{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "sol-max-0-153-4-retriever-r02-20260924T221413Z", "taskChecksum": "2d9d67db2be97473211bca2fcf89083c8c667b091790c3ee1556a5fea455a86a", "source": { "format": "codex-rollout", "sha256": "6fa4c75e71eba3a35787b7cac1801621aa32739d41f9e7f990a20239f25b50c9", "records": 728, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 121, "item_completed:AgentMessage": 22, "token_count": 97, "task_complete": 1 } }, "sessionId": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-24T23:00:35.808Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:35.808Z", "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": "2026-09-24T23:00:39.126Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:39.126Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-24T23:00:39.126Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-24T23:00:39.126Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-24T23:00:39.126Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:39.126Z", "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": "2026-09-24T23:00:39.127Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:39.127Z", "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": "2026-09-24T23:00:39.127Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:39.127Z", "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": "2026-09-24T23:00:39.127Z", "text": "\n /app\n sh\n 2026-09-24\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-24T23:00:39.128Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-24", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } }, { "kind": "context", "timestamp": "2026-09-24T23:00:39.129Z", "data": { "type": "turn_context", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-24", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-24T23:00:39.147Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:39.147Z", "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": "2026-09-24T23:00:46.761Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:00:46.761Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:00:48.024Z", "text": "I’ll inspect the dataset shape, schemas, eligibility density, and evaluator first, then build a scalable event-sampled two-tower baseline and iterate against public NDCG while keeping every artifact under `/app/solution/`. I’ll preserve the best valid ranking as soon as one is available." }, { "kind": "tool", "timestamp": "2026-09-24T23:00:49.937Z", "callId": "call_mpjABCgnvI2htJXScgx7Cx0N", "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/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,180p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:00:50.008Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\n/task/ranking_evaluation.py\n/task/requirements.txt\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_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_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/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:00:49.970Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ab97689087d28fb0d5ff8bfefef5", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 254, "reasoning_output_tokens": 73, "total_tokens": 14830 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 254, "reasoning_output_tokens": 73, "total_tokens": 14830 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 254, "reasoning_output_tokens": 73, "total_tokens": 14830 } } }, { "kind": "context", "timestamp": "2026-09-24T23:00:50.002Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2b636e3f-fccc-4dbd-9160-c30851567f76", "process_id": "35402", "command": [ "/bin/sh", "-lc", "pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,180p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,180p' /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\n/task/ranking_evaluation.py\n/task/requirements.txt\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_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_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/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "/app\n/task/ranking_evaluation.py\n/task/requirements.txt\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_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_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/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4511 }, "formatted_output": "/app\n/task/ranking_evaluation.py\n/task/requirements.txt\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_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_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/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-24T23:00:56.102Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:00:56.102Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:01.652Z", "callId": "call_o48XZeZQAaxKrdDumn3Bch5m", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(f'{p.name:40s} rows={pf.metadata.num_rows:,} groups={pf.metadata.num_row_groups} size={p.stat().st_size/1e6:.1f}MB schema={pf.schema_arrow.names}')\\n x=pd.read_parquet(p).head(3)\\n print(x.to_dict('records'))\\nfor p in sorted(D.glob('*.npz')):\\n with np.load(p) as z:\\n print(p.name, {k:(z[k].shape,str(z[k].dtype)) for k in z.files}, 'sizeMB',p.stat().st_size/1e6)\\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n counts=elig.sum(1)\\n print(' candidates min/mean/median/max',counts.min(),counts.mean(),np.median(counts),counts.max(),'density',elig.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:01.788Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:01.694Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5aba249bc87d29c32f7e986fd94ec", "usage": { "input_tokens": 18966, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 76, "total_tokens": 19359 }, "turn_token_usage": { "input_tokens": 33542, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 647, "reasoning_output_tokens": 149, "total_tokens": 34189 }, "thread_token_usage": { "input_tokens": 33542, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 647, "reasoning_output_tokens": 149, "total_tokens": 34189 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:01.783Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a205203d-ca96-4027-8d01-3a437904396e", "process_id": "86434", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(f'{p.name:40s} rows={pf.metadata.num_rows:,} groups={pf.metadata.num_row_groups} size={p.stat().st_size/1e6:.1f}MB schema={pf.schema_arrow.names}')\n x=pd.read_parquet(p).head(3)\n print(x.to_dict('records'))\nfor p in sorted(D.glob('*.npz')):\n with np.load(p) as z:\n print(p.name, {k:(z[k].shape,str(z[k].dtype)) for k in z.files}, 'sizeMB',p.stat().st_size/1e6)\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=elig.sum(1)\n print(' candidates min/mean/median/max',counts.min(),counts.mean(),np.median(counts),counts.max(),'density',elig.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(f'{p.name:40s} rows={pf.metadata.num_rows:,} groups={pf.metadata.num_row_groups} size={p.stat().st_size/1e6:.1f}MB schema={pf.schema_arrow.names}')\n x=pd.read_parquet(p).head(3)\n print(x.to_dict('records'))\nfor p in sorted(D.glob('*.npz')):\n with np.load(p) as z:\n print(p.name, {k:(z[k].shape,str(z[k].dtype)) for k in z.files}, 'sizeMB',p.stat().st_size/1e6)\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=elig.sum(1)\n print(' candidates min/mean/median/max',counts.min(),counts.mean(),np.median(counts),counts.max(),'density',elig.mean())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4870 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-24T23:01:09.833Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:09.833Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:12.008Z", "callId": "call_G2cEl1OrSjvWLs56kVNpLLEf", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"which -a python python3 || true\\nls -la /app /task | sed -n '1,160p'\\nfind / -maxdepth 4 -type f \\\\( -name 'python' -o -name 'python3' \\\\) 2>/dev/null | sed -n '1,80p'\\nenv | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:12.106Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 22:56 solution\n\n/task:\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root 0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCODEX_THREAD_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=5121eeb485b9\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:12.051Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5abae02cc87d285a06b714c3cde03", "usage": { "input_tokens": 19415, "cached_input_tokens": 18816, "cache_write_input_tokens": 0, "output_tokens": 159, "reasoning_output_tokens": 29, "total_tokens": 19574 }, "turn_token_usage": { "input_tokens": 52957, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 806, "reasoning_output_tokens": 178, "total_tokens": 53763 }, "thread_token_usage": { "input_tokens": 52957, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 806, "reasoning_output_tokens": 178, "total_tokens": 53763 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:12.101Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-04f61568-4947-48eb-baa6-1abe8b452cbb", "process_id": "11560", "command": [ "/bin/sh", "-lc", "which -a python python3 || true\nls -la /app /task | sed -n '1,160p'\nfind / -maxdepth 4 -type f \\( -name 'python' -o -name 'python3' \\) 2>/dev/null | sed -n '1,80p'\nenv | sort | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3 || true\nls -la /app /task | sed -n '1,160p'\nfind / -maxdepth 4 -type f \\( -name 'python' -o -name 'python3' \\) 2>/dev/null | sed -n '1,80p'\nenv | sort | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 22:56 solution\n\n/task:\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root 0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCODEX_THREAD_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=5121eeb485b9\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 22:56 solution\n\n/task:\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root 0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCODEX_THREAD_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=5121eeb485b9\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2400 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 24 22:56 solution\n\n/task:\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndrwxr-xr-x 1 root root 4096 Sep 24 23:00 ..\n-r--r--r-- 1 root root 0 Sep 24 22:42 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 22:43 data\n-r--r--r-- 1 root root 5774 Sep 24 22:42 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 22:42 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 22:42 requirements.txt\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCODEX_THREAD_ID=01a0d5a6-394b-7651-808b-02bcfc8d9573\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=5121eeb485b9\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-24T23:01:18.349Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:18.349Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:21.003Z", "callId": "call_VUZmH0ny4z42j42PY3Xw8bUf", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find / -maxdepth 5 -type d \\\\( -name 'site-packages' -o -name '.venv' -o -name 'venv' \\\\) 2>/dev/null | sed -n '1,120p'\\n/usr/local/bin/python -m pip list | sed -n '1,120p'\\n/usr/bin/python3 - <<'PY'\\ntry:\\n import pandas; print('system pandas',pandas.