{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "sol-medium-0-153-4-retriever-r04-20260924T221413Z", "taskChecksum": "0c0895d2b7173b62481a07f83efb31f63684cf378c530abc87e1ca18b8b367ea", "source": { "format": "codex-rollout", "sha256": "655934a39c38ee5d06e1f33e70805c1e78e381333232c3e94adc9da9d3887ff7", "records": 1005, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 103, "item_completed:AgentMessage": 9, "token_count": 175, "task_complete": 1 } }, "sessionId": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-25T01:03:29.259Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:03:29.259Z", "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": "2026-09-25T01:03:31.001Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:03:31.001Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-25T01:03:31.001Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-25T01:03:31.001Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-25T01:03:31.001Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:03:31.001Z", "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": "2026-09-25T01:03:31.001Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:03:31.001Z", "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": "2026-09-25T01:03:31.001Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:03:31.001Z", "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": "2026-09-25T01:03:31.001Z", "text": "\n /app\n sh\n 2026-09-25\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-25T01:03:31.003Z", "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": "2026-09-25", "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": "2026-09-25T01:03:31.004Z", "data": { "type": "turn_context", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "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": "medium", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "medium", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-25T01:03:31.028Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:03:31.028Z", "text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 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": "2026-09-25T01:03:35.527Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:35.527Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:03:36.688Z", "text": "I’ll inspect the data shapes, distributions, and evaluator, then build a scalable sampled-interaction two-tower pipeline. I’ll keep a valid submission saved early, use validation only for model selection, and refit on train+validation for the final ranking." }, { "kind": "tool", "timestamp": "2026-09-25T01:03:38.196Z", "callId": "call_FHuB762oLdu57LkN4ZHQ9bOa", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && cat /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:03:38.267Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:38.259Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e68e73d0-c21a-4f8f-a773-e1c42fc9227b", "process_id": "1805", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && cat /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,240p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "sed -n '1,220p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "cat /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6320 }, "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:03:38.266Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c863873887d299d0814a78441e5c", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 174, "reasoning_output_tokens": 22, "total_tokens": 14750 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 174, "reasoning_output_tokens": 22, "total_tokens": 14750 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 174, "reasoning_output_tokens": 22, "total_tokens": 14750 } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-25T01:03:47.803Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:47.803Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:03:49.243Z", "text": "The evaluator confirms that only the top 10 affect quality, while the file still needs 100 eligible items per user. I’m profiling event volume, eligibility density, and label/catalog overlap now; that will determine whether sequence-based contrastive training or a lighter popularity-personalization blend is the best CPU tradeoff." }, { "kind": "tool", "timestamp": "2026-09-25T01:03:53.493Z", "callId": "call_HTR918ByPnDGb7VTWoNtUrcY", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n m=pq.ParquetFile(p).metadata\\n print(p.name, m.num_rows, m.num_row_groups)\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\\nfor stage in ['validation','test']:\\n z=np.load(D/f'{stage}_eligible_candidates.npz')\\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:03:53.547Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:53.539Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c86a946887d2bf4e42323845dd97", "usage": { "input_tokens": 19026, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 718, "reasoning_output_tokens": 339, "total_tokens": 19744 }, "turn_token_usage": { "input_tokens": 33602, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 892, "reasoning_output_tokens": 361, "total_tokens": 34494 }, "thread_token_usage": { "input_tokens": 33602, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 892, "reasoning_output_tokens": 361, "total_tokens": 34494 } } }, { "kind": "context", "timestamp": "2026-09-25T01:03:53.543Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d9638bdb-050d-48f6-b38b-9b53cc937997", "process_id": "54267", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, m.num_rows, m.num_row_groups)\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, m.num_rows, m.num_row_groups)\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\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": 1620 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-25T01:03:56.434Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:56.434Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:03:57.750Z", "callId": "call_gyrRiFOeLEcsR2LkUYEY62zy", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"which python; python --version; ls -la /app; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:03:57.806Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:59 solution\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:03:57.801Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c87a177487d28e094c63a327df46", "usage": { "input_tokens": 19800, "cached_input_tokens": 18816, "cache_write_input_tokens": 0, "output_tokens": 96, "reasoning_output_tokens": 14, "total_tokens": 19896 }, "turn_token_usage": { "input_tokens": 53402, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 988, "reasoning_output_tokens": 375, "total_tokens": 54390 }, "thread_token_usage": { "input_tokens": 53402, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 988, "reasoning_output_tokens": 375, "total_tokens": 54390 } } }, { "kind": "context", "timestamp": "2026-09-25T01:03:57.803Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9f5414ed-a7b3-4194-9066-fd5fdd0379ab", "process_id": "91771", "command": [ "/bin/sh", "-lc", "which python; python --version; ls -la /app; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; python --version; ls -la /app; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:59 solution\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:59 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3330 }, "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:59 solution\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-25T01:04:00.395Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:00.395Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:01.758Z", "callId": "call_wXdfO2RMijYwWoIXjTnUJjnL", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"python -m pip list | head -30; find /opt /venv /tmp -maxdepth 4 -type f -name python 2>/dev/null | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:02.491Z", "content": "Script completed\nWall time 0.7 seconds\nOutput:\n\nPackage Version\n------- -------\npip 25.0.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:01.824Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c87e1ab887d2b0794173cdda7829", "usage": { "input_tokens": 20002, "cached_input_tokens": 19584, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 12, "total_tokens": 20099 }, "turn_token_usage": { "input_tokens": 73404, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 1085, "reasoning_output_tokens": 387, "total_tokens": 74489 }, "thread_token_usage": { "input_tokens": 73404, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 1085, "reasoning_output_tokens": 387, "total_tokens": 74489 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:02.487Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab36d694-48c9-481b-bc97-f892263b5726", "process_id": "49034", "command": [ "/bin/sh", "-lc", "python -m pip list | head -30; find /opt /venv /tmp -maxdepth 4 -type f -name python 2>/dev/null | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -m pip list | head -30; find /opt /venv /tmp -maxdepth 4 -type f -name python 2>/dev/null | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 566218734 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-25T01:04:05.996Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:05.996Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:08.511Z", "callId": "call_B3a5XJzNMOYUqoA6QAPMVfgl", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"find /task -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) -o -type l -name python 2>/dev/null; ls -la /task | head -30; env | sort | rg -i 'venv|python|path'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:08.598Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\n-r--r--r-- 1 root root 0 Sep 25 00:45 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:46 data\n-r--r--r-- 1 root root 5774 Sep 25 00:45 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:45 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:45 requirements.txt\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\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:08.553Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9ca71420-8367-4a85-b6e8-f7069dcee978", "process_id": "39682", "command": [ "/bin/sh", "-lc", "find /task -maxdepth 4 -type f \\( -name python -o -name python3 \\) -o -type l -name python 2>/dev/null; ls -la /task | head -30; env | sort | rg -i 'venv|python|path'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find /task -maxdepth 4 -type f \\( -name python -o -name python3 \\) -o -type l -name python 2>/dev/null; ls -la /task | head -30; env | sort | rg -i 'venv|python|path'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\n-r--r--r-- 1 root root 0 Sep 25 00:45 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:46 data\n-r--r--r-- 1 root root 5774 Sep 25 00:45 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:45 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:45 requirements.txt\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\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n", "stderr": "", "aggregated_output": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\n-r--r--r-- 1 root root 0 Sep 25 00:45 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:46 data\n-r--r--r-- 1 root root 5774 Sep 25 00:45 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:45 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:45 requirements.txt\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\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 1740 }, "formatted_output": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:59 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:03 ..\n-r--r--r-- 1 root root 0 Sep 25 00:45 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:46 data\n-r--r--r-- 1 root root 5774 Sep 25 00:45 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:45 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:45 requirements.txt\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\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:08.597Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c882e06c87d2a20726a6e641d7a6", "usage": { "input_tokens": 20136, "cached_input_tokens": 19840, "cache_write_input_tokens": 0, "output_tokens": 126, "reasoning_output_tokens": 16, "total_tokens": 20262 }, "turn_token_usage": { "input_tokens": 93540, "cached_input_tokens": 80512, "cache_write_input_tokens": 0, "output_tokens": 1211, "reasoning_output_tokens": 403, "total_tokens": 94751 }, "thread_token_usage": { "input_tokens": 93540, "cached_input_tokens": 80512, "cache_write_input_tokens": 0, "output_tokens": 1211, "reasoning_output_tokens": 403, "total_tokens": 94751 } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-25T01:04:11.663Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:11.663Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:13.311Z", "callId": "call_Qa61cQHq4VN7K4zaShBX6PNo", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"find / -type d \\\\( -name 'torch' -o -name 'pyarrow' \\\\) 2>/dev/null | head -20; find / -type f -name 'libtorch_cpu.so' 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:14.117Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:13.379Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c88908b887d282e0df51b43250ff", "usage": { "input_tokens": 20601, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 125, "reasoning_output_tokens": 26, "total_tokens": 20726 }, "turn_token_usage": { "input_tokens": 114141, "cached_input_tokens": 100480, "cache_write_input_tokens": 0, "output_tokens": 1336, "reasoning_output_tokens": 429, "total_tokens": 115477 }, "thread_token_usage": { "input_tokens": 114141, "cached_input_tokens": 100480, "cache_write_input_tokens": 0, "output_tokens": 1336, "reasoning_output_tokens": 429, "total_tokens": 115477 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:14.113Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ee4d07f5-b73d-45d5-9377-d1c18208a0e8", "process_id": "31056", "command": [ "/bin/sh", "-lc", "find / -type d \\( -name 'torch' -o -name 'pyarrow' \\) 2>/dev/null | head -20; find / -type f -name 'libtorch_cpu.so' 2>/dev/null | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type d \\( -name 'torch' -o -name 'pyarrow' \\) 2>/dev/null | head -20; find / -type f -name 'libtorch_cpu.so' 2>/dev/null | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 638083323 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-25T01:04:16.072Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:16.072Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:21.617Z", "callId": "call_9syEsfzabggFhOS5py2Ez1Kc", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n m=pq.ParquetFile(p).metadata\\n print(p.name, m.num_rows, m.num_row_groups)\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\\nfor stage in ['validation','test']:\\n z=np.load(D/f'{stage}_eligible_candidates.npz')\\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:24.051Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\ncandidate_catalog.parquet 30000 1\ntarget_users.parquet 8456 1\ntest_preference_state.parquet 484776 1\ntrain_dislikes.parquet 101075 1\ntrain_likes.parquet 814831 1\ntrain_listens.parquet 43484159 42\ntrain_undislikes.parquet 19960 1\ntrain_unlikes.parquet 292665 1\nval_dislikes.parquet 3563 1\nval_likes.parquet 37795 1\nval_listens.parquet 1501697 2\nval_undislikes.parquet 606 1\nval_unlikes.parquet 9346 1\nvalidation_preference_state.parquet 469124 1\ntargets 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 uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog 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 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24657. 28038.5 29242. 29852. 30000. ] density 0.9689102097130243\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24552. 28001. 29220. 29835. 30000.] density 0.9680439293598234\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:21.656Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c88e500c87d29f048c1b57d53bea", "usage": { "input_tokens": 20904, "cached_input_tokens": 20480, "cache_write_input_tokens": 0, "output_tokens": 330, "reasoning_output_tokens": 14, "total_tokens": 21234 }, "turn_token_usage": { "input_tokens": 135045, "cached_input_tokens": 120960, "cache_write_input_tokens": 0, "output_tokens": 1666, "reasoning_output_tokens": 443, "total_tokens": 136711 }, "thread_token_usage": { "input_tokens": 135045, "cached_input_tokens": 120960, "cache_write_input_tokens": 0, "output_tokens": 1666, "reasoning_output_tokens": 443, "total_tokens": 136711 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:24.047Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c2f53801-1cfb-40b9-9bde-6e937205f15c", "process_id": "1072", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, m.num_rows, m.num_row_groups)\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, m.num_rows, m.num_row_groups)\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet 30000 1\ntarget_users.parquet 8456 1\ntest_preference_state.parquet 484776 1\ntrain_dislikes.parquet 101075 1\ntrain_likes.parquet 814831 1\ntrain_listens.parquet 43484159 42\ntrain_undislikes.parquet 19960 1\ntrain_unlikes.parquet 292665 1\nval_dislikes.parquet 3563 1\nval_likes.parquet 37795 1\nval_listens.parquet 1501697 2\nval_undislikes.parquet 606 1\nval_unlikes.parquet 9346 1\nvalidation_preference_state.parquet 469124 1\ntargets 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 uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog 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 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24657. 28038.5 29242. 29852. 30000. ] density 0.9689102097130243\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24552. 28001. 29220. 29835. 30000.] density 0.9680439293598234\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet 30000 1\ntarget_users.parquet 8456 1\ntest_preference_state.parquet 484776 1\ntrain_dislikes.parquet 101075 1\ntrain_likes.parquet 814831 1\ntrain_listens.parquet 43484159 42\ntrain_undislikes.parquet 19960 1\ntrain_unlikes.parquet 292665 1\nval_dislikes.parquet 3563 1\nval_likes.parquet 37795 1\nval_listens.parquet 1501697 2\nval_undislikes.parquet 606 1\nval_unlikes.parquet 9346 1\nvalidation_preference_state.parquet 469124 1\ntargets 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 uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog 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 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24657. 28038.5 29242. 29852. 30000. ] density 0.9689102097130243\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24552. 28001. 29220. 29835. 30000.] density 0.9680439293598234\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 266664847 }, "formatted_output": "candidate_catalog.parquet 30000 1\ntarget_users.parquet 8456 1\ntest_preference_state.parquet 484776 1\ntrain_dislikes.parquet 101075 1\ntrain_likes.parquet 814831 1\ntrain_listens.parquet 43484159 42\ntrain_undislikes.parquet 19960 1\ntrain_unlikes.parquet 292665 1\nval_dislikes.parquet 3563 1\nval_likes.parquet 37795 1\nval_listens.parquet 1501697 2\nval_undislikes.parquet 606 1\nval_unlikes.parquet 9346 1\nvalidation_preference_state.parquet 469124 1\ntargets 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 uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog 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 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24657. 28038.5 29242. 29852. 30000. ] density 0.9689102097130243\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24552. 28001. 29220. 29835. 30000.] density 0.9680439293598234\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-25T01:04:29.127Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:29.127Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:34.240Z", "callId": "call_QtSf23mvDUiyc7sWlG9FAzIz", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+\\n+def rank(stage: str, path: Path) -> None:\\n+ cat = pd.read_parquet(D/'candidate_catalog.parquet')\\n+ with np.load(D/f'{stage}_eligible_candidates.npz') as z:\\n+ uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\\n+ rows=[]\\n+ for r,uid in enumerate(uids):\\n+ ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n+ top=items[ok][:100]\\n+ rows.extend((int(uid),int(it),j+1) for j,it in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\\n+\\n+if __name__=='__main__':\\n+ rank('validation',OUT/'validation_pop.parquet')\\n+ rank('test',OUT/'submission.parquet')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:04:34.348Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:34.249Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-aa1f2120-d95f-4918-94ad-c6c91a55381f", "changes": { "/app/solution/baseline.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef rank(stage: str, path: Path) -> None:\n cat = pd.read_parquet(D/'candidate_catalog.parquet')\n with np.load(D/f'{stage}_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\n rows=[]\n for r,uid in enumerate(uids):\n ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n top=items[ok][:100]\n rows.extend((int(uid),int(it),j+1) for j,it in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n\nif __name__=='__main__':\n rank('validation',OUT/'validation_pop.parquet')\n rank('test',OUT/'submission.parquet')\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:34.347Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8983f5887d2b8ca8e7b462dc539", "usage": { "input_tokens": 21855, "cached_input_tokens": 20736, "cache_write_input_tokens": 0, "output_tokens": 447, "reasoning_output_tokens": 138, "total_tokens": 22302 }, "turn_token_usage": { "input_tokens": 156900, "cached_input_tokens": 141696, "cache_write_input_tokens": 0, "output_tokens": 2113, "reasoning_output_tokens": 581, "total_tokens": 159013 }, "thread_token_usage": { "input_tokens": 156900, "cached_input_tokens": 141696, "cache_write_input_tokens": 0, "output_tokens": 2113, "reasoning_output_tokens": 581, "total_tokens": 159013 } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-25T01:04:36.320Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:36.320Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:37.608Z", "callId": "call_1E82Weor4mDZQDiKQcgBAmwI", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:44.662Z", "content": "Script completed\nWall time 7.1 seconds\nOutput:\n\n{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:37.661Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8a2b94c87d29937ef2f5d4469c5", "usage": { "input_tokens": 22326, "cached_input_tokens": 21632, "cache_write_input_tokens": 0, "output_tokens": 94, "reasoning_output_tokens": 13, "total_tokens": 22420 }, "turn_token_usage": { "input_tokens": 179226, "cached_input_tokens": 163328, "cache_write_input_tokens": 0, "output_tokens": 2207, "reasoning_output_tokens": 594, "total_tokens": 181433 }, "thread_token_usage": { "input_tokens": 179226, "cached_input_tokens": 163328, "cache_write_input_tokens": 0, "output_tokens": 2207, "reasoning_output_tokens": 594, "total_tokens": 181433 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:44.653Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-01046d41-9186-433c-8ce6-e64652dc1d28", "process_id": "54113", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 882591103 }, "formatted_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-25T01:04:48.111Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:48.111Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:04:56.782Z", "callId": "call_MVq4rjM8sSxRHfFJ0IOwxFTm", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,time\\nfrom pathlib import Path\\nD=Path('/task/data'); tar=pd.read_parquet(D/'target_users.parquet').uid\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); mp=dict(zip(cat.item_id,cat.popularity_rank))\\nls=pd.read_parquet(D/'val_listens.parquet'); lk=pd.read_parquet(D/'val_likes.parquet')\\nrel=pd.concat([ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[lk.is_organic==1][['uid','item_id']].assign(g=3)])\\nrel=rel[rel.uid.isin(set(tar)) & rel.item_id.isin(set(cat.item_id))].sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nprint('rel',len(rel),'users',rel.uid.nunique(),'grade',rel.g.value_counts().to_dict())\\nrr=rel.item_id.map(mp);print('poprank q',rr.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict(),'top10', (rr<=10).mean(),'top100',(rr<=100).mean(),'top1k',(rr<=1000).mean())\\nfor st in ['validation']:\\n pref=pd.read_parquet(D/f'{st}_preference_state.parquet')\\n x=rel.merge(pref,on=['uid','item_id'],how='left')\\n print(x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\\nprint('read target train')\\nt=time.time(); tr=pd.read_parquet(D/'train_listens.parquet',filters=[('uid','in',tar.tolist())]);print(len(tr),tr.memory_usage(deep=True).sum()/1e6,time.time()-t,tr.timestamp.min(),tr.timestamp.max())\\nprint(tr.head(),tr.dtypes)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:04:59.489Z", "content": "Script completed\nWall time 2.7 seconds\nOutput:\n\nrel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN 119421\nliked 55992\ndisliked 436\nName: count, dtype: int64 state disliked liked NaN\ng \n1 420 55320 111580\n3 16 672 7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130 uid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ntrack_length_seconds uint32\ndtype: object\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:56.850Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8ace31487d29f578ee6084294f4", "usage": { "input_tokens": 22487, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 534, "reasoning_output_tokens": 45, "total_tokens": 23021 }, "turn_token_usage": { "input_tokens": 201713, "cached_input_tokens": 185472, "cache_write_input_tokens": 0, "output_tokens": 2741, "reasoning_output_tokens": 639, "total_tokens": 204454 }, "thread_token_usage": { "input_tokens": 201713, "cached_input_tokens": 185472, "cache_write_input_tokens": 0, "output_tokens": 2741, "reasoning_output_tokens": 639, "total_tokens": 204454 } } }, { "kind": "context", "timestamp": "2026-09-25T01:04:59.474Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-37ba12c9-9a1e-4d29-81e0-a9e8a270c662", "process_id": "63639", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); tar=pd.read_parquet(D/'target_users.parquet').uid\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); mp=dict(zip(cat.item_id,cat.popularity_rank))\nls=pd.read_parquet(D/'val_listens.parquet'); lk=pd.read_parquet(D/'val_likes.parquet')\nrel=pd.concat([ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[lk.is_organic==1][['uid','item_id']].assign(g=3)])\nrel=rel[rel.uid.isin(set(tar)) & rel.item_id.isin(set(cat.item_id))].sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('rel',len(rel),'users',rel.uid.nunique(),'grade',rel.g.value_counts().to_dict())\nrr=rel.item_id.map(mp);print('poprank q',rr.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict(),'top10', (rr<=10).mean(),'top100',(rr<=100).mean(),'top1k',(rr<=1000).mean())\nfor st in ['validation']:\n pref=pd.read_parquet(D/f'{st}_preference_state.parquet')\n x=rel.merge(pref,on=['uid','item_id'],how='left')\n print(x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\nprint('read target train')\nt=time.time(); tr=pd.read_parquet(D/'train_listens.parquet',filters=[('uid','in',tar.tolist())]);print(len(tr),tr.memory_usage(deep=True).sum()/1e6,time.time()-t,tr.timestamp.min(),tr.timestamp.max())\nprint(tr.head(),tr.dtypes)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); tar=pd.read_parquet(D/'target_users.parquet').uid\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); mp=dict(zip(cat.item_id,cat.popularity_rank))\nls=pd.read_parquet(D/'val_listens.parquet'); lk=pd.read_parquet(D/'val_likes.parquet')\nrel=pd.concat([ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[lk.is_organic==1][['uid','item_id']].assign(g=3)])\nrel=rel[rel.uid.isin(set(tar)) & rel.item_id.isin(set(cat.item_id))].sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('rel',len(rel),'users',rel.uid.nunique(),'grade',rel.g.value_counts().to_dict())\nrr=rel.item_id.map(mp);print('poprank q',rr.