g.n.notna().mean(), 'by grade',g.groupby('g').n.apply(lambda x:x.notna().mean()).to_dict())\nprint(g.groupby('g')[['n','org','strong','last','meanr']].describe(percentiles=[.1,.5,.9]).to_string())\n# Eligible status for train pairs, prefs\npairs['elig']=[elig(u,i) for u,i in pairs[['uid','item_id']].itertuples(index=False,name=None)]\nprint('history pairs eligible frac',pairs.elig.mean(),'counts by user eligible history',pairs[pairs.elig].groupby('uid').size().describe())\nfor state in ['liked','disliked']:\n p=pd.read_parquet(B+'validation_preference_state.parquet'); p=p[p.state==state]\n p['elig']=[elig(u,i) for u,i in p[['uid','item_id']].itertuples(index=False,name=None)]\n print(state,len(p),'catalog',p.item_id.isin(cset).sum(),'eligible among catalog',p[p.item_id.isin(cset)].elig.mean())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 77, "nanos": 976093293 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:30.465Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d844e81a-fda2-4342-a9aa-390bc1113da6", "process_id": "71649", "command": [ "/bin/sh", "-lc", "kill 9464 9463 2>/dev/null || true; sleep 1; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 9464 9463 2>/dev/null || true; sleep 1; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1961242624\n", "stderr": "", "aggregated_output": "1961242624\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 853288923 }, "formatted_output": "1961242624\n" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-24T22:34:34.935Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:34.935Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:34:42.093Z", "callId": "call_EzFWwzSV2mkcKts5sgvNslUk", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np\\nB='/task/data/'\\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; its=z['item_ids']; mask=np.unpackbits(z['packed_eligible'][0],count=len(its),bitorder='little').astype(bool); eset=set(its[mask]); cset=set(its)\\nfor uid in [100,200,300,600,700,900,1000,1100]:\\n row=np.where(uids==uid)[0][0]; em=np.unpackbits(z['packed_eligible'][row],count=len(its),bitorder='little').astype(bool); es=set(its[em])\\n d=pd.read_parquet(B+'train_listens.parquet',filters=[('uid','=',uid)])\\n pairs=set(d.item_id); pos=set(d.loc[d.played_ratio_pct>=50,'item_id']); strong=set(d.loc[(d.played_ratio_pct>=80)&(d.is_organic==1),'item_id'])\\n pref=pd.read_parquet(B+'validation_preference_state.parquet',filters=[('uid','=',uid)])\\n print(uid,'events',len(d),'unique',len(pairs),'catalog unique',len(pairs&cset),'poscat',len(pos&cset),'strongorgcat',len(strong&cset),'eligible',len(es),'seen elig',len(pairs&es),'pos elig',len(pos&es),'prefs',pref.state.value_counts().to_dict(),'prefcat',pref.item_id.isin(cset).sum(),'pref elig',pref.item_id.isin(es).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:34:44.129Z", "content": "Script completed\nWall time 2.0 seconds\nOutput:\n\n100 events 3266 unique 830 catalog unique 682 poscat 600 strongorgcat 146 eligible 29318 seen elig 0 pos elig 0 prefs {'liked': 13, 'disliked': 2} prefcat 15 pref elig 0\n200 events 312 unique 195 catalog unique 168 poscat 163 strongorgcat 163 eligible 29832 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n300 events 699 unique 384 catalog unique 69 poscat 45 strongorgcat 42 eligible 29931 seen elig 0 pos elig 0 prefs {'liked': 3} prefcat 3 pref elig 0\n600 events 2476 unique 1060 catalog unique 680 poscat 657 strongorgcat 1 eligible 29320 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n700 events 9710 unique 2502 catalog unique 1653 poscat 1233 strongorgcat 387 eligible 28347 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n900 events 851 unique 519 catalog unique 334 poscat 26 strongorgcat 16 eligible 29666 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1000 events 694 unique 559 catalog unique 499 poscat 429 strongorgcat 0 eligible 29501 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1100 events 4217 unique 1682 catalog unique 1440 poscat 1337 strongorgcat 271 eligible 28560 seen elig 0 pos elig 0 prefs {'liked': 43, 'disliked': 1} prefcat 44 pref elig 0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:34:42.140Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a576a4cc87d28a79bcc66a370ba4", "usage": { "input_tokens": 33381, "cached_input_tokens": 33024, "cache_write_input_tokens": 0, "output_tokens": 601, "reasoning_output_tokens": 192, "total_tokens": 33982 }, "turn_token_usage": { "input_tokens": 388440, "cached_input_tokens": 357760, "cache_write_input_tokens": 0, "output_tokens": 5736, "reasoning_output_tokens": 1648, "total_tokens": 394176 }, "thread_token_usage": { "input_tokens": 388440, "cached_input_tokens": 357760, "cache_write_input_tokens": 0, "output_tokens": 5736, "reasoning_output_tokens": 1648, "total_tokens": 394176 } } }, { "kind": "context", "timestamp": "2026-09-24T22:34:44.125Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cee12ec8-c0f4-41f9-acc8-ca475da69ed1", "process_id": "65284", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np\nB='/task/data/'\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; its=z['item_ids']; mask=np.unpackbits(z['packed_eligible'][0],count=len(its),bitorder='little').astype(bool); eset=set(its[mask]); cset=set(its)\nfor uid in [100,200,300,600,700,900,1000,1100]:\n row=np.where(uids==uid)[0][0]; em=np.unpackbits(z['packed_eligible'][row],count=len(its),bitorder='little').astype(bool); es=set(its[em])\n d=pd.read_parquet(B+'train_listens.parquet',filters=[('uid','=',uid)])\n pairs=set(d.item_id); pos=set(d.loc[d.played_ratio_pct>=50,'item_id']); strong=set(d.loc[(d.played_ratio_pct>=80)&(d.is_organic==1),'item_id'])\n pref=pd.read_parquet(B+'validation_preference_state.parquet',filters=[('uid','=',uid)])\n print(uid,'events',len(d),'unique',len(pairs),'catalog unique',len(pairs&cset),'poscat',len(pos&cset),'strongorgcat',len(strong&cset),'eligible',len(es),'seen elig',len(pairs&es),'pos elig',len(pos&es),'prefs',pref.state.value_counts().to_dict(),'prefcat',pref.item_id.isin(cset).sum(),'pref elig',pref.item_id.isin(es).sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np\nB='/task/data/'\nz=np.load(B+'validation_eligible_candidates.npz'); uids=z['uids']; its=z['item_ids']; mask=np.unpackbits(z['packed_eligible'][0],count=len(its),bitorder='little').astype(bool); eset=set(its[mask]); cset=set(its)\nfor uid in [100,200,300,600,700,900,1000,1100]:\n row=np.where(uids==uid)[0][0]; em=np.unpackbits(z['packed_eligible'][row],count=len(its),bitorder='little').astype(bool); es=set(its[em])\n d=pd.read_parquet(B+'train_listens.parquet',filters=[('uid','=',uid)])\n pairs=set(d.item_id); pos=set(d.loc[d.played_ratio_pct>=50,'item_id']); strong=set(d.loc[(d.played_ratio_pct>=80)&(d.is_organic==1),'item_id'])\n pref=pd.read_parquet(B+'validation_preference_state.parquet',filters=[('uid','=',uid)])\n print(uid,'events',len(d),'unique',len(pairs),'catalog unique',len(pairs&cset),'poscat',len(pos&cset),'strongorgcat',len(strong&cset),'eligible',len(es),'seen elig',len(pairs&es),'pos elig',len(pos&es),'prefs',pref.state.value_counts().to_dict(),'prefcat',pref.item_id.isin(cset).sum(),'pref elig',pref.item_id.isin(es).sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "100 events 3266 unique 830 catalog unique 682 poscat 600 strongorgcat 146 eligible 29318 seen elig 0 pos elig 0 prefs {'liked': 13, 'disliked': 2} prefcat 15 pref elig 0\n200 events 312 unique 195 catalog unique 168 poscat 163 strongorgcat 163 eligible 29832 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n300 events 699 unique 384 catalog unique 69 poscat 45 strongorgcat 42 eligible 29931 seen elig 0 pos elig 0 prefs {'liked': 3} prefcat 3 pref elig 0\n600 events 2476 unique 1060 catalog unique 680 poscat 657 strongorgcat 1 eligible 29320 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n700 events 9710 unique 2502 catalog unique 1653 poscat 1233 strongorgcat 387 eligible 28347 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n900 events 851 unique 519 catalog unique 334 poscat 26 strongorgcat 16 eligible 29666 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1000 events 694 unique 559 catalog unique 499 poscat 429 strongorgcat 0 eligible 29501 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1100 events 4217 unique 1682 catalog unique 1440 poscat 1337 strongorgcat 271 eligible 28560 seen elig 0 pos elig 0 prefs {'liked': 43, 'disliked': 1} prefcat 44 pref elig 0\n", "stderr": "", "aggregated_output": "100 events 3266 unique 830 catalog unique 682 poscat 600 strongorgcat 146 eligible 29318 seen elig 0 pos elig 0 prefs {'liked': 13, 'disliked': 2} prefcat 15 pref elig 0\n200 events 312 unique 195 catalog unique 168 poscat 163 strongorgcat 163 eligible 29832 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n300 events 699 unique 384 catalog unique 69 poscat 45 strongorgcat 42 eligible 29931 seen elig 0 pos elig 0 prefs {'liked': 3} prefcat 3 pref elig 0\n600 events 2476 unique 1060 catalog unique 680 poscat 657 strongorgcat 1 eligible 29320 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n700 events 9710 unique 2502 catalog unique 1653 poscat 1233 strongorgcat 387 eligible 28347 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n900 events 851 unique 519 catalog unique 334 poscat 26 strongorgcat 16 eligible 29666 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1000 events 694 unique 559 catalog unique 499 poscat 429 strongorgcat 0 eligible 29501 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1100 events 4217 unique 1682 catalog unique 1440 poscat 1337 strongorgcat 271 eligible 28560 seen elig 0 pos elig 0 prefs {'liked': 43, 'disliked': 1} prefcat 44 pref elig 0\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 865972965 }, "formatted_output": "100 events 3266 unique 830 catalog unique 682 poscat 600 strongorgcat 146 eligible 29318 seen elig 0 pos elig 0 prefs {'liked': 13, 'disliked': 2} prefcat 15 pref elig 0\n200 events 312 unique 195 catalog unique 168 poscat 163 strongorgcat 163 eligible 29832 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n300 events 699 unique 384 catalog unique 69 poscat 45 strongorgcat 42 eligible 29931 seen elig 0 pos elig 0 prefs {'liked': 3} prefcat 3 pref elig 0\n600 events 2476 unique 1060 catalog unique 680 poscat 657 strongorgcat 1 eligible 29320 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n700 events 9710 unique 2502 catalog unique 1653 poscat 1233 strongorgcat 387 eligible 28347 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n900 events 851 unique 519 catalog unique 334 poscat 26 strongorgcat 16 eligible 29666 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1000 events 694 unique 559 catalog unique 499 poscat 429 strongorgcat 0 eligible 29501 seen elig 0 pos elig 0 prefs {} prefcat 0 pref elig 0\n1100 events 4217 unique 1682 catalog unique 1440 poscat 1337 strongorgcat 271 eligible 28560 seen elig 0 pos elig 0 prefs {'liked': 43, 'disliked': 1} prefcat 44 pref elig 0\n" } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-24T22:34:55.571Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:34:55.571Z", "text": "" } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-24T22:35:04.891Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:35:04.891Z", "text": "" } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-24T22:35:05.349Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:35:05.349Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:35:06.907Z", "text": "A useful structural fact emerged: eligibility removes every catalog track the user has already played. So this is genuinely a new-track problem—repeat-history heuristics are invalid. I’ve saved an eligibility-valid popularity fallback and am shifting the model toward taste-based collaborative discovery (user-neighbor and latent-factor signals), with organic 80% plays and likes weighted most heavily." }, { "kind": "tool", "timestamp": "2026-09-24T22:35:11.017Z", "callId": "call_gijoB64j5jSVJ0wPAlpvwEsJ", "name": "exec", "input": { "code": "let{output,...rest}=await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nz=np.load('/task/data/test_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\\nuid=[]; iid=[]; rank=[]\\nfor r,x in enumerate(u):\\n ids=it[np.flatnonzero(m[r])[:100]]\\n uid.extend([int(x)]*100); iid.extend(ids.tolist()); rank.extend(range(1,101))\\npd.DataFrame({'uid':uid,'item_id':iid,'rank':rank}).to_parquet('/app/solution/submission.parquet',index=False)\\nprint('saved fallback',len(uid))\\nPY\\nls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});text(rest);text(output);" }, "result": { "timestamp": "2026-09-24T22:35:12.778Z", "content": "Script completed\nWall time 1.8 seconds\nOutput:\n\n{\"chunk_id\":\"f9438b\",\"wall_time_seconds\":1.5929879059999998,\"exit_code\":0,\"original_token_count\":25}\nsaved fallback 845600\n-rw-r--r-- 1 agent agent 719K Sep 24 22:35 /app/solution/submission.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:35:11.114Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a5845ae087d2a875fbe77c7ce96a", "usage": { "input_tokens": 34391, "cached_input_tokens": 33152, "cache_write_input_tokens": 0, "output_tokens": 1377, "reasoning_output_tokens": 1059, "total_tokens": 35768 }, "turn_token_usage": { "input_tokens": 422831, "cached_input_tokens": 390912, "cache_write_input_tokens": 0, "output_tokens": 7113, "reasoning_output_tokens": 2707, "total_tokens": 429944 }, "thread_token_usage": { "input_tokens": 422831, "cached_input_tokens": 390912, "cache_write_input_tokens": 0, "output_tokens": 7113, "reasoning_output_tokens": 2707, "total_tokens": 429944 } } }, { "kind": "context", "timestamp": "2026-09-24T22:35:12.774Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eaf589d8-c054-428f-a307-6cbb9b682d7d", "process_id": "16893", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nz=np.load('/task/data/test_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nuid=[]; iid=[]; rank=[]\nfor r,x in enumerate(u):\n ids=it[np.flatnonzero(m[r])[:100]]\n uid.extend([int(x)]*100); iid.extend(ids.tolist()); rank.extend(range(1,101))\npd.DataFrame({'uid':uid,'item_id':iid,'rank':rank}).to_parquet('/app/solution/submission.parquet',index=False)\nprint('saved fallback',len(uid))\nPY\nls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nz=np.load('/task/data/test_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool)\nuid=[]; iid=[]; rank=[]\nfor r,x in enumerate(u):\n ids=it[np.flatnonzero(m[r])[:100]]\n uid.extend([int(x)]*100); iid.extend(ids.tolist()); rank.extend(range(1,101))\npd.DataFrame({'uid':uid,'item_id':iid,'rank':rank}).to_parquet('/app/solution/submission.parquet',index=False)\nprint('saved fallback',len(uid))\nPY\nls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "saved fallback 845600\n-rw-r--r-- 1 agent agent 719K Sep 24 22:35 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "saved fallback 845600\n-rw-r--r-- 1 agent agent 719K Sep 24 22:35 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 593001386 }, "formatted_output": "saved fallback 845600\n-rw-r--r-- 1 agent agent 719K Sep 24 22:35 /app/solution/submission.parquet\n" } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-24T22:35:23.105Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:35:23.105Z", "text": "" } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-24T22:35:31.651Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:35:31.651Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:35:34.855Z", "callId": "call_JadDQ9JSEE8SYsM7B2dV1qXB", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfor st in ['train','val']:\\n d=pd.read_parquet('/task/data/'+st+'_listens.parquet',columns=['is_organic','played_ratio_pct'])\\n print(st,len(d))\\n print(pd.crosstab(d.is_organic,pd.cut(d.played_ratio_pct,[-1,10,49,79,99,100,1000])).to_string())\\n print(d.groupby('is_organic').played_ratio_pct.describe(percentiles=[.1,.25,.5,.75,.8,.9,.95,.99]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:36:05.055Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:35:34.885Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a5a0f58c87d28e914f9ce6110b42", "usage": { "input_tokens": 35859, "cached_input_tokens": 34176, "cache_write_input_tokens": 0, "output_tokens": 1175, "reasoning_output_tokens": 986, "total_tokens": 37034 }, "turn_token_usage": { "input_tokens": 458690, "cached_input_tokens": 425088, "cache_write_input_tokens": 0, "output_tokens": 8288, "reasoning_output_tokens": 3693, "total_tokens": 466978 }, "thread_token_usage": { "input_tokens": 458690, "cached_input_tokens": 425088, "cache_write_input_tokens": 0, "output_tokens": 8288, "reasoning_output_tokens": 3693, "total_tokens": 466978 } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-24T22:36:07.661Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:36:07.661Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:36:09.502Z", "callId": "call_1VPa8mwIxI18nZyKpZ9qjZ6R", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for p in /proc/[0-9]*; do [ -r \\\"$p/cmdline\\\" ] || continue; c=$(tr '\\\\0' ' ' < \\\"$p/cmdline\\\"); case \\\"$c\\\" in *\\\"played_ratio_pct\\\"*) echo \\\"$p $c\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:36:09.607Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 &1 &1 &1 /dev/null || true; cat /sys/fs/cgroup/memory.current\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:36:13.465Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n1476603904\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:36:13.431Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6cb18f7c-15e1-494b-8505-b43240cef3ff", "process_id": "78272", "command": [ "/bin/sh", "-lc", "for p in /proc/[0-9]*; do [ -r \"$p/status\" ] || continue; pp=$(awk '/PPid/{print $2}' \"$p/status\"); case \"$pp\" in 9644) echo child $p; kill ${p##*/};; esac; done; kill 9644 2>/dev/null || true; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in /proc/[0-9]*; do [ -r \"$p/status\" ] || continue; pp=$(awk '/PPid/{print $2}' \"$p/status\"); case \"$pp\" in 9644) echo child $p; kill ${p##*/};; esac; done; kill 9644 2>/dev/null || true; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1476603904\n", "stderr": "", "aggregated_output": "1476603904\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4560 }, "formatted_output": "1476603904\n" } } }, { "kind": "context", "timestamp": "2026-09-24T22:36:13.463Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a5d9dcb887d28910b6a260ed13ff", "usage": { "input_tokens": 41724, "cached_input_tokens": 36864, "cache_write_input_tokens": 0, "output_tokens": 155, "reasoning_output_tokens": 22, "total_tokens": 41879 }, "turn_token_usage": { "input_tokens": 537471, "cached_input_tokens": 497664, "cache_write_input_tokens": 0, "output_tokens": 8573, "reasoning_output_tokens": 3734, "total_tokens": 546044 }, "thread_token_usage": { "input_tokens": 537471, "cached_input_tokens": 497664, "cache_write_input_tokens": 0, "output_tokens": 8573, "reasoning_output_tokens": 3734, "total_tokens": 546044 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-24T22:36:24.422Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:36:24.422Z", "text": "" } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-24T22:36:33.743Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:36:33.743Z", "text": "" } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-24T22:36:41.501Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:36:41.501Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:36:47.383Z", "callId": "call_Y8ZJ6DuzkhLdTSs9eFMLCqDr", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np,time\\nB='/task/data/'; cat=pd.read_parquet(B+'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); mp=np.full(ids.max()+1,-1,np.int32); mp[ids]=np.arange(len(ids),dtype=np.int32)\\nd=pd.read_parquet(B+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']); print('read',len(d),flush=True)\\nc=mp[d.item_id.to_numpy()]; ok=(c>=0); print('cat events',ok.sum(),flush=True)\\nfor name,m in [('all50',d.played_ratio_pct.to_numpy()>=50),('all80',d.played_ratio_pct.to_numpy()>=80),('org50',(d.played_ratio_pct.to_numpy()>=50)&(d.is_organic.to_numpy()==1)),('org80',(d.played_ratio_pct.to_numpy()>=80)&(d.is_organic.to_numpy()==1))]:\\n x=np.bincount(c[ok&m],minlength=len(ids)); np.save('/app/solution/pop_'+name+'.npy',x); print(name,x.sum(),x.max(),np.corrcoef(x,cat.positive_listens)[0,1],flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:36:48.562Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\nread 43484159\nTraceback (most recent call last):\n File \"\", line 4, in \nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:36:47.430Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a5ddda2c87d2a34f5e9a4d9668ae", "usage": { "input_tokens": 41907, "cached_input_tokens": 41600, "cache_write_input_tokens": 0, "output_tokens": 1795, "reasoning_output_tokens": 1459, "total_tokens": 43702 }, "turn_token_usage": { "input_tokens": 579378, "cached_input_tokens": 539264, "cache_write_input_tokens": 0, "output_tokens": 10368, "reasoning_output_tokens": 5193, "total_tokens": 589746 }, "thread_token_usage": { "input_tokens": 579378, "cached_input_tokens": 539264, "cache_write_input_tokens": 0, "output_tokens": 10368, "reasoning_output_tokens": 5193, "total_tokens": 589746 } } }, { "kind": "context", "timestamp": "2026-09-24T22:36:48.558Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d5066c72-0047-4a22-b401-0af95ba74ea2", "process_id": "31508", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nB='/task/data/'; cat=pd.read_parquet(B+'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); mp=np.full(ids.max()+1,-1,np.int32); mp[ids]=np.arange(len(ids),dtype=np.int32)\nd=pd.read_parquet(B+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']); print('read',len(d),flush=True)\nc=mp[d.item_id.to_numpy()]; ok=(c>=0); print('cat events',ok.sum(),flush=True)\nfor name,m in [('all50',d.played_ratio_pct.to_numpy()>=50),('all80',d.played_ratio_pct.to_numpy()>=80),('org50',(d.played_ratio_pct.to_numpy()>=50)&(d.is_organic.to_numpy()==1)),('org80',(d.played_ratio_pct.to_numpy()>=80)&(d.is_organic.to_numpy()==1))]:\n x=np.bincount(c[ok&m],minlength=len(ids)); np.save('/app/solution/pop_'+name+'.npy',x); print(name,x.sum(),x.max(),np.corrcoef(x,cat.positive_listens)[0,1],flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nB='/task/data/'; cat=pd.read_parquet(B+'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); mp=np.full(ids.max()+1,-1,np.int32); mp[ids]=np.arange(len(ids),dtype=np.int32)\nd=pd.read_parquet(B+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']); print('read',len(d),flush=True)\nc=mp[d.item_id.to_numpy()]; ok=(c>=0); print('cat events',ok.sum(),flush=True)\nfor name,m in [('all50',d.played_ratio_pct.to_numpy()>=50),('all80',d.played_ratio_pct.to_numpy()>=80),('org50',(d.played_ratio_pct.to_numpy()>=50)&(d.is_organic.to_numpy()==1)),('org80',(d.played_ratio_pct.to_numpy()>=80)&(d.is_organic.to_numpy()==1))]:\n x=np.bincount(c[ok&m],minlength=len(ids)); np.save('/app/solution/pop_'+name+'.npy',x); print(name,x.sum(),x.max(),np.corrcoef(x,cat.positive_listens)[0,1],flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "read 43484159\nTraceback (most recent call last):\n File \"\", line 4, in \nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n", "stderr": "", "aggregated_output": "read 43484159\nTraceback (most recent call last):\n File \"\", line 4, in \nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n", "exit_code": 1, "duration": { "secs": 1, "nanos": 6059465 }, "formatted_output": "read 43484159\nTraceback (most recent call last):\n File \"\", line 4, in \nIndexError: index 9390541 is out of bounds for axis 0 with size 9390295\n" } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-24T22:36:50.648Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:36:50.648Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:36:56.196Z", "callId": "call_tCQxBWDiB47EOlNWbL8lzCTG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np\\nB='/task/data/'; cat=pd.read_parquet(B+'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); mp=np.full(10_000_001,-1,np.int32); mp[ids]=np.arange(len(ids),dtype=np.int32)\\nd=pd.read_parquet(B+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']); print('read',len(d),flush=True)\\nc=mp[d.item_id.to_numpy()]; ok=(c>=0); print('cat events',ok.sum(),flush=True); rr=d.played_ratio_pct.to_numpy(); oo=d.is_organic.to_numpy()\\nfor name,m in [('all50',rr>=50),('all80',rr>=80),('org50',(rr>=50)&(oo==1)),('org80',(rr>=80)&(oo==1))]:\\n x=np.bincount(c[ok&m],minlength=len(ids)); np.save('/app/solution/pop_'+name+'.npy',x); print(name,x.sum(),x.max(),np.corrcoef(x,cat.positive_listens)[0,1],flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:36:58.201Z", "content": "Script completed\nWall time 2.0 seconds\nOutput:\n\nread 43484159\ncat events 31249613\nall50 20056329 26615 0.9999999999999998\nall80 18801138 25056 0.9996178554778983\norg50 9417793 20620 0.9277975729995728\norg80 8699160 19362 0.9295555258192555\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:36:56.249Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a600c6d087d28cd8b59282fecfe1", "usage": { "input_tokens": 43775, "cached_input_tokens": 41728, "cache_write_input_tokens": 0, "output_tokens": 361, "reasoning_output_tokens": 45, "total_tokens": 44136 }, "turn_token_usage": { "input_tokens": 623153, "cached_input_tokens": 580992, "cache_write_input_tokens": 0, "output_tokens": 10729, "reasoning_output_tokens": 5238, "total_tokens": 633882 }, "thread_token_usage": { "input_tokens": 623153, "cached_input_tokens": 580992, "cache_write_input_tokens": 0, "output_tokens": 10729, "reasoning_output_tokens": 5238, "total_tokens": 633882 } } }, { "kind": "context", "timestamp": "2026-09-24T22:36:58.197Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-104cec51-135e-4cc4-bd73-cdf5ce7eaca2", "process_id": "53178", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np\nB='/task/data/'; cat=pd.read_parquet(B+'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); mp=np.full(10_000_001,-1,np.int32); mp[ids]=np.arange(len(ids),dtype=np.int32)\nd=pd.read_parquet(B+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']); print('read',len(d),flush=True)\nc=mp[d.item_id.to_numpy()]; ok=(c>=0); print('cat events',ok.sum(),flush=True); rr=d.played_ratio_pct.to_numpy(); oo=d.is_organic.to_numpy()\nfor name,m in [('all50',rr>=50),('all80',rr>=80),('org50',(rr>=50)&(oo==1)),('org80',(rr>=80)&(oo==1))]:\n x=np.bincount(c[ok&m],minlength=len(ids)); np.save('/app/solution/pop_'+name+'.npy',x); print(name,x.sum(),x.max(),np.corrcoef(x,cat.positive_listens)[0,1],flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np\nB='/task/data/'; cat=pd.read_parquet(B+'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); mp=np.full(10_000_001,-1,np.int32); mp[ids]=np.arange(len(ids),dtype=np.int32)\nd=pd.read_parquet(B+'train_listens.parquet',columns=['item_id','is_organic','played_ratio_pct']); print('read',len(d),flush=True)\nc=mp[d.item_id.to_numpy()]; ok=(c>=0); print('cat events',ok.sum(),flush=True); rr=d.played_ratio_pct.to_numpy(); oo=d.is_organic.to_numpy()\nfor name,m in [('all50',rr>=50),('all80',rr>=80),('org50',(rr>=50)&(oo==1)),('org80',(rr>=80)&(oo==1))]:\n x=np.bincount(c[ok&m],minlength=len(ids)); np.save('/app/solution/pop_'+name+'.npy',x); print(name,x.sum(),x.max(),np.corrcoef(x,cat.positive_listens)[0,1],flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "read 43484159\ncat events 31249613\nall50 20056329 26615 0.9999999999999998\nall80 18801138 25056 0.9996178554778983\norg50 9417793 20620 0.9277975729995728\norg80 8699160 19362 0.9295555258192555\n", "stderr": "", "aggregated_output": "read 43484159\ncat events 31249613\nall50 20056329 26615 0.9999999999999998\nall80 18801138 25056 0.9996178554778983\norg50 9417793 20620 0.9277975729995728\norg80 8699160 19362 0.9295555258192555\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 834331156 }, "formatted_output": "read 43484159\ncat events 31249613\nall50 20056329 26615 0.9999999999999998\nall80 18801138 25056 0.9996178554778983\norg50 9417793 20620 0.9277975729995728\norg80 8699160 19362 0.9295555258192555\n" } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-24T22:37:00.193Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:37:00.193Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:37:05.794Z", "callId": "call_Djue08olBdUAI0WPEJnmINhZ", "name": "exec", "input": { "code": "// @exec: {\"yield_time_ms\": 30000, \"max_output_tokens\": 20000}\n\nconst r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nB='/task/data/'; z=np.load(B+'validation_eligible_candidates.npz'); us=z['uids']; ids=z['item_ids']; elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(ids),bitorder='little').astype(bool)\\nfor nm in ['all50','all80','org50','org80']:\\n p=np.load('/app/solution/pop_'+nm+'.npy'); order=np.argsort(-p,kind='stable'); U=[];I=[];R=[]\\n for r,u in enumerate(us):\\n take=order[elig[r,order]][:100]; U.extend([u]*100); I.extend(ids[take]); R.extend(range(1,101))\\n pd.DataFrame({'uid':U,'item_id':I,'rank':R}).to_parquet('/app/solution/validation_pop_'+nm+'.parquet',index=False)\\nPY\\nfor f in /app/solution/validation_pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:37:23.570Z", "content": "Script completed\nWall time 17.8 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_pop_all50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_all80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010727,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:37:05.895Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a60a9fcc87d2a02f96f4a4d4ed2b", "usage": { "input_tokens": 44248, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 39, "total_tokens": 44606 }, "turn_token_usage": { "input_tokens": 667401, "cached_input_tokens": 624640, "cache_write_input_tokens": 0, "output_tokens": 11087, "reasoning_output_tokens": 5277, "total_tokens": 678488 }, "thread_token_usage": { "input_tokens": 667401, "cached_input_tokens": 624640, "cache_write_input_tokens": 0, "output_tokens": 11087, "reasoning_output_tokens": 5277, "total_tokens": 678488 } } }, { "kind": "context", "timestamp": "2026-09-24T22:37:23.565Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60030199-5a20-4c01-95c1-0b4e6c197723", "process_id": "84064", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nB='/task/data/'; z=np.load(B+'validation_eligible_candidates.npz'); us=z['uids']; ids=z['item_ids']; elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(ids),bitorder='little').astype(bool)\nfor nm in ['all50','all80','org50','org80']:\n p=np.load('/app/solution/pop_'+nm+'.npy'); order=np.argsort(-p,kind='stable'); U=[];I=[];R=[]\n for r,u in enumerate(us):\n take=order[elig[r,order]][:100]; U.extend([u]*100); I.extend(ids[take]); R.extend(range(1,101))\n pd.DataFrame({'uid':U,'item_id':I,'rank':R}).to_parquet('/app/solution/validation_pop_'+nm+'.parquet',index=False)\nPY\nfor f in /app/solution/validation_pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nB='/task/data/'; z=np.load(B+'validation_eligible_candidates.npz'); us=z['uids']; ids=z['item_ids']; elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(ids),bitorder='little').astype(bool)\nfor nm in ['all50','all80','org50','org80']:\n p=np.load('/app/solution/pop_'+nm+'.npy'); order=np.argsort(-p,kind='stable'); U=[];I=[];R=[]\n for r,u in enumerate(us):\n take=order[elig[r,order]][:100]; U.extend([u]*100); I.extend(ids[take]); R.extend(range(1,101))\n pd.DataFrame({'uid':U,'item_id':I,'rank':R}).to_parquet('/app/solution/validation_pop_'+nm+'.parquet',index=False)\nPY\nfor f in /app/solution/validation_pop_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_pop_all50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_all80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010727,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_pop_all50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_all80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010727,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 607065072 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_pop_all50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_all80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010727,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org50.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010577,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_pop_org80.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010551,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-24T22:37:35.793Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:37:35.793Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:37:56.296Z", "callId": "call_VnEbrF2wDQeB4qSP6v6uHRFg", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/prepare_cache.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build compact sparse interaction matrices used by the ranker experiments.\\\"\\\"\\\"\\n+\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+N_USERS = 10_001 # Yambda user ids are 100, 200, ..., 1,000,000.\\n+N_ITEM_IDS = 10_000_001\\n+\\n+\\n+def matrix(rows, cols, values, shape):\\n+ ans = sparse.coo_matrix(\\n+ (values.astype(np.float32, copy=False), (rows, cols)), shape=shape\\n+ ).tocsr()\\n+ ans.sum_duplicates()\\n+ return ans\\n+\\n+\\n+def main():\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ item_to_catalog = np.full(N_ITEM_IDS, -1, dtype=np.int32)\\n+ item_to_catalog[catalog_ids] = np.arange(len(catalog_ids), dtype=np.int32)\\n+\\n+ listens = pd.read_parquet(\\n+ DATA / \\\"train_listens.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ rows = (listens.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\\n+ cols = listens.item_id.to_numpy(dtype=np.int32)\\n+ organic = listens.is_organic.to_numpy(dtype=np.uint8) == 1\\n+ ratio = listens.played_ratio_pct.to_numpy(dtype=np.uint16)\\n+ positive = ratio >= 50\\n+ strong = ratio >= 80\\n+ print(f\\\"loaded {len(listens):,} listens\\\", flush=True)\\n+\\n+ # Separate organic and recommendation-surface histories allow the validation\\n+ # sweep to choose their relative contribution without rereading raw logs.\\n+ keep = positive & organic\\n+ x_org = matrix(\\n+ rows[keep], cols[keep], np.ones(keep.sum(), dtype=np.float32),\\n+ (N_USERS, N_ITEM_IDS),\\n+ )\\n+ sparse.save_npz(OUT / \\\"train_x_org.npz\\\", x_org, compressed=False)\\n+ print(f\\\"x_org nnz={x_org.nnz:,}\\\", flush=True)\\n+ del x_org\\n+\\n+ keep = positive & ~organic\\n+ x_rec = matrix(\\n+ rows[keep], cols[keep], np.ones(keep.sum(), dtype=np.float32),\\n+ (N_USERS, N_ITEM_IDS),\\n+ )\\n+ sparse.save_npz(OUT / \\\"train_x_rec.npz\\\", x_rec, compressed=False)\\n+ print(f\\\"x_rec nnz={x_rec.nnz:,}\\\", flush=True)\\n+ del x_rec\\n+\\n+ catalog_cols = item_to_catalog[cols]\\n+ in_catalog = catalog_cols >= 0\\n+ keep = in_catalog & strong & organic\\n+ y_org80 = matrix(\\n+ rows[keep], catalog_cols[keep], np.ones(keep.sum(), dtype=np.float32),\\n+ (N_USERS, len(catalog_ids)),\\n+ )\\n+ sparse.save_npz(OUT / \\\"train_y_org80.npz\\\", y_org80, compressed=False)\\n+ print(f\\\"y_org80 nnz={y_org80.nnz:,}\\\", flush=True)\\n+ del y_org80\\n+\\n+ keep = in_catalog & positive\\n+ # Retain organic provenance in the aggregate rather than treating a\\n+ # platform-fed completion as equal evidence of intent.\\n+ values = 1.0 + 2.0 * organic[keep].astype(np.float32)\\n+ y_pos = matrix(\\n+ rows[keep], catalog_cols[keep], values,\\n+ (N_USERS, len(catalog_ids)),\\n+ )\\n+ sparse.save_npz(OUT / \\\"train_y_pos.npz\\\", y_pos, compressed=False)\\n+ print(f\\\"y_pos nnz={y_pos.nnz:,}\\\", flush=True)\\n+ del y_pos, listens, rows, cols, organic, ratio, positive, strong\\n+\\n+ likes = pd.read_parquet(\\n+ DATA / \\\"train_likes.parquet\\\", columns=[\\\"uid\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ )\\n+ lrows = (likes.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\\n+ lids = likes.item_id.to_numpy(dtype=np.int32)\\n+ lcols = item_to_catalog[lids]\\n+ keep = (lcols >= 0) & (likes.is_organic.to_numpy(dtype=np.uint8) == 1)\\n+ y_like = matrix(\\n+ lrows[keep], lcols[keep], np.ones(keep.sum(), dtype=np.float32),\\n+ (N_USERS, len(catalog_ids)),\\n+ )\\n+ sparse.save_npz(OUT / \\\"train_y_like.npz\\\", y_like, compressed=False)\\n+ print(f\\\"y_like nnz={y_like.nnz:,}\\\", flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:37:56.361Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:37:56.305Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-12a69c17-e922-4ee6-b85f-dd45b18857cb", "changes": { "/app/solution/prepare_cache.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build compact sparse interaction matrices used by the ranker experiments.\"\"\"\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nN_USERS = 10_001 # Yambda user ids are 100, 200, ..., 1,000,000.\nN_ITEM_IDS = 10_000_001\n\n\ndef matrix(rows, cols, values, shape):\n ans = sparse.coo_matrix(\n (values.astype(np.float32, copy=False), (rows, cols)), shape=shape\n ).tocsr()\n ans.sum_duplicates()\n return ans\n\n\ndef main():\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n item_to_catalog = np.full(N_ITEM_IDS, -1, dtype=np.int32)\n item_to_catalog[catalog_ids] = np.arange(len(catalog_ids), dtype=np.int32)\n\n listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n rows = (listens.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\n cols = listens.item_id.to_numpy(dtype=np.int32)\n organic = listens.is_organic.to_numpy(dtype=np.uint8) == 1\n ratio = listens.played_ratio_pct.to_numpy(dtype=np.uint16)\n positive = ratio >= 50\n strong = ratio >= 80\n print(f\"loaded {len(listens):,} listens\", flush=True)\n\n # Separate organic and recommendation-surface histories allow the validation\n # sweep to choose their relative contribution without rereading raw logs.\n keep = positive & organic\n x_org = matrix(\n rows[keep], cols[keep], np.ones(keep.sum(), dtype=np.float32),\n (N_USERS, N_ITEM_IDS),\n )\n sparse.save_npz(OUT / \"train_x_org.npz\", x_org, compressed=False)\n print(f\"x_org nnz={x_org.nnz:,}\", flush=True)\n del x_org\n\n keep = positive & ~organic\n x_rec = matrix(\n rows[keep], cols[keep], np.ones(keep.sum(), dtype=np.float32),\n (N_USERS, N_ITEM_IDS),\n )\n sparse.save_npz(OUT / \"train_x_rec.npz\", x_rec, compressed=False)\n print(f\"x_rec nnz={x_rec.nnz:,}\", flush=True)\n del x_rec\n\n catalog_cols = item_to_catalog[cols]\n in_catalog = catalog_cols >= 0\n keep = in_catalog & strong & organic\n y_org80 = matrix(\n rows[keep], catalog_cols[keep], np.ones(keep.sum(), dtype=np.float32),\n (N_USERS, len(catalog_ids)),\n )\n sparse.save_npz(OUT / \"train_y_org80.npz\", y_org80, compressed=False)\n print(f\"y_org80 nnz={y_org80.nnz:,}\", flush=True)\n del y_org80\n\n keep = in_catalog & positive\n # Retain organic provenance in the aggregate rather than treating a\n # platform-fed completion as equal evidence of intent.