__version__)\\nexcept Exception as e: print(e)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:21.960Z", "content": "Script completed\nWall time 1.0 seconds\nOutput:\n\n/usr/lib/python3.11/venv\n/usr/local/lib/python3.12/venv\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nPackage Version\n------- -------\npip 25.0.1\nNo module named 'pandas'\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:21.053Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5abb85ab087d2a1723aca392b77d6", "usage": { "input_tokens": 20312, "cached_input_tokens": 19200, "cache_write_input_tokens": 0, "output_tokens": 172, "reasoning_output_tokens": 15, "total_tokens": 20484 }, "turn_token_usage": { "input_tokens": 73269, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 978, "reasoning_output_tokens": 193, "total_tokens": 74247 }, "thread_token_usage": { "input_tokens": 73269, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 978, "reasoning_output_tokens": 193, "total_tokens": 74247 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:21.956Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d673687f-f648-4f36-bbf3-378d695a2234", "process_id": "73224", "command": [ "/bin/sh", "-lc", "find / -maxdepth 5 -type d \\( -name 'site-packages' -o -name '.venv' -o -name 'venv' \\) 2>/dev/null | sed -n '1,120p'\n/usr/local/bin/python -m pip list | sed -n '1,120p'\n/usr/bin/python3 - <<'PY'\ntry:\n import pandas; print('system pandas',pandas.__version__)\nexcept Exception as e: print(e)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -maxdepth 5 -type d \\( -name 'site-packages' -o -name '.venv' -o -name 'venv' \\) 2>/dev/null | sed -n '1,120p'\n/usr/local/bin/python -m pip list | sed -n '1,120p'\n/usr/bin/python3 - <<'PY'\ntry:\n import pandas; print('system pandas',pandas.__version__)\nexcept Exception as e: print(e)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/lib/python3.11/venv\n/usr/local/lib/python3.12/venv\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nPackage Version\n------- -------\npip 25.0.1\nNo module named 'pandas'\n", "stderr": "", "aggregated_output": "/usr/lib/python3.11/venv\n/usr/local/lib/python3.12/venv\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nPackage Version\n------- -------\npip 25.0.1\nNo module named 'pandas'\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 788881345 }, "formatted_output": "/usr/lib/python3.11/venv\n/usr/local/lib/python3.12/venv\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nPackage Version\n------- -------\npip 25.0.1\nNo module named 'pandas'\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-24T23:01:28.728Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:28.728Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:34.369Z", "callId": "call_y9d43HAaFCwxNLo3vMjpQlNH", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(f'{p.name:40s} rows={pf.metadata.num_rows:,} groups={pf.metadata.num_row_groups} size={p.stat().st_size/1e6:.1f}MB schema={pf.schema_arrow.names}')\\n x=pd.read_parquet(p).head(2)\\n print(x.to_dict('records'))\\nfor p in sorted(D.glob('*.npz')):\\n with np.load(p) as z:\\n print(p.name, {k:(z[k].shape,str(z[k].dtype)) for k in z.files}, 'sizeMB',p.stat().st_size/1e6)\\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n counts=elig.sum(1)\\n print(' candidates min/mean/median/max',counts.min(),counts.mean(),np.median(counts),counts.max(),'density',elig.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:37.768Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\ncandidate_catalog.parquet rows=30,000 groups=1 size=0.3MB schema=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n[{'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}]\ntarget_users.parquet rows=8,456 groups=1 size=0.1MB schema=['uid', 'recent_positive_listens', 'history_positive_listens']\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\ntest_preference_state.parquet rows=484,776 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntrain_dislikes.parquet rows=101,075 groups=1 size=0.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\ntrain_likes.parquet rows=814,831 groups=1 size=5.4MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\ntrain_listens.parquet rows=43,484,159 groups=42 size=319.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\ntrain_undislikes.parquet rows=19,960 groups=1 size=0.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\ntrain_unlikes.parquet rows=292,665 groups=1 size=1.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nval_dislikes.parquet rows=3,563 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nval_likes.parquet rows=37,795 groups=1 size=0.3MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nval_listens.parquet rows=1,501,697 groups=2 size=9.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nval_undislikes.parquet rows=606 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nval_unlikes.parquet rows=9,346 groups=1 size=0.1MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nvalidation_preference_state.parquet rows=469,124 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.736341\n candidates min/mean/median/max 24552 29041.3178807947 29220.0 30000 density 0.9680439293598234\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.573821\n candidates min/mean/median/max 24657 29067.306291390727 29242.0 30000 density 0.9689102097130243\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:34.418Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5abc242f887d2b9e10c4eb2c03175", "usage": { "input_tokens": 20575, "cached_input_tokens": 20096, "cache_write_input_tokens": 0, "output_tokens": 337, "reasoning_output_tokens": 15, "total_tokens": 20912 }, "turn_token_usage": { "input_tokens": 93844, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 1315, "reasoning_output_tokens": 208, "total_tokens": 95159 }, "thread_token_usage": { "input_tokens": 93844, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 1315, "reasoning_output_tokens": 208, "total_tokens": 95159 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:37.764Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-63042ce9-b578-4100-b29a-e2728678504d", "process_id": "12164", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(f'{p.name:40s} rows={pf.metadata.num_rows:,} groups={pf.metadata.num_row_groups} size={p.stat().st_size/1e6:.1f}MB schema={pf.schema_arrow.names}')\n x=pd.read_parquet(p).head(2)\n print(x.to_dict('records'))\nfor p in sorted(D.glob('*.npz')):\n with np.load(p) as z:\n print(p.name, {k:(z[k].shape,str(z[k].dtype)) for k in z.files}, 'sizeMB',p.stat().st_size/1e6)\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=elig.sum(1)\n print(' candidates min/mean/median/max',counts.min(),counts.mean(),np.median(counts),counts.max(),'density',elig.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(f'{p.name:40s} rows={pf.metadata.num_rows:,} groups={pf.metadata.num_row_groups} size={p.stat().st_size/1e6:.1f}MB schema={pf.schema_arrow.names}')\n x=pd.read_parquet(p).head(2)\n print(x.to_dict('records'))\nfor p in sorted(D.glob('*.npz')):\n with np.load(p) as z:\n print(p.name, {k:(z[k].shape,str(z[k].dtype)) for k in z.files}, 'sizeMB',p.stat().st_size/1e6)\n elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=elig.sum(1)\n print(' candidates min/mean/median/max',counts.min(),counts.mean(),np.median(counts),counts.max(),'density',elig.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet rows=30,000 groups=1 size=0.3MB schema=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n[{'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}]\ntarget_users.parquet rows=8,456 groups=1 size=0.1MB schema=['uid', 'recent_positive_listens', 'history_positive_listens']\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\ntest_preference_state.parquet rows=484,776 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntrain_dislikes.parquet rows=101,075 groups=1 size=0.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\ntrain_likes.parquet rows=814,831 groups=1 size=5.4MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\ntrain_listens.parquet rows=43,484,159 groups=42 size=319.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\ntrain_undislikes.parquet rows=19,960 groups=1 size=0.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\ntrain_unlikes.parquet rows=292,665 groups=1 size=1.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nval_dislikes.parquet rows=3,563 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nval_likes.parquet rows=37,795 groups=1 size=0.3MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nval_listens.parquet rows=1,501,697 groups=2 size=9.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nval_undislikes.parquet rows=606 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nval_unlikes.parquet rows=9,346 groups=1 size=0.1MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nvalidation_preference_state.parquet rows=469,124 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.736341\n candidates min/mean/median/max 24552 29041.3178807947 29220.0 30000 density 0.9680439293598234\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.573821\n candidates min/mean/median/max 24657 29067.306291390727 29242.0 30000 density 0.9689102097130243\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet rows=30,000 groups=1 size=0.3MB schema=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n[{'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}]\ntarget_users.parquet rows=8,456 groups=1 size=0.1MB schema=['uid', 'recent_positive_listens', 'history_positive_listens']\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\ntest_preference_state.parquet rows=484,776 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntrain_dislikes.parquet rows=101,075 groups=1 size=0.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\ntrain_likes.parquet rows=814,831 groups=1 size=5.4MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\ntrain_listens.parquet rows=43,484,159 groups=42 size=319.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\ntrain_undislikes.parquet rows=19,960 groups=1 size=0.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\ntrain_unlikes.parquet rows=292,665 groups=1 size=1.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nval_dislikes.parquet rows=3,563 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nval_likes.parquet rows=37,795 groups=1 size=0.3MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nval_listens.parquet rows=1,501,697 groups=2 size=9.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nval_undislikes.parquet rows=606 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nval_unlikes.parquet rows=9,346 groups=1 size=0.1MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nvalidation_preference_state.parquet rows=469,124 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.736341\n candidates min/mean/median/max 24552 29041.3178807947 29220.0 30000 density 0.9680439293598234\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.573821\n candidates min/mean/median/max 24657 29067.306291390727 29242.0 30000 density 0.9689102097130243\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 229903657 }, "formatted_output": "candidate_catalog.parquet rows=30,000 groups=1 size=0.3MB schema=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n[{'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}]\ntarget_users.parquet rows=8,456 groups=1 size=0.1MB schema=['uid', 'recent_positive_listens', 'history_positive_listens']\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\ntest_preference_state.parquet rows=484,776 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntrain_dislikes.parquet rows=101,075 groups=1 size=0.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\ntrain_likes.parquet rows=814,831 groups=1 size=5.4MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\ntrain_listens.parquet rows=43,484,159 groups=42 size=319.