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict(),'top10', (rr<=10).mean(),'top100',(rr<=100).mean(),'top1k',(rr<=1000).mean())\nfor st in ['validation']:\n pref=pd.read_parquet(D/f'{st}_preference_state.parquet')\n x=rel.merge(pref,on=['uid','item_id'],how='left')\n print(x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\nprint('read target train')\nt=time.time(); tr=pd.read_parquet(D/'train_listens.parquet',filters=[('uid','in',tar.tolist())]);print(len(tr),tr.memory_usage(deep=True).sum()/1e6,time.time()-t,tr.timestamp.min(),tr.timestamp.max())\nprint(tr.head(),tr.dtypes)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "rel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN 119421\nliked 55992\ndisliked 436\nName: count, dtype: int64 state disliked liked NaN\ng \n1 420 55320 111580\n3 16 672 7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130 uid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ntrack_length_seconds uint32\ndtype: object\n", "stderr": "", "aggregated_output": "rel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN 119421\nliked 55992\ndisliked 436\nName: count, dtype: int64 state disliked liked NaN\ng \n1 420 55320 111580\n3 16 672 7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130 uid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ntrack_length_seconds uint32\ndtype: object\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 527894144 }, "formatted_output": "rel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN 119421\nliked 55992\ndisliked 436\nName: count, dtype: int64 state disliked liked NaN\ng \n1 420 55320 111580\n3 16 672 7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130 uid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ntrack_length_seconds uint32\ndtype: object\n" } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-25T01:05:12.496Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:12.496Z", "text": "" } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-25T01:05:14.283Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:14.283Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:05:15.662Z", "text": "Two useful signals emerged: the catalog covers a broad popularity tail, and roughly one-third of future strong-listen labels are already in the cutoff’s active-like state. I’m benchmarking cutoff-correct history/explicit-state rankers first; these give a strong target for the neural towers and reveal how much value comes from repeat affinity versus collaborative generalization." }, { "kind": "tool", "timestamp": "2026-09-25T01:05:34.605Z", "callId": "call_RyUCCrxzwRtSqpiCd8Uq8Rap", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_rank.py\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D=Path('/task/data'); O=Path('/app/solution'); CUTOFF=24786800\\n+\\n+def main():\\n+ cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+ items=cat.item_id.to_numpy(np.int64); itemset=set(map(int,items))\\n+ pop={int(x): i for i,x in enumerate(items)}\\n+ targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+ tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ tr=tr[(tr.played_ratio_pct>=50)&tr.item_id.isin(itemset)].copy()\\n+ age=(CUTOFF-tr.timestamp.to_numpy())/86400.\\n+ tr['w7']=np.exp(-age/7)*(1+1.5*tr.is_organic.to_numpy())\\n+ tr['w30']=np.exp(-age/30)*(1+1.5*tr.is_organic.to_numpy())\\n+ tr['w90']=np.exp(-age/90)*(1+1.5*tr.is_organic.to_numpy())\\n+ tr['org']=tr.is_organic.astype(np.float32)\\n+ tr['strongorg']=((tr.is_organic==1)&(tr.played_ratio_pct>=80)).astype(np.float32)\\n+ ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w7=('w7','sum'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\\n+ del tr\\n+ pref=pd.read_parquet(D/'validation_preference_state.parquet')\\n+ liked=set(zip(pref.loc[pref.state=='liked','uid'].astype(int),pref.loc[pref.state=='liked','item_id'].astype(int)))\\n+ ag['liked']=[(int(u),int(i)) in liked for u,i in ag[['uid','item_id']].itertuples(index=False,name=None)]\\n+ # Also include liked catalog items without qualifying listens.\\n+ lp=pref[(pref.state=='liked')&pref.item_id.isin(itemset)][['uid','item_id']].copy()\\n+ ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\\n+ ag['popidx']=ag.item_id.map(pop)\\n+ methods={\\n+ 'count':'np.log1p(n)+.5*np.log1p(org)+2*liked',\\n+ 'recent':'w30+.2*w90+2*liked',\\n+ 'liked':'10*liked+w30+.2*w90',\\n+ 'organic':'np.log1p(strong)+.2*np.log1p(n)+3*liked',\\n+ 'last':'last/1e7+3*liked',\\n+ }\\n+ z=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; assert np.array_equal(items,z['item_ids'])\\n+ groups={int(u):g for u,g in ag.groupby('uid',sort=False)}\\n+ for name,expr in methods.items():\\n+ rows=[]\\n+ for r,u in enumerate(targets):\\n+ ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n+ g=groups.get(int(u))\\n+ chosen=[]\\n+ if g is not None:\\n+ n=g.n.to_numpy(); org=g.org.to_numpy(); strong=g.strong.to_numpy(); last=g['last'].to_numpy(); w30=g.w30.to_numpy(); w90=g.w90.to_numpy(); likedv=g.liked.to_numpy(); liked=likedv\\n+ score=eval(expr)\\n+ order=np.lexsort((g.popidx.to_numpy(),-score))\\n+ for j in order:\\n+ ii=int(g.item_id.iloc[j]); pi=pop[ii]\\n+ if ok[pi] and score[j]>0: chosen.append(ii)\\n+ if len(chosen)>=100: break\\n+ seen=set(chosen)\\n+ if len(chosen)<100:\\n+ for ii in items[ok]:\\n+ ii=int(ii)\\n+ if ii not in seen: chosen.append(ii)\\n+ if len(chosen)==100: break\\n+ rows.extend((int(u),ii,k+1) for k,ii in enumerate(chosen))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_{name}.parquet',index=False)\\n+\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:05:34.654Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:34.613Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-335c6112-f20f-4760-9471-ab005b7d0947", "changes": { "/app/solution/explore_rank.py": { "type": "add", "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD=Path('/task/data'); O=Path('/app/solution'); CUTOFF=24786800\n\ndef main():\n cat=pd.read_parquet(D/'candidate_catalog.parquet')\n items=cat.item_id.to_numpy(np.int64); itemset=set(map(int,items))\n pop={int(x): i for i,x in enumerate(items)}\n targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n tr=tr[(tr.played_ratio_pct>=50)&tr.item_id.isin(itemset)].copy()\n age=(CUTOFF-tr.timestamp.to_numpy())/86400.\n tr['w7']=np.exp(-age/7)*(1+1.5*tr.is_organic.to_numpy())\n tr['w30']=np.exp(-age/30)*(1+1.5*tr.is_organic.to_numpy())\n tr['w90']=np.exp(-age/90)*(1+1.5*tr.is_organic.to_numpy())\n tr['org']=tr.is_organic.astype(np.float32)\n tr['strongorg']=((tr.is_organic==1)&(tr.played_ratio_pct>=80)).astype(np.float32)\n ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w7=('w7','sum'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\n del tr\n pref=pd.read_parquet(D/'validation_preference_state.parquet')\n liked=set(zip(pref.loc[pref.state=='liked','uid'].astype(int),pref.loc[pref.state=='liked','item_id'].astype(int)))\n ag['liked']=[(int(u),int(i)) in liked for u,i in ag[['uid','item_id']].itertuples(index=False,name=None)]\n # Also include liked catalog items without qualifying listens.\n lp=pref[(pref.state=='liked')&pref.item_id.isin(itemset)][['uid','item_id']].copy()\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n ag['popidx']=ag.item_id.map(pop)\n methods={\n 'count':'np.log1p(n)+.5*np.log1p(org)+2*liked',\n 'recent':'w30+.2*w90+2*liked',\n 'liked':'10*liked+w30+.2*w90',\n 'organic':'np.log1p(strong)+.2*np.log1p(n)+3*liked',\n 'last':'last/1e7+3*liked',\n }\n z=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; assert np.array_equal(items,z['item_ids'])\n groups={int(u):g for u,g in ag.groupby('uid',sort=False)}\n for name,expr in methods.items():\n rows=[]\n for r,u in enumerate(targets):\n ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n g=groups.get(int(u))\n chosen=[]\n if g is not None:\n n=g.n.to_numpy(); org=g.org.to_numpy(); strong=g.strong.to_numpy(); last=g['last'].to_numpy(); w30=g.w30.to_numpy(); w90=g.w90.to_numpy(); likedv=g.liked.to_numpy(); liked=likedv\n score=eval(expr)\n order=np.lexsort((g.popidx.to_numpy(),-score))\n for j in order:\n ii=int(g.item_id.iloc[j]); pi=pop[ii]\n if ok[pi] and score[j]>0: chosen.append(ii)\n if len(chosen)>=100: break\n seen=set(chosen)\n if len(chosen)<100:\n for ii in items[ok]:\n ii=int(ii)\n if ii not in seen: chosen.append(ii)\n if len(chosen)==100: break\n rows.extend((int(u),ii,k+1) for k,ii in enumerate(chosen))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_{name}.parquet',index=False)\n\nif __name__=='__main__': main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/explore_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:05:34.653Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8bbaaac87d28c232555ab9110de", "usage": { "input_tokens": 23461, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 1849, "reasoning_output_tokens": 594, "total_tokens": 25310 }, "turn_token_usage": { "input_tokens": 225174, "cached_input_tokens": 207744, "cache_write_input_tokens": 0, "output_tokens": 4590, "reasoning_output_tokens": 1233, "total_tokens": 229764 }, "thread_token_usage": { "input_tokens": 225174, "cached_input_tokens": 207744, "cache_write_input_tokens": 0, "output_tokens": 4590, "reasoning_output_tokens": 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tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/explore_rank.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:05:41.670Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=83033", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:40.551Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8e1a59887d287f98f242910fcad", "usage": { "input_tokens": 25460, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 9, "total_tokens": 25544 }, "turn_token_usage": { "input_tokens": 275968, "cached_input_tokens": 256256, "cache_write_input_tokens": 0, "output_tokens": 4763, "reasoning_output_tokens": 1250, "total_tokens": 280731 }, "thread_token_usage": { "input_tokens": 275968, "cached_input_tokens": 256256, "cache_write_input_tokens": 0, "output_tokens": 4763, "reasoning_output_tokens": 1250, "total_tokens": 280731 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-25T01:05:42.730Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:42.730Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:43.434Z", "callId": "call_4GNN371jJ1hy8nRNYVaE18Ln", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:83033,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:05:48.443Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:43.483Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8e5d94487d2af1f64c02623d269", "usage": { "input_tokens": 25572, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 25626 }, "turn_token_usage": { "input_tokens": 301540, "cached_input_tokens": 281600, "cache_write_input_tokens": 0, "output_tokens": 4817, "reasoning_output_tokens": 1257, "total_tokens": 306357 }, "thread_token_usage": { "input_tokens": 301540, "cached_input_tokens": 281600, "cache_write_input_tokens": 0, "output_tokens": 4817, "reasoning_output_tokens": 1257, "total_tokens": 306357 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-25T01:05:50.086Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:50.086Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:51.505Z", "callId": "call_AWM2uhHbc2Yi2viOR2TT1zgH", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_{count,recent,liked,organic,last}.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:05:53.631Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:51.556Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8ecaa3087d2a2fea8a6628652fd", "usage": { "input_tokens": 25649, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 17, "total_tokens": 25752 }, "turn_token_usage": { "input_tokens": 327189, "cached_input_tokens": 306944, "cache_write_input_tokens": 0, "output_tokens": 4920, "reasoning_output_tokens": 1274, "total_tokens": 332109 }, "thread_token_usage": { "input_tokens": 327189, "cached_input_tokens": 306944, "cache_write_input_tokens": 0, "output_tokens": 4920, "reasoning_output_tokens": 1274, "total_tokens": 332109 } } }, { "kind": "context", "timestamp": "2026-09-25T01:05:53.627Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-22fc5533-87b0-4d44-a4a9-a2c184d94789", "process_id": "3842", "command": [ "/bin/sh", "-lc", "for f in solution/validation_{count,recent,liked,organic,last}.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_{count,recent,liked,organic,last}.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n", "exit_code": 1, "duration": { "secs": 1, "nanos": 958534024 }, "formatted_output": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-25T01:05:54.792Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:54.792Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:56.491Z", "callId": "call_RCXDXBbOvi5Ps1dxWACEYtiQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:06.704Z", "content": "Script completed\nWall time 10.2 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:56.568Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8f1d07887d284d236394a160295", "usage": { "input_tokens": 26142, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 11, "total_tokens": 26256 }, "turn_token_usage": { "input_tokens": 353331, "cached_input_tokens": 332416, "cache_write_input_tokens": 0, "output_tokens": 5034, "reasoning_output_tokens": 1285, "total_tokens": 358365 }, "thread_token_usage": { "input_tokens": 353331, "cached_input_tokens": 332416, "cache_write_input_tokens": 0, "output_tokens": 5034, "reasoning_output_tokens": 1285, "total_tokens": 358365 } } }, { "kind": "context", "timestamp": "2026-09-25T01:06:06.700Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86f49ad2-26ea-4030-b2b6-6554ab723acd", "process_id": "61143", "command": [ "/bin/sh", "-lc", "for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n", "exit_code": 1, "duration": { "secs": 10, "nanos": 45404879 }, "formatted_output": "Traceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n" } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-25T01:06:08.068Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:08.068Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:09.078Z", "callId": "call_NguRqaofRfz7ifX5UZy6Qbxl", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution; dmesg | tail -5 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:09.134Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ntotal 1.5M\n-rw-r--r-- 1 agent agent 805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:09.121Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ce8165ee-aa19-4a09-8ae5-03e7610fd732", "process_id": "84798", "command": [ "/bin/sh", "-lc", "ls -lh solution; dmesg | tail -5 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution; dmesg | tail -5 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 1.5M\n-rw-r--r-- 1 agent agent 805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n", "stderr": "", "aggregated_output": "total 1.5M\n-rw-r--r-- 1 agent agent 805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4510 }, "formatted_output": "total 1.5M\n-rw-r--r-- 1 agent agent 805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:06:09.133Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c8fee30c87d2a23576fb9ef95a0f", "usage": { "input_tokens": 28071, "cached_input_tokens": 25984, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 23, "total_tokens": 28160 }, "turn_token_usage": { "input_tokens": 381402, "cached_input_tokens": 358400, "cache_write_input_tokens": 0, "output_tokens": 5123, "reasoning_output_tokens": 1308, "total_tokens": 386525 }, "thread_token_usage": { "input_tokens": 381402, "cached_input_tokens": 358400, "cache_write_input_tokens": 0, "output_tokens": 5123, "reasoning_output_tokens": 1308, "total_tokens": 386525 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-25T01:06:15.940Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:15.940Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:19.044Z", "callId": "call_VXQpB7xnoDqRZmX63Z16jNBy", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.dataset as ds\\nD=ds.dataset('/task/data/train_listens.parquet',format='parquet')\\nfor days in [7,30,90,180]:\\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=50)\\n print(days,D.count_rows(filter=f))\\nfor days in [30,90,180]:\\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=80)&(ds.field('is_organic')==1)\\n print('org',days,D.count_rows(filter=f))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:22.052Z", "content": "Script completed\nWall time 3.0 seconds\nOutput:\n\n7 950150\n30 3883797\n90 10582861\n180 19038702\norg 30 1745899\norg 90 4791628\norg 180 8566731\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:19.089Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c901675c87d28e867c6f7914269a", "usage": { "input_tokens": 28301, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 498, "reasoning_output_tokens": 293, "total_tokens": 28799 }, "turn_token_usage": { "input_tokens": 409703, "cached_input_tokens": 386304, "cache_write_input_tokens": 0, "output_tokens": 5621, "reasoning_output_tokens": 1601, "total_tokens": 415324 }, "thread_token_usage": { "input_tokens": 409703, "cached_input_tokens": 386304, "cache_write_input_tokens": 0, "output_tokens": 5621, "reasoning_output_tokens": 1601, "total_tokens": 415324 } } }, { "kind": "context", "timestamp": "2026-09-25T01:06:22.048Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8e5a5b73-649f-43c2-b333-0de52168c2a8", "process_id": "75481", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.dataset as ds\nD=ds.dataset('/task/data/train_listens.parquet',format='parquet')\nfor days in [7,30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=50)\n print(days,D.count_rows(filter=f))\nfor days in [30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=80)&(ds.field('is_organic')==1)\n print('org',days,D.count_rows(filter=f))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.dataset as ds\nD=ds.dataset('/task/data/train_listens.parquet',format='parquet')\nfor days in [7,30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=50)\n print(days,D.count_rows(filter=f))\nfor days in [30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=80)&(ds.field('is_organic')==1)\n print('org',days,D.count_rows(filter=f))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "7 950150\n30 3883797\n90 10582861\n180 19038702\norg 30 1745899\norg 90 4791628\norg 180 8566731\n", "stderr": "", "aggregated_output": "7 950150\n30 3883797\n90 10582861\n180 19038702\norg 30 1745899\norg 90 4791628\norg 180 8566731\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 840682296 }, "formatted_output": "7 950150\n30 3883797\n90 10582861\n180 19038702\norg 30 1745899\norg 90 4791628\norg 180 8566731\n" } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-25T01:06:24.616Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:24.616Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:33.828Z", "callId": "call_TUHvw9ke2ZSmN7Fk9x44LweT", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/explore_rank.py\\n@@\\n- tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n- tr=tr[(tr.played_ratio_pct>=50)&tr.item_id.isin(itemset)].copy()\\n+ tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',CUTOFF-30*86400),('played_ratio_pct','>=',50)])\\n+ tr=tr[tr.item_id.isin(itemset)].copy()\\n@@\\n- tr['w7']=np.exp(-age/7)*(1+1.5*tr.is_organic.to_numpy())\\n tr['w30']=np.exp(-age/30)*(1+1.5*tr.is_organic.to_numpy())\\n tr['w90']=np.exp(-age/90)*(1+1.5*tr.is_organic.to_numpy())\\n@@\\n- ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w7=('w7','sum'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\\n+ ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\\n@@\\n- ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\\n+ ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:06:33.885Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:33.837Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-296b608d-9bef-406c-9f64-7205e64a595c", "changes": { "/app/solution/explore_rank.py": { "type": "update", "unified_diff": "@@ -11,6 +11,5 @@\n targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n- tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n- tr=tr[(tr.played_ratio_pct>=50)&tr.item_id.isin(itemset)].copy()\n+ tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',CUTOFF-30*86400),('played_ratio_pct','>=',50)])\n+ tr=tr[tr.item_id.isin(itemset)].copy()\n age=(CUTOFF-tr.timestamp.to_numpy())/86400.\n- tr['w7']=np.exp(-age/7)*(1+1.5*tr.is_organic.to_numpy())\n tr['w30']=np.exp(-age/30)*(1+1.5*tr.is_organic.to_numpy())\n@@ -19,3 +18,3 @@\n tr['strongorg']=((tr.is_organic==1)&(tr.played_ratio_pct>=80)).astype(np.float32)\n- ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w7=('w7','sum'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\n+ ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\n del tr\n@@ -26,3 +25,3 @@\n lp=pref[(pref.state=='liked')&pref.item_id.isin(itemset)][['uid','item_id']].copy()\n- ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n+ ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n ag['popidx']=ag.item_id.map(pop)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/explore_rank.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:06:33.884Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c90e47d887d29eb796b825d0b356", "usage": { "input_tokens": 28869, "cached_input_tokens": 28160, "cache_write_input_tokens": 0, "output_tokens": 603, "reasoning_output_tokens": 84, "total_tokens": 29472 }, "turn_token_usage": { "input_tokens": 438572, "cached_input_tokens": 414464, "cache_write_input_tokens": 0, "output_tokens": 6224, "reasoning_output_tokens": 1685, "total_tokens": 444796 }, "thread_token_usage": { "input_tokens": 438572, "cached_input_tokens": 414464, "cache_write_input_tokens": 0, "output_tokens": 6224, "reasoning_output_tokens": 1685, "total_tokens": 444796 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-25T01:06:35.490Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:35.490Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:36.687Z", "callId": "call_NpoFux9GZ0TOOvTSsPWPDZL0", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/explore_rank.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:06:37.852Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=16676", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:36.748Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c91a3c6487d29fbbbf73c73a02bb", "usage": { "input_tokens": 29496, "cached_input_tokens": 28672, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 6, "total_tokens": 29577 }, "turn_token_usage": { "input_tokens": 468068, "cached_input_tokens": 443136, "cache_write_input_tokens": 0, "output_tokens": 6305, "reasoning_output_tokens": 1691, "total_tokens": 474373 }, "thread_token_usage": { "input_tokens": 468068, "cached_input_tokens": 443136, "cache_write_input_tokens": 0, "output_tokens": 6305, "reasoning_output_tokens": 1691, "total_tokens": 474373 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:06:39.573Z", "callId": "call_zJe1hP9Gh1hyhsda0vqtzClR", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:16676,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:44.582Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n/app/solution/explore_rank.py:26: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:39.859Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c91e105c87d2ad98b7ed6a26aad1", "usage": { "input_tokens": 29605, "cached_input_tokens": 29312, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 29650 }, "turn_token_usage": { "input_tokens": 497673, "cached_input_tokens": 472448, "cache_write_input_tokens": 0, "output_tokens": 6350, "reasoning_output_tokens": 1691, "total_tokens": 504023 }, "thread_token_usage": { "input_tokens": 497673, "cached_input_tokens": 472448, "cache_write_input_tokens": 0, "output_tokens": 6350, "reasoning_output_tokens": 1691, "total_tokens": 504023 } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-25T01:06:46.970Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:46.970Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:48.864Z", "callId": "call_FpWwQpBnk18lwPSaDzomOYVL", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution/validation_*.parquet && for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:07:00.791Z", "content": "Script completed\nWall time 11.9 seconds\nOutput:\n\n-rw-r--r-- 1 agent agent 712K Sep 25 01:06 solution/validation_count.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 solution/validation_pop.parquet\n{\n \"model\": \"solution/validation_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:48.934Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c924c1b087d2be5cf293c7f624cf", "usage": { "input_tokens": 29797, "cached_input_tokens": 29440, "cache_write_input_tokens": 0, "output_tokens": 131, "reasoning_output_tokens": 17, "total_tokens": 29928 }, "turn_token_usage": { "input_tokens": 527470, "cached_input_tokens": 501888, "cache_write_input_tokens": 0, "output_tokens": 6481, "reasoning_output_tokens": 1708, "total_tokens": 533951 }, "thread_token_usage": { "input_tokens": 527470, "cached_input_tokens": 501888, "cache_write_input_tokens": 0, "output_tokens": 6481, "reasoning_output_tokens": 1708, "total_tokens": 533951 } } }, { "kind": "context", "timestamp": "2026-09-25T01:07:00.786Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9215924c-003a-4983-85ed-ad77ed16cbf3", "process_id": "59770", "command": [ "/bin/sh", "-lc", "ls -lh solution/validation_*.parquet && for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution/validation_*.parquet && for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "{\n \"model\": \"solution/validation_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n", "exit_code": 1, "duration": { "secs": 11, "nanos": 755510714 }, "formatted_output": "{\n \"model\": \"solution/validation_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n" } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-25T01:07:04.689Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:04.689Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:07:06.075Z", "callId": "call_QGvlG9lyq97pfI067nK0RO6V", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nx=pd.read_parquet('solution/validation_count.parquet');y=pd.read_parquet('solution/validation_pop.parquet');print((x.item_id!=y.item_id).sum(),x.head())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:07:07.683Z", "content": "Script completed\nWall time 1.6 seconds\nOutput:\n\n0 uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:07:07.639Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c935056887d2a9818af9a1767a6a", "usage": { "input_tokens": 31483, "cached_input_tokens": 29568, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 105, "total_tokens": 31698 }, "turn_token_usage": { "input_tokens": 558953, "cached_input_tokens": 531456, "cache_write_input_tokens": 0, "output_tokens": 6696, "reasoning_output_tokens": 1813, "total_tokens": 565649 }, "thread_token_usage": { "input_tokens": 558953, "cached_input_tokens": 531456, "cache_write_input_tokens": 0, "output_tokens": 6696, "reasoning_output_tokens": 1813, "total_tokens": 565649 } } }, { "kind": "context", "timestamp": "2026-09-25T01:07:07.678Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73451f8e-de85-41b2-8a7b-5e7966e7c06b", "process_id": "96960", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('solution/validation_count.parquet');y=pd.read_parquet('solution/validation_pop.parquet');print((x.item_id!=y.item_id).sum(),x.head())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('solution/validation_count.parquet');y=pd.read_parquet('solution/validation_pop.parquet');print((x.item_id!=y.item_id).sum(),x.head())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n", "stderr": "", "aggregated_output": "0 uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 437577823 }, "formatted_output": "0 uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\n" } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-25T01:07:10.211Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:10.211Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:07:17.368Z", "callId": "call_R0zPdsJ77JQPz2qozIUdnJtQ", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data/'; t=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int));cat=pd.read_parquet(D+'candidate_catalog.parquet'); col=dict(zip(cat.item_id.astype(int),range(30000)))\\nz=np.load(D+'validation_eligible_candidates.npz'); row=dict(zip(z['uids'].astype(int),range(len(z['uids']))));\\nls=pd.read_parquet(D+'val_listens.parquet');lk=pd.read_parquet(D+'val_likes.parquet'); rel=pd.concat([ls[(ls.uid.isin(t))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[(lk.uid.isin(t))&(lk.is_organic==1)][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\ndef ok(u,i):\\n c=col.get(int(i));r=row.get(int(u));return c is not None and bool((z['packed_eligible'][r,c//8]>>(c%8))&1)\\nrel=rel[[ok(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\\np=pd.read_parquet(D+'validation_preference_state.parquet');x=rel.merge(p,on=['uid','item_id'],how='left');print(len(rel),rel.uid.nunique(),x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:07:27.535Z", "content": "Script completed\nWall time 10.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:07:17.449Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c93be4e487d280febcac4e06f4fa", "usage": { "input_tokens": 31795, "cached_input_tokens": 31360, "cache_write_input_tokens": 0, "output_tokens": 462, "reasoning_output_tokens": 56, "total_tokens": 32257 }, "turn_token_usage": { "input_tokens": 590748, "cached_input_tokens": 562816, "cache_write_input_tokens": 0, "output_tokens": 7158, "reasoning_output_tokens": 1869, "total_tokens": 597906 }, "thread_token_usage": { "input_tokens": 590748, "cached_input_tokens": 562816, "cache_write_input_tokens": 0, "output_tokens": 7158, "reasoning_output_tokens": 1869, "total_tokens": 597906 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-25T01:07:38.766Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:38.766Z", "text": "" } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-25T01:07:48.086Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:48.086Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-25T01:07:56.530Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab78c3bf-f095-4f0e-98b1-5e4e5fe9cd34", "process_id": "16676", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/explore_rank.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/explore_rank.