\n values = 1.0 + 2.0 * organic[keep].astype(np.float32)\n y_pos = matrix(\n rows[keep], catalog_cols[keep], values,\n (N_USERS, len(catalog_ids)),\n )\n sparse.save_npz(OUT / \"train_y_pos.npz\", y_pos, compressed=False)\n print(f\"y_pos nnz={y_pos.nnz:,}\", flush=True)\n del y_pos, listens, rows, cols, organic, ratio, positive, strong\n\n likes = pd.read_parquet(\n DATA / \"train_likes.parquet\", columns=[\"uid\", \"item_id\", \"is_organic\"]\n )\n lrows = (likes.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\n lids = likes.item_id.to_numpy(dtype=np.int32)\n lcols = item_to_catalog[lids]\n keep = (lcols >= 0) & (likes.is_organic.to_numpy(dtype=np.uint8) == 1)\n y_like = matrix(\n lrows[keep], lcols[keep], np.ones(keep.sum(), dtype=np.float32),\n (N_USERS, len(catalog_ids)),\n )\n sparse.save_npz(OUT / \"train_y_like.npz\", y_like, compressed=False)\n print(f\"y_like nnz={y_like.nnz:,}\", flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/prepare_cache.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:37:56.360Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a623c5b087d2adbbdff633074887", "usage": { "input_tokens": 44821, "cached_input_tokens": 44032, "cache_write_input_tokens": 0, "output_tokens": 1770, "reasoning_output_tokens": 482, "total_tokens": 46591 }, "turn_token_usage": { "input_tokens": 712222, "cached_input_tokens": 668672, "cache_write_input_tokens": 0, "output_tokens": 12857, "reasoning_output_tokens": 5759, "total_tokens": 725079 }, "thread_token_usage": { "input_tokens": 712222, "cached_input_tokens": 668672, "cache_write_input_tokens": 0, "output_tokens": 12857, "reasoning_output_tokens": 5759, "total_tokens": 725079 } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-24T22:37:58.107Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:37:58.107Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:37:59.162Z", "callId": "call_X1AEHCepP300lHSYWYcSpJTW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/prepare_cache.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:38:00.355Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"be4660\",\"wall_time_seconds\":1.001317547,\"session_id\":81013,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:37:59.260Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a644a94887d28ef12af732df9db8", "usage": { "input_tokens": 46615, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 10, "total_tokens": 46691 }, "turn_token_usage": { "input_tokens": 758837, "cached_input_tokens": 713344, "cache_write_input_tokens": 0, "output_tokens": 12933, "reasoning_output_tokens": 5769, "total_tokens": 771770 }, "thread_token_usage": { "input_tokens": 758837, "cached_input_tokens": 713344, "cache_write_input_tokens": 0, "output_tokens": 12933, "reasoning_output_tokens": 5769, "total_tokens": 771770 } } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-24T22:38:01.415Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:38:01.415Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:38:02.078Z", "callId": "call_lBSrjyoouRmtnrJPAnjPUfuT", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81013,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:38:04.552Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\nloaded 43,484,159 listens\nx_org nnz=3,349,049\nx_rec nnz=6,141,177\ny_org80 nnz=1,950,643\ny_pos nnz=5,564,926\ny_like nnz=287,985\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:38:02.177Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6488fe087d293cbcbe8b36d99f0", "usage": { "input_tokens": 46748, "cached_input_tokens": 46464, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 46801 }, "turn_token_usage": { "input_tokens": 805585, "cached_input_tokens": 759808, "cache_write_input_tokens": 0, "output_tokens": 12986, "reasoning_output_tokens": 5775, "total_tokens": 818571 }, "thread_token_usage": { "input_tokens": 805585, "cached_input_tokens": 759808, "cache_write_input_tokens": 0, "output_tokens": 12986, "reasoning_output_tokens": 5775, "total_tokens": 818571 } } }, { "kind": "context", "timestamp": "2026-09-24T22:38:04.550Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8beaec2b-9326-46bb-851a-71447ad1e4c4", "process_id": "81013", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/prepare_cache.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/prepare_cache.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 43,484,159 listens\nx_org nnz=3,349,049\nx_rec nnz=6,141,177\ny_org80 nnz=1,950,643\ny_pos nnz=5,564,926\ny_like nnz=287,985\n", "stderr": "", "aggregated_output": "loaded 43,484,159 listens\nx_org nnz=3,349,049\nx_rec nnz=6,141,177\ny_org80 nnz=1,950,643\ny_pos nnz=5,564,926\ny_like nnz=287,985\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 197278658 }, "formatted_output": "loaded 43,484,159 listens\nx_org nnz=3,349,049\nx_rec nnz=6,141,177\ny_org80 nnz=1,950,643\ny_pos nnz=5,564,926\ny_like nnz=287,985\n" } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-24T22:38:14.724Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:38:14.724Z", "text": "" } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-24T22:38:24.061Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:38:24.061Z", "text": "" } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-24T22:38:29.048Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:38:29.048Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:39:03.395Z", "callId": "call_J6Buxf0p4Q9V5l8pJfbO6p1L", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/knn_experiment.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Validation-only collaborative-filtering sweep.\\n+\\n+This script never reads validation events. It builds rankings from training\\n+history and the validation-cutoff eligibility service; scores are obtained\\n+separately with the public evaluator.\\n+\\\"\\\"\\\"\\n+\\n+from pathlib import Path\\n+import gc\\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+TOP_NEIGHBORS = 400\\n+\\n+\\n+def tfidf_profile(x: sparse.csr_matrix, idf_power: float = 1.0):\\n+ x = x.astype(np.float32, copy=True)\\n+ # Repetition is useful, but sublinear: a person's 20th play should not\\n+ # overwhelm the rest of their taste profile.\\n+ x.data = np.log1p(x.data)\\n+ df = np.bincount(x.indices, minlength=x.shape[1])\\n+ idf = np.log1p(x.shape[0] / (1.0 + df)).astype(np.float32)\\n+ if idf_power != 1.0:\\n+ idf **= idf_power\\n+ x.data *= idf[x.indices]\\n+ x.eliminate_zeros()\\n+ normalize(x, norm=\\\"l2\\\", axis=1, copy=False)\\n+ return x\\n+\\n+\\n+def nearest(x: sparse.csr_matrix, target_rows: np.ndarray, name: str):\\n+ idx_path = OUT / f\\\"nbr_{name}_idx.npy\\\"\\n+ sim_path = OUT / f\\\"nbr_{name}_sim.npy\\\"\\n+ if idx_path.exists() and sim_path.exists():\\n+ return np.load(idx_path), np.load(sim_path)\\n+ n = len(target_rows)\\n+ all_idx = np.empty((n, TOP_NEIGHBORS), dtype=np.int32)\\n+ all_sim = np.empty((n, TOP_NEIGHBORS), dtype=np.float32)\\n+ for start in range(0, n, 256):\\n+ stop = min(n, start + 256)\\n+ sims = (x[target_rows[start:stop]] @ x.T).toarray().astype(np.float32)\\n+ sims[np.arange(stop - start), target_rows[start:stop]] = -1.0\\n+ part = np.argpartition(sims, -TOP_NEIGHBORS, axis=1)[:, -TOP_NEIGHBORS:]\\n+ vals = np.take_along_axis(sims, part, axis=1)\\n+ order = np.argsort(-vals, axis=1)\\n+ all_idx[start:stop] = np.take_along_axis(part, order, axis=1)\\n+ all_sim[start:stop] = np.take_along_axis(vals, order, axis=1)\\n+ print(f\\\"{name} neighbors {stop}/{n}\\\", flush=True)\\n+ np.save(idx_path, all_idx)\\n+ np.save(sim_path, all_sim)\\n+ return all_idx, all_sim\\n+\\n+\\n+def output_matrix(kind: str):\\n+ yorg = sparse.load_npz(OUT / \\\"train_y_org80.npz\\\").astype(np.float32)\\n+ ylike = sparse.load_npz(OUT / \\\"train_y_like.npz\\\").astype(np.float32)\\n+ if kind == \\\"obj\\\":\\n+ yorg.data = np.log1p(yorg.data)\\n+ ylike.data = np.ones_like(ylike.data) * 2.0\\n+ y = yorg + ylike\\n+ elif kind == \\\"orgbin\\\":\\n+ yorg.data = np.ones_like(yorg.data)\\n+ y = yorg\\n+ elif kind == \\\"pos\\\":\\n+ y = sparse.load_npz(OUT / \\\"train_y_pos.npz\\\").astype(np.float32)\\n+ y.data = np.log1p(y.data)\\n+ else:\\n+ raise ValueError(kind)\\n+ y.sum_duplicates()\\n+ return y.tocsr()\\n+\\n+\\n+def rank_variant(name, nbr_idx, nbr_sim, y, eligible, uids, item_ids,\\n+ k=100, sim_power=1.0, pop_weight=0.0):\\n+ pop = np.load(OUT / \\\"pop_all80.npy\\\").astype(np.float32)\\n+ pop = np.log1p(pop)\\n+ pop = (pop - pop.mean()) / pop.std()\\n+ out_uid, out_item, out_rank = [], [], []\\n+ n = len(uids)\\n+ for start in range(0, n, 128):\\n+ stop = min(n, start + 128)\\n+ rr = np.repeat(np.arange(stop - start), k)\\n+ cc = nbr_idx[start:stop, :k].reshape(-1)\\n+ vv = np.maximum(nbr_sim[start:stop, :k].reshape(-1), 0) ** sim_power\\n+ w = sparse.csr_matrix((vv, (rr, cc)), shape=(stop - start, y.shape[0]))\\n+ scores = (w @ y).toarray().astype(np.float32)\\n+ # Per-user neighbor vote normalization makes a small popularity prior\\n+ # comparable across users with very different history depths.\\n+ if pop_weight:\\n+ scale = np.maximum(scores.max(axis=1, keepdims=True), 1e-6)\\n+ scores += pop_weight * scale * pop[None, :]\\n+ scores[~eligible[start:stop]] = -np.inf\\n+ take = np.argpartition(scores, -100, axis=1)[:, -100:]\\n+ vals = np.take_along_axis(scores, take, axis=1)\\n+ order = np.argsort(-vals, axis=1)\\n+ take = np.take_along_axis(take, order, axis=1)\\n+ for j, uid in enumerate(uids[start:stop]):\\n+ out_uid.extend([int(uid)] * 100)\\n+ out_item.extend(item_ids[take[j]].tolist())\\n+ out_rank.extend(range(1, 101))\\n+ pd.DataFrame({\\\"uid\\\": out_uid, \\\"item_id\\\": out_item, \\\"rank\\\": out_rank}).to_parquet(\\n+ OUT / f\\\"validation_{name}.parquet\\\", index=False\\n+ )\\n+ print(f\\\"wrote {name}\\\", flush=True)\\n+\\n+\\n+def main():\\n+ artifact = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ uids = artifact[\\\"uids\\\"].astype(np.int64)\\n+ item_ids = artifact[\\\"item_ids\\\"].astype(np.int64)\\n+ eligible = np.unpackbits(\\n+ artifact[\\\"packed_eligible\\\"], axis=1, count=len(item_ids), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ target_rows = (uids // 100).astype(np.int32)\\n+\\n+ xorg = tfidf_profile(sparse.load_npz(OUT / \\\"train_x_org.npz\\\"))\\n+ oi, os = nearest(xorg, target_rows, \\\"org\\\")\\n+ del xorg\\n+ gc.collect()\\n+\\n+ xrec = tfidf_profile(sparse.load_npz(OUT / \\\"train_x_rec.npz\\\"))\\n+ ri, rs = nearest(xrec, target_rows, \\\"rec\\\")\\n+ del xrec\\n+ gc.collect()\\n+\\n+ for kind in (\\\"obj\\\", \\\"pos\\\"):\\n+ y = output_matrix(kind)\\n+ for k in (30, 100, 400):\\n+ rank_variant(f\\\"org_{kind}_k{k}\\\", oi, os, y, eligible, uids, item_ids, k=k)\\n+ rank_variant(f\\\"rec_{kind}_k100\\\", ri, rs, y, eligible, uids, item_ids, k=100)\\n+ del y\\n+ gc.collect()\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:39:03.483Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:39:03.404Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-350fd7e8-fbcd-4a53-86f0-0c82ca063953", "changes": { "/app/solution/knn_experiment.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Validation-only collaborative-filtering sweep.\n\nThis script never reads validation events. It builds rankings from training\nhistory and the validation-cutoff eligibility service; scores are obtained\nseparately with the public evaluator.\n\"\"\"\n\nfrom pathlib import Path\nimport gc\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\")\nTOP_NEIGHBORS = 400\n\n\ndef tfidf_profile(x: sparse.csr_matrix, idf_power: float = 1.0):\n x = x.astype(np.float32, copy=True)\n # Repetition is useful, but sublinear: a person's 20th play should not\n # overwhelm the rest of their taste profile.\n x.data = np.log1p(x.data)\n df = np.bincount(x.indices, minlength=x.shape[1])\n idf = np.log1p(x.shape[0] / (1.0 + df)).astype(np.float32)\n if idf_power != 1.0:\n idf **= idf_power\n x.data *= idf[x.indices]\n x.eliminate_zeros()\n normalize(x, norm=\"l2\", axis=1, copy=False)\n return x\n\n\ndef nearest(x: sparse.csr_matrix, target_rows: np.ndarray, name: str):\n idx_path = OUT / f\"nbr_{name}_idx.npy\"\n sim_path = OUT / f\"nbr_{name}_sim.npy\"\n if idx_path.exists() and sim_path.exists():\n return np.load(idx_path), np.load(sim_path)\n n = len(target_rows)\n all_idx = np.empty((n, TOP_NEIGHBORS), dtype=np.int32)\n all_sim = np.empty((n, TOP_NEIGHBORS), dtype=np.float32)\n for start in range(0, n, 256):\n stop = min(n, start + 256)\n sims = (x[target_rows[start:stop]] @ x.T).toarray().astype(np.float32)\n sims[np.arange(stop - start), target_rows[start:stop]] = -1.0\n part = np.argpartition(sims, -TOP_NEIGHBORS, axis=1)[:, -TOP_NEIGHBORS:]\n vals = np.take_along_axis(sims, part, axis=1)\n order = np.argsort(-vals, axis=1)\n all_idx[start:stop] = np.take_along_axis(part, order, axis=1)\n all_sim[start:stop] = np.take_along_axis(vals, order, axis=1)\n print(f\"{name} neighbors {stop}/{n}\", flush=True)\n np.save(idx_path, all_idx)\n np.save(sim_path, all_sim)\n return all_idx, all_sim\n\n\ndef output_matrix(kind: str):\n yorg = sparse.load_npz(OUT / \"train_y_org80.npz\").astype(np.float32)\n ylike = sparse.load_npz(OUT / \"train_y_like.npz\").astype(np.float32)\n if kind == \"obj\":\n yorg.data = np.log1p(yorg.data)\n ylike.data = np.ones_like(ylike.data) * 2.0\n y = yorg + ylike\n elif kind == \"orgbin\":\n yorg.data = np.ones_like(yorg.data)\n y = yorg\n elif kind == \"pos\":\n y = sparse.load_npz(OUT / \"train_y_pos.npz\").astype(np.float32)\n y.data = np.log1p(y.data)\n else:\n raise ValueError(kind)\n y.sum_duplicates()\n return y.tocsr()\n\n\ndef rank_variant(name, nbr_idx, nbr_sim, y, eligible, uids, item_ids,\n k=100, sim_power=1.0, pop_weight=0.0):\n pop = np.load(OUT / \"pop_all80.npy\").astype(np.float32)\n pop = np.log1p(pop)\n pop = (pop - pop.mean()) / pop.std()\n out_uid, out_item, out_rank = [], [], []\n n = len(uids)\n for start in range(0, n, 128):\n stop = min(n, start + 128)\n rr = np.repeat(np.arange(stop - start), k)\n cc = nbr_idx[start:stop, :k].reshape(-1)\n vv = np.maximum(nbr_sim[start:stop, :k].reshape(-1), 0) ** sim_power\n w = sparse.csr_matrix((vv, (rr, cc)), shape=(stop - start, y.shape[0]))\n scores = (w @ y).toarray().astype(np.float32)\n # Per-user neighbor vote normalization makes a small popularity prior\n # comparable across users with very different history depths.\n if pop_weight:\n scale = np.maximum(scores.max(axis=1, keepdims=True), 1e-6)\n scores += pop_weight * scale * pop[None, :]\n scores[~eligible[start:stop]] = -np.inf\n take = np.argpartition(scores, -100, axis=1)[:, -100:]\n vals = np.take_along_axis(scores, take, axis=1)\n order = np.argsort(-vals, axis=1)\n take = np.take_along_axis(take, order, axis=1)\n for j, uid in enumerate(uids[start:stop]):\n out_uid.extend([int(uid)] * 100)\n out_item.extend(item_ids[take[j]].tolist())\n out_rank.extend(range(1, 101))\n pd.DataFrame({\"uid\": out_uid, \"item_id\": out_item, \"rank\": out_rank}).to_parquet(\n OUT / f\"validation_{name}.parquet\", index=False\n )\n print(f\"wrote {name}\", flush=True)\n\n\ndef main():\n artifact = np.load(DATA / \"validation_eligible_candidates.npz\")\n uids = artifact[\"uids\"].astype(np.int64)\n item_ids = artifact[\"item_ids\"].astype(np.int64)\n eligible = np.unpackbits(\n artifact[\"packed_eligible\"], axis=1, count=len(item_ids), bitorder=\"little\"\n ).astype(bool)\n target_rows = (uids // 100).astype(np.int32)\n\n xorg = tfidf_profile(sparse.load_npz(OUT / \"train_x_org.npz\"))\n oi, os = nearest(xorg, target_rows, \"org\")\n del xorg\n gc.collect()\n\n xrec = tfidf_profile(sparse.load_npz(OUT / \"train_x_rec.npz\"))\n ri, rs = nearest(xrec, target_rows, \"rec\")\n del xrec\n gc.collect()\n\n for kind in (\"obj\", \"pos\"):\n y = output_matrix(kind)\n for k in (30, 100, 400):\n rank_variant(f\"org_{kind}_k{k}\", oi, os, y, eligible, uids, item_ids, k=k)\n rank_variant(f\"rec_{kind}_k100\", ri, rs, y, eligible, uids, item_ids, k=100)\n del y\n gc.collect()\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/knn_experiment.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:39:03.481Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a64cba3087d286274fcbd8d0cc7b", "usage": { "input_tokens": 46887, "cached_input_tokens": 46592, "cache_write_input_tokens": 0, "output_tokens": 3222, "reasoning_output_tokens": 1304, "total_tokens": 50109 }, "turn_token_usage": { "input_tokens": 852472, "cached_input_tokens": 806400, "cache_write_input_tokens": 0, "output_tokens": 16208, "reasoning_output_tokens": 7079, "total_tokens": 868680 }, "thread_token_usage": { "input_tokens": 852472, "cached_input_tokens": 806400, "cache_write_input_tokens": 0, "output_tokens": 16208, "reasoning_output_tokens": 7079, "total_tokens": 868680 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-24T22:39:04.757Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:39:04.757Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:39:05.842Z", "callId": "call_vqupDZlLMxMFsmp3xzBNuzs8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/knn_experiment.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:39:07.055Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"6542b1\",\"wall_time_seconds\":1.000993385,\"session_id\":6292,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:39:06.006Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a687d2d487d2b6afee9a02ee353b", "usage": { "input_tokens": 50133, "cached_input_tokens": 46720, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 8, "total_tokens": 50209 }, "turn_token_usage": { "input_tokens": 902605, "cached_input_tokens": 853120, "cache_write_input_tokens": 0, "output_tokens": 16284, "reasoning_output_tokens": 7087, "total_tokens": 918889 }, "thread_token_usage": { "input_tokens": 902605, "cached_input_tokens": 853120, "cache_write_input_tokens": 0, "output_tokens": 16284, "reasoning_output_tokens": 7087, "total_tokens": 918889 } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-24T22:39:08.747Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:39:08.747Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:39:09.431Z", "callId": "call_yymGdBUX2hGMe9X1uh6pUBjd", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6292,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:39:19.439Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\norg neighbors 256/8456\norg neighbors 512/8456\norg neighbors 768/8456\norg neighbors 1024/8456\norg neighbors 1280/8456\norg neighbors 1536/8456\norg neighbors 1792/8456\norg neighbors 2048/8456\norg neighbors 2304/8456\norg neighbors 2560/8456\norg neighbors 2816/8456\norg neighbors 3072/8456\norg neighbors 3328/8456\norg neighbors 3584/8456\norg neighbors 3840/8456\norg neighbors 4096/8456\norg neighbors 4352/8456\norg neighbors 4608/8456\norg neighbors 4864/8456\norg neighbors 5120/8456\norg neighbors 5376/8456\norg neighbors 5632/8456\norg neighbors 5888/8456\norg neighbors 6144/8456\norg neighbors 6400/8456\norg neighbors 6656/8456\norg neighbors 6912/8456\norg neighbors 7168/8456\norg neighbors 7424/8456\norg neighbors 7680/8456\norg neighbors 7936/8456\norg neighbors 8192/8456\norg neighbors 8448/8456\norg neighbors 8456/8456\nrec neighbors 256/8456\nrec neighbors 512/8456\nrec neighbors 768/8456\nrec neighbors 1024/8456\nrec neighbors 1280/8456\nrec neighbors 1536/8456\nrec neighbors 1792/8456\nrec neighbors 2048/8456\nrec neighbors 2304/8456\nrec neighbors 2560/8456\nrec neighbors 2816/8456\nrec neighbors 3072/8456\nrec neighbors 3328/8456\nrec neighbors 3584/8456\nrec neighbors 3840/8456\nrec neighbors 4096/8456\nrec neighbors 4352/8456\nrec neighbors 4608/8456\nrec neighbors 4864/8456\nrec neighbors 5120/8456\nrec neighbors 5376/8456\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:39:09.537Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a68b3a8c87d2b5ae8ce551ec7430", "usage": { "input_tokens": 50267, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 50323 }, "turn_token_usage": { "input_tokens": 952872, "cached_input_tokens": 903040, "cache_write_input_tokens": 0, "output_tokens": 16340, "reasoning_output_tokens": 7096, "total_tokens": 969212 }, "thread_token_usage": { "input_tokens": 952872, "cached_input_tokens": 903040, "cache_write_input_tokens": 0, "output_tokens": 16340, "reasoning_output_tokens": 7096, "total_tokens": 969212 } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-24T22:39:20.697Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:39:20.697Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:39:21.402Z", "callId": "call_qWPTxUc4lW7TarB1rfzxbIGi", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6292,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:39:31.410Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\nrec neighbors 5632/8456\nrec neighbors 5888/8456\nrec neighbors 6144/8456\nrec neighbors 6400/8456\nrec neighbors 6656/8456\nrec neighbors 6912/8456\nrec neighbors 7168/8456\nrec neighbors 7424/8456\nrec neighbors 7680/8456\nrec neighbors 7936/8456\nrec neighbors 8192/8456\nrec neighbors 8448/8456\nrec neighbors 8456/8456\nwrote org_obj_k30\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:39:21.493Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6979b9487d29310d14c84a9b5d7", "usage": { "input_tokens": 50835, "cached_input_tokens": 50048, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 50890 }, "turn_token_usage": { "input_tokens": 1003707, "cached_input_tokens": 953088, "cache_write_input_tokens": 0, "output_tokens": 16395, "reasoning_output_tokens": 7104, "total_tokens": 1020102 }, "thread_token_usage": { "input_tokens": 1003707, "cached_input_tokens": 953088, "cache_write_input_tokens": 0, "output_tokens": 16395, "reasoning_output_tokens": 7104, "total_tokens": 1020102 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-24T22:39:33.081Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:39:33.081Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:39:33.779Z", "callId": "call_ukGk6jSwvw3leS5ohQWkzKjN", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6292,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:39:43.787Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\nwrote org_obj_k100\nwrote org_obj_k400\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:39:33.882Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6a3992c87d2b18fd3e2a187c678", "usage": { "input_tokens": 51037, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 51101 }, "turn_token_usage": { "input_tokens": 1054744, "cached_input_tokens": 1003776, "cache_write_input_tokens": 0, "output_tokens": 16459, "reasoning_output_tokens": 7121, "total_tokens": 1071203 }, "thread_token_usage": { "input_tokens": 1054744, "cached_input_tokens": 1003776, "cache_write_input_tokens": 0, "output_tokens": 16459, "reasoning_output_tokens": 7121, "total_tokens": 1071203 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-24T22:39:45.742Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:39:45.742Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:39:50.426Z", "callId": "call_xm0CesnkeLxNWfiu8Jy8eTR8", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6292,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:40:00.435Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\nwrote rec_obj_k100\nwrote org_pos_k30\nwrote org_pos_k100\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:39:50.532Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6aff77887d2a14de00be65d148c", "usage": { "input_tokens": 51138, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 51191 }, "turn_token_usage": { "input_tokens": 1105882, "cached_input_tokens": 1054592, "cache_write_input_tokens": 0, "output_tokens": 16512, "reasoning_output_tokens": 7127, "total_tokens": 1122394 }, "thread_token_usage": { "input_tokens": 1105882, "cached_input_tokens": 1054592, "cache_write_input_tokens": 0, "output_tokens": 16512, "reasoning_output_tokens": 7127, "total_tokens": 1122394 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-24T22:40:01.936Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:40:01.936Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:40:02.623Z", "callId": "call_cpneStMujRAAeh41iftGDZ2M", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6292,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:40:12.632Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\nwrote org_pos_k400\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:40:02.711Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6c09c9487d28c34e08e3d6464e0", "usage": { "input_tokens": 51235, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 51288 }, "turn_token_usage": { "input_tokens": 1157117, "cached_input_tokens": 1105536, "cache_write_input_tokens": 0, "output_tokens": 16565, "reasoning_output_tokens": 7133, "total_tokens": 1173682 }, "thread_token_usage": { "input_tokens": 1157117, "cached_input_tokens": 1105536, "cache_write_input_tokens": 0, "output_tokens": 16565, "reasoning_output_tokens": 7133, "total_tokens": 1173682 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:40:16.342Z", "callId": "call_kBFpidVIhINyaHXxqt6eMclO", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":6292,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:40:17.694Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\nwrote rec_pos_k100\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:40:16.430Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6ccdc0c87d29b87312d408d3100", "usage": { "input_tokens": 51318, "cached_input_tokens": 51072, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 51363 }, "turn_token_usage": { "input_tokens": 1208435, "cached_input_tokens": 1156608, "cache_write_input_tokens": 0, "output_tokens": 16610, "reasoning_output_tokens": 7133, "total_tokens": 1225045 }, "thread_token_usage": { "input_tokens": 1208435, "cached_input_tokens": 1156608, "cache_write_input_tokens": 0, "output_tokens": 16610, "reasoning_output_tokens": 7133, "total_tokens": 1225045 } } }, { "kind": "context", "timestamp": "2026-09-24T22:40:17.692Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3a5dbcdf-a641-42a8-80cd-ba38392814a8", "process_id": "6292", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/knn_experiment.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/knn_experiment.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org neighbors 256/8456\norg neighbors 512/8456\norg neighbors 768/8456\norg neighbors 1024/8456\norg neighbors 1280/8456\norg neighbors 1536/8456\norg neighbors 1792/8456\norg neighbors 2048/8456\norg neighbors 2304/8456\norg neighbors 2560/8456\norg neighbors 2816/8456\norg neighbors 3072/8456\norg neighbors 3328/8456\norg neighbors 3584/8456\norg neighbors 3840/8456\norg neighbors 4096/8456\norg neighbors 4352/8456\norg neighbors 4608/8456\norg neighbors 4864/8456\norg neighbors 5120/8456\norg neighbors 5376/8456\norg neighbors 5632/8456\norg neighbors 5888/8456\norg neighbors 6144/8456\norg neighbors 6400/8456\norg neighbors 6656/8456\norg neighbors 6912/8456\norg neighbors 7168/8456\norg neighbors 7424/8456\norg neighbors 7680/8456\norg neighbors 7936/8456\norg neighbors 8192/8456\norg neighbors 8448/8456\norg neighbors 8456/8456\nrec neighbors 256/8456\nrec neighbors 512/8456\nrec neighbors 768/8456\nrec neighbors 1024/8456\nrec neighbors 1280/8456\nrec neighbors 1536/8456\nrec neighbors 1792/8456\nrec neighbors 2048/8456\nrec neighbors 2304/8456\nrec neighbors 2560/8456\nrec neighbors 2816/8456\nrec neighbors 3072/8456\nrec neighbors 3328/8456\nrec neighbors 3584/8456\nrec neighbors 3840/8456\nrec neighbors 4096/8456\nrec neighbors 4352/8456\nrec neighbors 4608/8456\nrec neighbors 4864/8456\nrec neighbors 5120/8456\nrec neighbors 5376/8456\nrec neighbors 5632/8456\nrec neighbors 5888/8456\nrec neighbors 6144/8456\nrec neighbors 6400/8456\nrec neighbors 6656/8456\nrec neighbors 6912/8456\nrec neighbors 7168/8456\nrec neighbors 7424/8456\nrec neighbors 7680/8456\nrec neighbors 7936/8456\nrec neighbors 8192/8456\nrec neighbors 8448/8456\nrec neighbors 8456/8456\nwrote org_obj_k30\nwrote org_obj_k100\nwrote org_obj_k400\nwrote rec_obj_k100\nwrote org_pos_k30\nwrote org_pos_k100\nwrote org_pos_k400\nwrote rec_pos_k100\n", "stderr": "", "aggregated_output": "org neighbors 256/8456\norg neighbors 512/8456\norg neighbors 768/8456\norg neighbors 1024/8456\norg neighbors 1280/8456\norg neighbors 1536/8456\norg neighbors 1792/8456\norg neighbors 2048/8456\norg neighbors 2304/8456\norg neighbors 2560/8456\norg neighbors 2816/8456\norg neighbors 3072/8456\norg neighbors 3328/8456\norg neighbors 3584/8456\norg neighbors 3840/8456\norg neighbors 4096/8456\norg neighbors 4352/8456\norg neighbors 4608/8456\norg neighbors 4864/8456\norg neighbors 5120/8456\norg neighbors 5376/8456\norg neighbors 5632/8456\norg neighbors 5888/8456\norg neighbors 6144/8456\norg neighbors 6400/8456\norg neighbors 6656/8456\norg neighbors 6912/8456\norg neighbors 7168/8456\norg neighbors 7424/8456\norg neighbors 7680/8456\norg neighbors 7936/8456\norg neighbors 8192/8456\norg neighbors 8448/8456\norg neighbors 8456/8456\nrec neighbors 256/8456\nrec neighbors 512/8456\nrec neighbors 768/8456\nrec neighbors 1024/8456\nrec neighbors 1280/8456\nrec neighbors 1536/8456\nrec neighbors 1792/8456\nrec neighbors 2048/8456\nrec neighbors 2304/8456\nrec neighbors 2560/8456\nrec neighbors 2816/8456\nrec neighbors 3072/8456\nrec neighbors 3328/8456\nrec neighbors 3584/8456\nrec neighbors 3840/8456\nrec neighbors 4096/8456\nrec neighbors 4352/8456\nrec neighbors 4608/8456\nrec neighbors 4864/8456\nrec neighbors 5120/8456\nrec neighbors 5376/8456\nrec neighbors 5632/8456\nrec neighbors 5888/8456\nrec neighbors 6144/8456\nrec neighbors 6400/8456\nrec neighbors 6656/8456\nrec neighbors 6912/8456\nrec neighbors 7168/8456\nrec neighbors 7424/8456\nrec neighbors 7680/8456\nrec neighbors 7936/8456\nrec neighbors 8192/8456\nrec neighbors 8448/8456\nrec neighbors 8456/8456\nwrote org_obj_k30\nwrote org_obj_k100\nwrote org_obj_k400\nwrote rec_obj_k100\nwrote org_pos_k30\nwrote org_pos_k100\nwrote org_pos_k400\nwrote rec_pos_k100\n", "exit_code": 0, "duration": { "secs": 71, "nanos": 637607460 }, "formatted_output": "org neighbors 256/8456\norg neighbors 512/8456\norg neighbors 768/8456\norg neighbors 1024/8456\norg neighbors 1280/8456\norg neighbors 1536/8456\norg neighbors 1792/8456\norg neighbors 2048/8456\norg neighbors 2304/8456\norg neighbors 2560/8456\norg neighbors 2816/8456\norg neighbors 3072/8456\norg neighbors 3328/8456\norg neighbors 3584/8456\norg neighbors 3840/8456\norg neighbors 4096/8456\norg neighbors 4352/8456\norg neighbors 4608/8456\norg neighbors 4864/8456\norg neighbors 5120/8456\norg neighbors 5376/8456\norg neighbors 5632/8456\norg neighbors 5888/8456\norg neighbors 6144/8456\norg neighbors 6400/8456\norg neighbors 6656/8456\norg neighbors 6912/8456\norg neighbors 7168/8456\norg neighbors 7424/8456\norg neighbors 7680/8456\norg neighbors 7936/8456\norg neighbors 8192/8456\norg neighbors 8448/8456\norg neighbors 8456/8456\nrec neighbors 256/8456\nrec neighbors 512/8456\nrec neighbors 768/8456\nrec neighbors 1024/8456\nrec neighbors 1280/8456\nrec neighbors 1536/8456\nrec neighbors 1792/8456\nrec neighbors 2048/8456\nrec neighbors 2304/8456\nrec neighbors 2560/8456\nrec neighbors 2816/8456\nrec neighbors 3072/8456\nrec neighbors 3328/8456\nrec neighbors 3584/8456\nrec neighbors 3840/8456\nrec neighbors 4096/8456\nrec neighbors 4352/8456\nrec neighbors 4608/8456\nrec neighbors 4864/8456\nrec neighbors 5120/8456\nrec neighbors 5376/8456\nrec neighbors 5632/8456\nrec neighbors 5888/8456\nrec neighbors 6144/8456\nrec neighbors 6400/8456\nrec neighbors 6656/8456\nrec neighbors 6912/8456\nrec neighbors 7168/8456\nrec neighbors 7424/8456\nrec neighbors 7680/8456\nrec neighbors 7936/8456\nrec neighbors 8192/8456\nrec neighbors 8448/8456\nrec neighbors 8456/8456\nwrote org_obj_k30\nwrote org_obj_k100\nwrote org_obj_k400\nwrote rec_obj_k100\nwrote org_pos_k30\nwrote org_pos_k100\nwrote org_pos_k400\nwrote rec_pos_k100\n" } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-24T22:40:19.557Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:40:19.557Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:40:21.083Z", "text": "The first collaborative sweep is built. It separates organic-seeking history from recommendation-surface history, uses rare-track TF–IDF to find taste neighbors, and then predicts unseen eligible tracks from neighbors’ strong organic plays and likes. I’m scoring neighbor depth and signal choices now; the popularity baseline is NDCG@10 ≈ 0.0107." }, { "kind": "tool", "timestamp": "2026-09-24T22:40:22.626Z", "callId": "call_a5A22ThuzFKg5dTDjRDWPvOt", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /app/solution/validation_org_*.parquet /app/solution/validation_rec_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:40:23.794Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"8f98e4\",\"wall_time_seconds\":1.001534251,\"session_id\":68302,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:40:22.737Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6d1dc6887d2bd9d6ce778b220ba", "usage": { "input_tokens": 51393, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 41, "total_tokens": 51608 }, "turn_token_usage": { "input_tokens": 1259828, "cached_input_tokens": 1207808, "cache_write_input_tokens": 0, "output_tokens": 16825, "reasoning_output_tokens": 7174, "total_tokens": 1276653 }, "thread_token_usage": { "input_tokens": 1259828, "cached_input_tokens": 1207808, "cache_write_input_tokens": 0, "output_tokens": 16825, "reasoning_output_tokens": 7174, "total_tokens": 1276653 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-24T22:40:24.858Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:40:24.858Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:40:25.557Z", "callId": "call_bbhF4Z8CFyuWBK6gYe3N29Hg", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":68302,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:40:43.489Z", "content": "Script completed\nWall time 17.9 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_org_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019948,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02149,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016452,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019837,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021859,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014409,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011933,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:40:25.662Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6d8019c87d2b9936a8243096537", "usage": { "input_tokens": 51667, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 51720 }, "turn_token_usage": { "input_tokens": 1311495, "cached_input_tokens": 1259008, "cache_write_input_tokens": 0, "output_tokens": 16878, "reasoning_output_tokens": 7180, "total_tokens": 1328373 }, "thread_token_usage": { "input_tokens": 1311495, "cached_input_tokens": 1259008, "cache_write_input_tokens": 0, "output_tokens": 16878, "reasoning_output_tokens": 7180, "total_tokens": 1328373 } } }, { "kind": "context", "timestamp": "2026-09-24T22:40:43.487Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8091ed8a-23a1-4a91-9e51-b4e73f465b2c", "process_id": "68302", "command": [ "/bin/sh", "-lc", "for f in /app/solution/validation_org_*.parquet /app/solution/validation_rec_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /app/solution/validation_org_*.parquet /app/solution/validation_rec_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_org_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019948,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02149,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016452,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019837,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021859,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014409,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011933,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_org_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019948,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02149,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016452,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019837,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021859,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014409,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011933,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 695583432 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_org_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019948,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02149,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_obj_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016452,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019837,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k30.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021859,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_org_pos_k400.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01608,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_obj_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014409,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_rec_pos_k100.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011933,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-24T22:40:53.685Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:40:53.685Z", "text": "" } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-24T22:41:02.997Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:41:02.997Z", "text": "" } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-24T22:41:03.746Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:41:03.746Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:41:22.098Z", "callId": "call_qC4dnOT1M1Cb9uZgAgDX3Th2", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/prepare_recent.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build recent-history variants for temporal validation tuning.