{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "sol-medium-0-153-4-retriever-r05-20260924T221413Z", "taskChecksum": "ae23330f58a3a024a66d3187797a246a18c8f88a9bd4f3c0652d0d735b090e85", "source": { "format": "codex-rollout", "sha256": "f5d4af7288ceffcca21758f970dd07aea2705500c3dfdb9257a25c000cc2cafe", "records": 563, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 80, "item_completed:AgentMessage": 10, "token_count": 83, "task_complete": 1 } }, "sessionId": "01a0d62d-3c3a-7571-950b-b50337da6ace", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-25T01:28:03.921Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:28:03.921Z", "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": "2026-09-25T01:28:05.252Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:28:05.252Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-25T01:28:05.252Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-25T01:28:05.252Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-25T01:28:05.252Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:28:05.252Z", "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": "2026-09-25T01:28:05.252Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:28:05.252Z", "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": "2026-09-25T01:28:05.252Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:28:05.252Z", "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": "2026-09-25T01:28:05.252Z", "text": "\n /app\n sh\n 2026-09-25\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-25T01:28:05.254Z", "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": "2026-09-25T01:28:05.255Z", "data": { "type": "turn_context", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "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": "2026-09-25T01:28:05.273Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T01:28:05.273Z", "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": "2026-09-25T01:28:06.836Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:06.836Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:28:08.046Z", "text": "I’ll first audit the data shapes, time ranges, candidate encoding, and available libraries. Then I’ll build a leakage-safe validation pipeline, preserve each valid ranking as I iterate, and refit the selected two-tower model on train plus validation history for the final submission." }, { "kind": "tool", "timestamp": "2026-09-25T01:28:09.952Z", "callId": "call_iQU01nZhhKLBiCYy9jU2x5e9", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:10.027Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_preference_state.parquet\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\"\"\"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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:09.987Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce25770887d2847e5b7faa4e8141", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 38, "total_tokens": 14791 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 38, "total_tokens": 14791 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 38, "total_tokens": 14791 } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:10.020Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c278293d-1bb5-4616-8ec6-148ca68406ca", "process_id": "96641", "command": [ "/bin/sh", "-lc", "pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_preference_state.parquet\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\"\"\"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", "stderr": "", "aggregated_output": "/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_preference_state.parquet\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\"\"\"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", "exit_code": 0, "duration": { "secs": 0, "nanos": 5070 }, "formatted_output": "/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/validation_preference_state.parquet\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\"\"\"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" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-25T01:28:11.218Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:11.218Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:28:12.420Z", "text": "The evaluator confirms that only the top 10 affect quality, while the artifact must still contain 100 eligible items per user. I’m profiling interaction density and catalog overlap now; that will determine whether the history encoder should emphasize recent sequences, weighted bags of tracks, or both." }, { "kind": "tool", "timestamp": "2026-09-25T01:28:19.431Z", "callId": "call_IpxB97irrGJZXR9qcixSd8hG", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, numpy as np, os\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n df=pd.read_parquet(p)\\n print(p.name, df.shape, list(df.columns), {c:str(df[c].dtype) for c in df.columns})\\n if 'timestamp' in df: print(' time',df.timestamp.min(),df.timestamp.max(),'users',df.uid.nunique(),'items',df.item_id.nunique())\\n elif 'uid' in df: print(' users',df.uid.nunique(), 'uid range',df.uid.min(),df.uid.max())\\nfor p in sorted(D.glob('*.npz')):\\n z=np.load(p)\\n print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print(' eligible',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max(), 'density',bits.mean())\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); print(cat.head(),cat.tail())\\ntgt=pd.read_parquet(D/'target_users.parquet'); print(tgt.head())\\nfor s in ['validation','test']:\\n df=pd.read_parquet(D/f'{s}_preference_state.parquet'); print(s,df.head(),df['state'].value_counts(), 'bothpairs',df.groupby(['uid','item_id']).size().gt(1).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:19.490Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:19.467Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce2a4c6087d2ab16bf86f18d20b0", "usage": { "input_tokens": 18927, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 476, "reasoning_output_tokens": 16, "total_tokens": 19403 }, "turn_token_usage": { "input_tokens": 33503, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 691, "reasoning_output_tokens": 54, "total_tokens": 34194 }, "thread_token_usage": { "input_tokens": 33503, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 691, "reasoning_output_tokens": 54, "total_tokens": 34194 } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:19.484Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d4edff04-6927-45b0-aa08-47e52376e360", "process_id": "65404", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pandas as pd, numpy as np, os\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n df=pd.read_parquet(p)\n print(p.name, df.shape, list(df.columns), {c:str(df[c].dtype) for c in df.columns})\n if 'timestamp' in df: print(' time',df.timestamp.min(),df.timestamp.max(),'users',df.uid.nunique(),'items',df.item_id.nunique())\n elif 'uid' in df: print(' users',df.uid.nunique(), 'uid range',df.uid.min(),df.uid.max())\nfor p in sorted(D.glob('*.npz')):\n z=np.load(p)\n print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print(' eligible',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max(), 'density',bits.mean())\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); print(cat.head(),cat.tail())\ntgt=pd.read_parquet(D/'target_users.parquet'); print(tgt.head())\nfor s in ['validation','test']:\n df=pd.read_parquet(D/f'{s}_preference_state.parquet'); print(s,df.head(),df['state'].value_counts(), 'bothpairs',df.groupby(['uid','item_id']).size().gt(1).sum())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pandas as pd, numpy as np, os\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n df=pd.read_parquet(p)\n print(p.name, df.shape, list(df.columns), {c:str(df[c].dtype) for c in df.columns})\n if 'timestamp' in df: print(' time',df.timestamp.min(),df.timestamp.max(),'users',df.uid.nunique(),'items',df.item_id.nunique())\n elif 'uid' in df: print(' users',df.uid.nunique(), 'uid range',df.uid.min(),df.uid.max())\nfor p in sorted(D.glob('*.npz')):\n z=np.load(p)\n print(p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print(' eligible',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max(), 'density',bits.mean())\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); print(cat.head(),cat.tail())\ntgt=pd.read_parquet(D/'target_users.parquet'); print(tgt.head())\nfor s in ['validation','test']:\n df=pd.read_parquet(D/f'{s}_preference_state.parquet'); print(s,df.head(),df['state'].value_counts(), 'bothpairs',df.groupby(['uid','item_id']).size().gt(1).sum())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4301 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-25T01:28:20.913Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:20.913Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:28:22.364Z", "callId": "call_dMZGZRFSFuN83jsiJsbATKzJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"which python python3 || true; ls -la /opt /app; find / -maxdepth 3 -type f -name python 2>/dev/null | sed -n '1,30p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:22.422Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:23 solution\n\n/opt:\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 01:23 task-venv\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:22.416Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-749edd96-bd91-4cb9-99c7-329c8c56aa4c", "process_id": "57675", "command": [ "/bin/sh", "-lc", "which python python3 || true; ls -la /opt /app; find / -maxdepth 3 -type f -name python 2>/dev/null | sed -n '1,30p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python python3 || true; ls -la /opt /app; find / -maxdepth 3 -type f -name python 2>/dev/null | sed -n '1,30p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:23 solution\n\n/opt:\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 01:23 task-venv\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:23 solution\n\n/opt:\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 01:23 task-venv\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5470 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:23 solution\n\n/opt:\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 01:23 .\ndrwxr-xr-x 1 root root 4096 Sep 25 01:28 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 01:23 task-venv\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:22.421Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce33c0fc87d2bb8662cc17e4d72b", "usage": { "input_tokens": 19459, "cached_input_tokens": 18688, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 24, "total_tokens": 19573 }, "turn_token_usage": { "input_tokens": 52962, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 805, "reasoning_output_tokens": 78, "total_tokens": 53767 }, "thread_token_usage": { "input_tokens": 52962, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 805, "reasoning_output_tokens": 78, "total_tokens": 53767 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:25.333Z", "callId": "call_1wj6xU4fAGW4IqyYPHb1xm0I", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"find /opt/task-venv -maxdepth 3 -type f -o -type l | sed -n '1,40p'; /opt/task-venv/bin/python -V; /opt/task-venv/bin/python -c 'import pandas,torch;print(pandas.__version__,torch.__version__)'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:29.759Z", "content": "Script completed\nWall time 4.4 seconds\nOutput:\n\n/opt/task-venv/lib64\n/opt/task-venv/pyvenv.cfg\n/opt/task-venv/.lock\n/opt/task-venv/bin/jupytext\n/opt/task-venv/bin/python3.12\n/opt/task-venv/bin/idna\n/opt/task-venv/bin/activate.csh\n/opt/task-venv/bin/chardetect\n/opt/task-venv/bin/python\n/opt/task-venv/bin/dotenv\n/opt/task-venv/bin/jupyter-migrate\n/opt/task-venv/bin/torchrun\n/opt/task-venv/bin/activate\n/opt/task-venv/bin/tqdm\n/opt/task-venv/bin/jupytext-config\n/opt/task-venv/bin/markdown-it\n/opt/task-venv/bin/jupyter-troubleshoot\n/opt/task-venv/bin/numpy-config\n/opt/task-venv/bin/jupyter-trust\n/opt/task-venv/bin/slugify\n/opt/task-venv/bin/activate.fish\n/opt/task-venv/bin/normalizer\n/opt/task-venv/bin/kaggle\n/opt/task-venv/bin/jupyter\n/opt/task-venv/bin/Activate.ps1\n/opt/task-venv/bin/pip\n/opt/task-venv/bin/isympy\n/opt/task-venv/bin/pip3\n/opt/task-venv/bin/jsonschema\n/opt/task-venv/bin/torchfrtrace\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/pip3.12\n/opt/task-venv/bin/f2py\nPython 3.12.13\n2.3.3 2.8.0+cpu\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:25.403Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce36cc7c87d284c57309a1a3a06f", "usage": { "input_tokens": 19753, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 0, "total_tokens": 19866 }, "turn_token_usage": { "input_tokens": 72715, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 918, "reasoning_output_tokens": 78, "total_tokens": 73633 }, "thread_token_usage": { "input_tokens": 72715, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 918, "reasoning_output_tokens": 78, "total_tokens": 73633 } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:29.755Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-563d4373-e0ad-4212-b3e8-3dc6ed11ebdb", "process_id": "65654", "command": [ "/bin/sh", "-lc", "find /opt/task-venv -maxdepth 3 -type f -o -type l | sed -n '1,40p'; /opt/task-venv/bin/python -V; /opt/task-venv/bin/python -c 'import pandas,torch;print(pandas.__version__,torch.__version__)'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find /opt/task-venv -maxdepth 3 -type f -o -type l | sed -n '1,40p'; /opt/task-venv/bin/python -V; /opt/task-venv/bin/python -c 'import pandas,torch;print(pandas.__version__,torch.__version__)'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 2.8.0+cpu\n", "stderr": "", "aggregated_output": "2.3.3 2.8.0+cpu\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 255337061 }, "formatted_output": "2.3.3 2.8.0+cpu\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T01:28:38.235Z", "callId": "call_SUIu8tpcCGA2tydtESaENAaE", "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')\\nfor p in sorted(D.glob('*.parquet')):\\n df=pd.read_parquet(p)\\n print('\\\\n',p.name, df.shape, list(df.columns))\\n if 'timestamp' in df: print('time',df.timestamp.min(),df.timestamp.max(),'users',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.mean())\\n elif 'uid' in df: print('users',df.uid.nunique(), 'rows/user',len(df)/df.uid.nunique())\\nfor p in sorted(D.glob('*.npz')):\\n z=np.load(p)\\n print('\\\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n print('eligible',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max(), 'density',bits.mean())\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); print('\\\\nCAT',cat.head().to_dict('records'),cat.tail().to_dict('records'))\\ntgt=pd.read_parquet(D/'target_users.parquet'); print('TGT',tgt.head().to_dict('records'))\\nfor s in ['validation','test']:\\n df=pd.read_parquet(D/f'{s}_preference_state.parquet'); print(s,df.head().to_dict('records'),df['state'].value_counts().to_dict(), 'bothpairs',df.groupby(['uid','item_id']).size().gt(1).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:28:42.535Z", "content": "Script completed\nWall time 4.3 seconds\nOutput:\n\n\n candidate_catalog.parquet (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n\n target_users.parquet (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']\nusers 8456 rows/user 1.0\n\n test_preference_state.parquet (484776, 3) ['uid', 'item_id', 'state']\nusers 7092 rows/user 68.35532994923858\n\n train_dislikes.parquet (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 75 24786580 users 5732 items 50876 organic 0.5172198862231017\n\n train_likes.parquet (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 80 24786790 users 8144 items 172303 organic 0.5667911505576003\n\n train_listens.parquet (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 0 24786795 users 9238 items 845395 organic 0.5169151828370419\n\n train_undislikes.parquet (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 15950 24785395 users 2769 items 14725 organic 0.8893286573146293\n\n train_unlikes.parquet (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 250 24786570 users 6197 items 113072 organic 0.9466967351750295\n\n val_dislikes.parquet (3563, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789920 25393280 users 955 items 3135 organic 0.5150154364299747\n\n val_likes.parquet (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24788815 25393390 users 4114 items 22902 organic 0.6662521497552586\n\n val_listens.parquet (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 24788600 25393395 users 8196 items 191747 organic 0.517072352145606\n\n val_undislikes.parquet (606, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24810715 25393035 users 205 items 579 organic 0.8696369636963697\n\n val_unlikes.parquet (9346, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789005 25393385 users 1313 items 8080 organic 0.9528140380911619\n\n validation_preference_state.parquet (469124, 3) ['uid', 'item_id', 'state']\nusers 7038 rows/user 66.65586814435919\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24552 29041.3178807947 30000 density 0.9680439293598234\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24657 29067.306291390727 30000 density 0.9689102097130243\n\nCAT [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}, {'item_id': 3542184, 'positive_listens': 24075, 'organic_positive_listens': 17699, 'popularity_rank': 4}, {'item_id': 9378983, 'positive_listens': 24036, 'organic_positive_listens': 18195, 'popularity_rank': 5}] [{'item_id': 1275692, 'positive_listens': 163, 'organic_positive_listens': 81, 'popularity_rank': 29996}, {'item_id': 1280011, 'positive_listens': 163, 'organic_positive_listens': 76, 'popularity_rank': 29997}, {'item_id': 1309246, 'positive_listens': 163, 'organic_positive_listens': 138, 'popularity_rank': 29998}, {'item_id': 1634958, 'positive_listens': 163, 'organic_positive_listens': 46, 'popularity_rank': 29999}, {'item_id': 1670524, 'positive_listens': 163, 'organic_positive_listens': 73, 'popularity_rank': 30000}]\nTGT [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}, {'uid': 600, 'recent_positive_listens': 1985.0, 'history_positive_listens': 2423}, {'uid': 700, 'recent_positive_listens': 1776.0, 'history_positive_listens': 6114}]\nvalidation [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 418435, 'disliked': 50689} bothpairs 1710\ntest [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 432659, 'disliked': 52117} bothpairs 1776\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:38.303Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce3df73487d2b51d7b147924b4b2", "usage": { "input_tokens": 20274, "cached_input_tokens": 19584, "cache_write_input_tokens": 0, "output_tokens": 432, "reasoning_output_tokens": 0, "total_tokens": 20706 }, "turn_token_usage": { "input_tokens": 92989, "cached_input_tokens": 83072, "cache_write_input_tokens": 0, "output_tokens": 1350, "reasoning_output_tokens": 78, "total_tokens": 94339 }, "thread_token_usage": { "input_tokens": 92989, "cached_input_tokens": 83072, "cache_write_input_tokens": 0, "output_tokens": 1350, "reasoning_output_tokens": 78, "total_tokens": 94339 } } }, { "kind": "context", "timestamp": "2026-09-25T01:28:42.530Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7dd3943e-31b6-4933-80ff-27bb2c34ef51", "process_id": "20942", "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')\nfor p in sorted(D.glob('*.parquet')):\n df=pd.read_parquet(p)\n print('\\n',p.name, df.shape, list(df.columns))\n if 'timestamp' in df: print('time',df.timestamp.min(),df.timestamp.max(),'users',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.mean())\n elif 'uid' in df: print('users',df.uid.nunique(), 'rows/user',len(df)/df.uid.nunique())\nfor p in sorted(D.glob('*.npz')):\n z=np.load(p)\n print('\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max(), 'density',bits.mean())\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); print('\\nCAT',cat.head().to_dict('records'),cat.tail().to_dict('records'))\ntgt=pd.read_parquet(D/'target_users.parquet'); print('TGT',tgt.head().to_dict('records'))\nfor s in ['validation','test']:\n df=pd.read_parquet(D/f'{s}_preference_state.parquet'); print(s,df.head().to_dict('records'),df['state'].value_counts().to_dict(), 'bothpairs',df.groupby(['uid','item_id']).size().gt(1).sum())\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')\nfor p in sorted(D.glob('*.parquet')):\n df=pd.read_parquet(p)\n print('\\n',p.name, df.shape, list(df.columns))\n if 'timestamp' in df: print('time',df.timestamp.min(),df.timestamp.max(),'users',df.uid.nunique(),'items',df.item_id.nunique(),'organic',df.is_organic.mean())\n elif 'uid' in df: print('users',df.uid.nunique(), 'rows/user',len(df)/df.uid.nunique())\nfor p in sorted(D.glob('*.npz')):\n z=np.load(p)\n print('\\n',p.name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max(), 'density',bits.mean())\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); print('\\nCAT',cat.head().to_dict('records'),cat.tail().to_dict('records'))\ntgt=pd.read_parquet(D/'target_users.parquet'); print('TGT',tgt.head().to_dict('records'))\nfor s in ['validation','test']:\n df=pd.read_parquet(D/f'{s}_preference_state.parquet'); print(s,df.head().to_dict('records'),df['state'].value_counts().to_dict(), 'bothpairs',df.groupby(['uid','item_id']).size().gt(1).sum())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n candidate_catalog.parquet (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n\n target_users.parquet (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']\nusers 8456 rows/user 1.0\n\n test_preference_state.parquet (484776, 3) ['uid', 'item_id', 'state']\nusers 7092 rows/user 68.35532994923858\n\n train_dislikes.parquet (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 75 24786580 users 5732 items 50876 organic 0.5172198862231017\n\n train_likes.parquet (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 80 24786790 users 8144 items 172303 organic 0.5667911505576003\n\n train_listens.parquet (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 0 24786795 users 9238 items 845395 organic 0.5169151828370419\n\n train_undislikes.parquet (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 15950 24785395 users 2769 items 14725 organic 0.8893286573146293\n\n train_unlikes.parquet (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 250 24786570 users 6197 items 113072 organic 0.9466967351750295\n\n val_dislikes.parquet (3563, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789920 25393280 users 955 items 3135 organic 0.5150154364299747\n\n val_likes.parquet (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24788815 25393390 users 4114 items 22902 organic 0.6662521497552586\n\n val_listens.parquet (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 24788600 25393395 users 8196 items 191747 organic 0.517072352145606\n\n val_undislikes.parquet (606, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24810715 25393035 users 205 items 579 organic 0.8696369636963697\n\n val_unlikes.parquet (9346, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789005 25393385 users 1313 items 8080 organic 0.9528140380911619\n\n validation_preference_state.parquet (469124, 3) ['uid', 'item_id', 'state']\nusers 7038 rows/user 66.65586814435919\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24552 29041.3178807947 30000 density 0.9680439293598234\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24657 29067.306291390727 30000 density 0.9689102097130243\n\nCAT [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}, {'item_id': 3542184, 'positive_listens': 24075, 'organic_positive_listens': 17699, 'popularity_rank': 4}, {'item_id': 9378983, 'positive_listens': 24036, 'organic_positive_listens': 18195, 'popularity_rank': 5}] [{'item_id': 1275692, 'positive_listens': 163, 'organic_positive_listens': 81, 'popularity_rank': 29996}, {'item_id': 1280011, 'positive_listens': 163, 'organic_positive_listens': 76, 'popularity_rank': 29997}, {'item_id': 1309246, 'positive_listens': 163, 'organic_positive_listens': 138, 'popularity_rank': 29998}, {'item_id': 1634958, 'positive_listens': 163, 'organic_positive_listens': 46, 'popularity_rank': 29999}, {'item_id': 1670524, 'positive_listens': 163, 'organic_positive_listens': 73, 'popularity_rank': 30000}]\nTGT [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}, {'uid': 600, 'recent_positive_listens': 1985.0, 'history_positive_listens': 2423}, {'uid': 700, 'recent_positive_listens': 1776.0, 'history_positive_listens': 6114}]\nvalidation [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 418435, 'disliked': 50689} bothpairs 1710\ntest [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 432659, 'disliked': 52117} bothpairs 1776\n", "stderr": "", "aggregated_output": "\n candidate_catalog.parquet (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n\n target_users.parquet (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']\nusers 8456 rows/user 1.0\n\n test_preference_state.parquet (484776, 3) ['uid', 'item_id', 'state']\nusers 7092 rows/user 68.35532994923858\n\n train_dislikes.parquet (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 75 24786580 users 5732 items 50876 organic 0.5172198862231017\n\n train_likes.parquet (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 80 24786790 users 8144 items 172303 organic 0.5667911505576003\n\n train_listens.parquet (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 0 24786795 users 9238 items 845395 organic 0.5169151828370419\n\n train_undislikes.parquet (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 15950 24785395 users 2769 items 14725 organic 0.8893286573146293\n\n train_unlikes.parquet (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 250 24786570 users 6197 items 113072 organic 0.9466967351750295\n\n val_dislikes.parquet (3563, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789920 25393280 users 955 items 3135 organic 0.5150154364299747\n\n val_likes.parquet (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24788815 25393390 users 4114 items 22902 organic 0.6662521497552586\n\n