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/explore_rank.py:26: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n", "stderr": "", "aggregated_output": "/app/solution/explore_rank.py:26: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n", "exit_code": 0, "duration": { "secs": 79, "nanos": 680884218 }, "formatted_output": "/app/solution/explore_rank.py:26: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-25T01:07:57.722Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:57.722Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-25T01:08:07.317Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:07.317Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-25T01:08:12.532Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:12.532Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:08:13.917Z", "text": "Historical items themselves are excluded by eligibility—the direct repeat-history variants collapse back to popularity. That makes the intended retrieval problem clear: learn item-to-item taste structure, then retrieve unseen eligible tracks. I’m implementing a neural DeepSets-style user tower over recent unique positive-history items and a separate item tower, trained with in-batch sampled softmax on later organic discoveries." }, { "kind": "tool", "timestamp": "2026-09-25T01:08:15.246Z", "callId": "call_vGeyzBxvKDLEPGkIcJSL12d6", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfor f in ['val_listens.parquet','val_likes.parquet']:\\n x=pd.read_parquet('/task/data/'+f,columns=['timestamp']);print(f,x.timestamp.min(),x.timestamp.max())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:08:16.869Z", "content": "Script completed\nWall time 1.6 seconds\nOutput:\n\nval_listens.parquet 24788600 25393395\nval_likes.parquet 24788815 25393390\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:08:15.293Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c94fbd2087d2b8c271531fb6cad6", "usage": { "input_tokens": 32280, "cached_input_tokens": 31616, "cache_write_input_tokens": 0, "output_tokens": 2552, "reasoning_output_tokens": 2360, "total_tokens": 34832 }, "turn_token_usage": { "input_tokens": 623028, "cached_input_tokens": 594432, "cache_write_input_tokens": 0, "output_tokens": 9710, "reasoning_output_tokens": 4229, "total_tokens": 632738 }, "thread_token_usage": { "input_tokens": 623028, "cached_input_tokens": 594432, "cache_write_input_tokens": 0, "output_tokens": 9710, "reasoning_output_tokens": 4229, "total_tokens": 632738 } } }, { "kind": "context", "timestamp": "2026-09-25T01:08:16.865Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cc7c0fa9-2964-42f3-8b42-1fdf9b35a135", "process_id": "25271", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor f in ['val_listens.parquet','val_likes.parquet']:\n x=pd.read_parquet('/task/data/'+f,columns=['timestamp']);print(f,x.timestamp.min(),x.timestamp.max())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor f in ['val_listens.parquet','val_likes.parquet']:\n x=pd.read_parquet('/task/data/'+f,columns=['timestamp']);print(f,x.timestamp.min(),x.timestamp.max())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "val_listens.parquet 24788600 25393395\nval_likes.parquet 24788815 25393390\n", "stderr": "", "aggregated_output": "val_listens.parquet 24788600 25393395\nval_likes.parquet 24788815 25393390\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 454180188 }, "formatted_output": "val_listens.parquet 24788600 25393395\nval_likes.parquet 24788815 25393390\n" } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-25T01:08:27.026Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:27.026Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:12.808Z", "callId": "call_yJjwgSRD5hVI5TFTXTVrtbWD", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_two_tower.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based neural two-tower retriever.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+import argparse, json, random\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+from torch.utils.data import DataLoader, TensorDataset\\n+\\n+DATA=Path('/task/data'); OUT=Path('/app/solution')\\n+TRAIN_CUTOFF=24786800\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History encoder: recency-weighted embedding pooling followed by an MLP.\\\"\\\"\\\"\\n+ def __init__(self,n_items:int,dim:int,max_hist:int):\\n+ super().__init__(); self.max_hist=max_hist\\n+ self.history_embedding=nn.Embedding(n_items+1,dim,padding_idx=0)\\n+ self.net=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim),nn.LayerNorm(dim))\\n+ w=torch.exp((torch.arange(max_hist)-max_hist+1)/48.0)\\n+ self.register_buffer('recency_weight',w)\\n+ def forward(self,hist):\\n+ mask=hist.ne(0); w=self.recency_weight[None,:]*mask\\n+ pooled=(self.history_embedding(hist)*w[...,None]).sum(1)/w.sum(1,keepdim=True).clamp_min(1e-6)\\n+ return self.net(pooled)\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently executable catalog encoder with ID and popularity inputs.\\\"\\\"\\\"\\n+ def __init__(self,n_items:int,dim:int,pop_features:torch.Tensor):\\n+ super().__init__(); self.item_embedding=nn.Embedding(n_items,dim)\\n+ self.register_buffer('pop_features',pop_features)\\n+ self.net=nn.Sequential(nn.Linear(dim+3,dim*2),nn.GELU(),nn.Linear(dim*2,dim),nn.LayerNorm(dim))\\n+ def forward(self,item_idx):\\n+ x=torch.cat([self.item_embedding(item_idx),self.pop_features[item_idx]],dim=-1)\\n+ return self.net(x)\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self,n_items,dim,max_hist,pop_features):\\n+ super().__init__(); self.user_tower=UserTower(n_items,dim,max_hist); self.item_tower=ItemTower(n_items,dim,pop_features)\\n+ self.logit_scale=nn.Parameter(torch.tensor(2.3))\\n+ def loss(self,hist,target):\\n+ u=nn.functional.normalize(self.user_tower(hist),dim=1)\\n+ v=nn.functional.normalize(self.item_tower(target),dim=1)\\n+ logits=u@v.T*self.logit_scale.exp().clamp(max=50)\\n+ # Duplicate items in a batch are alternative positives, not false negatives.\\n+ positive=target[:,None].eq(target[None,:])\\n+ return -(torch.logsumexp(logits.masked_fill(~positive,-1e9),1)-torch.logsumexp(logits,1)).mean()\\n+\\n+def load_events(stage,cutoff,item_lookup):\\n+ paths=[DATA/'train_listens.parquet']+([DATA/'val_listens.parquet'] if stage=='test' else [])\\n+ frames=[]\\n+ since=cutoff-120*86400\\n+ for p in paths:\\n+ x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',since),('played_ratio_pct','>=',50)])\\n+ raw=x.item_id.to_numpy(np.int64); valid=raw=0; x=x.loc[keep,['uid','timestamp','is_organic','played_ratio_pct']].copy(); x['idx']=idx[keep]; frames.append(x)\\n+ x=pd.concat(frames,ignore_index=True).sort_values(['uid','timestamp'],kind='stable')\\n+ return x\\n+\\n+def build_examples(events,targets,max_hist,n_per_user=14):\\n+ histories=[]; labels=[]\\n+ for uid,g in events.groupby('uid',sort=False):\\n+ # First encounter within the long lookback approximates a discovery event.\\n+ seq=g.drop_duplicates('idx',keep='first')\\n+ ids=seq.idx.to_numpy(np.int32)\\n+ organic=((seq.is_organic.to_numpy()==1)&(seq.played_ratio_pct.to_numpy()>=80))\\n+ positions=np.flatnonzero(organic & (np.arange(len(ids))>=12))\\n+ if not len(positions): continue\\n+ choose=positions[np.linspace(0,len(positions)-1,min(n_per_user,len(positions))).astype(int)]\\n+ for p in np.unique(choose):\\n+ h=ids[max(0,p-max_hist):p]+1\\n+ row=np.zeros(max_hist,np.int32); row[-len(h):]=h\\n+ histories.append(row);labels.append(ids[p])\\n+ return torch.from_numpy(np.asarray(histories,np.int32)).long(),torch.from_numpy(np.asarray(labels,np.int64))\\n+\\n+def build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\\n+ by={}\\n+ for uid,g in events.groupby('uid',sort=False):\\n+ ids=g.drop_duplicates('idx',keep='last').idx.to_numpy(np.int32)\\n+ by[int(uid)]=list(ids[-max_hist:])\\n+ # Active likes are user-side cutoff state. Append them as most-recent taste evidence.\\n+ pref=pd.read_parquet(pref_path)\\n+ pref=pref[pref.state.eq('liked')]\\n+ for uid,g in pref.groupby('uid',sort=False):\\n+ raw=g.item_id.to_numpy(np.int64); ok=raw=0]\\n+ if not len(liked): continue\\n+ base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\\n+ by[int(uid)]=base[-max_hist:]\\n+ out=np.zeros((len(uids),max_hist),np.int32)\\n+ for r,u in enumerate(uids):\\n+ h=np.asarray(by.get(int(u),[]),np.int32)+1\\n+ if len(h): out[r,-len(h):]=h\\n+ return torch.from_numpy(out).long()\\n+\\n+def train_and_rank(stage,dim,epochs,seed):\\n+ random.seed(seed);np.random.seed(seed);torch.manual_seed(seed);torch.set_num_threads(min(16,torch.get_num_threads()))\\n+ cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); n=len(items)\\n+ lookup=np.full(int(items.max())+1,-1,np.int32);lookup[items]=np.arange(n,dtype=np.int32)\\n+ cutoff=TRAIN_CUTOFF if stage=='validation' else 25393395\\n+ events=load_events(stage,cutoff,lookup)\\n+ max_hist=128; histories,labels=build_examples(events,None,max_hist)\\n+ print(f'{stage}: {len(events):,} positive events, {len(histories):,} training examples')\\n+ rank=cat.popularity_rank.to_numpy(np.float32)\\n+ pf=np.stack([np.log1p(cat.positive_listens.to_numpy())/12,np.log1p(cat.organic_positive_listens.to_numpy())/12,-np.log(rank)/12],1).astype('float32')\\n+ model=TwoTower(n,dim,max_hist,torch.from_numpy(pf))\\n+ loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\\n+ opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\\n+ model.train()\\n+ for ep in range(epochs):\\n+ total=0.;count=0\\n+ for h,y in loader:\\n+ opt.zero_grad(set_to_none=True);loss=model.loss(h,y);loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\\n+ total+=float(loss)*len(h);count+=len(h)\\n+ print(f'epoch {ep+1}/{epochs} loss={total/count:.4f}')\\n+ ckpt=OUT/f'model_{stage}.pt';torch.save({'state_dict':model.state_dict(),'dim':dim,'max_hist':max_hist,'stage':stage,'seed':seed},ckpt)\\n+ elig=np.load(DATA/f'{stage}_eligible_candidates.npz');uids=elig['uids'].astype(np.int64);packed=elig['packed_eligible']\\n+ pref=DATA/f'{stage}_preference_state.parquet'\\n+ h=build_inference_histories(events,uids,max_hist,pref,lookup)\\n+ model.eval(); rows=[]\\n+ with torch.no_grad():\\n+ ivec=nn.functional.normalize(model.item_tower(torch.arange(n)),dim=1)\\n+ for start in range(0,len(uids),128):\\n+ stop=min(start+128,len(uids));uvec=nn.functional.normalize(model.user_tower(h[start:stop]),dim=1)\\n+ scores=(uvec@ivec.T).numpy()\\n+ for j,r in enumerate(range(start,stop)):\\n+ ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool)\\n+ scores[j,~ok]=-np.inf\\n+ top=np.argpartition(scores[j],-100)[-100:];top=top[np.argsort(scores[j,top])[::-1]]\\n+ rows.extend((int(uids[r]),int(items[k]),q+1) for q,k in enumerate(top))\\n+ path=OUT/('validation.parquet' if stage=='validation' else 'submission.parquet')\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\\n+ (OUT/f'metrics_{stage}.json').write_text(json.dumps({'examples':len(histories),'epochs':epochs,'dim':dim,'seed':seed},indent=2))\\n+ print('wrote',path)\\n+\\n+if __name__=='__main__':\\n+ p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],required=True);p.add_argument('--dim',type=int,default=64);p.add_argument('--epochs',type=int,default=12);p.add_argument('--seed',type=int,default=2026)\\n+ a=p.parse_args();train_and_rank(a.stage,a.dim,a.epochs,a.seed)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:09:12.876Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:12.818Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-010728c7-366a-44b5-822d-fcde91369b79", "changes": { "/app/solution/train_two_tower.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-based neural two-tower retriever.\"\"\"\nfrom __future__ import annotations\nimport argparse, json, random\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader, TensorDataset\n\nDATA=Path('/task/data'); OUT=Path('/app/solution')\nTRAIN_CUTOFF=24786800\n\nclass UserTower(nn.Module):\n \"\"\"History encoder: recency-weighted embedding pooling followed by an MLP.\"\"\"\n def __init__(self,n_items:int,dim:int,max_hist:int):\n super().__init__(); self.max_hist=max_hist\n self.history_embedding=nn.Embedding(n_items+1,dim,padding_idx=0)\n self.net=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim),nn.LayerNorm(dim))\n w=torch.exp((torch.arange(max_hist)-max_hist+1)/48.0)\n self.register_buffer('recency_weight',w)\n def forward(self,hist):\n mask=hist.ne(0); w=self.recency_weight[None,:]*mask\n pooled=(self.history_embedding(hist)*w[...,None]).sum(1)/w.sum(1,keepdim=True).clamp_min(1e-6)\n return self.net(pooled)\n\nclass ItemTower(nn.Module):\n \"\"\"Independently executable catalog encoder with ID and popularity inputs.\"\"\"\n def __init__(self,n_items:int,dim:int,pop_features:torch.Tensor):\n super().__init__(); self.item_embedding=nn.Embedding(n_items,dim)\n self.register_buffer('pop_features',pop_features)\n self.net=nn.Sequential(nn.Linear(dim+3,dim*2),nn.GELU(),nn.Linear(dim*2,dim),nn.LayerNorm(dim))\n def forward(self,item_idx):\n x=torch.cat([self.item_embedding(item_idx),self.pop_features[item_idx]],dim=-1)\n return self.net(x)\n\nclass TwoTower(nn.Module):\n def __init__(self,n_items,dim,max_hist,pop_features):\n super().__init__(); self.user_tower=UserTower(n_items,dim,max_hist); self.item_tower=ItemTower(n_items,dim,pop_features)\n self.logit_scale=nn.Parameter(torch.tensor(2.3))\n def loss(self,hist,target):\n u=nn.functional.normalize(self.user_tower(hist),dim=1)\n v=nn.functional.normalize(self.item_tower(target),dim=1)\n logits=u@v.T*self.logit_scale.exp().clamp(max=50)\n # Duplicate items in a batch are alternative positives, not false negatives.\n positive=target[:,None].eq(target[None,:])\n return -(torch.logsumexp(logits.masked_fill(~positive,-1e9),1)-torch.logsumexp(logits,1)).mean()\n\ndef load_events(stage,cutoff,item_lookup):\n paths=[DATA/'train_listens.parquet']+([DATA/'val_listens.parquet'] if stage=='test' else [])\n frames=[]\n since=cutoff-120*86400\n for p in paths:\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',since),('played_ratio_pct','>=',50)])\n raw=x.item_id.to_numpy(np.int64); valid=raw=0; x=x.loc[keep,['uid','timestamp','is_organic','played_ratio_pct']].copy(); x['idx']=idx[keep]; frames.append(x)\n x=pd.concat(frames,ignore_index=True).sort_values(['uid','timestamp'],kind='stable')\n return x\n\ndef build_examples(events,targets,max_hist,n_per_user=14):\n histories=[]; labels=[]\n for uid,g in events.groupby('uid',sort=False):\n # First encounter within the long lookback approximates a discovery event.\n seq=g.drop_duplicates('idx',keep='first')\n ids=seq.idx.to_numpy(np.int32)\n organic=((seq.is_organic.to_numpy()==1)&(seq.played_ratio_pct.to_numpy()>=80))\n positions=np.flatnonzero(organic & (np.arange(len(ids))>=12))\n if not len(positions): continue\n choose=positions[np.linspace(0,len(positions)-1,min(n_per_user,len(positions))).astype(int)]\n for p in np.unique(choose):\n h=ids[max(0,p-max_hist):p]+1\n row=np.zeros(max_hist,np.int32); row[-len(h):]=h\n histories.append(row);labels.append(ids[p])\n return torch.from_numpy(np.asarray(histories,np.int32)).long(),torch.from_numpy(np.asarray(labels,np.int64))\n\ndef build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\n by={}\n for uid,g in events.groupby('uid',sort=False):\n ids=g.drop_duplicates('idx',keep='last').idx.to_numpy(np.int32)\n by[int(uid)]=list(ids[-max_hist:])\n # Active likes are user-side cutoff state. Append them as most-recent taste evidence.\n pref=pd.read_parquet(pref_path)\n pref=pref[pref.state.eq('liked')]\n for uid,g in pref.groupby('uid',sort=False):\n raw=g.item_id.to_numpy(np.int64); ok=raw=0]\n if not len(liked): continue\n base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\n by[int(uid)]=base[-max_hist:]\n out=np.zeros((len(uids),max_hist),np.int32)\n for r,u in enumerate(uids):\n h=np.asarray(by.get(int(u),[]),np.int32)+1\n if len(h): out[r,-len(h):]=h\n return torch.from_numpy(out).long()\n\ndef train_and_rank(stage,dim,epochs,seed):\n random.seed(seed);np.random.seed(seed);torch.manual_seed(seed);torch.set_num_threads(min(16,torch.get_num_threads()))\n cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); n=len(items)\n lookup=np.full(int(items.max())+1,-1,np.int32);lookup[items]=np.arange(n,dtype=np.int32)\n cutoff=TRAIN_CUTOFF if stage=='validation' else 25393395\n events=load_events(stage,cutoff,lookup)\n max_hist=128; histories,labels=build_examples(events,None,max_hist)\n print(f'{stage}: {len(events):,} positive events, {len(histories):,} training examples')\n rank=cat.popularity_rank.to_numpy(np.float32)\n pf=np.stack([np.log1p(cat.positive_listens.to_numpy())/12,np.log1p(cat.organic_positive_listens.to_numpy())/12,-np.log(rank)/12],1).astype('float32')\n model=TwoTower(n,dim,max_hist,torch.from_numpy(pf))\n loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\n opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n model.train()\n for ep in range(epochs):\n total=0.;count=0\n for h,y in loader:\n opt.zero_grad(set_to_none=True);loss=model.loss(h,y);loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\n total+=float(loss)*len(h);count+=len(h)\n print(f'epoch {ep+1}/{epochs} loss={total/count:.4f}')\n ckpt=OUT/f'model_{stage}.pt';torch.save({'state_dict':model.state_dict(),'dim':dim,'max_hist':max_hist,'stage':stage,'seed':seed},ckpt)\n elig=np.load(DATA/f'{stage}_eligible_candidates.npz');uids=elig['uids'].astype(np.int64);packed=elig['packed_eligible']\n pref=DATA/f'{stage}_preference_state.parquet'\n h=build_inference_histories(events,uids,max_hist,pref,lookup)\n model.eval(); rows=[]\n with torch.no_grad():\n ivec=nn.functional.normalize(model.item_tower(torch.arange(n)),dim=1)\n for start in range(0,len(uids),128):\n stop=min(start+128,len(uids));uvec=nn.functional.normalize(model.user_tower(h[start:stop]),dim=1)\n scores=(uvec@ivec.T).numpy()\n for j,r in enumerate(range(start,stop)):\n ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool)\n scores[j,~ok]=-np.inf\n top=np.argpartition(scores[j],-100)[-100:];top=top[np.argsort(scores[j,top])[::-1]]\n rows.extend((int(uids[r]),int(items[k]),q+1) for q,k in enumerate(top))\n path=OUT/('validation.parquet' if stage=='validation' else 'submission.parquet')\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n (OUT/f'metrics_{stage}.json').write_text(json.dumps({'examples':len(histories),'epochs':epochs,'dim':dim,'seed':seed},indent=2))\n print('wrote',path)\n\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],required=True);p.add_argument('--dim',type=int,default=64);p.add_argument('--epochs',type=int,default=12);p.add_argument('--seed',type=int,default=2026)\n a=p.parse_args();train_and_rank(a.stage,a.dim,a.epochs,a.seed)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_two_tower.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:09:12.874Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c981150887d291b661dc78d5c7f6", "usage": { "input_tokens": 34883, "cached_input_tokens": 32128, "cache_write_input_tokens": 0, "output_tokens": 3058, "reasoning_output_tokens": 505, "total_tokens": 37941 }, "turn_token_usage": { "input_tokens": 657911, "cached_input_tokens": 626560, "cache_write_input_tokens": 0, "output_tokens": 12768, "reasoning_output_tokens": 4734, "total_tokens": 670679 }, "thread_token_usage": { "input_tokens": 657911, "cached_input_tokens": 626560, "cache_write_input_tokens": 0, "output_tokens": 12768, "reasoning_output_tokens": 4734, "total_tokens": 670679 } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-25T01:09:14.702Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:09:14.702Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:16.117Z", "callId": "call_ilJGFp8VQQGcXadFoDLxRxWa", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:09:17.287Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=22656", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:16.190Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9b92e5487d297c1293eb50d4f6d", "usage": { "input_tokens": 37965, "cached_input_tokens": 34688, "cache_write_input_tokens": 0, "output_tokens": 94, "reasoning_output_tokens": 7, "total_tokens": 38059 }, "turn_token_usage": { "input_tokens": 695876, "cached_input_tokens": 661248, "cache_write_input_tokens": 0, "output_tokens": 12862, "reasoning_output_tokens": 4741, "total_tokens": 708738 }, "thread_token_usage": { "input_tokens": 695876, "cached_input_tokens": 661248, "cache_write_input_tokens": 0, "output_tokens": 12862, "reasoning_output_tokens": 4741, "total_tokens": 708738 } } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-25T01:09:18.831Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:09:18.831Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:19.510Z", "callId": "call_KpyjEBqXs9wnOGcfUy7s7Jih", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:22656,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:09:24.527Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:19.584Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9bd8c6487d294ca9dc83193e705", "usage": { "input_tokens": 38087, "cached_input_tokens": 37760, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 38143 }, "turn_token_usage": { "input_tokens": 733963, "cached_input_tokens": 699008, "cache_write_input_tokens": 0, "output_tokens": 12918, "reasoning_output_tokens": 4750, "total_tokens": 746881 }, "thread_token_usage": { "input_tokens": 733963, "cached_input_tokens": 699008, "cache_write_input_tokens": 0, "output_tokens": 12918, "reasoning_output_tokens": 4750, "total_tokens": 746881 } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-25T01:09:26.017Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:09:26.017Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:27.300Z", "callId": "call_FIxzUQh7Oybox0MMrHFX4RPp", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution/model_validation.pt solution/validation.parquet solution/metrics_validation.json 2>&1; ps aux | rg train_two | rg -v rg\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:09:27.421Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nls: cannot access 'solution/model_validation.pt': No such file or directory\nls: cannot access 'solution/validation.parquet': No such file or directory\nls: cannot access 'solution/metrics_validation.json': No such file or directory\n/bin/sh: 1: ps: not found\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:27.351Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4f49cc07-06df-435b-a162-c622ccb45836", "process_id": "14700", "command": [ "/bin/sh", "-lc", "ls -lh solution/model_validation.pt solution/validation.parquet solution/metrics_validation.json 2>&1; ps aux | rg train_two | rg -v rg" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution/model_validation.pt solution/validation.parquet solution/metrics_validation.json 2>&1; ps aux | rg train_two | rg -v rg" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "ls: cannot access 'solution/model_validation.pt': No such file or directory\nls: cannot access 'solution/validation.parquet': No such file or directory\nls: cannot access 'solution/metrics_validation.json': No such file or directory\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "ls: cannot access 'solution/model_validation.pt': No such file or directory\nls: cannot access 'solution/validation.parquet': No such file or directory\nls: cannot access 'solution/metrics_validation.json': No such file or directory\n/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5960 }, "formatted_output": "ls: cannot access 'solution/model_validation.pt': No such file or directory\nls: cannot access 'solution/validation.parquet': No such file or directory\nls: cannot access 'solution/metrics_validation.json': No such file or directory\n/bin/sh: 1: ps: not found\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:09:27.419Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9c4c42887d2845cc3bd92075b57", "usage": { "input_tokens": 38166, "cached_input_tokens": 37888, "cache_write_input_tokens": 0, "output_tokens": 103, "reasoning_output_tokens": 23, "total_tokens": 38269 }, "turn_token_usage": { "input_tokens": 772129, "cached_input_tokens": 736896, "cache_write_input_tokens": 0, "output_tokens": 13021, "reasoning_output_tokens": 4773, "total_tokens": 785150 }, "thread_token_usage": { "input_tokens": 772129, "cached_input_tokens": 736896, "cache_write_input_tokens": 0, "output_tokens": 13021, "reasoning_output_tokens": 4773, "total_tokens": 785150 } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-25T01:09:32.911Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:09:32.911Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:34.987Z", "callId": "call_A1E3npeXgP7a2XwbuyMevrO4", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n- since=cutoff-120*86400\\n+ since=cutoff-45*86400\\n@@\\n- x=pd.concat(frames,ignore_index=True).sort_values(['uid','timestamp'],kind='stable')\\n+ # Source files are ordered by uid/time, and train precedes validation.\\n+ x=pd.concat(frames,ignore_index=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:09:35.061Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:35.003Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-6a543440-85a9-4937-8c96-c6e971b23312", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -52,3 +52,3 @@\n frames=[]\n- since=cutoff-120*86400\n+ since=cutoff-45*86400\n for p in paths:\n@@ -57,3 +57,4 @@\n keep=idx>=0; x=x.loc[keep,['uid','timestamp','is_organic','played_ratio_pct']].copy(); x['idx']=idx[keep]; frames.append(x)\n- x=pd.concat(frames,ignore_index=True).sort_values(['uid','timestamp'],kind='stable')\n+ # Source files are ordered by uid/time, and train precedes validation.\n+ x=pd.concat(frames,ignore_index=True)\n return x\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:09:35.060Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9c7c52c87d2bd545f9d926591d8", "usage": { "input_tokens": 38353, "cached_input_tokens": 38016, "cache_write_input_tokens": 0, "output_tokens": 357, "reasoning_output_tokens": 228, "total_tokens": 38710 }, "turn_token_usage": { "input_tokens": 810482, "cached_input_tokens": 774912, "cache_write_input_tokens": 0, "output_tokens": 13378, "reasoning_output_tokens": 5001, "total_tokens": 823860 }, "thread_token_usage": { "input_tokens": 810482, "cached_input_tokens": 774912, "cache_write_input_tokens": 0, "output_tokens": 13378, "reasoning_output_tokens": 5001, "total_tokens": 823860 } } }, { "kind": "context", "timestamp": "2026-09-25T01:09:37.582Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-342c94f4-1fcf-4afb-a3f1-8bb290f3c401", "process_id": "83033", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/explore_rank.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/explore_rank.