\\\"\\\"\\\"\\n+\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+CUTOFF = 24_786_800\\n+DAY = 86_400\\n+N_USERS = 10_001\\n+N_ITEM_IDS = 10_000_001\\n+\\n+\\n+def mat(r, c, v, shape):\\n+ x = sparse.coo_matrix((v.astype(np.float32), (r, c)), shape=shape).tocsr()\\n+ x.sum_duplicates()\\n+ return x\\n+\\n+\\n+def main():\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ mapper = np.full(N_ITEM_IDS, -1, dtype=np.int32)\\n+ mapper[ids] = np.arange(len(ids), dtype=np.int32)\\n+ d = pd.read_parquet(\\n+ DATA / \\\"train_listens.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ r = (d.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\\n+ item = d.item_id.to_numpy(dtype=np.int32)\\n+ t = d.timestamp.to_numpy(dtype=np.int32)\\n+ org = d.is_organic.to_numpy(dtype=np.uint8) == 1\\n+ ratio = d.played_ratio_pct.to_numpy(dtype=np.uint16)\\n+ cc = mapper[item]\\n+ for days in (30, 90, 180):\\n+ recent = t >= CUTOFF - days * DAY\\n+ keep = recent & org & (ratio >= 50)\\n+ x = mat(r[keep], item[keep], np.ones(keep.sum()), (N_USERS, N_ITEM_IDS))\\n+ sparse.save_npz(OUT / f\\\"train_x_org_{days}d.npz\\\", x, compressed=False)\\n+ print(f\\\"x_org_{days}d nnz={x.nnz:,}\\\", flush=True)\\n+ del x\\n+\\n+ keep = recent & org & (ratio >= 80) & (cc >= 0)\\n+ y = mat(r[keep], cc[keep], np.ones(keep.sum()), (N_USERS, len(ids)))\\n+ sparse.save_npz(OUT / f\\\"train_y_org80_{days}d.npz\\\", y, compressed=False)\\n+ pop = np.asarray(y.sum(axis=0)).ravel()\\n+ np.save(OUT / f\\\"pop_org80_{days}d.npy\\\", pop)\\n+ print(f\\\"y_org80_{days}d nnz={y.nnz:,}\\\", flush=True)\\n+ del y\\n+\\n+ keep = recent & (ratio >= 50) & (cc >= 0)\\n+ values = 1.0 + 2.0 * org[keep].astype(np.float32)\\n+ y = mat(r[keep], cc[keep], values, (N_USERS, len(ids)))\\n+ sparse.save_npz(OUT / f\\\"train_y_pos_{days}d.npz\\\", y, compressed=False)\\n+ del y\\n+\\n+ # Exponential organic history retains long-run taste while emphasizing the\\n+ # last few months. Tiny weights older than 180 days are omitted.\\n+ keep = org & (ratio >= 50) & (t >= CUTOFF - 180 * DAY)\\n+ age_days = (CUTOFF - t[keep]).astype(np.float32) / DAY\\n+ weights = np.exp2(-age_days / 60.0)\\n+ x = mat(r[keep], item[keep], weights, (N_USERS, N_ITEM_IDS))\\n+ sparse.save_npz(OUT / \\\"train_x_org_decay60.npz\\\", x, compressed=False)\\n+ print(f\\\"x_org_decay60 nnz={x.nnz:,}\\\", flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:41:22.219Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:41:22.106Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-09612e73-0bab-4fc1-9335-eed0a4686515", "changes": { "/app/solution/prepare_recent.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build recent-history variants for temporal validation tuning.\"\"\"\n\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nCUTOFF = 24_786_800\nDAY = 86_400\nN_USERS = 10_001\nN_ITEM_IDS = 10_000_001\n\n\ndef mat(r, c, v, shape):\n x = sparse.coo_matrix((v.astype(np.float32), (r, c)), shape=shape).tocsr()\n x.sum_duplicates()\n return x\n\n\ndef main():\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n ids = catalog.item_id.to_numpy(dtype=np.int64)\n mapper = np.full(N_ITEM_IDS, -1, dtype=np.int32)\n mapper[ids] = np.arange(len(ids), dtype=np.int32)\n d = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n r = (d.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\n item = d.item_id.to_numpy(dtype=np.int32)\n t = d.timestamp.to_numpy(dtype=np.int32)\n org = d.is_organic.to_numpy(dtype=np.uint8) == 1\n ratio = d.played_ratio_pct.to_numpy(dtype=np.uint16)\n cc = mapper[item]\n for days in (30, 90, 180):\n recent = t >= CUTOFF - days * DAY\n keep = recent & org & (ratio >= 50)\n x = mat(r[keep], item[keep], np.ones(keep.sum()), (N_USERS, N_ITEM_IDS))\n sparse.save_npz(OUT / f\"train_x_org_{days}d.npz\", x, compressed=False)\n print(f\"x_org_{days}d nnz={x.nnz:,}\", flush=True)\n del x\n\n keep = recent & org & (ratio >= 80) & (cc >= 0)\n y = mat(r[keep], cc[keep], np.ones(keep.sum()), (N_USERS, len(ids)))\n sparse.save_npz(OUT / f\"train_y_org80_{days}d.npz\", y, compressed=False)\n pop = np.asarray(y.sum(axis=0)).ravel()\n np.save(OUT / f\"pop_org80_{days}d.npy\", pop)\n print(f\"y_org80_{days}d nnz={y.nnz:,}\", flush=True)\n del y\n\n keep = recent & (ratio >= 50) & (cc >= 0)\n values = 1.0 + 2.0 * org[keep].astype(np.float32)\n y = mat(r[keep], cc[keep], values, (N_USERS, len(ids)))\n sparse.save_npz(OUT / f\"train_y_pos_{days}d.npz\", y, compressed=False)\n del y\n\n # Exponential organic history retains long-run taste while emphasizing the\n # last few months. Tiny weights older than 180 days are omitted.\n keep = org & (ratio >= 50) & (t >= CUTOFF - 180 * DAY)\n age_days = (CUTOFF - t[keep]).astype(np.float32) / DAY\n weights = np.exp2(-age_days / 60.0)\n x = mat(r[keep], item[keep], weights, (N_USERS, N_ITEM_IDS))\n sparse.save_npz(OUT / \"train_x_org_decay60.npz\", x, compressed=False)\n print(f\"x_org_decay60 nnz={x.nnz:,}\", flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/prepare_recent.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:41:22.217Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a6ebaec887d2a5168ca3b1b65074", "usage": { "input_tokens": 52135, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 2091, "reasoning_output_tokens": 1068, "total_tokens": 54226 }, "turn_token_usage": { "input_tokens": 1363630, "cached_input_tokens": 1310464, "cache_write_input_tokens": 0, "output_tokens": 18969, "reasoning_output_tokens": 8248, "total_tokens": 1382599 }, "thread_token_usage": { "input_tokens": 1363630, "cached_input_tokens": 1310464, "cache_write_input_tokens": 0, "output_tokens": 18969, "reasoning_output_tokens": 8248, "total_tokens": 1382599 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:41:24.945Z", "callId": "call_jjZSj5kWnHo8koXBXOeYjjNx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/prepare_recent.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:41:26.156Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"438fa7\",\"wall_time_seconds\":1.001982159,\"session_id\":79266,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:41:25.153Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7129b1887d2b2dec75cad2b2aa6", "usage": { "input_tokens": 54250, "cached_input_tokens": 51968, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 0, "total_tokens": 54314 }, "turn_token_usage": { "input_tokens": 1417880, "cached_input_tokens": 1362432, "cache_write_input_tokens": 0, "output_tokens": 19033, "reasoning_output_tokens": 8248, "total_tokens": 1436913 }, "thread_token_usage": { "input_tokens": 1417880, "cached_input_tokens": 1362432, "cache_write_input_tokens": 0, "output_tokens": 19033, "reasoning_output_tokens": 8248, "total_tokens": 1436913 } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-24T22:41:27.957Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:41:27.957Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:41:28.662Z", "callId": "call_B9VQcs4ZqLFFfUVZdo10zzQp", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":79266,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:41:31.249Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:41:28.772Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a716646887d2b75a67cb9b11ce2c", "usage": { "input_tokens": 54371, "cached_input_tokens": 54016, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 54424 }, "turn_token_usage": { "input_tokens": 1472251, "cached_input_tokens": 1416448, "cache_write_input_tokens": 0, "output_tokens": 19086, "reasoning_output_tokens": 8254, "total_tokens": 1491337 }, "thread_token_usage": { "input_tokens": 1472251, "cached_input_tokens": 1416448, "cache_write_input_tokens": 0, "output_tokens": 19086, "reasoning_output_tokens": 8254, "total_tokens": 1491337 } } }, { "kind": "context", "timestamp": "2026-09-24T22:41:31.248Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-42b5e023-c219-41f8-8476-f92253fb1bc7", "process_id": "79266", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/prepare_recent.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/prepare_recent.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "x_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "stderr": "", "aggregated_output": "x_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 94026059 }, "formatted_output": "x_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-24T22:41:37.047Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:41:37.047Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:41:43.611Z", "callId": "call_nkKUqy4NRxdHZ45tcNEm0BLH", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np\\nfrom scipy import sparse\\nimport knn_experiment as k\\nz=np.load(k.DATA/'validation_eligible_candidates.npz'); us=z['uids'].astype(np.int64); ids=z['item_ids'].astype(np.int64); e=np.unpackbits(z['packed_eligible'],axis=1,count=len(ids),bitorder='little').astype(bool); tr=(us//100).astype(np.int32)\\nyfull=k.output_matrix('obj')\\nfor age in ['30d','90d','180d','decay60']:\\n x=k.tfidf_profile(sparse.load_npz(k.OUT/f'train_x_org_{age}.npz'))\\n ni,ns=k.nearest(x,tr,'org_'+age); del x; gc.collect()\\n for kk in [10,30,100]: k.rank_variant(f'org_{age}_fullobj_k{kk}',ni,ns,yfull,e,us,ids,k=kk)\\n if age in ['30d','90d','180d']:\\n y=sparse.load_npz(k.OUT/f'train_y_org80_{age}.npz').astype(np.float32); y.data=np.log1p(y.data)\\n k.rank_variant(f'org_{age}_recentobj_k30',ni,ns,y,e,us,ids,k=30)\\n del y\\n del ni,ns; gc.collect()\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:41:44.781Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"4fcb01\",\"wall_time_seconds\":1.000806525,\"session_id\":74092,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:41:43.723Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a71bd0a887d2a3603ac501965073", "usage": { "input_tokens": 54543, "cached_input_tokens": 54144, "cache_write_input_tokens": 0, "output_tokens": 614, "reasoning_output_tokens": 244, "total_tokens": 55157 }, "turn_token_usage": { "input_tokens": 1526794, "cached_input_tokens": 1470592, "cache_write_input_tokens": 0, "output_tokens": 19700, "reasoning_output_tokens": 8498, "total_tokens": 1546494 }, "thread_token_usage": { "input_tokens": 1526794, "cached_input_tokens": 1470592, "cache_write_input_tokens": 0, "output_tokens": 19700, "reasoning_output_tokens": 8498, "total_tokens": 1546494 } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-24T22:41:46.261Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:41:46.261Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:41:46.924Z", "callId": "call_knUJROF4QJLgSw7Yy5OL91Jl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74092,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:41:56.933Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\norg_30d neighbors 256/8456\norg_30d neighbors 512/8456\norg_30d neighbors 768/8456\norg_30d neighbors 1024/8456\norg_30d neighbors 1280/8456\norg_30d neighbors 1536/8456\norg_30d neighbors 1792/8456\norg_30d neighbors 2048/8456\norg_30d neighbors 2304/8456\norg_30d neighbors 2560/8456\norg_30d neighbors 2816/8456\norg_30d neighbors 3072/8456\norg_30d neighbors 3328/8456\norg_30d neighbors 3584/8456\norg_30d neighbors 3840/8456\norg_30d neighbors 4096/8456\norg_30d neighbors 4352/8456\norg_30d neighbors 4608/8456\norg_30d neighbors 4864/8456\norg_30d neighbors 5120/8456\norg_30d neighbors 5376/8456\norg_30d neighbors 5632/8456\norg_30d neighbors 5888/8456\norg_30d neighbors 6144/8456\norg_30d neighbors 6400/8456\norg_30d neighbors 6656/8456\norg_30d neighbors 6912/8456\norg_30d neighbors 7168/8456\norg_30d neighbors 7424/8456\norg_30d neighbors 7680/8456\norg_30d neighbors 7936/8456\norg_30d neighbors 8192/8456\norg_30d neighbors 8448/8456\norg_30d neighbors 8456/8456\nwrote org_30d_fullobj_k10\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:41:47.036Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a729004887d2945533783dfe6d20", "usage": { "input_tokens": 55215, "cached_input_tokens": 54400, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 55268 }, "turn_token_usage": { "input_tokens": 1582009, "cached_input_tokens": 1524992, "cache_write_input_tokens": 0, "output_tokens": 19753, "reasoning_output_tokens": 8504, "total_tokens": 1601762 }, "thread_token_usage": { "input_tokens": 1582009, "cached_input_tokens": 1524992, "cache_write_input_tokens": 0, "output_tokens": 19753, "reasoning_output_tokens": 8504, "total_tokens": 1601762 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-24T22:41:58.521Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:41:58.521Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:41:59.211Z", "callId": "call_YLInbtwbKNIrvIfWh0SIlvv5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74092,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:42:19.219Z", "content": "Script completed\nWall time 20.0 seconds\nOutput:\n\nwrote org_30d_fullobj_k30\nwrote org_30d_fullobj_k100\nwrote org_30d_recentobj_k30\norg_90d neighbors 256/8456\norg_90d neighbors 512/8456\norg_90d neighbors 768/8456\norg_90d neighbors 1024/8456\norg_90d neighbors 1280/8456\norg_90d neighbors 1536/8456\norg_90d neighbors 1792/8456\norg_90d neighbors 2048/8456\norg_90d neighbors 2304/8456\norg_90d neighbors 2560/8456\norg_90d neighbors 2816/8456\norg_90d neighbors 3072/8456\norg_90d neighbors 3328/8456\norg_90d neighbors 3584/8456\norg_90d neighbors 3840/8456\norg_90d neighbors 4096/8456\norg_90d neighbors 4352/8456\norg_90d neighbors 4608/8456\norg_90d neighbors 4864/8456\norg_90d neighbors 5120/8456\norg_90d neighbors 5376/8456\norg_90d neighbors 5632/8456\norg_90d neighbors 5888/8456\norg_90d neighbors 6144/8456\norg_90d neighbors 6400/8456\norg_90d neighbors 6656/8456\norg_90d neighbors 6912/8456\norg_90d neighbors 7168/8456\norg_90d neighbors 7424/8456\norg_90d neighbors 7680/8456\norg_90d neighbors 7936/8456\norg_90d neighbors 8192/8456\norg_90d neighbors 8448/8456\norg_90d neighbors 8456/8456\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:41:59.344Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7351d6487d291e4e43baeff52bd", "usage": { "input_tokens": 55707, "cached_input_tokens": 55040, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 55760 }, "turn_token_usage": { "input_tokens": 1637716, "cached_input_tokens": 1580032, "cache_write_input_tokens": 0, "output_tokens": 19806, "reasoning_output_tokens": 8510, "total_tokens": 1657522 }, "thread_token_usage": { "input_tokens": 1637716, "cached_input_tokens": 1580032, "cache_write_input_tokens": 0, "output_tokens": 19806, "reasoning_output_tokens": 8510, "total_tokens": 1657522 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-24T22:42:20.842Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:42:20.842Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:42:21.533Z", "callId": "call_R6FvsTwmlkc2fodvbbdiiqFW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74092,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:42:51.541Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nwrote org_90d_fullobj_k10\nwrote org_90d_fullobj_k30\nwrote org_90d_fullobj_k100\nwrote org_90d_recentobj_k30\norg_180d neighbors 256/8456\norg_180d neighbors 512/8456\norg_180d neighbors 768/8456\norg_180d neighbors 1024/8456\norg_180d neighbors 1280/8456\norg_180d neighbors 1536/8456\norg_180d neighbors 1792/8456\norg_180d neighbors 2048/8456\norg_180d neighbors 2304/8456\norg_180d neighbors 2560/8456\norg_180d neighbors 2816/8456\norg_180d neighbors 3072/8456\norg_180d neighbors 3328/8456\norg_180d neighbors 3584/8456\norg_180d neighbors 3840/8456\norg_180d neighbors 4096/8456\norg_180d neighbors 4352/8456\norg_180d neighbors 4608/8456\norg_180d neighbors 4864/8456\norg_180d neighbors 5120/8456\norg_180d neighbors 5376/8456\norg_180d neighbors 5632/8456\norg_180d neighbors 5888/8456\norg_180d neighbors 6144/8456\norg_180d neighbors 6400/8456\norg_180d neighbors 6656/8456\norg_180d neighbors 6912/8456\norg_180d neighbors 7168/8456\norg_180d neighbors 7424/8456\norg_180d neighbors 7680/8456\norg_180d neighbors 7936/8456\norg_180d neighbors 8192/8456\norg_180d neighbors 8448/8456\norg_180d neighbors 8456/8456\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:42:21.637Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a74b6a0487d2aa283fe9b83796dc", "usage": { "input_tokens": 56221, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 56274 }, "turn_token_usage": { "input_tokens": 1693937, "cached_input_tokens": 1635584, "cache_write_input_tokens": 0, "output_tokens": 19859, "reasoning_output_tokens": 8516, "total_tokens": 1713796 }, "thread_token_usage": { "input_tokens": 1693937, "cached_input_tokens": 1635584, "cache_write_input_tokens": 0, "output_tokens": 19859, "reasoning_output_tokens": 8516, "total_tokens": 1713796 } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-24T22:42:53.290Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:42:53.290Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:42:53.956Z", "callId": "call_eLubumkM9JedUuFJPCcDic7l", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74092,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:43:23.965Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nwrote org_180d_fullobj_k10\nwrote org_180d_fullobj_k30\nwrote org_180d_fullobj_k100\nwrote org_180d_recentobj_k30\norg_decay60 neighbors 256/8456\norg_decay60 neighbors 512/8456\norg_decay60 neighbors 768/8456\norg_decay60 neighbors 1024/8456\norg_decay60 neighbors 1280/8456\norg_decay60 neighbors 1536/8456\norg_decay60 neighbors 1792/8456\norg_decay60 neighbors 2048/8456\norg_decay60 neighbors 2304/8456\norg_decay60 neighbors 2560/8456\norg_decay60 neighbors 2816/8456\norg_decay60 neighbors 3072/8456\norg_decay60 neighbors 3328/8456\norg_decay60 neighbors 3584/8456\norg_decay60 neighbors 3840/8456\norg_decay60 neighbors 4096/8456\norg_decay60 neighbors 4352/8456\norg_decay60 neighbors 4608/8456\norg_decay60 neighbors 4864/8456\norg_decay60 neighbors 5120/8456\norg_decay60 neighbors 5376/8456\norg_decay60 neighbors 5632/8456\norg_decay60 neighbors 5888/8456\norg_decay60 neighbors 6144/8456\norg_decay60 neighbors 6400/8456\norg_decay60 neighbors 6656/8456\norg_decay60 neighbors 6912/8456\norg_decay60 neighbors 7168/8456\norg_decay60 neighbors 7424/8456\norg_decay60 neighbors 7680/8456\norg_decay60 neighbors 7936/8456\norg_decay60 neighbors 8192/8456\norg_decay60 neighbors 8448/8456\norg_decay60 neighbors 8456/8456\nwrote org_decay60_fullobj_k10\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:42:54.086Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a76bc63887d28acc46469a583ccc", "usage": { "input_tokens": 56746, "cached_input_tokens": 56064, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 56799 }, "turn_token_usage": { "input_tokens": 1750683, "cached_input_tokens": 1691648, "cache_write_input_tokens": 0, "output_tokens": 19912, "reasoning_output_tokens": 8522, "total_tokens": 1770595 }, "thread_token_usage": { "input_tokens": 1750683, "cached_input_tokens": 1691648, "cache_write_input_tokens": 0, "output_tokens": 19912, "reasoning_output_tokens": 8522, "total_tokens": 1770595 } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-24T22:43:25.017Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:43:25.017Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:43:25.721Z", "callId": "call_SOP53tWuzj5cUUmpQAfczONi", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74092,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:43:35.300Z", "content": "Script completed\nWall time 9.6 seconds\nOutput:\n\nwrote org_decay60_fullobj_k30\nwrote org_decay60_fullobj_k100\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:43:25.831Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a78c24dc87d2a8d32d325c0b6767", "usage": { "input_tokens": 57247, "cached_input_tokens": 56576, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 57300 }, "turn_token_usage": { "input_tokens": 1807930, "cached_input_tokens": 1748224, "cache_write_input_tokens": 0, "output_tokens": 19965, "reasoning_output_tokens": 8528, "total_tokens": 1827895 }, "thread_token_usage": { "input_tokens": 1807930, "cached_input_tokens": 1748224, "cache_write_input_tokens": 0, "output_tokens": 19965, "reasoning_output_tokens": 8528, "total_tokens": 1827895 } } }, { "kind": "context", "timestamp": "2026-09-24T22:43:35.298Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8fd552f0-091e-4920-bada-e457460ba462", "process_id": "74092", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nz=np.load(k.DATA/'validation_eligible_candidates.npz'); us=z['uids'].astype(np.int64); ids=z['item_ids'].astype(np.int64); e=np.unpackbits(z['packed_eligible'],axis=1,count=len(ids),bitorder='little').astype(bool); tr=(us//100).astype(np.int32)\nyfull=k.output_matrix('obj')\nfor age in ['30d','90d','180d','decay60']:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/f'train_x_org_{age}.npz'))\n ni,ns=k.nearest(x,tr,'org_'+age); del x; gc.collect()\n for kk in [10,30,100]: k.rank_variant(f'org_{age}_fullobj_k{kk}',ni,ns,yfull,e,us,ids,k=kk)\n if age in ['30d','90d','180d']:\n y=sparse.load_npz(k.OUT/f'train_y_org80_{age}.npz').astype(np.float32); y.data=np.log1p(y.data)\n k.rank_variant(f'org_{age}_recentobj_k30',ni,ns,y,e,us,ids,k=30)\n del y\n del ni,ns; gc.collect()\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nz=np.load(k.DATA/'validation_eligible_candidates.npz'); us=z['uids'].astype(np.int64); ids=z['item_ids'].astype(np.int64); e=np.unpackbits(z['packed_eligible'],axis=1,count=len(ids),bitorder='little').astype(bool); tr=(us//100).astype(np.int32)\nyfull=k.output_matrix('obj')\nfor age in ['30d','90d','180d','decay60']:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/f'train_x_org_{age}.npz'))\n ni,ns=k.nearest(x,tr,'org_'+age); del x; gc.collect()\n for kk in [10,30,100]: k.rank_variant(f'org_{age}_fullobj_k{kk}',ni,ns,yfull,e,us,ids,k=kk)\n if age in ['30d','90d','180d']:\n y=sparse.load_npz(k.OUT/f'train_y_org80_{age}.npz').astype(np.float32); y.data=np.log1p(y.data)\n k.rank_variant(f'org_{age}_recentobj_k30',ni,ns,y,e,us,ids,k=30)\n del y\n del ni,ns; gc.collect()\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org_30d neighbors 256/8456\norg_30d neighbors 512/8456\norg_30d neighbors 768/8456\norg_30d neighbors 1024/8456\norg_30d neighbors 1280/8456\norg_30d neighbors 1536/8456\norg_30d neighbors 1792/8456\norg_30d neighbors 2048/8456\norg_30d neighbors 2304/8456\norg_30d neighbors 2560/8456\norg_30d neighbors 2816/8456\norg_30d neighbors 3072/8456\norg_30d neighbors 3328/8456\norg_30d neighbors 3584/8456\norg_30d neighbors 3840/8456\norg_30d neighbors 4096/8456\norg_30d neighbors 4352/8456\norg_30d neighbors 4608/8456\norg_30d neighbors 4864/8456\norg_30d neighbors 5120/8456\norg_30d neighbors 5376/8456\norg_30d neighbors 5632/8456\norg_30d neighbors 5888/8456\norg_30d neighbors 6144/8456\norg_30d neighbors 6400/8456\norg_30d neighbors 6656/8456\norg_30d neighbors 6912/8456\norg_30d neighbors 7168/8456\norg_30d neighbors 7424/8456\norg_30d neighbors 7680/8456\norg_30d neighbors 7936/8456\norg_30d neighbors 8192/8456\norg_30d neighbors 8448/8456\norg_30d neighbors 8456/8456\nwrote org_30d_fullobj_k10\nwrote org_30d_fullobj_k30\nwrote org_30d_fullobj_k100\nwrote org_30d_recentobj_k30\norg_90d neighbors 256/8456\norg_90d neighbors 512/8456\norg_90d neighbors 768/8456\norg_90d neighbors 1024/8456\norg_90d neighbors 1280/8456\norg_90d neighbors 1536/8456\norg_90d neighbors 1792/8456\norg_90d neighbors 2048/8456\norg_90d neighbors 2304/8456\norg_90d neighbors 2560/8456\norg_90d neighbors 2816/8456\norg_90d neighbors 3072/8456\norg_90d neighbors 3328/8456\norg_90d neighbors 3584/8456\norg_90d neighbors 3840/8456\norg_90d neighbors 4096/8456\norg_90d neighbors 4352/8456\norg_90d neighbors 4608/8456\norg_90d neighbors 4864/8456\norg_90d neighbors 5120/8456\norg_90d neighbors 5376/8456\norg_90d neighbors 5632/8456\norg_90d neighbors 5888/8456\norg_90d neighbors 6144/8456\norg_90d neighbors 6400/8456\norg_90d neighbors 6656/8456\norg_90d neighbors 6912/8456\norg_90d neighbors 7168/8456\norg_90d neighbors 7424/8456\norg_90d neighbors 7680/8456\norg_90d neighbors 7936/8456\norg_90d neighbors 8192/8456\norg_90d neighbors 8448/8456\norg_90d neighbors 8456/8456\nwrote org_90d_fullobj_k10\nwrote org_90d_fullobj_k30\nwrote org_90d_fullobj_k100\nwrote org_90d_recentobj_k30\norg_180d neighbors 256/8456\norg_180d neighbors 512/8456\norg_180d neighbors 768/8456\norg_180d neighbors 1024/8456\norg_180d neighbors 1280/8456\norg_180d neighbors 1536/8456\norg_180d neighbors 1792/8456\norg_180d neighbors 2048/8456\norg_180d neighbors 2304/8456\norg_180d neighbors 2560/8456\norg_180d neighbors 2816/8456\norg_180d neighbors 3072/8456\norg_180d neighbors 3328/8456\norg_180d neighbors 3584/8456\norg_180d neighbors 3840/8456\norg_180d neighbors 4096/8456\norg_180d neighbors 4352/8456\norg_180d neighbors 4608/8456\norg_180d neighbors 4864/8456\norg_180d neighbors 5120/8456\norg_180d neighbors 5376/8456\norg_180d neighbors 5632/8456\norg_180d neighbors 5888/8456\norg_180d neighbors 6144/8456\norg_180d neighbors 6400/8456\norg_180d neighbors 6656/8456\norg_180d neighbors 6912/8456\norg_180d neighbors 7168/8456\norg_180d neighbors 7424/8456\norg_180d neighbors 7680/8456\norg_180d neighbors 7936/8456\norg_180d neighbors 8192/8456\norg_180d neighbors 8448/8456\norg_180d neighbors 8456/8456\nwrote org_180d_fullobj_k10\nwrote org_180d_fullobj_k30\nwrote org_180d_fullobj_k100\nwrote org_180d_recentobj_k30\norg_decay60 neighbors 256/8456\norg_decay60 neighbors 512/8456\norg_decay60 neighbors 768/8456\norg_decay60 neighbors 1024/8456\norg_decay60 neighbors 1280/8456\norg_decay60 neighbors 1536/8456\norg_decay60 neighbors 1792/8456\norg_decay60 neighbors 2048/8456\norg_decay60 neighbors 2304/8456\norg_decay60 neighbors 2560/8456\norg_decay60 neighbors 2816/8456\norg_decay60 neighbors 3072/8456\norg_decay60 neighbors 3328/8456\norg_decay60 neighbors 3584/8456\norg_decay60 neighbors 3840/8456\norg_decay60 neighbors 4096/8456\norg_decay60 neighbors 4352/8456\norg_decay60 neighbors 4608/8456\norg_decay60 neighbors 4864/8456\norg_decay60 neighbors 5120/8456\norg_decay60 neighbors 5376/8456\norg_decay60 neighbors 5632/8456\norg_decay60 neighbors 5888/8456\norg_decay60 neighbors 6144/8456\norg_decay60 neighbors 6400/8456\norg_decay60 neighbors 6656/8456\norg_decay60 neighbors 6912/8456\norg_decay60 neighbors 7168/8456\norg_decay60 neighbors 7424/8456\norg_decay60 neighbors 7680/8456\norg_decay60 neighbors 7936/8456\norg_decay60 neighbors 8192/8456\norg_decay60 neighbors 8448/8456\norg_decay60 neighbors 8456/8456\nwrote org_decay60_fullobj_k10\nwrote org_decay60_fullobj_k30\nwrote org_decay60_fullobj_k100\n", "stderr": "", "aggregated_output": "org_30d neighbors 256/8456\norg_30d neighbors 512/8456\norg_30d neighbors 768/8456\norg_30d neighbors 1024/8456\norg_30d neighbors 1280/8456\norg_30d neighbors 1536/8456\norg_30d neighbors 1792/8456\norg_30d neighbors 2048/8456\norg_30d neighbors 2304/8456\norg_30d neighbors 2560/8456\norg_30d neighbors 2816/8456\norg_30d neighbors 3072/8456\norg_30d neighbors 3328/8456\norg_30d neighbors 3584/8456\norg_30d neighbors 3840/8456\norg_30d neighbors 4096/8456\norg_30d neighbors 4352/8456\norg_30d neighbors 4608/8456\norg_30d neighbors 4864/8456\norg_30d neighbors 5120/8456\norg_30d neighbors 5376/8456\norg_30d neighbors 5632/8456\norg_30d neighbors 5888/8456\norg_30d neighbors 6144/8456\norg_30d neighbors 6400/8456\norg_30d neighbors 6656/8456\norg_30d neighbors 6912/8456\norg_30d neighbors 7168/8456\norg_30d neighbors 7424/8456\norg_30d neighbors 7680/8456\norg_30d neighbors 7936/8456\norg_30d neighbors 8192/8456\norg_30d neighbors 8448/8456\norg_30d neighbors 8456/8456\nwrote org_30d_fullobj_k10\nwrote org_30d_fullobj_k30\nwrote org_30d_fullobj_k100\nwrote org_30d_recentobj_k30\norg_90d neighbors 256/8456\norg_90d neighbors 512/8456\norg_90d neighbors 768/8456\norg_90d neighbors 1024/8456\norg_90d neighbors 1280/8456\norg_90d neighbors 1536/8456\norg_90d neighbors 1792/8456\norg_90d neighbors 2048/8456\norg_90d neighbors 2304/8456\norg_90d neighbors 2560/8456\norg_90d neighbors 2816/8456\norg_90d neighbors 3072/8456\norg_90d neighbors 3328/8456\norg_90d neighbors 3584/8456\norg_90d neighbors 3840/8456\norg_90d neighbors 4096/8456\norg_90d neighbors 4352/8456\norg_90d neighbors 4608/8456\norg_90d neighbors 4864/8456\norg_90d neighbors 5120/8456\norg_90d neighbors 5376/8456\norg_90d neighbors 5632/8456\norg_90d neighbors 5888/8456\norg_90d neighbors 6144/8456\norg_90d neighbors 6400/8456\norg_90d neighbors 6656/8456\norg_90d neighbors 6912/8456\norg_90d neighbors 7168/8456\norg_90d neighbors 7424/8456\norg_90d neighbors 7680/8456\norg_90d neighbors 7936/8456\norg_90d neighbors 8192/8456\norg_90d neighbors 8448/8456\norg_90d neighbors 8456/8456\nwrote org_90d_fullobj_k10\nwrote org_90d_fullobj_k30\nwrote org_90d_fullobj_k100\nwrote org_90d_recentobj_k30\norg_180d neighbors 256/8456\norg_180d neighbors 512/8456\norg_180d neighbors 768/8456\norg_180d neighbors 1024/8456\norg_180d neighbors 1280/8456\norg_180d neighbors 1536/8456\norg_180d neighbors 1792/8456\norg_180d neighbors 2048/8456\norg_180d neighbors 2304/8456\norg_180d neighbors 2560/8456\norg_180d neighbors 2816/8456\norg_180d neighbors 3072/8456\norg_180d neighbors 3328/8456\norg_180d neighbors 3584/8456\norg_180d neighbors 3840/8456\norg_180d neighbors 4096/8456\norg_180d neighbors 4352/8456\norg_180d neighbors 4608/8456\norg_180d neighbors 4864/8456\norg_180d neighbors 5120/8456\norg_180d neighbors 5376/8456\norg_180d neighbors 5632/8456\norg_180d neighbors 5888/8456\norg_180d neighbors 6144/8456\norg_180d neighbors 6400/8456\norg_180d neighbors 6656/8456\norg_180d neighbors 6912/8456\norg_180d neighbors 7168/8456\norg_180d neighbors 7424/8456\norg_180d neighbors 7680/8456\norg_180d neighbors 7936/8456\norg_180d neighbors 8192/8456\norg_180d neighbors 8448/8456\norg_180d neighbors 8456/8456\nwrote org_180d_fullobj_k10\nwrote org_180d_fullobj_k30\nwrote org_180d_fullobj_k100\nwrote org_180d_recentobj_k30\norg_decay60 neighbors 256/8456\norg_decay60 neighbors 512/8456\norg_decay60 neighbors 768/8456\norg_decay60 neighbors 1024/8456\norg_decay60 neighbors 1280/8456\norg_decay60 neighbors 1536/8456\norg_decay60 neighbors 1792/8456\norg_decay60 neighbors 2048/8456\norg_decay60 neighbors 2304/8456\norg_decay60 neighbors 2560/8456\norg_decay60 neighbors 2816/8456\norg_decay60 neighbors 3072/8456\norg_decay60 neighbors 3328/8456\norg_decay60 neighbors 3584/8456\norg_decay60 neighbors 3840/8456\norg_decay60 neighbors 4096/8456\norg_decay60 neighbors 4352/8456\norg_decay60 neighbors 4608/8456\norg_decay60 neighbors 4864/8456\norg_decay60 neighbors 5120/8456\norg_decay60 neighbors 5376/8456\norg_decay60 neighbors 5632/8456\norg_decay60 neighbors 5888/8456\norg_decay60 neighbors 6144/8456\norg_decay60 neighbors 6400/8456\norg_decay60 neighbors 6656/8456\norg_decay60 neighbors 6912/8456\norg_decay60 neighbors 7168/8456\norg_decay60 neighbors 7424/8456\norg_decay60 neighbors 7680/8456\norg_decay60 neighbors 7936/8456\norg_decay60 neighbors 8192/8456\norg_decay60 neighbors 8448/8456\norg_decay60 neighbors 8456/8456\nwrote org_decay60_fullobj_k10\nwrote org_decay60_fullobj_k30\nwrote org_decay60_fullobj_k100\n", "exit_code": 0, "duration": { "secs": 111, "nanos": 517342829 }, "formatted_output": "org_30d neighbors 256/8456\norg_30d neighbors 512/8456\norg_30d neighbors 768/8456\norg_30d neighbors 1024/8456\norg_30d neighbors 1280/8456\norg_30d neighbors 1536/8456\norg_30d neighbors 1792/8456\norg_30d neighbors 2048/8456\norg_30d neighbors 2304/8456\norg_30d neighbors 2560/8456\norg_30d neighbors 2816/8456\norg_30d neighbors 3072/8456\norg_30d neighbors 3328/8456\norg_30d neighbors 3584/8456\norg_30d neighbors 3840/8456\norg_30d neighbors 4096/8456\norg_30d neighbors 4352/8456\norg_30d neighbors 4608/8456\norg_30d neighbors 4864/8456\norg_30d neighbors 5120/8456\norg_30d neighbors 5376/8456\norg_30d neighbors 5632/8456\norg_30d neighbors 5888/8456\norg_30d neighbors 6144/8456\norg_30d neighbors 6400/8456\norg_30d neighbors 6656/8456\norg_30d neighbors 6912/8456\norg_30d neighbors 7168/8456\norg_30d neighbors 7424/8456\norg_30d neighbors 7680/8456\norg_30d neighbors 7936/8456\norg_30d neighbors 8192/8456\norg_30d neighbors 8448/8456\norg_30d neighbors 8456/8456\nwrote org_30d_fullobj_k10\nwrote org_30d_fullobj_k30\nwrote org_30d_fullobj_k100\nwrote org_30d_recentobj_k30\norg_90d neighbors 256/8456\norg_90d neighbors 512/8456\norg_90d neighbors 768/8456\norg_90d neighbors 1024/8456\norg_90d neighbors 1280/8456\norg_90d neighbors 1536/8456\norg_90d neighbors 1792/8456\norg_90d neighbors 2048/8456\norg_90d neighbors 2304/8456\norg_90d neighbors 2560/8456\norg_90d neighbors 2816/8456\norg_90d neighbors 3072/8456\norg_90d neighbors 3328/8456\norg_90d neighbors 3584/8456\norg_90d neighbors 3840/8456\norg_90d neighbors 4096/8456\norg_90d neighbors 4352/8456\norg_90d neighbors 4608/8456\norg_90d neighbors 4864/8456\norg_90d neighbors 5120/8456\norg_90d neighbors 5376/8456\norg_90d neighbors 5632/8456\norg_90d neighbors 5888/8456\norg_90d neighbors 6144/8456\norg_90d neighbors 6400/8456\norg_90d neighbors 6656/8456\norg_90d neighbors 6912/8456\norg_90d neighbors 7168/8456\norg_90d neighbors 7424/8456\norg_90d neighbors 7680/8456\norg_90d neighbors 7936/8456\norg_90d neighbors 8192/8456\norg_90d neighbors 8448/8456\norg_90d neighbors 8456/8456\nwrote org_90d_fullobj_k10\nwrote org_90d_fullobj_k30\nwrote org_90d_fullobj_k100\nwrote org_90d_recentobj_k30\norg_180d neighbors 256/8456\norg_180d neighbors 512/8456\norg_180d neighbors 768/8456\norg_180d neighbors 1024/8456\norg_180d neighbors 1280/8456\norg_180d neighbors 1536/8456\norg_180d neighbors 1792/8456\norg_180d neighbors 2048/8456\norg_180d neighbors 2304/8456\norg_180d neighbors 2560/8456\norg_180d neighbors 2816/8456\norg_180d neighbors 3072/8456\norg_180d neighbors 3328/8456\norg_180d neighbors 3584/8456\norg_180d neighbors 3840/8456\norg_180d neighbors 4096/8456\norg_180d neighbors 4352/8456\norg_180d neighbors 4608/8456\norg_180d neighbors 4864/8456\norg_180d neighbors 5120/8456\norg_180d neighbors 5376/8456\norg_180d neighbors 5632/8456\norg_180d neighbors 5888/8456\norg_180d neighbors 6144/8456\norg_180d neighbors 6400/8456\norg_180d neighbors 6656/8456\norg_180d neighbors 6912/8456\norg_180d neighbors 7168/8456\norg_180d neighbors 7424/8456\norg_180d neighbors 7680/8456\norg_180d neighbors 7936/8456\norg_180d neighbors 8192/8456\norg_180d neighbors 8448/8456\norg_180d neighbors 8456/8456\nwrote org_180d_fullobj_k10\nwrote org_180d_fullobj_k30\nwrote org_180d_fullobj_k100\nwrote org_180d_recentobj_k30\norg_decay60 neighbors 256/8456\norg_decay60 neighbors 512/8456\norg_decay60 neighbors 768/8456\norg_decay60 neighbors 1024/8456\norg_decay60 neighbors 1280/8456\norg_decay60 neighbors 1536/8456\norg_decay60 neighbors 1792/8456\norg_decay60 neighbors 2048/8456\norg_decay60 neighbors 2304/8456\norg_decay60 neighbors 2560/8456\norg_decay60 neighbors 2816/8456\norg_decay60 neighbors 3072/8456\norg_decay60 neighbors 3328/8456\norg_decay60 neighbors 3584/8456\norg_decay60 neighbors 3840/8456\norg_decay60 neighbors 4096/8456\norg_decay60 neighbors 4352/8456\norg_decay60 neighbors 4608/8456\norg_decay60 neighbors 4864/8456\norg_decay60 neighbors 5120/8456\norg_decay60 neighbors 5376/8456\norg_decay60 neighbors 5632/8456\norg_decay60 neighbors 5888/8456\norg_decay60 neighbors 6144/8456\norg_decay60 neighbors 6400/8456\norg_decay60 neighbors 6656/8456\norg_decay60 neighbors 6912/8456\norg_decay60 neighbors 7168/8456\norg_decay60 neighbors 7424/8456\norg_decay60 neighbors 7680/8456\norg_decay60 neighbors 7936/8456\norg_decay60 neighbors 8192/8456\norg_decay60 neighbors 8448/8456\norg_decay60 neighbors 8456/8456\nwrote org_decay60_fullobj_k10\nwrote org_decay60_fullobj_k30\nwrote org_decay60_fullobj_k100\n" } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-24T22:43:37.858Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:43:37.858Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:43:41.022Z", "callId": "call_8lkfJeBCYBoOueML8pJL9BjK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport glob,pandas as pd\\nfrom evaluate_public import validation_truth,load_submission,score,DATA\\nfrom ranking_evaluation import CandidateSet\\nt=set(pd.read_parquet(DATA/'target_users.parquet').uid.astype(int)); e=CandidateSet(DATA/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\\nfor f in sorted(glob.glob('/app/solution/validation_org_*d_*.parquet')):\\n print(Path(f).name,score(load_submission(Path(f),e,t),g),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:43:57.873Z", "content": "Script completed\nWall time 16.8 seconds\nOutput:\n\nvalidation_org_180d_fullobj_k10.parquet {'quality_ndcg@10': 0.016263, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k100.parquet {'quality_ndcg@10': 0.01988, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k30.parquet {'quality_ndcg@10': 0.019798, 'quality_users': 4263}\nvalidation_org_180d_recentobj_k30.parquet {'quality_ndcg@10': 0.023044, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k10.parquet {'quality_ndcg@10': 0.015221, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k100.parquet {'quality_ndcg@10': 0.021178, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k30.parquet {'quality_ndcg@10': 0.020511, 'quality_users': 4263}\nvalidation_org_30d_recentobj_k30.parquet {'quality_ndcg@10': 0.032651, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k10.parquet {'quality_ndcg@10': 0.017598, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k100.parquet {'quality_ndcg@10': 0.020642, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k30.parquet {'quality_ndcg@10': 0.020564, 'quality_users': 4263}\nvalidation_org_90d_recentobj_k30.parquet {'quality_ndcg@10': 0.02679, 'quality_users': 4263}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:43:41.170Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7977b5487d28d2231b5ca30a664", "usage": { "input_tokens": 57343, "cached_input_tokens": 57088, "cache_write_input_tokens": 0, "output_tokens": 251, "reasoning_output_tokens": 66, "total_tokens": 57594 }, "turn_token_usage": { "input_tokens": 1865273, "cached_input_tokens": 1805312, "cache_write_input_tokens": 0, "output_tokens": 20216, "reasoning_output_tokens": 8594, "total_tokens": 1885489 }, "thread_token_usage": { "input_tokens": 1865273, "cached_input_tokens": 1805312, "cache_write_input_tokens": 0, "output_tokens": 20216, "reasoning_output_tokens": 8594, "total_tokens": 1885489 } } }, { "kind": "context", "timestamp": "2026-09-24T22:43:57.869Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-89f30ad6-5834-478f-91f3-5b5e6c204d0f", "process_id": "40755", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport glob,pandas as pd\nfrom evaluate_public import validation_truth,load_submission,score,DATA\nfrom ranking_evaluation import CandidateSet\nt=set(pd.read_parquet(DATA/'target_users.parquet').uid.astype(int)); e=CandidateSet(DATA/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\nfor f in sorted(glob.glob('/app/solution/validation_org_*d_*.parquet')):\n print(Path(f).name,score(load_submission(Path(f),e,t),g),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport glob,pandas as pd\nfrom evaluate_public import validation_truth,load_submission,score,DATA\nfrom ranking_evaluation import CandidateSet\nt=set(pd.read_parquet(DATA/'target_users.parquet').uid.astype(int)); e=CandidateSet(DATA/'validation_eligible_candidates.npz'); g=validation_truth(e,t)\nfor f in sorted(glob.glob('/app/solution/validation_org_*d_*.parquet')):\n print(Path(f).name,score(load_submission(Path(f),e,t),g),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_org_180d_fullobj_k10.parquet {'quality_ndcg@10': 0.016263, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k100.parquet {'quality_ndcg@10': 0.01988, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k30.parquet {'quality_ndcg@10': 0.019798, 'quality_users': 4263}\nvalidation_org_180d_recentobj_k30.parquet {'quality_ndcg@10': 0.023044, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k10.parquet {'quality_ndcg@10': 0.015221, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k100.parquet {'quality_ndcg@10': 0.021178, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k30.parquet {'quality_ndcg@10': 0.020511, 'quality_users': 4263}\nvalidation_org_30d_recentobj_k30.parquet {'quality_ndcg@10': 0.032651, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k10.parquet {'quality_ndcg@10': 0.017598, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k100.parquet {'quality_ndcg@10': 0.020642, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k30.parquet {'quality_ndcg@10': 0.020564, 'quality_users': 4263}\nvalidation_org_90d_recentobj_k30.parquet {'quality_ndcg@10': 0.02679, 'quality_users': 4263}\n", "stderr": "", "aggregated_output": "validation_org_180d_fullobj_k10.parquet {'quality_ndcg@10': 0.016263, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k100.parquet {'quality_ndcg@10': 0.01988, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k30.parquet {'quality_ndcg@10': 0.019798, 'quality_users': 4263}\nvalidation_org_180d_recentobj_k30.parquet {'quality_ndcg@10': 0.023044, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k10.parquet {'quality_ndcg@10': 0.015221, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k100.parquet {'quality_ndcg@10': 0.021178, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k30.parquet {'quality_ndcg@10': 0.020511, 'quality_users': 4263}\nvalidation_org_30d_recentobj_k30.parquet {'quality_ndcg@10': 0.032651, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k10.parquet {'quality_ndcg@10': 0.017598, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k100.parquet {'quality_ndcg@10': 0.020642, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k30.parquet {'quality_ndcg@10': 0.020564, 'quality_users': 4263}\nvalidation_org_90d_recentobj_k30.parquet {'quality_ndcg@10': 0.02679, 'quality_users': 4263}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 672086860 }, "formatted_output": "validation_org_180d_fullobj_k10.parquet {'quality_ndcg@10': 0.016263, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k100.parquet {'quality_ndcg@10': 0.01988, 'quality_users': 4263}\nvalidation_org_180d_fullobj_k30.parquet {'quality_ndcg@10': 0.019798, 'quality_users': 4263}\nvalidation_org_180d_recentobj_k30.parquet {'quality_ndcg@10': 0.023044, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k10.parquet {'quality_ndcg@10': 0.015221, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k100.parquet {'quality_ndcg@10': 0.021178, 'quality_users': 4263}\nvalidation_org_30d_fullobj_k30.parquet {'quality_ndcg@10': 0.020511, 'quality_users': 4263}\nvalidation_org_30d_recentobj_k30.parquet {'quality_ndcg@10': 0.032651, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k10.parquet {'quality_ndcg@10': 0.017598, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k100.parquet {'quality_ndcg@10': 0.020642, 'quality_users': 4263}\nvalidation_org_90d_fullobj_k30.parquet {'quality_ndcg@10': 0.020564, 'quality_users': 4263}\nvalidation_org_90d_recentobj_k30.parquet {'quality_ndcg@10': 0.02679, 'quality_users': 4263}\n" } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-24T22:44:04.810Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:44:04.810Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:44:11.606Z", "callId": "call_o2eDPse5s3PmLvblqD23OfD9", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/prepare_recent.py\\n@@\\n- for days in (30, 90, 180):\\n+ windows = (7, 14, 30, 60, 90, 180)\\n+ for days in windows:\\n@@\\n x = mat(r[keep], item[keep], weights, (N_USERS, N_ITEM_IDS))\\n sparse.save_npz(OUT / \\\"train_x_org_decay60.npz\\\", x, compressed=False)\\n print(f\\\"x_org_decay60 nnz={x.nnz:,}\\\", flush=True)\\n+\\n+ likes = pd.read_parquet(\\n+ DATA / \\\"train_likes.