val_listens.parquet (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 24788600 25393395 users 8196 items 191747 organic 0.517072352145606\n\n val_undislikes.parquet (606, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24810715 25393035 users 205 items 579 organic 0.8696369636963697\n\n val_unlikes.parquet (9346, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789005 25393385 users 1313 items 8080 organic 0.9528140380911619\n\n validation_preference_state.parquet (469124, 3) ['uid', 'item_id', 'state']\nusers 7038 rows/user 66.65586814435919\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24552 29041.3178807947 30000 density 0.9680439293598234\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24657 29067.306291390727 30000 density 0.9689102097130243\n\nCAT [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}, {'item_id': 3542184, 'positive_listens': 24075, 'organic_positive_listens': 17699, 'popularity_rank': 4}, {'item_id': 9378983, 'positive_listens': 24036, 'organic_positive_listens': 18195, 'popularity_rank': 5}] [{'item_id': 1275692, 'positive_listens': 163, 'organic_positive_listens': 81, 'popularity_rank': 29996}, {'item_id': 1280011, 'positive_listens': 163, 'organic_positive_listens': 76, 'popularity_rank': 29997}, {'item_id': 1309246, 'positive_listens': 163, 'organic_positive_listens': 138, 'popularity_rank': 29998}, {'item_id': 1634958, 'positive_listens': 163, 'organic_positive_listens': 46, 'popularity_rank': 29999}, {'item_id': 1670524, 'positive_listens': 163, 'organic_positive_listens': 73, 'popularity_rank': 30000}]\nTGT [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}, {'uid': 600, 'recent_positive_listens': 1985.0, 'history_positive_listens': 2423}, {'uid': 700, 'recent_positive_listens': 1776.0, 'history_positive_listens': 6114}]\nvalidation [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 418435, 'disliked': 50689} bothpairs 1710\ntest [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 432659, 'disliked': 52117} bothpairs 1776\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 129123262 }, "formatted_output": "\n candidate_catalog.parquet (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n\n target_users.parquet (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']\nusers 8456 rows/user 1.0\n\n test_preference_state.parquet (484776, 3) ['uid', 'item_id', 'state']\nusers 7092 rows/user 68.35532994923858\n\n train_dislikes.parquet (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 75 24786580 users 5732 items 50876 organic 0.5172198862231017\n\n train_likes.parquet (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 80 24786790 users 8144 items 172303 organic 0.5667911505576003\n\n train_listens.parquet (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 0 24786795 users 9238 items 845395 organic 0.5169151828370419\n\n train_undislikes.parquet (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 15950 24785395 users 2769 items 14725 organic 0.8893286573146293\n\n train_unlikes.parquet (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 250 24786570 users 6197 items 113072 organic 0.9466967351750295\n\n val_dislikes.parquet (3563, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789920 25393280 users 955 items 3135 organic 0.5150154364299747\n\n val_likes.parquet (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24788815 25393390 users 4114 items 22902 organic 0.6662521497552586\n\n val_listens.parquet (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntime 24788600 25393395 users 8196 items 191747 organic 0.517072352145606\n\n val_undislikes.parquet (606, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24810715 25393035 users 205 items 579 organic 0.8696369636963697\n\n val_unlikes.parquet (9346, 4) ['uid', 'timestamp', 'item_id', 'is_organic']\ntime 24789005 25393385 users 1313 items 8080 organic 0.9528140380911619\n\n validation_preference_state.parquet (469124, 3) ['uid', 'item_id', 'state']\nusers 7038 rows/user 66.65586814435919\n\n test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24552 29041.3178807947 30000 density 0.9680439293598234\n\n validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible 24657 29067.306291390727 30000 density 0.9689102097130243\n\nCAT [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}, {'item_id': 3542184, 'positive_listens': 24075, 'organic_positive_listens': 17699, 'popularity_rank': 4}, {'item_id': 9378983, 'positive_listens': 24036, 'organic_positive_listens': 18195, 'popularity_rank': 5}] [{'item_id': 1275692, 'positive_listens': 163, 'organic_positive_listens': 81, 'popularity_rank': 29996}, {'item_id': 1280011, 'positive_listens': 163, 'organic_positive_listens': 76, 'popularity_rank': 29997}, {'item_id': 1309246, 'positive_listens': 163, 'organic_positive_listens': 138, 'popularity_rank': 29998}, {'item_id': 1634958, 'positive_listens': 163, 'organic_positive_listens': 46, 'popularity_rank': 29999}, {'item_id': 1670524, 'positive_listens': 163, 'organic_positive_listens': 73, 'popularity_rank': 30000}]\nTGT [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}, {'uid': 600, 'recent_positive_listens': 1985.0, 'history_positive_listens': 2423}, {'uid': 700, 'recent_positive_listens': 1776.0, 'history_positive_listens': 6114}]\nvalidation [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 418435, 'disliked': 50689} bothpairs 1710\ntest [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}, {'uid': 100, 'item_id': 1730273, 'state': 'liked'}, {'uid': 100, 'item_id': 2621343, 'state': 'liked'}] {'liked': 432659, 'disliked': 52117} bothpairs 1776\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-25T01:28:49.914Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:28:49.914Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:28:58.474Z", "callId": "call_QMGcmDPyKEyRCmcfwFq6yKYX", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"free -h; df -h /app; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\\n# validation label stats\\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nli=li[li.uid.isin(targets)&li.item_id.isin(cat)&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nla=la[la.uid.isin(targets)&la.item_id.isin(cat)&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\\nprint('strong',len(li),li.uid.nunique(),'likes',len(la),la.uid.nunique(),'union users',pd.concat([li,la]).uid.nunique())\\nprint('per user',pd.concat([li,la]).drop_duplicates().groupby('uid').size().describe([.5,.9,.99]))\\n# candidate popularity relevance ranks\\ncatdf=pd.read_parquet(D/'candidate_catalog.parquet')\\nrel=pd.concat([li.assign(g=1),la.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nrr=rel.merge(catdf[['item_id','popularity_rank']],on='item_id')\\nprint('rel pop rank',rr.groupby('g').popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9]).to_string())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:29:00.547Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\n/bin/sh: 1: free: not found\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\nstrong 170791 6027 likes 8528 2073 union users 6186\nper user count 6186.000000\nmean 28.407533\nstd 40.768883\nmin 1.000000\n50% 17.000000\n90% 67.000000\n99% 161.150000\nmax 1745.000000\ndtype: float64\nrel pop rank count mean std min 10% 25% 50% 75% 90% max\ng \n1 167201.0 7190.356134 7798.668689 1.0 205.0 988.00 4056.0 11221.0 20049.0 30000.0\n3 8528.0 8415.048311 8123.488549 1.0 442.0 1648.75 5554.0 13506.5 21728.7 29989.0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:28:58.476Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce4abf0c87d2ab0f86ca1e88905e", "usage": { "input_tokens": 22322, "cached_input_tokens": 20096, "cache_write_input_tokens": 0, "output_tokens": 730, "reasoning_output_tokens": 272, "total_tokens": 23052 }, "turn_token_usage": { "input_tokens": 115311, "cached_input_tokens": 103168, "cache_write_input_tokens": 0, "output_tokens": 2080, "reasoning_output_tokens": 350, "total_tokens": 117391 }, "thread_token_usage": { "input_tokens": 115311, "cached_input_tokens": 103168, "cache_write_input_tokens": 0, "output_tokens": 2080, "reasoning_output_tokens": 350, "total_tokens": 117391 } } }, { "kind": "context", "timestamp": "2026-09-25T01:29:00.543Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-84e98a61-3050-4381-804c-052f78baa220", "process_id": "13730", "command": [ "/bin/sh", "-lc", "free -h; df -h /app; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\n# validation label stats\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nli=li[li.uid.isin(targets)&li.item_id.isin(cat)&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nla=la[la.uid.isin(targets)&la.item_id.isin(cat)&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\nprint('strong',len(li),li.uid.nunique(),'likes',len(la),la.uid.nunique(),'union users',pd.concat([li,la]).uid.nunique())\nprint('per user',pd.concat([li,la]).drop_duplicates().groupby('uid').size().describe([.5,.9,.99]))\n# candidate popularity relevance ranks\ncatdf=pd.read_parquet(D/'candidate_catalog.parquet')\nrel=pd.concat([li.assign(g=1),la.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=rel.merge(catdf[['item_id','popularity_rank']],on='item_id')\nprint('rel pop rank',rr.groupby('g').popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9]).to_string())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; df -h /app; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\n# validation label stats\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nli=li[li.uid.isin(targets)&li.item_id.isin(cat)&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nla=la[la.uid.isin(targets)&la.item_id.isin(cat)&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\nprint('strong',len(li),li.uid.nunique(),'likes',len(la),la.uid.nunique(),'union users',pd.concat([li,la]).uid.nunique())\nprint('per user',pd.concat([li,la]).drop_duplicates().groupby('uid').size().describe([.5,.9,.99]))\n# candidate popularity relevance ranks\ncatdf=pd.read_parquet(D/'candidate_catalog.parquet')\nrel=pd.concat([li.assign(g=1),la.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=rel.merge(catdf[['item_id','popularity_rank']],on='item_id')\nprint('rel pop rank',rr.groupby('g').popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9]).to_string())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "strong 170791 6027 likes 8528 2073 union users 6186\nper user count 6186.000000\nmean 28.407533\nstd 40.768883\nmin 1.000000\n50% 17.000000\n90% 67.000000\n99% 161.150000\nmax 1745.000000\ndtype: float64\nrel pop rank count mean std min 10% 25% 50% 75% 90% max\ng \n1 167201.0 7190.356134 7798.668689 1.0 205.0 988.00 4056.0 11221.0 20049.0 30000.0\n3 8528.0 8415.048311 8123.488549 1.0 442.0 1648.75 5554.0 13506.5 21728.7 29989.0\n", "stderr": "", "aggregated_output": "strong 170791 6027 likes 8528 2073 union users 6186\nper user count 6186.000000\nmean 28.407533\nstd 40.768883\nmin 1.000000\n50% 17.000000\n90% 67.000000\n99% 161.150000\nmax 1745.000000\ndtype: float64\nrel pop rank count mean std min 10% 25% 50% 75% 90% max\ng \n1 167201.0 7190.356134 7798.668689 1.0 205.0 988.00 4056.0 11221.0 20049.0 30000.0\n3 8528.0 8415.048311 8123.488549 1.0 442.0 1648.75 5554.0 13506.5 21728.7 29989.0\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 902508036 }, "formatted_output": "strong 170791 6027 likes 8528 2073 union users 6186\nper user count 6186.000000\nmean 28.407533\nstd 40.768883\nmin 1.000000\n50% 17.000000\n90% 67.000000\n99% 161.150000\nmax 1745.000000\ndtype: float64\nrel pop rank count mean std min 10% 25% 50% 75% 90% max\ng \n1 167201.0 7190.356134 7798.668689 1.0 205.0 988.00 4056.0 11221.0 20049.0 30000.0\n3 8528.0 8415.048311 8123.488549 1.0 442.0 1648.75 5554.0 13506.5 21728.7 29989.0\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-25T01:29:10.641Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:10.641Z", "text": "" } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-25T01:29:18.048Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:18.048Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:29:19.296Z", "text": "The eligible sets are broad (about 29,000 tracks per user), and the public window has many relevant tracks per evaluated user, so a popularity-only retriever is unlikely to be enough. I’m establishing two reference points—catalog popularity and history-based repeat affinity—before spending CPU on contrastive training, so the neural model’s gain is measurable rather than assumed." }, { "kind": "tool", "timestamp": "2026-09-25T01:29:40.104Z", "callId": "call_ubMCePIFOHmutsrVcQMlmmJ0", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_baselines.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build inexpensive, leakage-safe validation baselines.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+\\n+targets = pd.read_parquet(D / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+items = catalog.item_id.to_numpy(np.int64)\\n+uid_order = np.sort(targets)\\n+item_order = np.sort(items)\\n+uid_to_row = {int(v): i for i, v in enumerate(targets)}\\n+item_to_col = {int(v): i for i, v in enumerate(items)}\\n+\\n+# Keep only conventional positive listens from target users to catalog tracks.\\n+parts = []\\n+pf = pq.ParquetFile(D / \\\"train_listens.parquet\\\")\\n+for batch in pf.iter_batches(batch_size=1_000_000, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]):\\n+ d = batch.to_pydict()\\n+ u = np.asarray(d[\\\"uid\\\"], dtype=np.int64)\\n+ t = np.asarray(d[\\\"timestamp\\\"], dtype=np.int64)\\n+ it = np.asarray(d[\\\"item_id\\\"], dtype=np.int64)\\n+ org = np.asarray(d[\\\"is_organic\\\"], dtype=np.int8)\\n+ ratio = np.asarray(d[\\\"played_ratio_pct\\\"], dtype=np.float32)\\n+ up = np.searchsorted(uid_order, u)\\n+ ip = np.searchsorted(item_order, it)\\n+ mask = ((up < len(uid_order)) & (uid_order[np.minimum(up, len(uid_order)-1)] == u) &\\n+ (ip < len(item_order)) & (item_order[np.minimum(ip, len(item_order)-1)] == it) & (ratio >= 50))\\n+ if mask.any():\\n+ parts.append(pd.DataFrame({\\\"uid\\\":u[mask], \\\"item_id\\\":it[mask], \\\"timestamp\\\":t[mask], \\\"organic\\\":org[mask]}))\\n+ print(f\\\"batch kept={sum(map(len,parts)):,}\\\", flush=True)\\n+\\n+hist = pd.concat(parts, ignore_index=True)\\n+agg = hist.groupby([\\\"uid\\\",\\\"item_id\\\"], sort=False).agg(\\n+ count=(\\\"timestamp\\\",\\\"size\\\"), organic=(\\\"organic\\\",\\\"sum\\\"), last=(\\\"timestamp\\\",\\\"max\\\")\\n+).reset_index()\\n+agg.to_parquet(O / \\\"train_catalog_history.parquet\\\", index=False)\\n+print(\\\"events\\\", len(hist), \\\"pairs\\\", len(agg))\\n+\\n+def write_rank(name, score_kind):\\n+ z=np.load(D/'validation_eligible_candidates.npz')\\n+ bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').astype(bool)\\n+ zitems=z['item_ids'].astype(np.int64); zrow={int(u):i for i,u in enumerate(z['uids'])}\\n+ # Popularity prior in supplied item ordering.\\n+ pop=np.arange(len(zitems),0,-1,dtype=np.float32)\\n+ byuid={int(u):g for u,g in agg.groupby('uid',sort=False)}\\n+ out=[]\\n+ for uid in targets:\\n+ s=pop.copy()\\n+ g=byuid.get(int(uid))\\n+ if g is not None:\\n+ cols=np.fromiter((item_to_col[int(x)] for x in g.item_id),dtype=np.int64,count=len(g))\\n+ if score_kind=='count':\\n+ bonus=np.log1p(g['count'].to_numpy())*40000 + g['organic'].to_numpy()*100\\n+ elif score_kind=='recent':\\n+ age=(24786800-g['last'].to_numpy())/86400\\n+ bonus=np.log1p(g['count'].to_numpy())*20000 + np.exp(-age/30)*80000 + g['organic'].to_numpy()*100\\n+ else: bonus=np.zeros(len(g))\\n+ s[cols]+=bonus\\n+ s[~bits[zrow[int(uid)]]]=-np.inf\\n+ top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\\n+ out.extend((int(uid),int(zitems[j]),r+1) for r,j in enumerate(top))\\n+ pd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet(O/f'{name}.parquet',index=False)\\n+\\n+write_rank('validation_pop','pop')\\n+write_rank('validation_repeat_count','count')\\n+write_rank('validation_repeat_recent','recent')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:29:40.148Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:29:40.113Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8c2dc524-d6d3-4cf7-86a8-90a70f513909", "changes": { "/app/solution/explore_baselines.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build inexpensive, leakage-safe validation baselines.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path(\"/task/data\")\nO = Path(\"/app/solution\")\n\ntargets = pd.read_parquet(D / \"target_users.parquet\").uid.to_numpy(np.int64)\ncatalog = pd.read_parquet(D / \"candidate_catalog.parquet\")\nitems = catalog.item_id.to_numpy(np.int64)\nuid_order = np.sort(targets)\nitem_order = np.sort(items)\nuid_to_row = {int(v): i for i, v in enumerate(targets)}\nitem_to_col = {int(v): i for i, v in enumerate(items)}\n\n# Keep only conventional positive listens from target users to catalog tracks.\nparts = []\npf = pq.ParquetFile(D / \"train_listens.parquet\")\nfor batch in pf.iter_batches(batch_size=1_000_000, columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]):\n d = batch.to_pydict()\n u = np.asarray(d[\"uid\"], dtype=np.int64)\n t = np.asarray(d[\"timestamp\"], dtype=np.int64)\n it = np.asarray(d[\"item_id\"], dtype=np.int64)\n org = np.asarray(d[\"is_organic\"], dtype=np.int8)\n ratio = np.asarray(d[\"played_ratio_pct\"], dtype=np.float32)\n up = np.searchsorted(uid_order, u)\n ip = np.searchsorted(item_order, it)\n mask = ((up < len(uid_order)) & (uid_order[np.minimum(up, len(uid_order)-1)] == u) &\n (ip < len(item_order)) & (item_order[np.minimum(ip, len(item_order)-1)] == it) & (ratio >= 50))\n if mask.any():\n parts.append(pd.DataFrame({\"uid\":u[mask], \"item_id\":it[mask], \"timestamp\":t[mask], \"organic\":org[mask]}))\n print(f\"batch kept={sum(map(len,parts)):,}\", flush=True)\n\nhist = pd.concat(parts, ignore_index=True)\nagg = hist.groupby([\"uid\",\"item_id\"], sort=False).agg(\n count=(\"timestamp\",\"size\"), organic=(\"organic\",\"sum\"), last=(\"timestamp\",\"max\")\n).reset_index()\nagg.to_parquet(O / \"train_catalog_history.parquet\", index=False)\nprint(\"events\", len(hist), \"pairs\", len(agg))\n\ndef write_rank(name, score_kind):\n z=np.load(D/'validation_eligible_candidates.npz')\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').astype(bool)\n zitems=z['item_ids'].astype(np.int64); zrow={int(u):i for i,u in enumerate(z['uids'])}\n # Popularity prior in supplied item ordering.\n pop=np.arange(len(zitems),0,-1,dtype=np.float32)\n byuid={int(u):g for u,g in agg.groupby('uid',sort=False)}\n out=[]\n for uid in targets:\n s=pop.copy()\n g=byuid.get(int(uid))\n if g is not None:\n cols=np.fromiter((item_to_col[int(x)] for x in g.item_id),dtype=np.int64,count=len(g))\n if score_kind=='count':\n bonus=np.log1p(g['count'].to_numpy())*40000 + g['organic'].to_numpy()*100\n elif score_kind=='recent':\n age=(24786800-g['last'].to_numpy())/86400\n bonus=np.log1p(g['count'].to_numpy())*20000 + np.exp(-age/30)*80000 + g['organic'].to_numpy()*100\n else: bonus=np.zeros(len(g))\n s[cols]+=bonus\n s[~bits[zrow[int(uid)]]]=-np.inf\n top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\n out.extend((int(uid),int(zitems[j]),r+1) for r,j in enumerate(top))\n pd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet(O/f'{name}.parquet',index=False)\n\nwrite_rank('validation_pop','pop')\nwrite_rank('validation_repeat_count','count')\nwrite_rank('validation_repeat_recent','recent')\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/explore_baselines.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:29:40.146Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce5cbd3487d2bed60deac082188c", "usage": { "input_tokens": 23372, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 2156, "reasoning_output_tokens": 910, "total_tokens": 25528 }, "turn_token_usage": { "input_tokens": 138683, "cached_input_tokens": 125312, "cache_write_input_tokens": 0, "output_tokens": 4236, "reasoning_output_tokens": 1260, "total_tokens": 142919 }, "thread_token_usage": { "input_tokens": 138683, "cached_input_tokens": 125312, "cache_write_input_tokens": 0, "output_tokens": 4236, "reasoning_output_tokens": 1260, "total_tokens": 142919 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-25T01:29:42.255Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:29:42.255Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:29:43.415Z", "callId": "call_Cv8Grmu697XTMqinCwhMa1RR", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -l solution/explore_baselines.py && /opt/task-venv/bin/python solution/explore_baselines.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:30:13.581Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"93a074\",\"wall_time_seconds\":30.001011139,\"session_id\":20502,\"original_token_count\":91,\"output\":\"-rw-r--r-- 1 agent agent 3404 Sep 25 01:29 solution/explore_baselines.py\\nbatch kept=444,136\\nbatch kept=900,355\\nbatch kept=1,329,225\\nbatch kept=1,773,315\\nbatch kept=2,220,892\\nbatch kept=2,694,851\\nbatch kept=3,169,708\\nbatch kept=3,634,986\\nbatch kept=4,072,361\\nbatch kept=4,507,902\\nbatch kept=4,982,336\\nbatch kept=5,442,836\\nbatch kept=5,914,841\\nbatch kept=6,384,722\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:29:43.481Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ce84c33487d2b7065920381e36e4", "usage": { "input_tokens": 25552, "cached_input_tokens": 23168, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 14, "total_tokens": 25638 }, "turn_token_usage": { "input_tokens": 164235, "cached_input_tokens": 148480, "cache_write_input_tokens": 0, "output_tokens": 4322, "reasoning_output_tokens": 1274, "total_tokens": 168557 }, "thread_token_usage": { "input_tokens": 164235, "cached_input_tokens": 148480, "cache_write_input_tokens": 0, "output_tokens": 4322, "reasoning_output_tokens": 1274, "total_tokens": 168557 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-25T01:30:14.672Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:14.672Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:30:15.345Z", "callId": "call_aEiZ5kuze3iSXBPfrJRMOgqJ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:20502,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:30:45.353Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nbatch kept=6,872,786\nbatch kept=7,356,913\nbatch kept=7,793,592\nbatch kept=8,209,202\nbatch kept=8,683,025\nbatch kept=9,154,647\nbatch kept=9,622,752\nbatch kept=10,102,567\nbatch kept=10,575,035\nbatch kept=10,999,239\nbatch kept=11,453,464\nbatch kept=11,899,851\nbatch kept=12,371,930\nbatch kept=12,836,030\nbatch kept=13,256,228\nbatch kept=13,723,681\nbatch kept=14,195,205\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:15.400Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cea5c10c87d29048b1bab4035ace", "usage": { "input_tokens": 25843, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 25898 }, "turn_token_usage": { "input_tokens": 190078, "cached_input_tokens": 173824, "cache_write_input_tokens": 0, "output_tokens": 4377, "reasoning_output_tokens": 1282, "total_tokens": 194455 }, "thread_token_usage": { "input_tokens": 190078, "cached_input_tokens": 173824, "cache_write_input_tokens": 0, "output_tokens": 4377, "reasoning_output_tokens": 1282, "total_tokens": 194455 } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-25T01:30:46.596Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:30:46.596Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:30:47.279Z", "callId": "call_bvPZo9vY83qFlQFdiZVZitaM", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:20502,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:31:17.287Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nbatch kept=14,650,619\nbatch kept=15,122,860\nbatch kept=15,585,117\nbatch kept=16,050,245\nbatch kept=16,502,221\nbatch kept=16,955,844\nbatch kept=17,417,609\nbatch kept=17,895,943\nbatch kept=18,348,811\nbatch kept=18,808,825\nbatch kept=19,271,922\nbatch kept=19,754,849\nbatch kept=19,959,853\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:30:47.345Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cec5a9f887d2895e55150d0cf476", "usage": { "input_tokens": 26074, "cached_input_tokens": 25728, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 13, "total_tokens": 26134 }, "turn_token_usage": { "input_tokens": 216152, "cached_input_tokens": 199552, "cache_write_input_tokens": 0, "output_tokens": 4437, "reasoning_output_tokens": 1295, "total_tokens": 220589 }, "thread_token_usage": { "input_tokens": 216152, "cached_input_tokens": 199552, "cache_write_input_tokens": 0, "output_tokens": 4437, "reasoning_output_tokens": 1295, "total_tokens": 220589 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-25T01:31:18.515Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:18.515Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:31:19.204Z", "callId": "call_OrXQphEmFrBJpcLlHP84hVPr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:20502,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:31:27.763Z", "content": "Script completed\nWall time 8.6 seconds\nOutput:\n\nevents 19959853 pairs 5514505\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:31:19.254Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cee5910887d2a38a3e7f17a3e47a", "usage": { "input_tokens": 26274, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 26330 }, "turn_token_usage": { "input_tokens": 242426, "cached_input_tokens": 225408, "cache_write_input_tokens": 0, "output_tokens": 4493, "reasoning_output_tokens": 1304, "total_tokens": 246919 }, "thread_token_usage": { "input_tokens": 242426, "cached_input_tokens": 225408, "cache_write_input_tokens": 0, "output_tokens": 4493, "reasoning_output_tokens": 1304, "total_tokens": 246919 } } }, { "kind": "context", "timestamp": "2026-09-25T01:31:27.757Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-955a139d-be23-4692-9746-ef98055374cd", "process_id": "20502", "command": [ "/bin/sh", "-lc", "ls -l solution/explore_baselines.py && /opt/task-venv/bin/python solution/explore_baselines.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -l solution/explore_baselines.py && /opt/task-venv/bin/python solution/explore_baselines.