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/explore_rank.py:27: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n", "stderr": "", "aggregated_output": "/app/solution/explore_rank.py:27: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n", "exit_code": 0, "duration": { "secs": 236, "nanos": 914723492 }, "formatted_output": "/app/solution/explore_rank.py:27: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n" } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-25T01:09:38.545Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:09:38.545Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:39.966Z", "callId": "call_3kWjJ6855N4zq8gmIroAU5CR", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:09:41.139Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=54367", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:40.053Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9cf636887d2a9e0a86e8873be9d", "usage": { "input_tokens": 38734, "cached_input_tokens": 38144, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 9, "total_tokens": 38832 }, "turn_token_usage": { "input_tokens": 849216, "cached_input_tokens": 813056, "cache_write_input_tokens": 0, "output_tokens": 13476, "reasoning_output_tokens": 5010, "total_tokens": 862692 }, "thread_token_usage": { "input_tokens": 849216, "cached_input_tokens": 813056, "cache_write_input_tokens": 0, "output_tokens": 13476, "reasoning_output_tokens": 5010, "total_tokens": 862692 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:09:44.230Z", "callId": "call_LyQ8lUmofVaF3tqzgtaS7Yh0", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:54367,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:09:49.240Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:44.306Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9d5559487d29e91cb55e36da8e8", "usage": { "input_tokens": 38860, "cached_input_tokens": 38528, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 38905 }, "turn_token_usage": { "input_tokens": 888076, "cached_input_tokens": 851584, "cache_write_input_tokens": 0, "output_tokens": 13521, "reasoning_output_tokens": 5010, "total_tokens": 901597 }, "thread_token_usage": { "input_tokens": 888076, "cached_input_tokens": 851584, "cache_write_input_tokens": 0, "output_tokens": 13521, "reasoning_output_tokens": 5010, "total_tokens": 901597 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-25T01:09:51.473Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:09:51.473Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:09:54.202Z", "callId": "call_d8gf0OwmCvsJnnDmBgBStfIh", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n- since=cutoff-45*86400\\n+ since=cutoff-21*86400\\n@@\\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',since),('played_ratio_pct','>=',50)])\\n+ print(f'loaded {p.name}: {len(x):,}',flush=True)\\n@@\\n- histories=[]; labels=[]\\n+ histories=[]; labels=[]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:09:54.234Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:09:54.214Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b2dbea52-3a4f-4199-83c6-6028536d2fce", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -52,5 +52,6 @@\n frames=[]\n- since=cutoff-45*86400\n+ since=cutoff-21*86400\n for p in paths:\n x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',since),('played_ratio_pct','>=',50)])\n+ print(f'loaded {p.name}: {len(x):,}',flush=True)\n raw=x.item_id.to_numpy(np.int64); valid=raw&1\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:10:09.719Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/bin/sh: 1: pgrep: not found\nls: cannot access 'solution/model_validation.pt': No such file or directory\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:09.661Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16600948-742b-4abb-a0cc-60da9712ff8c", "process_id": "25178", "command": [ "/bin/sh", "-lc", "pgrep -af train_two || true; ls -lh solution/model_validation.pt 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pgrep -af train_two || true; ls -lh solution/model_validation.pt 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: pgrep: not found\nls: cannot access 'solution/model_validation.pt': No such file or directory\n", "stderr": "", "aggregated_output": "/bin/sh: 1: pgrep: not found\nls: cannot access 'solution/model_validation.pt': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 10941 }, "formatted_output": "/bin/sh: 1: pgrep: not found\nls: cannot access 'solution/model_validation.pt': No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:10:09.683Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9edf44487d2ae9edfe4113eeca3", "usage": { "input_tokens": 39370, "cached_input_tokens": 39168, "cache_write_input_tokens": 0, "output_tokens": 165, "reasoning_output_tokens": 98, "total_tokens": 39535 }, "turn_token_usage": { "input_tokens": 1044827, "cached_input_tokens": 1007232, "cache_write_input_tokens": 0, "output_tokens": 14039, "reasoning_output_tokens": 5168, "total_tokens": 1058866 }, "thread_token_usage": { "input_tokens": 1044827, "cached_input_tokens": 1007232, "cache_write_input_tokens": 0, "output_tokens": 14039, "reasoning_output_tokens": 5168, "total_tokens": 1058866 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-25T01:10:12.264Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:10:12.264Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:10:13.724Z", "callId": "call_khCCcOfLaInxk2lDfQaurJgZ", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.max 2>/dev/null; cat /sys/fs/cgroup/memory.events 2>/dev/null; cat /sys/fs/cgroup/memory.current 2>/dev/null\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:10:13.841Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n8589934592\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n5001179136\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:13.818Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4b6917f-08c4-4c52-a4e0-411d8fbe79b6", "process_id": "87972", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.max 2>/dev/null; cat /sys/fs/cgroup/memory.events 2>/dev/null; cat /sys/fs/cgroup/memory.current 2>/dev/null" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.max 2>/dev/null; cat /sys/fs/cgroup/memory.events 2>/dev/null; cat /sys/fs/cgroup/memory.current 2>/dev/null" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "8589934592\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n5001179136\n", "stderr": "", "aggregated_output": "8589934592\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n5001179136\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3860 }, "formatted_output": "8589934592\nlow 0\nhigh 0\nmax 0\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 0\n5001179136\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:10:13.826Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9f1f03487d2a1b7085badce092a", "usage": { "input_tokens": 39586, "cached_input_tokens": 39168, "cache_write_input_tokens": 0, "output_tokens": 122, "reasoning_output_tokens": 31, "total_tokens": 39708 }, "turn_token_usage": { "input_tokens": 1084413, "cached_input_tokens": 1046400, "cache_write_input_tokens": 0, "output_tokens": 14161, "reasoning_output_tokens": 5199, "total_tokens": 1098574 }, "thread_token_usage": { "input_tokens": 1084413, "cached_input_tokens": 1046400, "cache_write_input_tokens": 0, "output_tokens": 14161, "reasoning_output_tokens": 5199, "total_tokens": 1098574 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-25T01:10:18.272Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:10:18.272Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:10:20.185Z", "callId": "call_M3oZSeaaoFCkhP6ZOwWt0fdr", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\"); case \\\"$c\\\" in *train_two*|*explore_rank*) echo \\\"$d $c\\\";; esac; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:10:20.304Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/proc/10035 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_two*|*explore_rank*) echo \"$d $c\";; esac; fi; done \n/proc/9908 /bin/sh -c /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9909 /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9953 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9954 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9988 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n/proc/9989 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:20.296Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b1ca131d-6718-4994-ad9f-0a31efc11e24", "process_id": "79762", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *train_two*|*explore_rank*) echo \"$d $c\";; esac; fi; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\\0' ' ' < \"$d/cmdline\"); case \"$c\" in *train_two*|*explore_rank*) echo \"$d $c\";; esac; fi; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/10035 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_two*|*explore_rank*) echo \"$d $c\";; esac; fi; done \n/proc/9908 /bin/sh -c /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9909 /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9953 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9954 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9988 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n/proc/9989 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n", "stderr": "", "aggregated_output": "/proc/10035 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_two*|*explore_rank*) echo \"$d $c\";; esac; fi; done \n/proc/9908 /bin/sh -c /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9909 /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9953 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9954 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9988 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n/proc/9989 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4381 }, "formatted_output": "/proc/10035 /bin/sh -c for d in /proc/[0-9]*; do if [ -r \"$d/cmdline\" ]; then c=$(tr '\u0000' ' ' < \"$d/cmdline\"); case \"$c\" in *train_two*|*explore_rank*) echo \"$d $c\";; esac; fi; done \n/proc/9908 /bin/sh -c /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9909 /opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9953 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9954 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12 \n/proc/9988 /bin/sh -c /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n/proc/9989 /opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10 \n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:10:20.301Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9f75d3887d293a0b1e908bb08d5", "usage": { "input_tokens": 39777, "cached_input_tokens": 39424, "cache_write_input_tokens": 0, "output_tokens": 163, "reasoning_output_tokens": 48, "total_tokens": 39940 }, "turn_token_usage": { "input_tokens": 1124190, "cached_input_tokens": 1085824, "cache_write_input_tokens": 0, "output_tokens": 14324, "reasoning_output_tokens": 5247, "total_tokens": 1138514 }, "thread_token_usage": { "input_tokens": 1124190, "cached_input_tokens": 1085824, "cache_write_input_tokens": 0, "output_tokens": 14324, "reasoning_output_tokens": 5247, "total_tokens": 1138514 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-25T01:10:22.502Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:10:22.502Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:10:24.065Z", "callId": "call_enZoVBoZ2gU49AS1qmIdgwT9", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 9908 9909 9953 9954; cat /sys/fs/cgroup/memory.current; for p in 9989; do awk '/VmRSS|VmSize/{print}' /proc/$p/status; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:10:24.176Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n5048614912\nVmSize:\t 3000108 kB\nVmRSS:\t 917000 kB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:24.152Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-06e55103-18e5-476a-b349-363b0f99a4f6", "process_id": "95846", "command": [ "/bin/sh", "-lc", "kill 9908 9909 9953 9954; cat /sys/fs/cgroup/memory.current; for p in 9989; do awk '/VmRSS|VmSize/{print}' /proc/$p/status; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 9908 9909 9953 9954; cat /sys/fs/cgroup/memory.current; for p in 9989; do awk '/VmRSS|VmSize/{print}' /proc/$p/status; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "5048614912\nVmSize:\t 3000108 kB\nVmRSS:\t 917000 kB\n", "stderr": "", "aggregated_output": "5048614912\nVmSize:\t 3000108 kB\nVmRSS:\t 917000 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6590 }, "formatted_output": "5048614912\nVmSize:\t 3000108 kB\nVmRSS:\t 917000 kB\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:10:24.170Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5c9fc8b2887d2865012f5053ddd20", "usage": { "input_tokens": 40249, "cached_input_tokens": 39552, "cache_write_input_tokens": 0, "output_tokens": 151, "reasoning_output_tokens": 55, "total_tokens": 40400 }, "turn_token_usage": { "input_tokens": 1164439, "cached_input_tokens": 1125376, "cache_write_input_tokens": 0, "output_tokens": 14475, "reasoning_output_tokens": 5302, "total_tokens": 1178914 }, "thread_token_usage": { "input_tokens": 1164439, "cached_input_tokens": 1125376, "cache_write_input_tokens": 0, "output_tokens": 14475, "reasoning_output_tokens": 5302, "total_tokens": 1178914 } } }, { "kind": "context", "timestamp": "2026-09-25T01:10:24.219Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5a51a10e-75a1-44e4-96ab-c3703303ee7f", "process_id": "54367", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 12" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "validation: 4,111,528 positive events, 89,386 training examples\n/app/solution/train_two_tower.py:115: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/12 loss=6.1924\n", "stderr": "", "aggregated_output": "validation: 4,111,528 positive events, 89,386 training examples\n/app/solution/train_two_tower.py:115: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/12 loss=6.1924\n", "exit_code": 143, "duration": { "secs": 44, "nanos": 82432059 }, "formatted_output": "validation: 4,111,528 positive events, 89,386 training examples\n/app/solution/train_two_tower.py:115: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/12 loss=6.1924\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:10:24.228Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ad7287b6-8988-40bc-ae89-4a45155b829c", "process_id": "22656", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --dim 64 --epochs 12" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/app/solution/train_two_tower.py:114: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\n", "stderr": "", "aggregated_output": "/app/solution/train_two_tower.py:114: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\n", "exit_code": 143, "duration": { "secs": 67, "nanos": 943629722 }, "formatted_output": "/app/solution/train_two_tower.py:114: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\n" } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-25T01:10:26.908Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:10:26.908Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:10:27.871Z", "callId": "call_Amvt697SxLW6UronxRuCeajB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73546,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:10:32.882Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nvalidation: 1,969,863 positive events, 73,883 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1945\nepoch 2/10 loss=6.1252\n\nSESSION_ID=73546", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:27.969Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca00711087d28e8d3e7dea9e0f62", "usage": { "input_tokens": 40449, "cached_input_tokens": 40064, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 44, "total_tokens": 40555 }, "turn_token_usage": { "input_tokens": 1204888, "cached_input_tokens": 1165440, "cache_write_input_tokens": 0, "output_tokens": 14581, "reasoning_output_tokens": 5346, "total_tokens": 1219469 }, "thread_token_usage": { "input_tokens": 1204888, "cached_input_tokens": 1165440, "cache_write_input_tokens": 0, "output_tokens": 14581, "reasoning_output_tokens": 5346, "total_tokens": 1219469 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-25T01:10:34.301Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:10:34.301Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:10:35.284Z", "callId": "call_D7NkLakwithORHcTLc94762M", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73546,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:10:40.295Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 3/10 loss=5.9864\nepoch 4/10 loss=5.7848\n\nSESSION_ID=73546", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:35.366Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca09135487d2813ede9a82018432", "usage": { "input_tokens": 40698, "cached_input_tokens": 40320, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 24, "total_tokens": 40784 }, "turn_token_usage": { "input_tokens": 1245586, "cached_input_tokens": 1205760, "cache_write_input_tokens": 0, "output_tokens": 14667, "reasoning_output_tokens": 5370, "total_tokens": 1260253 }, "thread_token_usage": { "input_tokens": 1245586, "cached_input_tokens": 1205760, "cache_write_input_tokens": 0, "output_tokens": 14667, "reasoning_output_tokens": 5370, "total_tokens": 1260253 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:10:42.357Z", "callId": "call_EINz3qSaTr0AvKkCsaRVBdJU", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73546,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:10:47.367Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 5/10 loss=5.5127\nepoch 6/10 loss=5.1935\n\nSESSION_ID=73546", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:42.432Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca108a1887d2bd844ac79f098d91", "usage": { "input_tokens": 40836, "cached_input_tokens": 40576, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 40896 }, "turn_token_usage": { "input_tokens": 1286422, "cached_input_tokens": 1246336, "cache_write_input_tokens": 0, "output_tokens": 14727, "reasoning_output_tokens": 5370, "total_tokens": 1301149 }, "thread_token_usage": { "input_tokens": 1286422, "cached_input_tokens": 1246336, "cache_write_input_tokens": 0, "output_tokens": 14727, "reasoning_output_tokens": 5370, "total_tokens": 1301149 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:10:49.536Z", "callId": "call_4nmyjl0yTLemln5zxLkcJ6Gf", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73546,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:10:54.546Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 7/10 loss=4.8423\nepoch 8/10 loss=4.4800\n\nSESSION_ID=73546", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:49.563Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca17ad6c87d29d8bb37e5edec535", "usage": { "input_tokens": 40948, "cached_input_tokens": 40704, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 41008 }, "turn_token_usage": { "input_tokens": 1327370, "cached_input_tokens": 1287040, "cache_write_input_tokens": 0, "output_tokens": 14787, "reasoning_output_tokens": 5370, "total_tokens": 1342157 }, "thread_token_usage": { "input_tokens": 1327370, "cached_input_tokens": 1287040, "cache_write_input_tokens": 0, "output_tokens": 14787, "reasoning_output_tokens": 5370, "total_tokens": 1342157 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:10:57.358Z", "callId": "call_riEttbeL2mkOdYaF7sLHz852", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73546,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:11:02.370Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 9/10 loss=4.1247\nepoch 10/10 loss=3.7825\n\nSESSION_ID=73546", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:10:57.420Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca1eb88087d2bef24792097f193e", "usage": { "input_tokens": 41060, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 41120 }, "turn_token_usage": { "input_tokens": 1368430, "cached_input_tokens": 1327872, "cache_write_input_tokens": 0, "output_tokens": 14847, "reasoning_output_tokens": 5370, "total_tokens": 1383277 }, "thread_token_usage": { "input_tokens": 1368430, "cached_input_tokens": 1327872, "cache_write_input_tokens": 0, "output_tokens": 14847, "reasoning_output_tokens": 5370, "total_tokens": 1383277 } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-25T01:11:03.668Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:03.668Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:11:04.667Z", "callId": "call_s8iyph4lOv3h1zf73s7JGs80", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:73546,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:11:06.457Z", "content": "Script completed\nWall time 1.8 seconds\nOutput:\n\nwrote /app/solution/validation.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:04.763Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca26955087d29c10b8e013ba2f4a", "usage": { "input_tokens": 41172, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 8, "total_tokens": 41242 }, "turn_token_usage": { "input_tokens": 1409602, "cached_input_tokens": 1368704, "cache_write_input_tokens": 0, "output_tokens": 14917, "reasoning_output_tokens": 5378, "total_tokens": 1424519 }, "thread_token_usage": { "input_tokens": 1409602, "cached_input_tokens": 1368704, "cache_write_input_tokens": 0, "output_tokens": 14917, "reasoning_output_tokens": 5378, "total_tokens": 1424519 } } }, { "kind": "context", "timestamp": "2026-09-25T01:11:06.455Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-636e98e6-608a-4480-bc4d-6c57c008e9b6", "process_id": "73546", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 64 --epochs 10" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded train_listens.parquet: 2,751,103\nvalidation: 1,969,863 positive events, 73,883 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1945\nepoch 2/10 loss=6.1252\nepoch 3/10 loss=5.9864\nepoch 4/10 loss=5.7848\nepoch 5/10 loss=5.5127\nepoch 6/10 loss=5.1935\nepoch 7/10 loss=4.8423\nepoch 8/10 loss=4.4800\nepoch 9/10 loss=4.1247\nepoch 10/10 loss=3.7825\nwrote /app/solution/validation.parquet\n", "stderr": "", "aggregated_output": "loaded train_listens.parquet: 2,751,103\nvalidation: 1,969,863 positive events, 73,883 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1945\nepoch 2/10 loss=6.1252\nepoch 3/10 loss=5.9864\nepoch 4/10 loss=5.7848\nepoch 5/10 loss=5.5127\nepoch 6/10 loss=5.1935\nepoch 7/10 loss=4.8423\nepoch 8/10 loss=4.4800\nepoch 9/10 loss=4.1247\nepoch 10/10 loss=3.7825\nwrote /app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 69, "nanos": 320710619 }, "formatted_output": "loaded train_listens.parquet: 2,751,103\nvalidation: 1,969,863 positive events, 73,883 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1945\nepoch 2/10 loss=6.1252\nepoch 3/10 loss=5.9864\nepoch 4/10 loss=5.7848\nepoch 5/10 loss=5.5127\nepoch 6/10 loss=5.1935\nepoch 7/10 loss=4.8423\nepoch 8/10 loss=4.4800\nepoch 9/10 loss=4.1247\nepoch 10/10 loss=3.7825\nwrote /app/solution/validation.parquet\n" } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-25T01:11:08.084Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:08.084Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:11:09.115Z", "callId": "call_HaJ2dDemWwmbszXCH57iaShK", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:11:12.569Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.001922,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:09.266Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca2aa4d087d2b0bbd996f90588a1", "usage": { "input_tokens": 41276, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 6, "total_tokens": 41348 }, "turn_token_usage": { "input_tokens": 1450878, "cached_input_tokens": 1409664, "cache_write_input_tokens": 0, "output_tokens": 14989, "reasoning_output_tokens": 5384, "total_tokens": 1465867 }, "thread_token_usage": { "input_tokens": 1450878, "cached_input_tokens": 1409664, "cache_write_input_tokens": 0, "output_tokens": 14989, "reasoning_output_tokens": 5384, "total_tokens": 1465867 } } }, { "kind": "context", "timestamp": "2026-09-25T01:11:12.565Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5cc3e87f-315f-49f3-956d-9cbc5d56df40", "process_id": "12278", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.001922,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.001922,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 285742318 }, "formatted_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.001922,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-25T01:11:21.694Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:21.694Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:11:23.449Z", "text": "The first neural-only validation pass is valid but underperforms popularity (0.001922 vs. 0.010710). Its sampled-softmax loss improves strongly, so it is learning taste structure, but the raw cosine score overpromotes tail items. I’m calibrating the learned dot product against catalog popularity inside the same exact two-tower dot-product decomposition; this is the key validation-stage model-selection step." }, { "kind": "tool", "timestamp": "2026-09-25T01:11:35.284Z", "callId": "call_zrUxYxYEc0ZKCl6m7V2jeKi5", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rank_blends.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Calibration sweep: popularity is an extra coordinate in the exact dot product.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np, pandas as pd, torch\\n+from torch import nn\\n+from train_two_tower import DATA, OUT, TwoTower, load_events, build_inference_histories, TRAIN_CUTOFF\\n+\\n+def main():\\n+ cat=pd.read_parquet(DATA/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);n=len(items)\\n+ lookup=np.full(int(items.max())+1,-1,np.int32);lookup[items]=np.arange(n,dtype=np.int32)\\n+ ck=torch.load(OUT/'model_validation.pt',map_location='cpu',weights_only=True)\\n+ rank=cat.popularity_rank.to_numpy(np.float32)\\n+ pf=np.stack([np.log1p(cat.positive_listens.to_numpy())/12,np.log1p(cat.organic_positive_listens.to_numpy())/12,-np.log(rank)/12],1).astype('float32')\\n+ m=TwoTower(n,ck['dim'],ck['max_hist'],torch.from_numpy(pf));m.load_state_dict(ck['state_dict']);m.eval()\\n+ ev=load_events('validation',TRAIN_CUTOFF,lookup)\\n+ z=np.load(DATA/'validation_eligible_candidates.npz');uids=z['uids'];packed=z['packed_eligible']\\n+ h=build_inference_histories(ev,uids,ck['max_hist'],DATA/'validation_preference_state.parquet',lookup)\\n+ with torch.no_grad():\\n+ iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\\n+ uv=nn.functional.normalize(m.user_tower(h),dim=1)\\n+ neural=(uv@iv.T).numpy()\\n+ pop=-np.log(rank)[None,:]\\n+ for alpha in [0.25,0.5,1,2,4,8,16]:\\n+ rows=[]\\n+ for r,u in enumerate(uids):\\n+ s=pop[0]+alpha*neural[r]\\n+ ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\\n+ top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\\n+ rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{alpha}.parquet',index=False)\\n+\\n+if __name__=='__main__':main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:11:35.379Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:35.292Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1831f07a-ae39-4882-872f-239ec2bb016d", "changes": { "/app/solution/rank_blends.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Calibration sweep: popularity is an extra coordinate in the exact dot product.\"\"\"\nfrom pathlib import Path\nimport numpy as np, pandas as pd, torch\nfrom torch import nn\nfrom train_two_tower import DATA, OUT, TwoTower, load_events, build_inference_histories, TRAIN_CUTOFF\n\ndef main():\n cat=pd.read_parquet(DATA/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);n=len(items)\n lookup=np.full(int(items.max())+1,-1,np.int32);lookup[items]=np.arange(n,dtype=np.int32)\n ck=torch.load(OUT/'model_validation.pt',map_location='cpu',weights_only=True)\n rank=cat.popularity_rank.to_numpy(np.float32)\n pf=np.stack([np.log1p(cat.positive_listens.to_numpy())/12,np.log1p(cat.organic_positive_listens.to_numpy())/12,-np.log(rank)/12],1).astype('float32')\n m=TwoTower(n,ck['dim'],ck['max_hist'],torch.from_numpy(pf));m.load_state_dict(ck['state_dict']);m.eval()\n ev=load_events('validation',TRAIN_CUTOFF,lookup)\n z=np.load(DATA/'validation_eligible_candidates.npz');uids=z['uids'];packed=z['packed_eligible']\n h=build_inference_histories(ev,uids,ck['max_hist'],DATA/'validation_preference_state.parquet',lookup)\n with torch.no_grad():\n iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\n uv=nn.functional.normalize(m.user_tower(h),dim=1)\n neural=(uv@iv.T).numpy()\n pop=-np.log(rank)[None,:]\n for alpha in [0.25,0.5,1,2,4,8,16]:\n rows=[]\n for r,u in enumerate(uids):\n s=pop[0]+alpha*neural[r]\n ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\n top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\n rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{alpha}.parquet',index=False)\n\nif __name__=='__main__':main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/rank_blends.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:11:35.378Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca30c16c87d296e68d58efb8dc97", "usage": { "input_tokens": 41414, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 1198, "reasoning_output_tokens": 445, "total_tokens": 42612 }, "turn_token_usage": { "input_tokens": 1492292, "cached_input_tokens": 1450752, "cache_write_input_tokens": 0, "output_tokens": 16187, "reasoning_output_tokens": 5829, "total_tokens": 1508479 }, "thread_token_usage": { "input_tokens": 1492292, "cached_input_tokens": 1450752, "cache_write_input_tokens": 0, "output_tokens": 16187, "reasoning_output_tokens": 5829, "total_tokens": 1508479 } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-25T01:11:38.022Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:38.022Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:11:39.293Z", "callId": "call_gabsIgLVI7uSnAsc9QNv1f8C", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/rank_blends.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:11:40.459Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=33355", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:39.381Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca47bb1487d2a9f069cd5e83895f", "usage": { "input_tokens": 42636, "cached_input_tokens": 41216, "cache_write_input_tokens": 0, "output_tokens": 87, "reasoning_output_tokens": 9, "total_tokens": 42723 }, "turn_token_usage": { "input_tokens": 1534928, "cached_input_tokens": 1491968, "cache_write_input_tokens": 0, "output_tokens": 16274, "reasoning_output_tokens": 5838, "total_tokens": 1551202 }, "thread_token_usage": { "input_tokens": 1534928, "cached_input_tokens": 1491968, "cache_write_input_tokens": 0, "output_tokens": 16274, "reasoning_output_tokens": 5838, "total_tokens": 1551202 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:11:42.795Z", "callId": "call_FgxfEnT0p42qxDanRCce36gf", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:33355,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:11:47.804Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nloaded train_listens.parquet: 2,751,103\n\nSESSION_ID=33355", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:42.846Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca4cb7cc87d290a930774e4f93a3", "usage": { "input_tokens": 42751, "cached_input_tokens": 42496, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 42811 }, "turn_token_usage": { "input_tokens": 1577679, "cached_input_tokens": 1534464, "cache_write_input_tokens": 0, "output_tokens": 16334, "reasoning_output_tokens": 5838, "total_tokens": 1594013 }, "thread_token_usage": { "input_tokens": 1577679, "cached_input_tokens": 1534464, "cache_write_input_tokens": 0, "output_tokens": 16334, "reasoning_output_tokens": 5838, "total_tokens": 1594013 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:11:50.909Z", "callId": "call_QpOTe6yOm7jrUJNGqfWuEs2L", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:33355,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output); 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if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:12:00.377Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:00.357Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca5c191887d2a16525ef58722083", "usage": { "input_tokens": 42941, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 43001 }, "turn_token_usage": { "input_tokens": 1663473, "cached_input_tokens": 1619712, "cache_write_input_tokens": 0, "output_tokens": 16454, "reasoning_output_tokens": 5838, "total_tokens": 1679927 }, "thread_token_usage": { "input_tokens": 1663473, "cached_input_tokens": 1619712, "cache_write_input_tokens": 0, "output_tokens": 16454, "reasoning_output_tokens": 5838, "total_tokens": 1679927 } } }, { "kind": "context", "timestamp": "2026-09-25T01:12:00.376Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fef91032-dce3-457a-931e-801af5bf57dd", "process_id": "33355", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/rank_blends.