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"],\\n+ )\\n+ lr = (likes.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\\n+ lt = likes.timestamp.to_numpy(dtype=np.int32)\\n+ li = likes.item_id.to_numpy(dtype=np.int32)\\n+ lc = mapper[li]\\n+ lo = likes.is_organic.to_numpy(dtype=np.uint8) == 1\\n+ for days in windows:\\n+ keep = (lt >= CUTOFF - days * DAY) & lo & (lc >= 0)\\n+ y = mat(lr[keep], lc[keep], np.ones(keep.sum()), (N_USERS, len(ids)))\\n+ sparse.save_npz(OUT / f\\\"train_y_like_{days}d.npz\\\", y, compressed=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:44:11.728Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:44:11.614Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a74b6308-f222-4ade-b907-dd3322f54334", "changes": { "/app/solution/prepare_recent.py": { "type": "update", "unified_diff": "@@ -38,3 +38,4 @@\n cc = mapper[item]\n- for days in (30, 90, 180):\n+ windows = (7, 14, 30, 60, 90, 180)\n+ for days in windows:\n recent = t >= CUTOFF - days * DAY\n@@ -69,2 +70,16 @@\n \n+ likes = pd.read_parquet(\n+ DATA / \"train_likes.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n+ )\n+ lr = (likes.uid.to_numpy(dtype=np.int32) // 100).astype(np.int32)\n+ lt = likes.timestamp.to_numpy(dtype=np.int32)\n+ li = likes.item_id.to_numpy(dtype=np.int32)\n+ lc = mapper[li]\n+ lo = likes.is_organic.to_numpy(dtype=np.uint8) == 1\n+ for days in windows:\n+ keep = (lt >= CUTOFF - days * DAY) & lo & (lc >= 0)\n+ y = mat(lr[keep], lc[keep], np.ones(keep.sum()), (N_USERS, len(ids)))\n+ sparse.save_npz(OUT / f\"train_y_like_{days}d.npz\", y, compressed=False)\n+\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/prepare_recent.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:44:11.726Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7ae0eec87d28077a6182f40d1d3", "usage": { "input_tokens": 58001, "cached_input_tokens": 57216, "cache_write_input_tokens": 0, "output_tokens": 682, "reasoning_output_tokens": 296, "total_tokens": 58683 }, "turn_token_usage": { "input_tokens": 1923274, "cached_input_tokens": 1862528, "cache_write_input_tokens": 0, "output_tokens": 20898, "reasoning_output_tokens": 8890, "total_tokens": 1944172 }, "thread_token_usage": { "input_tokens": 1923274, "cached_input_tokens": 1862528, "cache_write_input_tokens": 0, "output_tokens": 20898, "reasoning_output_tokens": 8890, "total_tokens": 1944172 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-24T22:44:13.997Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:44:13.997Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:44:15.045Z", "callId": "call_NTvLD6A0gIhIBXBEiPsTINf7", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/prepare_recent.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:44:16.255Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"54b93e\",\"wall_time_seconds\":1.001405152,\"session_id\":24664,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:44:15.146Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7bc153c87d2af7d8056113802a8", "usage": { "input_tokens": 58707, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 6, "total_tokens": 58779 }, "turn_token_usage": { "input_tokens": 1981981, "cached_input_tokens": 1920384, "cache_write_input_tokens": 0, "output_tokens": 20970, "reasoning_output_tokens": 8896, "total_tokens": 2002951 }, "thread_token_usage": { "input_tokens": 1981981, "cached_input_tokens": 1920384, "cache_write_input_tokens": 0, "output_tokens": 20970, "reasoning_output_tokens": 8896, "total_tokens": 2002951 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-24T22:44:17.380Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:44:17.380Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:44:20.318Z", "callId": "call_MP0kOmGy36u3VqdzGLHP5pw7", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":24664,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:44:22.996Z", "content": "Script completed\nWall time 2.7 seconds\nOutput:\n\nx_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:44:20.457Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7c0730c87d2bae88b3af09bd289", "usage": { "input_tokens": 58837, "cached_input_tokens": 58496, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 58890 }, "turn_token_usage": { "input_tokens": 2040818, "cached_input_tokens": 1978880, "cache_write_input_tokens": 0, "output_tokens": 21023, "reasoning_output_tokens": 8902, "total_tokens": 2061841 }, "thread_token_usage": { "input_tokens": 2040818, "cached_input_tokens": 1978880, "cache_write_input_tokens": 0, "output_tokens": 21023, "reasoning_output_tokens": 8902, "total_tokens": 2061841 } } }, { "kind": "context", "timestamp": "2026-09-24T22:44:22.994Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ba214845-4719-466c-8c5f-8518cd353134", "process_id": "24664", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/prepare_recent.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/prepare_recent.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "x_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "stderr": "", "aggregated_output": "x_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 741055310 }, "formatted_output": "x_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-24T22:44:34.613Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:44:34.613Z", "text": "" } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-24T22:44:38.495Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:44:38.495Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:44:58.681Z", "callId": "call_V4tmzEJfFyj3IT2N4h7WCEb9", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_knn.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fast public-window parameter sweep (validation labels are scoring-only).\\\"\\\"\\\"\\n+\\n+from pathlib import Path\\n+import sys\\n+\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+\\n+sys.path.insert(0, \\\"/task\\\")\\n+from evaluate_public import validation_truth # noqa: E402\\n+from ranking_evaluation import CandidateSet, ndcg_at_k # noqa: E402\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def truth_and_rows(uids):\\n+ targets = set(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.astype(int))\\n+ cand = CandidateSet(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ gold = validation_truth(cand, targets)\\n+ positions = np.array([i for i, u in enumerate(uids) if int(u) in gold], dtype=np.int32)\\n+ return gold, positions\\n+\\n+\\n+def objective_y(days, like_weight=2.0, binary=False, pop_penalty=0.0):\\n+ y = sparse.load_npz(OUT / f\\\"train_y_org80_{days}d.npz\\\").astype(np.float32)\\n+ y.data = np.ones_like(y.data) if binary else np.log1p(y.data)\\n+ yl = sparse.load_npz(OUT / f\\\"train_y_like_{days}d.npz\\\").astype(np.float32)\\n+ yl.data = np.ones_like(yl.data) * like_weight\\n+ y = (y + yl).tocsr()\\n+ if pop_penalty:\\n+ df = np.bincount(y.indices, minlength=y.shape[1]).astype(np.float32)\\n+ factor = np.power(np.maximum(df, 1), -pop_penalty)\\n+ y.data *= factor[y.indices]\\n+ return y\\n+\\n+\\n+def evaluate_config(nbr_idx, nbr_sim, y, eligible, uids, item_ids, gold,\\n+ positions, k, power=1.0, popularity=0.0):\\n+ pop = np.load(OUT / \\\"pop_all80.npy\\\").astype(np.float32)\\n+ pop = np.log1p(pop)\\n+ pop = (pop - pop.mean()) / pop.std()\\n+ vals = []\\n+ for q in range(0, len(positions), 128):\\n+ pp = positions[q:q + 128]\\n+ rr = np.repeat(np.arange(len(pp)), k)\\n+ cc = nbr_idx[pp, :k].reshape(-1)\\n+ vv = np.maximum(nbr_sim[pp, :k].reshape(-1), 0) ** power\\n+ w = sparse.csr_matrix((vv, (rr, cc)), shape=(len(pp), y.shape[0]))\\n+ score = (w @ y).toarray().astype(np.float32)\\n+ if popularity:\\n+ scale = np.maximum(score.max(axis=1, keepdims=True), 1e-6)\\n+ score += popularity * scale * pop[None, :]\\n+ score[~eligible[pp]] = -np.inf\\n+ top = np.argpartition(score, -10, axis=1)[:, -10:]\\n+ sv = np.take_along_axis(score, top, axis=1)\\n+ top = np.take_along_axis(top, np.argsort(-sv, axis=1), axis=1)\\n+ for j, p in enumerate(pp):\\n+ vals.append(ndcg_at_k(item_ids[top[j]].tolist(), gold[int(uids[p])], 10))\\n+ return float(np.mean(vals))\\n+\\n+\\n+def main():\\n+ z = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ uids, items = z[\\\"uids\\\"], z[\\\"item_ids\\\"]\\n+ eligible = np.unpackbits(\\n+ z[\\\"packed_eligible\\\"], axis=1, count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ gold, positions = truth_and_rows(uids)\\n+ ni = np.load(OUT / \\\"nbr_org_30d_idx.npy\\\")\\n+ ns = np.load(OUT / \\\"nbr_org_30d_sim.npy\\\")\\n+ for days in (7, 14, 30, 60, 90, 180):\\n+ y = objective_y(days)\\n+ for k in (10, 20, 30, 50, 100, 200):\\n+ score = evaluate_config(ni, ns, y, eligible, uids, items, gold, positions, k)\\n+ print(f\\\"out={days:3d} k={k:3d} score={score:.6f}\\\", flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:44:58.742Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:44:58.689Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8b8b17d0-2c24-4dd6-aee4-a1b7af616713", "changes": { "/app/solution/tune_knn.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Fast public-window parameter sweep (validation labels are scoring-only).\"\"\"\n\nfrom pathlib import Path\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\n\nsys.path.insert(0, \"/task\")\nfrom evaluate_public import validation_truth # noqa: E402\nfrom ranking_evaluation import CandidateSet, ndcg_at_k # noqa: E402\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\n\n\ndef truth_and_rows(uids):\n targets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\n cand = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n gold = validation_truth(cand, targets)\n positions = np.array([i for i, u in enumerate(uids) if int(u) in gold], dtype=np.int32)\n return gold, positions\n\n\ndef objective_y(days, like_weight=2.0, binary=False, pop_penalty=0.0):\n y = sparse.load_npz(OUT / f\"train_y_org80_{days}d.npz\").astype(np.float32)\n y.data = np.ones_like(y.data) if binary else np.log1p(y.data)\n yl = sparse.load_npz(OUT / f\"train_y_like_{days}d.npz\").astype(np.float32)\n yl.data = np.ones_like(yl.data) * like_weight\n y = (y + yl).tocsr()\n if pop_penalty:\n df = np.bincount(y.indices, minlength=y.shape[1]).astype(np.float32)\n factor = np.power(np.maximum(df, 1), -pop_penalty)\n y.data *= factor[y.indices]\n return y\n\n\ndef evaluate_config(nbr_idx, nbr_sim, y, eligible, uids, item_ids, gold,\n positions, k, power=1.0, popularity=0.0):\n pop = np.load(OUT / \"pop_all80.npy\").astype(np.float32)\n pop = np.log1p(pop)\n pop = (pop - pop.mean()) / pop.std()\n vals = []\n for q in range(0, len(positions), 128):\n pp = positions[q:q + 128]\n rr = np.repeat(np.arange(len(pp)), k)\n cc = nbr_idx[pp, :k].reshape(-1)\n vv = np.maximum(nbr_sim[pp, :k].reshape(-1), 0) ** power\n w = sparse.csr_matrix((vv, (rr, cc)), shape=(len(pp), y.shape[0]))\n score = (w @ y).toarray().astype(np.float32)\n if popularity:\n scale = np.maximum(score.max(axis=1, keepdims=True), 1e-6)\n score += popularity * scale * pop[None, :]\n score[~eligible[pp]] = -np.inf\n top = np.argpartition(score, -10, axis=1)[:, -10:]\n sv = np.take_along_axis(score, top, axis=1)\n top = np.take_along_axis(top, np.argsort(-sv, axis=1), axis=1)\n for j, p in enumerate(pp):\n vals.append(ndcg_at_k(item_ids[top[j]].tolist(), gold[int(uids[p])], 10))\n return float(np.mean(vals))\n\n\ndef main():\n z = np.load(DATA / \"validation_eligible_candidates.npz\")\n uids, items = z[\"uids\"], z[\"item_ids\"]\n eligible = np.unpackbits(\n z[\"packed_eligible\"], axis=1, count=len(items), bitorder=\"little\"\n ).astype(bool)\n gold, positions = truth_and_rows(uids)\n ni = np.load(OUT / \"nbr_org_30d_idx.npy\")\n ns = np.load(OUT / \"nbr_org_30d_sim.npy\")\n for days in (7, 14, 30, 60, 90, 180):\n y = objective_y(days)\n for k in (10, 20, 30, 50, 100, 200):\n score = evaluate_config(ni, ns, y, eligible, uids, items, gold, positions, k)\n print(f\"out={days:3d} k={k:3d} score={score:.6f}\", flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_knn.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:44:58.740Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7c731b887d2bda8d9b137698e34", "usage": { "input_tokens": 59086, "cached_input_tokens": 58624, "cache_write_input_tokens": 0, "output_tokens": 1855, "reasoning_output_tokens": 672, "total_tokens": 60941 }, "turn_token_usage": { "input_tokens": 2099904, "cached_input_tokens": 2037504, "cache_write_input_tokens": 0, "output_tokens": 22878, "reasoning_output_tokens": 9574, "total_tokens": 2122782 }, "thread_token_usage": { "input_tokens": 2099904, "cached_input_tokens": 2037504, "cache_write_input_tokens": 0, "output_tokens": 22878, "reasoning_output_tokens": 9574, "total_tokens": 2122782 } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-24T22:44:59.971Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:44:59.971Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:45:01.040Z", "callId": "call_lab6A0QBkjr5Br4Mwiv7HSIk", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/tune_knn.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:45:02.205Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"42b4ad\",\"wall_time_seconds\":1.000261814,\"session_id\":38812,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:45:01.142Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7eafd3487d2a65bcdd55dffa1ef", "usage": { "input_tokens": 60965, "cached_input_tokens": 58880, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 7, "total_tokens": 61039 }, "turn_token_usage": { "input_tokens": 2160869, "cached_input_tokens": 2096384, "cache_write_input_tokens": 0, "output_tokens": 22952, "reasoning_output_tokens": 9581, "total_tokens": 2183821 }, "thread_token_usage": { "input_tokens": 2160869, "cached_input_tokens": 2096384, "cache_write_input_tokens": 0, "output_tokens": 22952, "reasoning_output_tokens": 9581, "total_tokens": 2183821 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-24T22:45:04.552Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:45:04.552Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:45:05.258Z", "callId": "call_Z0I1pY1dTKE1zAGNIiQWnnOD", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38812,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:45:25.266Z", "content": "Script completed\nWall time 20.0 seconds\nOutput:\n\nout= 7 k= 10 score=0.023172\nout= 7 k= 20 score=0.026821\nout= 7 k= 30 score=0.031896\nout= 7 k= 50 score=0.037723\nout= 7 k=100 score=0.042783\nout= 7 k=200 score=0.045419\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:45:05.375Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a7ee63ac87d29278009e710f95f9", "usage": { "input_tokens": 61097, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 61150 }, "turn_token_usage": { "input_tokens": 2221966, "cached_input_tokens": 2157184, "cache_write_input_tokens": 0, "output_tokens": 23005, "reasoning_output_tokens": 9587, "total_tokens": 2244971 }, "thread_token_usage": { "input_tokens": 2221966, "cached_input_tokens": 2157184, "cache_write_input_tokens": 0, "output_tokens": 23005, "reasoning_output_tokens": 9587, "total_tokens": 2244971 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-24T22:45:27.813Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:45:27.813Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:45:28.507Z", "callId": "call_mQY7xNm1rgTi03U8Bf5hAnV3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38812,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:45:58.516Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nout= 14 k= 10 score=0.022146\nout= 14 k= 20 score=0.027197\nout= 14 k= 30 score=0.032468\nout= 14 k= 50 score=0.036990\nout= 14 k=100 score=0.041587\nout= 14 k=200 score=0.043316\nout= 30 k= 10 score=0.021869\nout= 30 k= 20 score=0.028195\nout= 30 k= 30 score=0.031947\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:45:28.625Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a805779c87d2abe84311d82fd450", "usage": { "input_tokens": 61267, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 61331 }, "turn_token_usage": { "input_tokens": 2283233, "cached_input_tokens": 2218112, "cache_write_input_tokens": 0, "output_tokens": 23069, "reasoning_output_tokens": 9604, "total_tokens": 2306302 }, "thread_token_usage": { "input_tokens": 2283233, "cached_input_tokens": 2218112, "cache_write_input_tokens": 0, "output_tokens": 23069, "reasoning_output_tokens": 9604, "total_tokens": 2306302 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-24T22:46:00.397Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:46:00.397Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:46:01.085Z", "callId": "call_YgCzMJOxwtyEDhnQklY0mIML", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38812,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:46:31.094Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nout= 30 k= 50 score=0.035076\nout= 30 k=100 score=0.037268\nout= 30 k=200 score=0.036943\nout= 60 k= 10 score=0.020264\nout= 60 k= 20 score=0.026013\nout= 60 k= 30 score=0.027507\nout= 60 k= 50 score=0.029224\nout= 60 k=100 score=0.031170\nout= 60 k=200 score=0.030119\nout= 90 k= 10 score=0.019108\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:46:01.239Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a826cbc487d293e9759e6baec446", "usage": { "input_tokens": 61487, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 29, "total_tokens": 61563 }, "turn_token_usage": { "input_tokens": 2344720, "cached_input_tokens": 2279168, "cache_write_input_tokens": 0, "output_tokens": 23145, "reasoning_output_tokens": 9633, "total_tokens": 2367865 }, "thread_token_usage": { "input_tokens": 2344720, "cached_input_tokens": 2279168, "cache_write_input_tokens": 0, "output_tokens": 23145, "reasoning_output_tokens": 9633, "total_tokens": 2367865 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-24T22:46:34.232Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:46:34.232Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:46:34.951Z", "callId": "call_glfQMXa8rrLxAv3r9fUIVjPJ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38812,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:47:02.391Z", "content": "Script completed\nWall time 27.4 seconds\nOutput:\n\nout= 90 k= 20 score=0.024187\nout= 90 k= 30 score=0.025519\nout= 90 k= 50 score=0.027180\nout= 90 k=100 score=0.028193\nout= 90 k=200 score=0.027127\nout=180 k= 10 score=0.016814\nout=180 k= 20 score=0.020825\nout=180 k= 30 score=0.021802\nout=180 k= 50 score=0.023256\nout=180 k=100 score=0.023534\nout=180 k=200 score=0.022183\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:46:35.068Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a84750dc87d288285ac7c5b99338", "usage": { "input_tokens": 61732, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 61785 }, "turn_token_usage": { "input_tokens": 2406452, "cached_input_tokens": 2340480, "cache_write_input_tokens": 0, "output_tokens": 23198, "reasoning_output_tokens": 9639, "total_tokens": 2429650 }, "thread_token_usage": { "input_tokens": 2406452, "cached_input_tokens": 2340480, "cache_write_input_tokens": 0, "output_tokens": 23198, "reasoning_output_tokens": 9639, "total_tokens": 2429650 } } }, { "kind": "context", "timestamp": "2026-09-24T22:47:02.390Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-babbec3f-142b-40d4-afb2-f990713cd0e6", "process_id": "38812", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/tune_knn.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/tune_knn.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "out= 7 k= 10 score=0.023172\nout= 7 k= 20 score=0.026821\nout= 7 k= 30 score=0.031896\nout= 7 k= 50 score=0.037723\nout= 7 k=100 score=0.042783\nout= 7 k=200 score=0.045419\nout= 14 k= 10 score=0.022146\nout= 14 k= 20 score=0.027197\nout= 14 k= 30 score=0.032468\nout= 14 k= 50 score=0.036990\nout= 14 k=100 score=0.041587\nout= 14 k=200 score=0.043316\nout= 30 k= 10 score=0.021869\nout= 30 k= 20 score=0.028195\nout= 30 k= 30 score=0.031947\nout= 30 k= 50 score=0.035076\nout= 30 k=100 score=0.037268\nout= 30 k=200 score=0.036943\nout= 60 k= 10 score=0.020264\nout= 60 k= 20 score=0.026013\nout= 60 k= 30 score=0.027507\nout= 60 k= 50 score=0.029224\nout= 60 k=100 score=0.031170\nout= 60 k=200 score=0.030119\nout= 90 k= 10 score=0.019108\nout= 90 k= 20 score=0.024187\nout= 90 k= 30 score=0.025519\nout= 90 k= 50 score=0.027180\nout= 90 k=100 score=0.028193\nout= 90 k=200 score=0.027127\nout=180 k= 10 score=0.016814\nout=180 k= 20 score=0.020825\nout=180 k= 30 score=0.021802\nout=180 k= 50 score=0.023256\nout=180 k=100 score=0.023534\nout=180 k=200 score=0.022183\n", "stderr": "", "aggregated_output": "out= 7 k= 10 score=0.023172\nout= 7 k= 20 score=0.026821\nout= 7 k= 30 score=0.031896\nout= 7 k= 50 score=0.037723\nout= 7 k=100 score=0.042783\nout= 7 k=200 score=0.045419\nout= 14 k= 10 score=0.022146\nout= 14 k= 20 score=0.027197\nout= 14 k= 30 score=0.032468\nout= 14 k= 50 score=0.036990\nout= 14 k=100 score=0.041587\nout= 14 k=200 score=0.043316\nout= 30 k= 10 score=0.021869\nout= 30 k= 20 score=0.028195\nout= 30 k= 30 score=0.031947\nout= 30 k= 50 score=0.035076\nout= 30 k=100 score=0.037268\nout= 30 k=200 score=0.036943\nout= 60 k= 10 score=0.020264\nout= 60 k= 20 score=0.026013\nout= 60 k= 30 score=0.027507\nout= 60 k= 50 score=0.029224\nout= 60 k=100 score=0.031170\nout= 60 k=200 score=0.030119\nout= 90 k= 10 score=0.019108\nout= 90 k= 20 score=0.024187\nout= 90 k= 30 score=0.025519\nout= 90 k= 50 score=0.027180\nout= 90 k=100 score=0.028193\nout= 90 k=200 score=0.027127\nout=180 k= 10 score=0.016814\nout=180 k= 20 score=0.020825\nout=180 k= 30 score=0.021802\nout=180 k= 50 score=0.023256\nout=180 k=100 score=0.023534\nout=180 k=200 score=0.022183\n", "exit_code": 0, "duration": { "secs": 121, "nanos": 185080725 }, "formatted_output": "out= 7 k= 10 score=0.023172\nout= 7 k= 20 score=0.026821\nout= 7 k= 30 score=0.031896\nout= 7 k= 50 score=0.037723\nout= 7 k=100 score=0.042783\nout= 7 k=200 score=0.045419\nout= 14 k= 10 score=0.022146\nout= 14 k= 20 score=0.027197\nout= 14 k= 30 score=0.032468\nout= 14 k= 50 score=0.036990\nout= 14 k=100 score=0.041587\nout= 14 k=200 score=0.043316\nout= 30 k= 10 score=0.021869\nout= 30 k= 20 score=0.028195\nout= 30 k= 30 score=0.031947\nout= 30 k= 50 score=0.035076\nout= 30 k=100 score=0.037268\nout= 30 k=200 score=0.036943\nout= 60 k= 10 score=0.020264\nout= 60 k= 20 score=0.026013\nout= 60 k= 30 score=0.027507\nout= 60 k= 50 score=0.029224\nout= 60 k=100 score=0.031170\nout= 60 k=200 score=0.030119\nout= 90 k= 10 score=0.019108\nout= 90 k= 20 score=0.024187\nout= 90 k= 30 score=0.025519\nout= 90 k= 50 score=0.027180\nout= 90 k=100 score=0.028193\nout= 90 k=200 score=0.027127\nout=180 k= 10 score=0.016814\nout=180 k= 20 score=0.020825\nout=180 k= 30 score=0.021802\nout=180 k= 50 score=0.023256\nout=180 k=100 score=0.023534\nout=180 k=200 score=0.022183\n" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-24T22:47:05.504Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:47:05.504Z", "text": "" } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-24T22:47:09.129Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:47:09.129Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T22:47:10.764Z", "text": "Recency is the dominant signal: matching on the last 30 days and voting from neighbors’ last 7 days lifts validation NDCG to about 0.0454—over 4× the baseline. I’m now tightening the trend window and testing whether personalized neighbor votes or recent global momentum should dominate, then I’ll refit the exact selected recipe through validation for the hidden window." }, { "kind": "tool", "timestamp": "2026-09-24T22:47:12.446Z", "callId": "call_WVsreqUz7cW8aQMDbw9vmbIj", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/prepare_recent.py\\n@@\\n- windows = (7, 14, 30, 60, 90, 180)\\n+ windows = (1, 3, 5, 7, 14, 30, 60, 90, 180)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" }, "result": { "timestamp": "2026-09-24T22:47:12.582Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:47:12.455Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-00ea1ac7-84c7-4d09-b299-0d87c758be46", "changes": { "/app/solution/prepare_recent.py": { "type": "update", "unified_diff": "@@ -38,3 +38,3 @@\n cc = mapper[item]\n- windows = (7, 14, 30, 60, 90, 180)\n+ windows = (1, 3, 5, 7, 14, 30, 60, 90, 180)\n for days in windows:\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/prepare_recent.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T22:47:12.581Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a866a85887d2a11144125690b970", "usage": { "input_tokens": 61963, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 501, "reasoning_output_tokens": 313, "total_tokens": 62464 }, "turn_token_usage": { "input_tokens": 2468415, "cached_input_tokens": 2402048, "cache_write_input_tokens": 0, "output_tokens": 23699, "reasoning_output_tokens": 9952, "total_tokens": 2492114 }, "thread_token_usage": { "input_tokens": 2468415, "cached_input_tokens": 2402048, "cache_write_input_tokens": 0, "output_tokens": 23699, "reasoning_output_tokens": 9952, "total_tokens": 2492114 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:47:14.893Z", "callId": "call_pDdoZxa5QhQZDygzdZXDv5CH", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/prepare_recent.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:47:16.064Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"ca8157\",\"wall_time_seconds\":1.000513368,\"session_id\":7250,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:47:15.008Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a871098c87d2876bac916aff2b4a", "usage": { "input_tokens": 62488, "cached_input_tokens": 61824, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 0, "total_tokens": 62552 }, "turn_token_usage": { "input_tokens": 2530903, "cached_input_tokens": 2463872, "cache_write_input_tokens": 0, "output_tokens": 23763, "reasoning_output_tokens": 9952, "total_tokens": 2554666 }, "thread_token_usage": { "input_tokens": 2530903, "cached_input_tokens": 2463872, "cache_write_input_tokens": 0, "output_tokens": 23763, "reasoning_output_tokens": 9952, "total_tokens": 2554666 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:47:17.955Z", "callId": "call_w92oFFxShNkEP1CXhhWRulVR", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7250,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:47:23.446Z", "content": "Script completed\nWall time 5.5 seconds\nOutput:\n\nx_org_1d nnz=52,688\ny_org80_1d nnz=33,553\nx_org_3d nnz=133,336\ny_org80_3d nnz=84,970\nx_org_5d nnz=203,180\ny_org80_5d nnz=129,377\nx_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:47:18.071Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a874462c87d2ba210633b8a1e735", "usage": { "input_tokens": 62609, "cached_input_tokens": 62336, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 62654 }, "turn_token_usage": { "input_tokens": 2593512, "cached_input_tokens": 2526208, "cache_write_input_tokens": 0, "output_tokens": 23808, "reasoning_output_tokens": 9952, "total_tokens": 2617320 }, "thread_token_usage": { "input_tokens": 2593512, "cached_input_tokens": 2526208, "cache_write_input_tokens": 0, "output_tokens": 23808, "reasoning_output_tokens": 9952, "total_tokens": 2617320 } } }, { "kind": "context", "timestamp": "2026-09-24T22:47:23.444Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fcfaf2d7-9c83-4b05-bc08-1433aec185ed", "process_id": "7250", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/prepare_recent.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/prepare_recent.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "x_org_1d nnz=52,688\ny_org80_1d nnz=33,553\nx_org_3d nnz=133,336\ny_org80_3d nnz=84,970\nx_org_5d nnz=203,180\ny_org80_5d nnz=129,377\nx_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "stderr": "", "aggregated_output": "x_org_1d nnz=52,688\ny_org80_1d nnz=33,553\nx_org_3d nnz=133,336\ny_org80_3d nnz=84,970\nx_org_5d nnz=203,180\ny_org80_5d nnz=129,377\nx_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 384362782 }, "formatted_output": "x_org_1d nnz=52,688\ny_org80_1d nnz=33,553\nx_org_3d nnz=133,336\ny_org80_3d nnz=84,970\nx_org_5d nnz=203,180\ny_org80_5d nnz=129,377\nx_org_7d nnz=271,919\ny_org80_7d nnz=173,200\nx_org_14d nnz=463,290\ny_org80_14d nnz=293,468\nx_org_30d nnz=811,383\ny_org80_30d nnz=509,847\nx_org_60d nnz=1,323,228\ny_org80_60d nnz=820,800\nx_org_90d nnz=1,715,135\ny_org80_90d nnz=1,056,497\nx_org_180d nnz=2,582,904\ny_org80_180d nnz=1,552,582\nx_org_decay60 nnz=2,582,904\n" } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-24T22:47:26.213Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:47:26.213Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:47:30.590Z", "callId": "call_kaChyVc3Vo2WU6NVFenKnsU6", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); g,p=t.truth_and_rows(u); ni=np.load(t.OUT/'nbr_org_30d_idx.npy'); ns=np.load(t.OUT/'nbr_org_30d_sim.npy')\\nfor d in [1,3,5,7,14]:\\n y=t.objective_y(d)\\n for k in [100,150,200,300,400]:\\n s=t.evaluate_config(ni,ns,y,e,u,it,g,p,k)\\n print('day',d,'k',k,'score',round(s,6),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:47:31.764Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"e789b8\",\"wall_time_seconds\":1.001304776,\"session_id\":93315,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:47:30.765Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a87baab487d2a935ed9927ccb78f", "usage": { "input_tokens": 62925, "cached_input_tokens": 62464, "cache_write_input_tokens": 0, "output_tokens": 347, "reasoning_output_tokens": 96, "total_tokens": 63272 }, "turn_token_usage": { "input_tokens": 2656437, "cached_input_tokens": 2588672, "cache_write_input_tokens": 0, "output_tokens": 24155, "reasoning_output_tokens": 10048, "total_tokens": 2680592 }, "thread_token_usage": { "input_tokens": 2656437, "cached_input_tokens": 2588672, "cache_write_input_tokens": 0, "output_tokens": 24155, "reasoning_output_tokens": 10048, "total_tokens": 2680592 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-24T22:47:33.751Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:47:33.751Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:47:34.438Z", "callId": "call_kd6TaDpP4YgNX0BT859SnEO3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":93315,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:48:04.447Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nday 1 k 100 score 0.028855\nday 1 k 150 score 0.032505\nday 1 k 200 score 0.034717\nday 1 k 300 score 0.036489\nday 1 k 400 score 0.03772\nday 3 k 100 score 0.037708\nday 3 k 150 score 0.041168\nday 3 k 200 score 0.042572\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:47:34.571Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a88402d487d29d0ec32e21810435", "usage": { "input_tokens": 63330, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 63385 }, "turn_token_usage": { "input_tokens": 2719767, "cached_input_tokens": 2651392, "cache_write_input_tokens": 0, "output_tokens": 24210, "reasoning_output_tokens": 10056, "total_tokens": 2743977 }, "thread_token_usage": { "input_tokens": 2719767, "cached_input_tokens": 2651392, "cache_write_input_tokens": 0, "output_tokens": 24210, "reasoning_output_tokens": 10056, "total_tokens": 2743977 } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-24T22:48:06.560Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:48:06.560Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:48:07.258Z", "callId": "call_2DcnXxS3OXWraj0ksTrHT79E", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":93315,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:48:37.266Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nday 3 k 300 score 0.043646\nday 3 k 400 score 0.044317\nday 5 k 100 score 0.039835\nday 5 k 150 score 0.042252\nday 5 k 200 score 0.043348\nday 5 k 300 score 0.044503\nday 5 k 400 score 0.045798\nday 7 k 100 score 0.042783\nday 7 k 150 score 0.044301\nday 7 k 200 score 0.045419\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:48:07.357Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a8a4a51487d2b26960351f6007a8", "usage": { "input_tokens": 63512, "cached_input_tokens": 63104, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 63565 }, "turn_token_usage": { "input_tokens": 2783279, "cached_input_tokens": 2714496, "cache_write_input_tokens": 0, "output_tokens": 24263, "reasoning_output_tokens": 10062, "total_tokens": 2807542 }, "thread_token_usage": { "input_tokens": 2783279, "cached_input_tokens": 2714496, "cache_write_input_tokens": 0, "output_tokens": 24263, "reasoning_output_tokens": 10062, "total_tokens": 2807542 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-24T22:48:38.468Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:48:38.468Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:48:39.162Z", "callId": "call_ydi1vlD7Njkx9kJ1Jl84RkaL", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":93315,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:48:56.868Z", "content": "Script completed\nWall time 17.7 seconds\nOutput:\n\nday 7 k 300 score 0.045554\nday 7 k 400 score 0.046086\nday 14 k 100 score 0.041587\nday 14 k 150 score 0.042856\nday 14 k 200 score 0.043316\nday 14 k 300 score 0.042881\nday 14 k 400 score 0.042722\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:48:39.294Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a8c57cd887d2b552091aa5d6e4a9", "usage": { "input_tokens": 63718, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 63771 }, "turn_token_usage": { "input_tokens": 2846997, "cached_input_tokens": 2777856, "cache_write_input_tokens": 0, "output_tokens": 