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "batch kept=444,136\nbatch kept=900,355\nbatch kept=1,329,225\nbatch kept=1,773,315\nbatch kept=2,220,892\nbatch kept=2,694,851\nbatch kept=3,169,708\nbatch kept=3,634,986\nbatch kept=4,072,361\nbatch kept=4,507,902\nbatch kept=4,982,336\nbatch kept=5,442,836\nbatch kept=5,914,841\nbatch kept=6,384,722\nbatch kept=6,872,786\nbatch kept=7,356,913\nbatch kept=7,793,592\nbatch kept=8,209,202\nbatch kept=8,683,025\nbatch kept=9,154,647\nbatch kept=9,622,752\nbatch kept=10,102,567\nbatch kept=10,575,035\nbatch kept=10,999,239\nbatch kept=11,453,464\nbatch kept=11,899,851\nbatch kept=12,371,930\nbatch kept=12,836,030\nbatch kept=13,256,228\nbatch kept=13,723,681\nbatch kept=14,195,205\nbatch kept=14,650,619\nbatch kept=15,122,860\nbatch kept=15,585,117\nbatch kept=16,050,245\nbatch kept=16,502,221\nbatch kept=16,955,844\nbatch kept=17,417,609\nbatch kept=17,895,943\nbatch kept=18,348,811\nbatch kept=18,808,825\nbatch kept=19,271,922\nbatch kept=19,754,849\nbatch kept=19,959,853\nevents 19959853 pairs 5514505\n", "stderr": "", "aggregated_output": "batch kept=444,136\nbatch kept=900,355\nbatch kept=1,329,225\nbatch kept=1,773,315\nbatch kept=2,220,892\nbatch kept=2,694,851\nbatch kept=3,169,708\nbatch kept=3,634,986\nbatch kept=4,072,361\nbatch kept=4,507,902\nbatch kept=4,982,336\nbatch kept=5,442,836\nbatch kept=5,914,841\nbatch kept=6,384,722\nbatch kept=6,872,786\nbatch kept=7,356,913\nbatch kept=7,793,592\nbatch kept=8,209,202\nbatch kept=8,683,025\nbatch kept=9,154,647\nbatch kept=9,622,752\nbatch kept=10,102,567\nbatch kept=10,575,035\nbatch kept=10,999,239\nbatch kept=11,453,464\nbatch kept=11,899,851\nbatch kept=12,371,930\nbatch kept=12,836,030\nbatch kept=13,256,228\nbatch kept=13,723,681\nbatch kept=14,195,205\nbatch kept=14,650,619\nbatch kept=15,122,860\nbatch kept=15,585,117\nbatch kept=16,050,245\nbatch kept=16,502,221\nbatch kept=16,955,844\nbatch kept=17,417,609\nbatch kept=17,895,943\nbatch kept=18,348,811\nbatch kept=18,808,825\nbatch kept=19,271,922\nbatch kept=19,754,849\nbatch kept=19,959,853\nevents 19959853 pairs 5514505\n", "exit_code": 0, "duration": { "secs": 104, "nanos": 177212881 }, "formatted_output": "batch kept=444,136\nbatch kept=900,355\nbatch kept=1,329,225\nbatch kept=1,773,315\nbatch kept=2,220,892\nbatch kept=2,694,851\nbatch kept=3,169,708\nbatch kept=3,634,986\nbatch kept=4,072,361\nbatch kept=4,507,902\nbatch kept=4,982,336\nbatch kept=5,442,836\nbatch kept=5,914,841\nbatch kept=6,384,722\nbatch kept=6,872,786\nbatch kept=7,356,913\nbatch kept=7,793,592\nbatch kept=8,209,202\nbatch kept=8,683,025\nbatch kept=9,154,647\nbatch kept=9,622,752\nbatch kept=10,102,567\nbatch kept=10,575,035\nbatch kept=10,999,239\nbatch kept=11,453,464\nbatch kept=11,899,851\nbatch kept=12,371,930\nbatch kept=12,836,030\nbatch kept=13,256,228\nbatch kept=13,723,681\nbatch kept=14,195,205\nbatch kept=14,650,619\nbatch kept=15,122,860\nbatch kept=15,585,117\nbatch kept=16,050,245\nbatch kept=16,502,221\nbatch kept=16,955,844\nbatch kept=17,417,609\nbatch kept=17,895,943\nbatch kept=18,348,811\nbatch kept=18,808,825\nbatch kept=19,271,922\nbatch kept=19,754,849\nbatch kept=19,959,853\nevents 19959853 pairs 5514505\n" } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-25T01:31:28.877Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:28.877Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:31:30.355Z", "callId": "call_s0JMVpoFnnQEO5Ih0Lu6A4mm", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_pop.parquet solution/validation_repeat_count.parquet solution/validation_repeat_recent.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:31:41.159Z", "content": "Script completed\nWall time 10.8 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{\n \"model\": \"solution/validation_repeat_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_repeat_recent.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:31:30.435Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5ceeff46487d2a5e2ab0f4388b0d1", "usage": { "input_tokens": 26364, "cached_input_tokens": 26112, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 9, "total_tokens": 26464 }, "turn_token_usage": { "input_tokens": 268790, "cached_input_tokens": 251520, "cache_write_input_tokens": 0, "output_tokens": 4593, "reasoning_output_tokens": 1313, "total_tokens": 273383 }, "thread_token_usage": { "input_tokens": 268790, "cached_input_tokens": 251520, "cache_write_input_tokens": 0, "output_tokens": 4593, "reasoning_output_tokens": 1313, "total_tokens": 273383 } } }, { "kind": "context", "timestamp": "2026-09-25T01:31:41.154Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9a01123b-0d62-45b8-9476-3787afdb8357", "process_id": "50580", "command": [ "/bin/sh", "-lc", "for f in solution/validation_pop.parquet solution/validation_repeat_count.parquet solution/validation_repeat_recent.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_pop.parquet solution/validation_repeat_count.parquet solution/validation_repeat_recent.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "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{\n \"model\": \"solution/validation_repeat_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_repeat_recent.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{\n \"model\": \"solution/validation_repeat_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_repeat_recent.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 634883041 }, "formatted_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_repeat_count.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_repeat_recent.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-25T01:31:52.366Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:31:52.366Z", "text": "" } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-25T01:32:02.558Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:32:02.558Z", "text": "" } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-25T01:32:12.127Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:32:12.127Z", "text": "" } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-25T01:32:22.447Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:32:22.447Z", "text": "" } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-25T01:32:32.054Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:32:32.054Z", "text": "" } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-25T01:32:32.750Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:32:32.750Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:32:34.368Z", "text": "The repeat-affinity variants collapse exactly to the popularity score because previously consumed tracks are excluded by eligibility—useful confirmation that this is genuinely a discovery task. I’m now training the required decomposition on a pseudo-future window inside `train_*`: the user tower pools only earlier positive history, and the item tower learns to separate newly discovered organic positives from sampled catalog negatives. Validation inference will then use the full training history only." }, { "kind": "tool", "timestamp": "2026-09-25T01:33:47.123Z", "callId": "call_YKhpfxtUmOWYSWiCinZoxT3j", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based neural two-tower ranker.\\n+\\n+No user-ID parameters or global user-item matrix are used. Training consumes\\n+lists of (history, future-positive) examples with sampled negatives.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import random\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+import torch\\n+from torch import nn\\n+import torch.nn.functional as F\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+DAY = 86_400\\n+PSEUDO_GAP = 1_800\\n+PSEUDO_DAYS = 7\\n+\\n+\\n+def seed_all(seed: int = 20260925) -> None:\\n+ random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\\n+\\n+\\n+def membership(values: np.ndarray, sorted_values: np.ndarray) -> np.ndarray:\\n+ p = np.searchsorted(sorted_values, values)\\n+ return (p < len(sorted_values)) & (sorted_values[np.minimum(p, len(sorted_values)-1)] == values)\\n+\\n+\\n+def aggregate_listens(files: list[Path], end: int | None, cache: Path) -> pd.DataFrame:\\n+ \\\"\\\"\\\"Stream positive catalog listens and return compact user-item features.\\\"\\\"\\\"\\n+ if cache.exists():\\n+ return pd.read_parquet(cache)\\n+ targets = np.sort(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64))\\n+ items = np.sort(pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").item_id.to_numpy(np.int64))\\n+ parts: list[pd.DataFrame] = []\\n+ for path in files:\\n+ pf = pq.ParquetFile(path)\\n+ for batch in pf.iter_batches(batch_size=1_000_000,\\n+ columns=[\\\"uid\\\",\\\"timestamp\\\",\\\"item_id\\\",\\\"is_organic\\\",\\\"played_ratio_pct\\\"]):\\n+ d=batch.to_pydict(); u=np.asarray(d['uid'],np.int64); t=np.asarray(d['timestamp'],np.int64)\\n+ it=np.asarray(d['item_id'],np.int64); org=np.asarray(d['is_organic'],np.int8)\\n+ ratio=np.asarray(d['played_ratio_pct'],np.float32)\\n+ m=membership(u,targets)&membership(it,items)&(ratio>=50)\\n+ if end is not None: m &= t <= end\\n+ if m.any(): parts.append(pd.DataFrame({'uid':u[m],'item_id':it[m],'timestamp':t[m],'organic':org[m]}))\\n+ events=pd.concat(parts,ignore_index=True)\\n+ agg=events.groupby(['uid','item_id'],sort=False).agg(count=('timestamp','size'),\\n+ organic=('organic','sum'),last=('timestamp','max')).reset_index()\\n+ agg.to_parquet(cache,index=False)\\n+ return agg\\n+\\n+\\n+def make_labels(listen_file: Path, like_file: Path, start: int, end: int,\\n+ history: pd.DataFrame, cache: Path) -> pd.DataFrame:\\n+ if cache.exists(): return pd.read_parquet(cache)\\n+ targets=set(pd.read_parquet(DATA/'target_users.parquet').uid.astype(int))\\n+ catalog=set(pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.astype(int))\\n+ li=pd.read_parquet(listen_file,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n+ li=li[(li.timestamp>=start)&(li.timestamp<=end)&(li.is_organic==1)&(li.played_ratio_pct>=80)\\n+ &li.uid.isin(targets)&li.item_id.isin(catalog)][['uid','item_id']].drop_duplicates().assign(weight=1.0)\\n+ la=pd.read_parquet(like_file,columns=['uid','timestamp','item_id','is_organic'])\\n+ la=la[(la.timestamp>=start)&(la.timestamp<=end)&(la.is_organic==1)\\n+ &la.uid.isin(targets)&la.item_id.isin(catalog)][['uid','item_id']].drop_duplicates().assign(weight=3.0)\\n+ labels=pd.concat([li,la]).sort_values('weight').drop_duplicates(['uid','item_id'],keep='last')\\n+ # Approximate cutoff eligibility: a previously positive track is not a new discovery.\\n+ seen=history[['uid','item_id']].drop_duplicates().assign(seen=1)\\n+ labels=labels.merge(seen,on=['uid','item_id'],how='left')\\n+ labels=labels[labels.seen.isna()].drop(columns='seen')\\n+ labels.to_parquet(cache,index=False)\\n+ return labels\\n+\\n+\\n+def add_preference_history(hist: pd.DataFrame, state_file: Path, cutoff: int) -> pd.DataFrame:\\n+ \\\"\\\"\\\"Fold active likes into pooling weights; disliked-only pairs are omitted.\\\"\\\"\\\"\\n+ state=pd.read_parquet(state_file)\\n+ liked=state[state.state=='liked'][['uid','item_id']].drop_duplicates()\\n+ cat=set(pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.astype(int))\\n+ liked=liked[liked.item_id.isin(cat)].assign(count=3,organic=3,last=cutoff)\\n+ both=pd.concat([hist,liked],ignore_index=True)\\n+ return both.groupby(['uid','item_id'],sort=False).agg(count=('count','sum'),organic=('organic','sum'),last=('last','max')).reset_index()\\n+\\n+\\n+def histories(hist: pd.DataFrame, uids: np.ndarray, item_to_col: dict[int,int], cutoff: int,\\n+ max_len: int=400) -> tuple[list[np.ndarray],list[np.ndarray],np.ndarray]:\\n+ by={int(u):g for u,g in hist.groupby('uid',sort=False)}\\n+ seqs=[]; weights=[]; stats=[]\\n+ for uid in uids:\\n+ g=by.get(int(uid))\\n+ if g is None or len(g)==0:\\n+ seqs.append(np.array([0],np.int64)); weights.append(np.array([0.0],np.float32)); stats.append([0.,0.]); continue\\n+ age=np.maximum(0,(cutoff-g['last'].to_numpy(np.float64))/DAY)\\n+ # Repetition, organic intent, and recency all matter; cap keeps serving bounded.\\n+ w=np.log1p(g['count'].to_numpy(np.float32))*(1+0.35*g['organic'].to_numpy()/np.maximum(g['count'],1))\\n+ w*=0.35+0.65*np.exp(-age/120.0)\\n+ order=np.argsort(w)[-max_len:]\\n+ ids=np.asarray([item_to_col[int(x)] for x in g.item_id.to_numpy()[order]],np.int64)\\n+ ww=w[order].astype(np.float32)\\n+ seqs.append(ids); weights.append(ww)\\n+ stats.append([np.log1p(len(g))/8.0, float(np.mean(g.organic/np.maximum(g['count'],1)))])\\n+ return seqs,weights,np.asarray(stats,np.float32)\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self,n_items:int,embed_dim:int=48,out_dim:int=64):\\n+ super().__init__()\\n+ self.history_embedding=nn.Embedding(n_items,embed_dim)\\n+ self.network=nn.Sequential(nn.Linear(embed_dim+2,96),nn.ReLU(),nn.Linear(96,out_dim))\\n+ def forward(self,flat:torch.Tensor,offsets:torch.Tensor,weights:torch.Tensor,stats:torch.Tensor):\\n+ pooled=F.embedding_bag(flat,self.history_embedding.weight,offsets,mode='sum',per_sample_weights=weights)\\n+ # Input weights are normalized per user by the collator.\\n+ return F.normalize(self.network(torch.cat([pooled,stats],1)),dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self,n_items:int,item_features:torch.Tensor,embed_dim:int=48,out_dim:int=64):\\n+ super().__init__(); self.embedding=nn.Embedding(n_items,embed_dim)\\n+ self.register_buffer('features',item_features)\\n+ self.network=nn.Sequential(nn.Linear(embed_dim+2,96),nn.ReLU(),nn.Linear(96,out_dim))\\n+ def forward(self,ids:torch.Tensor):\\n+ x=torch.cat([self.embedding(ids),self.features[ids]],-1)\\n+ return F.normalize(self.network(x),dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self,n_items:int,item_features:torch.Tensor):\\n+ super().__init__(); self.user_tower=UserTower(n_items); self.item_tower=ItemTower(n_items,item_features)\\n+ self.log_scale=nn.Parameter(torch.tensor(2.5))\\n+ def user(self,*args): return self.user_tower(*args)\\n+ def item(self,ids): return self.item_tower(ids)\\n+ def scale(self): return self.log_scale.exp().clamp(max=30)\\n+\\n+\\n+def collate(user_idx:list[int],seqs,weights,stats):\\n+ ids=[]; ws=[]; offsets=[]; at=0\\n+ for i in user_idx:\\n+ offsets.append(at); ids.append(seqs[i]); w=weights[i]; ws.append(w/max(float(w.sum()),1e-8)); at+=len(seqs[i])\\n+ return (torch.from_numpy(np.concatenate(ids)),torch.tensor(offsets,dtype=torch.long),\\n+ torch.from_numpy(np.concatenate(ws)),torch.from_numpy(stats[user_idx]))\\n+\\n+\\n+def train_model(train_sets:list[tuple[np.ndarray,list[np.ndarray],list[np.ndarray],np.ndarray,dict[int,np.ndarray],dict[int,np.ndarray]]],\\n+ item_features:torch.Tensor,epochs:int,seed:int=20260925)->TwoTower:\\n+ seed_all(seed); n=len(item_features); model=TwoTower(n,item_features)\\n+ opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\\n+ rng=np.random.default_rng(seed)\\n+ for epoch in range(epochs):\\n+ total=0.; steps=0\\n+ # Each cutoff supplies independent history-derived user examples.\\n+ for uids,seqs,ws,stats,pos_by_user,w_by_user in train_sets:\\n+ active=np.asarray([i for i,u in enumerate(uids) if int(u) in pos_by_user],np.int64); rng.shuffle(active)\\n+ for lo in range(0,len(active),128):\\n+ batch=active[lo:lo+128].tolist(); flat,off,pw,st=collate(batch,seqs,ws,stats)\\n+ uv=model.user(flat,off,pw,st)\\n+ # Four future positives per user (likes sampled more often), 96 uniform negatives.\\n+ p=np.empty((len(batch),4),np.int64)\\n+ for r,i in enumerate(batch):\\n+ uid=int(uids[i]); choices=pos_by_user[uid]; probs=w_by_user[uid]; probs=probs/probs.sum()\\n+ p[r]=rng.choice(choices,size=4,replace=True,p=probs)\\n+ neg=rng.integers(0,n,size=(len(batch),96),dtype=np.int64)\\n+ pv=model.item(torch.from_numpy(p)); nv=model.item(torch.from_numpy(neg))\\n+ ps=torch.einsum('bd,bkd->bk',uv,pv)*model.scale(); ns=torch.einsum('bd,bkd->bk',uv,nv)*model.scale()\\n+ # Pairwise sampled ranking loss directly optimizes ordering.\\n+ loss=-F.logsigmoid(ps[:,:,None]-ns[:,None,:]).mean()\\n+ opt.zero_grad(); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5.0); opt.step()\\n+ total+=float(loss); steps+=1\\n+ print(f'epoch {epoch+1:02d} loss={total/max(steps,1):.5f} scale={float(model.scale()):.2f}',flush=True)\\n+ return model\\n+\\n+\\n+@torch.no_grad()\\n+def rank(model:TwoTower,uids,seqs,ws,stats,elig_path:Path,out_path:Path):\\n+ model.eval(); z=np.load(elig_path); zitems=z['item_ids'].astype(np.int64)\\n+ zrow={int(u):i for i,u in enumerate(z['uids'])}; packed=z['packed_eligible']\\n+ item_vec=model.item(torch.arange(len(zitems))).cpu()\\n+ rows=[]\\n+ for lo in range(0,len(uids),128):\\n+ ix=list(range(lo,min(lo+128,len(uids)))); args=collate(ix,seqs,ws,stats)\\n+ uv=model.user(*args); scores=(uv@item_vec.T).numpy()\\n+ for r,i in enumerate(ix):\\n+ mask=np.unpackbits(packed[zrow[int(uids[i])]],count=len(zitems),bitorder='little').astype(bool)\\n+ scores[r,~mask]=-np.inf\\n+ top=np.argpartition(scores[r],-100)[-100:]; top=top[np.argsort(scores[r,top])[::-1]]\\n+ rows.extend((int(uids[i]),int(zitems[j]),k+1) for k,j in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out_path,index=False)\\n+\\n+\\n+def label_maps(labels:pd.DataFrame,item_to_col):\\n+ p={}; w={}\\n+ for uid,g in labels.groupby('uid'):\\n+ p[int(uid)]=np.asarray([item_to_col[int(x)] for x in g.item_id],np.int64)\\n+ w[int(uid)]=g.weight.to_numpy(np.float64)\\n+ return p,w\\n+\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','final'],required=True)\\n+ ap.add_argument('--epochs',type=int,default=18); ap.add_argument('--seed',type=int,default=20260925)\\n+ a=ap.parse_args(); OUT.mkdir(exist_ok=True)\\n+ targets=pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64)\\n+ cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64)\\n+ item_to_col={int(v):i for i,v in enumerate(items)}\\n+ feat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),\\n+ cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\\n+ item_features=torch.from_numpy(feat)\\n+\\n+ pseudo_end=TRAIN_END-PSEUDO_GAP-PSEUDO_DAYS*DAY\\n+ h0=aggregate_listens([DATA/'train_listens.parquet'],pseudo_end,OUT/'history_pseudo.parquet')\\n+ y0=make_labels(DATA/'train_listens.parquet',DATA/'train_likes.parquet',pseudo_end+PSEUDO_GAP,TRAIN_END,h0,OUT/'labels_pseudo.parquet')\\n+ s0,w0,st0=histories(h0,targets,item_to_col,pseudo_end); p0,pw0=label_maps(y0,item_to_col)\\n+ train_sets=[(targets,s0,w0,st0,p0,pw0)]\\n+ if a.stage=='validation':\\n+ h=aggregate_listens([DATA/'train_listens.parquet'],None,OUT/'history_train.parquet')\\n+ h=add_preference_history(h,DATA/'validation_preference_state.parquet',TRAIN_END)\\n+ seq,ww,stats=histories(h,targets,item_to_col,TRAIN_END)\\n+ elig=DATA/'validation_eligible_candidates.npz'; output=OUT/'validation.parquet'\\n+ else:\\n+ # Validation outcomes become a second supervised transition for final fitting.\\n+ htrain=aggregate_listens([DATA/'train_listens.parquet'],None,OUT/'history_train.parquet')\\n+ y1=make_labels(DATA/'val_listens.parquet',DATA/'val_likes.parquet',24_788_600,25_393_400,\\n+ htrain,OUT/'labels_validation_refit.parquet')\\n+ hs=add_preference_history(htrain,DATA/'validation_preference_state.parquet',TRAIN_END)\\n+ s1,w1,st1=histories(hs,targets,item_to_col,TRAIN_END); p1,pw1=label_maps(y1,item_to_col)\\n+ train_sets.append((targets,s1,w1,st1,p1,pw1))\\n+ h=aggregate_listens([DATA/'train_listens.parquet',DATA/'val_listens.parquet'],None,OUT/'history_train_val.parquet')\\n+ h=add_preference_history(h,DATA/'test_preference_state.parquet',25_395_200)\\n+ seq,ww,stats=histories(h,targets,item_to_col,25_395_200)\\n+ elig=DATA/'test_eligible_candidates.npz'; output=OUT/'submission.parquet'\\n+ print('examples',[(len(x[4]),sum(map(len,x[4].values()))) for x in train_sets],flush=True)\\n+ model=train_model(train_sets,item_features,a.epochs,a.seed)\\n+ torch.save({'state_dict':model.state_dict(),'items':items,'stage':a.stage},OUT/f'model_{a.stage}.pt')\\n+ rank(model,targets,seq,ww,stats,elig,output)\\n+ (OUT/f'metrics_{a.stage}.json').write_text(json.dumps({'epochs':a.epochs,'seed':a.seed},indent=2))\\n+ print(output)\\n+\\n+if __name__=='__main__': main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:33:47.174Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:33:47.133Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f2cda9c1-42c1-440a-8a43-85c4090f4eb8", "changes": { "/app/solution/train_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-based neural two-tower ranker.\n\nNo user-ID parameters or global user-item matrix are used. Training consumes\nlists of (history, future-positive) examples with sampled negatives.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_END = 24_786_800\nDAY = 86_400\nPSEUDO_GAP = 1_800\nPSEUDO_DAYS = 7\n\n\ndef seed_all(seed: int = 20260925) -> None:\n random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\n\n\ndef membership(values: np.ndarray, sorted_values: np.ndarray) -> np.ndarray:\n p = np.searchsorted(sorted_values, values)\n return (p < len(sorted_values)) & (sorted_values[np.minimum(p, len(sorted_values)-1)] == values)\n\n\ndef aggregate_listens(files: list[Path], end: int | None, cache: Path) -> pd.DataFrame:\n \"\"\"Stream positive catalog listens and return compact user-item features.\"\"\"\n if cache.exists():\n return pd.read_parquet(cache)\n targets = np.sort(pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64))\n items = np.sort(pd.read_parquet(DATA / \"candidate_catalog.parquet\").item_id.to_numpy(np.int64))\n parts: list[pd.DataFrame] = []\n for path in files:\n pf = pq.ParquetFile(path)\n for batch in pf.iter_batches(batch_size=1_000_000,\n columns=[\"uid\",\"timestamp\",\"item_id\",\"is_organic\",\"played_ratio_pct\"]):\n d=batch.to_pydict(); u=np.asarray(d['uid'],np.int64); t=np.asarray(d['timestamp'],np.int64)\n it=np.asarray(d['item_id'],np.int64); org=np.asarray(d['is_organic'],np.int8)\n ratio=np.asarray(d['played_ratio_pct'],np.float32)\n m=membership(u,targets)&membership(it,items)&(ratio>=50)\n if end is not None: m &= t <= end\n if m.any(): parts.append(pd.DataFrame({'uid':u[m],'item_id':it[m],'timestamp':t[m],'organic':org[m]}))\n events=pd.concat(parts,ignore_index=True)\n agg=events.groupby(['uid','item_id'],sort=False).agg(count=('timestamp','size'),\n organic=('organic','sum'),last=('timestamp','max')).reset_index()\n agg.to_parquet(cache,index=False)\n return agg\n\n\ndef make_labels(listen_file: Path, like_file: Path, start: int, end: int,\n history: pd.DataFrame, cache: Path) -> pd.DataFrame:\n if cache.exists(): return pd.read_parquet(cache)\n targets=set(pd.read_parquet(DATA/'target_users.parquet').uid.astype(int))\n catalog=set(pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.astype(int))\n li=pd.read_parquet(listen_file,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n li=li[(li.timestamp>=start)&(li.timestamp<=end)&(li.is_organic==1)&(li.played_ratio_pct>=80)\n &li.uid.isin(targets)&li.item_id.isin(catalog)][['uid','item_id']].drop_duplicates().assign(weight=1.0)\n la=pd.read_parquet(like_file,columns=['uid','timestamp','item_id','is_organic'])\n la=la[(la.timestamp>=start)&(la.timestamp<=end)&(la.is_organic==1)\n &la.uid.isin(targets)&la.item_id.isin(catalog)][['uid','item_id']].drop_duplicates().assign(weight=3.0)\n labels=pd.concat([li,la]).sort_values('weight').drop_duplicates(['uid','item_id'],keep='last')\n # Approximate cutoff eligibility: a previously positive track is not a new discovery.\n seen=history[['uid','item_id']].drop_duplicates().assign(seen=1)\n labels=labels.merge(seen,on=['uid','item_id'],how='left')\n labels=labels[labels.seen.isna()].drop(columns='seen')\n labels.to_parquet(cache,index=False)\n return labels\n\n\ndef add_preference_history(hist: pd.DataFrame, state_file: Path, cutoff: int) -> pd.DataFrame:\n \"\"\"Fold active likes into pooling weights; disliked-only pairs are omitted.\"\"\"\n state=pd.read_parquet(state_file)\n liked=state[state.state=='liked'][['uid','item_id']].drop_duplicates()\n cat=set(pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.astype(int))\n liked=liked[liked.item_id.isin(cat)].assign(count=3,organic=3,last=cutoff)\n both=pd.concat([hist,liked],ignore_index=True)\n return both.groupby(['uid','item_id'],sort=False).agg(count=('count','sum'),organic=('organic','sum'),last=('last','max')).reset_index()\n\n\ndef histories(hist: pd.DataFrame, uids: np.ndarray, item_to_col: dict[int,int], cutoff: int,\n max_len: int=400) -> tuple[list[np.ndarray],list[np.ndarray],np.ndarray]:\n by={int(u):g for u,g in hist.groupby('uid',sort=False)}\n seqs=[]; weights=[]; stats=[]\n for uid in uids:\n g=by.get(int(uid))\n if g is None or len(g)==0:\n seqs.append(np.array([0],np.int64)); weights.append(np.array([0.0],np.float32)); stats.append([0.,0.]); continue\n age=np.maximum(0,(cutoff-g['last'].to_numpy(np.float64))/DAY)\n # Repetition, organic intent, and recency all matter; cap keeps serving bounded.\n w=np.log1p(g['count'].to_numpy(np.float32))*(1+0.35*g['organic'].to_numpy()/np.maximum(g['count'],1))\n w*=0.35+0.65*np.exp(-age/120.0)\n order=np.argsort(w)[-max_len:]\n ids=np.asarray([item_to_col[int(x)] for x in g.item_id.to_numpy()[order]],np.int64)\n ww=w[order].astype(np.float32)\n seqs.append(ids); weights.append(ww)\n stats.append([np.log1p(len(g))/8.0, float(np.mean(g.organic/np.maximum(g['count'],1)))])\n return seqs,weights,np.asarray(stats,np.float32)\n\n\nclass UserTower(nn.Module):\n def __init__(self,n_items:int,embed_dim:int=48,out_dim:int=64):\n super().__init__()\n self.history_embedding=nn.Embedding(n_items,embed_dim)\n self.network=nn.Sequential(nn.Linear(embed_dim+2,96),nn.ReLU(),nn.Linear(96,out_dim))\n def forward(self,flat:torch.Tensor,offsets:torch.Tensor,weights:torch.Tensor,stats:torch.Tensor):\n pooled=F.embedding_bag(flat,self.history_embedding.weight,offsets,mode='sum',per_sample_weights=weights)\n # Input weights are normalized per user by the collator.\n return F.normalize(self.network(torch.cat([pooled,stats],1)),dim=1)\n\n\nclass ItemTower(nn.Module):\n def __init__(self,n_items:int,item_features:torch.Tensor,embed_dim:int=48,out_dim:int=64):\n super().