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/rank_blends.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded train_listens.parquet: 2,751,103\n", "stderr": "", "aggregated_output": "loaded train_listens.parquet: 2,751,103\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 918421628 }, "formatted_output": "loaded train_listens.parquet: 2,751,103\n" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-25T01:12:01.715Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:01.715Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:12:03.534Z", "callId": "call_PpbVwvAjM1WjRJACk47wXRll", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for a in 0.25 0.5 1 2 4 8 16; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_$a.parquet | tail -5 | tr '\\\\n' ' '; echo alpha=$a; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:12:26.930Z", "content": "Script completed\nWall time 23.4 seconds\nOutput:\n\n \"scores\": { \"quality_ndcg@10\": 0.010731, \"quality_users\": 4263 } } alpha=0.25\n \"scores\": { \"quality_ndcg@10\": 0.010709, \"quality_users\": 4263 } } alpha=0.5\n \"scores\": { \"quality_ndcg@10\": 0.010516, \"quality_users\": 4263 } } alpha=1\n \"scores\": { \"quality_ndcg@10\": 0.010659, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.010838, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.010683, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.009064, \"quality_users\": 4263 } } alpha=16\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:03.634Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca60cde487d2b839adbd58ad1ce0", "usage": { "input_tokens": 43024, "cached_input_tokens": 42752, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 7, "total_tokens": 43141 }, "turn_token_usage": { "input_tokens": 1706497, "cached_input_tokens": 1662464, "cache_write_input_tokens": 0, "output_tokens": 16571, "reasoning_output_tokens": 5845, "total_tokens": 1723068 }, "thread_token_usage": { "input_tokens": 1706497, "cached_input_tokens": 1662464, "cache_write_input_tokens": 0, "output_tokens": 16571, "reasoning_output_tokens": 5845, "total_tokens": 1723068 } } }, { "kind": "context", "timestamp": "2026-09-25T01:12:26.926Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c085153-03b7-4ea6-b313-c0ee82cfeeee", "process_id": "21175", "command": [ "/bin/sh", "-lc", "for a in 0.25 0.5 1 2 4 8 16; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_$a.parquet | tail -5 | tr '\\n' ' '; echo alpha=$a; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0.25 0.5 1 2 4 8 16; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_$a.parquet | tail -5 | tr '\\n' ' '; echo alpha=$a; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"scores\": { \"quality_ndcg@10\": 0.010731, \"quality_users\": 4263 } } alpha=0.25\n \"scores\": { \"quality_ndcg@10\": 0.010709, \"quality_users\": 4263 } } alpha=0.5\n \"scores\": { \"quality_ndcg@10\": 0.010516, \"quality_users\": 4263 } } alpha=1\n \"scores\": { \"quality_ndcg@10\": 0.010659, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.010838, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.010683, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.009064, \"quality_users\": 4263 } } alpha=16\n", "stderr": "", "aggregated_output": " \"scores\": { \"quality_ndcg@10\": 0.010731, \"quality_users\": 4263 } } alpha=0.25\n \"scores\": { \"quality_ndcg@10\": 0.010709, \"quality_users\": 4263 } } alpha=0.5\n \"scores\": { \"quality_ndcg@10\": 0.010516, \"quality_users\": 4263 } } alpha=1\n \"scores\": { \"quality_ndcg@10\": 0.010659, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.010838, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.010683, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.009064, \"quality_users\": 4263 } } alpha=16\n", "exit_code": 0, "duration": { "secs": 23, "nanos": 227296674 }, "formatted_output": " \"scores\": { \"quality_ndcg@10\": 0.010731, \"quality_users\": 4263 } } alpha=0.25\n \"scores\": { \"quality_ndcg@10\": 0.010709, \"quality_users\": 4263 } } alpha=0.5\n \"scores\": { \"quality_ndcg@10\": 0.010516, \"quality_users\": 4263 } } alpha=1\n \"scores\": { \"quality_ndcg@10\": 0.010659, \"quality_users\": 4263 } } alpha=2\n \"scores\": { \"quality_ndcg@10\": 0.010838, \"quality_users\": 4263 } } alpha=4\n \"scores\": { \"quality_ndcg@10\": 0.010683, \"quality_users\": 4263 } } alpha=8\n \"scores\": { \"quality_ndcg@10\": 0.009064, \"quality_users\": 4263 } } alpha=16\n" } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-25T01:12:34.477Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:34.477Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:12:36.222Z", "callId": "call_oNvo0HUqdWUIyEpnDlDRH0gp", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current; for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *train_two_tower.py*|*explore_rank.py*) echo \\\"$d $c\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:12:36.306Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n2174095360\n/proc/10291 /bin/sh -c cat /sys/fs/cgroup/memory.current; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *train_two_tower.py*|*explore_rank.py*) echo \"$d $c\";; esac; done \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:36.271Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9c4b5bcf-aa02-4e5f-a95f-1242908f1930", "process_id": "94767", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); 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for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *train_two_tower.py*|*explore_rank.py*) echo \"$d $c\";; esac; done \n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:12:36.304Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca7b1b8487d2864fa62d9aa6eee1", "usage": { "input_tokens": 43406, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 391, "reasoning_output_tokens": 271, "total_tokens": 43797 }, "turn_token_usage": { "input_tokens": 1749903, "cached_input_tokens": 1705344, "cache_write_input_tokens": 0, "output_tokens": 16962, "reasoning_output_tokens": 6116, "total_tokens": 1766865 }, "thread_token_usage": { "input_tokens": 1749903, "cached_input_tokens": 1705344, "cache_write_input_tokens": 0, "output_tokens": 16962, "reasoning_output_tokens": 6116, "total_tokens": 1766865 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-25T01:12:43.170Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:43.170Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:12:44.945Z", "callId": "call_Zo9TUY8NjzsEBf1zpUMaOja9", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n- since=cutoff-21*86400\\n+ since=cutoff-90*86400\\n@@\\n-def build_examples(events,targets,max_hist,n_per_user=14):\\n+def build_examples(events,targets,max_hist,n_per_user=28):\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:12:45.026Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:44.963Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-183fa623-0248-4440-9302-1fe7f841ffc1", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -52,3 +52,3 @@\n frames=[]\n- since=cutoff-21*86400\n+ since=cutoff-90*86400\n for p in paths:\n@@ -62,3 +62,3 @@\n \n-def build_examples(events,targets,max_hist,n_per_user=14):\n+def build_examples(events,targets,max_hist,n_per_user=28):\n histories=[]; labels=[]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:12:45.025Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca84964487d29a67050cdc0a2b41", "usage": { "input_tokens": 43909, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 424, "reasoning_output_tokens": 318, "total_tokens": 44333 }, "turn_token_usage": { "input_tokens": 1793812, "cached_input_tokens": 1748608, "cache_write_input_tokens": 0, "output_tokens": 17386, "reasoning_output_tokens": 6434, "total_tokens": 1811198 }, "thread_token_usage": { "input_tokens": 1793812, "cached_input_tokens": 1748608, "cache_write_input_tokens": 0, "output_tokens": 17386, "reasoning_output_tokens": 6434, "total_tokens": 1811198 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-25T01:12:46.260Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:46.260Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:12:47.785Z", "callId": "call_ItYDuamLP9lnN3TiVpChAl73", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 96 --epochs 12 --seed 2027\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:12:48.953Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:47.975Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca8d4a4087d2b4891ea8d06d929b", "usage": { "input_tokens": 44357, "cached_input_tokens": 43776, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 11, "total_tokens": 44462 }, "turn_token_usage": { "input_tokens": 1838169, "cached_input_tokens": 1792384, "cache_write_input_tokens": 0, "output_tokens": 17491, "reasoning_output_tokens": 6445, "total_tokens": 1855660 }, "thread_token_usage": { "input_tokens": 1838169, "cached_input_tokens": 1792384, "cache_write_input_tokens": 0, "output_tokens": 17491, "reasoning_output_tokens": 6445, "total_tokens": 1855660 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:12:51.636Z", "callId": "call_USwRS9mXFF9oNKLQRsiZqZs9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:12:56.653Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nloaded train_listens.parquet: 10,582,861\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:51.733Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca91542c87d2b049155969b2e82a", "usage": { "input_tokens": 44490, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 44550 }, "turn_token_usage": { "input_tokens": 1882659, "cached_input_tokens": 1836544, "cache_write_input_tokens": 0, "output_tokens": 17551, "reasoning_output_tokens": 6445, "total_tokens": 1900210 }, "thread_token_usage": { "input_tokens": 1882659, "cached_input_tokens": 1836544, "cache_write_input_tokens": 0, "output_tokens": 17551, "reasoning_output_tokens": 6445, "total_tokens": 1900210 } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-25T01:12:57.794Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:57.794Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:12:58.758Z", "callId": "call_dcmSlrybWbrV0P3D5tODA0Tj", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:13:03.768Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nvalidation: 7,755,443 positive events, 185,855 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:00.334Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ca98d81887d295cb52cab1c49eda", "usage": { "input_tokens": 44592, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 6, "total_tokens": 44660 }, "turn_token_usage": { "input_tokens": 1927251, "cached_input_tokens": 1880832, "cache_write_input_tokens": 0, "output_tokens": 17619, "reasoning_output_tokens": 6451, "total_tokens": 1944870 }, "thread_token_usage": { "input_tokens": 1927251, "cached_input_tokens": 1880832, "cache_write_input_tokens": 0, "output_tokens": 17619, "reasoning_output_tokens": 6451, "total_tokens": 1944870 } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-25T01:13:05.600Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:13:05.600Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:13:06.547Z", "callId": "call_OgCOXH5pA2xIjiO02LpoWlRS", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:13:11.556Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:06.659Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5caa01dc087d29e8e7305b4445bd0", "usage": { "input_tokens": 44779, "cached_input_tokens": 44416, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 11, "total_tokens": 44852 }, "turn_token_usage": { "input_tokens": 1972030, "cached_input_tokens": 1925248, "cache_write_input_tokens": 0, "output_tokens": 17692, "reasoning_output_tokens": 6462, "total_tokens": 1989722 }, "thread_token_usage": { "input_tokens": 1972030, "cached_input_tokens": 1925248, "cache_write_input_tokens": 0, "output_tokens": 17692, "reasoning_output_tokens": 6462, "total_tokens": 1989722 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:13:13.768Z", "callId": "call_5JyqWPPh0ezk3e6PAivUQJSd", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:13:18.793Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 1/12 loss=6.1397\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:13.917Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5caa7c6bc87d2a5e9527331c54ec7", "usage": { "input_tokens": 44880, "cached_input_tokens": 44544, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 44940 }, "turn_token_usage": { "input_tokens": 2016910, "cached_input_tokens": 1969792, "cache_write_input_tokens": 0, "output_tokens": 17752, "reasoning_output_tokens": 6462, "total_tokens": 2034662 }, "thread_token_usage": { "input_tokens": 2016910, "cached_input_tokens": 1969792, "cache_write_input_tokens": 0, "output_tokens": 17752, "reasoning_output_tokens": 6462, "total_tokens": 2034662 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:13:21.035Z", "callId": "call_jC3FqosAoYdJvM3JVgE2WSa5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:13:26.045Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:21.183Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5caaf341887d2b9029fc79349f3af", "usage": { "input_tokens": 44980, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 45040 }, "turn_token_usage": { "input_tokens": 2061890, "cached_input_tokens": 2014464, "cache_write_input_tokens": 0, "output_tokens": 17812, "reasoning_output_tokens": 6462, "total_tokens": 2079702 }, "thread_token_usage": { "input_tokens": 2061890, "cached_input_tokens": 2014464, "cache_write_input_tokens": 0, "output_tokens": 17812, "reasoning_output_tokens": 6462, "total_tokens": 2079702 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:13:30.358Z", "callId": "call_bkmXBo7gj0zRUYHkWIVBADG5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:13:35.368Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 2/12 loss=5.9723\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:30.455Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cab6643087d2bb6f7e148536439d", "usage": { "input_tokens": 45068, "cached_input_tokens": 44800, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 45128 }, "turn_token_usage": { "input_tokens": 2106958, "cached_input_tokens": 2059264, "cache_write_input_tokens": 0, "output_tokens": 17872, "reasoning_output_tokens": 6462, "total_tokens": 2124830 }, "thread_token_usage": { "input_tokens": 2106958, "cached_input_tokens": 2059264, "cache_write_input_tokens": 0, "output_tokens": 17872, "reasoning_output_tokens": 6462, "total_tokens": 2124830 } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-25T01:13:40.914Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:13:40.914Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:13:48.158Z", "callId": "call_eYSNEjIBumlFV3RaYnCKTlMv", "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 pathlib import Path\\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\\nfor name,s in scores.items():\\n order=np.argsort(-s,kind='stable');rows=[]\\n for r,u in enumerate(z['uids']):\\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\\nPY\\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\\\\n' ' '; echo $n; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:14:18.336Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=8336", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:48.285Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cabf9a3487d2925ea55a98460658", "usage": { "input_tokens": 45168, "cached_input_tokens": 44928, "cache_write_input_tokens": 0, "output_tokens": 609, "reasoning_output_tokens": 191, "total_tokens": 45777 }, "turn_token_usage": { "input_tokens": 2152126, "cached_input_tokens": 2104192, "cache_write_input_tokens": 0, "output_tokens": 18481, "reasoning_output_tokens": 6653, "total_tokens": 2170607 }, "thread_token_usage": { "input_tokens": 2152126, "cached_input_tokens": 2104192, "cache_write_input_tokens": 0, "output_tokens": 18481, "reasoning_output_tokens": 6653, "total_tokens": 2170607 } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-25T01:14:24.016Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:24.016Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:14:24.017Z", "callId": "call_wjJD3cFSWyWiPE0bZcI9pkTT", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8336,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:14:29.026Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8336", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:14:24.111Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5caea8eec87d2b32a0b69c587aa6a", "usage": { "input_tokens": 45805, "cached_input_tokens": 44928, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 10, "total_tokens": 45877 }, "turn_token_usage": { "input_tokens": 2197931, "cached_input_tokens": 2149120, "cache_write_input_tokens": 0, "output_tokens": 18553, "reasoning_output_tokens": 6663, "total_tokens": 2216484 }, "thread_token_usage": { "input_tokens": 2197931, "cached_input_tokens": 2149120, "cache_write_input_tokens": 0, "output_tokens": 18553, "reasoning_output_tokens": 6663, "total_tokens": 2216484 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-25T01:14:30.895Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:30.895Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:14:31.851Z", "callId": "call_RVhDxMqzOLNJOcLKeeRpvgHr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:14:36.861Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 3/12 loss=5.7017\nepoch 4/12 loss=5.3605\nepoch 5/12 loss=5.0090\nepoch 6/12 loss=4.6653\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:14:31.966Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5caf5376887d2804c0b408fa358cd", "usage": { "input_tokens": 45905, "cached_input_tokens": 45568, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 17, "total_tokens": 45984 }, "turn_token_usage": { "input_tokens": 2243836, "cached_input_tokens": 2194688, "cache_write_input_tokens": 0, "output_tokens": 18632, "reasoning_output_tokens": 6680, "total_tokens": 2262468 }, "thread_token_usage": { "input_tokens": 2243836, "cached_input_tokens": 2194688, "cache_write_input_tokens": 0, "output_tokens": 18632, "reasoning_output_tokens": 6680, "total_tokens": 2262468 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-25T01:14:38.210Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:38.210Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:14:39.177Z", "callId": "call_KdYttOHsheDNPVQ84qSXxu2r", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8336,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:14:44.188Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8336", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:14:39.315Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cafd181087d2a6e40181174c25bf", "usage": { "input_tokens": 46060, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 7, "total_tokens": 46129 }, "turn_token_usage": { "input_tokens": 2289896, "cached_input_tokens": 2240384, "cache_write_input_tokens": 0, "output_tokens": 18701, "reasoning_output_tokens": 6687, "total_tokens": 2308597 }, "thread_token_usage": { "input_tokens": 2289896, "cached_input_tokens": 2240384, "cache_write_input_tokens": 0, "output_tokens": 18701, "reasoning_output_tokens": 6687, "total_tokens": 2308597 } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-25T01:14:45.572Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:45.572Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:14:46.547Z", "callId": "call_7kzDLebj3A7FSxnZ6yAuP7q3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:14:51.557Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 7/12 loss=4.3371\n\nSESSION_ID=23650", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:14:46.634Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb045ecc87d28402a7102f659934", "usage": { "input_tokens": 46157, "cached_input_tokens": 45824, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 20, "total_tokens": 46239 }, "turn_token_usage": { "input_tokens": 2336053, "cached_input_tokens": 2286208, "cache_write_input_tokens": 0, "output_tokens": 18783, "reasoning_output_tokens": 6707, "total_tokens": 2354836 }, "thread_token_usage": { "input_tokens": 2336053, "cached_input_tokens": 2286208, "cache_write_input_tokens": 0, "output_tokens": 18783, "reasoning_output_tokens": 6707, "total_tokens": 2354836 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:14:53.486Z", "callId": "call_9Lt1tLHrv3hI6MPF9Es3bk4p", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:23650,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); 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if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:15:58.277Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\nwrote /app/solution/validation.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:15:56.910Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb4ad11887d2b40e92110e0f926c", "usage": { "input_tokens": 47043, "cached_input_tokens": 46720, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 6, "total_tokens": 47111 }, "turn_token_usage": { "input_tokens": 2756008, "cached_input_tokens": 2703616, "cache_write_input_tokens": 0, "output_tokens": 19331, "reasoning_output_tokens": 6713, "total_tokens": 2775339 }, "thread_token_usage": { "input_tokens": 2756008, "cached_input_tokens": 2703616, "cache_write_input_tokens": 0, "output_tokens": 19331, "reasoning_output_tokens": 6713, "total_tokens": 2775339 } } }, { "kind": "context", "timestamp": "2026-09-25T01:15:58.275Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e4b9b3e7-190c-4bba-b688-f37375689b6f", "process_id": "23650", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 96 --epochs 12 --seed 2027" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 96 --epochs 12 --seed 2027" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded train_listens.parquet: 10,582,861\nvalidation: 7,755,443 positive events, 185,855 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/12 loss=6.1397\nepoch 2/12 loss=5.9723\nepoch 3/12 loss=5.7017\nepoch 4/12 loss=5.3605\nepoch 5/12 loss=5.0090\nepoch 6/12 loss=4.6653\nepoch 7/12 loss=4.3371\nepoch 8/12 loss=4.0337\nepoch 9/12 loss=3.7555\nepoch 10/12 loss=3.5045\nepoch 11/12 loss=3.2760\nepoch 12/12 loss=3.0684\nwrote /app/solution/validation.parquet\n", "stderr": "", "aggregated_output": "loaded train_listens.parquet: 10,582,861\nvalidation: 7,755,443 positive events, 185,855 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/12 loss=6.1397\nepoch 2/12 loss=5.9723\nepoch 3/12 loss=5.7017\nepoch 4/12 loss=5.3605\nepoch 5/12 loss=5.0090\nepoch 6/12 loss=4.6653\nepoch 7/12 loss=4.3371\nepoch 8/12 loss=4.0337\nepoch 9/12 loss=3.7555\nepoch 10/12 loss=3.5045\nepoch 11/12 loss=3.2760\nepoch 12/12 loss=3.0684\nwrote /app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 190, "nanos": 323314763 }, "formatted_output": "loaded train_listens.parquet: 10,582,861\nvalidation: 7,755,443 positive events, 185,855 training examples\n/app/solution/train_two_tower.py:116: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/12 loss=6.1397\nepoch 2/12 loss=5.9723\nepoch 3/12 loss=5.7017\nepoch 4/12 loss=5.3605\nepoch 5/12 loss=5.0090\nepoch 6/12 loss=4.6653\nepoch 7/12 loss=4.3371\nepoch 8/12 loss=4.0337\nepoch 9/12 loss=3.7555\nepoch 10/12 loss=3.5045\nepoch 11/12 loss=3.2760\nepoch 12/12 loss=3.0684\nwrote /app/solution/validation.parquet\n" } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-25T01:15:59.805Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:15:59.805Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:00.795Z", "callId": "call_gkX9FR9Rn1QlLSuBtYtMiyob", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8336,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:16:05.804Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8336", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:00.892Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb4e851487d2ba6f22c58cf74f0c", "usage": { "input_tokens": 47145, "cached_input_tokens": 46848, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 28, "total_tokens": 47235 }, "turn_token_usage": { "input_tokens": 2803153, "cached_input_tokens": 2750464, "cache_write_input_tokens": 0, "output_tokens": 19421, "reasoning_output_tokens": 6741, "total_tokens": 2822574 }, "thread_token_usage": { "input_tokens": 2803153, "cached_input_tokens": 2750464, "cache_write_input_tokens": 0, "output_tokens": 19421, "reasoning_output_tokens": 6741, "total_tokens": 2822574 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-25T01:16:07.333Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:07.333Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:09.153Z", "callId": "call_Z76itz05pwiJydFuu6jv6CCU", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *validation_global*) echo \\\"$d $c\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:09.263Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/proc/10358 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(z['uids']):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\n' ' '; echo $n; done \n/proc/10425 /bin/sh -c ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global*) echo \"$d $c\";; esac; done \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:09.203Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d49646ef-ab5c-438d-94f6-1709d04aa925", "process_id": "84274", "command": [ "/bin/sh", "-lc", "ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global*) echo \"$d $c\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global*) echo \"$d $c\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/10358 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(z['uids']):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\n' ' '; echo $n; done \n/proc/10425 /bin/sh -c ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global*) echo \"$d $c\";; esac; done \n", "stderr": "", "aggregated_output": "/proc/10358 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(z['uids']):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\n' ' '; echo $n; done \n/proc/10425 /bin/sh -c ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global*) echo \"$d $c\";; esac; done \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3500 }, "formatted_output": "/proc/10358 /bin/sh -c /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(z['uids']):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\n' ' '; echo $n; done \n/proc/10425 /bin/sh -c ls -lh solution/validation_global_* 2>/dev/null; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global*) echo \"$d $c\";; esac; done \n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:09.261Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb55fe4c87d28e32266b4dc2f5df", "usage": { "input_tokens": 47263, "cached_input_tokens": 46976, "cache_write_input_tokens": 0, "output_tokens": 138, "reasoning_output_tokens": 24, "total_tokens": 47401 }, "turn_token_usage": { "input_tokens": 2850416, "cached_input_tokens": 2797440, "cache_write_input_tokens": 0, "output_tokens": 19559, "reasoning_output_tokens": 6765, "total_tokens": 2869975 }, "thread_token_usage": { "input_tokens": 2850416, "cached_input_tokens": 2797440, "cache_write_input_tokens": 0, "output_tokens": 19559, "reasoning_output_tokens": 6765, "total_tokens": 2869975 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-25T01:16:12.008Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:12.008Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:13.119Z", "callId": "call_JIQ9DeBEqRe8JwECtcQzzlL9", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"cat /proc/10358/task/10358/children; cat /sys/fs/cgroup/memory.current\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:13.247Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n10359 2296872960\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:13.158Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-10aae0e5-ee55-4b38-8359-206eb9aa8b70", "process_id": "29644", "command": [ "/bin/sh", "-lc", "cat /proc/10358/task/10358/children; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /proc/10358/task/10358/children", "name": "children", "path": "/proc/10358/task/10358/children" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "10359 2296872960\n", "stderr": "", "aggregated_output": "10359 2296872960\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4760 }, "formatted_output": "10359 2296872960\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:13.246Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb59d19887d2bc87cfdc83bec493", "usage": { "input_tokens": 47853, "cached_input_tokens": 47104, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 14, "total_tokens": 47936 }, "turn_token_usage": { "input_tokens": 2898269, "cached_input_tokens": 2844544, "cache_write_input_tokens": 0, "output_tokens": 19642, "reasoning_output_tokens": 6779, "total_tokens": 2917911 }, "thread_token_usage": { "input_tokens": 2898269, "cached_input_tokens": 2844544, "cache_write_input_tokens": 0, "output_tokens": 19642, "reasoning_output_tokens": 6779, "total_tokens": 2917911 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-25T01:16:15.787Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:15.787Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:16.836Z", "callId": "call_OaMYDOVg6YNkQIRN6akz8570", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:20.346Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003457,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:16.940Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb5d884887d2acbe9ca7fed37fac", "usage": { "input_tokens": 47967, "cached_input_tokens": 47616, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 63, "total_tokens": 48096 }, "turn_token_usage": { "input_tokens": 2946236, "cached_input_tokens": 2892160, "cache_write_input_tokens": 0, "output_tokens": 19771, "reasoning_output_tokens": 6842, "total_tokens": 2966007 }, "thread_token_usage": { "input_tokens": 2946236, "cached_input_tokens": 2892160, "cache_write_input_tokens": 0, "output_tokens": 19771, "reasoning_output_tokens": 6842, "total_tokens": 2966007 } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:20.342Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f8bfd0d2-880f-4c9e-82d2-5f6f000a2d39", "process_id": "48216", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003457,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003457,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 340062350 }, "formatted_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003457,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-25T01:16:24.133Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:24.133Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:25.023Z", "callId": "call_ICwQMk6rnCllmAWanGxB1CgN", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:25.154Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n2297995264\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:25.060Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2942e1a2-ff1f-4ceb-b874-b59a4c737f8e", "process_id": "10388", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2297995264\n", "stderr": "", "aggregated_output": "2297995264\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3740 }, "formatted_output": "2297995264\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:25.153Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb65e3d087d28babee2d5b87c632", "usage": { "input_tokens": 48162, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 49, "total_tokens": 48267 }, "turn_token_usage": { "input_tokens": 2994398, "cached_input_tokens": 2939904, "cache_write_input_tokens": 0, "output_tokens": 19876, "reasoning_output_tokens": 6891, "total_tokens": 3014274 }, "thread_token_usage": { "input_tokens": 2994398, "cached_input_tokens": 2939904, "cache_write_input_tokens": 0, "output_tokens": 19876, "reasoning_output_tokens": 6891, "total_tokens": 3014274 } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-25T01:16:26.779Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:26.779Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:28.048Z", "callId": "call_lhtPcv4OgtmNirgJ8ypqfNVV", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/rank_blends.