24316, "reasoning_output_tokens": 10068, "total_tokens": 2871313 }, "thread_token_usage": { "input_tokens": 2846997, "cached_input_tokens": 2777856, "cache_write_input_tokens": 0, "output_tokens": 24316, "reasoning_output_tokens": 10068, "total_tokens": 2871313 } } }, { "kind": "context", "timestamp": "2026-09-24T22:48:56.866Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fc7d0b3c-9db9-485a-a1bd-2d46382894aa", "process_id": "93315", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); g,p=t.truth_and_rows(u); ni=np.load(t.OUT/'nbr_org_30d_idx.npy'); ns=np.load(t.OUT/'nbr_org_30d_sim.npy')\nfor d in [1,3,5,7,14]:\n y=t.objective_y(d)\n for k in [100,150,200,300,400]:\n s=t.evaluate_config(ni,ns,y,e,u,it,g,p,k)\n print('day',d,'k',k,'score',round(s,6),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); g,p=t.truth_and_rows(u); ni=np.load(t.OUT/'nbr_org_30d_idx.npy'); ns=np.load(t.OUT/'nbr_org_30d_sim.npy')\nfor d in [1,3,5,7,14]:\n y=t.objective_y(d)\n for k in [100,150,200,300,400]:\n s=t.evaluate_config(ni,ns,y,e,u,it,g,p,k)\n print('day',d,'k',k,'score',round(s,6),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "day 1 k 100 score 0.028855\nday 1 k 150 score 0.032505\nday 1 k 200 score 0.034717\nday 1 k 300 score 0.036489\nday 1 k 400 score 0.03772\nday 3 k 100 score 0.037708\nday 3 k 150 score 0.041168\nday 3 k 200 score 0.042572\nday 3 k 300 score 0.043646\nday 3 k 400 score 0.044317\nday 5 k 100 score 0.039835\nday 5 k 150 score 0.042252\nday 5 k 200 score 0.043348\nday 5 k 300 score 0.044503\nday 5 k 400 score 0.045798\nday 7 k 100 score 0.042783\nday 7 k 150 score 0.044301\nday 7 k 200 score 0.045419\nday 7 k 300 score 0.045554\nday 7 k 400 score 0.046086\nday 14 k 100 score 0.041587\nday 14 k 150 score 0.042856\nday 14 k 200 score 0.043316\nday 14 k 300 score 0.042881\nday 14 k 400 score 0.042722\n", "stderr": "", "aggregated_output": "day 1 k 100 score 0.028855\nday 1 k 150 score 0.032505\nday 1 k 200 score 0.034717\nday 1 k 300 score 0.036489\nday 1 k 400 score 0.03772\nday 3 k 100 score 0.037708\nday 3 k 150 score 0.041168\nday 3 k 200 score 0.042572\nday 3 k 300 score 0.043646\nday 3 k 400 score 0.044317\nday 5 k 100 score 0.039835\nday 5 k 150 score 0.042252\nday 5 k 200 score 0.043348\nday 5 k 300 score 0.044503\nday 5 k 400 score 0.045798\nday 7 k 100 score 0.042783\nday 7 k 150 score 0.044301\nday 7 k 200 score 0.045419\nday 7 k 300 score 0.045554\nday 7 k 400 score 0.046086\nday 14 k 100 score 0.041587\nday 14 k 150 score 0.042856\nday 14 k 200 score 0.043316\nday 14 k 300 score 0.042881\nday 14 k 400 score 0.042722\n", "exit_code": 0, "duration": { "secs": 86, "nanos": 102930216 }, "formatted_output": "day 1 k 100 score 0.028855\nday 1 k 150 score 0.032505\nday 1 k 200 score 0.034717\nday 1 k 300 score 0.036489\nday 1 k 400 score 0.03772\nday 3 k 100 score 0.037708\nday 3 k 150 score 0.041168\nday 3 k 200 score 0.042572\nday 3 k 300 score 0.043646\nday 3 k 400 score 0.044317\nday 5 k 100 score 0.039835\nday 5 k 150 score 0.042252\nday 5 k 200 score 0.043348\nday 5 k 300 score 0.044503\nday 5 k 400 score 0.045798\nday 7 k 100 score 0.042783\nday 7 k 150 score 0.044301\nday 7 k 200 score 0.045419\nday 7 k 300 score 0.045554\nday 7 k 400 score 0.046086\nday 14 k 100 score 0.041587\nday 14 k 150 score 0.042856\nday 14 k 200 score 0.043316\nday 14 k 300 score 0.042881\nday 14 k 400 score 0.042722\n" } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-24T22:49:01.621Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:01.621Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:49:07.452Z", "callId": "call_WgagdmbNEkcgVBxoHLVcEezA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); g,p=t.truth_and_rows(u)\\nfrom ranking_evaluation import ndcg_at_k\\nfor d in [1,3,5,7,14,30,60,90,180]:\\n y=t.objective_y(d); s=np.asarray(y.sum(0)).ravel(); vals=[]\\n for q in range(0,len(p),256):\\n pp=p[q:q+256]; sc=np.broadcast_to(s,(len(pp),len(s))).copy(); sc[~e[pp]]=-1\\n top=np.argpartition(sc,-10,axis=1)[:,-10:]; sv=np.take_along_axis(sc,top,1); top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\\n vals.extend(ndcg_at_k(it[top[j]].tolist(),g[int(u[x])],10) for j,x in enumerate(pp))\\n print(d,float(np.mean(vals)),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:49:14.387Z", "content": "Script completed\nWall time 6.9 seconds\nOutput:\n\n1 0.03483208495045985\n3 0.03347270106025616\n5 0.03267312868800765\n7 0.03206340073133369\n14 0.028708786527114998\n30 0.020689660637662668\n60 0.01770902837388687\n90 0.016760764363332906\n180 0.013098946985601346\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:07.580Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a8d9117487d2b41daf1f54c2dcd3", "usage": { "input_tokens": 63885, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 538, "reasoning_output_tokens": 205, "total_tokens": 64423 }, "turn_token_usage": { "input_tokens": 2910882, "cached_input_tokens": 2841344, "cache_write_input_tokens": 0, "output_tokens": 24854, "reasoning_output_tokens": 10273, "total_tokens": 2935736 }, "thread_token_usage": { "input_tokens": 2910882, "cached_input_tokens": 2841344, "cache_write_input_tokens": 0, "output_tokens": 24854, "reasoning_output_tokens": 10273, "total_tokens": 2935736 } } }, { "kind": "context", "timestamp": "2026-09-24T22:49:14.383Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e111e9cf-9335-42b3-b3f0-601d0849b906", "process_id": "91078", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); g,p=t.truth_and_rows(u)\nfrom ranking_evaluation import ndcg_at_k\nfor d in [1,3,5,7,14,30,60,90,180]:\n y=t.objective_y(d); s=np.asarray(y.sum(0)).ravel(); vals=[]\n for q in range(0,len(p),256):\n pp=p[q:q+256]; sc=np.broadcast_to(s,(len(pp),len(s))).copy(); sc[~e[pp]]=-1\n top=np.argpartition(sc,-10,axis=1)[:,-10:]; sv=np.take_along_axis(sc,top,1); top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\n vals.extend(ndcg_at_k(it[top[j]].tolist(),g[int(u[x])],10) for j,x in enumerate(pp))\n print(d,float(np.mean(vals)),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); g,p=t.truth_and_rows(u)\nfrom ranking_evaluation import ndcg_at_k\nfor d in [1,3,5,7,14,30,60,90,180]:\n y=t.objective_y(d); s=np.asarray(y.sum(0)).ravel(); vals=[]\n for q in range(0,len(p),256):\n pp=p[q:q+256]; sc=np.broadcast_to(s,(len(pp),len(s))).copy(); sc[~e[pp]]=-1\n top=np.argpartition(sc,-10,axis=1)[:,-10:]; sv=np.take_along_axis(sc,top,1); top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\n vals.extend(ndcg_at_k(it[top[j]].tolist(),g[int(u[x])],10) for j,x in enumerate(pp))\n print(d,float(np.mean(vals)),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1 0.03483208495045985\n3 0.03347270106025616\n5 0.03267312868800765\n7 0.03206340073133369\n14 0.028708786527114998\n30 0.020689660637662668\n60 0.01770902837388687\n90 0.016760764363332906\n180 0.013098946985601346\n", "stderr": "", "aggregated_output": "1 0.03483208495045985\n3 0.03347270106025616\n5 0.03267312868800765\n7 0.03206340073133369\n14 0.028708786527114998\n30 0.020689660637662668\n60 0.01770902837388687\n90 0.016760764363332906\n180 0.013098946985601346\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 730583646 }, "formatted_output": "1 0.03483208495045985\n3 0.03347270106025616\n5 0.03267312868800765\n7 0.03206340073133369\n14 0.028708786527114998\n30 0.020689660637662668\n60 0.01770902837388687\n90 0.016760764363332906\n180 0.013098946985601346\n" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-24T22:49:22.582Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:22.582Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:49:32.874Z", "callId": "call_D73MDf1C90FddeGERMCP552t", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nfrom scipy import sparse\\nimport tune_knn as t\\nfrom ranking_evaluation import ndcg_at_k\\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); gold,pos=t.truth_and_rows(u); ni=np.load(t.OUT/'nbr_org_30d_idx.npy'); ns=np.load(t.OUT/'nbr_org_30d_sim.npy'); y=t.objective_y(7)\\nglobs={d:np.asarray(t.objective_y(d).sum(0)).ravel().astype(np.float32) for d in [1,3,5,7]}\\nfor d in globs: globs[d]/=globs[d].max()\\nconfigs=[('base',None,0.)]+[(f'g{d}_a{a}',d,a) for d in globs for a in [.1,.25,.5,.75]]\\nres={n:[] for n,_,_ in configs}; k=400\\nfor q in range(0,len(pos),128):\\n pp=pos[q:q+128]; rr=np.repeat(np.arange(len(pp)),k); cc=ni[pp,:k].ravel(); vv=ns[pp,:k].ravel(); w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),y.shape[0])); sc=(w@y).toarray().astype(np.float32); sc/=np.maximum(sc.max(1,keepdims=True),1e-9)\\n for name,d,a in configs:\\n s=sc if d is None else sc*(1-a)+globs[d][None,:]*a\\n s=np.where(e[pp],s,-np.inf); top=np.argpartition(s,-10,axis=1)[:,-10:]; sv=np.take_along_axis(s,top,1); top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\\n res[name].extend(ndcg_at_k(it[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\\n print(q+len(pp),flush=True)\\nfor name in res: print(name,round(float(np.mean(res[name])),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:49:34.056Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"99e4c7\",\"wall_time_seconds\":1.001114148,\"session_id\":23436,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:33.003Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a8ea94c887d2a154f5d0e4375315", "usage": { "input_tokens": 64545, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 948, "reasoning_output_tokens": 368, "total_tokens": 65493 }, "turn_token_usage": { "input_tokens": 2975427, "cached_input_tokens": 2905088, "cache_write_input_tokens": 0, "output_tokens": 25802, "reasoning_output_tokens": 10641, "total_tokens": 3001229 }, "thread_token_usage": { "input_tokens": 2975427, "cached_input_tokens": 2905088, "cache_write_input_tokens": 0, "output_tokens": 25802, "reasoning_output_tokens": 10641, "total_tokens": 3001229 } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-24T22:49:35.512Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:35.512Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:49:36.215Z", "callId": "call_XGpV5yA63YtkPlL2rZ0AFCgm", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":23436,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:49:46.794Z", "content": "Script completed\nWall time 10.6 seconds\nOutput:\n\n128\n256\n384\n512\n640\n768\n896\n1024\n1152\n1280\n1408\n1536\n1664\n1792\n1920\n2048\n2176\n2304\n2432\n2560\n2688\n2816\n2944\n3072\n3200\n3328\n3456\n3584\n3712\n3840\n3968\n4096\n4224\n4263\nbase 0.046086\ng1_a0.1 0.047376\ng1_a0.25 0.047091\ng1_a0.5 0.04332\ng1_a0.75 0.038592\ng3_a0.1 0.047089\ng3_a0.25 0.046941\ng3_a0.5 0.042882\ng3_a0.75 0.037982\ng5_a0.1 0.047124\ng5_a0.25 0.046928\ng5_a0.5 0.042696\ng5_a0.75 0.037578\ng7_a0.1 0.046646\ng7_a0.25 0.046529\ng7_a0.5 0.042262\ng7_a0.75 0.036135\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:36.329Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a8fe4b1c87d28f27b37235152d8a", "usage": { "input_tokens": 65552, "cached_input_tokens": 64384, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 65605 }, "turn_token_usage": { "input_tokens": 3040979, "cached_input_tokens": 2969472, "cache_write_input_tokens": 0, "output_tokens": 25855, "reasoning_output_tokens": 10647, "total_tokens": 3066834 }, "thread_token_usage": { "input_tokens": 3040979, "cached_input_tokens": 2969472, "cache_write_input_tokens": 0, "output_tokens": 25855, "reasoning_output_tokens": 10647, "total_tokens": 3066834 } } }, { "kind": "context", "timestamp": "2026-09-24T22:49:46.792Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c70e4512-fcba-481a-adae-bceae1e0424d", "process_id": "23436", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); gold,pos=t.truth_and_rows(u); ni=np.load(t.OUT/'nbr_org_30d_idx.npy'); ns=np.load(t.OUT/'nbr_org_30d_sim.npy'); y=t.objective_y(7)\nglobs={d:np.asarray(t.objective_y(d).sum(0)).ravel().astype(np.float32) for d in [1,3,5,7]}\nfor d in globs: globs[d]/=globs[d].max()\nconfigs=[('base',None,0.)]+[(f'g{d}_a{a}',d,a) for d in globs for a in [.1,.25,.5,.75]]\nres={n:[] for n,_,_ in configs}; k=400\nfor q in range(0,len(pos),128):\n pp=pos[q:q+128]; rr=np.repeat(np.arange(len(pp)),k); cc=ni[pp,:k].ravel(); vv=ns[pp,:k].ravel(); w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),y.shape[0])); sc=(w@y).toarray().astype(np.float32); sc/=np.maximum(sc.max(1,keepdims=True),1e-9)\n for name,d,a in configs:\n s=sc if d is None else sc*(1-a)+globs[d][None,:]*a\n s=np.where(e[pp],s,-np.inf); top=np.argpartition(s,-10,axis=1)[:,-10:]; sv=np.take_along_axis(s,top,1); top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\n res[name].extend(ndcg_at_k(it[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\n print(q+len(pp),flush=True)\nfor name in res: print(name,round(float(np.mean(res[name])),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); gold,pos=t.truth_and_rows(u); ni=np.load(t.OUT/'nbr_org_30d_idx.npy'); ns=np.load(t.OUT/'nbr_org_30d_sim.npy'); y=t.objective_y(7)\nglobs={d:np.asarray(t.objective_y(d).sum(0)).ravel().astype(np.float32) for d in [1,3,5,7]}\nfor d in globs: globs[d]/=globs[d].max()\nconfigs=[('base',None,0.)]+[(f'g{d}_a{a}',d,a) for d in globs for a in [.1,.25,.5,.75]]\nres={n:[] for n,_,_ in configs}; k=400\nfor q in range(0,len(pos),128):\n pp=pos[q:q+128]; rr=np.repeat(np.arange(len(pp)),k); cc=ni[pp,:k].ravel(); vv=ns[pp,:k].ravel(); w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),y.shape[0])); sc=(w@y).toarray().astype(np.float32); sc/=np.maximum(sc.max(1,keepdims=True),1e-9)\n for name,d,a in configs:\n s=sc if d is None else sc*(1-a)+globs[d][None,:]*a\n s=np.where(e[pp],s,-np.inf); top=np.argpartition(s,-10,axis=1)[:,-10:]; sv=np.take_along_axis(s,top,1); top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\n res[name].extend(ndcg_at_k(it[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\n print(q+len(pp),flush=True)\nfor name in res: print(name,round(float(np.mean(res[name])),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "128\n256\n384\n512\n640\n768\n896\n1024\n1152\n1280\n1408\n1536\n1664\n1792\n1920\n2048\n2176\n2304\n2432\n2560\n2688\n2816\n2944\n3072\n3200\n3328\n3456\n3584\n3712\n3840\n3968\n4096\n4224\n4263\nbase 0.046086\ng1_a0.1 0.047376\ng1_a0.25 0.047091\ng1_a0.5 0.04332\ng1_a0.75 0.038592\ng3_a0.1 0.047089\ng3_a0.25 0.046941\ng3_a0.5 0.042882\ng3_a0.75 0.037982\ng5_a0.1 0.047124\ng5_a0.25 0.046928\ng5_a0.5 0.042696\ng5_a0.75 0.037578\ng7_a0.1 0.046646\ng7_a0.25 0.046529\ng7_a0.5 0.042262\ng7_a0.75 0.036135\n", "stderr": "", "aggregated_output": "128\n256\n384\n512\n640\n768\n896\n1024\n1152\n1280\n1408\n1536\n1664\n1792\n1920\n2048\n2176\n2304\n2432\n2560\n2688\n2816\n2944\n3072\n3200\n3328\n3456\n3584\n3712\n3840\n3968\n4096\n4224\n4263\nbase 0.046086\ng1_a0.1 0.047376\ng1_a0.25 0.047091\ng1_a0.5 0.04332\ng1_a0.75 0.038592\ng3_a0.1 0.047089\ng3_a0.25 0.046941\ng3_a0.5 0.042882\ng3_a0.75 0.037982\ng5_a0.1 0.047124\ng5_a0.25 0.046928\ng5_a0.5 0.042696\ng5_a0.75 0.037578\ng7_a0.1 0.046646\ng7_a0.25 0.046529\ng7_a0.5 0.042262\ng7_a0.75 0.036135\n", "exit_code": 0, "duration": { "secs": 13, "nanos": 736758241 }, "formatted_output": "128\n256\n384\n512\n640\n768\n896\n1024\n1152\n1280\n1408\n1536\n1664\n1792\n1920\n2048\n2176\n2304\n2432\n2560\n2688\n2816\n2944\n3072\n3200\n3328\n3456\n3584\n3712\n3840\n3968\n4096\n4224\n4263\nbase 0.046086\ng1_a0.1 0.047376\ng1_a0.25 0.047091\ng1_a0.5 0.04332\ng1_a0.75 0.038592\ng3_a0.1 0.047089\ng3_a0.25 0.046941\ng3_a0.5 0.042882\ng3_a0.75 0.037982\ng5_a0.1 0.047124\ng5_a0.25 0.046928\ng5_a0.5 0.042696\ng5_a0.75 0.037578\ng7_a0.1 0.046646\ng7_a0.25 0.046529\ng7_a0.5 0.042262\ng7_a0.75 0.036135\n" } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-24T22:49:50.073Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:50.073Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:49:54.808Z", "callId": "call_bzWjfMWb17RMkF0OThrmi0Di", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np\\nfrom scipy import sparse\\nimport knn_experiment as k\\nimport tune_knn as t\\nz=np.load(k.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); tr=(u//100).astype(np.int32); g,p=t.truth_and_rows(u); y=t.objective_y(7)\\nfor age in ['7d','14d','60d','90d']:\\n x=k.tfidf_profile(sparse.load_npz(k.OUT/f'train_x_org_{age}.npz')); ni,ns=k.nearest(x,tr,'org_'+age); del x; gc.collect()\\n for kk in [100,200,400]: print(age,kk,t.evaluate_config(ni,ns,y,e,u,it,g,p,kk),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:49:55.976Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5d01ad\",\"wall_time_seconds\":1.001957881,\"session_id\":72240,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:54.981Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a90afd1887d28c8850afc196a566", "usage": { "input_tokens": 65922, "cached_input_tokens": 65408, "cache_write_input_tokens": 0, "output_tokens": 395, "reasoning_output_tokens": 123, "total_tokens": 66317 }, "turn_token_usage": { "input_tokens": 3106901, "cached_input_tokens": 3034880, "cache_write_input_tokens": 0, "output_tokens": 26250, "reasoning_output_tokens": 10770, "total_tokens": 3133151 }, "thread_token_usage": { "input_tokens": 3106901, "cached_input_tokens": 3034880, "cache_write_input_tokens": 0, "output_tokens": 26250, "reasoning_output_tokens": 10770, "total_tokens": 3133151 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-24T22:49:58.469Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:49:58.469Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:49:59.193Z", "callId": "call_pVqGhDGZMaI8zQorVHwUXiOq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":72240,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:50:29.201Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\norg_7d neighbors 256/8456\norg_7d neighbors 512/8456\norg_7d neighbors 768/8456\norg_7d neighbors 1024/8456\norg_7d neighbors 1280/8456\norg_7d neighbors 1536/8456\norg_7d neighbors 1792/8456\norg_7d neighbors 2048/8456\norg_7d neighbors 2304/8456\norg_7d neighbors 2560/8456\norg_7d neighbors 2816/8456\norg_7d neighbors 3072/8456\norg_7d neighbors 3328/8456\norg_7d neighbors 3584/8456\norg_7d neighbors 3840/8456\norg_7d neighbors 4096/8456\norg_7d neighbors 4352/8456\norg_7d neighbors 4608/8456\norg_7d neighbors 4864/8456\norg_7d neighbors 5120/8456\norg_7d neighbors 5376/8456\norg_7d neighbors 5632/8456\norg_7d neighbors 5888/8456\norg_7d neighbors 6144/8456\norg_7d neighbors 6400/8456\norg_7d neighbors 6656/8456\norg_7d neighbors 6912/8456\norg_7d neighbors 7168/8456\norg_7d neighbors 7424/8456\norg_7d neighbors 7680/8456\norg_7d neighbors 7936/8456\norg_7d neighbors 8192/8456\norg_7d neighbors 8448/8456\norg_7d neighbors 8456/8456\n7d 100 0.03491509539269168\n7d 200 0.03768300919161347\n7d 400 0.03775298583203186\norg_14d neighbors 256/8456\norg_14d neighbors 512/8456\norg_14d neighbors 768/8456\norg_14d neighbors 1024/8456\norg_14d neighbors 1280/8456\norg_14d neighbors 1536/8456\norg_14d neighbors 1792/8456\norg_14d neighbors 2048/8456\norg_14d neighbors 2304/8456\norg_14d neighbors 2560/8456\norg_14d neighbors 2816/8456\norg_14d neighbors 3072/8456\norg_14d neighbors 3328/8456\norg_14d neighbors 3584/8456\norg_14d neighbors 3840/8456\norg_14d neighbors 4096/8456\norg_14d neighbors 4352/8456\norg_14d neighbors 4608/8456\norg_14d neighbors 4864/8456\norg_14d neighbors 5120/8456\norg_14d neighbors 5376/8456\norg_14d neighbors 5632/8456\norg_14d neighbors 5888/8456\norg_14d neighbors 6144/8456\norg_14d neighbors 6400/8456\norg_14d neighbors 6656/8456\norg_14d neighbors 6912/8456\norg_14d neighbors 7168/8456\norg_14d neighbors 7424/8456\norg_14d neighbors 7680/8456\norg_14d neighbors 7936/8456\norg_14d neighbors 8192/8456\norg_14d neighbors 8448/8456\norg_14d neighbors 8456/8456\n14d 100 0.03965336783900798\n14d 200 0.042403522968775434\n14d 400 0.043067854445372755\norg_60d neighbors 256/8456\norg_60d neighbors 512/8456\norg_60d neighbors 768/8456\norg_60d neighbors 1024/8456\norg_60d neighbors 1280/8456\norg_60d neighbors 1536/8456\norg_60d neighbors 1792/8456\norg_60d neighbors 2048/8456\norg_60d neighbors 2304/8456\norg_60d neighbors 2560/8456\norg_60d neighbors 2816/8456\norg_60d neighbors 3072/8456\norg_60d neighbors 3328/8456\norg_60d neighbors 3584/8456\norg_60d neighbors 3840/8456\norg_60d neighbors 4096/8456\norg_60d neighbors 4352/8456\norg_60d neighbors 4608/8456\norg_60d neighbors 4864/8456\norg_60d neighbors 5120/8456\norg_60d neighbors 5376/8456\norg_60d neighbors 5632/8456\norg_60d neighbors 5888/8456\norg_60d neighbors 6144/8456\norg_60d neighbors 6400/8456\norg_60d neighbors 6656/8456\norg_60d neighbors 6912/8456\norg_60d neighbors 7168/8456\norg_60d neighbors 7424/8456\norg_60d neighbors 7680/8456\norg_60d neighbors 7936/8456\norg_60d neighbors 8192/8456\norg_60d neighbors 8448/8456\norg_60d neighbors 8456/8456\n60d 100 0.045549175891299344\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:49:59.305Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9142e9c87d2bfa903e8bb1430d3", "usage": { "input_tokens": 66375, "cached_input_tokens": 65792, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 66428 }, "turn_token_usage": { "input_tokens": 3173276, "cached_input_tokens": 3100672, "cache_write_input_tokens": 0, "output_tokens": 26303, "reasoning_output_tokens": 10776, "total_tokens": 3199579 }, "thread_token_usage": { "input_tokens": 3173276, "cached_input_tokens": 3100672, "cache_write_input_tokens": 0, "output_tokens": 26303, "reasoning_output_tokens": 10776, "total_tokens": 3199579 } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-24T22:50:31.072Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:31.072Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:50:31.793Z", "callId": "call_s5Ak260KDLMzkJEvdE2dYDDp", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":72240,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:50:44.138Z", "content": "Script completed\nWall time 12.3 seconds\nOutput:\n\n60d 200 0.0473678943489192\n60d 400 0.048387671502560625\n90d 100 0.04603199166521877\n90d 200 0.04820047309677232\n90d 400 0.049108749753125874\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:31.883Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a93561d087d2801159519aab6420", "usage": { "input_tokens": 67764, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 67822 }, "turn_token_usage": { "input_tokens": 3241040, "cached_input_tokens": 3166848, "cache_write_input_tokens": 0, "output_tokens": 26361, "reasoning_output_tokens": 10787, "total_tokens": 3267401 }, "thread_token_usage": { "input_tokens": 3241040, "cached_input_tokens": 3166848, "cache_write_input_tokens": 0, "output_tokens": 26361, "reasoning_output_tokens": 10787, "total_tokens": 3267401 } } }, { "kind": "context", "timestamp": "2026-09-24T22:50:44.136Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3843b5d-6cd4-4991-8f59-4f7b982e91e5", "process_id": "72240", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); tr=(u//100).astype(np.int32); g,p=t.truth_and_rows(u); y=t.objective_y(7)\nfor age in ['7d','14d','60d','90d']:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/f'train_x_org_{age}.npz')); ni,ns=k.nearest(x,tr,'org_'+age); del x; gc.collect()\n for kk in [100,200,400]: print(age,kk,t.evaluate_config(ni,ns,y,e,u,it,g,p,kk),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz'); u=z['uids']; it=z['item_ids']; e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool); tr=(u//100).astype(np.int32); g,p=t.truth_and_rows(u); y=t.objective_y(7)\nfor age in ['7d','14d','60d','90d']:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/f'train_x_org_{age}.npz')); ni,ns=k.nearest(x,tr,'org_'+age); del x; gc.collect()\n for kk in [100,200,400]: print(age,kk,t.evaluate_config(ni,ns,y,e,u,it,g,p,kk),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org_7d neighbors 256/8456\norg_7d neighbors 512/8456\norg_7d neighbors 768/8456\norg_7d neighbors 1024/8456\norg_7d neighbors 1280/8456\norg_7d neighbors 1536/8456\norg_7d neighbors 1792/8456\norg_7d neighbors 2048/8456\norg_7d neighbors 2304/8456\norg_7d neighbors 2560/8456\norg_7d neighbors 2816/8456\norg_7d neighbors 3072/8456\norg_7d neighbors 3328/8456\norg_7d neighbors 3584/8456\norg_7d neighbors 3840/8456\norg_7d neighbors 4096/8456\norg_7d neighbors 4352/8456\norg_7d neighbors 4608/8456\norg_7d neighbors 4864/8456\norg_7d neighbors 5120/8456\norg_7d neighbors 5376/8456\norg_7d neighbors 5632/8456\norg_7d neighbors 5888/8456\norg_7d neighbors 6144/8456\norg_7d neighbors 6400/8456\norg_7d neighbors 6656/8456\norg_7d neighbors 6912/8456\norg_7d neighbors 7168/8456\norg_7d neighbors 7424/8456\norg_7d neighbors 7680/8456\norg_7d neighbors 7936/8456\norg_7d neighbors 8192/8456\norg_7d neighbors 8448/8456\norg_7d neighbors 8456/8456\n7d 100 0.03491509539269168\n7d 200 0.03768300919161347\n7d 400 0.03775298583203186\norg_14d neighbors 256/8456\norg_14d neighbors 512/8456\norg_14d neighbors 768/8456\norg_14d neighbors 1024/8456\norg_14d neighbors 1280/8456\norg_14d neighbors 1536/8456\norg_14d neighbors 1792/8456\norg_14d neighbors 2048/8456\norg_14d neighbors 2304/8456\norg_14d neighbors 2560/8456\norg_14d neighbors 2816/8456\norg_14d neighbors 3072/8456\norg_14d neighbors 3328/8456\norg_14d neighbors 3584/8456\norg_14d neighbors 3840/8456\norg_14d neighbors 4096/8456\norg_14d neighbors 4352/8456\norg_14d neighbors 4608/8456\norg_14d neighbors 4864/8456\norg_14d neighbors 5120/8456\norg_14d neighbors 5376/8456\norg_14d neighbors 5632/8456\norg_14d neighbors 5888/8456\norg_14d neighbors 6144/8456\norg_14d neighbors 6400/8456\norg_14d neighbors 6656/8456\norg_14d neighbors 6912/8456\norg_14d neighbors 7168/8456\norg_14d neighbors 7424/8456\norg_14d neighbors 7680/8456\norg_14d neighbors 7936/8456\norg_14d neighbors 8192/8456\norg_14d neighbors 8448/8456\norg_14d neighbors 8456/8456\n14d 100 0.03965336783900798\n14d 200 0.042403522968775434\n14d 400 0.043067854445372755\norg_60d neighbors 256/8456\norg_60d neighbors 512/8456\norg_60d neighbors 768/8456\norg_60d neighbors 1024/8456\norg_60d neighbors 1280/8456\norg_60d neighbors 1536/8456\norg_60d neighbors 1792/8456\norg_60d neighbors 2048/8456\norg_60d neighbors 2304/8456\norg_60d neighbors 2560/8456\norg_60d neighbors 2816/8456\norg_60d neighbors 3072/8456\norg_60d neighbors 3328/8456\norg_60d neighbors 3584/8456\norg_60d neighbors 3840/8456\norg_60d neighbors 4096/8456\norg_60d neighbors 4352/8456\norg_60d neighbors 4608/8456\norg_60d neighbors 4864/8456\norg_60d neighbors 5120/8456\norg_60d neighbors 5376/8456\norg_60d neighbors 5632/8456\norg_60d neighbors 5888/8456\norg_60d neighbors 6144/8456\norg_60d neighbors 6400/8456\norg_60d neighbors 6656/8456\norg_60d neighbors 6912/8456\norg_60d neighbors 7168/8456\norg_60d neighbors 7424/8456\norg_60d neighbors 7680/8456\norg_60d neighbors 7936/8456\norg_60d neighbors 8192/8456\norg_60d neighbors 8448/8456\norg_60d neighbors 8456/8456\n60d 100 0.045549175891299344\n60d 200 0.0473678943489192\n60d 400 0.048387671502560625\n90d 100 0.04603199166521877\n90d 200 0.04820047309677232\n90d 400 0.049108749753125874\n", "stderr": "", "aggregated_output": "org_7d neighbors 256/8456\norg_7d neighbors 512/8456\norg_7d neighbors 768/8456\norg_7d neighbors 1024/8456\norg_7d neighbors 1280/8456\norg_7d neighbors 1536/8456\norg_7d neighbors 1792/8456\norg_7d neighbors 2048/8456\norg_7d neighbors 2304/8456\norg_7d neighbors 2560/8456\norg_7d neighbors 2816/8456\norg_7d neighbors 3072/8456\norg_7d neighbors 3328/8456\norg_7d neighbors 3584/8456\norg_7d neighbors 3840/8456\norg_7d neighbors 4096/8456\norg_7d neighbors 4352/8456\norg_7d neighbors 4608/8456\norg_7d neighbors 4864/8456\norg_7d neighbors 5120/8456\norg_7d neighbors 5376/8456\norg_7d neighbors 5632/8456\norg_7d neighbors 5888/8456\norg_7d neighbors 6144/8456\norg_7d neighbors 6400/8456\norg_7d neighbors 6656/8456\norg_7d neighbors 6912/8456\norg_7d neighbors 7168/8456\norg_7d neighbors 7424/8456\norg_7d neighbors 7680/8456\norg_7d neighbors 7936/8456\norg_7d neighbors 8192/8456\norg_7d neighbors 8448/8456\norg_7d neighbors 8456/8456\n7d 100 0.03491509539269168\n7d 200 0.03768300919161347\n7d 400 0.03775298583203186\norg_14d neighbors 256/8456\norg_14d neighbors 512/8456\norg_14d neighbors 768/8456\norg_14d neighbors 1024/8456\norg_14d neighbors 1280/8456\norg_14d neighbors 1536/8456\norg_14d neighbors 1792/8456\norg_14d neighbors 2048/8456\norg_14d neighbors 2304/8456\norg_14d neighbors 2560/8456\norg_14d neighbors 2816/8456\norg_14d neighbors 3072/8456\norg_14d neighbors 3328/8456\norg_14d neighbors 3584/8456\norg_14d neighbors 3840/8456\norg_14d neighbors 4096/8456\norg_14d neighbors 4352/8456\norg_14d neighbors 4608/8456\norg_14d neighbors 4864/8456\norg_14d neighbors 5120/8456\norg_14d neighbors 5376/8456\norg_14d neighbors 5632/8456\norg_14d neighbors 5888/8456\norg_14d neighbors 6144/8456\norg_14d neighbors 6400/8456\norg_14d neighbors 6656/8456\norg_14d neighbors 6912/8456\norg_14d neighbors 7168/8456\norg_14d neighbors 7424/8456\norg_14d neighbors 7680/8456\norg_14d neighbors 7936/8456\norg_14d neighbors 8192/8456\norg_14d neighbors 8448/8456\norg_14d neighbors 8456/8456\n14d 100 0.03965336783900798\n14d 200 0.042403522968775434\n14d 400 0.043067854445372755\norg_60d neighbors 256/8456\norg_60d neighbors 512/8456\norg_60d neighbors 768/8456\norg_60d neighbors 1024/8456\norg_60d neighbors 1280/8456\norg_60d neighbors 1536/8456\norg_60d neighbors 1792/8456\norg_60d neighbors 2048/8456\norg_60d neighbors 2304/8456\norg_60d neighbors 2560/8456\norg_60d neighbors 2816/8456\norg_60d neighbors 3072/8456\norg_60d neighbors 3328/8456\norg_60d neighbors 3584/8456\norg_60d neighbors 3840/8456\norg_60d neighbors 4096/8456\norg_60d neighbors 4352/8456\norg_60d neighbors 4608/8456\norg_60d neighbors 4864/8456\norg_60d neighbors 5120/8456\norg_60d neighbors 5376/8456\norg_60d neighbors 5632/8456\norg_60d neighbors 5888/8456\norg_60d neighbors 6144/8456\norg_60d neighbors 6400/8456\norg_60d neighbors 6656/8456\norg_60d neighbors 6912/8456\norg_60d neighbors 7168/8456\norg_60d neighbors 7424/8456\norg_60d neighbors 7680/8456\norg_60d neighbors 7936/8456\norg_60d neighbors 8192/8456\norg_60d neighbors 8448/8456\norg_60d neighbors 8456/8456\n60d 100 0.045549175891299344\n60d 200 0.0473678943489192\n60d 400 0.048387671502560625\n90d 100 0.04603199166521877\n90d 200 0.04820047309677232\n90d 400 0.049108749753125874\n", "exit_code": 0, "duration": { "secs": 49, "nanos": 162113032 }, "formatted_output": "org_7d neighbors 256/8456\norg_7d neighbors 512/8456\norg_7d neighbors 768/8456\norg_7d neighbors 1024/8456\norg_7d neighbors 1280/8456\norg_7d neighbors 1536/8456\norg_7d neighbors 1792/8456\norg_7d neighbors 2048/8456\norg_7d neighbors 2304/8456\norg_7d neighbors 2560/8456\norg_7d neighbors 2816/8456\norg_7d neighbors 3072/8456\norg_7d neighbors 3328/8456\norg_7d neighbors 3584/8456\norg_7d neighbors 3840/8456\norg_7d neighbors 4096/8456\norg_7d neighbors 4352/8456\norg_7d neighbors 4608/8456\norg_7d neighbors 4864/8456\norg_7d neighbors 5120/8456\norg_7d neighbors 5376/8456\norg_7d neighbors 5632/8456\norg_7d neighbors 5888/8456\norg_7d neighbors 6144/8456\norg_7d neighbors 6400/8456\norg_7d neighbors 6656/8456\norg_7d neighbors 6912/8456\norg_7d neighbors 7168/8456\norg_7d neighbors 7424/8456\norg_7d neighbors 7680/8456\norg_7d neighbors 7936/8456\norg_7d neighbors 8192/8456\norg_7d neighbors 8448/8456\norg_7d neighbors 8456/8456\n7d 100 0.03491509539269168\n7d 200 0.03768300919161347\n7d 400 0.03775298583203186\norg_14d neighbors 256/8456\norg_14d neighbors 512/8456\norg_14d neighbors 768/8456\norg_14d neighbors 1024/8456\norg_14d neighbors 1280/8456\norg_14d neighbors 1536/8456\norg_14d neighbors 1792/8456\norg_14d neighbors 2048/8456\norg_14d neighbors 2304/8456\norg_14d neighbors 2560/8456\norg_14d neighbors 2816/8456\norg_14d neighbors 3072/8456\norg_14d neighbors 3328/8456\norg_14d neighbors 3584/8456\norg_14d neighbors 3840/8456\norg_14d neighbors 4096/8456\norg_14d neighbors 4352/8456\norg_14d neighbors 4608/8456\norg_14d neighbors 4864/8456\norg_14d neighbors 5120/8456\norg_14d neighbors 5376/8456\norg_14d neighbors 5632/8456\norg_14d neighbors 5888/8456\norg_14d neighbors 6144/8456\norg_14d neighbors 6400/8456\norg_14d neighbors 6656/8456\norg_14d neighbors 6912/8456\norg_14d neighbors 7168/8456\norg_14d neighbors 7424/8456\norg_14d neighbors 7680/8456\norg_14d neighbors 7936/8456\norg_14d neighbors 8192/8456\norg_14d neighbors 8448/8456\norg_14d neighbors 8456/8456\n14d 100 0.03965336783900798\n14d 200 0.042403522968775434\n14d 400 0.043067854445372755\norg_60d neighbors 256/8456\norg_60d neighbors 512/8456\norg_60d neighbors 768/8456\norg_60d neighbors 1024/8456\norg_60d neighbors 1280/8456\norg_60d neighbors 1536/8456\norg_60d neighbors 1792/8456\norg_60d neighbors 2048/8456\norg_60d neighbors 2304/8456\norg_60d neighbors 2560/8456\norg_60d neighbors 2816/8456\norg_60d neighbors 3072/8456\norg_60d neighbors 3328/8456\norg_60d neighbors 3584/8456\norg_60d neighbors 3840/8456\norg_60d neighbors 4096/8456\norg_60d neighbors 4352/8456\norg_60d neighbors 4608/8456\norg_60d neighbors 4864/8456\norg_60d neighbors 5120/8456\norg_60d neighbors 5376/8456\norg_60d neighbors 5632/8456\norg_60d neighbors 5888/8456\norg_60d neighbors 6144/8456\norg_60d neighbors 6400/8456\norg_60d neighbors 6656/8456\norg_60d neighbors 6912/8456\norg_60d neighbors 7168/8456\norg_60d neighbors 7424/8456\norg_60d neighbors 7680/8456\norg_60d neighbors 7936/8456\norg_60d neighbors 8192/8456\norg_60d neighbors 8448/8456\norg_60d neighbors 8456/8456\n60d 100 0.045549175891299344\n60d 200 0.0473678943489192\n60d 400 0.048387671502560625\n90d 100 0.04603199166521877\n90d 200 0.04820047309677232\n90d 400 0.049108749753125874\n" } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-24T22:50:46.423Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:46.423Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:50:50.497Z", "callId": "call_0EP94I3gvGw1etL7oAiH84Ql", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7)\\nfor nm in ['org_90d','org_180d','org_decay60','org']:\\n ni=np.load(t.OUT/f'nbr_{nm}_idx.npy');ns=np.load(t.OUT/f'nbr_{nm}_sim.npy')\\n for k in [100,200,400]: print(nm,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:50:51.665Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"0413ad\",\"wall_time_seconds\":1.001207801,\"session_id\":1049,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:50.668Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a944728887d2a719fb338a4074f9", "usage": { "input_tokens": 67915, "cached_input_tokens": 67584, "cache_write_input_tokens": 0, "output_tokens": 296, "reasoning_output_tokens": 61, "total_tokens": 68211 }, "turn_token_usage": { "input_tokens": 3308955, "cached_input_tokens": 3234432, "cache_write_input_tokens": 0, "output_tokens": 26657, "reasoning_output_tokens": 10848, "total_tokens": 3335612 }, "thread_token_usage": { "input_tokens": 3308955, "cached_input_tokens": 3234432, "cache_write_input_tokens": 0, "output_tokens": 26657, "reasoning_output_tokens": 10848, "total_tokens": 3335612 } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-24T22:50:52.836Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:50:52.836Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:50:53.537Z", "callId": "call_WXdqE5AhOorX9TSiiCPs2ToQ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":1049,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:51:23.545Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\norg_90d 100 0.04603199166521877\norg_90d 200 0.04820047309677232\norg_90d 400 0.049108749753125874\norg_180d 100 0.04439036241238781\norg_180d 200 0.04792449109254625\norg_180d 400 0.04855632772382862\norg_decay60 100 0.04624104576823856\norg_decay60 200 0.04861100287564322\norg_decay60 400 0.04915887733111542\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:50:53.635Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a94bd82087d2a08ee68332f9f1c0", "usage": { "input_tokens": 68268, "cached_input_tokens": 67712, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 68321 }, "turn_token_usage": { "input_tokens": 3377223, "cached_input_tokens": 3302144, "cache_write_input_tokens": 0, "output_tokens": 26710, "reasoning_output_tokens": 10854, "total_tokens": 3403933 }, "thread_token_usage": { "input_tokens": 3377223, "cached_input_tokens": 3302144, "cache_write_input_tokens": 0, "output_tokens": 26710, "reasoning_output_tokens": 10854, "total_tokens": 3403933 } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-24T22:51:24.906Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:51:24.906Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:51:25.593Z", "callId": "call_tLEK92Bbjoe3Ti8lOtkmOcQP", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":1049,\"chars\":\"\",\"yield_time_ms\":20000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:51:31.881Z", "content": "Script completed\nWall time 6.3 seconds\nOutput:\n\norg 100 0.0437766762060616\norg 200 0.04719073157371033\norg 400 0.04956888596514391\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:51:25.748Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a96bbb5887d2aec323c8e9008049", "usage": { "input_tokens": 68485, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 68539 }, "turn_token_usage": { "input_tokens": 3445708, "cached_input_tokens": 3370240, "cache_write_input_tokens": 0, "output_tokens": 26764, "reasoning_output_tokens": 10861, "total_tokens": 3472472 }, "thread_token_usage": { "input_tokens": 3445708, "cached_input_tokens": 3370240, "cache_write_input_tokens": 0, "output_tokens": 26764, "reasoning_output_tokens": 10861, "total_tokens": 3472472 } } }, { "kind": "context", "timestamp": "2026-09-24T22:51:31.879Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9e8bf3bf-2d2c-4081-94ed-111615303964", "process_id": "1049", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7)\nfor nm in ['org_90d','org_180d','org_decay60','org']:\n ni=np.load(t.OUT/f'nbr_{nm}_idx.npy');ns=np.load(t.OUT/f'nbr_{nm}_sim.npy')\n for k in [100,200,400]: print(nm,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz'); u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7)\nfor nm in ['org_90d','org_180d','org_decay60','org']:\n ni=np.load(t.OUT/f'nbr_{nm}_idx.npy');ns=np.load(t.OUT/f'nbr_{nm}_sim.npy')\n for k in [100,200,400]: print(nm,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org_90d 100 0.04603199166521877\norg_90d 200 0.04820047309677232\norg_90d 400 0.049108749753125874\norg_180d 100 0.04439036241238781\norg_180d 200 0.04792449109254625\norg_180d 400 0.04855632772382862\norg_decay60 100 0.04624104576823856\norg_decay60 200 0.04861100287564322\norg_decay60 400 0.04915887733111542\norg 100 