__init__(); self.embedding=nn.Embedding(n_items,embed_dim)\n self.register_buffer('features',item_features)\n self.network=nn.Sequential(nn.Linear(embed_dim+2,96),nn.ReLU(),nn.Linear(96,out_dim))\n def forward(self,ids:torch.Tensor):\n x=torch.cat([self.embedding(ids),self.features[ids]],-1)\n return F.normalize(self.network(x),dim=-1)\n\n\nclass TwoTower(nn.Module):\n def __init__(self,n_items:int,item_features:torch.Tensor):\n super().__init__(); self.user_tower=UserTower(n_items); self.item_tower=ItemTower(n_items,item_features)\n self.log_scale=nn.Parameter(torch.tensor(2.5))\n def user(self,*args): return self.user_tower(*args)\n def item(self,ids): return self.item_tower(ids)\n def scale(self): return self.log_scale.exp().clamp(max=30)\n\n\ndef collate(user_idx:list[int],seqs,weights,stats):\n ids=[]; ws=[]; offsets=[]; at=0\n for i in user_idx:\n offsets.append(at); ids.append(seqs[i]); w=weights[i]; ws.append(w/max(float(w.sum()),1e-8)); at+=len(seqs[i])\n return (torch.from_numpy(np.concatenate(ids)),torch.tensor(offsets,dtype=torch.long),\n torch.from_numpy(np.concatenate(ws)),torch.from_numpy(stats[user_idx]))\n\n\ndef train_model(train_sets:list[tuple[np.ndarray,list[np.ndarray],list[np.ndarray],np.ndarray,dict[int,np.ndarray],dict[int,np.ndarray]]],\n item_features:torch.Tensor,epochs:int,seed:int=20260925)->TwoTower:\n seed_all(seed); n=len(item_features); model=TwoTower(n,item_features)\n opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n rng=np.random.default_rng(seed)\n for epoch in range(epochs):\n total=0.; steps=0\n # Each cutoff supplies independent history-derived user examples.\n for uids,seqs,ws,stats,pos_by_user,w_by_user in train_sets:\n active=np.asarray([i for i,u in enumerate(uids) if int(u) in pos_by_user],np.int64); rng.shuffle(active)\n for lo in range(0,len(active),128):\n batch=active[lo:lo+128].tolist(); flat,off,pw,st=collate(batch,seqs,ws,stats)\n uv=model.user(flat,off,pw,st)\n # Four future positives per user (likes sampled more often), 96 uniform negatives.\n p=np.empty((len(batch),4),np.int64)\n for r,i in enumerate(batch):\n uid=int(uids[i]); choices=pos_by_user[uid]; probs=w_by_user[uid]; probs=probs/probs.sum()\n p[r]=rng.choice(choices,size=4,replace=True,p=probs)\n neg=rng.integers(0,n,size=(len(batch),96),dtype=np.int64)\n pv=model.item(torch.from_numpy(p)); nv=model.item(torch.from_numpy(neg))\n ps=torch.einsum('bd,bkd->bk',uv,pv)*model.scale(); ns=torch.einsum('bd,bkd->bk',uv,nv)*model.scale()\n # Pairwise sampled ranking loss directly optimizes ordering.\n loss=-F.logsigmoid(ps[:,:,None]-ns[:,None,:]).mean()\n opt.zero_grad(); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5.0); opt.step()\n total+=float(loss); steps+=1\n print(f'epoch {epoch+1:02d} loss={total/max(steps,1):.5f} scale={float(model.scale()):.2f}',flush=True)\n return model\n\n\n@torch.no_grad()\ndef rank(model:TwoTower,uids,seqs,ws,stats,elig_path:Path,out_path:Path):\n model.eval(); z=np.load(elig_path); zitems=z['item_ids'].astype(np.int64)\n zrow={int(u):i for i,u in enumerate(z['uids'])}; packed=z['packed_eligible']\n item_vec=model.item(torch.arange(len(zitems))).cpu()\n rows=[]\n for lo in range(0,len(uids),128):\n ix=list(range(lo,min(lo+128,len(uids)))); args=collate(ix,seqs,ws,stats)\n uv=model.user(*args); scores=(uv@item_vec.T).numpy()\n for r,i in enumerate(ix):\n mask=np.unpackbits(packed[zrow[int(uids[i])]],count=len(zitems),bitorder='little').astype(bool)\n scores[r,~mask]=-np.inf\n top=np.argpartition(scores[r],-100)[-100:]; top=top[np.argsort(scores[r,top])[::-1]]\n rows.extend((int(uids[i]),int(zitems[j]),k+1) for k,j in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out_path,index=False)\n\n\ndef label_maps(labels:pd.DataFrame,item_to_col):\n p={}; w={}\n for uid,g in labels.groupby('uid'):\n p[int(uid)]=np.asarray([item_to_col[int(x)] for x in g.item_id],np.int64)\n w[int(uid)]=g.weight.to_numpy(np.float64)\n return p,w\n\n\ndef main():\n ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','final'],required=True)\n ap.add_argument('--epochs',type=int,default=18); ap.add_argument('--seed',type=int,default=20260925)\n a=ap.parse_args(); OUT.mkdir(exist_ok=True)\n targets=pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64)\n cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64)\n item_to_col={int(v):i for i,v in enumerate(items)}\n feat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),\n cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\n item_features=torch.from_numpy(feat)\n\n pseudo_end=TRAIN_END-PSEUDO_GAP-PSEUDO_DAYS*DAY\n h0=aggregate_listens([DATA/'train_listens.parquet'],pseudo_end,OUT/'history_pseudo.parquet')\n y0=make_labels(DATA/'train_listens.parquet',DATA/'train_likes.parquet',pseudo_end+PSEUDO_GAP,TRAIN_END,h0,OUT/'labels_pseudo.parquet')\n s0,w0,st0=histories(h0,targets,item_to_col,pseudo_end); p0,pw0=label_maps(y0,item_to_col)\n train_sets=[(targets,s0,w0,st0,p0,pw0)]\n if a.stage=='validation':\n h=aggregate_listens([DATA/'train_listens.parquet'],None,OUT/'history_train.parquet')\n h=add_preference_history(h,DATA/'validation_preference_state.parquet',TRAIN_END)\n seq,ww,stats=histories(h,targets,item_to_col,TRAIN_END)\n elig=DATA/'validation_eligible_candidates.npz'; output=OUT/'validation.parquet'\n else:\n # Validation outcomes become a second supervised transition for final fitting.\n htrain=aggregate_listens([DATA/'train_listens.parquet'],None,OUT/'history_train.parquet')\n y1=make_labels(DATA/'val_listens.parquet',DATA/'val_likes.parquet',24_788_600,25_393_400,\n htrain,OUT/'labels_validation_refit.parquet')\n hs=add_preference_history(htrain,DATA/'validation_preference_state.parquet',TRAIN_END)\n s1,w1,st1=histories(hs,targets,item_to_col,TRAIN_END); p1,pw1=label_maps(y1,item_to_col)\n train_sets.append((targets,s1,w1,st1,p1,pw1))\n h=aggregate_listens([DATA/'train_listens.parquet',DATA/'val_listens.parquet'],None,OUT/'history_train_val.parquet')\n h=add_preference_history(h,DATA/'test_preference_state.parquet',25_395_200)\n seq,ww,stats=histories(h,targets,item_to_col,25_395_200)\n elig=DATA/'test_eligible_candidates.npz'; output=OUT/'submission.parquet'\n print('examples',[(len(x[4]),sum(map(len,x[4].values()))) for x in train_sets],flush=True)\n model=train_model(train_sets,item_features,a.epochs,a.seed)\n torch.save({'state_dict':model.state_dict(),'items':items,'stage':a.stage},OUT/f'model_{a.stage}.pt')\n rank(model,targets,seq,ww,stats,elig,output)\n (OUT/f'metrics_{a.stage}.json').write_text(json.dumps({'epochs':a.epochs,'seed':a.seed},indent=2))\n print(output)\n\nif __name__=='__main__': main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:33:47.173Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cefd570c87d2b3b0b50e2c8e8a3a", "usage": { "input_tokens": 26621, "cached_input_tokens": 26240, "cache_write_input_tokens": 0, "output_tokens": 6954, "reasoning_output_tokens": 2623, "total_tokens": 33575 }, "turn_token_usage": { "input_tokens": 295411, "cached_input_tokens": 277760, "cache_write_input_tokens": 0, "output_tokens": 11547, "reasoning_output_tokens": 3936, "total_tokens": 306958 }, "thread_token_usage": { "input_tokens": 295411, "cached_input_tokens": 277760, "cache_write_input_tokens": 0, "output_tokens": 11547, "reasoning_output_tokens": 3936, "total_tokens": 306958 } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-25T01:33:49.539Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:33:49.539Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:33:51.376Z", "callId": "call_PGAnqTo2tm14fpEFNFd76zD7", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"cp solution/train_catalog_history.parquet solution/history_train.parquet && /opt/task-venv/bin/python -m py_compile solution/train_ranker.py && OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 18\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:34:21.542Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"3291a6\",\"wall_time_seconds\":30.000608841,\"session_id\":86749,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:33:51.436Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cf7b6dc087d2b7e903f6530bfa30", "usage": { "input_tokens": 33599, "cached_input_tokens": 26496, "cache_write_input_tokens": 0, "output_tokens": 188, "reasoning_output_tokens": 77, "total_tokens": 33787 }, "turn_token_usage": { "input_tokens": 329010, "cached_input_tokens": 304256, "cache_write_input_tokens": 0, "output_tokens": 11735, "reasoning_output_tokens": 4013, "total_tokens": 340745 }, "thread_token_usage": { "input_tokens": 329010, "cached_input_tokens": 304256, "cache_write_input_tokens": 0, "output_tokens": 11735, "reasoning_output_tokens": 4013, "total_tokens": 340745 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-25T01:34:22.652Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:34:22.652Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:34:23.342Z", "callId": "call_ciGchE0IkVlp536tMB4iENx0", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:86749,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:34:53.351Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:23.398Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cf9dc25087d2b98935c54a091cb7", "usage": { "input_tokens": 33845, "cached_input_tokens": 33408, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 33903 }, "turn_token_usage": { "input_tokens": 362855, "cached_input_tokens": 337664, "cache_write_input_tokens": 0, "output_tokens": 11793, "reasoning_output_tokens": 4024, "total_tokens": 374648 }, "thread_token_usage": { "input_tokens": 362855, "cached_input_tokens": 337664, "cache_write_input_tokens": 0, "output_tokens": 11793, "reasoning_output_tokens": 4024, "total_tokens": 374648 } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-25T01:34:54.354Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:34:54.354Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:34:55.050Z", "callId": "call_eVLZE7vDKNan90wwlxfPlw9K", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:86749,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:35:25.059Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:34:55.131Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cfbd885087d2937c4e4258ce4134", "usage": { "input_tokens": 33926, "cached_input_tokens": 33664, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 33979 }, "turn_token_usage": { "input_tokens": 396781, "cached_input_tokens": 371328, "cache_write_input_tokens": 0, "output_tokens": 11846, "reasoning_output_tokens": 4030, "total_tokens": 408627 }, "thread_token_usage": { "input_tokens": 396781, "cached_input_tokens": 371328, "cache_write_input_tokens": 0, "output_tokens": 11846, "reasoning_output_tokens": 4030, "total_tokens": 408627 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-25T01:35:27.176Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:35:27.176Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:35:28.283Z", "text": "The training job is in its one-time streaming preprocessing pass over the 43.5 million listens. The implementation caps each served history at 400 weighted tracks, so request-time user computation stays bounded even though training can learn from the full log." }, { "kind": "tool", "timestamp": "2026-09-25T01:35:28.987Z", "callId": "call_DJznPX39JU3Hpxh4CJ0hSP8f", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:86749,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:35:33.579Z", "content": "Script completed\nWall time 4.6 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"/app/solution/train_ranker.py\", line 244, in \n if __name__=='__main__': main()\n ^^^^^^\n File \"/app/solution/train_ranker.py\", line 218, in main\n s0,w0,st0=histories(h0,targets,item_to_col,pseudo_end); p0,pw0=label_maps(y0,item_to_col)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_ranker.py\", line 106, in histories\n ww=w[order].astype(np.float32)\n ~^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1165, in __getitem__\n return self._get_with(key)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1192, in _get_with\n return self.loc[key]\n ~~~~~~~~^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n return self._getitem_axis(maybe_callable, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n return self._getitem_iterable(key, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n keyarr, indexer = self._get_listlike_indexer(key, axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n self._raise_if_missing(keyarr, indexer, axis_name)\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([ 4, 8, 10, 11, 14, 20, 26, 34, 43, 44,\\n ...\\n 36, 54, 19, 6, 109, 46, 70, 5, 32, 16],\\n dtype='int64', length=163)] are in the [index]\"\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:35:29.046Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cfde596c87d2b03a1911ed111028", "usage": { "input_tokens": 34002, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 11, "total_tokens": 34116 }, "turn_token_usage": { "input_tokens": 430783, "cached_input_tokens": 405120, "cache_write_input_tokens": 0, "output_tokens": 11960, "reasoning_output_tokens": 4041, "total_tokens": 442743 }, "thread_token_usage": { "input_tokens": 430783, "cached_input_tokens": 405120, "cache_write_input_tokens": 0, "output_tokens": 11960, "reasoning_output_tokens": 4041, "total_tokens": 442743 } } }, { "kind": "context", "timestamp": "2026-09-25T01:35:33.577Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-08df6aef-1697-4c4a-a273-e2dcf4fd7de0", "process_id": "86749", "command": [ "/bin/sh", "-lc", "cp solution/train_catalog_history.parquet solution/history_train.parquet && /opt/task-venv/bin/python -m py_compile solution/train_ranker.py && OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 18" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/train_catalog_history.parquet solution/history_train.parquet && /opt/task-venv/bin/python -m py_compile solution/train_ranker.py && OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 18" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/train_ranker.py\", line 244, in \n if __name__=='__main__': main()\n ^^^^^^\n File \"/app/solution/train_ranker.py\", line 218, in main\n s0,w0,st0=histories(h0,targets,item_to_col,pseudo_end); p0,pw0=label_maps(y0,item_to_col)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_ranker.py\", line 106, in histories\n ww=w[order].astype(np.float32)\n ~^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1165, in __getitem__\n return self._get_with(key)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1192, in _get_with\n return self.loc[key]\n ~~~~~~~~^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n return self._getitem_axis(maybe_callable, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n return self._getitem_iterable(key, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n keyarr, indexer = self._get_listlike_indexer(key, axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n self._raise_if_missing(keyarr, indexer, axis_name)\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([ 4, 8, 10, 11, 14, 20, 26, 34, 43, 44,\\n ...\\n 36, 54, 19, 6, 109, 46, 70, 5, 32, 16],\\n dtype='int64', length=163)] are in the [index]\"\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/train_ranker.py\", line 244, in \n if __name__=='__main__': main()\n ^^^^^^\n File \"/app/solution/train_ranker.py\", line 218, in main\n s0,w0,st0=histories(h0,targets,item_to_col,pseudo_end); p0,pw0=label_maps(y0,item_to_col)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_ranker.py\", line 106, in histories\n ww=w[order].astype(np.float32)\n ~^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1165, in __getitem__\n return self._get_with(key)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1192, in _get_with\n return self.loc[key]\n ~~~~~~~~^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n return self._getitem_axis(maybe_callable, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n return self._getitem_iterable(key, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n keyarr, indexer = self._get_listlike_indexer(key, axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n self._raise_if_missing(keyarr, indexer, axis_name)\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([ 4, 8, 10, 11, 14, 20, 26, 34, 43, 44,\\n ...\\n 36, 54, 19, 6, 109, 46, 70, 5, 32, 16],\\n dtype='int64', length=163)] are in the [index]\"\n", "exit_code": 1, "duration": { "secs": 102, "nanos": 36301254 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/train_ranker.py\", line 244, in \n if __name__=='__main__': main()\n ^^^^^^\n File \"/app/solution/train_ranker.py\", line 218, in main\n s0,w0,st0=histories(h0,targets,item_to_col,pseudo_end); p0,pw0=label_maps(y0,item_to_col)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_ranker.py\", line 106, in histories\n ww=w[order].astype(np.float32)\n ~^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1165, in __getitem__\n return self._get_with(key)\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/series.py\", line 1192, in _get_with\n return self.loc[key]\n ~~~~~~~~^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n return self._getitem_axis(maybe_callable, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n return self._getitem_iterable(key, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n keyarr, indexer = self._get_listlike_indexer(key, axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n self._raise_if_missing(keyarr, indexer, axis_name)\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6261, in _raise_if_missing\n raise KeyError(f\"None of [{key}] are in the [{axis_name}]\")\nKeyError: \"None of [Index([ 4, 8, 10, 11, 14, 20, 26, 34, 43, 44,\\n ...\\n 36, 54, 19, 6, 109, 46, 70, 5, 32, 16],\\n dtype='int64', length=163)] are in the [index]\"\n" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-25T01:35:34.965Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:35:34.965Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:35:37.371Z", "callId": "call_AuueYhtoBp1N8CoCT5JrL5hp", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- w=np.log1p(g['count'].to_numpy(np.float32))*(1+0.35*g['organic'].to_numpy()/np.maximum(g['count'],1))\\n+ counts=g['count'].to_numpy(np.float32)\\n+ organic=g['organic'].to_numpy(np.float32)\\n+ w=np.log1p(counts)*(1+0.35*organic/np.maximum(counts,1))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:35:37.460Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:35:37.380Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-770ac965-549c-49ba-9ba6-c71023d1120d", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -101,3 +101,5 @@\n # Repetition, organic intent, and recency all matter; cap keeps serving bounded.\n- w=np.log1p(g['count'].to_numpy(np.float32))*(1+0.35*g['organic'].to_numpy()/np.maximum(g['count'],1))\n+ counts=g['count'].to_numpy(np.float32)\n+ organic=g['organic'].to_numpy(np.float32)\n+ w=np.log1p(counts)*(1+0.35*organic/np.maximum(counts,1))\n w*=0.35+0.65*np.exp(-age/120.0)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:35:37.459Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cfe5cc6887d29c6e3112fcc47bf8", "usage": { "input_tokens": 34825, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 160, "reasoning_output_tokens": 18, "total_tokens": 34985 }, "turn_token_usage": { "input_tokens": 465608, "cached_input_tokens": 438912, "cache_write_input_tokens": 0, "output_tokens": 12120, "reasoning_output_tokens": 4059, "total_tokens": 477728 }, "thread_token_usage": { "input_tokens": 465608, "cached_input_tokens": 438912, "cache_write_input_tokens": 0, "output_tokens": 12120, "reasoning_output_tokens": 4059, "total_tokens": 477728 } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-25T01:35:38.631Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:35:38.631Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:35:39.902Z", "callId": "call_dghPswaloWHcfYxFh5O91AlK", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 18\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:36:10.071Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"3a9093\",\"wall_time_seconds\":30.001460659,\"session_id\":70997,\"original_token_count\":193,\"output\":\"examples [(4652, 47934)]\\n/app/solution/train_ranker.py:176: 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); steps+=1\\nepoch 01 loss=0.70803 scale=11.75\\nepoch 02 loss=0.58849 scale=11.90\\nepoch 03 loss=0.49469 scale=12.01\\nepoch 04 loss=0.41940 scale=12.15\\nepoch 05 loss=0.36228 scale=12.27\\nepoch 06 loss=0.32525 scale=12.40\\nepoch 07 loss=0.29401 scale=12.50\\nepoch 08 loss=0.26806 scale=12.62\\nepoch 09 loss=0.24056 scale=12.80\\nepoch 10 loss=0.22725 scale=12.92\\nepoch 11 loss=0.21596 scale=13.06\\nepoch 12 loss=0.20165 scale=13.14\\nepoch 13 loss=0.19279 scale=13.30\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:35:39.998Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5cfe9b5d487d296b1d37c92a2a36b", "usage": { "input_tokens": 35009, "cached_input_tokens": 34688, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 10, "total_tokens": 35098 }, "turn_token_usage": { "input_tokens": 500617, "cached_input_tokens": 473600, "cache_write_input_tokens": 0, "output_tokens": 12209, "reasoning_output_tokens": 4069, "total_tokens": 512826 }, "thread_token_usage": { "input_tokens": 500617, "cached_input_tokens": 473600, "cache_write_input_tokens": 0, "output_tokens": 12209, "reasoning_output_tokens": 4069, "total_tokens": 512826 } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-25T01:36:11.294Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:36:11.294Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:36:12.075Z", "callId": "call_hhkJLfJzifZR1dc0NBFTAC3b", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:70997,chars:\"\",yield_time_ms:30000,max_output_tokens:4000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:36:17.490Z", "content": "Script completed\nWall time 5.4 seconds\nOutput:\n\nepoch 14 loss=0.18404 scale=13.50\nepoch 15 loss=0.17407 scale=13.65\nepoch 16 loss=0.16639 scale=13.83\nepoch 17 loss=0.15982 scale=13.96\nepoch 18 loss=0.15107 scale=14.15\n/app/solution/validation.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:12.139Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d00a508487d2a30604297b471521", "usage": { "input_tokens": 35445, "cached_input_tokens": 34816, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 35501 }, "turn_token_usage": { "input_tokens": 536062, "cached_input_tokens": 508416, "cache_write_input_tokens": 0, "output_tokens": 12265, "reasoning_output_tokens": 4078, "total_tokens": 548327 }, "thread_token_usage": { "input_tokens": 536062, "cached_input_tokens": 508416, "cache_write_input_tokens": 0, "output_tokens": 12265, "reasoning_output_tokens": 4078, "total_tokens": 548327 } } }, { "kind": "context", "timestamp": "2026-09-25T01:36:17.488Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-388a1550-fb93-4060-b9bf-d340b5ab73a0", "process_id": "70997", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 18" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 18" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "examples [(4652, 47934)]\n/app/solution/train_ranker.py:176: 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); steps+=1\nepoch 01 loss=0.70803 scale=11.75\nepoch 02 loss=0.58849 scale=11.90\nepoch 03 loss=0.49469 scale=12.01\nepoch 04 loss=0.41940 scale=12.15\nepoch 05 loss=0.36228 scale=12.27\nepoch 06 loss=0.32525 scale=12.40\nepoch 07 loss=0.29401 scale=12.50\nepoch 08 loss=0.26806 scale=12.62\nepoch 09 loss=0.24056 scale=12.80\nepoch 10 loss=0.22725 scale=12.92\nepoch 11 loss=0.21596 scale=13.06\nepoch 12 loss=0.20165 scale=13.14\nepoch 13 loss=0.19279 scale=13.30\nepoch 14 loss=0.18404 scale=13.50\nepoch 15 loss=0.17407 scale=13.65\nepoch 16 loss=0.16639 scale=13.83\nepoch 17 loss=0.15982 scale=13.96\nepoch 18 loss=0.15107 scale=14.15\n/app/solution/validation.parquet\n", "stderr": "", "aggregated_output": "examples [(4652, 47934)]\n/app/solution/train_ranker.py:176: 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); steps+=1\nepoch 01 loss=0.70803 scale=11.75\nepoch 02 loss=0.58849 scale=11.90\nepoch 03 loss=0.49469 scale=12.01\nepoch 04 loss=0.41940 scale=12.15\nepoch 05 loss=0.36228 scale=12.27\nepoch 06 loss=0.32525 scale=12.40\nepoch 07 loss=0.29401 scale=12.50\nepoch 08 loss=0.26806 scale=12.62\nepoch 09 loss=0.24056 scale=12.80\nepoch 10 loss=0.22725 scale=12.92\nepoch 11 loss=0.21596 scale=13.06\nepoch 12 loss=0.20165 scale=13.14\nepoch 13 loss=0.19279 scale=13.30\nepoch 14 loss=0.18404 scale=13.50\nepoch 15 loss=0.17407 scale=13.65\nepoch 16 loss=0.16639 scale=13.83\nepoch 17 loss=0.15982 scale=13.96\nepoch 18 loss=0.15107 scale=14.15\n/app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 37, "nanos": 420368029 }, "formatted_output": "examples [(4652, 47934)]\n/app/solution/train_ranker.py:176: 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); steps+=1\nepoch 01 loss=0.70803 scale=11.75\nepoch 02 loss=0.58849 scale=11.90\nepoch 03 loss=0.49469 scale=12.01\nepoch 04 loss=0.41940 scale=12.15\nepoch 05 loss=0.36228 scale=12.27\nepoch 06 loss=0.32525 scale=12.40\nepoch 07 loss=0.29401 scale=12.50\nepoch 08 loss=0.26806 scale=12.62\nepoch 09 loss=0.24056 scale=12.80\nepoch 10 loss=0.22725 scale=12.92\nepoch 11 loss=0.21596 scale=13.06\nepoch 12 loss=0.20165 scale=13.14\nepoch 13 loss=0.19279 scale=13.30\nepoch 14 loss=0.18404 scale=13.50\nepoch 15 loss=0.17407 scale=13.65\nepoch 16 loss=0.16639 scale=13.83\nepoch 17 loss=0.15982 scale=13.96\nepoch 18 loss=0.15107 scale=14.15\n/app/solution/validation.parquet\n" } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-25T01:36:19.990Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:36:19.990Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:36:21.018Z", "callId": "call_Pv2eLW0BMu9skaWJMOJVscPw", "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\":30000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:36:24.420Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020104,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:21.101Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d011aed087d2811c46653edc0069", "usage": { "input_tokens": 35607, "cached_input_tokens": 35328, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 6, "total_tokens": 35679 }, "turn_token_usage": { "input_tokens": 571669, "cached_input_tokens": 543744, "cache_write_input_tokens": 0, "output_tokens": 12337, "reasoning_output_tokens": 4084, "total_tokens": 584006 }, "thread_token_usage": { "input_tokens": 571669, "cached_input_tokens": 543744, "cache_write_input_tokens": 0, "output_tokens": 12337, "reasoning_output_tokens": 4084, "total_tokens": 584006 } } }, { "kind": "context", "timestamp": "2026-09-25T01:36:24.416Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a312e72a-afcb-44f8-a6f3-e72d78263a55", "process_id": "72364", "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\": 0.020104,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020104,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 233462477 }, "formatted_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020104,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-25T01:36:30.776Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:36:30.776Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:36:32.264Z", "text": "The first neural run reaches public NDCG@10 = 0.020104, about 88% above the 0.010710 popularity reference. I’m preserving that valid ranking and testing a small score-level popularity calibration plus training regularization/epoch choices; these retain the exact dot-product form by encoding any selected global item prior inside the item tower and a constant user coordinate." } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-25T01:36:34.063Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:36:34.063Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:36:45.758Z", "callId": "call_YricEXOpqrYvmcVWiCMUqUrQ", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_blends.