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\ntrain_undislikes.parquet rows=19,960 groups=1 size=0.2MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\ntrain_unlikes.parquet rows=292,665 groups=1 size=1.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nval_dislikes.parquet rows=3,563 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nval_likes.parquet rows=37,795 groups=1 size=0.3MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nval_listens.parquet rows=1,501,697 groups=2 size=9.9MB schema=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nval_undislikes.parquet rows=606 groups=1 size=0.0MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nval_unlikes.parquet rows=9,346 groups=1 size=0.1MB schema=['uid', 'timestamp', 'item_id', 'is_organic']\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nvalidation_preference_state.parquet rows=469,124 groups=1 size=1.1MB schema=['uid', 'item_id', 'state']\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.736341\n candidates min/mean/median/max 24552 29041.3178807947 29220.0 30000 density 0.9680439293598234\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} sizeMB 7.573821\n candidates min/mean/median/max 24657 29067.306291390727 29242.0 30000 density 0.9689102097130243\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-24T23:01:50.755Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:50.755Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:01:52.029Z", "text": "The benchmark has 8,456 users, 30,000 catalog items, and 43.5M training listens. Eligibility is broad (~97% of catalog), so quality—not candidate filtering—is the main challenge. I’m now measuring how much validation relevance comes from repeats, active likes, recency, and popularity; that will determine the history encoder and sampling weights before spending CPU on training." }, { "kind": "tool", "timestamp": "2026-09-24T23:02:05.590Z", "callId": "call_9VdRXuAhCAT4i6tmAsUHUWk7", "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, pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\nt=pd.read_parquet(D/'target_users.parquet'); us=set(t.uid.astype(int))\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nprint('targets stats',t.describe().to_dict())\\nprint('uid range',t.uid.min(),t.uid.max(),'catalog id range',cat.item_id.min(),cat.item_id.max())\\n# val label summary\\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\\nwith np.load(D/'validation_eligible_candidates.npz') as z:\\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; ui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(items)}\\ndef elig(df):\\n rows=np.fromiter((ui.get(x,-1) for x in df.uid),dtype=np.int32,count=len(df)); cols=np.fromiter((ii.get(x,-1) for x in df.item_id),dtype=np.int32,count=len(df)); ok=(rows>=0)&(cols>=0); inds=np.flatnonzero(ok); ok[inds]=((packed[rows[inds],cols[inds]//8]>>(cols[inds]%8))&1).astype(bool); return ok\\nstrong=vl[(vl.uid.isin(us))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\\nlikes=vk[(vk.uid.isin(us))&(vk.is_organic==1)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\\nstrong=strong[elig(strong)]; likes=likes[elig(likes)]\\nrel=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nprint('val events range',vl.timestamp.min(),vl.timestamp.max(),'truth rows',len(rel),'users',rel.uid.nunique(),'grades',rel.grade.value_counts().to_dict(),'items',rel.item_id.nunique())\\nprint('rels/user',rel.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\\nrr=rel.merge(cat[['item_id','popularity_rank','positive_listens','organic_positive_listens']],on='item_id',how='left')\\nprint('rel catalog coverage',rr.popularity_rank.notna().mean(),'pop ranks',rr.popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\\nprint('top popular relevance fraction thresholds', {n:float((rr.popularity_rank<=n).mean()) for n in [10,50,100,500,1000,5000,10000,30000]})\\nprint('rel timestamp day counts',((rel.timestamp-24804800)//86400).value_counts().sort_index().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:02:05.634Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/bin/sh: 1: free: not found\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:05.627Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5abd207dc87d287331ee6bf8cda67", "usage": { "input_tokens": 22547, "cached_input_tokens": 20352, "cache_write_input_tokens": 0, "output_tokens": 1136, "reasoning_output_tokens": 249, "total_tokens": 23683 }, "turn_token_usage": { "input_tokens": 116391, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 2451, "reasoning_output_tokens": 457, "total_tokens": 118842 }, "thread_token_usage": { "input_tokens": 116391, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 2451, "reasoning_output_tokens": 457, "total_tokens": 118842 } } }, { "kind": "context", "timestamp": "2026-09-24T23:02:05.629Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-205d4672-96bc-463e-8486-e1db4d9756e3", "process_id": "19544", "command": [ "/bin/sh", "-lc", "free -h && df -h /app && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nt=pd.read_parquet(D/'target_users.parquet'); us=set(t.uid.astype(int))\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets stats',t.describe().to_dict())\nprint('uid range',t.uid.min(),t.uid.max(),'catalog id range',cat.item_id.min(),cat.item_id.max())\n# val label summary\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; ui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(items)}\ndef elig(df):\n rows=np.fromiter((ui.get(x,-1) for x in df.uid),dtype=np.int32,count=len(df)); cols=np.fromiter((ii.get(x,-1) for x in df.item_id),dtype=np.int32,count=len(df)); ok=(rows>=0)&(cols>=0); inds=np.flatnonzero(ok); ok[inds]=((packed[rows[inds],cols[inds]//8]>>(cols[inds]%8))&1).astype(bool); return ok\nstrong=vl[(vl.uid.isin(us))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nlikes=vk[(vk.uid.isin(us))&(vk.is_organic==1)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nstrong=strong[elig(strong)]; likes=likes[elig(likes)]\nrel=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('val events range',vl.timestamp.min(),vl.timestamp.max(),'truth rows',len(rel),'users',rel.uid.nunique(),'grades',rel.grade.value_counts().to_dict(),'items',rel.item_id.nunique())\nprint('rels/user',rel.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nrr=rel.merge(cat[['item_id','popularity_rank','positive_listens','organic_positive_listens']],on='item_id',how='left')\nprint('rel catalog coverage',rr.popularity_rank.notna().mean(),'pop ranks',rr.popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nprint('top popular relevance fraction thresholds', {n:float((rr.popularity_rank<=n).mean()) for n in [10,50,100,500,1000,5000,10000,30000]})\nprint('rel timestamp day counts',((rel.timestamp-24804800)//86400).value_counts().sort_index().to_dict())\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, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nt=pd.read_parquet(D/'target_users.parquet'); us=set(t.uid.astype(int))\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets stats',t.describe().to_dict())\nprint('uid range',t.uid.min(),t.uid.max(),'catalog id range',cat.item_id.min(),cat.item_id.max())\n# val label summary\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; ui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(items)}\ndef elig(df):\n rows=np.fromiter((ui.get(x,-1) for x in df.uid),dtype=np.int32,count=len(df)); cols=np.fromiter((ii.get(x,-1) for x in df.item_id),dtype=np.int32,count=len(df)); ok=(rows>=0)&(cols>=0); inds=np.flatnonzero(ok); ok[inds]=((packed[rows[inds],cols[inds]//8]>>(cols[inds]%8))&1).astype(bool); return ok\nstrong=vl[(vl.uid.isin(us))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nlikes=vk[(vk.uid.isin(us))&(vk.is_organic==1)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nstrong=strong[elig(strong)]; likes=likes[elig(likes)]\nrel=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('val events range',vl.timestamp.min(),vl.timestamp.max(),'truth rows',len(rel),'users',rel.uid.nunique(),'grades',rel.grade.value_counts().to_dict(),'items',rel.item_id.nunique())\nprint('rels/user',rel.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nrr=rel.merge(cat[['item_id','popularity_rank','positive_listens','organic_positive_listens']],on='item_id',how='left')\nprint('rel catalog coverage',rr.popularity_rank.notna().mean(),'pop ranks',rr.popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nprint('top popular relevance fraction thresholds', {n:float((rr.popularity_rank<=n).mean()) for n in [10,50,100,500,1000,5000,10000,30000]})\nprint('rel timestamp day counts',((rel.timestamp-24804800)//86400).value_counts().sort_index().to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: free: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: free: not found\n", "exit_code": 127, "duration": { "secs": 0, "nanos": 3710 }, "formatted_output": "/bin/sh: 1: free: not found\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-24T23:02:11.960Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:02:11.960Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:02:26.209Z", "callId": "call_wgtza7GT1SsaPQqrvoXti7ic", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,8p' /proc/meminfo\\ndf -h /app\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nt=pd.read_parquet(D/'target_users.parquet'); us=set(t.uid.astype(int))\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nprint('targets stats',t.describe().to_dict())\\nprint('uid range',t.uid.min(),t.uid.max(),'catalog id range',cat.item_id.min(),cat.item_id.max())\\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\\nwith np.load(D/'validation_eligible_candidates.npz') as z:\\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; ui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(items)}\\ndef elig(df):\\n rows=np.fromiter((ui.get(x,-1) for x in df.uid),dtype=np.int32,count=len(df)); cols=np.fromiter((ii.get(x,-1) for x in df.item_id),dtype=np.int32,count=len(df)); ok=(rows>=0)&(cols>=0); inds=np.flatnonzero(ok); ok[inds]=((packed[rows[inds],cols[inds]//8]>>(cols[inds]%8))&1).astype(bool); return ok\\nstrong=vl[(vl.uid.isin(us))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\\nlikes=vk[(vk.uid.isin(us))&(vk.is_organic==1)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\\nstrong=strong[elig(strong)]; likes=likes[elig(likes)]\\nrel=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nprint('val events range',vl.timestamp.min(),vl.timestamp.max(),'truth rows',len(rel),'users',rel.uid.nunique(),'grades',rel.grade.value_counts().to_dict(),'items',rel.item_id.nunique())\\nprint('rels/user',rel.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\\nrr=rel.merge(cat[['item_id','popularity_rank','positive_listens','organic_positive_listens']],on='item_id',how='left')\\nprint('rel catalog coverage',rr.popularity_rank.notna().mean(),'pop ranks',rr.popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\\nprint('top popular relevance fraction thresholds', {n:float((rr.popularity_rank<=n).mean()) for n in [10,50,100,500,1000,5000,10000,30000]})\\nprint('rel timestamp day counts',((rel.timestamp-24804800)//86400).value_counts().sort_index().