0.0437766762060616\norg 200 0.04719073157371033\norg 400 0.04956888596514391\n", "stderr": "", "aggregated_output": "org_90d 100 0.04603199166521877\norg_90d 200 0.04820047309677232\norg_90d 400 0.049108749753125874\norg_180d 100 0.04439036241238781\norg_180d 200 0.04792449109254625\norg_180d 400 0.04855632772382862\norg_decay60 100 0.04624104576823856\norg_decay60 200 0.04861100287564322\norg_decay60 400 0.04915887733111542\norg 100 0.0437766762060616\norg 200 0.04719073157371033\norg 400 0.04956888596514391\n", "exit_code": 0, "duration": { "secs": 41, "nanos": 215806455 }, "formatted_output": "org_90d 100 0.04603199166521877\norg_90d 200 0.04820047309677232\norg_90d 400 0.049108749753125874\norg_180d 100 0.04439036241238781\norg_180d 200 0.04792449109254625\norg_180d 400 0.04855632772382862\norg_decay60 100 0.04624104576823856\norg_decay60 200 0.04861100287564322\norg_decay60 400 0.04915887733111542\norg 100 0.0437766762060616\norg 200 0.04719073157371033\norg 400 0.04956888596514391\n" } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-24T22:51:35.758Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:51:35.758Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:51:40.304Z", "callId": "call_uDFQJQh8jk9oQ897IUBXzVb7", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np\\nfrom scipy import sparse\\nimport knn_experiment as k\\nimport tune_knn as t\\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,p=t.truth_and_rows(u);y=t.objective_y(7)\\nk.TOP_NEIGHBORS=1000\\nx=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'));ni,ns=k.nearest(x,tr,'org1000');del x;gc.collect()\\nfor kk in [400,600,800,1000]: print(kk,t.evaluate_config(ni,ns,y,e,u,it,g,p,kk),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:51:41.471Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"9a570b\",\"wall_time_seconds\":1.00147888,\"session_id\":18699,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:51:40.480Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9741c0487d2a9083122406b8934", "usage": { "input_tokens": 68601, "cached_input_tokens": 68352, "cache_write_input_tokens": 0, "output_tokens": 336, "reasoning_output_tokens": 75, "total_tokens": 68937 }, "turn_token_usage": { "input_tokens": 3514309, "cached_input_tokens": 3438592, "cache_write_input_tokens": 0, "output_tokens": 27100, "reasoning_output_tokens": 10936, "total_tokens": 3541409 }, "thread_token_usage": { "input_tokens": 3514309, "cached_input_tokens": 3438592, "cache_write_input_tokens": 0, "output_tokens": 27100, "reasoning_output_tokens": 10936, "total_tokens": 3541409 } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-24T22:51:44.677Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:51:44.677Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:51:45.390Z", "callId": "call_mhxOQgLS2ZmgHfM0DzjiBRlx", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":18699,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:51:59.019Z", "content": "Script completed\nWall time 13.6 seconds\nOutput:\n\norg1000 neighbors 256/8456\norg1000 neighbors 512/8456\norg1000 neighbors 768/8456\norg1000 neighbors 1024/8456\norg1000 neighbors 1280/8456\norg1000 neighbors 1536/8456\norg1000 neighbors 1792/8456\norg1000 neighbors 2048/8456\norg1000 neighbors 2304/8456\norg1000 neighbors 2560/8456\norg1000 neighbors 2816/8456\norg1000 neighbors 3072/8456\norg1000 neighbors 3328/8456\norg1000 neighbors 3584/8456\norg1000 neighbors 3840/8456\norg1000 neighbors 4096/8456\norg1000 neighbors 4352/8456\norg1000 neighbors 4608/8456\norg1000 neighbors 4864/8456\norg1000 neighbors 5120/8456\norg1000 neighbors 5376/8456\norg1000 neighbors 5632/8456\norg1000 neighbors 5888/8456\norg1000 neighbors 6144/8456\norg1000 neighbors 6400/8456\norg1000 neighbors 6656/8456\norg1000 neighbors 6912/8456\norg1000 neighbors 7168/8456\norg1000 neighbors 7424/8456\norg1000 neighbors 7680/8456\norg1000 neighbors 7936/8456\norg1000 neighbors 8192/8456\norg1000 neighbors 8448/8456\norg1000 neighbors 8456/8456\n400 0.04956888596514391\n600 0.048898718880188145\n800 0.04841987524537575\n1000 0.048048009169577625\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:51:45.501Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a97db6fc87d2ba066363da1c9ec8", "usage": { "input_tokens": 68995, "cached_input_tokens": 68480, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 69048 }, "turn_token_usage": { "input_tokens": 3583304, "cached_input_tokens": 3507072, "cache_write_input_tokens": 0, "output_tokens": 27153, "reasoning_output_tokens": 10942, "total_tokens": 3610457 }, "thread_token_usage": { "input_tokens": 3583304, "cached_input_tokens": 3507072, "cache_write_input_tokens": 0, "output_tokens": 27153, "reasoning_output_tokens": 10942, "total_tokens": 3610457 } } }, { "kind": "context", "timestamp": "2026-09-24T22:51:59.017Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-462e6fcc-ad33-441b-9e81-6a0d7e862c7e", "process_id": "18699", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,p=t.truth_and_rows(u);y=t.objective_y(7)\nk.TOP_NEIGHBORS=1000\nx=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'));ni,ns=k.nearest(x,tr,'org1000');del x;gc.collect()\nfor kk in [400,600,800,1000]: print(kk,t.evaluate_config(ni,ns,y,e,u,it,g,p,kk),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,p=t.truth_and_rows(u);y=t.objective_y(7)\nk.TOP_NEIGHBORS=1000\nx=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'));ni,ns=k.nearest(x,tr,'org1000');del x;gc.collect()\nfor kk in [400,600,800,1000]: print(kk,t.evaluate_config(ni,ns,y,e,u,it,g,p,kk),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org1000 neighbors 256/8456\norg1000 neighbors 512/8456\norg1000 neighbors 768/8456\norg1000 neighbors 1024/8456\norg1000 neighbors 1280/8456\norg1000 neighbors 1536/8456\norg1000 neighbors 1792/8456\norg1000 neighbors 2048/8456\norg1000 neighbors 2304/8456\norg1000 neighbors 2560/8456\norg1000 neighbors 2816/8456\norg1000 neighbors 3072/8456\norg1000 neighbors 3328/8456\norg1000 neighbors 3584/8456\norg1000 neighbors 3840/8456\norg1000 neighbors 4096/8456\norg1000 neighbors 4352/8456\norg1000 neighbors 4608/8456\norg1000 neighbors 4864/8456\norg1000 neighbors 5120/8456\norg1000 neighbors 5376/8456\norg1000 neighbors 5632/8456\norg1000 neighbors 5888/8456\norg1000 neighbors 6144/8456\norg1000 neighbors 6400/8456\norg1000 neighbors 6656/8456\norg1000 neighbors 6912/8456\norg1000 neighbors 7168/8456\norg1000 neighbors 7424/8456\norg1000 neighbors 7680/8456\norg1000 neighbors 7936/8456\norg1000 neighbors 8192/8456\norg1000 neighbors 8448/8456\norg1000 neighbors 8456/8456\n400 0.04956888596514391\n600 0.048898718880188145\n800 0.04841987524537575\n1000 0.048048009169577625\n", "stderr": "", "aggregated_output": "org1000 neighbors 256/8456\norg1000 neighbors 512/8456\norg1000 neighbors 768/8456\norg1000 neighbors 1024/8456\norg1000 neighbors 1280/8456\norg1000 neighbors 1536/8456\norg1000 neighbors 1792/8456\norg1000 neighbors 2048/8456\norg1000 neighbors 2304/8456\norg1000 neighbors 2560/8456\norg1000 neighbors 2816/8456\norg1000 neighbors 3072/8456\norg1000 neighbors 3328/8456\norg1000 neighbors 3584/8456\norg1000 neighbors 3840/8456\norg1000 neighbors 4096/8456\norg1000 neighbors 4352/8456\norg1000 neighbors 4608/8456\norg1000 neighbors 4864/8456\norg1000 neighbors 5120/8456\norg1000 neighbors 5376/8456\norg1000 neighbors 5632/8456\norg1000 neighbors 5888/8456\norg1000 neighbors 6144/8456\norg1000 neighbors 6400/8456\norg1000 neighbors 6656/8456\norg1000 neighbors 6912/8456\norg1000 neighbors 7168/8456\norg1000 neighbors 7424/8456\norg1000 neighbors 7680/8456\norg1000 neighbors 7936/8456\norg1000 neighbors 8192/8456\norg1000 neighbors 8448/8456\norg1000 neighbors 8456/8456\n400 0.04956888596514391\n600 0.048898718880188145\n800 0.04841987524537575\n1000 0.048048009169577625\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 547176099 }, "formatted_output": "org1000 neighbors 256/8456\norg1000 neighbors 512/8456\norg1000 neighbors 768/8456\norg1000 neighbors 1024/8456\norg1000 neighbors 1280/8456\norg1000 neighbors 1536/8456\norg1000 neighbors 1792/8456\norg1000 neighbors 2048/8456\norg1000 neighbors 2304/8456\norg1000 neighbors 2560/8456\norg1000 neighbors 2816/8456\norg1000 neighbors 3072/8456\norg1000 neighbors 3328/8456\norg1000 neighbors 3584/8456\norg1000 neighbors 3840/8456\norg1000 neighbors 4096/8456\norg1000 neighbors 4352/8456\norg1000 neighbors 4608/8456\norg1000 neighbors 4864/8456\norg1000 neighbors 5120/8456\norg1000 neighbors 5376/8456\norg1000 neighbors 5632/8456\norg1000 neighbors 5888/8456\norg1000 neighbors 6144/8456\norg1000 neighbors 6400/8456\norg1000 neighbors 6656/8456\norg1000 neighbors 6912/8456\norg1000 neighbors 7168/8456\norg1000 neighbors 7424/8456\norg1000 neighbors 7680/8456\norg1000 neighbors 7936/8456\norg1000 neighbors 8192/8456\norg1000 neighbors 8448/8456\norg1000 neighbors 8456/8456\n400 0.04956888596514391\n600 0.048898718880188145\n800 0.04841987524537575\n1000 0.048048009169577625\n" } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-24T22:52:00.963Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:52:00.963Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:04.868Z", "callId": "call_DMNJ8ucSqCDSCzRIdRF4sqL7", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7);ni=np.load(t.OUT/'nbr_org_idx.npy');ns=np.load(t.OUT/'nbr_org_sim.npy')\\nfor pw in [.25,.5,.75,1,1.25,1.5,2,3]: print(pw,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=pw),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:52:06.055Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"8078aa\",\"wall_time_seconds\":1.000518748,\"session_id\":2631,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:04.998Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a98f42a887d2a04b607d4beacc0b", "usage": { "input_tokens": 69487, "cached_input_tokens": 68864, "cache_write_input_tokens": 0, "output_tokens": 273, "reasoning_output_tokens": 49, "total_tokens": 69760 }, "turn_token_usage": { "input_tokens": 3652791, "cached_input_tokens": 3575936, "cache_write_input_tokens": 0, "output_tokens": 27426, "reasoning_output_tokens": 10991, "total_tokens": 3680217 }, "thread_token_usage": { "input_tokens": 3652791, "cached_input_tokens": 3575936, "cache_write_input_tokens": 0, "output_tokens": 27426, "reasoning_output_tokens": 10991, "total_tokens": 3680217 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:52:08.026Z", "callId": "call_arpttV3hcRv9fEux24FBwtDA", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2631,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:52:30.307Z", "content": "Script completed\nWall time 22.3 seconds\nOutput:\n\n0.25 0.049138441655798705\n0.5 0.04938996861046462\n0.75 0.04972509477359094\n1 0.04956888596514391\n1.25 0.04901535077391609\n1.5 0.048715716497393736\n2 0.04644648915415835\n3 0.03983022788935454\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:08.144Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9965f0c87d29654f45b70d2b14e", "usage": { "input_tokens": 69817, "cached_input_tokens": 69248, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 69862 }, "turn_token_usage": { "input_tokens": 3722608, "cached_input_tokens": 3645184, "cache_write_input_tokens": 0, "output_tokens": 27471, "reasoning_output_tokens": 10991, "total_tokens": 3750079 }, "thread_token_usage": { "input_tokens": 3722608, "cached_input_tokens": 3645184, "cache_write_input_tokens": 0, "output_tokens": 27471, "reasoning_output_tokens": 10991, "total_tokens": 3750079 } } }, { "kind": "context", "timestamp": "2026-09-24T22:52:30.305Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e887fffb-314d-49e9-ae4a-34d7821f0e6e", "process_id": "2631", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7);ni=np.load(t.OUT/'nbr_org_idx.npy');ns=np.load(t.OUT/'nbr_org_sim.npy')\nfor pw in [.25,.5,.75,1,1.25,1.5,2,3]: print(pw,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=pw),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7);ni=np.load(t.OUT/'nbr_org_idx.npy');ns=np.load(t.OUT/'nbr_org_sim.npy')\nfor pw in [.25,.5,.75,1,1.25,1.5,2,3]: print(pw,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=pw),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0.25 0.049138441655798705\n0.5 0.04938996861046462\n0.75 0.04972509477359094\n1 0.04956888596514391\n1.25 0.04901535077391609\n1.5 0.048715716497393736\n2 0.04644648915415835\n3 0.03983022788935454\n", "stderr": "", "aggregated_output": "0.25 0.049138441655798705\n0.5 0.04938996861046462\n0.75 0.04972509477359094\n1 0.04956888596514391\n1.25 0.04901535077391609\n1.5 0.048715716497393736\n2 0.04644648915415835\n3 0.03983022788935454\n", "exit_code": 0, "duration": { "secs": 25, "nanos": 250672215 }, "formatted_output": "0.25 0.049138441655798705\n0.5 0.04938996861046462\n0.75 0.04972509477359094\n1 0.04956888596514391\n1.25 0.04901535077391609\n1.5 0.048715716497393736\n2 0.04644648915415835\n3 0.03983022788935454\n" } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-24T22:52:35.992Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:52:35.992Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:41.380Z", "callId": "call_ByNICLv0yM3fKAhCbISIPRTo", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idx.npy');ns=np.load(t.OUT/'nbr_org_sim.npy')\\nfor w in [0,.25,.5,1,2,4,8]:\\n y=t.objective_y(7,like_weight=w)\\n print('like',w,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.75),flush=True)\\nfor b in [True,False]:\\n for pen in [.1,.25,-.1]:\\n y=t.objective_y(7,like_weight=1,binary=b,pop_penalty=pen)\\n print('binary',b,'pen',pen,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.75),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:52:42.555Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"dbd0bb\",\"wall_time_seconds\":1.001342962,\"session_id\":12908,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:41.564Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9ae94d487d2848758fe85f9e4c1", "usage": { "input_tokens": 69983, "cached_input_tokens": 69632, "cache_write_input_tokens": 0, "output_tokens": 443, "reasoning_output_tokens": 135, "total_tokens": 70426 }, "turn_token_usage": { "input_tokens": 3792591, "cached_input_tokens": 3714816, "cache_write_input_tokens": 0, "output_tokens": 27914, "reasoning_output_tokens": 11126, "total_tokens": 3820505 }, "thread_token_usage": { "input_tokens": 3792591, "cached_input_tokens": 3714816, "cache_write_input_tokens": 0, "output_tokens": 27914, "reasoning_output_tokens": 11126, "total_tokens": 3820505 } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-24T22:52:43.699Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:52:43.699Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:52:44.369Z", "callId": "call_tVFwtRcd63uy9gJvCzTE4MvV", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":12908,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:53:14.377Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nlike 0 0.04849736826595881\nlike 0.25 0.04870073254400032\nlike 0.5 0.04877474297894327\nlike 1 0.04888076198610076\nlike 2 0.04972509477359094\nlike 4 0.0486548176862347\nlike 8 0.04706863951378384\nbinary True pen 0.1 0.04845157584765077\nbinary True pen 0.25 0.04961936609329029\nbinary True pen -0.1 0.04755252310029428\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:52:44.470Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9bac44087d2b12a04f69f781ff3", "usage": { "input_tokens": 70484, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70537 }, "turn_token_usage": { "input_tokens": 3863075, "cached_input_tokens": 3784576, "cache_write_input_tokens": 0, "output_tokens": 27967, "reasoning_output_tokens": 11132, "total_tokens": 3891042 }, "thread_token_usage": { "input_tokens": 3863075, "cached_input_tokens": 3784576, "cache_write_input_tokens": 0, "output_tokens": 27967, "reasoning_output_tokens": 11132, "total_tokens": 3891042 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-24T22:53:19.588Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:53:19.588Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:53:19.589Z", "callId": "call_OoeAckcZINQ8z6SlDboMxjIq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":12908,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:53:21.854Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\nbinary False pen 0.1 0.049780855202100543\nbinary False pen 0.25 0.0493142145252186\nbinary False pen -0.1 0.0483708179888533\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:53:19.687Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9daa96c87d2ae571de0b406f730", "usage": { "input_tokens": 70706, "cached_input_tokens": 70272, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 70761 }, "turn_token_usage": { "input_tokens": 3933781, "cached_input_tokens": 3854848, "cache_write_input_tokens": 0, "output_tokens": 28022, "reasoning_output_tokens": 11140, "total_tokens": 3961803 }, "thread_token_usage": { "input_tokens": 3933781, "cached_input_tokens": 3854848, "cache_write_input_tokens": 0, "output_tokens": 28022, "reasoning_output_tokens": 11140, "total_tokens": 3961803 } } }, { "kind": "context", "timestamp": "2026-09-24T22:53:21.852Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-14c14b57-2e0c-47d2-96cf-37a26e28e56d", "process_id": "12908", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idx.npy');ns=np.load(t.OUT/'nbr_org_sim.npy')\nfor w in [0,.25,.5,1,2,4,8]:\n y=t.objective_y(7,like_weight=w)\n print('like',w,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.75),flush=True)\nfor b in [True,False]:\n for pen in [.1,.25,-.1]:\n y=t.objective_y(7,like_weight=1,binary=b,pop_penalty=pen)\n print('binary',b,'pen',pen,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.75),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idx.npy');ns=np.load(t.OUT/'nbr_org_sim.npy')\nfor w in [0,.25,.5,1,2,4,8]:\n y=t.objective_y(7,like_weight=w)\n print('like',w,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.75),flush=True)\nfor b in [True,False]:\n for pen in [.1,.25,-.1]:\n y=t.objective_y(7,like_weight=1,binary=b,pop_penalty=pen)\n print('binary',b,'pen',pen,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.75),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "like 0 0.04849736826595881\nlike 0.25 0.04870073254400032\nlike 0.5 0.04877474297894327\nlike 1 0.04888076198610076\nlike 2 0.04972509477359094\nlike 4 0.0486548176862347\nlike 8 0.04706863951378384\nbinary True pen 0.1 0.04845157584765077\nbinary True pen 0.25 0.04961936609329029\nbinary True pen -0.1 0.04755252310029428\nbinary False pen 0.1 0.049780855202100543\nbinary False pen 0.25 0.0493142145252186\nbinary False pen -0.1 0.0483708179888533\n", "stderr": "", "aggregated_output": "like 0 0.04849736826595881\nlike 0.25 0.04870073254400032\nlike 0.5 0.04877474297894327\nlike 1 0.04888076198610076\nlike 2 0.04972509477359094\nlike 4 0.0486548176862347\nlike 8 0.04706863951378384\nbinary True pen 0.1 0.04845157584765077\nbinary True pen 0.25 0.04961936609329029\nbinary True pen -0.1 0.04755252310029428\nbinary False pen 0.1 0.049780855202100543\nbinary False pen 0.25 0.0493142145252186\nbinary False pen -0.1 0.0483708179888533\n", "exit_code": 0, "duration": { "secs": 40, "nanos": 298555249 }, "formatted_output": "like 0 0.04849736826595881\nlike 0.25 0.04870073254400032\nlike 0.5 0.04877474297894327\nlike 1 0.04888076198610076\nlike 2 0.04972509477359094\nlike 4 0.0486548176862347\nlike 8 0.04706863951378384\nbinary True pen 0.1 0.04845157584765077\nbinary True pen 0.25 0.04961936609329029\nbinary True pen -0.1 0.04755252310029428\nbinary False pen 0.1 0.049780855202100543\nbinary False pen 0.25 0.0493142145252186\nbinary False pen -0.1 0.0483708179888533\n" } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-24T22:53:29.274Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:53:29.274Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:53:34.280Z", "callId": "call_M5bapFHzb9NPi30Fc3kbMEBD", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np\\nfrom scipy import sparse\\nimport knn_experiment as k\\nimport tune_knn as t\\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\\nfor pw in [.5,.75,1.25,1.5]:\\n x=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'),idf_power=pw); ni,ns=k.nearest(x,tr,f'org_idf{pw}');del x;gc.collect();s=t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.75);print('idf',pw,s,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:53:35.454Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"f4c050\",\"wall_time_seconds\":1.000923604,\"session_id\":25123,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:53:34.453Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5a9e21c8887d289d455c293fa63e7", "usage": { "input_tokens": 70835, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 623, "reasoning_output_tokens": 338, "total_tokens": 71458 }, "turn_token_usage": { "input_tokens": 4004616, "cached_input_tokens": 3925376, "cache_write_input_tokens": 0, "output_tokens": 28645, "reasoning_output_tokens": 11478, "total_tokens": 4033261 }, "thread_token_usage": { "input_tokens": 4004616, "cached_input_tokens": 3925376, "cache_write_input_tokens": 0, "output_tokens": 28645, "reasoning_output_tokens": 11478, "total_tokens": 4033261 } } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-24T22:53:40.817Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:53:40.817Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:53:40.818Z", "callId": "call_evDWA6PIRBAauIRyjaQ4LgMf", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":25123,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:54:10.826Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\norg_idf0.5 neighbors 256/8456\norg_idf0.5 neighbors 512/8456\norg_idf0.5 neighbors 768/8456\norg_idf0.5 neighbors 1024/8456\norg_idf0.5 neighbors 1280/8456\norg_idf0.5 neighbors 1536/8456\norg_idf0.5 neighbors 1792/8456\norg_idf0.5 neighbors 2048/8456\norg_idf0.5 neighbors 2304/8456\norg_idf0.5 neighbors 2560/8456\norg_idf0.5 neighbors 2816/8456\norg_idf0.5 neighbors 3072/8456\norg_idf0.5 neighbors 3328/8456\norg_idf0.5 neighbors 3584/8456\norg_idf0.5 neighbors 3840/8456\norg_idf0.5 neighbors 4096/8456\norg_idf0.5 neighbors 4352/8456\norg_idf0.5 neighbors 4608/8456\norg_idf0.5 neighbors 4864/8456\norg_idf0.5 neighbors 5120/8456\norg_idf0.5 neighbors 5376/8456\norg_idf0.5 neighbors 5632/8456\norg_idf0.5 neighbors 5888/8456\norg_idf0.5 neighbors 6144/8456\norg_idf0.5 neighbors 6400/8456\norg_idf0.5 neighbors 6656/8456\norg_idf0.5 neighbors 6912/8456\norg_idf0.5 neighbors 7168/8456\norg_idf0.5 neighbors 7424/8456\norg_idf0.5 neighbors 7680/8456\norg_idf0.5 neighbors 7936/8456\norg_idf0.5 neighbors 8192/8456\norg_idf0.5 neighbors 8448/8456\norg_idf0.5 neighbors 8456/8456\nidf 0.5 0.04873073521633579\norg_idf0.75 neighbors 256/8456\norg_idf0.75 neighbors 512/8456\norg_idf0.75 neighbors 768/8456\norg_idf0.75 neighbors 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t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\nfor pw in [.5,.75,1.25,1.5]:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'),idf_power=pw); ni,ns=k.nearest(x,tr,f'org_idf{pw}');del x;gc.collect();s=t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.75);print('idf',pw,s,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\nfor pw in [.5,.75,1.25,1.5]:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'),idf_power=pw); ni,ns=k.nearest(x,tr,f'org_idf{pw}');del x;gc.collect();s=t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.75);print('idf',pw,s,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org_idf0.5 neighbors 256/8456\norg_idf0.5 neighbors 512/8456\norg_idf0.5 neighbors 768/8456\norg_idf0.5 neighbors 1024/8456\norg_idf0.5 neighbors 1280/8456\norg_idf0.5 neighbors 1536/8456\norg_idf0.5 neighbors 1792/8456\norg_idf0.5 neighbors 2048/8456\norg_idf0.5 neighbors 2304/8456\norg_idf0.5 neighbors 2560/8456\norg_idf0.5 neighbors 2816/8456\norg_idf0.5 neighbors 3072/8456\norg_idf0.5 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1280/8456\norg_idf0.5 neighbors 1536/8456\norg_idf0.5 neighbors 1792/8456\norg_idf0.5 neighbors 2048/8456\norg_idf0.5 neighbors 2304/8456\norg_idf0.5 neighbors 2560/8456\norg_idf0.5 neighbors 2816/8456\norg_idf0.5 neighbors 3072/8456\norg_idf0.5 neighbors 3328/8456\norg_idf0.5 neighbors 3584/8456\norg_idf0.5 neighbors 3840/8456\norg_idf0.5 neighbors 4096/8456\norg_idf0.5 neighbors 4352/8456\norg_idf0.5 neighbors 4608/8456\norg_idf0.5 neighbors 4864/8456\norg_idf0.5 neighbors 5120/8456\norg_idf0.5 neighbors 5376/8456\norg_idf0.5 neighbors 5632/8456\norg_idf0.5 neighbors 5888/8456\norg_idf0.5 neighbors 6144/8456\norg_idf0.5 neighbors 6400/8456\norg_idf0.5 neighbors 6656/8456\norg_idf0.5 neighbors 6912/8456\norg_idf0.5 neighbors 7168/8456\norg_idf0.5 neighbors 7424/8456\norg_idf0.5 neighbors 7680/8456\norg_idf0.5 neighbors 7936/8456\norg_idf0.5 neighbors 8192/8456\norg_idf0.5 neighbors 8448/8456\norg_idf0.5 neighbors 8456/8456\nidf 0.5 0.04873073521633579\norg_idf0.75 neighbors 256/8456\norg_idf0.75 neighbors 512/8456\norg_idf0.75 neighbors 768/8456\norg_idf0.75 neighbors 1024/8456\norg_idf0.75 neighbors 1280/8456\norg_idf0.75 neighbors 1536/8456\norg_idf0.75 neighbors 1792/8456\norg_idf0.75 neighbors 2048/8456\norg_idf0.75 neighbors 2304/8456\norg_idf0.75 neighbors 2560/8456\norg_idf0.75 neighbors 2816/8456\norg_idf0.75 neighbors 3072/8456\norg_idf0.75 neighbors 3328/8456\norg_idf0.75 neighbors 3584/8456\norg_idf0.75 neighbors 3840/8456\norg_idf0.75 neighbors 4096/8456\norg_idf0.75 neighbors 4352/8456\norg_idf0.75 neighbors 4608/8456\norg_idf0.75 neighbors 4864/8456\norg_idf0.75 neighbors 5120/8456\norg_idf0.75 neighbors 5376/8456\norg_idf0.75 neighbors 5632/8456\norg_idf0.75 neighbors 5888/8456\norg_idf0.75 neighbors 6144/8456\norg_idf0.75 neighbors 6400/8456\norg_idf0.75 neighbors 6656/8456\norg_idf0.75 neighbors 6912/8456\norg_idf0.75 neighbors 7168/8456\norg_idf0.75 neighbors 7424/8456\norg_idf0.75 neighbors 7680/8456\norg_idf0.75 neighbors 7936/8456\norg_idf0.75 neighbors 8192/8456\norg_idf0.75 neighbors 8448/8456\norg_idf0.75 neighbors 8456/8456\nidf 0.75 0.04969463381641732\norg_idf1.25 neighbors 256/8456\norg_idf1.25 neighbors 512/8456\norg_idf1.25 neighbors 768/8456\norg_idf1.25 neighbors 1024/8456\norg_idf1.25 neighbors 1280/8456\norg_idf1.25 neighbors 1536/8456\norg_idf1.25 neighbors 1792/8456\norg_idf1.25 neighbors 2048/8456\norg_idf1.25 neighbors 2304/8456\norg_idf1.25 neighbors 2560/8456\norg_idf1.25 neighbors 2816/8456\norg_idf1.25 neighbors 3072/8456\norg_idf1.25 neighbors 3328/8456\norg_idf1.25 neighbors 3584/8456\norg_idf1.25 neighbors 3840/8456\norg_idf1.25 neighbors 4096/8456\norg_idf1.25 neighbors 4352/8456\norg_idf1.25 neighbors 4608/8456\norg_idf1.25 neighbors 4864/8456\norg_idf1.25 neighbors 5120/8456\norg_idf1.25 neighbors 5376/8456\norg_idf1.25 neighbors 5632/8456\norg_idf1.25 neighbors 5888/8456\norg_idf1.25 neighbors 6144/8456\norg_idf1.25 neighbors 6400/8456\norg_idf1.25 neighbors 6656/8456\norg_idf1.25 neighbors 6912/8456\norg_idf1.25 neighbors 7168/8456\norg_idf1.25 neighbors 7424/8456\norg_idf1.25 neighbors 7680/8456\norg_idf1.25 neighbors 7936/8456\norg_idf1.25 neighbors 8192/8456\norg_idf1.25 neighbors 8448/8456\norg_idf1.25 neighbors 8456/8456\nidf 1.25 0.04958945325443481\norg_idf1.5 neighbors 256/8456\norg_idf1.5 neighbors 512/8456\norg_idf1.5 neighbors 768/8456\norg_idf1.5 neighbors 1024/8456\norg_idf1.5 neighbors 1280/8456\norg_idf1.5 neighbors 1536/8456\norg_idf1.5 neighbors 1792/8456\norg_idf1.5 neighbors 2048/8456\norg_idf1.5 neighbors 2304/8456\norg_idf1.5 neighbors 2560/8456\norg_idf1.5 neighbors 2816/8456\norg_idf1.5 neighbors 3072/8456\norg_idf1.5 neighbors 3328/8456\norg_idf1.5 neighbors 3584/8456\norg_idf1.5 neighbors 3840/8456\norg_idf1.5 neighbors 4096/8456\norg_idf1.5 neighbors 4352/8456\norg_idf1.5 neighbors 4608/8456\norg_idf1.5 neighbors 4864/8456\norg_idf1.5 neighbors 5120/8456\norg_idf1.5 neighbors 5376/8456\norg_idf1.5 neighbors 5632/8456\norg_idf1.5 neighbors 5888/8456\norg_idf1.5 neighbors 6144/8456\norg_idf1.5 neighbors 6400/8456\norg_idf1.5 neighbors 6656/8456\norg_idf1.5 neighbors 6912/8456\norg_idf1.5 neighbors 7168/8456\norg_idf1.5 neighbors 7424/8456\norg_idf1.5 neighbors 7680/8456\norg_idf1.5 neighbors 7936/8456\norg_idf1.5 neighbors 8192/8456\norg_idf1.5 neighbors 8448/8456\norg_idf1.5 neighbors 8456/8456\nidf 1.5 0.05037259301094281\n" } } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-24T22:54:13.770Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:54:13.770Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:54:18.730Z", "callId": "call_b9PpDQW1qEPppNRuvac7DVOn", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np\\nfrom scipy import sparse\\nimport knn_experiment as k\\nimport tune_knn as t\\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\\nfor pw in [1.75,2,2.5,3]:\\n x=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'),idf_power=pw);ni,ns=k.nearest(x,tr,f'org_idf{pw}');del x;gc.collect();print('idf',pw,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.75),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:54:19.900Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"abcaa6\",\"wall_time_seconds\":1.001257815,\"session_id\":94709,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:54:18.869Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa13053487d28800778ad6d28877", "usage": { "input_tokens": 73546, "cached_input_tokens": 71296, "cache_write_input_tokens": 0, "output_tokens": 326, "reasoning_output_tokens": 44, "total_tokens": 73872 }, "turn_token_usage": { "input_tokens": 4149678, "cached_input_tokens": 4067328, "cache_write_input_tokens": 0, "output_tokens": 29026, "reasoning_output_tokens": 11530, "total_tokens": 4178704 }, "thread_token_usage": { "input_tokens": 4149678, "cached_input_tokens": 4067328, "cache_write_input_tokens": 0, "output_tokens": 29026, "reasoning_output_tokens": 11530, "total_tokens": 4178704 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-24T22:54:21.424Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:54:21.424Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:54:22.123Z", "callId": "call_fvo00Oj9tYWLIaldOWHE3h6I", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":94709,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:54:52.132Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\norg_idf1.75 neighbors 256/8456\norg_idf1.75 neighbors 512/8456\norg_idf1.75 neighbors 768/8456\norg_idf1.75 neighbors 1024/8456\norg_idf1.75 neighbors 1280/8456\norg_idf1.75 neighbors 1536/8456\norg_idf1.75 neighbors 1792/8456\norg_idf1.75 neighbors 2048/8456\norg_idf1.75 neighbors 2304/8456\norg_idf1.75 neighbors 2560/8456\norg_idf1.75 neighbors 2816/8456\norg_idf1.75 neighbors 3072/8456\norg_idf1.75 neighbors 3328/8456\norg_idf1.75 neighbors 3584/8456\norg_idf1.75 neighbors 3840/8456\norg_idf1.75 neighbors 4096/8456\norg_idf1.75 neighbors 4352/8456\norg_idf1.75 neighbors 4608/8456\norg_idf1.75 neighbors 4864/8456\norg_idf1.75 neighbors 5120/8456\norg_idf1.75 neighbors 5376/8456\norg_idf1.75 neighbors 5632/8456\norg_idf1.75 neighbors 5888/8456\norg_idf1.75 neighbors 6144/8456\norg_idf1.75 neighbors 6400/8456\norg_idf1.75 neighbors 6656/8456\norg_idf1.75 neighbors 6912/8456\norg_idf1.75 neighbors 7168/8456\norg_idf1.75 neighbors 7424/8456\norg_idf1.75 neighbors 7680/8456\norg_idf1.75 neighbors 7936/8456\norg_idf1.75 neighbors 8192/8456\norg_idf1.75 neighbors 8448/8456\norg_idf1.75 neighbors 8456/8456\nidf 1.75 0.04987329993050412\norg_idf2 neighbors 256/8456\norg_idf2 neighbors 512/8456\norg_idf2 neighbors 768/8456\norg_idf2 neighbors 1024/8456\norg_idf2 neighbors 1280/8456\norg_idf2 neighbors 1536/8456\norg_idf2 neighbors 1792/8456\norg_idf2 neighbors 2048/8456\norg_idf2 neighbors 2304/8456\norg_idf2 neighbors 2560/8456\norg_idf2 neighbors 2816/8456\norg_idf2 neighbors 3072/8456\norg_idf2 neighbors 3328/8456\norg_idf2 neighbors 3584/8456\norg_idf2 neighbors 3840/8456\norg_idf2 neighbors 4096/8456\norg_idf2 neighbors 4352/8456\norg_idf2 neighbors 4608/8456\norg_idf2 neighbors 4864/8456\norg_idf2 neighbors 5120/8456\norg_idf2 neighbors 5376/8456\norg_idf2 neighbors 5632/8456\norg_idf2 neighbors 5888/8456\norg_idf2 neighbors 6144/8456\norg_idf2 neighbors 6400/8456\norg_idf2 neighbors 6656/8456\norg_idf2 neighbors 6912/8456\norg_idf2 neighbors 7168/8456\norg_idf2 neighbors 7424/8456\norg_idf2 neighbors 7680/8456\norg_idf2 neighbors 7936/8456\norg_idf2 neighbors 8192/8456\norg_idf2 neighbors 8448/8456\norg_idf2 neighbors 8456/8456\nidf 2 0.049580233555678206\norg_idf2.5 neighbors 256/8456\norg_idf2.5 neighbors 512/8456\norg_idf2.5 neighbors 768/8456\norg_idf2.5 neighbors 1024/8456\norg_idf2.5 neighbors 1280/8456\norg_idf2.5 neighbors 1536/8456\norg_idf2.5 neighbors 1792/8456\norg_idf2.5 neighbors 2048/8456\norg_idf2.5 neighbors 2304/8456\norg_idf2.5 neighbors 2560/8456\norg_idf2.5 neighbors 2816/8456\norg_idf2.5 neighbors 3072/8456\norg_idf2.5 neighbors 3328/8456\norg_idf2.5 neighbors 3584/8456\norg_idf2.5 neighbors 3840/8456\norg_idf2.5 neighbors 4096/8456\norg_idf2.5 neighbors 4352/8456\norg_idf2.5 neighbors 4608/8456\norg_idf2.5 neighbors 4864/8456\norg_idf2.5 neighbors 5120/8456\norg_idf2.5 neighbors 5376/8456\norg_idf2.5 neighbors 5632/8456\norg_idf2.5 neighbors 5888/8456\norg_idf2.5 neighbors 6144/8456\norg_idf2.5 neighbors 6400/8456\norg_idf2.5 neighbors 6656/8456\norg_idf2.5 neighbors 6912/8456\norg_idf2.5 neighbors 7168/8456\norg_idf2.5 neighbors 7424/8456\norg_idf2.5 neighbors 7680/8456\norg_idf2.5 neighbors 7936/8456\norg_idf2.5 neighbors 8192/8456\norg_idf2.5 neighbors 8448/8456\norg_idf2.5 neighbors 8456/8456\nidf 2.5 0.04847559107186812\norg_idf3 neighbors 256/8456\norg_idf3 neighbors 512/8456\norg_idf3 neighbors 768/8456\norg_idf3 neighbors 1024/8456\norg_idf3 neighbors 1280/8456\norg_idf3 neighbors 1536/8456\norg_idf3 neighbors 1792/8456\norg_idf3 neighbors 2048/8456\norg_idf3 neighbors 2304/8456\norg_idf3 neighbors 2560/8456\norg_idf3 neighbors 2816/8456\norg_idf3 neighbors 3072/8456\norg_idf3 neighbors 3328/8456\norg_idf3 neighbors 3584/8456\norg_idf3 neighbors 3840/8456\norg_idf3 neighbors 4096/8456\norg_idf3 neighbors 4352/8456\norg_idf3 neighbors 4608/8456\norg_idf3 neighbors 4864/8456\norg_idf3 neighbors 5120/8456\norg_idf3 neighbors 5376/8456\norg_idf3 neighbors 5632/8456\norg_idf3 neighbors 5888/8456\norg_idf3 neighbors 6144/8456\norg_idf3 neighbors 6400/8456\norg_idf3 neighbors 6656/8456\norg_idf3 neighbors 6912/8456\norg_idf3 neighbors 7168/8456\norg_idf3 neighbors 7424/8456\norg_idf3 neighbors 7680/8456\norg_idf3 neighbors 7936/8456\norg_idf3 neighbors 8192/8456\norg_idf3 neighbors 8448/8456\norg_idf3 neighbors 8456/8456\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:54:22.245Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa1c181887d280f39d43754b31f2", "usage": { "input_tokens": 73929, "cached_input_tokens": 73344, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 73984 }, "turn_token_usage": { "input_tokens": 4223607, "cached_input_tokens": 4140672, "cache_write_input_tokens": 0, "output_tokens": 29081, "reasoning_output_tokens": 11538, "total_tokens": 4252688 }, "thread_token_usage": { "input_tokens": 4223607, "cached_input_tokens": 4140672, "cache_write_input_tokens": 0, "output_tokens": 29081, "reasoning_output_tokens": 11538, "total_tokens": 4252688 } } }, { "kind": "context", "timestamp": "2026-09-24T22:54:54.571Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e53373b1-848c-4a56-8d4f-f8d36eb54254", "process_id": "94709", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\nfor pw in [1.75,2,2.5,3]:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'),idf_power=pw);ni,ns=k.nearest(x,tr,f'org_idf{pw}');del x;gc.collect();print('idf',pw,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.75),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\nfor pw in [1.75,2,2.5,3]:\n x=k.tfidf_profile(sparse.load_npz(k.OUT/'train_x_org.npz'),idf_power=pw);ni,ns=k.nearest(x,tr,f'org_idf{pw}');del x;gc.collect();print('idf',pw,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.75),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "org_idf1.75 neighbors 256/8456\norg_idf1.75 neighbors 512/8456\norg_idf1.75 neighbors 768/8456\norg_idf1.75 neighbors 1024/8456\norg_idf1.75 neighbors 1280/8456\norg_idf1.75 neighbors 1536/8456\norg_idf1.75 neighbors 1792/8456\norg_idf1.75 neighbors 2048/8456\norg_idf1.75 neighbors 2304/8456\norg_idf1.75 neighbors 2560/8456\norg_idf1.75 neighbors 2816/8456\norg_idf1.75 neighbors 3072/8456\norg_idf1.75 neighbors 3328/8456\norg_idf1.75 neighbors 3584/8456\norg_idf1.75 neighbors 3840/8456\norg_idf1.75 neighbors 4096/8456\norg_idf1.75 neighbors 4352/8456\norg_idf1.75 neighbors 4608/8456\norg_idf1.75 neighbors 4864/8456\norg_idf1.75 neighbors 5120/8456\norg_idf1.75 neighbors 5376/8456\norg_idf1.75 neighbors 5632/8456\norg_idf1.75 neighbors 5888/8456\norg_idf1.75 neighbors 6144/8456\norg_idf1.75 neighbors 6400/8456\norg_idf1.75 neighbors 6656/8456\norg_idf1.75 neighbors 6912/8456\norg_idf1.75 neighbors 7168/8456\norg_idf1.75 neighbors 7424/8456\norg_idf1.75 neighbors 7680/8456\norg_idf1.75 neighbors 7936/8456\norg_idf1.75 neighbors 8192/8456\norg_idf1.75 neighbors 8448/8456\norg_idf1.75 neighbors 8456/8456\nidf 1.75 0.04987329993050412\norg_idf2 neighbors 256/8456\norg_idf2 neighbors 512/8456\norg_idf2 neighbors 768/8456\norg_idf2 neighbors 1024/8456\norg_idf2 neighbors 1280/8456\norg_idf2 neighbors 1536/8456\norg_idf2 neighbors 1792/8456\norg_idf2 neighbors 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[ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:54:55.482Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:54:55.497Z", "callId": "call_henm8fBsJ1Qbh0c7rWFBr8YZ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":94709,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:54:55.613Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nidf 3 0.0465714609501824\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:54:55.611Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa3c521087d291dcda70676ee9e4", "usage": { "input_tokens": 75806, "cached_input_tokens": 73728, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 75864 }, "turn_token_usage": { "input_tokens": 4299413, "cached_input_tokens": 4214400, "cache_write_input_tokens": 0, "output_tokens": 29139, "reasoning_output_tokens": 11549, "total_tokens": 4328552 }, "thread_token_usage": { "input_tokens": 4299413, "cached_input_tokens": 4214400, "cache_write_input_tokens": 0, "output_tokens": 29139, "reasoning_output_tokens": 11549, "total_tokens": 4328552 