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Create validation rankings with an exact extra dot-product popularity coordinate.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np, pandas as pd, torch\\n+import train_ranker as tr\\n+\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64)\\n+item_to_col={int(v):i for i,v in enumerate(items)}\\n+feat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\\n+model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\\n+h=pd.read_parquet(O/'history_train.parquet'); h=tr.add_preference_history(h,D/'validation_preference_state.parquet',tr.TRAIN_END)\\n+seq,ww,stats=tr.histories(h,targets,item_to_col,tr.TRAIN_END)\\n+z=np.load(D/'validation_eligible_candidates.npz'); zitems=z['item_ids'].astype(np.int64); zr={int(u):i for i,u in enumerate(z['uids'])}\\n+ivec=model.item(torch.arange(len(items))).detach(); prior=feat[:,0]\\n+alphas=[0.01,0.025,0.05,0.075,0.1,0.15,0.2]\\n+outs={a:[] for a in alphas}\\n+with torch.no_grad():\\n+ for lo in range(0,len(targets),128):\\n+ ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\\n+ for r,i in enumerate(ix):\\n+ mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\\n+ for a in alphas:\\n+ s=base[r]+a*prior; s[~mask]=-np.inf\\n+ top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\\n+ outs[a].extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\\n+for a,rows in outs.items(): pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:36:45.853Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:45.766Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-07a15072-ad3c-48a0-824f-15cd7aeedcca", "changes": { "/app/solution/tune_blends.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Create validation rankings with an exact extra dot-product popularity coordinate.\"\"\"\nfrom pathlib import Path\nimport numpy as np, pandas as pd, torch\nimport train_ranker as tr\n\nD=Path('/task/data'); O=Path('/app/solution')\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64)\nitem_to_col={int(v):i for i,v in enumerate(items)}\nfeat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\nmodel=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\nh=pd.read_parquet(O/'history_train.parquet'); h=tr.add_preference_history(h,D/'validation_preference_state.parquet',tr.TRAIN_END)\nseq,ww,stats=tr.histories(h,targets,item_to_col,tr.TRAIN_END)\nz=np.load(D/'validation_eligible_candidates.npz'); zitems=z['item_ids'].astype(np.int64); zr={int(u):i for i,u in enumerate(z['uids'])}\nivec=model.item(torch.arange(len(items))).detach(); prior=feat[:,0]\nalphas=[0.01,0.025,0.05,0.075,0.1,0.15,0.2]\nouts={a:[] for a in alphas}\nwith torch.no_grad():\n for lo in range(0,len(targets),128):\n ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\n for r,i in enumerate(ix):\n mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\n for a in alphas:\n s=base[r]+a*prior; s[~mask]=-np.inf\n top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\n outs[a].extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\nfor a,rows in outs.items(): pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_blends.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:36:45.851Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d018a23487d291d3816a3a295f9d", "usage": { "input_tokens": 35745, "cached_input_tokens": 35456, "cache_write_input_tokens": 0, "output_tokens": 1140, "reasoning_output_tokens": 386, "total_tokens": 36885 }, "turn_token_usage": { "input_tokens": 607414, "cached_input_tokens": 579200, "cache_write_input_tokens": 0, "output_tokens": 13477, "reasoning_output_tokens": 4470, "total_tokens": 620891 }, "thread_token_usage": { "input_tokens": 607414, "cached_input_tokens": 579200, "cache_write_input_tokens": 0, "output_tokens": 13477, "reasoning_output_tokens": 4470, "total_tokens": 620891 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:36:48.800Z", "callId": "call_GTRLpkzf0WMh1cShEHWxx6wZ", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:36:52.799Z", "content": "Script completed\nWall time 4.0 seconds\nOutput:\n\n{\"chunk_id\":\"bb252d\",\"wall_time_seconds\":3.830039415,\"exit_code\":1,\"original_token_count\":424,\"output\":\"Traceback (most recent call last):\\n File \\\"/app/solution/tune_blends.py\\\", line 12, in \\n model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\\\", line 1529, in load\\n raise pickle.UnpicklingError(_get_wo_message(str(e))) from None\\n_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, \\u001b[1mdo those steps only if you trust the source of the checkpoint\\u001b[0m. \\n\\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\\n\\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\\n\\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy._core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.\\n\\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:48.928Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d02e1c7887d2b47637873708d083", "usage": { "input_tokens": 36909, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 0, "total_tokens": 37022 }, "turn_token_usage": { "input_tokens": 644323, "cached_input_tokens": 614784, "cache_write_input_tokens": 0, "output_tokens": 13590, "reasoning_output_tokens": 4470, "total_tokens": 657913 }, "thread_token_usage": { "input_tokens": 644323, "cached_input_tokens": 614784, "cache_write_input_tokens": 0, "output_tokens": 13590, "reasoning_output_tokens": 4470, "total_tokens": 657913 } } }, { "kind": "context", "timestamp": "2026-09-25T01:36:52.795Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-afcc47b7-b41d-4417-9268-2931a0bd7220", "process_id": "33796", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' '; echo; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/tune_blends.py\", line 12, in \n model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1529, in load\n raise pickle.UnpicklingError(_get_wo_message(str(e))) from None\n_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy._core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/tune_blends.py\", line 12, in \n model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1529, in load\n raise pickle.UnpicklingError(_get_wo_message(str(e))) from None\n_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy._core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.\n", "exit_code": 1, "duration": { "secs": 3, "nanos": 830036795 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/tune_blends.py\", line 12, in \n model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/serialization.py\", line 1529, in load\n raise pickle.UnpicklingError(_get_wo_message(str(e))) from None\n_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy._core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.\n\nCheck the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.\n" } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-25T01:36:54.880Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:36:54.880Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:36:56.883Z", "callId": "call_iH2N9TAyUclsuGIrmxqG8CBA", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blends.py\\n@@\\n-model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\\n+model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict']); model.eval()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:36:56.966Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:36:56.892Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ca103656-bf4a-4e02-96d7-5dfc5b92eb54", "changes": { "/app/solution/tune_blends.py": { "type": "update", "unified_diff": "@@ -11,3 +11,3 @@\n feat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\n-model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=True)['state_dict']); model.eval()\n+model=tr.TwoTower(len(items),torch.from_numpy(feat)); model.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict']); model.eval()\n h=pd.read_parquet(O/'history_train.parquet'); h=tr.add_preference_history(h,D/'validation_preference_state.parquet',tr.TRAIN_END)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blends.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:36:56.965Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d034fbb487d2ba7f2e7a620e7212", "usage": { "input_tokens": 37481, "cached_input_tokens": 36736, "cache_write_input_tokens": 0, "output_tokens": 136, "reasoning_output_tokens": 11, "total_tokens": 37617 }, "turn_token_usage": { "input_tokens": 681804, "cached_input_tokens": 651520, "cache_write_input_tokens": 0, "output_tokens": 13726, "reasoning_output_tokens": 4481, "total_tokens": 695530 }, "thread_token_usage": { "input_tokens": 681804, "cached_input_tokens": 651520, "cache_write_input_tokens": 0, "output_tokens": 13726, "reasoning_output_tokens": 4481, "total_tokens": 695530 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:36:59.931Z", "callId": "call_7hBrlKFqd8HoDmOhJe8HPAjm", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":7000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:37:30.098Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"d7bbfa\",\"wall_time_seconds\":30.001143119,\"session_id\":93942,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:37:00.047Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d039328087d284898303cce22961", "usage": { "input_tokens": 37641, "cached_input_tokens": 37248, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 0, "total_tokens": 37754 }, "turn_token_usage": { "input_tokens": 719445, "cached_input_tokens": 688768, "cache_write_input_tokens": 0, "output_tokens": 13839, "reasoning_output_tokens": 4481, "total_tokens": 733284 }, "thread_token_usage": { "input_tokens": 719445, "cached_input_tokens": 688768, "cache_write_input_tokens": 0, "output_tokens": 13839, "reasoning_output_tokens": 4481, "total_tokens": 733284 } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-25T01:37:31.072Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:37:31.072Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:37:31.769Z", "callId": "call_DZld6eTJTBf1lZqIcDTqFSbG", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:93942,chars:\"\",yield_time_ms:30000,max_output_tokens:7000}); 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The unblended 0.020104 artifact remains safely preserved while this runs." }, { "kind": "tool", "timestamp": "2026-09-25T01:41:57.892Z", "callId": "call_NgPoXqHKbNn3iEf6cC3jpqLW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:93942,chars:\"\",yield_time_ms:60000,max_output_tokens:7000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:42:28.901Z", "content": "Script running with cell ID 35\nWall time 31.0 seconds\nOutput:\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:41:57.991Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d160c87487d2a8f82d886b592721", "usage": { "input_tokens": 38629, "cached_input_tokens": 38400, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 24, "total_tokens": 38746 }, "turn_token_usage": { "input_tokens": 1101453, "cached_input_tokens": 1068160, "cache_write_input_tokens": 0, "output_tokens": 14555, "reasoning_output_tokens": 4636, "total_tokens": 1116008 }, "thread_token_usage": { "input_tokens": 1101453, "cached_input_tokens": 1068160, "cache_write_input_tokens": 0, "output_tokens": 14555, "reasoning_output_tokens": 4636, "total_tokens": 1116008 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-25T01:42:30.858Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:42:30.858Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:42:31.239Z", "callId": "call_wa013mY0GIf8O5UTWND7Oql8", "name": "wait", "input": { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 7000 }, "result": { "timestamp": "2026-09-25T01:42:57.902Z", "content": "Script completed\nWall time 26.7 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:42:31.342Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d185195087d297d2158fca71bfa6", "usage": { "input_tokens": 38774, "cached_input_tokens": 38400, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 21, "total_tokens": 38828 }, "turn_token_usage": { "input_tokens": 1140227, "cached_input_tokens": 1106560, "cache_write_input_tokens": 0, "output_tokens": 14609, "reasoning_output_tokens": 4657, "total_tokens": 1154836 }, "thread_token_usage": { "input_tokens": 1140227, "cached_input_tokens": 1106560, "cache_write_input_tokens": 0, "output_tokens": 14609, "reasoning_output_tokens": 4657, "total_tokens": 1154836 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-25T01:42:59.884Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:42:59.884Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:43:01.698Z", "callId": "call_QGyd9jqffORyFDvSOhjCzsil", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution/validation_blend*; for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tail -n 5 | tr '\\\\n' ' '; echo \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":7000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:43:03.897Z", "content": "Script completed\nWall time 2.2 seconds\nOutput:\n\n{\"chunk_id\":\"45f193\",\"wall_time_seconds\":2.030279254,\"exit_code\":0,\"original_token_count\":382,\"output\":\"ls: cannot access 'solution/validation_blend*': No such file or directory\\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_blend_*.parquet'\\nsolution/validation_blend_*.parquet\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:43:01.809Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d1a2231087d2ace0891a85d1c480", "usage": { "input_tokens": 38851, "cached_input_tokens": 38656, "cache_write_input_tokens": 0, "output_tokens": 131, "reasoning_output_tokens": 23, "total_tokens": 38982 }, "turn_token_usage": { "input_tokens": 1179078, "cached_input_tokens": 1145216, "cache_write_input_tokens": 0, "output_tokens": 14740, "reasoning_output_tokens": 4680, "total_tokens": 1193818 }, "thread_token_usage": { "input_tokens": 1179078, "cached_input_tokens": 1145216, "cache_write_input_tokens": 0, "output_tokens": 14740, "reasoning_output_tokens": 4680, "total_tokens": 1193818 } } }, { "kind": "context", "timestamp": "2026-09-25T01:43:03.893Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5129f002-c5b8-41b4-9e50-562050063fd7", "process_id": "23267", "command": [ "/bin/sh", "-lc", "ls -lh solution/validation_blend*; for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -n 5 | tr '\\n' ' '; echo \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution/validation_blend*; for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tail -n 5 | tr '\\n' ' '; echo \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "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_blend_*.parquet'\nsolution/validation_blend_*.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_blend_*.parquet'\nsolution/validation_blend_*.parquet\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 30266323 }, "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_blend_*.parquet'\nsolution/validation_blend_*.parquet\n" } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-25T01:43:08.258Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:43:08.258Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:43:18.809Z", "callId": "call_H5C7SezrvvBn1DghZXQeH24J", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blends.py\\n@@\\n-alphas=[0.01,0.025,0.05,0.075,0.1,0.15,0.2]\\n-outs={a:[] for a in alphas}\\n-with torch.no_grad():\\n- for lo in range(0,len(targets),128):\\n- ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\\n- for r,i in enumerate(ix):\\n- mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\\n- for a in alphas:\\n- s=base[r]+a*prior; s[~mask]=-np.inf\\n- top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\\n- outs[a].extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\\n-for a,rows in outs.items(): pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\\n+alphas=[0.025,0.075,0.15]\\n+for a in alphas:\\n+ rows=[]\\n+ with torch.no_grad():\\n+ for lo in range(0,len(targets),128):\\n+ ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\\n+ for r,i in enumerate(ix):\\n+ mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\\n+ s=base[r]+a*prior; s[~mask]=-np.inf\\n+ top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\\n+ rows.extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\\n+ print(a,flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:43:18.828Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:43:18.814Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d1a815c087d2a7935fcfee6c8838", "usage": { "input_tokens": 39451, "cached_input_tokens": 38656, "cache_write_input_tokens": 0, "output_tokens": 770, "reasoning_output_tokens": 192, "total_tokens": 40221 }, "turn_token_usage": { "input_tokens": 1218529, "cached_input_tokens": 1183872, "cache_write_input_tokens": 0, "output_tokens": 15510, "reasoning_output_tokens": 4872, "total_tokens": 1234039 }, "thread_token_usage": { "input_tokens": 1218529, "cached_input_tokens": 1183872, "cache_write_input_tokens": 0, "output_tokens": 15510, "reasoning_output_tokens": 4872, "total_tokens": 1234039 } } }, { "kind": "context", "timestamp": "2026-09-25T01:43:18.823Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-9321a768-a50c-4fd9-a582-17a3f3ec0b9a", "changes": { "/app/solution/tune_blends.py": { "type": "update", "unified_diff": "@@ -16,13 +16,14 @@\n ivec=model.item(torch.arange(len(items))).detach(); prior=feat[:,0]\n-alphas=[0.01,0.025,0.05,0.075,0.1,0.15,0.2]\n-outs={a:[] for a in alphas}\n-with torch.no_grad():\n- for lo in range(0,len(targets),128):\n- ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\n- for r,i in enumerate(ix):\n- mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\n- for a in alphas:\n+alphas=[0.025,0.075,0.15]\n+for a in alphas:\n+ rows=[]\n+ with torch.no_grad():\n+ for lo in range(0,len(targets),128):\n+ ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\n+ for r,i in enumerate(ix):\n+ mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\n s=base[r]+a*prior; s[~mask]=-np.inf\n top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\n- outs[a].extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\n-for a,rows in outs.items(): pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\n+ rows.extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\n+ print(a,flush=True)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blends.py\n", "stderr": "" } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-25T01:43:20.526Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:43:20.526Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:43:21.705Z", "callId": "call_qqg4mEl9NxQijB93ul9tagdB", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:43:51.872Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"8613ac\",\"wall_time_seconds\":30.001284302,\"session_id\":45997,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:43:21.790Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d1b7502087d2a3f69944628af21c", "usage": { "input_tokens": 40245, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 6, "total_tokens": 40324 }, "turn_token_usage": { "input_tokens": 1258774, "cached_input_tokens": 1223168, "cache_write_input_tokens": 0, "output_tokens": 15589, "reasoning_output_tokens": 4878, "total_tokens": 1274363 }, "thread_token_usage": { "input_tokens": 1258774, "cached_input_tokens": 1223168, "cache_write_input_tokens": 0, "output_tokens": 15589, "reasoning_output_tokens": 4878, "total_tokens": 1274363 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-25T01:43:54.194Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:43:54.194Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:43:54.880Z", "callId": "call_dPd82scQJEpdanIVPyEwaruk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:45997,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); 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text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:47:09.469Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:46:39.567Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d27d45e487d2bbca1ae279e56991", "usage": { "input_tokens": 40875, "cached_input_tokens": 40576, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 40920 }, "turn_token_usage": { "input_tokens": 1502377, "cached_input_tokens": 1465088, "cache_write_input_tokens": 0, "output_tokens": 16013, "reasoning_output_tokens": 5026, "total_tokens": 1518390 }, "thread_token_usage": { "input_tokens": 1502377, "cached_input_tokens": 1465088, "cache_write_input_tokens": 0, "output_tokens": 16013, "reasoning_output_tokens": 5026, "total_tokens": 1518390 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-25T01:47:12.602Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:47:12.602Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:47:14.881Z", "callId": "call_LtFuXJilx3kMKRcsHNA9a4Fs", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *tune_blends*) echo \\\"$d $c\\\"; awk '/VmRSS|VmSize/ {print}' \\\"$d/status\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:47:14.942Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nMemTotal: 15988972 kB\nMemFree: 1730616 kB\nMemAvailable: 12695528 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\n/proc/9719 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\n' ' '; echo; done \nVmSize:\t 2592 kB\nVmRSS:\t 1736 kB\n/proc/9720 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 3522160 kB\nVmRSS:\t 1543400 kB\n/proc/9842 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2592 kB\nVmRSS:\t 1696 kB\n/proc/9843 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2982092 kB\nVmRSS:\t 1005920 kB\n/proc/9901 /bin/sh -c awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *tune_blends*) echo \"$d $c\"; awk '/VmRSS|VmSize/ {print}' \"$d/status\";; esac; done \nVmSize:\t 2592 kB\nVmRSS:\t 1732 kB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:47:14.936Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d29db11c87d29192cbf8ea0a605b", "usage": { "input_tokens": 40943, "cached_input_tokens": 40704, "cache_write_input_tokens": 0, "output_tokens": 252, "reasoning_output_tokens": 108, "total_tokens": 41195 }, "turn_token_usage": { "input_tokens": 1543320, "cached_input_tokens": 1505792, "cache_write_input_tokens": 0, "output_tokens": 16265, "reasoning_output_tokens": 5134, "total_tokens": 1559585 }, "thread_token_usage": { "input_tokens": 1543320, "cached_input_tokens": 1505792, "cache_write_input_tokens": 0, "output_tokens": 16265, "reasoning_output_tokens": 5134, "total_tokens": 1559585 } } }, { "kind": "context", "timestamp": "2026-09-25T01:47:14.938Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b141d0fe-3aae-43dd-ab51-447b2a1de9be", "process_id": "44487", "command": [ "/bin/sh", "-lc", "awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *tune_blends*) echo \"$d $c\"; awk '/VmRSS|VmSize/ {print}' \"$d/status\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *tune_blends*) echo \"$d $c\"; awk '/VmRSS|VmSize/ {print}' \"$d/status\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "MemTotal: 15988972 kB\nMemFree: 1730616 kB\nMemAvailable: 12695528 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\n/proc/9719 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\n' ' '; echo; done \nVmSize:\t 2592 kB\nVmRSS:\t 1736 kB\n/proc/9720 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 3522160 kB\nVmRSS:\t 1543400 kB\n/proc/9842 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2592 kB\nVmRSS:\t 1696 kB\n/proc/9843 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2982092 kB\nVmRSS:\t 1005920 kB\n/proc/9901 /bin/sh -c awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *tune_blends*) echo \"$d $c\"; awk '/VmRSS|VmSize/ {print}' \"$d/status\";; esac; done \nVmSize:\t 2592 kB\nVmRSS:\t 1732 kB\n", "stderr": "", "aggregated_output": "MemTotal: 15988972 kB\nMemFree: 1730616 kB\nMemAvailable: 12695528 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\n/proc/9719 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\n' ' '; echo; done \nVmSize:\t 2592 kB\nVmRSS:\t 1736 kB\n/proc/9720 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 3522160 kB\nVmRSS:\t 1543400 kB\n/proc/9842 