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:02:28.349Z", "content": "Script completed\nWall time 2.1 seconds\nOutput:\n\nMemTotal: 15988964 kB\nMemFree: 2027992 kB\nMemAvailable: 14965924 kB\nBuffers: 270488 kB\nCached: 12238796 kB\nSwapCached: 0 kB\nActive: 491544 kB\nInactive: 12315972 kB\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\ntargets stats {'uid': {'count': 8456.0, 'mean': 502289.1083254494, 'std': 287953.3959120005, 'min': 100.0, '25%': 254375.0, '50%': 503850.0, '75%': 751725.0, 'max': 1000000.0}, 'recent_positive_listens': {'count': 8456.0, 'mean': 1251.0767502365184, 'std': 1414.3409035684056, 'min': 20.0, '25%': 281.0, '50%': 784.5, '75%': 1706.25, 'max': 19592.0}, 'history_positive_listens': {'count': 8456.0, 'mean': 3236.440752128666, 'std': 3697.5752855873343, 'min': 20.0, '25%': 630.0, '50%': 1920.5, '75%': 4494.25, 'max': 26597.0}}\nuid range 100 1000000 catalog id range 206 9390294\nval events range 24788600 25393395 truth rows 37769 users 4263 grades {1: 33352, 3: 4417} items 14124\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\nrel catalog coverage 1.0 pop ranks {'count': 37769.0, 'mean': 8463.087743916969, 'std': 8117.953262366348, 'min': 1.0, '10%': 428.0, '25%': 1733.0, '50%': 5622.0, '75%': 13374.0, '90%': 21680.200000000004, '95%': 25693.599999999984, '99%': 29101.0, 'max': 30000.0}\ntop popular relevance fraction thresholds {10: 0.005030580634912229, 50: 0.017924753104397787, 100: 0.031877995181233285, 500: 0.11130821573247902, 1000: 0.17535545023696683, 5000: 0.4705975800259472, 10000: 0.6592708305753395, 30000: 1.0}\nrel timestamp day counts {0: 5921, 1: 5266, 2: 5237, 3: 4658, 4: 4863, 5: 6095, 6: 5461, 49710: 268}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:26.239Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5abede67087d2af65ef54d1291ec3", "usage": { "input_tokens": 23717, "cached_input_tokens": 22400, "cache_write_input_tokens": 0, "output_tokens": 816, "reasoning_output_tokens": 16, "total_tokens": 24533 }, "turn_token_usage": { "input_tokens": 140108, "cached_input_tokens": 126336, "cache_write_input_tokens": 0, "output_tokens": 3267, "reasoning_output_tokens": 473, "total_tokens": 143375 }, "thread_token_usage": { "input_tokens": 140108, "cached_input_tokens": 126336, "cache_write_input_tokens": 0, "output_tokens": 3267, "reasoning_output_tokens": 473, "total_tokens": 143375 } } }, { "kind": "context", "timestamp": "2026-09-24T23:02:28.345Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb4dc4c5-3670-4508-881b-d297d6fbf82d", "process_id": "30479", "command": [ "/bin/sh", "-lc", "sed -n '1,8p' /proc/meminfo\ndf -h /app\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nt=pd.read_parquet(D/'target_users.parquet'); us=set(t.uid.astype(int))\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets stats',t.describe().to_dict())\nprint('uid range',t.uid.min(),t.uid.max(),'catalog id range',cat.item_id.min(),cat.item_id.max())\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; ui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(items)}\ndef elig(df):\n rows=np.fromiter((ui.get(x,-1) for x in df.uid),dtype=np.int32,count=len(df)); cols=np.fromiter((ii.get(x,-1) for x in df.item_id),dtype=np.int32,count=len(df)); ok=(rows>=0)&(cols>=0); inds=np.flatnonzero(ok); ok[inds]=((packed[rows[inds],cols[inds]//8]>>(cols[inds]%8))&1).astype(bool); return ok\nstrong=vl[(vl.uid.isin(us))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nlikes=vk[(vk.uid.isin(us))&(vk.is_organic==1)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nstrong=strong[elig(strong)]; likes=likes[elig(likes)]\nrel=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('val events range',vl.timestamp.min(),vl.timestamp.max(),'truth rows',len(rel),'users',rel.uid.nunique(),'grades',rel.grade.value_counts().to_dict(),'items',rel.item_id.nunique())\nprint('rels/user',rel.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nrr=rel.merge(cat[['item_id','popularity_rank','positive_listens','organic_positive_listens']],on='item_id',how='left')\nprint('rel catalog coverage',rr.popularity_rank.notna().mean(),'pop ranks',rr.popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nprint('top popular relevance fraction thresholds', {n:float((rr.popularity_rank<=n).mean()) for n in [10,50,100,500,1000,5000,10000,30000]})\nprint('rel timestamp day counts',((rel.timestamp-24804800)//86400).value_counts().sort_index().to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,8p' /proc/meminfo\ndf -h /app\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nt=pd.read_parquet(D/'target_users.parquet'); us=set(t.uid.astype(int))\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets stats',t.describe().to_dict())\nprint('uid range',t.uid.min(),t.uid.max(),'catalog id range',cat.item_id.min(),cat.item_id.max())\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic','timestamp'])\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; ui={x:i for i,x in enumerate(u)}; ii={x:i for i,x in enumerate(items)}\ndef elig(df):\n rows=np.fromiter((ui.get(x,-1) for x in df.uid),dtype=np.int32,count=len(df)); cols=np.fromiter((ii.get(x,-1) for x in df.item_id),dtype=np.int32,count=len(df)); ok=(rows>=0)&(cols>=0); inds=np.flatnonzero(ok); ok[inds]=((packed[rows[inds],cols[inds]//8]>>(cols[inds]%8))&1).astype(bool); return ok\nstrong=vl[(vl.uid.isin(us))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nlikes=vk[(vk.uid.isin(us))&(vk.is_organic==1)][['uid','item_id','timestamp']].drop_duplicates(['uid','item_id'])\nstrong=strong[elig(strong)]; likes=likes[elig(likes)]\nrel=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('val events range',vl.timestamp.min(),vl.timestamp.max(),'truth rows',len(rel),'users',rel.uid.nunique(),'grades',rel.grade.value_counts().to_dict(),'items',rel.item_id.nunique())\nprint('rels/user',rel.groupby('uid').size().describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nrr=rel.merge(cat[['item_id','popularity_rank','positive_listens','organic_positive_listens']],on='item_id',how='left')\nprint('rel catalog coverage',rr.popularity_rank.notna().mean(),'pop ranks',rr.popularity_rank.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nprint('top popular relevance fraction thresholds', {n:float((rr.popularity_rank<=n).mean()) for n in [10,50,100,500,1000,5000,10000,30000]})\nprint('rel timestamp day counts',((rel.timestamp-24804800)//86400).value_counts().sort_index().to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "targets stats {'uid': {'count': 8456.0, 'mean': 502289.1083254494, 'std': 287953.3959120005, 'min': 100.0, '25%': 254375.0, '50%': 503850.0, '75%': 751725.0, 'max': 1000000.0}, 'recent_positive_listens': {'count': 8456.0, 'mean': 1251.0767502365184, 'std': 1414.3409035684056, 'min': 20.0, '25%': 281.0, '50%': 784.5, '75%': 1706.25, 'max': 19592.0}, 'history_positive_listens': {'count': 8456.0, 'mean': 3236.440752128666, 'std': 3697.5752855873343, 'min': 20.0, '25%': 630.0, '50%': 1920.5, '75%': 4494.25, 'max': 26597.0}}\nuid range 100 1000000 catalog id range 206 9390294\nval events range 24788600 25393395 truth rows 37769 users 4263 grades {1: 33352, 3: 4417} items 14124\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\nrel catalog coverage 1.0 pop ranks {'count': 37769.0, 'mean': 8463.087743916969, 'std': 8117.953262366348, 'min': 1.0, '10%': 428.0, '25%': 1733.0, '50%': 5622.0, '75%': 13374.0, '90%': 21680.200000000004, '95%': 25693.599999999984, '99%': 29101.0, 'max': 30000.0}\ntop popular relevance fraction thresholds {10: 0.005030580634912229, 50: 0.017924753104397787, 100: 0.031877995181233285, 500: 0.11130821573247902, 1000: 0.17535545023696683, 5000: 0.4705975800259472, 10000: 0.6592708305753395, 30000: 1.0}\nrel timestamp day counts {0: 5921, 1: 5266, 2: 5237, 3: 4658, 4: 4863, 5: 6095, 6: 5461, 49710: 268}\n", "stderr": "", "aggregated_output": "targets stats {'uid': {'count': 8456.0, 'mean': 502289.1083254494, 'std': 287953.3959120005, 'min': 100.0, '25%': 254375.0, '50%': 503850.0, '75%': 751725.0, 'max': 1000000.0}, 'recent_positive_listens': {'count': 8456.0, 'mean': 1251.0767502365184, 'std': 1414.3409035684056, 'min': 20.0, '25%': 281.0, '50%': 784.5, '75%': 1706.25, 'max': 19592.0}, 'history_positive_listens': {'count': 8456.0, 'mean': 3236.440752128666, 'std': 3697.5752855873343, 'min': 20.0, '25%': 630.0, '50%': 1920.5, '75%': 4494.25, 'max': 26597.0}}\nuid range 100 1000000 catalog id range 206 9390294\nval events range 24788600 25393395 truth rows 37769 users 4263 grades {1: 33352, 3: 4417} items 14124\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\nrel catalog coverage 1.0 pop ranks {'count': 37769.0, 'mean': 8463.087743916969, 'std': 8117.953262366348, 'min': 1.0, '10%': 428.0, '25%': 1733.0, '50%': 5622.0, '75%': 13374.0, '90%': 21680.200000000004, '95%': 25693.599999999984, '99%': 29101.0, 'max': 30000.0}\ntop popular relevance fraction thresholds {10: 0.005030580634912229, 50: 0.017924753104397787, 100: 0.031877995181233285, 500: 0.11130821573247902, 1000: 0.17535545023696683, 5000: 0.4705975800259472, 10000: 0.6592708305753395, 30000: 1.0}\nrel timestamp day counts {0: 5921, 1: 5266, 2: 5237, 3: 4658, 4: 4863, 5: 6095, 6: 5461, 49710: 268}\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 970063668 }, "formatted_output": "targets stats {'uid': {'count': 8456.0, 'mean': 502289.1083254494, 'std': 287953.3959120005, 'min': 100.0, '25%': 254375.0, '50%': 503850.0, '75%': 751725.0, 'max': 1000000.0}, 'recent_positive_listens': {'count': 8456.0, 'mean': 1251.0767502365184, 'std': 1414.3409035684056, 'min': 20.0, '25%': 281.0, '50%': 784.5, '75%': 1706.25, 'max': 19592.0}, 'history_positive_listens': {'count': 8456.0, 'mean': 3236.440752128666, 'std': 3697.5752855873343, 'min': 20.0, '25%': 630.0, '50%': 1920.5, '75%': 4494.25, 'max': 26597.0}}\nuid range 100 1000000 catalog id range 206 9390294\nval events range 24788600 25393395 truth rows 37769 users 4263 grades {1: 33352, 3: 4417} items 14124\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '10%': 1.0, '25%': 2.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\nrel catalog coverage 1.0 pop ranks {'count': 37769.0, 'mean': 8463.087743916969, 'std': 8117.953262366348, 'min': 1.0, '10%': 428.0, '25%': 1733.0, '50%': 5622.0, '75%': 13374.0, '90%': 21680.200000000004, '95%': 25693.599999999984, '99%': 29101.0, 'max': 30000.0}\ntop popular relevance fraction thresholds {10: 0.005030580634912229, 50: 0.017924753104397787, 100: 0.031877995181233285, 500: 0.11130821573247902, 1000: 0.17535545023696683, 5000: 0.4705975800259472, 10000: 0.6592708305753395, 30000: 1.0}\nrel timestamp day counts {0: 5921, 1: 5266, 2: 5237, 3: 4658, 4: 4863, 5: 6095, 6: 5461, 49710: 268}\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-24T23:02:40.280Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:02:40.280Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:02:45.035Z", "callId": "call_lDtPvg5QPunDmXOmCwGR9qIF", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/usr/bin/time -v /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, time\\nfrom pathlib import Path\\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet',columns=['uid']).uid\\nst=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),x.memory_usage(deep=True).sum()/1e9,'GB',time.time()-st)\\nprint(x.dtypes, 'uids',x.uid.nunique(),'items',x.item_id.nunique(),'ranges',x.timestamp.min(),x.timestamp.max())\\nx=x[x.uid.isin(targets)]; print('target rows',len(x),x.memory_usage(deep=True).sum()/1e9,'uids',x.uid.nunique(),'items',x.item_id.nunique())\\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id)\\nprint('catalog fraction events',x.item_id.isin(cat).mean(),'distinct catalog listened',x[x.item_id.isin(cat)].item_id.nunique())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:02:45.075Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/bin/sh: 1: /usr/bin/time: not found\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:45.059Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ac04871887d2b026fc6d821d1dd1", "usage": { "input_tokens": 25398, "cached_input_tokens": 23552, "cache_write_input_tokens": 0, "output_tokens": 639, "reasoning_output_tokens": 337, "total_tokens": 26037 }, "turn_token_usage": { "input_tokens": 165506, "cached_input_tokens": 