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:16:29.215Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=57828", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:28.140Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": 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}, "result": { "timestamp": "2026-09-25T01:16:36.967Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nloaded train_listens.parquet: 10,582,861\n\nSESSION_ID=57828", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:32.062Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb6d697087d2bd2637c3465fad54", "usage": { "input_tokens": 48416, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 48476 }, "turn_token_usage": { "input_tokens": 3091109, "cached_input_tokens": 3036032, "cache_write_input_tokens": 0, "output_tokens": 20029, "reasoning_output_tokens": 6906, "total_tokens": 3111138 }, "thread_token_usage": { "input_tokens": 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/sys/fs/cgroup/memory.current; cat /proc/10359/status | rg 'State|VmRSS'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:16:46.860Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n4314189824\nState:\tR (running)\nVmRSS:\t 182552 kB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:46.768Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f00d63f3-045e-430a-bc4e-8c138ad97d89", "process_id": "49294", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current; cat /proc/10359/status | rg 'State|VmRSS'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" }, { "type": "read", "cmd": "cat /proc/10359/status", "name": "status", "path": "/proc/10359/status" }, { "type": "search", "cmd": "rg 'State|VmRSS'", "query": "State|VmRSS", "path": null } ], "source": "unified_exec_startup", "status": "completed", "stdout": "4314189824\nState:\tR (running)\nVmRSS:\t 182552 kB\n", "stderr": "", "aggregated_output": "4314189824\nState:\tR (running)\nVmRSS:\t 182552 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4360 }, "formatted_output": "4314189824\nState:\tR (running)\nVmRSS:\t 182552 kB\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:46.858Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb7c53d887d2b144bf58b18e2143", "usage": { "input_tokens": 48606, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 17, "total_tokens": 48695 }, "turn_token_usage": { "input_tokens": 3188233, "cached_input_tokens": 3132672, "cache_write_input_tokens": 0, "output_tokens": 20178, "reasoning_output_tokens": 6923, "total_tokens": 3208411 }, "thread_token_usage": { "input_tokens": 3188233, "cached_input_tokens": 3132672, "cache_write_input_tokens": 0, "output_tokens": 20178, "reasoning_output_tokens": 6923, "total_tokens": 3208411 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-25T01:16:49.029Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:49.029Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:50.056Z", "callId": "call_TZHI1u17UxJBdrd1fkGYtzjr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:57828,chars:\"\",yield_time_ms:1000,max_output_tokens:1000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:16:50.263Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:50.120Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb7f2ac087d29243c5d514137a10", "usage": { "input_tokens": 48739, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 38, "total_tokens": 48839 }, "turn_token_usage": { "input_tokens": 3236972, "cached_input_tokens": 3181056, "cache_write_input_tokens": 0, "output_tokens": 20278, "reasoning_output_tokens": 6961, "total_tokens": 3257250 }, "thread_token_usage": { "input_tokens": 3236972, "cached_input_tokens": 3181056, "cache_write_input_tokens": 0, "output_tokens": 20278, "reasoning_output_tokens": 6961, "total_tokens": 3257250 } } }, { "kind": "context", "timestamp": "2026-09-25T01:16:50.260Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b1cec2e8-97b7-46a4-83c1-93f5c74a1774", "process_id": "57828", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/rank_blends.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/rank_blends.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded train_listens.parquet: 10,582,861\n", "stderr": "", "aggregated_output": "loaded train_listens.parquet: 10,582,861\n", "exit_code": 0, "duration": { "secs": 22, "nanos": 46895144 }, "formatted_output": "loaded train_listens.parquet: 10,582,861\n" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-25T01:16:55.471Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:16:55.471Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:16:55.472Z", "callId": "call_F41DeMAEUIl6P2wRZXdCAGb0", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for a in 0.25 0.5 1 2 4 8 16; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_$a.parquet | rg 'quality_ndcg'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:17:19.359Z", "content": "Script completed\nWall time 23.9 seconds\nOutput:\n\n \"quality_ndcg@10\": 0.010715,\n \"quality_ndcg@10\": 0.010718,\n \"quality_ndcg@10\": 0.010764,\n \"quality_ndcg@10\": 0.010767,\n \"quality_ndcg@10\": 0.01094,\n \"quality_ndcg@10\": 0.011497,\n \"quality_ndcg@10\": 0.011586,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:55.582Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb82a61087d299df1fdc98f93d58", "usage": { "input_tokens": 48862, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 125, "reasoning_output_tokens": 9, "total_tokens": 48987 }, "turn_token_usage": { "input_tokens": 3285834, "cached_input_tokens": 3229568, "cache_write_input_tokens": 0, "output_tokens": 20403, "reasoning_output_tokens": 6970, "total_tokens": 3306237 }, "thread_token_usage": { "input_tokens": 3285834, "cached_input_tokens": 3229568, "cache_write_input_tokens": 0, "output_tokens": 20403, "reasoning_output_tokens": 6970, "total_tokens": 3306237 } } }, { "kind": "context", "timestamp": "2026-09-25T01:17:19.355Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5e3118ae-222c-4244-9ac7-d9d79994b6cf", "process_id": "76259", "command": [ "/bin/sh", "-lc", "for a in 0.25 0.5 1 2 4 8 16; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_$a.parquet | rg 'quality_ndcg'; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for a in 0.25 0.5 1 2 4 8 16; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_$a.parquet | rg 'quality_ndcg'; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.010715,\n \"quality_ndcg@10\": 0.010718,\n \"quality_ndcg@10\": 0.010764,\n \"quality_ndcg@10\": 0.010767,\n \"quality_ndcg@10\": 0.01094,\n \"quality_ndcg@10\": 0.011497,\n \"quality_ndcg@10\": 0.011586,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.010715,\n \"quality_ndcg@10\": 0.010718,\n \"quality_ndcg@10\": 0.010764,\n \"quality_ndcg@10\": 0.010767,\n \"quality_ndcg@10\": 0.01094,\n \"quality_ndcg@10\": 0.011497,\n \"quality_ndcg@10\": 0.011586,\n", "exit_code": 0, "duration": { "secs": 23, "nanos": 717041890 }, "formatted_output": " \"quality_ndcg@10\": 0.010715,\n \"quality_ndcg@10\": 0.010718,\n \"quality_ndcg@10\": 0.010764,\n \"quality_ndcg@10\": 0.010767,\n \"quality_ndcg@10\": 0.01094,\n \"quality_ndcg@10\": 0.011497,\n \"quality_ndcg@10\": 0.011586,\n" } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-25T01:17:23.050Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:23.050Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:24.018Z", "callId": "call_RfhwQ0jcz7ajd9704KBKvqqX", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8336,chars:\"\",yield_time_ms:1000,max_output_tokens:3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:17:29.026Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8336", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:24.146Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cb9f9dac87d2a25381b06d47ac61", "usage": { "input_tokens": 49108, "cached_input_tokens": 48640, "cache_write_input_tokens": 0, "output_tokens": 199, "reasoning_output_tokens": 137, "total_tokens": 49307 }, "turn_token_usage": { "input_tokens": 3334942, "cached_input_tokens": 3278208, "cache_write_input_tokens": 0, "output_tokens": 20602, "reasoning_output_tokens": 7107, "total_tokens": 3355544 }, "thread_token_usage": { "input_tokens": 3334942, "cached_input_tokens": 3278208, "cache_write_input_tokens": 0, "output_tokens": 20602, "reasoning_output_tokens": 7107, "total_tokens": 3355544 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-25T01:17:32.475Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:32.475Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:33.526Z", "callId": "call_EJPYjxCZmJY2iNCDZXU2TYPx", "name": "exec", "input": { "code": "let{output,...rest}=await tools.exec_command({cmd:\"kill 10358 10359 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});text(rest);text(output);\n" }, "result": { "timestamp": "2026-09-25T01:17:33.651Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"f1032f\",\"wall_time_seconds\":0.00000283,\"exit_code\":0,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:33.555Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-660f8538-fe90-40c0-ba0e-410785dc9dfc", "process_id": "8336", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(z['uids']):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\\n' ' '; echo $n; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; assert np.array_equal(items,cat.item_id)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable');rows=[]\n for r,u in enumerate(z['uids']):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);top=order[ok[order]][:100]\n rows.extend((int(u),int(items[k]),j+1) for j,k in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | tail -5 | tr '\\n' ' '; echo $n; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 225, "nanos": 221740042 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:17:33.561Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9261bb5e-06ef-4543-97f5-134f64b2b48b", "process_id": "52667", "command": [ "/bin/sh", "-lc", "kill 10358 10359 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 10358 10359 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 2830 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:17:33.650Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": 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"call_3iMux86eP7d3RJtUmGReijVN", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n def build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\\n@@\\n- pref=pd.read_parquet(pref_path)\\n- pref=pref[pref.state.eq('liked')]\\n- for uid,g in pref.groupby('uid',sort=False):\\n- raw=g.item_id.to_numpy(np.int64); ok=raw=0]\\n- if not len(liked): continue\\n- base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\\n- by[int(uid)]=base[-max_hist:]\\n+ if pref_path is not None:\\n+ pref=pd.read_parquet(pref_path)\\n+ pref=pref[pref.state.eq('liked')]\\n+ for uid,g in pref.groupby('uid',sort=False):\\n+ raw=g.item_id.to_numpy(np.int64); ok=raw=0]\\n+ if not len(liked): continue\\n+ base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\\n+ by[int(uid)]=base[-max_hist:]\\n*** Update File: /app/solution/rank_blends.py\\n@@\\n- h=build_inference_histories(ev,uids,ck['max_hist'],DATA/'validation_preference_state.parquet',lookup)\\n- with torch.no_grad():\\n- iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\\n- uv=nn.functional.normalize(m.user_tower(h),dim=1)\\n- neural=(uv@iv.T).numpy()\\n- pop=-np.log(rank)[None,:]\\n- for alpha in [0.25,0.5,1,2,4,8,16]:\\n- rows=[]\\n- for r,u in enumerate(uids):\\n- s=pop[0]+alpha*neural[r]\\n- ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\\n- top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\\n- rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\\n- pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{alpha}.parquet',index=False)\\n+ pop=-np.log(rank)[None,:]\\n+ with torch.no_grad(): iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\\n+ for mode,pref in [('pref',DATA/'validation_preference_state.parquet'),('nopref',None)]:\\n+ h=build_inference_histories(ev,uids,ck['max_hist'],pref,lookup)\\n+ with torch.no_grad(): neural=(nn.functional.normalize(m.user_tower(h),dim=1)@iv.T).numpy()\\n+ for alpha in [8,12,16,20,24,32]:\\n+ rows=[]\\n+ for r,u in enumerate(uids):\\n+ s=pop[0]+alpha*neural[r]\\n+ ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\\n+ top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\\n+ rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{mode}_{alpha}.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:17:55.894Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:55.808Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ae1d18f3-d3d4-41b1-a5c7-a6eefdaf4f37", "changes": { "/app/solution/rank_blends.py": { "type": "update", "unified_diff": "@@ -16,16 +16,15 @@\n z=np.load(DATA/'validation_eligible_candidates.npz');uids=z['uids'];packed=z['packed_eligible']\n- h=build_inference_histories(ev,uids,ck['max_hist'],DATA/'validation_preference_state.parquet',lookup)\n- with torch.no_grad():\n- iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\n- uv=nn.functional.normalize(m.user_tower(h),dim=1)\n- neural=(uv@iv.T).numpy()\n pop=-np.log(rank)[None,:]\n- for alpha in [0.25,0.5,1,2,4,8,16]:\n- rows=[]\n- for r,u in enumerate(uids):\n- s=pop[0]+alpha*neural[r]\n- ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\n- top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\n- rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\n- pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{alpha}.parquet',index=False)\n+ with torch.no_grad(): iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\n+ for mode,pref in [('pref',DATA/'validation_preference_state.parquet'),('nopref',None)]:\n+ h=build_inference_histories(ev,uids,ck['max_hist'],pref,lookup)\n+ with torch.no_grad(): neural=(nn.functional.normalize(m.user_tower(h),dim=1)@iv.T).numpy()\n+ for alpha in [8,12,16,20,24,32]:\n+ rows=[]\n+ for r,u in enumerate(uids):\n+ s=pop[0]+alpha*neural[r]\n+ ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\n+ top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\n+ rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{mode}_{alpha}.parquet',index=False)\n \n", "move_path": null }, "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -84,9 +84,10 @@\n # Active likes are user-side cutoff state. Append them as most-recent taste evidence.\n- pref=pd.read_parquet(pref_path)\n- pref=pref[pref.state.eq('liked')]\n- for uid,g in pref.groupby('uid',sort=False):\n- raw=g.item_id.to_numpy(np.int64); ok=raw=0]\n- if not len(liked): continue\n- base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\n- by[int(uid)]=base[-max_hist:]\n+ if pref_path is not None:\n+ pref=pd.read_parquet(pref_path)\n+ pref=pref[pref.state.eq('liked')]\n+ for uid,g in pref.groupby('uid',sort=False):\n+ raw=g.item_id.to_numpy(np.int64); ok=raw=0]\n+ if not len(liked): continue\n+ base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\n+ by[int(uid)]=base[-max_hist:]\n out=np.zeros((len(uids),max_hist),np.int32)\n", "move_path": null } }, "status": "completed", "stdout": "Success. 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"kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:26.325Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:33.377Z", "callId": "call_z5mVNecw0I3N3QYWf73kqwi9", "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 pathlib import Path\\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N)\\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\\nfor name,s in scores.items():\\n order=np.argsort(-s,kind='stable')\\n for r in range(N):\\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);out_i[r*100:(r+1)*100]=items[order[ok[order]][:100]]\\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:20:03.542Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=65664", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:33.509Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cc1acd1887d2867be75aaf89001d", "usage": { "input_tokens": 51780, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 514, "reasoning_output_tokens": 116, "total_tokens": 52294 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"cache_write_input_tokens": 0, "output_tokens": 23258, "reasoning_output_tokens": 7537, "total_tokens": 4126136 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:20:33.080Z", "callId": "call_V0eneTTS1UeUJCwkWGlJZrcN", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:65664,chars:\"\",yield_time_ms:1000,max_output_tokens:1000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:20:38.087Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=65664", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:33.174Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cc5e692c87d283a8fedd409fa97c", "usage": { "input_tokens": 52634, "cached_input_tokens": 52352, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 52694 }, "turn_token_usage": { "input_tokens": 4155512, "cached_input_tokens": 4092032, "cache_write_input_tokens": 0, "output_tokens": 23318, "reasoning_output_tokens": 7537, "total_tokens": 4178830 }, "thread_token_usage": { "input_tokens": 4155512, "cached_input_tokens": 4092032, "cache_write_input_tokens": 0, "output_tokens": 23318, "reasoning_output_tokens": 7537, "total_tokens": 4178830 } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-25T01:20:39.865Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:39.865Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:42.021Z", "callId": "call_ZF9cJ13AFLr3uxPCCraXsyad", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *validation_global_*) kill ${d#/proc/} 2>/dev/null || true;; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:20:42.187Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:42.063Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-58e6045a-e56a-4b6c-940e-08e2faeb9361", "process_id": "22284", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global_*) kill ${d#/proc/} 2>/dev/null || true;; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *validation_global_*) kill ${d#/proc/} 2>/dev/null || true;; esac; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 0, "nanos": 3141 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:42.140Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9113b0c9-7c74-4232-88a0-bfcc3e464632", "process_id": "65664", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable')\n for r in range(N):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);out_i[r*100:(r+1)*100]=items[order[ok[order]][:100]]\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable')\n for r in range(N):\n ok=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);out_i[r*100:(r+1)*100]=items[order[ok[order]][:100]]\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 68, "nanos": 600157223 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:42.185Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cc664af887d2845f3e49f410c9dd", "usage": { "input_tokens": 52722, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 25, "total_tokens": 52856 }, "turn_token_usage": { "input_tokens": 4208234, "cached_input_tokens": 4144512, "cache_write_input_tokens": 0, "output_tokens": 23452, "reasoning_output_tokens": 7562, "total_tokens": 4231686 }, "thread_token_usage": { "input_tokens": 4208234, "cached_input_tokens": 4144512, "cache_write_input_tokens": 0, "output_tokens": 23452, "reasoning_output_tokens": 7562, "total_tokens": 4231686 } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-25T01:20:45.866Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:45.866Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:55.622Z", "callId": "call_VhqxXLzVVPJYXAv8mO9RbvXB", "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 pathlib import Path\\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N);bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\\nfor name,s in scores.items():\\n order=np.argsort(-s,kind='stable')[:1000]\\n for r in range(N): out_i[r*100:(r+1)*100]=items[order[bits[r,order]][:100]]\\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\\nPY\\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:21:11.344Z", "content": "Script completed\nWall time 15.7 seconds\nOutput:\n\n \"quality_ndcg@10\": 3.2e-05,\n \"quality_ndcg@10\": 0.010205,\n \"quality_ndcg@10\": 0.004558,\n \"quality_ndcg@10\": 0.010563,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:55.706Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cc6a89e487d288c8e311dc6e1713", "usage": { "input_tokens": 52879, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 547, "reasoning_output_tokens": 107, "total_tokens": 53426 }, "turn_token_usage": { "input_tokens": 4261113, "cached_input_tokens": 4196992, "cache_write_input_tokens": 0, "output_tokens": 23999, "reasoning_output_tokens": 7669, "total_tokens": 4285112 }, "thread_token_usage": { "input_tokens": 4261113, "cached_input_tokens": 4196992, "cache_write_input_tokens": 0, "output_tokens": 23999, "reasoning_output_tokens": 7669, "total_tokens": 4285112 } } }, { "kind": "context", "timestamp": "2026-09-25T01:21:11.340Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-084059be-2173-4bda-8b2d-f236915cb580", "process_id": "4350", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N);bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable')[:1000]\n for r in range(N): out_i[r*100:(r+1)*100]=items[order[bits[r,order]][:100]]\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N);bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable')[:1000]\n for r in range(N): out_i[r*100:(r+1)*100]=items[order[bits[r,order]][:100]]\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 3.2e-05,\n \"quality_ndcg@10\": 0.010205,\n \"quality_ndcg@10\": 0.004558,\n \"quality_ndcg@10\": 0.010563,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 3.2e-05,\n \"quality_ndcg@10\": 0.010205,\n \"quality_ndcg@10\": 0.004558,\n \"quality_ndcg@10\": 0.010563,\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 553213704 }, "formatted_output": " \"quality_ndcg@10\": 3.2e-05,\n \"quality_ndcg@10\": 0.010205,\n \"quality_ndcg@10\": 0.004558,\n \"quality_ndcg@10\": 0.010563,\n" } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-25T01:21:21.769Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:21.769Z", "text": "" } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-25T01:21:23.579Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:23.579Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:21:25.141Z", "text": "Calibration lifts validation to 0.012125, with the selected score still exactly representable as one dot product by appending a popularity coordinate to both tower outputs. Active-like state was slightly harmful as an inference-time sequence append, so I’m retaining it as an available cutoff feature but excluding that append in the selected configuration. I’m adding organic-like events as higher-value contrastive targets before the final refit." }, { "kind": "tool", "timestamp": "2026-09-25T01:21:37.040Z", "callId": "call_MD4A8un8uvF8JA6WsFlVPeCI", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n-def build_examples(events,targets,max_hist,n_per_user=28):\\n+def load_likes(stage,cutoff,item_lookup):\\n+ paths=[DATA/'train_likes.parquet']+([DATA/'val_likes.parquet'] if stage=='test' else [])\\n+ frames=[]\\n+ for p in paths:\\n+ x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic'],filters=[('timestamp','>=',cutoff-90*86400),('is_organic','==',1)])\\n+ raw=x.item_id.to_numpy(np.int64);valid=raw=0;x=x.loc[keep,['uid','timestamp']].copy();x['idx']=idx[keep];frames.append(x)\\n+ return pd.concat(frames,ignore_index=True)\\n+\\n+def build_examples(events,likes,max_hist,n_per_user=28):\\n histories=[]; labels=[]\\n+ likes_by={int(u):g for u,g in likes.groupby('uid',sort=False)} if likes is not None else {}\\n for uid,g in events.groupby('uid',sort=False):\\n@@\\n row=np.zeros(max_hist,np.int32); row[-len(h):]=h\\n histories.append(row);labels.append(ids[p])\\n+ # Likes match the grade-3 channel. The target itself is removed from the\\n+ # preceding listen bag, emulating eligibility at a pre-discovery cutoff.\\n+ lg=likes_by.get(int(uid))\\n+ if lg is not None:\\n+ take=np.linspace(0,len(lg)-1,min(12,len(lg))).astype(int)\\n+ times=g.timestamp.to_numpy(); gids=g.idx.to_numpy(np.int32)\\n+ for q in np.unique(take):\\n+ target=int(lg.idx.iloc[q]); p=np.searchsorted(times,int(lg.timestamp.iloc[q]),side='left')\\n+ hh=gids[:p]; hh=hh[hh!=target]\\n+ if len(hh)<8: continue\\n+ # Most-recent occurrence per item, preserving recent order.\\n+ _,rev=np.unique(hh[::-1],return_index=True); h=hh[np.sort(len(hh)-1-rev)][-max_hist:]+1\\n+ row=np.zeros(max_hist,np.int32);row[-len(h):]=h;histories.append(row);labels.append(target)\\n@@\\n- histories,labels=build_examples(events,None,max_hist)\\n+ likes=load_likes(stage,cutoff,lookup)\\n+ histories,labels=build_examples(events,likes,max_hist)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:21:37.168Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\napply_patch verification failed: Failed to find expected lines in /app/solution/train_two_tower.py:\n histories,labels=build_examples(events,None,max_hist)", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:37.166Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cc8788ec87d289541405207c7ba6", "usage": { "input_tokens": 53507, "cached_input_tokens": 52736, "cache_write_input_tokens": 0, "output_tokens": 1356, "reasoning_output_tokens": 608, "total_tokens": 54863 }, "turn_token_usage": { "input_tokens": 4314620, "cached_input_tokens": 4249728, "cache_write_input_tokens": 0, "output_tokens": 25355, "reasoning_output_tokens": 8277, "total_tokens": 4339975 }, "thread_token_usage": { "input_tokens": 4314620, "cached_input_tokens": 4249728, "cache_write_input_tokens": 0, "output_tokens": 25355, "reasoning_output_tokens": 8277, "total_tokens": 4339975 } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-25T01:21:46.539Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:46.539Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:21:46.540Z", "callId": "call_pz24li2z0XstV92eusiy5SuE", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"rg -n 'def build|histories,labels|def load_events' solution/train_two_tower.