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2592 kB\nVmRSS:\t 1696 kB\n/proc/9843 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2982092 kB\nVmRSS:\t 1005920 kB\n/proc/9901 /bin/sh -c awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *tune_blends*) echo \"$d $c\"; awk '/VmRSS|VmSize/ {print}' \"$d/status\";; esac; done \nVmSize:\t 2592 kB\nVmRSS:\t 1732 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4660 }, "formatted_output": "MemTotal: 15988972 kB\nMemFree: 1730616 kB\nMemAvailable: 12695528 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\n/proc/9719 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py && for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\n' ' '; echo; done \nVmSize:\t 2592 kB\nVmRSS:\t 1736 kB\n/proc/9720 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 3522160 kB\nVmRSS:\t 1543400 kB\n/proc/9842 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2592 kB\nVmRSS:\t 1696 kB\n/proc/9843 /opt/task-venv/bin/python solution/tune_blends.py \nVmSize:\t 2982092 kB\nVmRSS:\t 1005920 kB\n/proc/9901 /bin/sh -c awk '/MemTotal|MemFree|MemAvailable|SwapTotal|SwapFree/ {print}' /proc/meminfo; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *tune_blends*) echo \"$d $c\"; awk '/VmRSS|VmSize/ {print}' \"$d/status\";; esac; done \nVmSize:\t 2592 kB\nVmRSS:\t 1732 kB\n" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-25T01:47:18.314Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:47:18.314Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:47:19.604Z", "callId": "call_5V52qHyUtw9buUbhfLB0Bkm8", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 9720 9719 2>/dev/null || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:47:19.702Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nMemTotal: 15988972 kB\nMemAvailable: 12672144 kB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:47:19.643Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-64b82f19-99f5-4694-8f3d-eb394424684e", "process_id": "7140", "command": [ "/bin/sh", "-lc", "kill 9720 9719 2>/dev/null || true; 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do awk '/State|VmRSS|voluntary_ctxt_switches|nonvoluntary_ctxt_switches/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; done; ls -lh solution/validation_blend* 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "State:\tR (running)\nVmRSS:\t 1005920 kB\nvoluntary_ctxt_switches:\t30\nnonvoluntary_ctxt_switches:\t707\n0", "stderr": "", "aggregated_output": "State:\tR (running)\nVmRSS:\t 1005920 kB\nvoluntary_ctxt_switches:\t30\nnonvoluntary_ctxt_switches:\t707\n0", "exit_code": 0, "duration": { "secs": 0, "nanos": 4010 }, "formatted_output": "State:\tR (running)\nVmRSS:\t 1005920 kB\nvoluntary_ctxt_switches:\t30\nnonvoluntary_ctxt_switches:\t707\n0" } } }, { "kind": "context", "timestamp": "2026-09-25T01:47:58.680Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d2c9705487d29d6e15a8bee7fb90", "usage": { "input_tokens": 41900, "cached_input_tokens": 41600, "cache_write_input_tokens": 0, "output_tokens": 248, "reasoning_output_tokens": 126, "total_tokens": 42148 }, "turn_token_usage": { "input_tokens": 1668676, "cached_input_tokens": 1629568, "cache_write_input_tokens": 0, "output_tokens": 16710, "reasoning_output_tokens": 5332, "total_tokens": 1685386 }, "thread_token_usage": { "input_tokens": 1668676, "cached_input_tokens": 1629568, "cache_write_input_tokens": 0, "output_tokens": 16710, "reasoning_output_tokens": 5332, "total_tokens": 1685386 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-25T01:48:00.095Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:48:00.095Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:48:00.825Z", "callId": "call_drCBez1bPoivhx9FeWoWK0td", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:45997,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); 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uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\\n- for r,i in enumerate(ix):\\n- mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\\n- s=base[r]+a*prior; s[~mask]=-np.inf\\n- top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\\n- rows.extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\\n- pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\\n- print(a,flush=True)\\n+# One expensive tower-scoring pass. Small priors can only reorder plausible\\n+# neural candidates, so retain 2,000 per user for the calibration sweep.\\n+short_ids=[]; short_scores=[]\\n+with torch.no_grad():\\n+ for lo in range(0,len(targets),128):\\n+ ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\\n+ for r,i in enumerate(ix):\\n+ mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\\n+ base[r,~mask]=-np.inf; top=np.argpartition(base[r],-2000)[-2000:]\\n+ short_ids.append(top); short_scores.append(base[r,top])\\n+short_ids=np.asarray(short_ids); short_scores=np.asarray(short_scores)\\n+for a in [0.025,0.075,0.15]:\\n+ rows=[]\\n+ for i,uid in enumerate(targets):\\n+ cand=short_ids[i]; s=short_scores[i]+a*prior[cand]\\n+ take=np.argpartition(s,-100)[-100:]; take=take[np.argsort(s[take])[::-1]]; top=cand[take]\\n+ rows.extend((int(uid),int(items[j]),k+1) for k,j in enumerate(top))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False); print(a,flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:49:25.878Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:49:25.798Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-9c2934a3-4d57-4cdb-b68d-66c498612d01", "changes": { "/app/solution/tune_blends.py": { "type": "update", "unified_diff": "@@ -16,14 +16,19 @@\n ivec=model.item(torch.arange(len(items))).detach(); prior=feat[:,0]\n-alphas=[0.025,0.075,0.15]\n-for a in alphas:\n+# One expensive tower-scoring pass. Small priors can only reorder plausible\n+# neural candidates, so retain 2,000 per user for the calibration sweep.\n+short_ids=[]; short_scores=[]\n+with torch.no_grad():\n+ for lo in range(0,len(targets),128):\n+ ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\n+ for r,i in enumerate(ix):\n+ mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\n+ base[r,~mask]=-np.inf; top=np.argpartition(base[r],-2000)[-2000:]\n+ short_ids.append(top); short_scores.append(base[r,top])\n+short_ids=np.asarray(short_ids); short_scores=np.asarray(short_scores)\n+for a in [0.025,0.075,0.15]:\n rows=[]\n- with torch.no_grad():\n- for lo in range(0,len(targets),128):\n- ix=list(range(lo,min(lo+128,len(targets)))); uv=model.user(*tr.collate(ix,seq,ww,stats)); base=(uv@ivec.T).numpy()\n- for r,i in enumerate(ix):\n- mask=np.unpackbits(z['packed_eligible'][zr[int(targets[i])]],count=len(items),bitorder='little').astype(bool)\n- s=base[r]+a*prior; s[~mask]=-np.inf\n- top=np.argpartition(s,-100)[-100:]; top=top[np.argsort(s[top])[::-1]]\n- rows.extend((int(targets[i]),int(items[j]),k+1) for k,j in enumerate(top))\n- pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False)\n- print(a,flush=True)\n+ for i,uid in enumerate(targets):\n+ cand=short_ids[i]; s=short_scores[i]+a*prior[cand]\n+ take=np.argpartition(s,-100)[-100:]; take=take[np.argsort(s[take])[::-1]]; top=cand[take]\n+ rows.extend((int(uid),int(items[j]),k+1) for k,j in enumerate(top))\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_blend_{a}.parquet',index=False); print(a,flush=True)\n", "move_path": null } }, "status": "completed", "stdout": "Success. 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text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:54:46.193Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:54:16.301Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d446660087d2b542a9a42343fdfd", "usage": { "input_tokens": 44228, "cached_input_tokens": 44032, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 12, "total_tokens": 44287 }, "turn_token_usage": { "input_tokens": 2277207, "cached_input_tokens": 2233344, "cache_write_input_tokens": 0, "output_tokens": 18452, "reasoning_output_tokens": 5764, "total_tokens": 2295659 }, "thread_token_usage": { "input_tokens": 2277207, "cached_input_tokens": 2233344, "cache_write_input_tokens": 0, "output_tokens": 18452, "reasoning_output_tokens": 5764, "total_tokens": 2295659 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-25T01:54:49.206Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:54:49.206Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:54:52.408Z", "callId": "call_hahQv8G8neZV1IHK9jUlLKrz", "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 \\\"$c\\\" in *python*) echo \\\"$d $c\\\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:54:52.492Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/proc/10039 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \n/proc/10040 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \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\nState:\tR (running)\nVmRSS:\t 1798232 kB\nThreads:\t21\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:54:52.476Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ec887f8-e7d6-4b02-9155-1cfab89ec262", "process_id": "40423", "command": [ "/bin/sh", "-lc", "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/proc/10039 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \n/proc/10040 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \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\nState:\tR (running)\nVmRSS:\t 1798232 kB\nThreads:\t21\n", "stderr": "", "aggregated_output": "/proc/10039 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \n/proc/10040 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \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\nState:\tR (running)\nVmRSS:\t 1798232 kB\nThreads:\t21\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2490 }, "formatted_output": "/proc/10039 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \n/proc/10040 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *python*) echo \"$d $c\";; esac; done | sed -n '1,30p'; awk '/State|VmRSS|Threads/ {print}' /proc/$(for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *solution/tune_blends.py*) basename $d;; esac; done | tail -1)/status \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\nState:\tR (running)\nVmRSS:\t 1798232 kB\nThreads:\t21\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:54:52.491Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d466656487d281ac7b380b5af62b", "usage": { "input_tokens": 44310, "cached_input_tokens": 44032, "cache_write_input_tokens": 0, "output_tokens": 205, "reasoning_output_tokens": 20, "total_tokens": 44515 }, "turn_token_usage": { "input_tokens": 2321517, "cached_input_tokens": 2277376, "cache_write_input_tokens": 0, "output_tokens": 18657, "reasoning_output_tokens": 5784, "total_tokens": 2340174 }, "thread_token_usage": { "input_tokens": 2321517, "cached_input_tokens": 2277376, "cache_write_input_tokens": 0, "output_tokens": 18657, "reasoning_output_tokens": 5784, "total_tokens": 2340174 } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-25T01:54:58.203Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:54:58.203Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:54:58.666Z", "callId": "call_04pVJMUhfLZVQpnn2beft3ms", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:25101,chars:\"\",yield_time_ms:60000,max_output_tokens:3000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:55:29.674Z", "content": "Script running with cell ID 64\nWall time 31.0 seconds\nOutput:\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:54:58.739Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d46cc2a887d28f422de5346bbde3", "usage": { "input_tokens": 45249, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 291, "reasoning_output_tokens": 244, "total_tokens": 45540 }, "turn_token_usage": { "input_tokens": 2366766, "cached_input_tokens": 2321536, "cache_write_input_tokens": 0, "output_tokens": 18948, "reasoning_output_tokens": 6028, "total_tokens": 2385714 }, "thread_token_usage": { "input_tokens": 2366766, "cached_input_tokens": 2321536, "cache_write_input_tokens": 0, "output_tokens": 18948, "reasoning_output_tokens": 6028, "total_tokens": 2385714 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-25T01:55:30.790Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:55:30.790Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:55:31.184Z", "callId": "call_98TeWQ6FxLHA1jRv7mUbXcPf", "name": "wait", "input": { "cell_id": "64", "yield_time_ms": 30000, "max_tokens": 3000 }, "result": { "timestamp": "2026-09-25T01:55:58.675Z", "content": "Script completed\nWall time 27.5 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:55:31.296Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d491db7487d2b94c52eb9996a1d1", "usage": { "input_tokens": 45568, "cached_input_tokens": 45056, "cache_write_input_tokens": 0, "output_tokens": 41, "reasoning_output_tokens": 8, "total_tokens": 45609 }, "turn_token_usage": { "input_tokens": 2412334, "cached_input_tokens": 2366592, "cache_write_input_tokens": 0, "output_tokens": 18989, "reasoning_output_tokens": 6036, "total_tokens": 2431323 }, "thread_token_usage": { "input_tokens": 2412334, "cached_input_tokens": 2366592, "cache_write_input_tokens": 0, "output_tokens": 18989, "reasoning_output_tokens": 6036, "total_tokens": 2431323 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-25T01:56:00.026Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:56:00.026Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:56:02.286Z", "callId": "call_Bpzyq0RQhhZsuLkT2gHPgdt9", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\" 2>/dev/null); case \\\"$c\\\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \\\"$(basename $d) $c\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:56:02.400Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n10090 /bin/sh -c ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \"$(basename $d) $c\";; esac; done \n9962 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \n9963 /opt/task-venv/bin/python solution/tune_blends.py \n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:02.348Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cddf7ce3-0d3e-4a73-a144-1960501e00ea", "process_id": "79226", "command": [ "/bin/sh", "-lc", "ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \"$(basename $d) $c\";; esac; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \"$(basename $d) $c\";; esac; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "10090 /bin/sh -c ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \"$(basename $d) $c\";; esac; done \n9962 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \n9963 /opt/task-venv/bin/python solution/tune_blends.py \n", "stderr": "", "aggregated_output": "10090 /bin/sh -c ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \"$(basename $d) $c\";; esac; done \n9962 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \n9963 /opt/task-venv/bin/python solution/tune_blends.py \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2350 }, "formatted_output": "10090 /bin/sh -c ls -lh solution/validation_blend* 2>/dev/null || true; for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *'/opt/task-venv/bin/python solution/tune_blends.py'*) echo \"$(basename $d) $c\";; esac; done \n9962 /bin/sh -c OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py \n9963 /opt/task-venv/bin/python solution/tune_blends.py \n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:56:02.398Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4af158487d28d51cebbad94b1e7", "usage": { "input_tokens": 45632, "cached_input_tokens": 45440, "cache_write_input_tokens": 0, "output_tokens": 145, "reasoning_output_tokens": 11, "total_tokens": 45777 }, "turn_token_usage": { "input_tokens": 2457966, "cached_input_tokens": 2412032, "cache_write_input_tokens": 0, "output_tokens": 19134, "reasoning_output_tokens": 6047, "total_tokens": 2477100 }, "thread_token_usage": { "input_tokens": 2457966, "cached_input_tokens": 2412032, "cache_write_input_tokens": 0, "output_tokens": 19134, "reasoning_output_tokens": 6047, "total_tokens": 2477100 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-25T01:56:04.558Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:56:04.558Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:56:05.831Z", "callId": "call_AcW5o7NR7lwUjn51MxGp6nKU", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"awk '{print $14,$15,$22}' /proc/9963/stat; awk '/Cpus_allowed_list/ {print}' /proc/9963/status\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:56:05.939Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n39475 498 258983\nCpus_allowed_list:\t0-7\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:05.869Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bc9c097d-1f39-476d-acb1-d97b6ba7612d", "process_id": "30931", "command": [ "/bin/sh", "-lc", "awk '{print $14,$15,$22}' /proc/9963/stat; awk '/Cpus_allowed_list/ {print}' /proc/9963/status" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "awk '{print $14,$15,$22}' /proc/9963/stat", "name": "stat", "path": "/proc/9963/stat" }, { "type": "read", "cmd": "awk '/Cpus_allowed_list/ {print}' /proc/9963/status", "name": "status", "path": "/proc/9963/status" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "39475 498 258983\nCpus_allowed_list:\t0-7\n", "stderr": "", "aggregated_output": "39475 498 258983\nCpus_allowed_list:\t0-7\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2120 }, "formatted_output": "39475 498 258983\nCpus_allowed_list:\t0-7\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:56:05.937Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4b2b41c87d281375f3b65eafe70", "usage": { "input_tokens": 45945, "cached_input_tokens": 45440, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 62, "total_tokens": 46087 }, "turn_token_usage": { "input_tokens": 2503911, "cached_input_tokens": 2457472, "cache_write_input_tokens": 0, "output_tokens": 19276, "reasoning_output_tokens": 6109, "total_tokens": 2523187 }, "thread_token_usage": { "input_tokens": 2503911, "cached_input_tokens": 2457472, "cache_write_input_tokens": 0, "output_tokens": 19276, "reasoning_output_tokens": 6109, "total_tokens": 2523187 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-25T01:56:08.754Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:56:08.754Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:56:09.940Z", "callId": "call_gNpEhPCGrChkBzSB1h0Qw5pa", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"awk '{print $14,$15}' /proc/9963/stat 2>/dev/null || true; ls -lh solution/validation_blend* 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:56:10.044Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n39881 503\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:09.978Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e9f07e8d-9c83-4e6a-8e52-eb2bf2a2710b", "process_id": "36926", "command": [ "/bin/sh", "-lc", "awk '{print $14,$15}' /proc/9963/stat 2>/dev/null || true; ls -lh solution/validation_blend* 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "awk '{print $14,$15}' /proc/9963/stat 2>/dev/null || true; ls -lh solution/validation_blend* 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "39881 503\n", "stderr": "", "aggregated_output": "39881 503\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2590 }, "formatted_output": "39881 503\n" } } }, { "kind": "context", "timestamp": "2026-09-25T01:56:10.043Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4b6370487d2aa54ce50ed7a14f8", "usage": { "input_tokens": 46128, "cached_input_tokens": 45824, "cache_write_input_tokens": 0, "output_tokens": 166, "reasoning_output_tokens": 81, "total_tokens": 46294 }, "turn_token_usage": { "input_tokens": 2550039, "cached_input_tokens": 2503296, "cache_write_input_tokens": 0, "output_tokens": 19442, "reasoning_output_tokens": 6190, "total_tokens": 2569481 }, "thread_token_usage": { "input_tokens": 2550039, "cached_input_tokens": 2503296, "cache_write_input_tokens": 0, "output_tokens": 19442, "reasoning_output_tokens": 6190, "total_tokens": 2569481 } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-25T01:56:11.417Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:56:11.417Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:56:12.368Z", "callId": "call_0r9nDG8AX0YbFmZ7MwRUOYyn", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"kill 9963 9962 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:56:12.477Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:12.403Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fa4aeba1-83c4-4a59-824b-c538e93a2bdb", "process_id": "54932", "command": [ "/bin/sh", "-lc", "kill 9963 9962 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 9963 9962 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 1700 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:56:12.458Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9cd9e619-0f69-48df-a88a-c030cd9014c2", "process_id": "25101", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_blends.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 403, "nanos": 992876520 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:56:12.476Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4ba4fcc87d2a7380a9f257b6a39", "usage": { "input_tokens": 46322, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 21, "total_tokens": 46404 }, "turn_token_usage": { "input_tokens": 2596361, "cached_input_tokens": 2549248, "cache_write_input_tokens": 0, "output_tokens": 19524, "reasoning_output_tokens": 6211, "total_tokens": 2615885 }, "thread_token_usage": { "input_tokens": 2596361, "cached_input_tokens": 2549248, "cache_write_input_tokens": 0, "output_tokens": 19524, "reasoning_output_tokens": 6211, "total_tokens": 2615885 } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-25T01:56:17.095Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:56:17.095Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:56:27.534Z", "callId": "call_WiK8iyiuLep9kO273N6WmEml", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_top100.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Cheap calibration within an already retrieved neural top-100.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np, pandas as pd, torch\\n+import train_ranker as tr\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\n+cat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(items)}\\n+feat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\\n+m=tr.TwoTower(len(items),torch.from_numpy(feat)); m.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict']); m.eval()\\n+h=tr.add_preference_history(pd.read_parquet(O/'history_train.parquet'),D/'validation_preference_state.parquet',tr.TRAIN_END)\\n+seq,ww,stats=tr.histories(h,targets,mp,tr.TRAIN_END)\\n+old=pd.read_parquet(O/'validation.parquet').sort_values(['uid','rank']); grouped={int(u):g.item_id.to_numpy() for u,g in old.groupby('uid',sort=False)}\\n+uvs=[]\\n+with torch.no_grad():\\n+ for lo in range(0,len(targets),256):\\n+ ix=list(range(lo,min(lo+256,len(targets)))); uvs.append(m.user(*tr.collate(ix,seq,ww,stats)))\\n+ uvs=torch.cat(uvs)\\n+ for alpha in [0.01,0.025,0.05,0.1,0.2]:\\n+ rows=[]\\n+ for i,u in enumerate(targets):\\n+ c=np.asarray([mp[int(x)] for x in grouped[int(u)]],np.int64); iv=m.item(torch.from_numpy(c))\\n+ score=(iv@uvs[i]).numpy()+alpha*feat[c,0]; order=np.argsort(score)[::-1]\\n+ rows.extend((int(u),int(items[c[j]]),r+1) for r,j in enumerate(order))\\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_topblend_{alpha}.parquet',index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:56:27.617Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:27.542Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-9077112c-4827-444d-a3ba-e2a3a9386f26", "changes": { "/app/solution/tune_top100.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Cheap calibration within an already retrieved neural top-100.\"\"\"\nfrom pathlib import Path\nimport numpy as np, pandas as pd, torch\nimport train_ranker as tr\nD=Path('/task/data'); O=Path('/app/solution')\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(items)}\nfeat=np.stack([np.log1p(cat.positive_listens)/np.log1p(cat.positive_listens.max()),cat.organic_positive_listens/np.maximum(cat.positive_listens,1)],1).astype(np.float32)\nm=tr.TwoTower(len(items),torch.from_numpy(feat)); m.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict']); m.eval()\nh=tr.add_preference_history(pd.read_parquet(O/'history_train.parquet'),D/'validation_preference_state.parquet',tr.TRAIN_END)\nseq,ww,stats=tr.histories(h,targets,mp,tr.TRAIN_END)\nold=pd.read_parquet(O/'validation.parquet').sort_values(['uid','rank']); grouped={int(u):g.item_id.to_numpy() for u,g in old.groupby('uid',sort=False)}\nuvs=[]\nwith torch.no_grad():\n for lo in range(0,len(targets),256):\n ix=list(range(lo,min(lo+256,len(targets)))); uvs.append(m.user(*tr.collate(ix,seq,ww,stats)))\n uvs=torch.cat(uvs)\n for alpha in [0.01,0.025,0.05,0.1,0.2]:\n rows=[]\n for i,u in enumerate(targets):\n c=np.asarray([mp[int(x)] for x in grouped[int(u)]],np.int64); iv=m.item(torch.from_numpy(c))\n score=(iv@uvs[i]).numpy()+alpha*feat[c,0]; order=np.argsort(score)[::-1]\n rows.extend((int(u),int(items[c[j]]),r+1) for r,j in enumerate(order))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_topblend_{alpha}.parquet',index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_top100.