149888, "cache_write_input_tokens": 0, "output_tokens": 3906, "reasoning_output_tokens": 810, "total_tokens": 169412 }, "thread_token_usage": { "input_tokens": 165506, "cached_input_tokens": 149888, "cache_write_input_tokens": 0, "output_tokens": 3906, "reasoning_output_tokens": 810, "total_tokens": 169412 } } }, { "kind": "context", "timestamp": "2026-09-24T23:02:45.071Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5768c1e8-38e4-46fe-b9af-80da9075262f", "process_id": "83152", "command": [ "/bin/sh", "-lc", "/usr/bin/time -v /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet',columns=['uid']).uid\nst=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),x.memory_usage(deep=True).sum()/1e9,'GB',time.time()-st)\nprint(x.dtypes, 'uids',x.uid.nunique(),'items',x.item_id.nunique(),'ranges',x.timestamp.min(),x.timestamp.max())\nx=x[x.uid.isin(targets)]; print('target rows',len(x),x.memory_usage(deep=True).sum()/1e9,'uids',x.uid.nunique(),'items',x.item_id.nunique())\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id)\nprint('catalog fraction events',x.item_id.isin(cat).mean(),'distinct catalog listened',x[x.item_id.isin(cat)].item_id.nunique())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/usr/bin/time -v /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet',columns=['uid']).uid\nst=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),x.memory_usage(deep=True).sum()/1e9,'GB',time.time()-st)\nprint(x.dtypes, 'uids',x.uid.nunique(),'items',x.item_id.nunique(),'ranges',x.timestamp.min(),x.timestamp.max())\nx=x[x.uid.isin(targets)]; print('target rows',len(x),x.memory_usage(deep=True).sum()/1e9,'uids',x.uid.nunique(),'items',x.item_id.nunique())\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id)\nprint('catalog fraction events',x.item_id.isin(cat).mean(),'distinct catalog listened',x[x.item_id.isin(cat)].item_id.nunique())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: /usr/bin/time: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: /usr/bin/time: not found\n", "exit_code": 127, "duration": { "secs": 0, "nanos": 2300 }, "formatted_output": "/bin/sh: 1: /usr/bin/time: not found\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-24T23:02:51.765Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:02:51.765Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:02:57.344Z", "callId": "call_hmeUrX8ldvGYBRZlw1xrp1MA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, time, resource\\nfrom pathlib import Path\\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet',columns=['uid']).uid\\nst=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),x.memory_usage(deep=True).sum()/1e9,'GB',time.time()-st,'rssGB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1e6)\\nprint(x.dtypes, 'uids',x.uid.nunique(),'items',x.item_id.nunique(),'ranges',x.timestamp.min(),x.timestamp.max())\\nx=x[x.uid.isin(targets)]; print('target rows',len(x),x.memory_usage(deep=True).sum()/1e9,'uids',x.uid.nunique(),'items',x.item_id.nunique())\\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id)\\nprint('catalog fraction events',x.item_id.isin(cat).mean(),'distinct catalog listened',x[x.item_id.isin(cat)].item_id.nunique())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:03:08.831Z", "content": "Script completed\nWall time 11.5 seconds\nOutput:\n\nloaded 43484159 0.652262517 GB 0.44429492950439453 rssGB 1.500108\nuid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ndtype: object uids 9238 items 845395 ranges 0 24786795\ntarget rows 43222297 0.994112831 uids 8456 items 839582\ncatalog fraction events 0.7186693479062438 distinct catalog listened 30000\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:57.386Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ac1585ac87d295d077c1f6c0bf27", "usage": { "input_tokens": 26074, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 15, "total_tokens": 26408 }, "turn_token_usage": { "input_tokens": 191580, "cached_input_tokens": 175104, "cache_write_input_tokens": 0, "output_tokens": 4240, "reasoning_output_tokens": 825, "total_tokens": 195820 }, "thread_token_usage": { "input_tokens": 191580, "cached_input_tokens": 175104, "cache_write_input_tokens": 0, "output_tokens": 4240, "reasoning_output_tokens": 825, "total_tokens": 195820 } } }, { "kind": "context", "timestamp": "2026-09-24T23:03:08.821Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b8b21d4a-515e-497f-bbff-13e9882e3340", "process_id": "84425", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time, resource\nfrom pathlib import Path\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet',columns=['uid']).uid\nst=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),x.memory_usage(deep=True).sum()/1e9,'GB',time.time()-st,'rssGB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1e6)\nprint(x.dtypes, 'uids',x.uid.nunique(),'items',x.item_id.nunique(),'ranges',x.timestamp.min(),x.timestamp.max())\nx=x[x.uid.isin(targets)]; print('target rows',len(x),x.memory_usage(deep=True).sum()/1e9,'uids',x.uid.nunique(),'items',x.item_id.nunique())\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id)\nprint('catalog fraction events',x.item_id.isin(cat).mean(),'distinct catalog listened',x[x.item_id.isin(cat)].item_id.nunique())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time, resource\nfrom pathlib import Path\nD=Path('/task/data'); targets=pd.read_parquet(D/'target_users.parquet',columns=['uid']).uid\nst=time.time(); x=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('loaded',len(x),x.memory_usage(deep=True).sum()/1e9,'GB',time.time()-st,'rssGB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1e6)\nprint(x.dtypes, 'uids',x.uid.nunique(),'items',x.item_id.nunique(),'ranges',x.timestamp.min(),x.timestamp.max())\nx=x[x.uid.isin(targets)]; print('target rows',len(x),x.memory_usage(deep=True).sum()/1e9,'uids',x.uid.nunique(),'items',x.item_id.nunique())\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id)\nprint('catalog fraction events',x.item_id.isin(cat).mean(),'distinct catalog listened',x[x.item_id.isin(cat)].item_id.nunique())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 43484159 0.652262517 GB 0.44429492950439453 rssGB 1.500108\nuid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ndtype: object uids 9238 items 845395 ranges 0 24786795\ntarget rows 43222297 0.994112831 uids 8456 items 839582\ncatalog fraction events 0.7186693479062438 distinct catalog listened 30000\n", "stderr": "", "aggregated_output": "loaded 43484159 0.652262517 GB 0.44429492950439453 rssGB 1.500108\nuid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ndtype: object uids 9238 items 845395 ranges 0 24786795\ntarget rows 43222297 0.994112831 uids 8456 items 839582\ncatalog fraction events 0.7186693479062438 distinct catalog listened 30000\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 312206773 }, "formatted_output": "loaded 43484159 0.652262517 GB 0.44429492950439453 rssGB 1.500108\nuid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ndtype: object uids 9238 items 845395 ranges 0 24786795\ntarget rows 43222297 0.994112831 uids 8456 items 839582\ncatalog fraction events 0.7186693479062438 distinct catalog listened 30000\n" } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-24T23:03:26.808Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:03:26.808Z", "text": "" } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-24T23:03:37.727Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:03:37.727Z", "text": "" } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-24T23:03:42.231Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:03:42.231Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:00.235Z", "callId": "call_6DIvmdz652wx8shWGnEe17R4", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, time\\nfrom pathlib import Path\\nD=Path('/task/data'); CUT=24786800\\n# labels no need elig? derive like eval quickly and map eligibility\\nT=pd.read_parquet(D/'target_users.parquet'); us=T.uid.to_numpy(np.int64); uidpos={x:i for i,x in enumerate(us)}\\nC=pd.read_parquet(D/'candidate_catalog.parquet'); its=C.item_id.to_numpy(np.int64); ipos={x:i for i,x in enumerate(its)}\\nwith np.load(D/'validation_eligible_candidates.npz') as z: zu=z['uids']; zp=z['packed_eligible']\\n# same order likely\\nprint('elig uid same',np.array_equal(zu,us))\\ndef prep(df, grade):\\n ui=df.uid.map(uidpos).fillna(-1).to_numpy(np.int32); ii=df.item_id.map(ipos).fillna(-1).to_numpy(np.int32); ok=(ui>=0)&(ii>=0); ix=np.flatnonzero(ok); ok[ix]=((zp[ui[ix],ii[ix]//8]>>(ii[ix]%8))&1)>0\\n return pd.DataFrame({'uidx':ui[ok],'iidx':ii[ok],'uid':df.uid.to_numpy()[ok],'item_id':df.item_id.to_numpy()[ok],'grade':grade})\\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)].drop_duplicates(['uid','item_id']);\\nvk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[vk.is_organic==1].drop_duplicates(['uid','item_id']);\\nrel=pd.concat([prep(vl,1),prep(vk,3)]).sort_values('grade').drop_duplicates(['uidx','iidx'],keep='last'); rel['key']=rel.uidx.astype(np.int64)*30000+rel.iidx\\nrkeys=rel.key.to_numpy(); print('rel',len(rel),rel.uid.nunique())\\n# active pref overlaps\\np=pd.read_parquet(D/'validation_preference_state.parquet'); pui=p.uid.map(uidpos).fillna(-1).to_numpy(np.int32); pii=p.item_id.map(ipos).fillna(-1).to_numpy(np.int32); pk=pui.astype(np.int64)*30000+pii\\nfor state in ['liked','disliked']:\\n s=set(pk[(p.state.to_numpy()==state)&(pui>=0)&(pii>=0)])\\n print(state,'pairs',len(s),'rel overlap',np.isin(rkeys,np.fromiter(s,np.int64)).mean(),'grade3 overlap',np.isin(rel.loc[rel.grade==3,'key'],np.fromiter(s,np.int64)).mean())\\n# listens scan only relevant pair stats, and overall relevant historical threshold\\ncols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\\nx=pd.read_parquet(D/'train_listens.parquet',columns=cols)\\nui=x.uid.map(uidpos).fillna(-1).to_numpy(np.int32); ii=x.item_id.map(ipos).fillna(-1).to_numpy(np.int32); ok=(ui>=0)&(ii>=0); keys=ui.astype(np.int64)*30000+ii\\n# sorted rel and search\\norder=np.argsort(rkeys); sr=rkeys[order]; loc=np.searchsorted(sr,keys); match=ok&(loc=80).sum()),avg_ratio=('played_ratio_pct','mean')).reset_index()\\nr=rel.merge(agg,on='key',how='left');\\nprint('historical listen overlap',r.n.notna().mean(),'by grade',r.groupby('grade').n.apply(lambda x:x.notna().mean()).to_dict())\\nfor days in [1,3,7,14,30,60,90,180,287]:\\n print('within',days,'days',((CUT-r['last'])<=days*86400).fillna(False).mean())\\nprint('n distribution relevant',r.n.fillna(0).describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\\nprint('organic distribution relevant',r.organic.fillna(0).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\\n# likes historic exact events\\nlk=pd.read_parquet(D/'train_likes.parquet'); lui=lk.uid.map(uidpos).fillna(-1).to_numpy(np.int32); lii=lk.item_id.map(ipos).fillna(-1).to_numpy(np.int32); lkeys=lui.astype(np.int64)*30000+lii\\nprint('any prior like rel',np.isin(rkeys,np.unique(lkeys[(lui>=0)&(lii>=0)])).mean())\\n# eligibility exclusion overlap active prefs\\nfor state in ['liked','disliked']:\\n mask=(p.state.to_numpy()==state)&(pui>=0)&(pii>=0); eu=((zp[pui[mask],pii[mask]//8]>>(pii[mask]%8))&1)>0\\n print(state,'eligible rate',eu.