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:21:46.629Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n50:def load_events(stage,cutoff,item_lookup):\n63:def build_examples(events,targets,max_hist,n_per_user=28):\n79:def build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\n105: max_hist=128; histories,labels=build_examples(events,None,max_hist)\n110: loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:46.579Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-48393202-8ca7-4e45-a952-0cc7bfb42ecf", "process_id": "86581", "command": [ "/bin/sh", "-lc", "rg -n 'def build|histories,labels|def load_events' solution/train_two_tower.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "search", "cmd": "rg -n 'def build|histories,labels|def load_events' solution/train_two_tower.py", "query": "def build|histories,labels|def load_events", "path": "train_two_tower.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "50:def load_events(stage,cutoff,item_lookup):\n63:def build_examples(events,targets,max_hist,n_per_user=28):\n79:def build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\n105: max_hist=128; histories,labels=build_examples(events,None,max_hist)\n110: loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\n", "stderr": "", "aggregated_output": "50:def load_events(stage,cutoff,item_lookup):\n63:def build_examples(events,targets,max_hist,n_per_user=28):\n79:def build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\n105: max_hist=128; histories,labels=build_examples(events,None,max_hist)\n110: loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4700 }, "formatted_output": "50:def load_events(stage,cutoff,item_lookup):\n63:def build_examples(events,targets,max_hist,n_per_user=28):\n79:def build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\n105: max_hist=128; histories,labels=build_examples(events,None,max_hist)\n110: loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:21:46.628Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cca19edc87d2a02e032ba6440a3e", "usage": { "input_tokens": 54922, "cached_input_tokens": 53376, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 7, "total_tokens": 54998 }, "turn_token_usage": { "input_tokens": 4369542, "cached_input_tokens": 4303104, "cache_write_input_tokens": 0, "output_tokens": 25431, "reasoning_output_tokens": 8284, "total_tokens": 4394973 }, "thread_token_usage": { "input_tokens": 4369542, "cached_input_tokens": 4303104, "cache_write_input_tokens": 0, "output_tokens": 25431, "reasoning_output_tokens": 8284, "total_tokens": 4394973 } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-25T01:21:48.143Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:48.143Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:21:59.444Z", "callId": "call_2SDoOP60LECJu2HwcMlzky8Y", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n-def build_examples(events,targets,max_hist,n_per_user=28):\\n- histories=[]; labels=[]\\n+def load_likes(stage,cutoff,item_lookup):\\n+ paths=[DATA/'train_likes.parquet']+([DATA/'val_likes.parquet'] if stage=='test' else [])\\n+ frames=[]\\n+ for p in paths:\\n+ x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic'],filters=[('timestamp','>=',cutoff-90*86400),('is_organic','==',1)])\\n+ raw=x.item_id.to_numpy(np.int64);valid=raw=0;x=x.loc[keep,['uid','timestamp']].copy();x['idx']=idx[keep];frames.append(x)\\n+ return pd.concat(frames,ignore_index=True)\\n+\\n+def build_examples(events,likes,max_hist,n_per_user=28):\\n+ histories=[]; labels=[]\\n+ likes_by={int(u):g for u,g in likes.groupby('uid',sort=False)} if likes is not None else {}\\n@@\\n for p in np.unique(choose):\\n h=ids[max(0,p-max_hist):p]+1\\n row=np.zeros(max_hist,np.int32); row[-len(h):]=h\\n histories.append(row);labels.append(ids[p])\\n+ lg=likes_by.get(int(uid))\\n+ if lg is not None:\\n+ take=np.linspace(0,len(lg)-1,min(12,len(lg))).astype(int)\\n+ times=g.timestamp.to_numpy(); gids=g.idx.to_numpy(np.int32)\\n+ for q in np.unique(take):\\n+ target=int(lg.idx.iloc[q]); p=np.searchsorted(times,int(lg.timestamp.iloc[q]),side='left')\\n+ hh=gids[:p]; hh=hh[hh!=target]\\n+ if len(hh)<8: continue\\n+ _,rev=np.unique(hh[::-1],return_index=True); h=hh[np.sort(len(hh)-1-rev)][-max_hist:]+1\\n+ row=np.zeros(max_hist,np.int32);row[-len(h):]=h;histories.append(row);labels.append(target)\\n@@\\n- max_hist=128; histories,labels=build_examples(events,None,max_hist)\\n+ likes=load_likes(stage,cutoff,lookup)\\n+ max_hist=128; histories,labels=build_examples(events,likes,max_hist)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:21:59.570Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:21:59.453Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-3e6c3fd8-9b4e-4931-be81-8a9e12137d7b", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -62,4 +62,14 @@\n \n-def build_examples(events,targets,max_hist,n_per_user=28):\n+def load_likes(stage,cutoff,item_lookup):\n+ paths=[DATA/'train_likes.parquet']+([DATA/'val_likes.parquet'] if stage=='test' else [])\n+ frames=[]\n+ for p in paths:\n+ x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic'],filters=[('timestamp','>=',cutoff-90*86400),('is_organic','==',1)])\n+ raw=x.item_id.to_numpy(np.int64);valid=raw=0;x=x.loc[keep,['uid','timestamp']].copy();x['idx']=idx[keep];frames.append(x)\n+ return pd.concat(frames,ignore_index=True)\n+\n+def build_examples(events,likes,max_hist,n_per_user=28):\n histories=[]; labels=[]\n+ likes_by={int(u):g for u,g in likes.groupby('uid',sort=False)} if likes is not None else {}\n for uid,g in events.groupby('uid',sort=False):\n@@ -76,2 +86,12 @@\n histories.append(row);labels.append(ids[p])\n+ lg=likes_by.get(int(uid))\n+ if lg is not None:\n+ take=np.linspace(0,len(lg)-1,min(12,len(lg))).astype(int)\n+ times=g.timestamp.to_numpy(); gids=g.idx.to_numpy(np.int32)\n+ for q in np.unique(take):\n+ target=int(lg.idx.iloc[q]); p=np.searchsorted(times,int(lg.timestamp.iloc[q]),side='left')\n+ hh=gids[:p]; hh=hh[hh!=target]\n+ if len(hh)<8: continue\n+ _,rev=np.unique(hh[::-1],return_index=True); h=hh[np.sort(len(hh)-1-rev)][-max_hist:]+1\n+ row=np.zeros(max_hist,np.int32);row[-len(h):]=h;histories.append(row);labels.append(target)\n return torch.from_numpy(np.asarray(histories,np.int32)).long(),torch.from_numpy(np.asarray(labels,np.int64))\n@@ -104,3 +124,4 @@\n events=load_events(stage,cutoff,lookup)\n- max_hist=128; histories,labels=build_examples(events,None,max_hist)\n+ likes=load_likes(stage,cutoff,lookup)\n+ max_hist=128; histories,labels=build_examples(events,likes,max_hist)\n print(f'{stage}: {len(events):,} positive events, {len(histories):,} training examples')\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:21:59.568Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ccab000c87d2b9d6b6fc48fbcb4a", "usage": { "input_tokens": 55115, "cached_input_tokens": 54784, "cache_write_input_tokens": 0, "output_tokens": 644, "reasoning_output_tokens": 7, "total_tokens": 55759 }, "turn_token_usage": { "input_tokens": 4424657, "cached_input_tokens": 4357888, "cache_write_input_tokens": 0, "output_tokens": 26075, "reasoning_output_tokens": 8291, "total_tokens": 4450732 }, "thread_token_usage": { "input_tokens": 4424657, "cached_input_tokens": 4357888, "cache_write_input_tokens": 0, "output_tokens": 26075, "reasoning_output_tokens": 8291, "total_tokens": 4450732 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-25T01:22:02.996Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:02.996Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:04.568Z", "callId": "call_p6yAza8TRULrtczFlLzJf8GD", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/train_two_tower.py --stage validation --dim 96 --epochs 10 --seed 2027\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:22:05.734Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=75564", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:04.676Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ccb7dce887d2956b993a470f5d42", "usage": { "input_tokens": 55783, "cached_input_tokens": 54912, "cache_write_input_tokens": 0, "output_tokens": 155, "reasoning_output_tokens": 61, "total_tokens": 55938 }, "turn_token_usage": { "input_tokens": 4480440, "cached_input_tokens": 4412800, "cache_write_input_tokens": 0, "output_tokens": 26230, "reasoning_output_tokens": 8352, "total_tokens": 4506670 }, "thread_token_usage": { "input_tokens": 4480440, "cached_input_tokens": 4412800, "cache_write_input_tokens": 0, "output_tokens": 26230, "reasoning_output_tokens": 8352, "total_tokens": 4506670 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:22:07.834Z", "callId": "call_ILCuDlW7pw254b0LElM5Fhz1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:75564,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:22:12.843Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nloaded train_listens.parquet: 10,582,861\n\nSESSION_ID=75564", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:07.948Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ccbdf0d087d281db3c7854550f40", "usage": { "input_tokens": 55966, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 56026 }, "turn_token_usage": { "input_tokens": 4536406, "cached_input_tokens": 4468352, "cache_write_input_tokens": 0, "output_tokens": 26290, "reasoning_output_tokens": 8352, "total_tokens": 4562696 }, "thread_token_usage": { "input_tokens": 4536406, "cached_input_tokens": 4468352, "cache_write_input_tokens": 0, "output_tokens": 26290, "reasoning_output_tokens": 8352, "total_tokens": 4562696 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:22:16.277Z", "callId": "call_42GmXtvTsGtWbZQ0nVspSglk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:75564,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:22:21.285Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nvalidation: 7,755,443 positive events, 222,902 training examples\n\nSESSION_ID=75564", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:16.340Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ccc50c5487d2aed9693381c3f70a", "usage": { "input_tokens": 56068, "cached_input_tokens": 55808, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 56128 }, "turn_token_usage": { "input_tokens": 4592474, "cached_input_tokens": 4524160, "cache_write_input_tokens": 0, "output_tokens": 26350, "reasoning_output_tokens": 8352, "total_tokens": 4618824 }, "thread_token_usage": { "input_tokens": 4592474, "cached_input_tokens": 4524160, "cache_write_input_tokens": 0, "output_tokens": 26350, "reasoning_output_tokens": 8352, "total_tokens": 4618824 } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-25T01:22:23.594Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:23.594Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:23.857Z", "callId": "call_TDphN9KQRtqNhXg0md4i1v8U", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:75564,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); 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(Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1295\nepoch 2/10 loss=5.9365\nepoch 3/10 loss=5.6279\nepoch 4/10 loss=5.2869\nepoch 5/10 loss=4.9592\nepoch 6/10 loss=4.6449\nepoch 7/10 loss=4.3473\nepoch 8/10 loss=4.0726\nepoch 9/10 loss=3.8206\nepoch 10/10 loss=3.5941\nwrote /app/solution/validation.parquet\n", "stderr": "", "aggregated_output": "loaded train_listens.parquet: 10,582,861\nvalidation: 7,755,443 positive events, 222,902 training examples\n/app/solution/train_two_tower.py:138: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1295\nepoch 2/10 loss=5.9365\nepoch 3/10 loss=5.6279\nepoch 4/10 loss=5.2869\nepoch 5/10 loss=4.9592\nepoch 6/10 loss=4.6449\nepoch 7/10 loss=4.3473\nepoch 8/10 loss=4.0726\nepoch 9/10 loss=3.8206\nepoch 10/10 loss=3.5941\nwrote /app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 184, "nanos": 587625777 }, "formatted_output": "loaded train_listens.parquet: 10,582,861\nvalidation: 7,755,443 positive events, 222,902 training examples\n/app/solution/train_two_tower.py:138: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1295\nepoch 2/10 loss=5.9365\nepoch 3/10 loss=5.6279\nepoch 4/10 loss=5.2869\nepoch 5/10 loss=4.9592\nepoch 6/10 loss=4.6449\nepoch 7/10 loss=4.3473\nepoch 8/10 loss=4.0726\nepoch 9/10 loss=3.8206\nepoch 10/10 loss=3.5941\nwrote /app/solution/validation.parquet\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:25:11.013Z", "callId": "call_ZlmBb52QPvE5kyQbqOx3Oiul", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:75564,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:25:11.157Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nwrote /app/solution/validation.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:25:11.155Z", "data": { "type": "token_usage_record", "thread_id": 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train_listens.parquet: 10,582,861\n" } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-25T01:25:58.503Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:25:58.503Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:26:00.727Z", "callId": "call_QrPkf9Yjsl8wX2Q7lsFc02rY", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for m in pref nopref; do for a in 8 12 16 20 24 32; do s=$(/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_blend_${m}_${a}.parquet | rg quality_ndcg); echo $m $a $s; done; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:26:30.909Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\npref 8 \"quality_ndcg@10\": 0.011342,\npref 12 \"quality_ndcg@10\": 0.011642,\npref 16 \"quality_ndcg@10\": 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tools.write_stdin({session_id:32124,chars:\"\",yield_time_ms:1000,max_output_tokens:1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:26:41.514Z", "content": "Script completed\nWall time 1.0 seconds\nOutput:\n\nnopref 32 \"quality_ndcg@10\": 0.012162,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:26:40.647Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cdced45087d2ac50129836c800bc", "usage": { "input_tokens": 59414, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 59459 }, "turn_token_usage": { "input_tokens": 6381584, "cached_input_tokens": 6304384, "cache_write_input_tokens": 0, "output_tokens": 28368, 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"unified_exec_startup", "status": "completed", "stdout": "pref 8 \"quality_ndcg@10\": 0.011342,\npref 12 \"quality_ndcg@10\": 0.011642,\npref 16 \"quality_ndcg@10\": 0.011946,\npref 20 \"quality_ndcg@10\": 0.01205,\npref 24 \"quality_ndcg@10\": 0.012041,\npref 32 \"quality_ndcg@10\": 0.011219,\nnopref 8 \"quality_ndcg@10\": 0.011269,\nnopref 12 \"quality_ndcg@10\": 0.011699,\nnopref 16 \"quality_ndcg@10\": 0.011728,\nnopref 20 \"quality_ndcg@10\": 0.012336,\nnopref 24 \"quality_ndcg@10\": 0.012491,\nnopref 32 \"quality_ndcg@10\": 0.012162,\n", "stderr": "", "aggregated_output": "pref 8 \"quality_ndcg@10\": 0.011342,\npref 12 \"quality_ndcg@10\": 0.011642,\npref 16 \"quality_ndcg@10\": 0.011946,\npref 20 \"quality_ndcg@10\": 0.01205,\npref 24 \"quality_ndcg@10\": 0.012041,\npref 32 \"quality_ndcg@10\": 0.011219,\nnopref 8 \"quality_ndcg@10\": 0.011269,\nnopref 12 \"quality_ndcg@10\": 0.011699,\nnopref 16 \"quality_ndcg@10\": 0.011728,\nnopref 20 \"quality_ndcg@10\": 0.012336,\nnopref 24 \"quality_ndcg@10\": 0.012491,\nnopref 32 \"quality_ndcg@10\": 0.012162,\n", "exit_code": 0, "duration": { "secs": 40, "nanos": 604883979 }, "formatted_output": "pref 8 \"quality_ndcg@10\": 0.011342,\npref 12 \"quality_ndcg@10\": 0.011642,\npref 16 \"quality_ndcg@10\": 0.011946,\npref 20 \"quality_ndcg@10\": 0.01205,\npref 24 \"quality_ndcg@10\": 0.012041,\npref 32 \"quality_ndcg@10\": 0.011219,\nnopref 8 \"quality_ndcg@10\": 0.011269,\nnopref 12 \"quality_ndcg@10\": 0.011699,\nnopref 16 \"quality_ndcg@10\": 0.011728,\nnopref 20 \"quality_ndcg@10\": 0.012336,\nnopref 24 \"quality_ndcg@10\": 0.012491,\nnopref 32 \"quality_ndcg@10\": 0.012162,\n" } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-25T01:26:49.167Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:26:49.167Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:26:58.670Z", "callId": "call_AYiuctWu1wMsSysBXWCCu1GW", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n def loss(self,hist,target):\\n@@\\n return -(torch.logsumexp(logits.masked_fill(~positive,-1e9),1)-torch.logsumexp(logits,1)).mean()\\n+ def serving_user_vector(self,hist,alpha=24.0):\\n+ \\\"\\\"\\\"Augmented request-time vector; its last coordinate calibrates popularity.\\\"\\\"\\\"\\n+ u=nn.functional.normalize(self.user_tower(hist),dim=1)*alpha**0.5\\n+ return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\\n+ def serving_item_vector(self,item_idx,alpha=24.0):\\n+ \\\"\\\"\\\"Offline vector. Dotting this with serving_user_vector is the final score.\\\"\\\"\\\"\\n+ v=nn.functional.normalize(self.item_tower(item_idx),dim=1)*alpha**0.5\\n+ log_pop_rank=self.item_tower.pop_features[item_idx,2:3]*12.0\\n+ return torch.cat([v,log_pop_rank],dim=1)\\n@@\\n- pref=DATA/f'{stage}_preference_state.parquet'\\n- h=build_inference_histories(events,uids,max_hist,pref,lookup)\\n+ # Validation selected listening history without the explicit-like append.\\n+ # Preference state remains available for experiments and eligibility is\\n+ # always platform-owned, but appending unordered likes reduced NDCG.\\n+ h=build_inference_histories(events,uids,max_hist,None,lookup)\\n@@\\n- ivec=nn.functional.normalize(model.item_tower(torch.arange(n)),dim=1)\\n+ ivec=model.serving_item_vector(torch.arange(n))\\n+ np.save(OUT/f'item_vectors_{stage}.npy',ivec.numpy().astype('float32'))\\n for start in range(0,len(uids),128):\\n- stop=min(start+128,len(uids));uvec=nn.functional.normalize(model.user_tower(h[start:stop]),dim=1)\\n+ stop=min(start+128,len(uids));uvec=model.serving_user_vector(h[start:stop])\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:26:58.779Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:26:58.680Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-184d7e0f-009c-46fb-8c5f-3b0ef9c31868", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -48,2 +48,11 @@\n return -(torch.logsumexp(logits.masked_fill(~positive,-1e9),1)-torch.logsumexp(logits,1)).mean()\n+ def serving_user_vector(self,hist,alpha=24.0):\n+ \"\"\"Augmented request-time vector; its last coordinate calibrates popularity.\"\"\"\n+ u=nn.functional.normalize(self.user_tower(hist),dim=1)*alpha**0.5\n+ return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\n+ def serving_item_vector(self,item_idx,alpha=24.0):\n+ \"\"\"Offline vector. Dotting this with serving_user_vector is the final score.\"\"\"\n+ v=nn.functional.normalize(self.item_tower(item_idx),dim=1)*alpha**0.5\n+ log_pop_rank=self.item_tower.pop_features[item_idx,2:3]*12.0\n+ return torch.cat([v,log_pop_rank],dim=1)\n \n@@ -141,9 +150,12 @@\n elig=np.load(DATA/f'{stage}_eligible_candidates.npz');uids=elig['uids'].astype(np.int64);packed=elig['packed_eligible']\n- pref=DATA/f'{stage}_preference_state.parquet'\n- h=build_inference_histories(events,uids,max_hist,pref,lookup)\n+ # Validation selected listening history without the explicit-like append.\n+ # Preference state remains available for experiments and eligibility is\n+ # always platform-owned, but appending unordered likes reduced NDCG.\n+ h=build_inference_histories(events,uids,max_hist,None,lookup)\n model.eval(); rows=[]\n with torch.no_grad():\n- ivec=nn.functional.normalize(model.item_tower(torch.arange(n)),dim=1)\n+ ivec=model.serving_item_vector(torch.arange(n))\n+ np.save(OUT/f'item_vectors_{stage}.npy',ivec.numpy().astype('float32'))\n for start in range(0,len(uids),128):\n- stop=min(start+128,len(uids));uvec=nn.functional.normalize(model.user_tower(h[start:stop]),dim=1)\n+ stop=min(start+128,len(uids));uvec=model.serving_user_vector(h[start:stop])\n scores=(uvec@ivec.T).numpy()\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:26:58.777Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cdd1afb487d2bd87f352abdb872e", "usage": { "input_tokens": 59499, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 891, "reasoning_output_tokens": 355, "total_tokens": 60390 }, "turn_token_usage": { "input_tokens": 6441083, "cached_input_tokens": 6363648, "cache_write_input_tokens": 0, "output_tokens": 29259, "reasoning_output_tokens": 8793, "total_tokens": 6470342 }, "thread_token_usage": { "input_tokens": 6441083, "cached_input_tokens": 6363648, "cache_write_input_tokens": 0, "output_tokens": 29259, "reasoning_output_tokens": 8793, "total_tokens": 6470342 } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-25T01:27:00.656Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:00.656Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:27:02.352Z", "text": "The like-target augmentation improved the selected calibrated model to 0.012491 NDCG@10. I’ve now frozen the validation choices: 96 latent dimensions, 90-day history, organic-listen plus organic-like contrastive targets, no unordered like-state append, and calibration weight 24. I’m refitting that exact setup on train+validation and will validate every final row against test eligibility." }, { "kind": "tool", "timestamp": "2026-09-25T01:27:03.885Z", "callId": "call_sJ5owCFe7VB2FjqSeJfhTuWd", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u solution/train_two_tower.py --stage test --dim 96 --epochs 10 --seed 2027\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:05.055Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:03.995Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cde3358c87d2a529681c55476f85", "usage": { "input_tokens": 60414, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 25, "total_tokens": 60622 }, "turn_token_usage": { "input_tokens": 6501497, "cached_input_tokens": 6422912, "cache_write_input_tokens": 0, "output_tokens": 29467, "reasoning_output_tokens": 8818, "total_tokens": 6530964 }, "thread_token_usage": { "input_tokens": 6501497, "cached_input_tokens": 6422912, "cache_write_input_tokens": 0, "output_tokens": 29467, "reasoning_output_tokens": 8818, "total_tokens": 6530964 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:27:07.144Z", "callId": "call_HQdUOmSOMMH2MPGPCSgkPBA2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:12.153Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nloaded train_listens.parquet: 9,878,394\nloaded val_listens.parquet: 964,959\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:07.299Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cde93c9487d289b7f679251b4469", "usage": { "input_tokens": 60650, "cached_input_tokens": 60288, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 60710 }, "turn_token_usage": { "input_tokens": 6562147, "cached_input_tokens": 6483200, "cache_write_input_tokens": 0, "output_tokens": 29527, "reasoning_output_tokens": 8818, "total_tokens": 6591674 }, "thread_token_usage": { "input_tokens": 6562147, "cached_input_tokens": 6483200, "cache_write_input_tokens": 0, "output_tokens": 29527, "reasoning_output_tokens": 8818, "total_tokens": 6591674 } } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-25T01:27:14.536Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:14.536Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:27:16.200Z", "callId": "call_CEYks6zXKkzRWp1m7A8651rB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:21.210Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\ntest: 7,887,086 positive events, 226,895 training examples\n/app/solution/train_two_tower.py:147: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:16.381Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cdf0959c87d29ff898b876cdff38", "usage": { "input_tokens": 60764, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 24, "total_tokens": 60850 }, "turn_token_usage": { "input_tokens": 6622911, "cached_input_tokens": 6543616, "cache_write_input_tokens": 0, "output_tokens": 29613, "reasoning_output_tokens": 8842, "total_tokens": 6652524 }, "thread_token_usage": { "input_tokens": 6622911, "cached_input_tokens": 6543616, "cache_write_input_tokens": 0, "output_tokens": 29613, "reasoning_output_tokens": 8842, "total_tokens": 6652524 } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-25T01:27:23.012Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:23.012Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:27:24.195Z", "callId": "call_e6FP1KRGrZZAckdB1SqrtxNv", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:29.214Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:24.363Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cdf986fc87d2a09e758d45feb7c2", "usage": { "input_tokens": 60969, "cached_input_tokens": 60544, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 6, "total_tokens": 61037 }, "turn_token_usage": { "input_tokens": 6683880, "cached_input_tokens": 6604160, "cache_write_input_tokens": 0, "output_tokens": 29681, "reasoning_output_tokens": 8848, "total_tokens": 6713561 }, "thread_token_usage": { "input_tokens": 6683880, "cached_input_tokens": 6604160, "cache_write_input_tokens": 0, "output_tokens": 29681, "reasoning_output_tokens": 8848, "total_tokens": 6713561 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:27:31.278Z", "callId": "call_sLep0zjKdQA3VtrThXWjlenn", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:36.291Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:31.430Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce01642887d28524bcfa0b5f0be2", "usage": { "input_tokens": 61065, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61125 }, "turn_token_usage": { "input_tokens": 6744945, "cached_input_tokens": 6664960, "cache_write_input_tokens": 0, "output_tokens": 29741, "reasoning_output_tokens": 8848, "total_tokens": 6774686 }, "thread_token_usage": { "input_tokens": 6744945, "cached_input_tokens": 6664960, "cache_write_input_tokens": 0, "output_tokens": 29741, "reasoning_output_tokens": 8848, "total_tokens": 6774686 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:27:38.473Z", "callId": "call_T9oXM92TEIKALzsNBVmrsVxo", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:43.487Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 1/10 loss=6.1303\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:38.595Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce08909887d2a7fe8694ecad6824", "usage": { "input_tokens": 61153, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61213 }, "turn_token_usage": { "input_tokens": 6806098, "cached_input_tokens": 6725888, "cache_write_input_tokens": 0, "output_tokens": 29801, "reasoning_output_tokens": 8848, "total_tokens": 6835899 }, "thread_token_usage": { "input_tokens": 6806098, "cached_input_tokens": 6725888, "cache_write_input_tokens": 0, "output_tokens": 29801, "reasoning_output_tokens": 8848, "total_tokens": 6835899 } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-25T01:27:44.801Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:27:44.801Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:27:45.768Z", "callId": "call_t8uIewG4jLdg1l3EEL946wT7", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:50.779Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:45.938Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce0fac0c87d2b40eef1de023287c", "usage": { "input_tokens": 61253, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 8, "total_tokens": 61323 }, "turn_token_usage": { "input_tokens": 6867351, "cached_input_tokens": 6786816, "cache_write_input_tokens": 0, "output_tokens": 29871, "reasoning_output_tokens": 8856, "total_tokens": 6897222 }, "thread_token_usage": { "input_tokens": 6867351, "cached_input_tokens": 6786816, "cache_write_input_tokens": 0, "output_tokens": 29871, "reasoning_output_tokens": 8856, "total_tokens": 6897222 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:27:53.782Z", "callId": "call_QqdJOUmULD3k66NDxrSt0geW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:27:58.790Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 2/10 loss=5.9230\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:27:53.958Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce16ff4487d2a30193ad7919e465", "usage": { "input_tokens": 61351, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61411 }, "turn_token_usage": { "input_tokens": 6928702, "cached_input_tokens": 6847872, "cache_write_input_tokens": 0, "output_tokens": 29931, "reasoning_output_tokens": 8856, "total_tokens": 6958633 }, "thread_token_usage": { "input_tokens": 6928702, "cached_input_tokens": 6847872, "cache_write_input_tokens": 0, "output_tokens": 29931, "reasoning_output_tokens": 8856, "total_tokens": 6958633 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:00.956Z", "callId": "call_zv5zx4NQ0eXix8si5QIrQRca", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); 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if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:28:13.034Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 3/10 loss=5.6029\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:08.156Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce2627ec87d29b6ec898108e77dd", "usage": { "input_tokens": 61539, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61599 }, "turn_token_usage": { "input_tokens": 7051692, "cached_input_tokens": 6970368, "cache_write_input_tokens": 0, "output_tokens": 30051, "reasoning_output_tokens": 8856, "total_tokens": 7081743 }, "thread_token_usage": { "input_tokens": 7051692, "cached_input_tokens": 6970368, "cache_write_input_tokens": 0, "output_tokens": 30051, "reasoning_output_tokens": 8856, "total_tokens": 7081743 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:17.889Z", "callId": "call_T7tv5G9o0angxL4t8zqyieH2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:28:22.904Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:18.055Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce2f441c87d2b793648aa5faab86", "usage": { "input_tokens": 61639, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61699 }, "turn_token_usage": { "input_tokens": 7113331, "cached_input_tokens": 7031680, "cache_write_input_tokens": 0, "output_tokens": 30111, "reasoning_output_tokens": 8856, "total_tokens": 7143442 }, "thread_token_usage": { "input_tokens": 7113331, "cached_input_tokens": 7031680, "cache_write_input_tokens": 0, "output_tokens": 30111, "reasoning_output_tokens": 8856, "total_tokens": 7143442 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:26.661Z", "callId": "call_vggGKf0mhiiQ7l2BidYF5FrY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:28:31.675Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 4/10 loss=5.2651\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:26.769Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce3716b487d2b2ddce28e44733e9", "usage": { "input_tokens": 61727, "cached_input_tokens": 61440, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61787 }, "turn_token_usage": { "input_tokens": 7175058, "cached_input_tokens": 7093120, "cache_write_input_tokens": 0, "output_tokens": 30171, "reasoning_output_tokens": 8856, "total_tokens": 7205229 }, "thread_token_usage": { "input_tokens": 7175058, "cached_input_tokens": 7093120, "cache_write_input_tokens": 0, "output_tokens": 30171, "reasoning_output_tokens": 8856, "total_tokens": 7205229 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:33.931Z", "callId": "call_h7lv81lPVf3ipEK1Wrvoxjgv", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:28:38.942Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:34.058Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce3fe30087d29e018078497d7ce7", "usage": { "input_tokens": 61827, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61887 }, "turn_token_usage": { "input_tokens": 7236885, "cached_input_tokens": 7154688, "cache_write_input_tokens": 0, "output_tokens": 30231, "reasoning_output_tokens": 8856, "total_tokens": 7267116 }, "thread_token_usage": { "input_tokens": 7236885, "cached_input_tokens": 7154688, "cache_write_input_tokens": 0, "output_tokens": 30231, "reasoning_output_tokens": 8856, "total_tokens": 7267116 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:41.299Z", "callId": "call_tTREseubarn8RK11owVBYa73", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:28:46.311Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nepoch 5/10 loss=4.9455\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:41.456Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce47368487d29fe578131e205cad", "usage": { "input_tokens": 61915, "cached_input_tokens": 61696, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 61975 }, "turn_token_usage": { "input_tokens": 7298800, "cached_input_tokens": 7216384, "cache_write_input_tokens": 0, "output_tokens": 30291, "reasoning_output_tokens": 8856, "total_tokens": 7329091 }, "thread_token_usage": { "input_tokens": 7298800, "cached_input_tokens": 7216384, "cache_write_input_tokens": 0, "output_tokens": 30291, "reasoning_output_tokens": 8856, "total_tokens": 7329091 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:48.526Z", "callId": "call_OEjXBO4wJYMrlIhJ11A0WwMK", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:28:53.535Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n\nSESSION_ID=8860", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:48.678Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce4e810c87d2a8db2193aad4d084", "usage": { "input_tokens": 62015, "cached_input_tokens": 61696, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 62075 }, "turn_token_usage": { "input_tokens": 7360815, "cached_input_tokens": 7278080, "cache_write_input_tokens": 0, "output_tokens": 30351, "reasoning_output_tokens": 8856, "total_tokens": 7391166 }, "thread_token_usage": { "input_tokens": 7360815, "cached_input_tokens": 7278080, "cache_write_input_tokens": 0, "output_tokens": 30351, "reasoning_output_tokens": 8856, "total_tokens": 7391166 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:56.005Z", "callId": "call_Z8V9wOHBWCzDph1lk4bCS2fh", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:8860,chars:\"\",yield_time_ms:1000,max_output_tokens:1500});\ntext(r.output); 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if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:30:05.197Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\nepoch 10/10 loss=3.6207\nwrote /app/solution/submission.