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:56:27.616Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4bccea487d2ab2e57f62b8c2d54", "usage": { "input_tokens": 46427, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 782, "reasoning_output_tokens": 180, "total_tokens": 47209 }, "turn_token_usage": { "input_tokens": 2642788, "cached_input_tokens": 2595328, "cache_write_input_tokens": 0, "output_tokens": 20306, "reasoning_output_tokens": 6391, "total_tokens": 2663094 }, "thread_token_usage": { "input_tokens": 2642788, "cached_input_tokens": 2595328, "cache_write_input_tokens": 0, "output_tokens": 20306, "reasoning_output_tokens": 6391, "total_tokens": 2663094 } } }, { "kind": "tool", "timestamp": "2026-09-25T01:56:30.620Z", "callId": "call_X15dAGYj01KHwWZytj0hjfSI", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_top100.py && for f in solution/validation_topblend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | grep 'quality_ndcg'; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:57:00.785Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"a37139\",\"wall_time_seconds\":30.000617744,\"session_id\":37743,\"original_token_count\":9,\"output\":\" \\\"quality_ndcg@10\\\": 0.020077,\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:56:30.725Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4cbee6487d2bcf8f942fffc2ee3", "usage": { "input_tokens": 47233, "cached_input_tokens": 46208, "cache_write_input_tokens": 0, "output_tokens": 111, "reasoning_output_tokens": 0, "total_tokens": 47344 }, "turn_token_usage": { "input_tokens": 2690021, "cached_input_tokens": 2641536, "cache_write_input_tokens": 0, "output_tokens": 20417, "reasoning_output_tokens": 6391, "total_tokens": 2710438 }, "thread_token_usage": { "input_tokens": 2690021, "cached_input_tokens": 2641536, "cache_write_input_tokens": 0, "output_tokens": 20417, "reasoning_output_tokens": 6391, "total_tokens": 2710438 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-25T01:57:02.039Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:02.039Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:57:02.739Z", "callId": "call_meqxlv4rbcsua4n1eStP7E5T", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:37743,chars:\"\",yield_time_ms:30000,max_output_tokens:4000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:57:11.735Z", "content": "Script completed\nWall time 9.0 seconds\nOutput:\n\n \"quality_ndcg@10\": 0.020113,\n \"quality_ndcg@10\": 0.020071,\n \"quality_ndcg@10\": 0.020483,\n \"quality_ndcg@10\": 0.020128,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:57:02.859Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4ecfb3887d2912a90763f11298e", "usage": { "input_tokens": 47415, "cached_input_tokens": 47104, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 47472 }, "turn_token_usage": { "input_tokens": 2737436, "cached_input_tokens": 2688640, "cache_write_input_tokens": 0, "output_tokens": 20474, "reasoning_output_tokens": 6401, "total_tokens": 2757910 }, "thread_token_usage": { "input_tokens": 2737436, "cached_input_tokens": 2688640, "cache_write_input_tokens": 0, "output_tokens": 20474, "reasoning_output_tokens": 6401, "total_tokens": 2757910 } } }, { "kind": "context", "timestamp": "2026-09-25T01:57:11.734Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-daf37b5d-3bb7-4bcb-9fa0-a8accce5e7c4", "process_id": "37743", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_top100.py && for f in solution/validation_topblend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep 'quality_ndcg'; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/tune_top100.py && for f in solution/validation_topblend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | grep 'quality_ndcg'; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.020077,\n \"quality_ndcg@10\": 0.020113,\n \"quality_ndcg@10\": 0.020071,\n \"quality_ndcg@10\": 0.020483,\n \"quality_ndcg@10\": 0.020128,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.020077,\n \"quality_ndcg@10\": 0.020113,\n \"quality_ndcg@10\": 0.020071,\n \"quality_ndcg@10\": 0.020483,\n \"quality_ndcg@10\": 0.020128,\n", "exit_code": 0, "duration": { "secs": 40, "nanos": 949889315 }, "formatted_output": " \"quality_ndcg@10\": 0.020077,\n \"quality_ndcg@10\": 0.020113,\n \"quality_ndcg@10\": 0.020071,\n \"quality_ndcg@10\": 0.020483,\n \"quality_ndcg@10\": 0.020128,\n" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-25T01:57:22.042Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:22.042Z", "text": "" } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-25T01:57:30.863Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:30.863Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:57:32.518Z", "text": "A lightweight calibration within the retrieved set improved public NDCG@10 to 0.020483 at popularity weight 0.1. I’ll use that as a 65th dot-product coordinate—user-side value 1, item-side value 0.1 times normalized log-popularity—so the deployed score remains exactly one inner product and item vectors remain independently precomputable. Next I’m refitting on both observed windows and generating the final eligible top 100." }, { "kind": "tool", "timestamp": "2026-09-25T01:57:40.459Z", "callId": "call_bpSAJmi7iZTZeLqrPg13M7xG", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n def scale(self): return self.log_scale.exp().clamp(max=30)\\n+ def retrieval_user(self,*args):\\n+ \\\"\\\"\\\"65-D serving vector; final coordinate supports an exact item prior.\\\"\\\"\\\"\\n+ u=self.user(*args)\\n+ return torch.cat([u,torch.ones((len(u),1),dtype=u.dtype,device=u.device)],1)\\n+ def retrieval_item(self,ids,popularity_weight:float=0.1):\\n+ v=self.item(ids)\\n+ return torch.cat([v,popularity_weight*self.item_tower.features[ids,:1]],1)\\n@@\\n- item_vec=model.item(torch.arange(len(zitems))).cpu()\\n+ item_vec=model.retrieval_item(torch.arange(len(zitems))).cpu()\\n@@\\n- uv=model.user(*args); scores=(uv@item_vec.T).numpy()\\n+ uv=model.retrieval_user(*args); scores=(uv@item_vec.T).numpy()\\n@@\\n- h=aggregate_listens([DATA/'train_listens.parquet',DATA/'val_listens.parquet'],None,OUT/'history_train_val.parquet')\\n+ combined_cache=OUT/'history_train_val.parquet'\\n+ if combined_cache.exists():\\n+ h=pd.read_parquet(combined_cache)\\n+ else:\\n+ hv=aggregate_listens([DATA/'val_listens.parquet'],None,OUT/'history_val.parquet')\\n+ h=pd.concat([htrain,hv],ignore_index=True).groupby(['uid','item_id'],sort=False).agg(\\n+ count=('count','sum'),organic=('organic','sum'),last=('last','max')).reset_index()\\n+ h.to_parquet(combined_cache,index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:57:44.677Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:57:40.467Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8b663577-c9cc-487c-b4ed-f06a1e6b7916", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -141,2 +141,9 @@\n def scale(self): return self.log_scale.exp().clamp(max=30)\n+ def retrieval_user(self,*args):\n+ \"\"\"65-D serving vector; final coordinate supports an exact item prior.\"\"\"\n+ u=self.user(*args)\n+ return torch.cat([u,torch.ones((len(u),1),dtype=u.dtype,device=u.device)],1)\n+ def retrieval_item(self,ids,popularity_weight:float=0.1):\n+ v=self.item(ids)\n+ return torch.cat([v,popularity_weight*self.item_tower.features[ids,:1]],1)\n \n@@ -184,3 +191,3 @@\n zrow={int(u):i for i,u in enumerate(z['uids'])}; packed=z['packed_eligible']\n- item_vec=model.item(torch.arange(len(zitems))).cpu()\n+ item_vec=model.retrieval_item(torch.arange(len(zitems))).cpu()\n rows=[]\n@@ -188,3 +195,3 @@\n ix=list(range(lo,min(lo+128,len(uids)))); args=collate(ix,seqs,ws,stats)\n- uv=model.user(*args); scores=(uv@item_vec.T).numpy()\n+ uv=model.retrieval_user(*args); scores=(uv@item_vec.T).numpy()\n for r,i in enumerate(ix):\n@@ -234,3 +241,10 @@\n train_sets.append((targets,s1,w1,st1,p1,pw1))\n- h=aggregate_listens([DATA/'train_listens.parquet',DATA/'val_listens.parquet'],None,OUT/'history_train_val.parquet')\n+ combined_cache=OUT/'history_train_val.parquet'\n+ if combined_cache.exists():\n+ h=pd.read_parquet(combined_cache)\n+ else:\n+ hv=aggregate_listens([DATA/'val_listens.parquet'],None,OUT/'history_val.parquet')\n+ h=pd.concat([htrain,hv],ignore_index=True).groupby(['uid','item_id'],sort=False).agg(\n+ count=('count','sum'),organic=('organic','sum'),last=('last','max')).reset_index()\n+ h.to_parquet(combined_cache,index=False)\n h=add_preference_history(h,DATA/'test_preference_state.parquet',25_395_200)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:57:44.675Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d4f7ee0887d2989cf5b3bd2794b3", "usage": { "input_tokens": 47551, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 1542, "reasoning_output_tokens": 990, "total_tokens": 49093 }, "turn_token_usage": { "input_tokens": 2784987, "cached_input_tokens": 2735872, "cache_write_input_tokens": 0, "output_tokens": 22016, "reasoning_output_tokens": 7391, "total_tokens": 2807003 }, "thread_token_usage": { "input_tokens": 2784987, "cached_input_tokens": 2735872, "cache_write_input_tokens": 0, "output_tokens": 22016, "reasoning_output_tokens": 7391, "total_tokens": 2807003 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-25T01:57:46.720Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:57:46.720Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:57:48.008Z", "callId": "call_S7In249jaC5nieJcc2LNHLd4", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:58:18.174Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\"chunk_id\":\"1ac3a0\",\"wall_time_seconds\":30.000828888,\"session_id\":68743,\"original_token_count\":86,\"output\":\"examples [(4652, 47934), (4666, 44794)]\\n/app/solution/train_ranker.py:183: 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); steps+=1\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:57:48.120Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d518f17487d2b0fce909ef146cb4", "usage": { "input_tokens": 49117, "cached_input_tokens": 47360, "cache_write_input_tokens": 0, "output_tokens": 93, "reasoning_output_tokens": 14, "total_tokens": 49210 }, "turn_token_usage": { "input_tokens": 2834104, "cached_input_tokens": 2783232, "cache_write_input_tokens": 0, "output_tokens": 22109, "reasoning_output_tokens": 7405, "total_tokens": 2856213 }, "thread_token_usage": { "input_tokens": 2834104, "cached_input_tokens": 2783232, "cache_write_input_tokens": 0, "output_tokens": 22109, "reasoning_output_tokens": 7405, "total_tokens": 2856213 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-25T01:58:20.763Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:58:20.763Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:58:21.516Z", "callId": "call_VEa5dm09BCRsFexTwyvd7qsK", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:68743,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:58:51.526Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nepoch 01 loss=0.66808 scale=11.76\nepoch 02 loss=0.52203 scale=11.89\nepoch 03 loss=0.43261 scale=11.98\nepoch 04 loss=0.37685 scale=12.10\nepoch 05 loss=0.33293 scale=12.25\nepoch 06 loss=0.30805 scale=12.39\nepoch 07 loss=0.28340 scale=12.60\nepoch 08 loss=0.26622 scale=12.72\nepoch 09 loss=0.25189 scale=12.84\nepoch 10 loss=0.23824 scale=13.02\nepoch 11 loss=0.22852 scale=13.12\nepoch 12 loss=0.21401 scale=13.35\nepoch 13 loss=0.20668 scale=13.48\nepoch 14 loss=0.19771 scale=13.68\nepoch 15 loss=0.18661 scale=13.89\nepoch 16 loss=0.17933 scale=14.12\nepoch 17 loss=0.17216 scale=14.33\nepoch 18 loss=0.16750 scale=14.42\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:58:21.618Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d53a63d487d2aa5020429b892c78", "usage": { "input_tokens": 49358, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 83, "reasoning_output_tokens": 36, "total_tokens": 49441 }, "turn_token_usage": { "input_tokens": 2883462, "cached_input_tokens": 2832128, "cache_write_input_tokens": 0, "output_tokens": 22192, "reasoning_output_tokens": 7441, "total_tokens": 2905654 }, "thread_token_usage": { "input_tokens": 2883462, "cached_input_tokens": 2832128, "cache_write_input_tokens": 0, "output_tokens": 22192, "reasoning_output_tokens": 7441, "total_tokens": 2905654 } } }, { "kind": "context", "timestamp": "2026-09-25T01:58:52.437Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d749bcb2-2cf3-4258-98b5-742b3eacaa41", "process_id": "68743", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "examples [(4652, 47934), (4666, 44794)]\n/app/solution/train_ranker.py:183: 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); steps+=1\nepoch 01 loss=0.66808 scale=11.76\nepoch 02 loss=0.52203 scale=11.89\nepoch 03 loss=0.43261 scale=11.98\nepoch 04 loss=0.37685 scale=12.10\nepoch 05 loss=0.33293 scale=12.25\nepoch 06 loss=0.30805 scale=12.39\nepoch 07 loss=0.28340 scale=12.60\nepoch 08 loss=0.26622 scale=12.72\nepoch 09 loss=0.25189 scale=12.84\nepoch 10 loss=0.23824 scale=13.02\nepoch 11 loss=0.22852 scale=13.12\nepoch 12 loss=0.21401 scale=13.35\nepoch 13 loss=0.20668 scale=13.48\nepoch 14 loss=0.19771 scale=13.68\nepoch 15 loss=0.18661 scale=13.89\nepoch 16 loss=0.17933 scale=14.12\nepoch 17 loss=0.17216 scale=14.33\nepoch 18 loss=0.16750 scale=14.42\n/app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "examples [(4652, 47934), (4666, 44794)]\n/app/solution/train_ranker.py:183: 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); steps+=1\nepoch 01 loss=0.66808 scale=11.76\nepoch 02 loss=0.52203 scale=11.89\nepoch 03 loss=0.43261 scale=11.98\nepoch 04 loss=0.37685 scale=12.10\nepoch 05 loss=0.33293 scale=12.25\nepoch 06 loss=0.30805 scale=12.39\nepoch 07 loss=0.28340 scale=12.60\nepoch 08 loss=0.26622 scale=12.72\nepoch 09 loss=0.25189 scale=12.84\nepoch 10 loss=0.23824 scale=13.02\nepoch 11 loss=0.22852 scale=13.12\nepoch 12 loss=0.21401 scale=13.35\nepoch 13 loss=0.20668 scale=13.48\nepoch 14 loss=0.19771 scale=13.68\nepoch 15 loss=0.18661 scale=13.89\nepoch 16 loss=0.17933 scale=14.12\nepoch 17 loss=0.17216 scale=14.33\nepoch 18 loss=0.16750 scale=14.42\n/app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 64, "nanos": 265898530 }, "formatted_output": "examples [(4652, 47934), (4666, 44794)]\n/app/solution/train_ranker.py:183: 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); steps+=1\nepoch 01 loss=0.66808 scale=11.76\nepoch 02 loss=0.52203 scale=11.89\nepoch 03 loss=0.43261 scale=11.98\nepoch 04 loss=0.37685 scale=12.10\nepoch 05 loss=0.33293 scale=12.25\nepoch 06 loss=0.30805 scale=12.39\nepoch 07 loss=0.28340 scale=12.60\nepoch 08 loss=0.26622 scale=12.72\nepoch 09 loss=0.25189 scale=12.84\nepoch 10 loss=0.23824 scale=13.02\nepoch 11 loss=0.22852 scale=13.12\nepoch 12 loss=0.21401 scale=13.35\nepoch 13 loss=0.20668 scale=13.48\nepoch 14 loss=0.19771 scale=13.68\nepoch 15 loss=0.18661 scale=13.89\nepoch 16 loss=0.17933 scale=14.12\nepoch 17 loss=0.17216 scale=14.33\nepoch 18 loss=0.16750 scale=14.42\n/app/solution/submission.parquet\n" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-25T01:58:52.800Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:58:52.800Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:58:53.577Z", "callId": "call_I2p23y615I0w3NbcHUkBnN5N", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:68743,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:58:53.629Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/app/solution/submission.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:58:53.628Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d55bbb6887d2b303fb96476e7480", "usage": { "input_tokens": 49734, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 49791 }, "turn_token_usage": { "input_tokens": 2933196, "cached_input_tokens": 2881280, "cache_write_input_tokens": 0, "output_tokens": 22249, "reasoning_output_tokens": 7451, "total_tokens": 2955445 }, "thread_token_usage": { "input_tokens": 2933196, "cached_input_tokens": 2881280, "cache_write_input_tokens": 0, "output_tokens": 22249, "reasoning_output_tokens": 7451, "total_tokens": 2955445 } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-25T01:59:04.960Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:04.960Z", "text": "" } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-25T01:59:07.396Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:07.396Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:59:14.512Z", "callId": "call_XUsxgfetkp2TrJZ6Vt1QZ3eT", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class UserTower(nn.Module):\\n@@\\n def forward(self,flat:torch.Tensor,offsets:torch.Tensor,weights:torch.Tensor,stats:torch.Tensor):\\n@@\\n return F.normalize(self.network(torch.cat([pooled,stats],1)),dim=1)\\n+ def serving_vector(self,*args):\\n+ u=self.forward(*args)\\n+ return torch.cat([u,torch.ones((len(u),1),dtype=u.dtype,device=u.device)],1)\\n@@\\n class ItemTower(nn.Module):\\n@@\\n def forward(self,ids:torch.Tensor):\\n@@\\n return F.normalize(self.network(x),dim=-1)\\n+ def serving_vector(self,ids:torch.Tensor,popularity_weight:float=0.1):\\n+ v=self.forward(ids)\\n+ return torch.cat([v,popularity_weight*self.features[ids,:1]],1)\\n@@\\n def retrieval_user(self,*args):\\n \\\"\\\"\\\"65-D serving vector; final coordinate supports an exact item prior.\\\"\\\"\\\"\\n- u=self.user(*args)\\n- return torch.cat([u,torch.ones((len(u),1),dtype=u.dtype,device=u.device)],1)\\n+ return self.user_tower.serving_vector(*args)\\n def retrieval_item(self,ids,popularity_weight:float=0.1):\\n- v=self.item(ids)\\n- return torch.cat([v,popularity_weight*self.item_tower.features[ids,:1]],1)\\n+ return self.item_tower.serving_vector(ids,popularity_weight)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T01:59:14.600Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:59:14.520Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f226a85d-737c-45d3-9f7e-ae34ea72dbfe", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -122,2 +122,5 @@\n return F.normalize(self.network(torch.cat([pooled,stats],1)),dim=1)\n+ def serving_vector(self,*args):\n+ u=self.forward(*args)\n+ return torch.cat([u,torch.ones((len(u),1),dtype=u.dtype,device=u.device)],1)\n \n@@ -132,2 +135,5 @@\n return F.normalize(self.network(x),dim=-1)\n+ def serving_vector(self,ids:torch.Tensor,popularity_weight:float=0.1):\n+ v=self.forward(ids)\n+ return torch.cat([v,popularity_weight*self.features[ids,:1]],1)\n \n@@ -143,7 +149,5 @@\n \"\"\"65-D serving vector; final coordinate supports an exact item prior.\"\"\"\n- u=self.user(*args)\n- return torch.cat([u,torch.ones((len(u),1),dtype=u.dtype,device=u.device)],1)\n+ return self.user_tower.serving_vector(*args)\n def retrieval_item(self,ids,popularity_weight:float=0.1):\n- v=self.item(ids)\n- return torch.cat([v,popularity_weight*self.item_tower.features[ids,:1]],1)\n+ return self.item_tower.serving_vector(ids,popularity_weight)\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:59:14.599Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d55de34c87d2950519ebbdd3ab35", "usage": { "input_tokens": 49822, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 1049, "reasoning_output_tokens": 645, "total_tokens": 50871 }, "turn_token_usage": { "input_tokens": 2983018, "cached_input_tokens": 2930816, "cache_write_input_tokens": 0, "output_tokens": 23298, "reasoning_output_tokens": 8096, "total_tokens": 3006316 }, "thread_token_usage": { "input_tokens": 2983018, "cached_input_tokens": 2930816, "cache_write_input_tokens": 0, "output_tokens": 23298, "reasoning_output_tokens": 8096, "total_tokens": 3006316 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-25T01:59:17.645Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:17.645Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:59:24.069Z", "callId": "call_iMqPdmI7u3OXm7EXEhDv9afv", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python - <<'PY'\\nimport sys\\nsys.path.insert(0,'/app/solution')\\nimport train_ranker as tr\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, torch\\nD=Path('/task/data'); O=Path('/app/solution')\\nt=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); c=pd.read_parquet(D/'candidate_catalog.parquet'); items=c.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(items)}\\nf=np.stack([np.log1p(c.positive_listens)/np.log1p(c.positive_listens.max()),c.organic_positive_listens/np.maximum(c.positive_listens,1)],1).astype(np.float32)\\nm=tr.TwoTower(len(items),torch.from_numpy(f)); m.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict'])\\nh=tr.add_preference_history(pd.read_parquet(O/'history_train.parquet'),D/'validation_preference_state.parquet',tr.TRAIN_END)\\ns,w,st=tr.histories(h,t,mp,tr.TRAIN_END)\\ntr.rank(m,t,s,w,st,D/'validation_eligible_candidates.npz',O/'validation_globalblend.parquet')\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_globalblend.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T01:59:40.054Z", "content": "Script completed\nWall time 16.0 seconds\nOutput:\n\n{\"chunk_id\":\"8f4a49\",\"wall_time_seconds\":15.818842132,\"exit_code\":0,\"original_token_count\":34,\"output\":\"{\\n \\\"model\\\": \\\"solution/validation_globalblend.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.020483,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:59:24.177Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d572df4887d29c2ee5922d4e27c3", "usage": { "input_tokens": 50895, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 378, "reasoning_output_tokens": 12, "total_tokens": 51273 }, "turn_token_usage": { "input_tokens": 3033913, "cached_input_tokens": 2980480, "cache_write_input_tokens": 0, "output_tokens": 23676, "reasoning_output_tokens": 8108, "total_tokens": 3057589 }, "thread_token_usage": { "input_tokens": 3033913, "cached_input_tokens": 2980480, "cache_write_input_tokens": 0, "output_tokens": 23676, "reasoning_output_tokens": 8108, "total_tokens": 3057589 } } }, { "kind": "context", "timestamp": "2026-09-25T01:59:40.050Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-734d68de-4c35-4bce-8b33-9ba52e03f689", "process_id": "89260", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/app/solution')\nimport train_ranker as tr\nfrom pathlib import Path\nimport pandas as pd, numpy as np, torch\nD=Path('/task/data'); O=Path('/app/solution')\nt=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); c=pd.read_parquet(D/'candidate_catalog.parquet'); items=c.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(items)}\nf=np.stack([np.log1p(c.positive_listens)/np.log1p(c.positive_listens.max()),c.organic_positive_listens/np.maximum(c.positive_listens,1)],1).astype(np.float32)\nm=tr.TwoTower(len(items),torch.from_numpy(f)); m.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict'])\nh=tr.add_preference_history(pd.read_parquet(O/'history_train.parquet'),D/'validation_preference_state.parquet',tr.TRAIN_END)\ns,w,st=tr.histories(h,t,mp,tr.TRAIN_END)\ntr.rank(m,t,s,w,st,D/'validation_eligible_candidates.npz',O/'validation_globalblend.parquet')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_globalblend.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/app/solution')\nimport train_ranker as tr\nfrom pathlib import Path\nimport pandas as pd, numpy as np, torch\nD=Path('/task/data'); O=Path('/app/solution')\nt=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); c=pd.read_parquet(D/'candidate_catalog.parquet'); items=c.item_id.to_numpy(np.int64); mp={int(x):i for i,x in enumerate(items)}\nf=np.stack([np.log1p(c.positive_listens)/np.log1p(c.positive_listens.max()),c.organic_positive_listens/np.maximum(c.positive_listens,1)],1).astype(np.float32)\nm=tr.TwoTower(len(items),torch.from_numpy(f)); m.load_state_dict(torch.load(O/'model_validation.pt',weights_only=False)['state_dict'])\nh=tr.add_preference_history(pd.read_parquet(O/'history_train.parquet'),D/'validation_preference_state.parquet',tr.TRAIN_END)\ns,w,st=tr.histories(h,t,mp,tr.TRAIN_END)\ntr.rank(m,t,s,w,st,D/'validation_eligible_candidates.npz',O/'validation_globalblend.parquet')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_globalblend.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_globalblend.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020483,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_globalblend.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020483,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 818815132 }, "formatted_output": "{\n \"model\": \"solution/validation_globalblend.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020483,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-25T01:59:52.521Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:52.521Z", "text": "" } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-25T01:59:54.059Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:59:54.059Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:59:55.252Z", "text": "The exact all-candidate dot-product ranking reproduces the calibrated public score: NDCG@10 = 0.020483 over 4,263 scored users. The final refit and submission are complete; I’m now running the private-stage-equivalent structural checks (user coverage, ranks, duplicates, and test eligibility) and documenting the reproducible command." }, { "kind": "tool", "timestamp": "2026-09-25T01:59:58.292Z", "callId": "call_rDlR7JROFFbwpAEX4ALW8cFy", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\nt=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet(p),t); e=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\\nprint({'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':len(bad),'duplicates':int(f.duplicated(['uid','item_id']).sum()),'bytes':p.stat().st_size})\\nprint(f.head(12).to_string(index=False))\\nPY\\nls -lh solution/model_final.pt solution/submission.parquet solution/validation_globalblend.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:00:01.019Z", "content": "Script completed\nWall time 2.7 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'bytes': 1655698}\n uid item_id rank\n 100 4954011 1\n 100 237151 2\n 100 2621516 3\n 100 2736766 4\n 100 6511826 5\n 100 8250570 6\n 100 1758776 7\n 100 2055777 8\n 100 5927996 9\n 100 7958357 10\n 100 2408923 11\n 100 5488609 12\n-rw-r--r-- 1 agent agent 12M Sep 25 01:58 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:58 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:59 solution/validation_globalblend.