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:04:05.930Z", "content": "Script completed\nWall time 5.7 seconds\nOutput:\n\nelig uid same True\nrel 37769 4263\nliked pairs 418435 rel overlap 0.0 grade3 overlap 0.0\ndisliked pairs 50689 rel overlap 0.0 grade3 overlap 0.0\nhistorical listen overlap 0.0 by grade {1: 0.0, 3: 0.0}\nwithin 1 days 0.0\nwithin 3 days 0.0\nwithin 7 days 0.0\nwithin 14 days 0.0\nwithin 30 days 0.0\nwithin 60 days 0.0\nwithin 90 days 0.0\nwithin 180 days 0.0\nwithin 287 days 0.0\nn distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '10%': 0.0, '25%': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\norganic distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\nany prior like rel 0.0\nliked eligible rate 0.0\ndisliked eligible rate 0.0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:00.280Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ac2d048c87d298da3b1ec39c07e7", "usage": { "input_tokens": 26551, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 2473, "reasoning_output_tokens": 1201, "total_tokens": 29024 }, "turn_token_usage": { "input_tokens": 218131, "cached_input_tokens": 200960, "cache_write_input_tokens": 0, "output_tokens": 6713, "reasoning_output_tokens": 2026, "total_tokens": 224844 }, "thread_token_usage": { "input_tokens": 218131, "cached_input_tokens": 200960, "cache_write_input_tokens": 0, "output_tokens": 6713, "reasoning_output_tokens": 2026, "total_tokens": 224844 } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:05.926Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31225cc1-66d1-4591-b750-0e3ad124a08c", "process_id": "61321", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); CUT=24786800\n# labels no need elig? derive like eval quickly and map eligibility\nT=pd.read_parquet(D/'target_users.parquet'); us=T.uid.to_numpy(np.int64); uidpos={x:i for i,x in enumerate(us)}\nC=pd.read_parquet(D/'candidate_catalog.parquet'); its=C.item_id.to_numpy(np.int64); ipos={x:i for i,x in enumerate(its)}\nwith np.load(D/'validation_eligible_candidates.npz') as z: zu=z['uids']; zp=z['packed_eligible']\n# same order likely\nprint('elig uid same',np.array_equal(zu,us))\ndef prep(df, grade):\n ui=df.uid.map(uidpos).fillna(-1).to_numpy(np.int32); ii=df.item_id.map(ipos).fillna(-1).to_numpy(np.int32); ok=(ui>=0)&(ii>=0); ix=np.flatnonzero(ok); ok[ix]=((zp[ui[ix],ii[ix]//8]>>(ii[ix]%8))&1)>0\n return pd.DataFrame({'uidx':ui[ok],'iidx':ii[ok],'uid':df.uid.to_numpy()[ok],'item_id':df.item_id.to_numpy()[ok],'grade':grade})\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)].drop_duplicates(['uid','item_id']);\nvk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[vk.is_organic==1].drop_duplicates(['uid','item_id']);\nrel=pd.concat([prep(vl,1),prep(vk,3)]).sort_values('grade').drop_duplicates(['uidx','iidx'],keep='last'); rel['key']=rel.uidx.astype(np.int64)*30000+rel.iidx\nrkeys=rel.key.to_numpy(); print('rel',len(rel),rel.uid.nunique())\n# active pref overlaps\np=pd.read_parquet(D/'validation_preference_state.parquet'); pui=p.uid.map(uidpos).fillna(-1).to_numpy(np.int32); pii=p.item_id.map(ipos).fillna(-1).to_numpy(np.int32); pk=pui.astype(np.int64)*30000+pii\nfor state in ['liked','disliked']:\n s=set(pk[(p.state.to_numpy()==state)&(pui>=0)&(pii>=0)])\n print(state,'pairs',len(s),'rel overlap',np.isin(rkeys,np.fromiter(s,np.int64)).mean(),'grade3 overlap',np.isin(rel.loc[rel.grade==3,'key'],np.fromiter(s,np.int64)).mean())\n# listens scan only relevant pair stats, and overall relevant historical threshold\ncols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\nx=pd.read_parquet(D/'train_listens.parquet',columns=cols)\nui=x.uid.map(uidpos).fillna(-1).to_numpy(np.int32); ii=x.item_id.map(ipos).fillna(-1).to_numpy(np.int32); ok=(ui>=0)&(ii>=0); keys=ui.astype(np.int64)*30000+ii\n# sorted rel and search\norder=np.argsort(rkeys); sr=rkeys[order]; loc=np.searchsorted(sr,keys); match=ok&(loc=80).sum()),avg_ratio=('played_ratio_pct','mean')).reset_index()\nr=rel.merge(agg,on='key',how='left');\nprint('historical listen overlap',r.n.notna().mean(),'by grade',r.groupby('grade').n.apply(lambda x:x.notna().mean()).to_dict())\nfor days in [1,3,7,14,30,60,90,180,287]:\n print('within',days,'days',((CUT-r['last'])<=days*86400).fillna(False).mean())\nprint('n distribution relevant',r.n.fillna(0).describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nprint('organic distribution relevant',r.organic.fillna(0).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\n# likes historic exact events\nlk=pd.read_parquet(D/'train_likes.parquet'); lui=lk.uid.map(uidpos).fillna(-1).to_numpy(np.int32); lii=lk.item_id.map(ipos).fillna(-1).to_numpy(np.int32); lkeys=lui.astype(np.int64)*30000+lii\nprint('any prior like rel',np.isin(rkeys,np.unique(lkeys[(lui>=0)&(lii>=0)])).mean())\n# eligibility exclusion overlap active prefs\nfor state in ['liked','disliked']:\n mask=(p.state.to_numpy()==state)&(pui>=0)&(pii>=0); eu=((zp[pui[mask],pii[mask]//8]>>(pii[mask]%8))&1)>0\n print(state,'eligible rate',eu.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data'); CUT=24786800\n# labels no need elig? derive like eval quickly and map eligibility\nT=pd.read_parquet(D/'target_users.parquet'); us=T.uid.to_numpy(np.int64); uidpos={x:i for i,x in enumerate(us)}\nC=pd.read_parquet(D/'candidate_catalog.parquet'); its=C.item_id.to_numpy(np.int64); ipos={x:i for i,x in enumerate(its)}\nwith np.load(D/'validation_eligible_candidates.npz') as z: zu=z['uids']; zp=z['packed_eligible']\n# same order likely\nprint('elig uid same',np.array_equal(zu,us))\ndef prep(df, grade):\n ui=df.uid.map(uidpos).fillna(-1).to_numpy(np.int32); ii=df.item_id.map(ipos).fillna(-1).to_numpy(np.int32); ok=(ui>=0)&(ii>=0); ix=np.flatnonzero(ok); ok[ix]=((zp[ui[ix],ii[ix]//8]>>(ii[ix]%8))&1)>0\n return pd.DataFrame({'uidx':ui[ok],'iidx':ii[ok],'uid':df.uid.to_numpy()[ok],'item_id':df.item_id.to_numpy()[ok],'grade':grade})\nvl=pd.read_parquet(D/'val_listens.parquet'); vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)].drop_duplicates(['uid','item_id']);\nvk=pd.read_parquet(D/'val_likes.parquet'); vk=vk[vk.is_organic==1].drop_duplicates(['uid','item_id']);\nrel=pd.concat([prep(vl,1),prep(vk,3)]).sort_values('grade').drop_duplicates(['uidx','iidx'],keep='last'); rel['key']=rel.uidx.astype(np.int64)*30000+rel.iidx\nrkeys=rel.key.to_numpy(); print('rel',len(rel),rel.uid.nunique())\n# active pref overlaps\np=pd.read_parquet(D/'validation_preference_state.parquet'); pui=p.uid.map(uidpos).fillna(-1).to_numpy(np.int32); pii=p.item_id.map(ipos).fillna(-1).to_numpy(np.int32); pk=pui.astype(np.int64)*30000+pii\nfor state in ['liked','disliked']:\n s=set(pk[(p.state.to_numpy()==state)&(pui>=0)&(pii>=0)])\n print(state,'pairs',len(s),'rel overlap',np.isin(rkeys,np.fromiter(s,np.int64)).mean(),'grade3 overlap',np.isin(rel.loc[rel.grade==3,'key'],np.fromiter(s,np.int64)).mean())\n# listens scan only relevant pair stats, and overall relevant historical threshold\ncols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\nx=pd.read_parquet(D/'train_listens.parquet',columns=cols)\nui=x.uid.map(uidpos).fillna(-1).to_numpy(np.int32); ii=x.item_id.map(ipos).fillna(-1).to_numpy(np.int32); ok=(ui>=0)&(ii>=0); keys=ui.astype(np.int64)*30000+ii\n# sorted rel and search\norder=np.argsort(rkeys); sr=rkeys[order]; loc=np.searchsorted(sr,keys); match=ok&(loc=80).sum()),avg_ratio=('played_ratio_pct','mean')).reset_index()\nr=rel.merge(agg,on='key',how='left');\nprint('historical listen overlap',r.n.notna().mean(),'by grade',r.groupby('grade').n.apply(lambda x:x.notna().mean()).to_dict())\nfor days in [1,3,7,14,30,60,90,180,287]:\n print('within',days,'days',((CUT-r['last'])<=days*86400).fillna(False).mean())\nprint('n distribution relevant',r.n.fillna(0).describe(percentiles=[.1,.25,.5,.75,.9,.95,.99]).to_dict())\nprint('organic distribution relevant',r.organic.fillna(0).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\n# likes historic exact events\nlk=pd.read_parquet(D/'train_likes.parquet'); lui=lk.uid.map(uidpos).fillna(-1).to_numpy(np.int32); lii=lk.item_id.map(ipos).fillna(-1).to_numpy(np.int32); lkeys=lui.astype(np.int64)*30000+lii\nprint('any prior like rel',np.isin(rkeys,np.unique(lkeys[(lui>=0)&(lii>=0)])).mean())\n# eligibility exclusion overlap active prefs\nfor state in ['liked','disliked']:\n mask=(p.state.to_numpy()==state)&(pui>=0)&(pii>=0); eu=((zp[pui[mask],pii[mask]//8]>>(pii[mask]%8))&1)>0\n print(state,'eligible rate',eu.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "elig uid same True\nrel 37769 4263\nliked pairs 418435 rel overlap 0.0 grade3 overlap 0.0\ndisliked pairs 50689 rel overlap 0.0 grade3 overlap 0.0\nhistorical listen overlap 0.0 by grade {1: 0.0, 3: 0.0}\nwithin 1 days 0.0\nwithin 3 days 0.0\nwithin 7 days 0.0\nwithin 14 days 0.0\nwithin 30 days 0.0\nwithin 60 days 0.0\nwithin 90 days 0.0\nwithin 180 days 0.0\nwithin 287 days 0.0\nn distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '10%': 0.0, '25%': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\norganic distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\nany prior like rel 0.0\nliked eligible rate 0.0\ndisliked eligible rate 0.0\n", "stderr": "", "aggregated_output": "elig uid same True\nrel 37769 4263\nliked pairs 418435 rel overlap 0.0 grade3 overlap 0.0\ndisliked pairs 50689 rel overlap 0.0 grade3 overlap 0.0\nhistorical listen overlap 0.0 by grade {1: 0.0, 3: 0.0}\nwithin 1 days 0.0\nwithin 3 days 0.0\nwithin 7 days 0.0\nwithin 14 days 0.0\nwithin 30 days 0.0\nwithin 60 days 0.0\nwithin 90 days 0.0\nwithin 180 days 0.0\nwithin 287 days 0.0\nn distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '10%': 0.0, '25%': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\norganic distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\nany prior like rel 0.0\nliked eligible rate 0.0\ndisliked eligible rate 0.0\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 525548387 }, "formatted_output": "elig uid same True\nrel 37769 4263\nliked pairs 418435 rel overlap 0.0 grade3 overlap 0.0\ndisliked pairs 50689 rel overlap 0.0 grade3 overlap 0.0\nhistorical listen overlap 0.0 by grade {1: 0.0, 3: 0.0}\nwithin 1 days 0.0\nwithin 3 days 0.0\nwithin 7 days 0.0\nwithin 14 days 0.0\nwithin 30 days 0.0\nwithin 60 days 0.0\nwithin 90 days 0.0\nwithin 180 days 0.0\nwithin 287 days 0.0\nn distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '10%': 0.0, '25%': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\norganic distribution relevant {'count': 37769.0, 'mean': 0.0, 'std': 0.0, 'min': 0.0, '50%': 0.0, '75%': 0.0, '90%': 0.0, '95%': 0.0, '99%': 0.0, 'max': 0.0}\nany prior like rel 0.0\nliked eligible rate 0.0\ndisliked eligible rate 0.0\n" } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-24T23:04:14.644Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:14.644Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:04:16.957Z", "text": "A key finding: eligibility excludes every previously listened, liked, or disliked user–item pair. So repeat/recency shortcuts score zero by construction. The useful signal is collaborative similarity—learning which unseen tracks fit a user’s historical taste—plus calibrated popularity. I’m saving a popularity fallback now, then training on earlier-cutoff → later-organic-event examples that mirror this discovery setup." }, { "kind": "tool", "timestamp": "2026-09-24T23:04:22.991Z", "callId": "call_hIxcCXzzB3eVP7dl2CYF92Yo", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/make_popularity_baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Create structurally valid popularity rankings for either cutoff.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"test\\\"), required=True)\\n+ parser.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = parser.parse_args()\\n+\\n+ data = Path(\\\"/task/data\\\")\\n+ archive = data / f\\\"{args.stage}_eligible_candidates.npz\\\"\\n+ with np.load(archive, allow_pickle=False) as artifact:\\n+ uids = artifact[\\\"uids\\\"].astype(np.int64)\\n+ item_ids = artifact[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = artifact[\\\"packed_eligible\\\"]\\n+\\n+ rows: list[tuple[int, int, int]] = []\\n+ # item_ids has catalog-popularity order, so the first eligible entries are\\n+ # a deterministic global-popularity fallback.