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:02.721Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce9530f887d28c2d09b06d7ba131", "usage": { "input_tokens": 62767, "cached_input_tokens": 62464, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 62827 }, "turn_token_usage": { "input_tokens": 7860343, "cached_input_tokens": 7775488, "cache_write_input_tokens": 0, "output_tokens": 30831, "reasoning_output_tokens": 8856, "total_tokens": 7891174 }, "thread_token_usage": { "input_tokens": 7860343, "cached_input_tokens": 7775488, "cache_write_input_tokens": 0, "output_tokens": 30831, "reasoning_output_tokens": 8856, "total_tokens": 7891174 } } }, { "kind": "context", "timestamp": "2026-09-25T01:30:05.195Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c0d29307-716c-4531-8834-cdfefcd91846", "process_id": "8860", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage test --dim 96 --epochs 10 --seed 2027" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u solution/train_two_tower.py --stage test --dim 96 --epochs 10 --seed 2027" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded train_listens.parquet: 9,878,394\nloaded val_listens.parquet: 964,959\ntest: 7,887,086 positive events, 226,895 training examples\n/app/solution/train_two_tower.py:147: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1303\nepoch 2/10 loss=5.9230\nepoch 3/10 loss=5.6029\nepoch 4/10 loss=5.2651\nepoch 5/10 loss=4.9455\nepoch 6/10 loss=4.6405\nepoch 7/10 loss=4.3525\nepoch 8/10 loss=4.0867\nepoch 9/10 loss=3.8460\nepoch 10/10 loss=3.6207\nwrote /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "loaded train_listens.parquet: 9,878,394\nloaded val_listens.parquet: 964,959\ntest: 7,887,086 positive events, 226,895 training examples\n/app/solution/train_two_tower.py:147: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1303\nepoch 2/10 loss=5.9230\nepoch 3/10 loss=5.6029\nepoch 4/10 loss=5.2651\nepoch 5/10 loss=4.9455\nepoch 6/10 loss=4.6405\nepoch 7/10 loss=4.3525\nepoch 8/10 loss=4.0867\nepoch 9/10 loss=3.8460\nepoch 10/10 loss=3.6207\nwrote /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 181, "nanos": 140908364 }, "formatted_output": "loaded train_listens.parquet: 9,878,394\nloaded val_listens.parquet: 964,959\ntest: 7,887,086 positive events, 226,895 training examples\n/app/solution/train_two_tower.py:147: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1303\nepoch 2/10 loss=5.9230\nepoch 3/10 loss=5.6029\nepoch 4/10 loss=5.2651\nepoch 5/10 loss=4.9455\nepoch 6/10 loss=4.6405\nepoch 7/10 loss=4.3525\nepoch 8/10 loss=4.0867\nepoch 9/10 loss=3.8460\nepoch 10/10 loss=3.6207\nwrote /app/solution/submission.parquet\n" } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-25T01:30:11.662Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:11.662Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:30:21.224Z", "callId": "call_E1rL1exzaE8rZGhDxtM3ETW6", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+`train_two_tower.py` is the complete CPU PyTorch training and retrieval program.\\n+It never constructs or factorizes a global user-item interaction matrix. Training\\n+examples are sampled from per-user event sequences and use in-batch negatives.\\n+\\n+## Model\\n+\\n+- **User tower:** a trainable history-item embedding table, exponential recency\\n+ pooling over the latest 128 distinct positive-listen items, then a\\n+ `Linear(96,192) -> GELU -> Linear(192,96) -> LayerNorm` network. There is no\\n+ user-ID embedding.\\n+- **Item tower:** a separate trainable item embedding plus three catalog\\n+ popularity features, followed by the same 2-layer MLP and LayerNorm.\\n+- The serving vectors are 97-dimensional. Normalized neural outputs occupy the\\n+ first 96 coordinates; the last coordinate implements the validation-selected\\n+ popularity calibration. Thus the complete final score is **exactly one dot\\n+ product**: `24 * cosine(neural_user, neural_item) - log(popularity_rank)`.\\n+ `item_vectors_test.npy` contains the independently precomputed catalog side.\\n+\\n+The loss is duplicate-aware in-batch sampled softmax. Positives are later\\n+organic listens completed at least 80%, plus organic likes (the higher-value\\n+grade-3 channel). A 90-day history is used. The final checkpoint is refit on\\n+train plus validation events, treating validation solely as observed history.\\n+Eligibility is applied only after scoring and remains platform-owned.\\n+\\n+## Validation and final build\\n+\\n+Public validation NDCG@10: **0.012491** (4,263 scored users). The selected\\n+validation ranking is `validation.parquet`.\\n+\\n+Reproduce the final refit and submission with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_two_tower.py \\\\\\n+ --stage test --dim 96 --epochs 10 --seed 2027\\n+```\\n+\\n+The command writes `submission.parquet`, `model_test.pt`, and the offline item\\n+vectors, all under `/app/solution/`.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:30:21.342Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:21.233Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-48886eac-ee99-4a83-8d6c-626b2e5d4204", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You two-tower ranker\n\n`train_two_tower.py` is the complete CPU PyTorch training and retrieval program.\nIt never constructs or factorizes a global user-item interaction matrix. Training\nexamples are sampled from per-user event sequences and use in-batch negatives.\n\n## Model\n\n- **User tower:** a trainable history-item embedding table, exponential recency\n pooling over the latest 128 distinct positive-listen items, then a\n `Linear(96,192) -> GELU -> Linear(192,96) -> LayerNorm` network. There is no\n user-ID embedding.\n- **Item tower:** a separate trainable item embedding plus three catalog\n popularity features, followed by the same 2-layer MLP and LayerNorm.\n- The serving vectors are 97-dimensional. Normalized neural outputs occupy the\n first 96 coordinates; the last coordinate implements the validation-selected\n popularity calibration. Thus the complete final score is **exactly one dot\n product**: `24 * cosine(neural_user, neural_item) - log(popularity_rank)`.\n `item_vectors_test.npy` contains the independently precomputed catalog side.\n\nThe loss is duplicate-aware in-batch sampled softmax. Positives are later\norganic listens completed at least 80%, plus organic likes (the higher-value\ngrade-3 channel). A 90-day history is used. The final checkpoint is refit on\ntrain plus validation events, treating validation solely as observed history.\nEligibility is applied only after scoring and remains platform-owned.\n\n## Validation and final build\n\nPublic validation NDCG@10: **0.012491** (4,263 scored users). The selected\nvalidation ranking is `validation.parquet`.\n\nReproduce the final refit and submission with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_two_tower.py \\\n --stage test --dim 96 --epochs 10 --seed 2027\n```\n\nThe command writes `submission.parquet`, `model_test.pt`, and the offline item\nvectors, all under `/app/solution/`.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:30:21.340Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ce9d6b2087d290158fef9f32a832", "usage": { "input_tokens": 62873, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 819, "reasoning_output_tokens": 280, "total_tokens": 63692 }, "turn_token_usage": { "input_tokens": 7923216, "cached_input_tokens": 7838080, "cache_write_input_tokens": 0, "output_tokens": 31650, "reasoning_output_tokens": 9136, "total_tokens": 7954866 }, "thread_token_usage": { "input_tokens": 7923216, "cached_input_tokens": 7838080, "cache_write_input_tokens": 0, "output_tokens": 31650, "reasoning_output_tokens": 9136, "total_tokens": 7954866 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:30:32.121Z", "callId": "call_AvxvXoQNj0F9hPLmjIuklwPC", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\\nbad=0\\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nvec=np.load('/app/solution/item_vectors_test.npy')\\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:31:02.291Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:32.291Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ceadc35c87d2b9519d8e163416ce", "usage": { "input_tokens": 63716, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 382, "reasoning_output_tokens": 0, "total_tokens": 64098 }, "turn_token_usage": { "input_tokens": 7986932, "cached_input_tokens": 7900800, "cache_write_input_tokens": 0, "output_tokens": 32032, "reasoning_output_tokens": 9136, "total_tokens": 8018964 }, "thread_token_usage": { "input_tokens": 7986932, "cached_input_tokens": 7900800, "cache_write_input_tokens": 0, "output_tokens": 32032, "reasoning_output_tokens": 9136, "total_tokens": 8018964 } } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-25T01:31:08.885Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:08.885Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:31:10.777Z", "callId": "call_MaZbqOq5SbTXAX6Hr8AesKEw", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \\\"$d $c\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:31:10.970Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nWarning: truncated output (original token count: 5842)\nTotal output lines: 453\n\n/proc/11659 /bin/sh -c cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 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=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 …3842 tokens truncated…t 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 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 \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:31:10.828Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8dc908a5-ec2b-497d-888a-2ab617c91407", "process_id": "80774", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/11659 /bin/sh -c cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 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=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 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)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 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=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 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)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 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=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in 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 implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed 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 defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt 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- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 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 /dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:31:26.708Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n11661 ", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:31:21.857Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cee5cb7c87d29f6023512ca8e10f", "usage": { "input_tokens": 66516, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 35, "total_tokens": 66621 }, "turn_token_usage": { "input_tokens": 8183871, "cached_input_tokens": 8042112, "cache_write_input_tokens": 0, "output_tokens": 32508, "reasoning_output_tokens": 9326, "total_tokens": 8216379 }, "thread_token_usage": { "input_tokens": 8183871, "cached_input_tokens": 8042112, "cache_write_input_tokens": 0, "output_tokens": 32508, "reasoning_output_tokens": 9326, "total_tokens": 8216379 } } }, { "kind": "context", "timestamp": "2026-09-25T01:31:26.703Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5c7947af-b88b-450b-b1af-de18b7e67c7c", "process_id": "73637", "command": [ "/bin/sh", "-lc", "sleep 5; cat /proc/11659/task/11659/children 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sleep 5; cat /proc/11659/task/11659/children 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "11661 ", "stderr": "", "aggregated_output": "11661 ", "exit_code": 0, "duration": { "secs": 4, "nanos": 853431012 }, "formatted_output": "11661 " } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-25T01:31:30.037Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:30.037Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:31:37.334Z", "callId": "call_wGNsOaNG8jAfXYl2JCdIz5M6", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 11659 11661 2>/dev/null || true\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\\nD=Path('/task/data');x=pd.read_parquet('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\\nrow={int(v):r for r,v in enumerate(u)};col={int(v):c for c,v in enumerate(items)};bad=0\\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\\n r=row[int(uid)];c=col.get(int(item),-1);bad += c<0 or not ((int(packed[r,c//8])>>(c%8))&1)\\nvec=np.load('/app/solution/item_vectors_test.npy')\\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-25T01:31:43.554Z", "content": "Script completed\nWall time 6.2 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012491,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent 12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent 23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:31:37.372Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-17f4fc6f-d593-4fbd-8c32-d9c7727defbd", "process_id": "45956", "command": [ "/bin/sh", "-lc", "cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 65, "nanos": 82409298 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:31:37.504Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ceef2b6087d29ac4dbb2e1772a50", "usage": { "input_tokens": 66647, "cached_input_tokens": 66304, "cache_write_input_tokens": 0, "output_tokens": 484, "reasoning_output_tokens": 79, "total_tokens": 67131 }, "turn_token_usage": { "input_tokens": 8250518, "cached_input_tokens": 8108416, "cache_write_input_tokens": 0, "output_tokens": 32992, "reasoning_output_tokens": 9405, "total_tokens": 8283510 }, "thread_token_usage": { "input_tokens": 8250518, "cached_input_tokens": 8108416, "cache_write_input_tokens": 0, "output_tokens": 32992, "reasoning_output_tokens": 9405, "total_tokens": 8283510 } } }, { "kind": "context", "timestamp": "2026-09-25T01:31:43.550Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d635235-5a35-4911-8e92-f4d6085c92f4", "process_id": "32955", "command": [ "/bin/sh", "-lc", "kill 11659 11661 2>/dev/null || true\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');x=pd.read_parquet('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nrow={int(v):r for r,v in enumerate(u)};col={int(v):c for c,v in enumerate(items)};bad=0\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(uid)];c=col.get(int(item),-1);bad += c<0 or not ((int(packed[r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 11659 11661 2>/dev/null || true\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');x=pd.read_parquet('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nrow={int(v):r for r,v in enumerate(u)};col={int(v):c for c,v in enumerate(items)};bad=0\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(uid)];c=col.get(int(item),-1);bad += c<0 or not ((int(packed[r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012491,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent 12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent 23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012491,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent 12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent 23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 49423125 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012491,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent 12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent 23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n" } } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-25T01:31:53.650Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:53.650Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:32:01.529Z", "callId": "call_tUskFvRrB7CdNBm0A8HLILKt", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n class UserTower(nn.Module):\\n@@\\n def forward(self,hist):\\n@@\\n return self.net(pooled)\\n+ def serving_vector(self,hist,alpha=24.0):\\n+ u=nn.functional.normalize(self(hist),dim=1)*alpha**0.5\\n+ return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\\n@@\\n class ItemTower(nn.Module):\\n@@\\n def forward(self,item_idx):\\n@@\\n return self.net(x)\\n+ def serving_vector(self,item_idx,alpha=24.0):\\n+ v=nn.functional.normalize(self(item_idx),dim=1)*alpha**0.5\\n+ log_pop_rank=self.pop_features[item_idx,2:3]*12.0\\n+ return torch.cat([v,log_pop_rank],dim=1)\\n@@\\n def serving_user_vector(self,hist,alpha=24.0):\\n \\\"\\\"\\\"Augmented request-time vector; its last coordinate calibrates popularity.\\\"\\\"\\\"\\n- u=nn.functional.normalize(self.user_tower(hist),dim=1)*alpha**0.5\\n- return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\\n+ return self.user_tower.serving_vector(hist,alpha)\\n@@\\n- v=nn.functional.normalize(self.item_tower(item_idx),dim=1)*alpha**0.5\\n- log_pop_rank=self.item_tower.pop_features[item_idx,2:3]*12.0\\n- return torch.cat([v,log_pop_rank],dim=1)\\n+ return self.item_tower.serving_vector(item_idx,alpha)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:32:01.670Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:32:01.539Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1c524d03-5ab6-409d-a90a-a30afa63300d", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -26,2 +26,5 @@\n return self.net(pooled)\n+ def serving_vector(self,hist,alpha=24.0):\n+ u=nn.functional.normalize(self(hist),dim=1)*alpha**0.5\n+ return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\n \n@@ -36,2 +39,6 @@\n return self.net(x)\n+ def serving_vector(self,item_idx,alpha=24.0):\n+ v=nn.functional.normalize(self(item_idx),dim=1)*alpha**0.5\n+ log_pop_rank=self.pop_features[item_idx,2:3]*12.0\n+ return torch.cat([v,log_pop_rank],dim=1)\n \n@@ -50,9 +57,6 @@\n \"\"\"Augmented request-time vector; its last coordinate calibrates popularity.\"\"\"\n- u=nn.functional.normalize(self.user_tower(hist),dim=1)*alpha**0.5\n- return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\n+ return self.user_tower.serving_vector(hist,alpha)\n def serving_item_vector(self,item_idx,alpha=24.0):\n \"\"\"Offline vector. Dotting this with serving_user_vector is the final score.\"\"\"\n- v=nn.functional.normalize(self.item_tower(item_idx),dim=1)*alpha**0.5\n- log_pop_rank=self.item_tower.pop_features[item_idx,2:3]*12.0\n- return torch.cat([v,log_pop_rank],dim=1)\n+ return self.item_tower.serving_vector(item_idx,alpha)\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:32:01.668Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5ceffd15c87d2a8113c81f80a5a8e", "usage": { "input_tokens": 67345, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 912, "reasoning_output_tokens": 479, "total_tokens": 68257 }, "turn_token_usage": { "input_tokens": 8317863, "cached_input_tokens": 8174848, "cache_write_input_tokens": 0, "output_tokens": 33904, "reasoning_output_tokens": 9884, "total_tokens": 8351767 }, "thread_token_usage": { "input_tokens": 8317863, "cached_input_tokens": 8174848, "cache_write_input_tokens": 0, "output_tokens": 33904, "reasoning_output_tokens": 9884, "total_tokens": 8351767 } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-25T01:32:04.770Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:32:04.770Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:32:06.342Z", "text": "The final refit and retrieval are complete. Structural validation passed for all 845,600 rows: 8,456 users, exactly 100 unique ranked items each, zero test-eligibility violations. The offline item index contains 30,000 finite 97-dimensional vectors, and the saved validation artifact reproduces 0.012491 NDCG@10." }, { "kind": "tool", "timestamp": "2026-09-25T01:32:07.839Z", "callId": "call_eeJ8Y7nTKsBZRWsCE3bh0IVN", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/rank_blends.py solution/baseline.py && git status --short 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:32:08.018Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:32:07.983Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cf123ee887d2a209827dc1535538", "usage": { "input_tokens": 68281, "cached_input_tokens": 67200, "cache_write_input_tokens": 0, "output_tokens": 212, "reasoning_output_tokens": 43, "total_tokens": 68493 }, "turn_token_usage": { "input_tokens": 8386144, "cached_input_tokens": 8242048, "cache_write_input_tokens": 0, "output_tokens": 34116, "reasoning_output_tokens": 9927, "total_tokens": 8420260 }, "thread_token_usage": { "input_tokens": 8386144, "cached_input_tokens": 8242048, "cache_write_input_tokens": 0, "output_tokens": 34116, "reasoning_output_tokens": 9927, "total_tokens": 8420260 } } }, { "kind": "context", "timestamp": "2026-09-25T01:32:08.014Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b9ca28f2-67ee-4f36-bb25-42ec70a0f688", "process_id": "85882", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/rank_blends.py solution/baseline.py && git status --short 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/rank_blends.py solution/baseline.py && git status --short 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3800 }, "formatted_output": "" } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-25T01:32:16.244Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:32:16.244Z", "text": "Completed the CPU PyTorch two-tower ranker.\n\n- Final submission: [submission.parquet](/app/solution/submission.parquet)\n- Training and inference: [train_two_tower.py](/app/solution/train_two_tower.py)\n- Documentation: [README.md](/app/solution/README.md)\n- Final checkpoint: [model_test.pt](/app/solution/model_test.pt)\n- Precomputed item vectors: [item_vectors_test.npy](/app/solution/item_vectors_test.npy)\n- Public validation NDCG@10: **0.012491**\n\nValidation confirmed 845,600 rows, 100 unique items for each of 8,456 users, and zero eligibility violations." }, { "kind": "context", "timestamp": "2026-09-25T01:32:16.389Z", "data": { "type": "token_usage_record", "thread_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "session_id": "01a0d616-bbb3-7910-9e4f-da52424b2d7e", "root_turn_id": "01a0d616-bbc3-7890-9a1f-8d6df2534cce", "response_id": "resp_0e695b31ff5f241c016ab5cf18751c87d2877dd8a53dc53338", "usage": { "input_tokens": 68516, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 0, "total_tokens": 68665 }, "turn_token_usage": { "input_tokens": 8454660, "cached_input_tokens": 8310144, "cache_write_input_tokens": 0, "output_tokens": 34265, "reasoning_output_tokens": 9927, "total_tokens": 8488925 }, "thread_token_usage": { "input_tokens": 8454660, "cached_input_tokens": 8310144, "cache_write_input_tokens": 0, "output_tokens": 34265, "reasoning_output_tokens": 9927, "total_tokens": 8488925 } } }, { "kind": "context", "timestamp": "2026-09-25T01:32:16.409Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ac68fccd-613c-4136-ae1c-743a56180edb", "process_id": "8704", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/'; t=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int));cat=pd.read_parquet(D+'candidate_catalog.parquet'); col=dict(zip(cat.item_id.astype(int),range(30000)))\nz=np.load(D+'validation_eligible_candidates.npz'); row=dict(zip(z['uids'].astype(int),range(len(z['uids']))));\nls=pd.read_parquet(D+'val_listens.parquet');lk=pd.read_parquet(D+'val_likes.parquet'); rel=pd.concat([ls[(ls.uid.isin(t))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[(lk.uid.isin(t))&(lk.is_organic==1)][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\ndef ok(u,i):\n c=col.get(int(i));r=row.get(int(u));return c is not None and bool((z['packed_eligible'][r,c//8]>>(c%8))&1)\nrel=rel[[ok(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\np=pd.read_parquet(D+'validation_preference_state.parquet');x=rel.merge(p,on=['uid','item_id'],how='left');print(len(rel),rel.uid.nunique(),x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/'; t=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int));cat=pd.read_parquet(D+'candidate_catalog.parquet'); col=dict(zip(cat.item_id.astype(int),range(30000)))\nz=np.load(D+'validation_eligible_candidates.npz'); row=dict(zip(z['uids'].astype(int),range(len(z['uids']))));\nls=pd.read_parquet(D+'val_listens.parquet');lk=pd.read_parquet(D+'val_likes.parquet'); rel=pd.concat([ls[(ls.uid.isin(t))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[(lk.uid.isin(t))&(lk.is_organic==1)][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\ndef ok(u,i):\n c=col.get(int(i));r=row.get(int(u));return c is not None and bool((z['packed_eligible'][r,c//8]>>(c%8))&1)\nrel=rel[[ok(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\np=pd.read_parquet(D+'validation_preference_state.parquet');x=rel.merge(p,on=['uid','item_id'],how='left');print(len(rel),rel.uid.nunique(),x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": -1, "duration": { "secs": 1498, "nanos": 876663523 }, "formatted_output": "" } } } ] } ] }