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-24T22:54:58.062Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:54:58.062Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:02.384Z", "callId": "call_5Q1INEEsTXLDqbErv5FpENwW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy')\\nfor pw in [.4,.5,.6,.75,.9,1]:\\n for k in [200,300,400]: print(pw,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k,power=pw),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:55:03.554Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"92395d\",\"wall_time_seconds\":1.000965219,\"session_id\":12384,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:02.561Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa3fd31487d2adffed20a39690e7", "usage": { "input_tokens": 75900, "cached_input_tokens": 75648, "cache_write_input_tokens": 0, "output_tokens": 302, "reasoning_output_tokens": 53, "total_tokens": 76202 }, "turn_token_usage": { "input_tokens": 4375313, "cached_input_tokens": 4290048, "cache_write_input_tokens": 0, "output_tokens": 29441, "reasoning_output_tokens": 11602, "total_tokens": 4404754 }, "thread_token_usage": { "input_tokens": 4375313, "cached_input_tokens": 4290048, "cache_write_input_tokens": 0, "output_tokens": 29441, "reasoning_output_tokens": 11602, "total_tokens": 4404754 } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-24T22:55:04.775Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:55:04.775Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:05.458Z", "callId": "call_ZvsJCwf8Bv1fuzDxllXlMqsh", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":12384,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:55:35.467Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n0.4 200 0.047675838417918864\n0.4 300 0.04966130065752067\n0.4 400 0.05011378202580846\n0.5 200 0.047877091756657136\n0.5 300 0.04947361342481757\n0.5 400 0.05033537975074719\n0.6 200 0.047741880789409524\n0.6 300 0.04945246259643548\n0.6 400 0.050469187456490164\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:05.585Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa47c9cc87d29381c23cb9b6c336", "usage": { "input_tokens": 76259, "cached_input_tokens": 75776, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 76312 }, "turn_token_usage": { "input_tokens": 4451572, "cached_input_tokens": 4365824, "cache_write_input_tokens": 0, "output_tokens": 29494, "reasoning_output_tokens": 11608, "total_tokens": 4481066 }, "thread_token_usage": { "input_tokens": 4451572, "cached_input_tokens": 4365824, "cache_write_input_tokens": 0, "output_tokens": 29494, "reasoning_output_tokens": 11608, "total_tokens": 4481066 } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-24T22:55:36.906Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:55:36.906Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:55:37.597Z", "callId": "call_4fKAEZTteYTDVEZJOfewR9vB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":12384,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:56:00.564Z", "content": "Script completed\nWall time 23.0 seconds\nOutput:\n\n0.75 200 0.046950577171059035\n0.75 300 0.04930069475538652\n0.75 400 0.05037259301094281\n0.9 200 0.046353679204616804\n0.9 300 0.04869915690309911\n0.9 400 0.05023394691360532\n1 200 0.04582443099533877\n1 300 0.048701283174150886\n1 400 0.05001317131608239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:55:37.745Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa67a91487d285a9b9063f15630c", "usage": { "input_tokens": 76470, "cached_input_tokens": 76032, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 76523 }, "turn_token_usage": { "input_tokens": 4528042, "cached_input_tokens": 4441856, "cache_write_input_tokens": 0, "output_tokens": 29547, "reasoning_output_tokens": 11614, "total_tokens": 4557589 }, "thread_token_usage": { "input_tokens": 4528042, "cached_input_tokens": 4441856, "cache_write_input_tokens": 0, "output_tokens": 29547, "reasoning_output_tokens": 11614, "total_tokens": 4557589 } } }, { "kind": "context", "timestamp": "2026-09-24T22:56:00.563Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dccd172b-5ae1-4a70-900b-387480e23e8b", "process_id": "12384", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy')\nfor pw in [.4,.5,.6,.75,.9,1]:\n for k in [200,300,400]: print(pw,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k,power=pw),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy')\nfor pw in [.4,.5,.6,.75,.9,1]:\n for k in [200,300,400]: print(pw,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k,power=pw),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0.4 200 0.047675838417918864\n0.4 300 0.04966130065752067\n0.4 400 0.05011378202580846\n0.5 200 0.047877091756657136\n0.5 300 0.04947361342481757\n0.5 400 0.05033537975074719\n0.6 200 0.047741880789409524\n0.6 300 0.04945246259643548\n0.6 400 0.050469187456490164\n0.75 200 0.046950577171059035\n0.75 300 0.04930069475538652\n0.75 400 0.05037259301094281\n0.9 200 0.046353679204616804\n0.9 300 0.04869915690309911\n0.9 400 0.05023394691360532\n1 200 0.04582443099533877\n1 300 0.048701283174150886\n1 400 0.05001317131608239\n", "stderr": "", "aggregated_output": "0.4 200 0.047675838417918864\n0.4 300 0.04966130065752067\n0.4 400 0.05011378202580846\n0.5 200 0.047877091756657136\n0.5 300 0.04947361342481757\n0.5 400 0.05033537975074719\n0.6 200 0.047741880789409524\n0.6 300 0.04945246259643548\n0.6 400 0.050469187456490164\n0.75 200 0.046950577171059035\n0.75 300 0.04930069475538652\n0.75 400 0.05037259301094281\n0.9 200 0.046353679204616804\n0.9 300 0.04869915690309911\n0.9 400 0.05023394691360532\n1 200 0.04582443099533877\n1 300 0.048701283174150886\n1 400 0.05001317131608239\n", "exit_code": 0, "duration": { "secs": 58, "nanos": 9442257 }, "formatted_output": "0.4 200 0.047675838417918864\n0.4 300 0.04966130065752067\n0.4 400 0.05011378202580846\n0.5 200 0.047877091756657136\n0.5 300 0.04947361342481757\n0.5 400 0.05033537975074719\n0.6 200 0.047741880789409524\n0.6 300 0.04945246259643548\n0.6 400 0.050469187456490164\n0.75 200 0.046950577171059035\n0.75 300 0.04930069475538652\n0.75 400 0.05037259301094281\n0.9 200 0.046353679204616804\n0.9 300 0.04869915690309911\n0.9 400 0.05023394691360532\n1 200 0.04582443099533877\n1 300 0.048701283174150886\n1 400 0.05001317131608239\n" } } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-24T22:56:10.896Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:10.896Z", "text": "" } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-24T22:56:12.542Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:12.542Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:56:21.884Z", "callId": "call_x6p9fOWnccA58u6sDzlwdxKE", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nfrom scipy import sparse\\nfrom sklearn.decomposition import TruncatedSVD\\nfrom sklearn.preprocessing import normalize\\nimport tune_knn as t\\nfrom ranking_evaluation import ndcg_at_k\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];items=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);rows=(u//100).astype(int)\\nx=sparse.load_npz(t.OUT/'train_x_org.npz')[:,items].astype(np.float32);x.data=np.log1p(x.data)\\n# TF-IDF-style column weighting softens blockbuster domination.\\ndf=np.bincount(x.indices,minlength=x.shape[1]); idf=np.log1p(x.shape[0]/(1+df)).astype(np.float32); x.data*=idf[x.indices]**0.5\\nprint('matrix',x.shape,x.nnz,flush=True)\\nsvd=TruncatedSVD(n_components=192,n_iter=7,random_state=1); U=svd.fit_transform(x); V=svd.components_;print('fit',svd.explained_variance_ratio_.sum(),flush=True)\\nfor dim in [32,64,96,128,192]:\\n vals=[]\\n for q in range(0,len(pos),128):\\n pp=pos[q:q+128]; sc=U[rows[pp],:dim]@V[:dim];sc[~e[pp]]=-np.inf;top=np.argpartition(sc,-10,axis=1)[:,-10:];sv=np.take_along_axis(sc,top,1);top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\\n vals.extend(ndcg_at_k(items[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\\n print(dim,float(np.mean(vals)),flush=True)\\nnp.save(t.OUT/'svd_U.npy',U);np.save(t.OUT/'svd_V.npy',V)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:56:23.061Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"13b203\",\"wall_time_seconds\":1.001308136,\"session_id\":38426,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:56:22.061Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa80cb4487d286cbd27986e39d25", "usage": { "input_tokens": 76675, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 1128, "reasoning_output_tokens": 600, "total_tokens": 77803 }, "turn_token_usage": { "input_tokens": 4604717, "cached_input_tokens": 4518144, "cache_write_input_tokens": 0, "output_tokens": 30675, "reasoning_output_tokens": 12214, "total_tokens": 4635392 }, "thread_token_usage": { "input_tokens": 4604717, "cached_input_tokens": 4518144, "cache_write_input_tokens": 0, "output_tokens": 30675, "reasoning_output_tokens": 12214, "total_tokens": 4635392 } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-24T22:56:24.353Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:24.353Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:56:25.056Z", "callId": "call_ypJAT2zLcZ0AhvdVOWF23zSc", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38426,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:56:35.477Z", "content": "Script completed\nWall time 10.4 seconds\nOutput:\n\nmatrix (10001, 30000) 2060124\nfit 0.2742408\n32 0.020527410282807145\n64 0.022048820031123652\n96 0.023488318718117497\n128 0.023798676257337822\n192 0.02516005217611165\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:56:25.174Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aa973d0487d2b717e29497296f00", "usage": { "input_tokens": 77860, "cached_input_tokens": 76544, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 77913 }, "turn_token_usage": { "input_tokens": 4682577, "cached_input_tokens": 4594688, "cache_write_input_tokens": 0, "output_tokens": 30728, "reasoning_output_tokens": 12220, "total_tokens": 4713305 }, "thread_token_usage": { "input_tokens": 4682577, "cached_input_tokens": 4594688, "cache_write_input_tokens": 0, "output_tokens": 30728, "reasoning_output_tokens": 12220, "total_tokens": 4713305 } } }, { "kind": "context", "timestamp": "2026-09-24T22:56:35.475Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4b615d49-8713-4d2d-b600-eb485e9d9ca6", "process_id": "38426", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];items=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);rows=(u//100).astype(int)\nx=sparse.load_npz(t.OUT/'train_x_org.npz')[:,items].astype(np.float32);x.data=np.log1p(x.data)\n# TF-IDF-style column weighting softens blockbuster domination.\ndf=np.bincount(x.indices,minlength=x.shape[1]); idf=np.log1p(x.shape[0]/(1+df)).astype(np.float32); x.data*=idf[x.indices]**0.5\nprint('matrix',x.shape,x.nnz,flush=True)\nsvd=TruncatedSVD(n_components=192,n_iter=7,random_state=1); U=svd.fit_transform(x); V=svd.components_;print('fit',svd.explained_variance_ratio_.sum(),flush=True)\nfor dim in [32,64,96,128,192]:\n vals=[]\n for q in range(0,len(pos),128):\n pp=pos[q:q+128]; sc=U[rows[pp],:dim]@V[:dim];sc[~e[pp]]=-np.inf;top=np.argpartition(sc,-10,axis=1)[:,-10:];sv=np.take_along_axis(sc,top,1);top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\n vals.extend(ndcg_at_k(items[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\n print(dim,float(np.mean(vals)),flush=True)\nnp.save(t.OUT/'svd_U.npy',U);np.save(t.OUT/'svd_V.npy',V)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];items=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);rows=(u//100).astype(int)\nx=sparse.load_npz(t.OUT/'train_x_org.npz')[:,items].astype(np.float32);x.data=np.log1p(x.data)\n# TF-IDF-style column weighting softens blockbuster domination.\ndf=np.bincount(x.indices,minlength=x.shape[1]); idf=np.log1p(x.shape[0]/(1+df)).astype(np.float32); x.data*=idf[x.indices]**0.5\nprint('matrix',x.shape,x.nnz,flush=True)\nsvd=TruncatedSVD(n_components=192,n_iter=7,random_state=1); U=svd.fit_transform(x); V=svd.components_;print('fit',svd.explained_variance_ratio_.sum(),flush=True)\nfor dim in [32,64,96,128,192]:\n vals=[]\n for q in range(0,len(pos),128):\n pp=pos[q:q+128]; sc=U[rows[pp],:dim]@V[:dim];sc[~e[pp]]=-np.inf;top=np.argpartition(sc,-10,axis=1)[:,-10:];sv=np.take_along_axis(sc,top,1);top=np.take_along_axis(top,np.argsort(-sv,axis=1),1)\n vals.extend(ndcg_at_k(items[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\n print(dim,float(np.mean(vals)),flush=True)\nnp.save(t.OUT/'svd_U.npy',U);np.save(t.OUT/'svd_V.npy',V)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "matrix (10001, 30000) 2060124\nfit 0.2742408\n32 0.020527410282807145\n64 0.022048820031123652\n96 0.023488318718117497\n128 0.023798676257337822\n192 0.02516005217611165\n", "stderr": "", "aggregated_output": "matrix (10001, 30000) 2060124\nfit 0.2742408\n32 0.020527410282807145\n64 0.022048820031123652\n96 0.023488318718117497\n128 0.023798676257337822\n192 0.02516005217611165\n", "exit_code": 0, "duration": { "secs": 13, "nanos": 416037750 }, "formatted_output": "matrix (10001, 30000) 2060124\nfit 0.2742408\n32 0.020527410282807145\n64 0.022048820031123652\n96 0.023488318718117497\n128 0.023798676257337822\n192 0.02516005217611165\n" } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-24T22:56:46.395Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:46.395Z", "text": "" } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-24T22:56:51.424Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:56:51.424Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:57:04.057Z", "callId": "call_NVB4BV2pDMYz4PwGLvaDqKmU", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nfrom scipy import sparse\\nimport tune_knn as t\\nfrom ranking_evaluation import ndcg_at_k\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy');days=[1,3,5,7,14];ys={d:t.objective_y(d,like_weight=2,pop_penalty=.1) for d in days}; glob=np.asarray(ys[1].sum(0)).ravel().astype(np.float32);glob/=glob.max()\\nconfigs=[('base',{7:1},0)]\\nfor d in [1,3,5,14]:\\n for a in [.05,.1,.2,.3,.5]: configs.append((f'd{d}a{a}',{7:1-a,d:a},0))\\nfor a in [.03,.05,.075,.1,.15]:configs.append((f'glob{a}',{7:1},a))\\nconfigs += [('d1.1g.05',{7:.9,1:.1},.05),('d3.1g.05',{7:.9,3:.1},.05),('d5.1g.05',{7:.9,5:.1},.05)]\\nres={n:[] for n,_,_ in configs};k=400\\nfor q in range(0,len(pos),128):\\n pp=pos[q:q+128];rr=np.repeat(np.arange(len(pp)),k);cc=ni[pp,:k].ravel();vv=np.maximum(ns[pp,:k].ravel(),0)**.6;w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),ys[7].shape[0]));scores={}\\n for d,y in ys.items():\\n sc=(w@y).toarray().astype(np.float32);sc/=np.maximum(sc.max(1,keepdims=True),1e-9);scores[d]=sc\\n for name,mix,ga in configs:\\n sc=sum(a*scores[d] for d,a in mix.items())+ga*glob[None,:];sc=np.where(e[pp],sc,-np.inf);top=np.argpartition(sc,-10,axis=1)[:,-10:];sv=np.take_along_axis(sc,top,1);top=np.take_along_axis(top,np.argsort(-sv,axis=1),1);res[name].extend(ndcg_at_k(it[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\\nfor n in res:print(n,round(float(np.mean(res[n])),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:57:05.255Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"b181a7\",\"wall_time_seconds\":1.00157901,\"session_id\":21127,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:57:04.169Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aaa3a9e087d2b396f195fb078891", "usage": { "input_tokens": 78013, "cached_input_tokens": 77696, "cache_write_input_tokens": 0, "output_tokens": 1500, "reasoning_output_tokens": 790, "total_tokens": 79513 }, "turn_token_usage": { "input_tokens": 4760590, "cached_input_tokens": 4672384, "cache_write_input_tokens": 0, "output_tokens": 32228, "reasoning_output_tokens": 13010, "total_tokens": 4792818 }, "thread_token_usage": { "input_tokens": 4760590, "cached_input_tokens": 4672384, "cache_write_input_tokens": 0, "output_tokens": 32228, "reasoning_output_tokens": 13010, "total_tokens": 4792818 } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-24T22:57:09.496Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:09.496Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:57:09.498Z", "callId": "call_sudv0Nw49FidCGeunQg2fbo6", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":21127,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:57:39.506Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:57:09.593Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aac1741887d285aa0d3db30aeab2", "usage": { "input_tokens": 79571, "cached_input_tokens": 77824, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 79624 }, "turn_token_usage": { "input_tokens": 4840161, "cached_input_tokens": 4750208, "cache_write_input_tokens": 0, "output_tokens": 32281, "reasoning_output_tokens": 13016, "total_tokens": 4872442 }, "thread_token_usage": { "input_tokens": 4840161, "cached_input_tokens": 4750208, "cache_write_input_tokens": 0, "output_tokens": 32281, "reasoning_output_tokens": 13016, "total_tokens": 4872442 } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-24T22:57:42.023Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:57:42.023Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:57:42.728Z", "callId": "call_9qt0c62G41H5xiNsjwpTruCq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":21127,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:57:59.348Z", "content": "Script completed\nWall time 16.6 seconds\nOutput:\n\nbase 0.050469\nd1a0.05 0.050253\nd1a0.1 0.050137\nd1a0.2 0.049738\nd1a0.3 0.049853\nd1a0.5 0.047219\nd3a0.05 0.050425\nd3a0.1 0.050135\nd3a0.2 0.050186\nd3a0.3 0.049886\nd3a0.5 0.049178\nd5a0.05 0.050551\nd5a0.1 0.050543\nd5a0.2 0.050407\nd5a0.3 0.050673\nd5a0.5 0.050252\nd14a0.05 0.050289\nd14a0.1 0.050574\nd14a0.2 0.050329\nd14a0.3 0.050238\nd14a0.5 0.04968\nglob0.03 0.050406\nglob0.05 0.050543\nglob0.075 0.050532\nglob0.1 0.050493\nglob0.15 0.050366\nd1.1g.05 0.050207\nd3.1g.05 0.050458\nd5.1g.05 0.05064\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:57:42.895Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aae3c1dc87d2aebcec7e44e61389", "usage": { "input_tokens": 79647, "cached_input_tokens": 79360, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 79700 }, "turn_token_usage": { "input_tokens": 4919808, "cached_input_tokens": 4829568, "cache_write_input_tokens": 0, "output_tokens": 32334, "reasoning_output_tokens": 13022, "total_tokens": 4952142 }, "thread_token_usage": { "input_tokens": 4919808, "cached_input_tokens": 4829568, "cache_write_input_tokens": 0, "output_tokens": 32334, "reasoning_output_tokens": 13022, "total_tokens": 4952142 } } }, { "kind": "context", "timestamp": "2026-09-24T22:57:59.346Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2505ca69-6d33-4de6-a453-75c1e00e2c49", "process_id": "21127", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy');days=[1,3,5,7,14];ys={d:t.objective_y(d,like_weight=2,pop_penalty=.1) for d in days}; glob=np.asarray(ys[1].sum(0)).ravel().astype(np.float32);glob/=glob.max()\nconfigs=[('base',{7:1},0)]\nfor d in [1,3,5,14]:\n for a in [.05,.1,.2,.3,.5]: configs.append((f'd{d}a{a}',{7:1-a,d:a},0))\nfor a in [.03,.05,.075,.1,.15]:configs.append((f'glob{a}',{7:1},a))\nconfigs += [('d1.1g.05',{7:.9,1:.1},.05),('d3.1g.05',{7:.9,3:.1},.05),('d5.1g.05',{7:.9,5:.1},.05)]\nres={n:[] for n,_,_ in configs};k=400\nfor q in range(0,len(pos),128):\n pp=pos[q:q+128];rr=np.repeat(np.arange(len(pp)),k);cc=ni[pp,:k].ravel();vv=np.maximum(ns[pp,:k].ravel(),0)**.6;w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),ys[7].shape[0]));scores={}\n for d,y in ys.items():\n sc=(w@y).toarray().astype(np.float32);sc/=np.maximum(sc.max(1,keepdims=True),1e-9);scores[d]=sc\n for name,mix,ga in configs:\n sc=sum(a*scores[d] for d,a in mix.items())+ga*glob[None,:];sc=np.where(e[pp],sc,-np.inf);top=np.argpartition(sc,-10,axis=1)[:,-10:];sv=np.take_along_axis(sc,top,1);top=np.take_along_axis(top,np.argsort(-sv,axis=1),1);res[name].extend(ndcg_at_k(it[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\nfor n in res:print(n,round(float(np.mean(res[n])),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy');days=[1,3,5,7,14];ys={d:t.objective_y(d,like_weight=2,pop_penalty=.1) for d in days}; glob=np.asarray(ys[1].sum(0)).ravel().astype(np.float32);glob/=glob.max()\nconfigs=[('base',{7:1},0)]\nfor d in [1,3,5,14]:\n for a in [.05,.1,.2,.3,.5]: configs.append((f'd{d}a{a}',{7:1-a,d:a},0))\nfor a in [.03,.05,.075,.1,.15]:configs.append((f'glob{a}',{7:1},a))\nconfigs += [('d1.1g.05',{7:.9,1:.1},.05),('d3.1g.05',{7:.9,3:.1},.05),('d5.1g.05',{7:.9,5:.1},.05)]\nres={n:[] for n,_,_ in configs};k=400\nfor q in range(0,len(pos),128):\n pp=pos[q:q+128];rr=np.repeat(np.arange(len(pp)),k);cc=ni[pp,:k].ravel();vv=np.maximum(ns[pp,:k].ravel(),0)**.6;w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),ys[7].shape[0]));scores={}\n for d,y in ys.items():\n sc=(w@y).toarray().astype(np.float32);sc/=np.maximum(sc.max(1,keepdims=True),1e-9);scores[d]=sc\n for name,mix,ga in configs:\n sc=sum(a*scores[d] for d,a in mix.items())+ga*glob[None,:];sc=np.where(e[pp],sc,-np.inf);top=np.argpartition(sc,-10,axis=1)[:,-10:];sv=np.take_along_axis(sc,top,1);top=np.take_along_axis(top,np.argsort(-sv,axis=1),1);res[name].extend(ndcg_at_k(it[top[j]].tolist(),gold[int(u[p])],10) for j,p in enumerate(pp))\nfor n in res:print(n,round(float(np.mean(res[n])),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base 0.050469\nd1a0.05 0.050253\nd1a0.1 0.050137\nd1a0.2 0.049738\nd1a0.3 0.049853\nd1a0.5 0.047219\nd3a0.05 0.050425\nd3a0.1 0.050135\nd3a0.2 0.050186\nd3a0.3 0.049886\nd3a0.5 0.049178\nd5a0.05 0.050551\nd5a0.1 0.050543\nd5a0.2 0.050407\nd5a0.3 0.050673\nd5a0.5 0.050252\nd14a0.05 0.050289\nd14a0.1 0.050574\nd14a0.2 0.050329\nd14a0.3 0.050238\nd14a0.5 0.04968\nglob0.03 0.050406\nglob0.05 0.050543\nglob0.075 0.050532\nglob0.1 0.050493\nglob0.15 0.050366\nd1.1g.05 0.050207\nd3.1g.05 0.050458\nd5.1g.05 0.05064\n", "stderr": "", "aggregated_output": "base 0.050469\nd1a0.05 0.050253\nd1a0.1 0.050137\nd1a0.2 0.049738\nd1a0.3 0.049853\nd1a0.5 0.047219\nd3a0.05 0.050425\nd3a0.1 0.050135\nd3a0.2 0.050186\nd3a0.3 0.049886\nd3a0.5 0.049178\nd5a0.05 0.050551\nd5a0.1 0.050543\nd5a0.2 0.050407\nd5a0.3 0.050673\nd5a0.5 0.050252\nd14a0.05 0.050289\nd14a0.1 0.050574\nd14a0.2 0.050329\nd14a0.3 0.050238\nd14a0.5 0.04968\nglob0.03 0.050406\nglob0.05 0.050543\nglob0.075 0.050532\nglob0.1 0.050493\nglob0.15 0.050366\nd1.1g.05 0.050207\nd3.1g.05 0.050458\nd5.1g.05 0.05064\n", "exit_code": 0, "duration": { "secs": 55, "nanos": 93301642 }, "formatted_output": "base 0.050469\nd1a0.05 0.050253\nd1a0.1 0.050137\nd1a0.2 0.049738\nd1a0.3 0.049853\nd1a0.5 0.047219\nd3a0.05 0.050425\nd3a0.1 0.050135\nd3a0.2 0.050186\nd3a0.3 0.049886\nd3a0.5 0.049178\nd5a0.05 0.050551\nd5a0.1 0.050543\nd5a0.2 0.050407\nd5a0.3 0.050673\nd5a0.5 0.050252\nd14a0.05 0.050289\nd14a0.1 0.050574\nd14a0.2 0.050329\nd14a0.3 0.050238\nd14a0.5 0.04968\nglob0.03 0.050406\nglob0.05 0.050543\nglob0.075 0.050532\nglob0.1 0.050493\nglob0.15 0.050366\nd1.1g.05 0.050207\nd3.1g.05 0.050458\nd5.1g.05 0.05064\n" } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-24T22:58:04.533Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:58:04.533Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:58:16.657Z", "callId": "call_TZKXShCrRLqyeBZnGCmwR9Dc", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nfrom scipy import sparse\\nimport tune_knn as t\\nfrom ranking_evaluation import ndcg_at_k\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy');y=t.objective_y(7,like_weight=2,pop_penalty=.1);gscore=np.asarray(t.objective_y(1,like_weight=2,pop_penalty=.1).sum(0)).ravel().astype(np.float32);gscore/=gscore.max();k=400\\nout=[]\\nfor q in range(0,len(pos),128):\\n pp=pos[q:q+128];rr=np.repeat(np.arange(len(pp)),k);cc=ni[pp,:k].ravel();vv=np.maximum(ns[pp,:k].ravel(),0)**.6;w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),y.shape[0]));base=(w@y).toarray().astype(np.float32);base/=np.maximum(base.max(1,keepdims=True),1e-9)\\n for j,p in enumerate(pp):\\n row={'uid':int(u[p])}\\n for name,sc in [('knn',base[j]),('glob',gscore),('mix',base[j]+.05*gscore)]:\\n s=np.where(e[p],sc,-np.inf);top=np.argpartition(s,-10)[-10:];top=top[np.argsort(-s[top])];row[name]=ndcg_at_k(it[top].tolist(),gold[int(u[p])],10)\\n out.append(row)\\ndf=pd.DataFrame(out);x=sparse.load_npz(t.OUT/'train_x_org.npz');cnt=np.diff(x.indptr);df['org_n']=cnt[(df.uid.to_numpy()//100).astype(int)];tu=pd.read_parquet(t.DATA/'target_users.parquet').set_index('uid');df['recent']=df.uid.map(tu.recent_positive_listens);df['hist']=df.uid.map(tu.history_positive_listens)\\nfor col in ['org_n','recent','hist']:\\n df['bin']=pd.qcut(df[col],10,duplicates='drop');print('\\\\n',col);print(df.groupby('bin',observed=True)[['knn','glob','mix',col]].mean().to_string())\\nprint('overall',df[['knn','glob','mix']].mean());df.to_parquet(t.OUT/'validation_user_scores.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:58:17.857Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"167a26\",\"wall_time_seconds\":1.00133308,\"session_id\":7349,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:58:16.800Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aaf789b887d2bb618300d640b062", "usage": { "input_tokens": 80059, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 897, "reasoning_output_tokens": 215, "total_tokens": 80956 }, "turn_token_usage": { "input_tokens": 4999867, "cached_input_tokens": 4909056, "cache_write_input_tokens": 0, "output_tokens": 33231, "reasoning_output_tokens": 13237, "total_tokens": 5033098 }, "thread_token_usage": { "input_tokens": 4999867, "cached_input_tokens": 4909056, "cache_write_input_tokens": 0, "output_tokens": 33231, "reasoning_output_tokens": 13237, "total_tokens": 5033098 } } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-24T22:58:19.896Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:58:19.896Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:58:20.599Z", "callId": "call_g28338a7rhgbaWV3xckN43My", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":7349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:58:22.687Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\n\n org_n\n knn glob mix org_n\nbin \n(-0.001, 63.0] 0.060129 0.043004 0.060600 33.849188\n(63.0, 122.0] 0.047172 0.030123 0.047136 93.202326\n(122.0, 187.0] 0.048781 0.029780 0.048017 154.496471\n(187.0, 260.8] 0.065493 0.046576 0.065280 222.785203\n(260.8, 347.0] 0.055989 0.048861 0.056778 304.508159\n(347.0, 451.0] 0.043226 0.028710 0.043678 399.406542\n(451.0, 590.4] 0.056174 0.034002 0.056593 522.182464\n(590.4, 779.6] 0.051792 0.037817 0.051166 675.753521\n(779.6, 1106.0] 0.042542 0.027432 0.042737 925.885246\n(1106.0, 6806.0] 0.033624 0.020998 0.033660 1604.321596\n\n recent\n knn glob mix recent\nbin \n(19.999, 142.0] 0.055844 0.047505 0.055941 80.635731\n(142.0, 292.0] 0.036227 0.023264 0.036244 218.375887\n(292.0, 492.6] 0.055277 0.039572 0.055087 386.569412\n(492.6, 714.8] 0.064485 0.036944 0.063757 601.363850\n(714.8, 992.0] 0.033884 0.019858 0.034282 850.303738\n(992.0, 1293.0] 0.050366 0.036808 0.051672 1139.441315\n(1293.0, 1705.4] 0.048731 0.040980 0.049910 1482.004706\n(1705.4, 2273.2] 0.059375 0.035389 0.059457 1958.126761\n(2273.2, 3313.4] 0.054584 0.036961 0.053604 2749.570423\n(3313.4, 19592.0] 0.045853 0.029827 0.045412 4888.964871\n\n hist\n knn glob mix hist\nbin \n(19.999, 250.4] 0.057552 0.042298 0.057897 127.274005\n(250.4, 557.4] 0.062867 0.042909 0.063006 393.262911\n(557.4, 979.6] 0.040701 0.029004 0.040354 759.448357\n(979.6, 1542.8] 0.045484 0.031094 0.046586 1257.086854\n(1542.8, 2252.0] 0.058525 0.038518 0.058473 1876.311475\n(2252.0, 3153.4] 0.057867 0.045029 0.058092 2703.995305\n(3153.4, 4304.4] 0.045371 0.031827 0.045464 3715.678404\n(4304.4, 5937.2] 0.042734 0.031090 0.042984 5069.809859\n(5937.2, 8880.8] 0.051030 0.027930 0.050265 7141.406103\n(8880.8, 24495.0] 0.042544 0.027526 0.042296 12521.063232\noverall knn 0.050469\nglob 0.034723\nmix 0.050543\ndtype: float64\nTraceback (most recent call last):\n File \"\", line 17, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_decorators.py\", line 333, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3124, in to_parquet\n return to_parquet(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 482, in to_parquet\n impl.write(\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 229, in write\n self.api.parquet.write_table(\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1928, in write_table\n writer.write_table(table, row_group_size=row_group_size)\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1116, in write_table\n self.writer.write_table(table, row_group_size=row_group_size)\n File \"pyarrow/_parquet.pyx\", line 2242, in pyarrow._parquet.ParquetWriter.write_table\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from dictionary>, indices=int8, ordered=1> to struct using function cast_struct\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:58:20.729Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab0a0e1887d2819dda30e6f89e9c", "usage": { "input_tokens": 81013, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 81066 }, "turn_token_usage": { "input_tokens": 5080880, "cached_input_tokens": 4988928, "cache_write_input_tokens": 0, "output_tokens": 33284, "reasoning_output_tokens": 13243, "total_tokens": 5114164 }, "thread_token_usage": { "input_tokens": 5080880, "cached_input_tokens": 4988928, "cache_write_input_tokens": 0, "output_tokens": 33284, "reasoning_output_tokens": 13243, "total_tokens": 5114164 } } }, { "kind": "context", "timestamp": "2026-09-24T22:58:22.686Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-51390b0a-68b2-4e73-9ee1-f1e687bedd66", "process_id": "7349", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy');y=t.objective_y(7,like_weight=2,pop_penalty=.1);gscore=np.asarray(t.objective_y(1,like_weight=2,pop_penalty=.1).sum(0)).ravel().astype(np.float32);gscore/=gscore.max();k=400\nout=[]\nfor q in range(0,len(pos),128):\n pp=pos[q:q+128];rr=np.repeat(np.arange(len(pp)),k);cc=ni[pp,:k].ravel();vv=np.maximum(ns[pp,:k].ravel(),0)**.6;w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),y.shape[0]));base=(w@y).toarray().astype(np.float32);base/=np.maximum(base.max(1,keepdims=True),1e-9)\n for j,p in enumerate(pp):\n row={'uid':int(u[p])}\n for name,sc in [('knn',base[j]),('glob',gscore),('mix',base[j]+.05*gscore)]:\n s=np.where(e[p],sc,-np.inf);top=np.argpartition(s,-10)[-10:];top=top[np.argsort(-s[top])];row[name]=ndcg_at_k(it[top].tolist(),gold[int(u[p])],10)\n out.append(row)\ndf=pd.DataFrame(out);x=sparse.load_npz(t.OUT/'train_x_org.npz');cnt=np.diff(x.indptr);df['org_n']=cnt[(df.uid.to_numpy()//100).astype(int)];tu=pd.read_parquet(t.DATA/'target_users.parquet').set_index('uid');df['recent']=df.uid.map(tu.recent_positive_listens);df['hist']=df.uid.map(tu.history_positive_listens)\nfor col in ['org_n','recent','hist']:\n df['bin']=pd.qcut(df[col],10,duplicates='drop');print('\\n',col);print(df.groupby('bin',observed=True)[['knn','glob','mix',col]].mean().to_string())\nprint('overall',df[['knn','glob','mix']].mean());df.to_parquet(t.OUT/'validation_user_scores.parquet',index=False)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution:/task /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nimport tune_knn as t\nfrom ranking_evaluation import ndcg_at_k\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);gold,pos=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_org_idf1.5_idx.npy');ns=np.load(t.OUT/'nbr_org_idf1.5_sim.npy');y=t.objective_y(7,like_weight=2,pop_penalty=.1);gscore=np.asarray(t.objective_y(1,like_weight=2,pop_penalty=.1).sum(0)).ravel().astype(np.float32);gscore/=gscore.max();k=400\nout=[]\nfor q in range(0,len(pos),128):\n pp=pos[q:q+128];rr=np.repeat(np.arange(len(pp)),k);cc=ni[pp,:k].ravel();vv=np.maximum(ns[pp,:k].ravel(),0)**.6;w=sparse.csr_matrix((vv,(rr,cc)),shape=(len(pp),y.shape[0]));base=(w@y).toarray().astype(np.float32);base/=np.maximum(base.max(1,keepdims=True),1e-9)\n for j,p in enumerate(pp):\n row={'uid':int(u[p])}\n for name,sc in [('knn',base[j]),('glob',gscore),('mix',base[j]+.05*gscore)]:\n s=np.where(e[p],sc,-np.inf);top=np.argpartition(s,-10)[-10:];top=top[np.argsort(-s[top])];row[name]=ndcg_at_k(it[top].tolist(),gold[int(u[p])],10)\n out.append(row)\ndf=pd.DataFrame(out);x=sparse.load_npz(t.OUT/'train_x_org.npz');cnt=np.diff(x.indptr);df['org_n']=cnt[(df.uid.to_numpy()//100).astype(int)];tu=pd.read_parquet(t.DATA/'target_users.parquet').set_index('uid');df['recent']=df.uid.map(tu.recent_positive_listens);df['hist']=df.uid.map(tu.history_positive_listens)\nfor col in ['org_n','recent','hist']:\n df['bin']=pd.qcut(df[col],10,duplicates='drop');print('\\n',col);print(df.groupby('bin',observed=True)[['knn','glob','mix',col]].mean().to_string())\nprint('overall',df[['knn','glob','mix']].mean());df.to_parquet(t.OUT/'validation_user_scores.parquet',index=False)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "\n org_n\n knn glob mix org_n\nbin \n(-0.001, 63.0] 0.060129 0.043004 0.060600 33.849188\n(63.0, 122.0] 0.047172 0.030123 0.047136 93.202326\n(122.0, 187.0] 0.048781 0.029780 0.048017 154.496471\n(187.0, 260.8] 0.065493 0.046576 0.065280 222.785203\n(260.8, 347.0] 0.055989 0.048861 0.056778 304.508159\n(347.0, 451.0] 0.043226 0.028710 0.043678 399.406542\n(451.0, 590.4] 0.056174 0.034002 0.056593 522.182464\n(590.4, 779.6] 0.051792 0.037817 0.051166 675.753521\n(779.6, 1106.0] 0.042542 0.027432 0.042737 925.885246\n(1106.0, 6806.0] 0.033624 0.020998 0.033660 1604.321596\n\n recent\n knn glob mix recent\nbin \n(19.999, 142.0] 0.055844 0.047505 0.055941 80.635731\n(142.0, 292.0] 0.036227 0.023264 0.036244 218.375887\n(292.0, 492.6] 0.055277 0.039572 0.055087 386.569412\n(492.6, 714.8] 0.064485 0.036944 0.063757 601.363850\n(714.8, 992.0] 0.033884 0.019858 0.034282 850.303738\n(992.0, 1293.0] 0.050366 0.036808 0.051672 1139.441315\n(1293.0, 1705.4] 0.048731 0.040980 0.049910 1482.004706\n(1705.4, 2273.2] 0.059375 0.035389 0.059457 1958.126761\n(2273.2, 3313.4] 0.054584 0.036961 0.053604 2749.570423\n(3313.4, 19592.0] 0.045853 0.029827 0.045412 4888.964871\n\n hist\n knn glob mix hist\nbin \n(19.999, 250.4] 0.057552 0.042298 0.057897 127.274005\n(250.4, 557.4] 0.062867 0.042909 0.063006 393.262911\n(557.4, 979.6] 0.040701 0.029004 0.040354 759.448357\n(979.6, 1542.8] 0.045484 0.031094 0.046586 1257.086854\n(1542.8, 2252.0] 0.058525 0.038518 0.058473 1876.311475\n(2252.0, 3153.4] 0.057867 0.045029 0.058092 2703.995305\n(3153.4, 4304.4] 0.045371 0.031827 0.045464 3715.678404\n(4304.4, 5937.2] 0.042734 0.031090 0.042984 5069.809859\n(5937.2, 8880.8] 0.051030 0.027930 0.050265 7141.406103\n(8880.8, 24495.0] 0.042544 0.027526 0.042296 12521.063232\noverall knn 0.050469\nglob 0.034723\nmix 0.050543\ndtype: float64\nTraceback (most recent call last):\n File \"\", line 17, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_decorators.py\", line 333, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3124, in to_parquet\n return to_parquet(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 482, in to_parquet\n impl.write(\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 229, in write\n self.api.parquet.write_table(\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1928, in write_table\n writer.write_table(table, row_group_size=row_group_size)\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1116, in write_table\n self.writer.write_table(table, row_group_size=row_group_size)\n File \"pyarrow/_parquet.pyx\", line 2242, in pyarrow._parquet.ParquetWriter.write_table\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from dictionary>, indices=int8, ordered=1> to struct using function cast_struct\n", "stderr": "", "aggregated_output": "\n org_n\n knn glob mix org_n\nbin \n(-0.001, 63.0] 0.060129 0.043004 0.060600 33.849188\n(63.0, 122.0] 0.047172 0.030123 0.047136 93.202326\n(122.0, 187.0] 0.048781 0.029780 0.048017 154.496471\n(187.0, 260.8] 0.065493 0.046576 0.065280 222.785203\n(260.8, 347.0] 0.055989 0.048861 0.056778 304.508159\n(347.0, 451.0] 0.043226 0.028710 0.043678 399.406542\n(451.0, 590.4] 0.056174 0.034002 0.056593 522.182464\n(590.4, 779.6] 0.051792 0.037817 0.051166 675.753521\n(779.6, 1106.0] 0.042542 0.027432 0.042737 925.885246\n(1106.0, 6806.0] 0.033624 0.020998 0.033660 1604.321596\n\n recent\n knn glob mix recent\nbin \n(19.999, 142.0] 0.055844 0.047505 0.055941 80.635731\n(142.0, 292.0] 0.036227 0.023264 0.036244 218.375887\n(292.0, 492.6] 0.055277 0.039572 0.055087 386.569412\n(492.6, 714.8] 0.064485 0.036944 0.063757 601.363850\n(714.8, 992.0] 0.033884 0.019858 0.034282 850.303738\n(992.0, 1293.0] 0.050366 0.036808 0.051672 1139.441315\n(1293.0, 1705.4] 0.048731 0.040980 0.049910 1482.004706\n(1705.4, 2273.2] 0.059375 0.035389 0.059457 1958.126761\n(2273.2, 3313.4] 0.054584 0.036961 0.053604 2749.570423\n(3313.4, 19592.0] 0.045853 0.029827 0.045412 4888.964871\n\n hist\n knn glob mix hist\nbin \n(19.999, 250.4] 0.057552 0.042298 0.057897 127.274005\n(250.4, 557.4] 0.062867 0.042909 0.063006 393.262911\n(557.4, 979.6] 0.040701 0.029004 0.040354 759.448357\n(979.6, 1542.8] 0.045484 0.031094 0.046586 1257.086854\n(1542.8, 2252.0] 0.058525 0.038518 0.058473 1876.311475\n(2252.0, 3153.4] 0.057867 0.045029 0.058092 2703.995305\n(3153.4, 4304.4] 0.045371 0.031827 0.045464 3715.678404\n(4304.4, 5937.2] 0.042734 0.031090 0.042984 5069.809859\n(5937.2, 8880.8] 0.051030 0.027930 0.050265 7141.406103\n(8880.8, 24495.0] 0.042544 0.027526 0.042296 12521.063232\noverall knn 0.050469\nglob 0.034723\nmix 0.050543\ndtype: float64\nTraceback (most recent call last):\n File \"\", line 17, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_decorators.py\", line 333, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3124, in to_parquet\n return to_parquet(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 482, in to_parquet\n impl.write(\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 229, in write\n self.api.parquet.write_table(\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1928, in write_table\n writer.write_table(table, row_group_size=row_group_size)\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1116, in write_table\n self.writer.write_table(table, row_group_size=row_group_size)\n File \"pyarrow/_parquet.pyx\", line 2242, in pyarrow._parquet.ParquetWriter.write_table\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from dictionary>, indices=int8, ordered=1> to struct using function cast_struct\n", "exit_code": 1, "duration": { "secs": 5, "nanos": 831049523 }, "formatted_output": "\n org_n\n knn glob mix org_n\nbin \n(-0.001, 63.0] 0.060129 0.043004 0.060600 33.849188\n(63.0, 122.0] 0.047172 