\\n+ for row, uid in enumerate(uids):\\n+ mask = np.unpackbits(\\n+ packed[row], count=len(item_ids), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = item_ids[mask][:100]\\n+ rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\\n+ args.output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(\\n+ args.output, index=False\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:04:23.052Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:23.000Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-041e5467-3c47-4976-8d8b-3292bd7fd459", "changes": { "/app/solution/make_popularity_baseline.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Create structurally valid popularity rankings for either cutoff.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=(\"validation\", \"test\"), required=True)\n parser.add_argument(\"--output\", type=Path, required=True)\n args = parser.parse_args()\n\n data = Path(\"/task/data\")\n archive = data / f\"{args.stage}_eligible_candidates.npz\"\n with np.load(archive, allow_pickle=False) as artifact:\n uids = artifact[\"uids\"].astype(np.int64)\n item_ids = artifact[\"item_ids\"].astype(np.int64)\n packed = artifact[\"packed_eligible\"]\n\n rows: list[tuple[int, int, int]] = []\n # item_ids has catalog-popularity order, so the first eligible entries are\n # a deterministic global-popularity fallback.\n for row, uid in enumerate(uids):\n mask = np.unpackbits(\n packed[row], count=len(item_ids), bitorder=\"little\"\n ).astype(bool)\n chosen = item_ids[mask][:100]\n rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\n args.output.parent.mkdir(parents=True, exist_ok=True)\n pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(\n args.output, index=False\n )\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/make_popularity_baseline.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:23.051Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ac661a7087d29cef221d7037bdd2", "usage": { "input_tokens": 29428, "cached_input_tokens": 26368, "cache_write_input_tokens": 0, "output_tokens": 819, "reasoning_output_tokens": 256, "total_tokens": 30247 }, "turn_token_usage": { "input_tokens": 247559, "cached_input_tokens": 227328, "cache_write_input_tokens": 0, "output_tokens": 7532, "reasoning_output_tokens": 2282, "total_tokens": 255091 }, "thread_token_usage": { "input_tokens": 247559, "cached_input_tokens": 227328, "cache_write_input_tokens": 0, "output_tokens": 7532, "reasoning_output_tokens": 2282, "total_tokens": 255091 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-24T23:04:25.469Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:25.469Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:27.955Z", "callId": "call_cP4D0APsKTZRgiWvGQot67OP", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/make_popularity_baseline.py --stage validation --output /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /app/solution/make_popularity_baseline.py --stage test --output /app/solution/submission.parquet && ls -lh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:04:37.066Z", "content": "Script completed\nWall time 9.1 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 719K Sep 24 23:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:28.048Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ac7766d887d2b1a1305303b1602d", "usage": { "input_tokens": 30271, "cached_input_tokens": 29312, "cache_write_input_tokens": 0, "output_tokens": 163, "reasoning_output_tokens": 16, "total_tokens": 30434 }, "turn_token_usage": { "input_tokens": 277830, "cached_input_tokens": 256640, "cache_write_input_tokens": 0, "output_tokens": 7695, "reasoning_output_tokens": 2298, "total_tokens": 285525 }, "thread_token_usage": { "input_tokens": 277830, "cached_input_tokens": 256640, "cache_write_input_tokens": 0, "output_tokens": 7695, "reasoning_output_tokens": 2298, "total_tokens": 285525 } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:37.062Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-954edfdd-6b03-422b-8647-25f6f70f936b", "process_id": "45765", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/make_popularity_baseline.py --stage validation --output /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /app/solution/make_popularity_baseline.py --stage test --output /app/solution/submission.parquet && ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/make_popularity_baseline.py --stage validation --output /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /app/solution/make_popularity_baseline.py --stage test --output /app/solution/submission.parquet && ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 719K Sep 24 23:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 719K Sep 24 23:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 941610525 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 719K Sep 24 23:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n" } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-24T23:04:51.816Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:51.816Z", "text": "" } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-24T23:05:05.394Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:05:05.394Z", "text": "" } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-24T23:05:19.308Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:05:19.308Z", "text": "" } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-24T23:05:33.585Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:05:33.585Z", "text": "" } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-24T23:05:47.278Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:05:47.278Z", "text": "" } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-24T23:06:01.120Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:01.120Z", "text": "" } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-24T23:06:14.826Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:14.826Z", "text": "" } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-24T23:06:28.612Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:28.612Z", "text": "" } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-24T23:06:42.842Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:42.842Z", "text": "" } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-24T23:06:55.478Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:55.478Z", "text": "" } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-24T23:07:08.656Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:08.656Z", "text": "" } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-24T23:07:15.177Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:15.177Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:07:16.181Z", "callId": "call_jDrKBwOrh5oPllhbudFBffLm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"getconf _NPROCESSORS_ONLN\\n/opt/task-venv/bin/python - <<'PY'\\nimport torch\\nprint(torch.__version__, torch.get_num_threads(), torch.__config__.show())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:07:19.376Z", "content": "Script completed\nWall time 3.2 seconds\nOutput:\n\n8\n2.8.0+cpu 4 PyTorch built with:\n - GCC 13.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a1cb3cc05d46d198467bebbb6e8fba50a325d4e7, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=0, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, \n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:07:16.224Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ac85586887d29968d452f51b75c2", "usage": { "input_tokens": 30590, "cached_input_tokens": 30080, "cache_write_input_tokens": 0, "output_tokens": 6087, "reasoning_output_tokens": 5995, "total_tokens": 36677 }, "turn_token_usage": { "input_tokens": 308420, "cached_input_tokens": 286720, "cache_write_input_tokens": 0, "output_tokens": 13782, "reasoning_output_tokens": 8293, "total_tokens": 322202 }, "thread_token_usage": { "input_tokens": 308420, "cached_input_tokens": 286720, "cache_write_input_tokens": 0, "output_tokens": 13782, "reasoning_output_tokens": 8293, "total_tokens": 322202 } } }, { "kind": "context", "timestamp": "2026-09-24T23:07:19.373Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6c9c61ea-3181-4bf0-b41b-088882a08193", "process_id": "5453", "command": [ "/bin/sh", "-lc", "getconf _NPROCESSORS_ONLN\n/opt/task-venv/bin/python - <<'PY'\nimport torch\nprint(torch.__version__, torch.get_num_threads(), torch.__config__.show())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "getconf _NPROCESSORS_ONLN\n/opt/task-venv/bin/python - <<'PY'\nimport torch\nprint(torch.__version__, torch.get_num_threads(), torch.__config__.show())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.8.0+cpu 4 PyTorch built with:\n - GCC 13.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a1cb3cc05d46d198467bebbb6e8fba50a325d4e7, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=0, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, \n\n", "stderr": "", "aggregated_output": "2.8.0+cpu 4 PyTorch built with:\n - GCC 13.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a1cb3cc05d46d198467bebbb6e8fba50a325d4e7, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=0, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, \n\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 27830138 }, "formatted_output": "2.8.0+cpu 4 PyTorch built with:\n - GCC 13.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a1cb3cc05d46d198467bebbb6e8fba50a325d4e7, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=0, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, \n\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-24T23:07:34.078Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:34.078Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-24T23:07:45.483Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:45.483Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:07:47.668Z", "callId": "call_3FLDGjZHw5UcQ3cOxP9pWFrh", "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 name in ['train_listens','train_likes','train_unlikes','train_dislikes','train_undislikes','val_listens','val_likes']:\\n x=pd.read_parquet(D/(name+'.parquet'),columns=['uid','timestamp'])\\n a=x.uid.to_numpy(); t=x.timestamp.to_numpy(); print(name,'uid sorted',np.all(a[1:]>=a[:-1]),'within time violations',np.sum((a[1:]==a[:-1])&(t[1:]=a[:-1]),'within time violations',np.sum((a[1:]==a[:-1])&(t[1:]=a[:-1]),'within time violations',np.sum((a[1:]==a[:-1])&(t[1:] None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+@dataclass\\n+class EventSource:\\n+ \\\"\\\"\\\"One uid-sorted event source with O(1) per-user slicing.\\\"\\\"\\\"\\n+\\n+ uid: np.ndarray\\n+ timestamp: np.ndarray\\n+ item: np.ndarray\\n+ fields: dict[str, np.ndarray]\\n+ starts: np.ndarray\\n+\\n+ def user(self, user_index: int) -> tuple[np.ndarray, np.ndarray, dict[str, np.ndarray]]:\\n+ lo, hi = int(self.starts[user_index]), int(self.starts[user_index + 1])\\n+ return (\\n+ self.timestamp[lo:hi],\\n+ self.item[lo:hi],\\n+ {key: value[lo:hi] for key, value in self.fields.items()},\\n+ )\\n+\\n+\\n+def dense_lookup(values: np.ndarray) -> np.ndarray:\\n+ lookup = np.full(int(values.max()) + 1, -1, dtype=np.int32)\\n+ lookup[values.astype(np.int64)] = np.arange(len(values), dtype=np.int32)\\n+ return lookup\\n+\\n+\\n+def mapped(values: np.ndarray, lookup: np.ndarray) -> np.ndarray:\\n+ result = np.full(len(values), -1, dtype=np.int32)\\n+ ok = values < len(lookup)\\n+ result[ok] = lookup[values[ok].astype(np.int64)]\\n+ return result\\n+\\n+\\n+def load_source(\\n+ path: Path,\\n+ uid_lookup: np.ndarray,\\n+ item_lookup: np.ndarray,\\n+ n_users: int,\\n+ extra_columns: Iterable[str],\\n+) -> EventSource:\\n+ columns = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", *extra_columns]\\n+ frame = pd.read_parquet(path, columns=columns)\\n+ uid = mapped(frame.uid.to_numpy(), uid_lookup)\\n+ item = mapped(frame.item_id.to_numpy(), item_lookup)\\n+ keep = (uid >= 0) & (item >= 0)\\n+ uid = uid[keep]\\n+ timestamp = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep].astype(np.int32)\\n+ item = item[keep]\\n+ fields = {\\n+ column: frame[column].to_numpy(copy=False)[keep].copy()\\n+ for column in extra_columns\\n+ }\\n+ # Input parquet is sorted by uid then timestamp. Filtering preserves order.