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:59:58.368Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d58c44b087d2bdd2118168835a34", "usage": { "input_tokens": 51381, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 955, "reasoning_output_tokens": 602, "total_tokens": 52336 }, "turn_token_usage": { "input_tokens": 3085294, "cached_input_tokens": 3031168, "cache_write_input_tokens": 0, "output_tokens": 24631, "reasoning_output_tokens": 8710, "total_tokens": 3109925 }, "thread_token_usage": { "input_tokens": 3085294, "cached_input_tokens": 3031168, "cache_write_input_tokens": 0, "output_tokens": 24631, "reasoning_output_tokens": 8710, "total_tokens": 3109925 } } }, { "kind": "context", "timestamp": "2026-09-25T02:00:01.014Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4da42e49-fb30-4f6d-bcec-9e2f5e298538", "process_id": "33961", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nt=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet(p),t); e=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\nprint({'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':len(bad),'duplicates':int(f.duplicated(['uid','item_id']).sum()),'bytes':p.stat().st_size})\nprint(f.head(12).to_string(index=False))\nPY\nls -lh solution/model_final.pt solution/submission.parquet solution/validation_globalblend.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nt=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet(p),t); e=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\nprint({'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':len(bad),'duplicates':int(f.duplicated(['uid','item_id']).sum()),'bytes':p.stat().st_size})\nprint(f.head(12).to_string(index=False))\nPY\nls -lh solution/model_final.pt solution/submission.parquet solution/validation_globalblend.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'bytes': 1655698}\n uid item_id rank\n 100 4954011 1\n 100 237151 2\n 100 2621516 3\n 100 2736766 4\n 100 6511826 5\n 100 8250570 6\n 100 1758776 7\n 100 2055777 8\n 100 5927996 9\n 100 7958357 10\n 100 2408923 11\n 100 5488609 12\n-rw-r--r-- 1 agent agent 12M Sep 25 01:58 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:58 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:59 solution/validation_globalblend.parquet\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'bytes': 1655698}\n uid item_id rank\n 100 4954011 1\n 100 237151 2\n 100 2621516 3\n 100 2736766 4\n 100 6511826 5\n 100 8250570 6\n 100 1758776 7\n 100 2055777 8\n 100 5927996 9\n 100 7958357 10\n 100 2408923 11\n 100 5488609 12\n-rw-r--r-- 1 agent agent 12M Sep 25 01:58 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:58 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:59 solution/validation_globalblend.parquet\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 559866575 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'bytes': 1655698}\n uid item_id rank\n 100 4954011 1\n 100 237151 2\n 100 2621516 3\n 100 2736766 4\n 100 6511826 5\n 100 8250570 6\n 100 1758776 7\n 100 2055777 8\n 100 5927996 9\n 100 7958357 10\n 100 2408923 11\n 100 5488609 12\n-rw-r--r-- 1 agent agent 12M Sep 25 01:58 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:58 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 01:59 solution/validation_globalblend.parquet\n" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-25T02:00:07.743Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:00:07.743Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:00:20.215Z", "callId": "call_9q5NpJf0yurO4TeXETibCc05", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- total+=float(loss); steps+=1\\n+ total+=float(loss.detach()); steps+=1\\n*** Add File: /app/solution/README.md\\n+# History-based PyTorch two-tower ranker\\n+\\n+`submission.parquet` is the final test-cutoff ranking. It has 845,600 rows\\n+(100 for each of 8,456 target users), integer ranks 1–100, no repeated item per\\n+user, and zero violations against `test_eligible_candidates.npz`.\\n+\\n+## Model\\n+\\n+The model has no user-ID embedding and never constructs or decomposes a global\\n+user-item matrix. Preprocessing streams event rows and retains per-user sparse\\n+histories and sparse future-positive lists.\\n+\\n+The user tower embeds up to 400 history tracks into 48 dimensions, pools them\\n+with count, organic-intent, and recency weights, concatenates two activity\\n+statistics, and applies an MLP `50 -> 96 -> 64` with ReLU and L2 normalization.\\n+The item tower concatenates a trainable 48-D item embedding with normalized\\n+log-popularity and organic-share features, then applies a separate\\n+`50 -> 96 -> 64` MLP and L2 normalization. A calibrated coordinate is appended\\n+to both outputs: `1` on the user side and `0.1 * normalized_log_popularity` on\\n+the item side. Thus the serving vectors are both 65-D and the complete score is\\n+exactly their dot product. Item vectors are independent of users and can be\\n+precomputed in a MIPS index; request-time work is one bounded user-tower pass.\\n+\\n+Training uses sampled pairwise logistic (BPR) loss. For each history-derived\\n+user example it samples four organic future positives (organic likes receive\\n+3x sampling weight) and 96 catalog negatives. Model selection uses a temporal\\n+pseudo-window wholly inside `train_*`; validation inference uses only training\\n+history plus the supplied validation-cutoff preference state. The final model\\n+is refit with both the pseudo-window and now-observed validation transition,\\n+then uses `train_* + val_*` history and test-cutoff preference state.\\n+\\n+## Validation\\n+\\n+The exact all-candidate 65-D dot-product ranking in\\n+`validation_globalblend.parquet` scores **0.020483 NDCG@10** on 4,263 public\\n+quality users. The catalog-popularity reference scored 0.010710.\\n+\\n+## Reproduce the final submission\\n+\\n+From `/app`:\\n+\\n+```sh\\n+OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\\\\n+ /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\\n+```\\n+\\n+This writes `solution/model_final.pt` and `solution/submission.parquet`.\\n+Intermediate preprocessing caches are confined to `solution/`.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T02:00:20.335Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:00:20.223Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-17e2aad0-2d1b-41d0-a366-38cf54d55201", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -186,3 +186,3 @@\n opt.zero_grad(); loss.backward(); nn.utils.clip_grad_norm_(model.parameters(),5.0); opt.step()\n- total+=float(loss); steps+=1\n+ total+=float(loss.detach()); steps+=1\n print(f'epoch {epoch+1:02d} loss={total/max(steps,1):.5f} scale={float(model.scale()):.2f}',flush=True)\n", "move_path": null }, "/app/solution/README.md": { "type": "add", "content": "# History-based PyTorch two-tower ranker\n\n`submission.parquet` is the final test-cutoff ranking. It has 845,600 rows\n(100 for each of 8,456 target users), integer ranks 1–100, no repeated item per\nuser, and zero violations against `test_eligible_candidates.npz`.\n\n## Model\n\nThe model has no user-ID embedding and never constructs or decomposes a global\nuser-item matrix. Preprocessing streams event rows and retains per-user sparse\nhistories and sparse future-positive lists.\n\nThe user tower embeds up to 400 history tracks into 48 dimensions, pools them\nwith count, organic-intent, and recency weights, concatenates two activity\nstatistics, and applies an MLP `50 -> 96 -> 64` with ReLU and L2 normalization.\nThe item tower concatenates a trainable 48-D item embedding with normalized\nlog-popularity and organic-share features, then applies a separate\n`50 -> 96 -> 64` MLP and L2 normalization. A calibrated coordinate is appended\nto both outputs: `1` on the user side and `0.1 * normalized_log_popularity` on\nthe item side. Thus the serving vectors are both 65-D and the complete score is\nexactly their dot product. Item vectors are independent of users and can be\nprecomputed in a MIPS index; request-time work is one bounded user-tower pass.\n\nTraining uses sampled pairwise logistic (BPR) loss. For each history-derived\nuser example it samples four organic future positives (organic likes receive\n3x sampling weight) and 96 catalog negatives. Model selection uses a temporal\npseudo-window wholly inside `train_*`; validation inference uses only training\nhistory plus the supplied validation-cutoff preference state. The final model\nis refit with both the pseudo-window and now-observed validation transition,\nthen uses `train_* + val_*` history and test-cutoff preference state.\n\n## Validation\n\nThe exact all-candidate 65-D dot-product ranking in\n`validation_globalblend.parquet` scores **0.020483 NDCG@10** on 4,263 public\nquality users. The catalog-popularity reference scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`:\n\n```sh\nOMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\n```\n\nThis writes `solution/model_final.pt` and `solution/submission.parquet`.\nIntermediate preprocessing caches are confined to `solution/`.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T02:00:20.334Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d5a2e21887d280410372b883fdd1", "usage": { "input_tokens": 52618, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 850, "reasoning_output_tokens": 154, "total_tokens": 53468 }, "turn_token_usage": { "input_tokens": 3137912, "cached_input_tokens": 3082368, "cache_write_input_tokens": 0, "output_tokens": 25481, "reasoning_output_tokens": 8864, "total_tokens": 3163393 }, "thread_token_usage": { "input_tokens": 3137912, "cached_input_tokens": 3082368, "cache_write_input_tokens": 0, "output_tokens": 25481, "reasoning_output_tokens": 8864, "total_tokens": 3163393 } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-25T02:00:28.174Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:00:28.174Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T02:00:30.326Z", "callId": "call_0YGkSIMX8K0XVvT0LrpA0Di2", "name": "exec", "input": { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/explore_baselines.py solution/tune_blends.py solution/tune_top100.py && find solution -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":8000}); text(r.output);\n" }, "result": { "timestamp": "2026-09-25T02:00:30.498Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\nREADME.md 2309 bytes\nexplore_baselines.py 3404 bytes\nhistory_pseudo.parquet 44445511 bytes\nhistory_train.parquet 45809381 bytes\nhistory_train_val.parquet 47295058 bytes\nhistory_val.parquet 2875162 bytes\nlabels_pseudo.parquet 209876 bytes\nlabels_validation_refit.parquet 201696 bytes\nmetrics_final.json 38 bytes\nmetrics_validation.json 38 bytes\nmodel_final.pt 12127495 bytes\nmodel_validation.pt 12127585 bytes\nsubmission.parquet 1655698 bytes\ntrain_catalog_history.parquet 45809381 bytes\ntrain_ranker.py 14775 bytes\ntune_blends.py 2303 bytes\ntune_top100.py 1713 bytes\nvalidation.parquet 1564394 bytes\nvalidation_globalblend.parquet 1530024 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_repeat_count.parquet 728599 bytes\nvalidation_repeat_recent.parquet 728599 bytes\nvalidation_topblend_0.01.parquet 1564400 bytes\nvalidation_topblend_0.025.parquet 1564385 bytes\nvalidation_topblend_0.05.parquet 1564381 bytes\nvalidation_topblend_0.1.parquet 1564394 bytes\nvalidation_topblend_0.2.parquet 1564400 bytes\n# History-based PyTorch two-tower ranker\n\n`submission.parquet` is the final test-cutoff ranking. It has 845,600 rows\n(100 for each of 8,456 target users), integer ranks 1–100, no repeated item per\nuser, and zero violations against `test_eligible_candidates.npz`.\n\n## Model\n\nThe model has no user-ID embedding and never constructs or decomposes a global\nuser-item matrix. Preprocessing streams event rows and retains per-user sparse\nhistories and sparse future-positive lists.\n\nThe user tower embeds up to 400 history tracks into 48 dimensions, pools them\nwith count, organic-intent, and recency weights, concatenates two activity\nstatistics, and applies an MLP `50 -> 96 -> 64` with ReLU and L2 normalization.\nThe item tower concatenates a trainable 48-D item embedding with normalized\nlog-popularity and organic-share features, then applies a separate\n`50 -> 96 -> 64` MLP and L2 normalization. A calibrated coordinate is appended\nto both outputs: `1` on the user side and `0.1 * normalized_log_popularity` on\nthe item side. Thus the serving vectors are both 65-D and the complete score is\nexactly their dot product. Item vectors are independent of users and can be\nprecomputed in a MIPS index; request-time work is one bounded user-tower pass.\n\nTraining uses sampled pairwise logistic (BPR) loss. For each history-derived\nuser example it samples four organic future positives (organic likes receive\n3x sampling weight) and 96 catalog negatives. Model selection uses a temporal\npseudo-window wholly inside `train_*`; validation inference uses only training\nhistory plus the supplied validation-cutoff preference state. The final model\nis refit with both the pseudo-window and now-observed validation transition,\nthen uses `train_* + val_*` history and test-cutoff preference state.\n\n## Validation\n\nThe exact all-candidate 65-D dot-product ranking in\n`validation_globalblend.parquet` scores **0.020483 NDCG@10** on 4,263 public\nquality users. The catalog-popularity reference scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`:\n\n```sh\nOMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\n```\n\nThis writes `solution/model_final.pt` and `solution/submission.parquet`.\nIntermediate preprocessing caches are confined to `solution/`.\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T02:00:30.395Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d5b4a21c87d2ba6df87837477143", "usage": { "input_tokens": 53492, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 351, "reasoning_output_tokens": 232, "total_tokens": 53843 }, "turn_token_usage": { "input_tokens": 3191404, "cached_input_tokens": 3134848, "cache_write_input_tokens": 0, "output_tokens": 25832, "reasoning_output_tokens": 9096, "total_tokens": 3217236 }, "thread_token_usage": { "input_tokens": 3191404, "cached_input_tokens": 3134848, "cache_write_input_tokens": 0, "output_tokens": 25832, "reasoning_output_tokens": 9096, "total_tokens": 3217236 } } }, { "kind": "context", "timestamp": "2026-09-25T02:00:30.493Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3417034e-bbaa-43d9-a38c-0970a4dc8e92", "process_id": "67218", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/explore_baselines.py solution/tune_blends.py solution/tune_top100.py && find solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,240p' solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/explore_baselines.py solution/tune_blends.py solution/tune_top100.py && find solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,240p' solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "README.md 2309 bytes\nexplore_baselines.py 3404 bytes\nhistory_pseudo.parquet 44445511 bytes\nhistory_train.parquet 45809381 bytes\nhistory_train_val.parquet 47295058 bytes\nhistory_val.parquet 2875162 bytes\nlabels_pseudo.parquet 209876 bytes\nlabels_validation_refit.parquet 201696 bytes\nmetrics_final.json 38 bytes\nmetrics_validation.json 38 bytes\nmodel_final.pt 12127495 bytes\nmodel_validation.pt 12127585 bytes\nsubmission.parquet 1655698 bytes\ntrain_catalog_history.parquet 45809381 bytes\ntrain_ranker.py 14775 bytes\ntune_blends.py 2303 bytes\ntune_top100.py 1713 bytes\nvalidation.parquet 1564394 bytes\nvalidation_globalblend.parquet 1530024 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_repeat_count.parquet 728599 bytes\nvalidation_repeat_recent.parquet 728599 bytes\nvalidation_topblend_0.01.parquet 1564400 bytes\nvalidation_topblend_0.025.parquet 1564385 bytes\nvalidation_topblend_0.05.parquet 1564381 bytes\nvalidation_topblend_0.1.parquet 1564394 bytes\nvalidation_topblend_0.2.parquet 1564400 bytes\n# History-based PyTorch two-tower ranker\n\n`submission.parquet` is the final test-cutoff ranking. It has 845,600 rows\n(100 for each of 8,456 target users), integer ranks 1–100, no repeated item per\nuser, and zero violations against `test_eligible_candidates.npz`.\n\n## Model\n\nThe model has no user-ID embedding and never constructs or decomposes a global\nuser-item matrix. Preprocessing streams event rows and retains per-user sparse\nhistories and sparse future-positive lists.\n\nThe user tower embeds up to 400 history tracks into 48 dimensions, pools them\nwith count, organic-intent, and recency weights, concatenates two activity\nstatistics, and applies an MLP `50 -> 96 -> 64` with ReLU and L2 normalization.\nThe item tower concatenates a trainable 48-D item embedding with normalized\nlog-popularity and organic-share features, then applies a separate\n`50 -> 96 -> 64` MLP and L2 normalization. A calibrated coordinate is appended\nto both outputs: `1` on the user side and `0.1 * normalized_log_popularity` on\nthe item side. Thus the serving vectors are both 65-D and the complete score is\nexactly their dot product. Item vectors are independent of users and can be\nprecomputed in a MIPS index; request-time work is one bounded user-tower pass.\n\nTraining uses sampled pairwise logistic (BPR) loss. For each history-derived\nuser example it samples four organic future positives (organic likes receive\n3x sampling weight) and 96 catalog negatives. Model selection uses a temporal\npseudo-window wholly inside `train_*`; validation inference uses only training\nhistory plus the supplied validation-cutoff preference state. The final model\nis refit with both the pseudo-window and now-observed validation transition,\nthen uses `train_* + val_*` history and test-cutoff preference state.\n\n## Validation\n\nThe exact all-candidate 65-D dot-product ranking in\n`validation_globalblend.parquet` scores **0.020483 NDCG@10** on 4,263 public\nquality users. The catalog-popularity reference scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`:\n\n```sh\nOMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\n```\n\nThis writes `solution/model_final.pt` and `solution/submission.parquet`.\nIntermediate preprocessing caches are confined to `solution/`.\n", "stderr": "", "aggregated_output": "README.md 2309 bytes\nexplore_baselines.py 3404 bytes\nhistory_pseudo.parquet 44445511 bytes\nhistory_train.parquet 45809381 bytes\nhistory_train_val.parquet 47295058 bytes\nhistory_val.parquet 2875162 bytes\nlabels_pseudo.parquet 209876 bytes\nlabels_validation_refit.parquet 201696 bytes\nmetrics_final.json 38 bytes\nmetrics_validation.json 38 bytes\nmodel_final.pt 12127495 bytes\nmodel_validation.pt 12127585 bytes\nsubmission.parquet 1655698 bytes\ntrain_catalog_history.parquet 45809381 bytes\ntrain_ranker.py 14775 bytes\ntune_blends.py 2303 bytes\ntune_top100.py 1713 bytes\nvalidation.parquet 1564394 bytes\nvalidation_globalblend.parquet 1530024 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_repeat_count.parquet 728599 bytes\nvalidation_repeat_recent.parquet 728599 bytes\nvalidation_topblend_0.01.parquet 1564400 bytes\nvalidation_topblend_0.025.parquet 1564385 bytes\nvalidation_topblend_0.05.parquet 1564381 bytes\nvalidation_topblend_0.1.parquet 1564394 bytes\nvalidation_topblend_0.2.parquet 1564400 bytes\n# History-based PyTorch two-tower ranker\n\n`submission.parquet` is the final test-cutoff ranking. It has 845,600 rows\n(100 for each of 8,456 target users), integer ranks 1–100, no repeated item per\nuser, and zero violations against `test_eligible_candidates.npz`.\n\n## Model\n\nThe model has no user-ID embedding and never constructs or decomposes a global\nuser-item matrix. Preprocessing streams event rows and retains per-user sparse\nhistories and sparse future-positive lists.\n\nThe user tower embeds up to 400 history tracks into 48 dimensions, pools them\nwith count, organic-intent, and recency weights, concatenates two activity\nstatistics, and applies an MLP `50 -> 96 -> 64` with ReLU and L2 normalization.\nThe item tower concatenates a trainable 48-D item embedding with normalized\nlog-popularity and organic-share features, then applies a separate\n`50 -> 96 -> 64` MLP and L2 normalization. A calibrated coordinate is appended\nto both outputs: `1` on the user side and `0.1 * normalized_log_popularity` on\nthe item side. Thus the serving vectors are both 65-D and the complete score is\nexactly their dot product. Item vectors are independent of users and can be\nprecomputed in a MIPS index; request-time work is one bounded user-tower pass.\n\nTraining uses sampled pairwise logistic (BPR) loss. For each history-derived\nuser example it samples four organic future positives (organic likes receive\n3x sampling weight) and 96 catalog negatives. Model selection uses a temporal\npseudo-window wholly inside `train_*`; validation inference uses only training\nhistory plus the supplied validation-cutoff preference state. The final model\nis refit with both the pseudo-window and now-observed validation transition,\nthen uses `train_* + val_*` history and test-cutoff preference state.\n\n## Validation\n\nThe exact all-candidate 65-D dot-product ranking in\n`validation_globalblend.parquet` scores **0.020483 NDCG@10** on 4,263 public\nquality users. The catalog-popularity reference scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`:\n\n```sh\nOMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\n```\n\nThis writes `solution/model_final.pt` and `solution/submission.parquet`.\nIntermediate preprocessing caches are confined to `solution/`.\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4570 }, "formatted_output": "README.md 2309 bytes\nexplore_baselines.py 3404 bytes\nhistory_pseudo.parquet 44445511 bytes\nhistory_train.parquet 45809381 bytes\nhistory_train_val.parquet 47295058 bytes\nhistory_val.parquet 2875162 bytes\nlabels_pseudo.parquet 209876 bytes\nlabels_validation_refit.parquet 201696 bytes\nmetrics_final.json 38 bytes\nmetrics_validation.json 38 bytes\nmodel_final.pt 12127495 bytes\nmodel_validation.pt 12127585 bytes\nsubmission.parquet 1655698 bytes\ntrain_catalog_history.parquet 45809381 bytes\ntrain_ranker.py 14775 bytes\ntune_blends.py 2303 bytes\ntune_top100.py 1713 bytes\nvalidation.parquet 1564394 bytes\nvalidation_globalblend.parquet 1530024 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_repeat_count.parquet 728599 bytes\nvalidation_repeat_recent.parquet 728599 bytes\nvalidation_topblend_0.01.parquet 1564400 bytes\nvalidation_topblend_0.025.parquet 1564385 bytes\nvalidation_topblend_0.05.parquet 1564381 bytes\nvalidation_topblend_0.1.parquet 1564394 bytes\nvalidation_topblend_0.2.parquet 1564400 bytes\n# History-based PyTorch two-tower ranker\n\n`submission.parquet` is the final test-cutoff ranking. It has 845,600 rows\n(100 for each of 8,456 target users), integer ranks 1–100, no repeated item per\nuser, and zero violations against `test_eligible_candidates.npz`.\n\n## Model\n\nThe model has no user-ID embedding and never constructs or decomposes a global\nuser-item matrix. Preprocessing streams event rows and retains per-user sparse\nhistories and sparse future-positive lists.\n\nThe user tower embeds up to 400 history tracks into 48 dimensions, pools them\nwith count, organic-intent, and recency weights, concatenates two activity\nstatistics, and applies an MLP `50 -> 96 -> 64` with ReLU and L2 normalization.\nThe item tower concatenates a trainable 48-D item embedding with normalized\nlog-popularity and organic-share features, then applies a separate\n`50 -> 96 -> 64` MLP and L2 normalization. A calibrated coordinate is appended\nto both outputs: `1` on the user side and `0.1 * normalized_log_popularity` on\nthe item side. Thus the serving vectors are both 65-D and the complete score is\nexactly their dot product. Item vectors are independent of users and can be\nprecomputed in a MIPS index; request-time work is one bounded user-tower pass.\n\nTraining uses sampled pairwise logistic (BPR) loss. For each history-derived\nuser example it samples four organic future positives (organic likes receive\n3x sampling weight) and 96 catalog negatives. Model selection uses a temporal\npseudo-window wholly inside `train_*`; validation inference uses only training\nhistory plus the supplied validation-cutoff preference state. The final model\nis refit with both the pseudo-window and now-observed validation transition,\nthen uses `train_* + val_*` history and test-cutoff preference state.\n\n## Validation\n\nThe exact all-candidate 65-D dot-product ranking in\n`validation_globalblend.parquet` scores **0.020483 NDCG@10** on 4,263 public\nquality users. The catalog-popularity reference scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`:\n\n```sh\nOMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 18\n```\n\nThis writes `solution/model_final.pt` and `solution/submission.parquet`.\nIntermediate preprocessing caches are confined to `solution/`.\n" } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-25T02:00:34.457Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T02:00:34.457Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T02:00:37.155Z", "text": "Completed the CPU PyTorch two-tower ranker.\n\n- Final artifact: [submission.parquet](/app/solution/submission.parquet)\n- Training/inference code: [train_ranker.py](/app/solution/train_ranker.py)\n- Documentation: [README.md](/app/solution/README.md)\n- Final model: [model_final.pt](/app/solution/model_final.pt)\n- Public validation NDCG@10: **0.020483**\n- Submission: **845,600 rows**, 8,456 users, 100 unique eligible items per user, zero structural or eligibility violations.\n- Serving score is exactly a dot product between independently generated 65-dimensional user and item vectors." }, { "kind": "context", "timestamp": "2026-09-25T02:00:37.215Z", "data": { "type": "token_usage_record", "thread_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "session_id": "01a0d62d-3c3a-7571-950b-b50337da6ace", "root_turn_id": "01a0d62d-3c49-78e1-8bdc-81a811f00a25", "response_id": "resp_0cbd4d915e8492f3016ab5d5bec98087d2831870831cedf4a5", "usage": { "input_tokens": 54692, "cached_input_tokens": 53376, "cache_write_input_tokens": 0, "output_tokens": 212, "reasoning_output_tokens": 61, "total_tokens": 54904 }, "turn_token_usage": { "input_tokens": 3246096, "cached_input_tokens": 3188224, "cache_write_input_tokens": 0, "output_tokens": 26044, "reasoning_output_tokens": 9157, "total_tokens": 3272140 }, "thread_token_usage": { "input_tokens": 3246096, "cached_input_tokens": 3188224, "cache_write_input_tokens": 0, "output_tokens": 26044, "reasoning_output_tokens": 9157, "total_tokens": 3272140 } } } ] } ] }