0.030123 0.047136 93.202326\n(122.0, 187.0] 0.048781 0.029780 0.048017 154.496471\n(187.0, 260.8] 0.065493 0.046576 0.065280 222.785203\n(260.8, 347.0] 0.055989 0.048861 0.056778 304.508159\n(347.0, 451.0] 0.043226 0.028710 0.043678 399.406542\n(451.0, 590.4] 0.056174 0.034002 0.056593 522.182464\n(590.4, 779.6] 0.051792 0.037817 0.051166 675.753521\n(779.6, 1106.0] 0.042542 0.027432 0.042737 925.885246\n(1106.0, 6806.0] 0.033624 0.020998 0.033660 1604.321596\n\n recent\n knn glob mix recent\nbin \n(19.999, 142.0] 0.055844 0.047505 0.055941 80.635731\n(142.0, 292.0] 0.036227 0.023264 0.036244 218.375887\n(292.0, 492.6] 0.055277 0.039572 0.055087 386.569412\n(492.6, 714.8] 0.064485 0.036944 0.063757 601.363850\n(714.8, 992.0] 0.033884 0.019858 0.034282 850.303738\n(992.0, 1293.0] 0.050366 0.036808 0.051672 1139.441315\n(1293.0, 1705.4] 0.048731 0.040980 0.049910 1482.004706\n(1705.4, 2273.2] 0.059375 0.035389 0.059457 1958.126761\n(2273.2, 3313.4] 0.054584 0.036961 0.053604 2749.570423\n(3313.4, 19592.0] 0.045853 0.029827 0.045412 4888.964871\n\n hist\n knn glob mix hist\nbin \n(19.999, 250.4] 0.057552 0.042298 0.057897 127.274005\n(250.4, 557.4] 0.062867 0.042909 0.063006 393.262911\n(557.4, 979.6] 0.040701 0.029004 0.040354 759.448357\n(979.6, 1542.8] 0.045484 0.031094 0.046586 1257.086854\n(1542.8, 2252.0] 0.058525 0.038518 0.058473 1876.311475\n(2252.0, 3153.4] 0.057867 0.045029 0.058092 2703.995305\n(3153.4, 4304.4] 0.045371 0.031827 0.045464 3715.678404\n(4304.4, 5937.2] 0.042734 0.031090 0.042984 5069.809859\n(5937.2, 8880.8] 0.051030 0.027930 0.050265 7141.406103\n(8880.8, 24495.0] 0.042544 0.027526 0.042296 12521.063232\noverall knn 0.050469\nglob 0.034723\nmix 0.050543\ndtype: float64\nTraceback (most recent call last):\n File \"\", line 17, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_decorators.py\", line 333, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 3124, in to_parquet\n return to_parquet(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 482, in to_parquet\n impl.write(\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 229, in write\n self.api.parquet.write_table(\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1928, in write_table\n writer.write_table(table, row_group_size=row_group_size)\n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py\", line 1116, in write_table\n self.writer.write_table(table, row_group_size=row_group_size)\n File \"pyarrow/_parquet.pyx\", line 2242, in pyarrow._parquet.ParquetWriter.write_table\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from dictionary>, indices=int8, ordered=1> to struct using function cast_struct\n" } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-24T22:58:30.377Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:58:30.377Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:58:37.040Z", "callId": "call_xfrxuXCBFwV4mVrB2wgipEji", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np,pandas as pd\\nfrom scipy import sparse\\nimport knn_experiment as k\\nimport tune_knn as t\\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\\np=pd.read_parquet(k.DATA/'validation_preference_state.parquet');p=p[p.state=='liked'];r=(p.uid.to_numpy(dtype=np.int32)//100);c=p.item_id.to_numpy(dtype=np.int32);L=sparse.coo_matrix((np.ones(len(p),np.float32),(r,c)),shape=(10001,10000001)).tocsr()\\nbase=sparse.load_npz(k.OUT/'train_x_org.npz').astype(np.float32)\\nfor wt in [1,3,10]:\\n x=base+wt*L;x=k.tfidf_profile(x,idf_power=1.5);ni,ns=k.nearest(x,tr,f'orglike{wt}');del x;gc.collect();print('likeprof',wt,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.6),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:58:38.254Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5c71c4\",\"wall_time_seconds\":1.000998044,\"session_id\":41654,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:58:37.169Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab0edd7c87d2bbe04afc361a30c8", "usage": { "input_tokens": 82621, "cached_input_tokens": 80896, "cache_write_input_tokens": 0, "output_tokens": 741, "reasoning_output_tokens": 365, "total_tokens": 83362 }, "turn_token_usage": { "input_tokens": 5163501, "cached_input_tokens": 5069824, "cache_write_input_tokens": 0, "output_tokens": 34025, "reasoning_output_tokens": 13608, "total_tokens": 5197526 }, "thread_token_usage": { "input_tokens": 5163501, "cached_input_tokens": 5069824, "cache_write_input_tokens": 0, "output_tokens": 34025, "reasoning_output_tokens": 13608, "total_tokens": 5197526 } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-24T22:58:41.383Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:58:41.383Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:58:42.054Z", "callId": "call_J2XZolPnGCvcHxJOtZCjoL3n", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":41654,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:59:05.548Z", "content": "Script completed\nWall time 23.5 seconds\nOutput:\n\norglike1 neighbors 256/8456\norglike1 neighbors 512/8456\norglike1 neighbors 768/8456\norglike1 neighbors 1024/8456\norglike1 neighbors 1280/8456\norglike1 neighbors 1536/8456\norglike1 neighbors 1792/8456\norglike1 neighbors 2048/8456\norglike1 neighbors 2304/8456\norglike1 neighbors 2560/8456\norglike1 neighbors 2816/8456\norglike1 neighbors 3072/8456\norglike1 neighbors 3328/8456\norglike1 neighbors 3584/8456\norglike1 neighbors 3840/8456\norglike1 neighbors 4096/8456\norglike1 neighbors 4352/8456\norglike1 neighbors 4608/8456\norglike1 neighbors 4864/8456\norglike1 neighbors 5120/8456\norglike1 neighbors 5376/8456\norglike1 neighbors 5632/8456\norglike1 neighbors 5888/8456\norglike1 neighbors 6144/8456\norglike1 neighbors 6400/8456\norglike1 neighbors 6656/8456\norglike1 neighbors 6912/8456\norglike1 neighbors 7168/8456\norglike1 neighbors 7424/8456\norglike1 neighbors 7680/8456\norglike1 neighbors 7936/8456\norglike1 neighbors 8192/8456\norglike1 neighbors 8448/8456\norglike1 neighbors 8456/8456\nlikeprof 1 0.050388600752613213\norglike3 neighbors 256/8456\norglike3 neighbors 512/8456\norglike3 neighbors 768/8456\norglike3 neighbors 1024/8456\norglike3 neighbors 1280/8456\norglike3 neighbors 1536/8456\norglike3 neighbors 1792/8456\norglike3 neighbors 2048/8456\norglike3 neighbors 2304/8456\norglike3 neighbors 2560/8456\norglike3 neighbors 2816/8456\norglike3 neighbors 3072/8456\norglike3 neighbors 3328/8456\norglike3 neighbors 3584/8456\norglike3 neighbors 3840/8456\norglike3 neighbors 4096/8456\norglike3 neighbors 4352/8456\norglike3 neighbors 4608/8456\norglike3 neighbors 4864/8456\norglike3 neighbors 5120/8456\norglike3 neighbors 5376/8456\norglike3 neighbors 5632/8456\norglike3 neighbors 5888/8456\norglike3 neighbors 6144/8456\norglike3 neighbors 6400/8456\norglike3 neighbors 6656/8456\norglike3 neighbors 6912/8456\norglike3 neighbors 7168/8456\norglike3 neighbors 7424/8456\norglike3 neighbors 7680/8456\norglike3 neighbors 7936/8456\norglike3 neighbors 8192/8456\norglike3 neighbors 8448/8456\norglike3 neighbors 8456/8456\nlikeprof 3 0.05053169339935649\norglike10 neighbors 256/8456\norglike10 neighbors 512/8456\norglike10 neighbors 768/8456\norglike10 neighbors 1024/8456\norglike10 neighbors 1280/8456\norglike10 neighbors 1536/8456\norglike10 neighbors 1792/8456\norglike10 neighbors 2048/8456\norglike10 neighbors 2304/8456\norglike10 neighbors 2560/8456\norglike10 neighbors 2816/8456\norglike10 neighbors 3072/8456\norglike10 neighbors 3328/8456\norglike10 neighbors 3584/8456\norglike10 neighbors 3840/8456\norglike10 neighbors 4096/8456\norglike10 neighbors 4352/8456\norglike10 neighbors 4608/8456\norglike10 neighbors 4864/8456\norglike10 neighbors 5120/8456\norglike10 neighbors 5376/8456\norglike10 neighbors 5632/8456\norglike10 neighbors 5888/8456\norglike10 neighbors 6144/8456\norglike10 neighbors 6400/8456\norglike10 neighbors 6656/8456\norglike10 neighbors 6912/8456\norglike10 neighbors 7168/8456\norglike10 neighbors 7424/8456\norglike10 neighbors 7680/8456\norglike10 neighbors 7936/8456\norglike10 neighbors 8192/8456\norglike10 neighbors 8448/8456\norglike10 neighbors 8456/8456\nlikeprof 10 0.05160541135248133\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:58:42.196Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab1e6f9487d2ab02d046002e156b", "usage": { "input_tokens": 83421, "cached_input_tokens": 82432, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 83476 }, "turn_token_usage": { "input_tokens": 5246922, "cached_input_tokens": 5152256, "cache_write_input_tokens": 0, "output_tokens": 34080, "reasoning_output_tokens": 13616, "total_tokens": 5281002 }, "thread_token_usage": { "input_tokens": 5246922, "cached_input_tokens": 5152256, "cache_write_input_tokens": 0, "output_tokens": 34080, "reasoning_output_tokens": 13616, "total_tokens": 5281002 } } }, { "kind": "context", "timestamp": "2026-09-24T22:59:05.546Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-36e9159d-da2f-4f47-8529-ec5a815e1314", "process_id": "41654", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np,pandas as pd\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\np=pd.read_parquet(k.DATA/'validation_preference_state.parquet');p=p[p.state=='liked'];r=(p.uid.to_numpy(dtype=np.int32)//100);c=p.item_id.to_numpy(dtype=np.int32);L=sparse.coo_matrix((np.ones(len(p),np.float32),(r,c)),shape=(10001,10000001)).tocsr()\nbase=sparse.load_npz(k.OUT/'train_x_org.npz').astype(np.float32)\nfor wt in [1,3,10]:\n x=base+wt*L;x=k.tfidf_profile(x,idf_power=1.5);ni,ns=k.nearest(x,tr,f'orglike{wt}');del x;gc.collect();print('likeprof',wt,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.6),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np,pandas as pd\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1)\np=pd.read_parquet(k.DATA/'validation_preference_state.parquet');p=p[p.state=='liked'];r=(p.uid.to_numpy(dtype=np.int32)//100);c=p.item_id.to_numpy(dtype=np.int32);L=sparse.coo_matrix((np.ones(len(p),np.float32),(r,c)),shape=(10001,10000001)).tocsr()\nbase=sparse.load_npz(k.OUT/'train_x_org.npz').astype(np.float32)\nfor wt in [1,3,10]:\n x=base+wt*L;x=k.tfidf_profile(x,idf_power=1.5);ni,ns=k.nearest(x,tr,f'orglike{wt}');del x;gc.collect();print('likeprof',wt,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.6),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "orglike1 neighbors 256/8456\norglike1 neighbors 512/8456\norglike1 neighbors 768/8456\norglike1 neighbors 1024/8456\norglike1 neighbors 1280/8456\norglike1 neighbors 1536/8456\norglike1 neighbors 1792/8456\norglike1 neighbors 2048/8456\norglike1 neighbors 2304/8456\norglike1 neighbors 2560/8456\norglike1 neighbors 2816/8456\norglike1 neighbors 3072/8456\norglike1 neighbors 3328/8456\norglike1 neighbors 3584/8456\norglike1 neighbors 3840/8456\norglike1 neighbors 4096/8456\norglike1 neighbors 4352/8456\norglike1 neighbors 4608/8456\norglike1 neighbors 4864/8456\norglike1 neighbors 5120/8456\norglike1 neighbors 5376/8456\norglike1 neighbors 5632/8456\norglike1 neighbors 5888/8456\norglike1 neighbors 6144/8456\norglike1 neighbors 6400/8456\norglike1 neighbors 6656/8456\norglike1 neighbors 6912/8456\norglike1 neighbors 7168/8456\norglike1 neighbors 7424/8456\norglike1 neighbors 7680/8456\norglike1 neighbors 7936/8456\norglike1 neighbors 8192/8456\norglike1 neighbors 8448/8456\norglike1 neighbors 8456/8456\nlikeprof 1 0.050388600752613213\norglike3 neighbors 256/8456\norglike3 neighbors 512/8456\norglike3 neighbors 768/8456\norglike3 neighbors 1024/8456\norglike3 neighbors 1280/8456\norglike3 neighbors 1536/8456\norglike3 neighbors 1792/8456\norglike3 neighbors 2048/8456\norglike3 neighbors 2304/8456\norglike3 neighbors 2560/8456\norglike3 neighbors 2816/8456\norglike3 neighbors 3072/8456\norglike3 neighbors 3328/8456\norglike3 neighbors 3584/8456\norglike3 neighbors 3840/8456\norglike3 neighbors 4096/8456\norglike3 neighbors 4352/8456\norglike3 neighbors 4608/8456\norglike3 neighbors 4864/8456\norglike3 neighbors 5120/8456\norglike3 neighbors 5376/8456\norglike3 neighbors 5632/8456\norglike3 neighbors 5888/8456\norglike3 neighbors 6144/8456\norglike3 neighbors 6400/8456\norglike3 neighbors 6656/8456\norglike3 neighbors 6912/8456\norglike3 neighbors 7168/8456\norglike3 neighbors 7424/8456\norglike3 neighbors 7680/8456\norglike3 neighbors 7936/8456\norglike3 neighbors 8192/8456\norglike3 neighbors 8448/8456\norglike3 neighbors 8456/8456\nlikeprof 3 0.05053169339935649\norglike10 neighbors 256/8456\norglike10 neighbors 512/8456\norglike10 neighbors 768/8456\norglike10 neighbors 1024/8456\norglike10 neighbors 1280/8456\norglike10 neighbors 1536/8456\norglike10 neighbors 1792/8456\norglike10 neighbors 2048/8456\norglike10 neighbors 2304/8456\norglike10 neighbors 2560/8456\norglike10 neighbors 2816/8456\norglike10 neighbors 3072/8456\norglike10 neighbors 3328/8456\norglike10 neighbors 3584/8456\norglike10 neighbors 3840/8456\norglike10 neighbors 4096/8456\norglike10 neighbors 4352/8456\norglike10 neighbors 4608/8456\norglike10 neighbors 4864/8456\norglike10 neighbors 5120/8456\norglike10 neighbors 5376/8456\norglike10 neighbors 5632/8456\norglike10 neighbors 5888/8456\norglike10 neighbors 6144/8456\norglike10 neighbors 6400/8456\norglike10 neighbors 6656/8456\norglike10 neighbors 6912/8456\norglike10 neighbors 7168/8456\norglike10 neighbors 7424/8456\norglike10 neighbors 7680/8456\norglike10 neighbors 7936/8456\norglike10 neighbors 8192/8456\norglike10 neighbors 8448/8456\norglike10 neighbors 8456/8456\nlikeprof 10 0.05160541135248133\n", "stderr": "", "aggregated_output": "orglike1 neighbors 256/8456\norglike1 neighbors 512/8456\norglike1 neighbors 768/8456\norglike1 neighbors 1024/8456\norglike1 neighbors 1280/8456\norglike1 neighbors 1536/8456\norglike1 neighbors 1792/8456\norglike1 neighbors 2048/8456\norglike1 neighbors 2304/8456\norglike1 neighbors 2560/8456\norglike1 neighbors 2816/8456\norglike1 neighbors 3072/8456\norglike1 neighbors 3328/8456\norglike1 neighbors 3584/8456\norglike1 neighbors 3840/8456\norglike1 neighbors 4096/8456\norglike1 neighbors 4352/8456\norglike1 neighbors 4608/8456\norglike1 neighbors 4864/8456\norglike1 neighbors 5120/8456\norglike1 neighbors 5376/8456\norglike1 neighbors 5632/8456\norglike1 neighbors 5888/8456\norglike1 neighbors 6144/8456\norglike1 neighbors 6400/8456\norglike1 neighbors 6656/8456\norglike1 neighbors 6912/8456\norglike1 neighbors 7168/8456\norglike1 neighbors 7424/8456\norglike1 neighbors 7680/8456\norglike1 neighbors 7936/8456\norglike1 neighbors 8192/8456\norglike1 neighbors 8448/8456\norglike1 neighbors 8456/8456\nlikeprof 1 0.050388600752613213\norglike3 neighbors 256/8456\norglike3 neighbors 512/8456\norglike3 neighbors 768/8456\norglike3 neighbors 1024/8456\norglike3 neighbors 1280/8456\norglike3 neighbors 1536/8456\norglike3 neighbors 1792/8456\norglike3 neighbors 2048/8456\norglike3 neighbors 2304/8456\norglike3 neighbors 2560/8456\norglike3 neighbors 2816/8456\norglike3 neighbors 3072/8456\norglike3 neighbors 3328/8456\norglike3 neighbors 3584/8456\norglike3 neighbors 3840/8456\norglike3 neighbors 4096/8456\norglike3 neighbors 4352/8456\norglike3 neighbors 4608/8456\norglike3 neighbors 4864/8456\norglike3 neighbors 5120/8456\norglike3 neighbors 5376/8456\norglike3 neighbors 5632/8456\norglike3 neighbors 5888/8456\norglike3 neighbors 6144/8456\norglike3 neighbors 6400/8456\norglike3 neighbors 6656/8456\norglike3 neighbors 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tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport gc,numpy as np,pandas as pd\\nfrom scipy import sparse\\nimport knn_experiment as k\\nimport tune_knn as t\\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1);p=pd.read_parquet(k.DATA/'validation_preference_state.parquet');p=p[p.state=='liked'];L=sparse.coo_matrix((np.ones(len(p),np.float32),((p.uid.to_numpy(np.int32)//100),p.item_id.to_numpy(np.int32))),shape=(10001,10000001)).tocsr();base=sparse.load_npz(k.OUT/'train_x_org.npz').astype(np.float32)\\nfor wt in [30,100]:\\n x=k.tfidf_profile(base+wt*L,idf_power=1.5);ni,ns=k.nearest(x,tr,f'orglike{wt}');del x;gc.collect();print(wt,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.6),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:59:17.264Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"0036aa\",\"wall_time_seconds\":1.000250418,\"session_id\":90587,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:59:16.217Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab39ba4487d2a91b59122c3cf62b", "usage": { "input_tokens": 84654, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 385, "reasoning_output_tokens": 25, "total_tokens": 85039 }, "turn_token_usage": { "input_tokens": 5331576, "cached_input_tokens": 5235456, "cache_write_input_tokens": 0, "output_tokens": 34465, "reasoning_output_tokens": 13641, "total_tokens": 5366041 }, "thread_token_usage": { "input_tokens": 5331576, "cached_input_tokens": 5235456, "cache_write_input_tokens": 0, "output_tokens": 34465, "reasoning_output_tokens": 13641, "total_tokens": 5366041 } } }, { "kind": "tool", "timestamp": "2026-09-24T22:59:20.474Z", "callId": "call_4HP2wuhpL7gbe2GuvvOIAI3Y", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":90587,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T22:59:35.806Z", "content": "Script completed\nWall time 15.3 seconds\nOutput:\n\norglike30 neighbors 256/8456\norglike30 neighbors 512/8456\norglike30 neighbors 768/8456\norglike30 neighbors 1024/8456\norglike30 neighbors 1280/8456\norglike30 neighbors 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8456/8456\n100 0.05104273595868935\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:59:20.600Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab4578f487d2aca50239c5825068", "usage": { "input_tokens": 85096, "cached_input_tokens": 84480, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 85141 }, "turn_token_usage": { "input_tokens": 5416672, "cached_input_tokens": 5319936, "cache_write_input_tokens": 0, "output_tokens": 34510, "reasoning_output_tokens": 13641, "total_tokens": 5451182 }, "thread_token_usage": { "input_tokens": 5416672, "cached_input_tokens": 5319936, "cache_write_input_tokens": 0, "output_tokens": 34510, "reasoning_output_tokens": 13641, "total_tokens": 5451182 } } }, { "kind": "context", "timestamp": "2026-09-24T22:59:35.804Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dbf4949b-aa66-43ab-824d-7f1089b0724b", "process_id": "90587", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np,pandas as pd\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1);p=pd.read_parquet(k.DATA/'validation_preference_state.parquet');p=p[p.state=='liked'];L=sparse.coo_matrix((np.ones(len(p),np.float32),((p.uid.to_numpy(np.int32)//100),p.item_id.to_numpy(np.int32))),shape=(10001,10000001)).tocsr();base=sparse.load_npz(k.OUT/'train_x_org.npz').astype(np.float32)\nfor wt in [30,100]:\n x=k.tfidf_profile(base+wt*L,idf_power=1.5);ni,ns=k.nearest(x,tr,f'orglike{wt}');del x;gc.collect();print(wt,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.6),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport gc,numpy as np,pandas as pd\nfrom scipy import sparse\nimport knn_experiment as k\nimport tune_knn as t\nz=np.load(k.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);tr=(u//100).astype(np.int32);g,pos=t.truth_and_rows(u);y=t.objective_y(7,like_weight=2,pop_penalty=.1);p=pd.read_parquet(k.DATA/'validation_preference_state.parquet');p=p[p.state=='liked'];L=sparse.coo_matrix((np.ones(len(p),np.float32),((p.uid.to_numpy(np.int32)//100),p.item_id.to_numpy(np.int32))),shape=(10001,10000001)).tocsr();base=sparse.load_npz(k.OUT/'train_x_org.npz').astype(np.float32)\nfor wt in [30,100]:\n x=k.tfidf_profile(base+wt*L,idf_power=1.5);ni,ns=k.nearest(x,tr,f'orglike{wt}');del x;gc.collect();print(wt,t.evaluate_config(ni,ns,y,e,u,it,g,pos,400,power=.6),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "orglike30 neighbors 256/8456\norglike30 neighbors 512/8456\norglike30 neighbors 768/8456\norglike30 neighbors 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4864/8456\norglike30 neighbors 5120/8456\norglike30 neighbors 5376/8456\norglike30 neighbors 5632/8456\norglike30 neighbors 5888/8456\norglike30 neighbors 6144/8456\norglike30 neighbors 6400/8456\norglike30 neighbors 6656/8456\norglike30 neighbors 6912/8456\norglike30 neighbors 7168/8456\norglike30 neighbors 7424/8456\norglike30 neighbors 7680/8456\norglike30 neighbors 7936/8456\norglike30 neighbors 8192/8456\norglike30 neighbors 8448/8456\norglike30 neighbors 8456/8456\n30 0.05119910663705973\norglike100 neighbors 256/8456\norglike100 neighbors 512/8456\norglike100 neighbors 768/8456\norglike100 neighbors 1024/8456\norglike100 neighbors 1280/8456\norglike100 neighbors 1536/8456\norglike100 neighbors 1792/8456\norglike100 neighbors 2048/8456\norglike100 neighbors 2304/8456\norglike100 neighbors 2560/8456\norglike100 neighbors 2816/8456\norglike100 neighbors 3072/8456\norglike100 neighbors 3328/8456\norglike100 neighbors 3584/8456\norglike100 neighbors 3840/8456\norglike100 neighbors 4096/8456\norglike100 neighbors 4352/8456\norglike100 neighbors 4608/8456\norglike100 neighbors 4864/8456\norglike100 neighbors 5120/8456\norglike100 neighbors 5376/8456\norglike100 neighbors 5632/8456\norglike100 neighbors 5888/8456\norglike100 neighbors 6144/8456\norglike100 neighbors 6400/8456\norglike100 neighbors 6656/8456\norglike100 neighbors 6912/8456\norglike100 neighbors 7168/8456\norglike100 neighbors 7424/8456\norglike100 neighbors 7680/8456\norglike100 neighbors 7936/8456\norglike100 neighbors 8192/8456\norglike100 neighbors 8448/8456\norglike100 neighbors 8456/8456\n100 0.05104273595868935\n" } } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-24T22:59:44.558Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:59:44.558Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:59:49.577Z", "callId": "call_EwooOZEUUbSQnD1SLWUI5Txt", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nfrom scipy import sparse\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy')\\nfor d in [1,3,5,7,14,30]:\\n y=sparse.load_npz(t.OUT/f'train_y_pos_{d}d.npz').astype(np.float32);y.data=np.log1p(y.data);df=np.bincount(y.indices,minlength=y.shape[1]);fac=np.maximum(df,1)**-.1;y.data*=fac[y.indices]\\n print('pos',d,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.6),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T22:59:50.759Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"f4169e\",\"wall_time_seconds\":1.001832247,\"session_id\":90093,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:59:49.761Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab58048487d28142c25e455b46b9", "usage": { "input_tokens": 85928, "cached_input_tokens": 84864, "cache_write_input_tokens": 0, "output_tokens": 565, "reasoning_output_tokens": 279, "total_tokens": 86493 }, "turn_token_usage": { "input_tokens": 5502600, "cached_input_tokens": 5404800, "cache_write_input_tokens": 0, "output_tokens": 35075, "reasoning_output_tokens": 13920, "total_tokens": 5537675 }, "thread_token_usage": { "input_tokens": 5502600, "cached_input_tokens": 5404800, "cache_write_input_tokens": 0, "output_tokens": 35075, "reasoning_output_tokens": 13920, "total_tokens": 5537675 } } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-24T22:59:55.157Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T22:59:55.157Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T22:59:55.851Z", "callId": "call_9hoP8xZEFnTW5sXeoAxiChHz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":90093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T23:00:08.346Z", "content": "Script completed\nWall time 12.5 seconds\nOutput:\n\npos 1 0.04438040271762136\npos 3 0.05123961382195779\npos 5 0.052656093991723024\npos 7 0.05294446343913489\npos 14 0.047951619493501685\npos 30 0.03837429417995782\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T22:59:55.991Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab66fa6c87d298d4898110d1d230", "usage": { "input_tokens": 86551, "cached_input_tokens": 85760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 86604 }, "turn_token_usage": { "input_tokens": 5589151, "cached_input_tokens": 5490560, "cache_write_input_tokens": 0, "output_tokens": 35128, "reasoning_output_tokens": 13926, "total_tokens": 5624279 }, "thread_token_usage": { "input_tokens": 5589151, "cached_input_tokens": 5490560, "cache_write_input_tokens": 0, "output_tokens": 35128, "reasoning_output_tokens": 13926, "total_tokens": 5624279 } } }, { "kind": "context", "timestamp": "2026-09-24T23:00:08.344Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9b5f80e1-6b44-497d-ba22-ac041f0d1a9d", "process_id": "90093", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy')\nfor d in [1,3,5,7,14,30]:\n y=sparse.load_npz(t.OUT/f'train_y_pos_{d}d.npz').astype(np.float32);y.data=np.log1p(y.data);df=np.bincount(y.indices,minlength=y.shape[1]);fac=np.maximum(df,1)**-.1;y.data*=fac[y.indices]\n print('pos',d,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.6),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy')\nfor d in [1,3,5,7,14,30]:\n y=sparse.load_npz(t.OUT/f'train_y_pos_{d}d.npz').astype(np.float32);y.data=np.log1p(y.data);df=np.bincount(y.indices,minlength=y.shape[1]);fac=np.maximum(df,1)**-.1;y.data*=fac[y.indices]\n print('pos',d,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.6),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "pos 1 0.04438040271762136\npos 3 0.05123961382195779\npos 5 0.052656093991723024\npos 7 0.05294446343913489\npos 14 0.047951619493501685\npos 30 0.03837429417995782\n", "stderr": "", "aggregated_output": "pos 1 0.04438040271762136\npos 3 0.05123961382195779\npos 5 0.052656093991723024\npos 7 0.05294446343913489\npos 14 0.047951619493501685\npos 30 0.03837429417995782\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 587195030 }, "formatted_output": "pos 1 0.04438040271762136\npos 3 0.05123961382195779\npos 5 0.052656093991723024\npos 7 0.05294446343913489\npos 14 0.047951619493501685\npos 30 0.03837429417995782\n" } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-24T23:00:14.915Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:00:14.915Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:00:20.130Z", "callId": "call_NOAgIq9pTJgzN1dtRiXoJPyN", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nfrom scipy import sparse\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy');base=sparse.load_npz(t.OUT/'train_y_pos_7d.npz').astype(np.float32);base.data=np.log1p(base.data);df=np.bincount(base.indices,minlength=base.shape[1]).astype(np.float32)\\nfor pen in [-.1,0,.05,.1,.15,.2,.3,.4]:\\n y=base.copy();fac=np.maximum(df,1)**(-pen);y.data*=fac[y.indices];print('pen',pen,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.6),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T23:00:21.297Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"070833\",\"wall_time_seconds\":1.001172247,\"session_id\":70092,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:00:20.303Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab78962887d2851be7eb740911ec", "usage": { "input_tokens": 86705, "cached_input_tokens": 86400, "cache_write_input_tokens": 0, "output_tokens": 490, "reasoning_output_tokens": 191, "total_tokens": 87195 }, "turn_token_usage": { "input_tokens": 5675856, "cached_input_tokens": 5576960, "cache_write_input_tokens": 0, "output_tokens": 35618, "reasoning_output_tokens": 14117, "total_tokens": 5711474 }, "thread_token_usage": { "input_tokens": 5675856, "cached_input_tokens": 5576960, "cache_write_input_tokens": 0, "output_tokens": 35618, "reasoning_output_tokens": 14117, "total_tokens": 5711474 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:00:26.773Z", "callId": "call_poVhFhwTmpfFV3ctzuzJ2uiO", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":70092,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T23:00:41.943Z", "content": "Script completed\nWall time 15.2 seconds\nOutput:\n\npen -0.1 0.05022915662364997\npen 0 0.05160114895794032\npen 0.05 0.05257545952595658\npen 0.1 0.05294446343913489\npen 0.15 0.05407443187011977\npen 0.2 0.05449981070553227\npen 0.3 0.05435908146674422\npen 0.4 0.050303113148401586\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:00:26.913Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab8581e487d2a8b94e62ba13099a", "usage": { "input_tokens": 87251, "cached_input_tokens": 86528, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 87296 }, "turn_token_usage": { "input_tokens": 5763107, "cached_input_tokens": 5663488, "cache_write_input_tokens": 0, "output_tokens": 35663, "reasoning_output_tokens": 14117, "total_tokens": 5798770 }, "thread_token_usage": { "input_tokens": 5763107, "cached_input_tokens": 5663488, "cache_write_input_tokens": 0, "output_tokens": 35663, "reasoning_output_tokens": 14117, "total_tokens": 5798770 } } }, { "kind": "context", "timestamp": "2026-09-24T23:00:41.941Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fd45fc2b-7825-4e7a-a636-0a73dcda4c03", "process_id": "70092", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy');base=sparse.load_npz(t.OUT/'train_y_pos_7d.npz').astype(np.float32);base.data=np.log1p(base.data);df=np.bincount(base.indices,minlength=base.shape[1]).astype(np.float32)\nfor pen in [-.1,0,.05,.1,.15,.2,.3,.4]:\n y=base.copy();fac=np.maximum(df,1)**(-pen);y.data*=fac[y.indices];print('pen',pen,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.6),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy');base=sparse.load_npz(t.OUT/'train_y_pos_7d.npz').astype(np.float32);base.data=np.log1p(base.data);df=np.bincount(base.indices,minlength=base.shape[1]).astype(np.float32)\nfor pen in [-.1,0,.05,.1,.15,.2,.3,.4]:\n y=base.copy();fac=np.maximum(df,1)**(-pen);y.data*=fac[y.indices];print('pen',pen,t.evaluate_config(ni,ns,y,e,u,it,g,p,400,power=.6),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "pen -0.1 0.05022915662364997\npen 0 0.05160114895794032\npen 0.05 0.05257545952595658\npen 0.1 0.05294446343913489\npen 0.15 0.05407443187011977\npen 0.2 0.05449981070553227\npen 0.3 0.05435908146674422\npen 0.4 0.050303113148401586\n", "stderr": "", "aggregated_output": "pen -0.1 0.05022915662364997\npen 0 0.05160114895794032\npen 0.05 0.05257545952595658\npen 0.1 0.05294446343913489\npen 0.15 0.05407443187011977\npen 0.2 0.05449981070553227\npen 0.3 0.05435908146674422\npen 0.4 0.050303113148401586\n", "exit_code": 0, "duration": { "secs": 21, "nanos": 644757640 }, "formatted_output": "pen -0.1 0.05022915662364997\npen 0 0.05160114895794032\npen 0.05 0.05257545952595658\npen 0.1 0.05294446343913489\npen 0.15 0.05407443187011977\npen 0.2 0.05449981070553227\npen 0.3 0.05435908146674422\npen 0.4 0.050303113148401586\n" } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-24T23:00:50.384Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:00:50.384Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:00:55.510Z", "callId": "call_vdWXZxBZkcoFxNtGNz2Wf9dx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nfrom scipy import sparse\\nimport tune_knn as t\\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy');y=sparse.load_npz(t.OUT/'train_y_pos_7d.npz').astype(np.float32);y.data=np.log1p(y.data);df=np.bincount(y.indices,minlength=y.shape[1]);fac=np.maximum(df,1)**-.2;y.data*=fac[y.indices]\\nfor pw in [.3,.4,.5,.6,.7,.8,1]:\\n for k in [200,300,400]:print(pw,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k,power=pw),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000}); text(JSON.stringify(r));" }, "result": { "timestamp": "2026-09-24T23:00:56.676Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5a8b69\",\"wall_time_seconds\":1.000182863,\"session_id\":50840,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:00:55.587Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5ab9a245c87d2a641171101954238", "usage": { "input_tokens": 87437, "cached_input_tokens": 87040, "cache_write_input_tokens": 0, "output_tokens": 315, "reasoning_output_tokens": 23, "total_tokens": 87752 }, "turn_token_usage": { "input_tokens": 5850544, "cached_input_tokens": 5750528, "cache_write_input_tokens": 0, "output_tokens": 35978, "reasoning_output_tokens": 14140, "total_tokens": 5886522 }, "thread_token_usage": { "input_tokens": 5850544, "cached_input_tokens": 5750528, "cache_write_input_tokens": 0, "output_tokens": 35978, "reasoning_output_tokens": 14140, "total_tokens": 5886522 } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-24T23:01:04.375Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:04.375Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:05.050Z", "callId": "call_dZkWrbBothu4j5Com9pWpMKk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":50840,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T23:01:35.058Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n0.3 200 0.05096124048068468\n0.3 300 0.05334042784214741\n0.3 400 0.05407381745707422\n0.4 200 0.0510306783503808\n0.4 300 0.05343881898446143\n0.4 400 0.05448883108084471\n0.5 200 0.051151385623060916\n0.5 300 0.05352317169241952\n0.5 400 0.054241216252554666\n0.6 200 0.050924662881252886\n0.6 300 0.053552833355279356\n0.6 400 0.05449981070553227\n0.7 200 0.05092332999320765\n0.7 300 0.053239248507137626\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:05.231Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5aba9414087d29199510e7b3f0587", "usage": { "input_tokens": 87811, "cached_input_tokens": 87296, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 87864 }, "turn_token_usage": { "input_tokens": 5938355, "cached_input_tokens": 5837824, "cache_write_input_tokens": 0, "output_tokens": 36031, "reasoning_output_tokens": 14146, "total_tokens": 5974386 }, "thread_token_usage": { "input_tokens": 5938355, "cached_input_tokens": 5837824, "cache_write_input_tokens": 0, "output_tokens": 36031, "reasoning_output_tokens": 14146, "total_tokens": 5974386 } } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-24T23:01:41.586Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:41.586Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:42.475Z", "callId": "call_nLesj3Xz7IBSWtx71uKxx2a5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":50840,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);" }, "result": { "timestamp": "2026-09-24T23:01:52.161Z", "content": "Script completed\nWall time 9.7 seconds\nOutput:\n\n0.7 400 0.05457008687815929\n0.8 200 0.050911640421580834\n0.8 300 0.053112357523465846\n0.8 400 0.054604893524766274\n1 200 0.050297168055663546\n1 300 0.0527150884703791\n1 400 0.05373558045090158\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:42.610Z", "data": { "type": "token_usage_record", "thread_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "session_id": "01a0d58b-f9de-7830-99f5-a3cc9a1c7731", "root_turn_id": "01a0d58b-f9ed-7b51-b0a4-8d575f6cba55", "response_id": "resp_01cf521b01775913016ab5abcf41e887d2b9e54b71e678114b", "usage": { "input_tokens": 88097, "cached_input_tokens": 87680, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 88150 }, "turn_token_usage": { "input_tokens": 6026452, "cached_input_tokens": 5925504, "cache_write_input_tokens": 0, "output_tokens": 36084, "reasoning_output_tokens": 14152, "total_tokens": 6062536 }, "thread_token_usage": { "input_tokens": 6026452, "cached_input_tokens": 5925504, "cache_write_input_tokens": 0, "output_tokens": 36084, "reasoning_output_tokens": 14152, "total_tokens": 6062536 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:52.158Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-29b4bd16-36e6-41da-8590-4e64d90198d0", "process_id": "50840", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy');y=sparse.load_npz(t.OUT/'train_y_pos_7d.npz').astype(np.float32);y.data=np.log1p(y.data);df=np.bincount(y.indices,minlength=y.shape[1]);fac=np.maximum(df,1)**-.2;y.data*=fac[y.indices]\nfor pw in [.3,.4,.5,.6,.7,.8,1]:\n for k in [200,300,400]:print(pw,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k,power=pw),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nfrom scipy import sparse\nimport tune_knn as t\nz=np.load(t.DATA/'validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];e=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little').astype(bool);g,p=t.truth_and_rows(u);ni=np.load(t.OUT/'nbr_orglike10_idx.npy');ns=np.load(t.OUT/'nbr_orglike10_sim.npy');y=sparse.load_npz(t.OUT/'train_y_pos_7d.npz').astype(np.float32);y.data=np.log1p(y.data);df=np.bincount(y.indices,minlength=y.shape[1]);fac=np.maximum(df,1)**-.2;y.data*=fac[y.indices]\nfor pw in [.3,.4,.5,.6,.7,.8,1]:\n for k in [200,300,400]:print(pw,k,t.evaluate_config(ni,ns,y,e,u,it,g,p,k,power=pw),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0.3 200 0.05096124048068468\n0.3 300 0.05334042784214741\n0.3 400 0.05407381745707422\n0.4 200 0.0510306783503808\n0.4 300 0.05343881898446143\n0.4 400 0.05448883108084471\n0.5 200 0.051151385623060916\n0.5 300 0.05352317169241952\n0.5 400 0.054241216252554666\n0.6 200 0.050924662881252886\n0.6 300 0.053552833355279356\n0.6 400 0.05449981070553227\n0.7 200 0.05092332999320765\n0.7 300 0.053239248507137626\n0.7 400 0.05457008687815929\n0.8 200 0.050911640421580834\n0.8 300 0.053112357523465846\n0.8 400 0.054604893524766274\n1 200 0.050297168055663546\n1 300 0.0527150884703791\n1 400 0.05373558045090158\n", "stderr": "", "aggregated_output": "0.3 200 0.05096124048068468\n0.3 300 0.05334042784214741\n0.3 400 0.05407381745707422\n0.4 200 0.0510306783503808\n0.4 300 0.05343881898446143\n0.4 400 0.05448883108084471\n0.5 200 0.051151385623060916\n0.5 300 0.05352317169241952\n0.5 400 0.054241216252554666\n0.6 200 0.050924662881252886\n0.6 300 0.053552833355279356\n0.6 400 0.05449981070553227\n0.7 200 0.05092332999320765\n0.7 300 0.053239248507137626\n0.7 400 0.05457008687815929\n0.8 200 0.050911640421580834\n0.8 300 0.053112357523465846\n0.8 400 0.054604893524766274\n1 200 0.050297168055663546\n1 300 0.0527150884703791\n1 400 0.05373558045090158\n", "exit_code": 0, "duration": { "secs": 56, "nanos": 482795752 }, "formatted_output": "0.3 200 0.05096124048068468\n0.3 300 0.05334042784214741\n0.3 400 0.05407381745707422\n0.4 200 0.0510306783503808\n0.4 300 0.05343881898446143\n0.4 400 0.05448883108084471\n0.5 200 0.051151385623060916\n0.5 300 0.05352317169241952\n0.5 400 0.054241216252554666\n0.6 200 0.050924662881252886\n0.6 300 0.053552833355279356\n0.6 400 0.05449981070553227\n0.7 200 0.05092332999320765\n0.7 300 0.053239248507137626\n0.7 400 0.05457008687815929\n0.8 200 0.050911640421580834\n0.8 300 0.053112357523465846\n0.8 400 0.054604893524766274\n1 200 0.050297168055663546\n1 300 0.0527150884703791\n1 400 0.05373558045090158\n" } } } ] } ] }