\\n+ starts = np.searchsorted(uid, np.arange(n_users + 1), side=\\\"left\\\").astype(np.int64)\\n+ del frame\\n+ gc.collect()\\n+ return EventSource(uid, timestamp, item, fields, starts)\\n+\\n+\\n+def join_user_parts(\\n+ sources: list[EventSource], user_index: int\\n+) -> tuple[np.ndarray, np.ndarray, dict[str, np.ndarray]]:\\n+ parts = [source.user(user_index) for source in sources]\\n+ if not parts:\\n+ return np.empty(0, np.int32), np.empty(0, np.int32), {}\\n+ times = np.concatenate([part[0] for part in parts])\\n+ items = np.concatenate([part[1] for part in parts])\\n+ keys = parts[0][2].keys()\\n+ fields = {key: np.concatenate([part[2][key] for part in parts]) for key in keys}\\n+ # Sources are chronological stage blocks (train followed by validation).\\n+ return times, items, fields\\n+\\n+\\n+def pad_recent(values: np.ndarray, width: int) -> np.ndarray:\\n+ out = np.full(width, PAD, dtype=np.int32)\\n+ if len(values):\\n+ chosen = values[-width:]\\n+ out[: len(chosen)] = chosen\\n+ return out\\n+\\n+\\n+def pad_spread(values: np.ndarray, width: int) -> np.ndarray:\\n+ out = np.full(width, PAD, dtype=np.int32)\\n+ if len(values):\\n+ if len(values) > width:\\n+ positions = np.linspace(0, len(values) - 1, width).astype(np.int64)\\n+ values = values[positions]\\n+ out[: len(values)] = values\\n+ return out\\n+\\n+\\n+def pad_state(values: Iterable[int], width: int) -> np.ndarray:\\n+ # Lower catalog index means better-supported/more-popular source embedding.\\n+ chosen = np.asarray(sorted(values)[:width], dtype=np.int32)\\n+ out = np.full(width, PAD, dtype=np.int32)\\n+ out[: len(chosen)] = chosen\\n+ return out\\n+\\n+\\n+def state_from_actions(\\n+ action_time: np.ndarray,\\n+ action_item: np.ndarray,\\n+ action_kind: np.ndarray,\\n+ cutoff: int,\\n+) -> tuple[set[int], set[int]]:\\n+ liked: set[int] = set()\\n+ disliked: set[int] = set()\\n+ for item, kind in zip(\\n+ action_item[action_time <= cutoff], action_kind[action_time <= cutoff]\\n+ ):\\n+ value = int(item)\\n+ if kind == 1:\\n+ liked.add(value)\\n+ elif kind == 2:\\n+ liked.discard(value)\\n+ elif kind == 3:\\n+ disliked.add(value)\\n+ else:\\n+ disliked.discard(value)\\n+ return liked, disliked\\n+\\n+\\n+def make_context(\\n+ listen_time: np.ndarray,\\n+ listen_item: np.ndarray,\\n+ organic: np.ndarray,\\n+ ratio: np.ndarray,\\n+ liked: set[int],\\n+ disliked: set[int],\\n+ cutoff: int,\\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\\n+ end = int(np.searchsorted(listen_time, cutoff, side=\\\"right\\\"))\\n+ times = listen_time[:end]\\n+ items = listen_item[:end]\\n+ org = organic[:end]\\n+ completion = ratio[:end]\\n+\\n+ positive = completion >= 50\\n+ positive_items = items[positive]\\n+ recent = pad_recent(positive_items, K_RECENT)\\n+\\n+ long_mask = (org == 1) & (completion >= 80) & (times >= cutoff - 180 * 86_400)\\n+ organic_items = items[long_mask]\\n+ long_organic = pad_spread(organic_items, K_ORGANIC)\\n+\\n+ recent_90 = positive & (times >= cutoff - 90 * 86_400)\\n+ features = np.asarray(\\n+ [\\n+ math.log1p(int(positive.sum())) / 10.0,\\n+ math.log1p(int(recent_90.sum())) / 8.0,\\n+ math.log1p(int(long_mask.sum())) / 8.0,\\n+ math.log1p(len(liked)) / 5.0,\\n+ math.log1p(len(disliked)) / 4.0,\\n+ float(org[positive].mean()) if positive.any() else 0.0,\\n+ ],\\n+ dtype=np.float32,\\n+ )\\n+ return (\\n+ recent,\\n+ long_organic,\\n+ pad_state(liked, K_LIKED),\\n+ pad_state(disliked, K_DISLIKED),\\n+ features,\\n+ )\\n+\\n+\\n+def active_state_from_file(\\n+ path: Path,\\n+ uid_lookup: np.ndarray,\\n+ item_lookup: np.ndarray,\\n+ n_users: int,\\n+) -> tuple[list[set[int]], list[set[int]]]:\\n+ frame = pd.read_parquet(path)\\n+ uid = mapped(frame.uid.to_numpy(), uid_lookup)\\n+ item = mapped(frame.item_id.to_numpy(), item_lookup)\\n+ state = frame.state.to_numpy()\\n+ liked = [set() for _ in range(n_users)]\\n+ disliked = [set() for _ in range(n_users)]\\n+ for user, track, value in zip(uid, item, state):\\n+ if user < 0 or track < 0:\\n+ continue\\n+ (liked if value == \\\"liked\\\" else disliked)[int(user)].add(int(track))\\n+ return liked, disliked\\n+\\n+\\n+def prepare(stage: str, output: Path, snapshots: int) -> None:\\n+ \\\"\\\"\\\"Create rolling training contexts and the requested inference context.\\\"\\\"\\\"\\n+ started = time.time()\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n+ uids = targets.uid.to_numpy(dtype=np.int64)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ item_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ if len(item_ids) != N_ITEMS:\\n+ raise ValueError(f\\\"expected {N_ITEMS} catalog items\\\")\\n+ uid_lookup = dense_lookup(uids)\\n+ item_lookup = dense_lookup(item_ids)\\n+ n_users = len(uids)\\n+\\n+ include_validation = stage == \\\"test\\\"\\n+ prefixes = [\\\"train\\\", \\\"val\\\"] if include_validation else [\\\"train\\\"]\\n+ listens = [\\n+ load_source(\\n+ DATA / f\\\"{prefix}_listens.parquet\\\",\\n+ uid_lookup,\\n+ item_lookup,\\n+ n_users,\\n+ (\\\"is_organic\\\", \\\"played_ratio_pct\\\"),\\n+ )\\n+ for prefix in prefixes\\n+ ]\\n+\\n+ action_specs = ((\\\"likes\\\", 1), (\\\"unlikes\\\", 2), (\\\"dislikes\\\", 3), (\\\"undislikes\\\", 4))\\n+ actions: list[tuple[EventSource, int]] = []\\n+ likes_sources: list[EventSource] = []\\n+ for prefix in prefixes:\\n+ for family, kind in action_specs:\\n+ source = load_source(\\n+ DATA / f\\\"{prefix}_{family}.parquet\\\",\\n+ uid_lookup,\\n+ item_lookup,\\n+ n_users,\\n+ (\\\"is_organic\\\",),\\n+ )\\n+ actions.append((source, kind))\\n+ if family == \\\"likes\\\":\\n+ likes_sources.append(source)\\n+\\n+ inference_cutoff = VALIDATION_END if include_validation else TRAIN_CUTOFF\\n+ latest_training_cutoff = TRAIN_CUTOFF if include_validation else TRAIN_CUTOFF - STRIDE\\n+ cutoffs = np.asarray(\\n+ sorted(latest_training_cutoff - np.arange(snapshots) * STRIDE), dtype=np.int64\\n+ )\\n+ pref_file = DATA / (\\\"test_preference_state.parquet\\\" if include_validation else \\\"validation_preference_state.parquet\\\")\\n+ inference_liked, inference_disliked = active_state_from_file(\\n+ pref_file, uid_lookup, item_lookup, n_users\\n+ )\\n+\\n+ train_recent: list[np.ndarray] = []\\n+ train_organic: list[np.ndarray] = []\\n+ train_liked: list[np.ndarray] = []\\n+ train_disliked: list[np.ndarray] = []\\n+ train_features: list[np.ndarray] = []\\n+ context_age: list[float] = []\\n+ label_context: list[int] = []\\n+ label_item: list[int] = []\\n+ label_grade: list[int] = []\\n+ infer_recent = np.empty((n_users, K_RECENT), np.int32)\\n+ infer_organic = np.empty((n_users, K_ORGANIC), np.int32)\\n+ infer_liked = np.empty((n_users, K_LIKED), np.int32)\\n+ infer_disliked = np.empty((n_users, K_DISLIKED), np.int32)\\n+ infer_features = np.empty((n_users, 6), np.float32)\\n+\\n+ for user in range(n_users):\\n+ lt, li, lf = join_user_parts(listens, user)\\n+ lo = lf[\\\"is_organic\\\"].astype(np.uint8, copy=False)\\n+ lr = lf[\\\"played_ratio_pct\\\"].astype(np.uint16, copy=False)\\n+\\n+ action_parts: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []\\n+ for source, kind in actions:\\n+ at, ai, _ = source.user(user)\\n+ if len(at):\\n+ action_parts.append((at, ai, np.full(len(at), kind, np.uint8)))\\n+ if action_parts:\\n+ at = np.concatenate([part[0] for part in action_parts])\\n+ ai = np.concatenate([part[1] for part in action_parts])\\n+ ak = np.concatenate([part[2] for part in action_parts])\\n+ order = np.argsort(at, kind=\\\"stable\\\")\\n+ at, ai, ak = at[order], ai[order], ak[order]\\n+ else:\\n+ at = np.empty(0, np.int32)\\n+ ai = np.empty(0, np.int32)\\n+ ak = np.empty(0, np.uint8)\\n+\\n+ kt, ki, kf = join_user_parts(likes_sources, user)\\n+ ko = kf.get(\\\"is_organic\\\", np.empty(0, np.uint8))\\n+\\n+ # Earliest event per item approximates the historical eligibility rule.\\n+ all_time = np.concatenate((lt, at))\\n+ all_item = np.concatenate((li, ai))\\n+ if len(all_time):\\n+ time_order = np.argsort(all_time, kind=\\\"stable\\\")\\n+ chronological_items = all_item[time_order]\\n+ chronological_times = all_time[time_order]\\n+ seen_items, first_positions = np.unique(\\n+ chronological_items, return_index=True\\n+ )\\n+ first_times = chronological_times[first_positions]\\n+ else:\\n+ seen_items = np.empty(0, np.int32)\\n+ first_times = np.empty(0, np.int32)\\n+\\n+ for cutoff_number, cutoff in enumerate(cutoffs):\\n+ start, stop = int(cutoff + GAP), int(cutoff + GAP + WINDOW)\\n+ left, right = np.searchsorted(lt, (start, stop), side=\\\"left\\\")\\n+ positive_mask = (lo[left:right] == 1) & (lr[left:right] >= 80)\\n+ future_items = li[left:right][positive_mask]\\n+ grades: dict[int, int] = {int(item): 1 for item in future_items}\\n+ kleft, kright = np.searchsorted(kt, (start, stop), side=\\\"left\\\")\\n+ for item in ki[kleft:kright][ko[kleft:kright] == 1]:\\n+ grades[int(item)] = 3\\n+ if grades:\\n+ candidate_items = np.fromiter(grades.keys(), dtype=np.int32)\\n+ positions = np.searchsorted(seen_items, candidate_items)\\n+ prior = np.zeros(len(candidate_items), dtype=bool)\\n+ valid = positions < len(seen_items)\\n+ valid_indices = np.flatnonzero(valid)\\n+ exact = np.zeros(len(candidate_items), dtype=bool)\\n+ exact[valid_indices] = seen_items[positions[valid_indices]] == candidate_items[valid_indices]\\n+ exact_indices = np.flatnonzero(exact)\\n+ prior[exact_indices] = first_times[positions[exact_indices]] <= cutoff\\n+ candidate_items = candidate_items[~prior]\\n+ else:\\n+ candidate_items = np.empty(0, np.int32)\\n+ if not len(candidate_items):\\n+ continue\\n+ liked, disliked = state_from_actions(at, ai, ak, int(cutoff))\\n+ context = make_context(lt, li, lo, lr, liked, disliked, int(cutoff))\\n+ context_index = len(train_recent)\\n+ train_recent.append(context[0])\\n+ train_organic.append(context[1])\\n+ train_liked.append(context[2])\\n+ train_disliked.append(context[3])\\n+ train_features.append(context[4])\\n+ context_age.append(float(len(cutoffs) - 1 - cutoff_number))\\n+ for item in candidate_items:\\n+ label_context.append(context_index)\\n+ label_item.append(int(item))\\n+ label_grade.append(grades[int(item)])\\n+\\n+ inference_context = make_context(\\n+ lt,\\n+ li,\\n+ lo,\\n+ lr,\\n+ inference_liked[user],\\n+ inference_disliked[user],\\n+ inference_cutoff,\\n+ )\\n+ infer_recent[user], infer_organic[user], infer_liked[user], infer_disliked[user], infer_features[user] = inference_context\\n+ if (user + 1) % 1000 == 0:\\n+ print(\\n+ f\\\"prepared {user + 1}/{n_users} users, \\\"\\n+ f\\\"{len(train_recent):,} contexts, {len(label_item):,} positives\\\",\\n+ flush=True,\\n+ )\\n+\\n+ arrays = {\\n+ \\\"uids\\\": uids,\\n+ \\\"item_ids\\\": item_ids,\\n+ \\\"item_features\\\": np.column_stack(\\n+ [\\n+ np.log1p(catalog.positive_listens.to_numpy()),\\n+ np.log1p(catalog.organic_positive_listens.to_numpy()),\\n+ np.log1p(catalog.organic_positive_listens.to_numpy())\\n+ - np.log1p(catalog.positive_listens.to_numpy()),\\n+ -np.log1p(catalog.popularity_rank.to_numpy()),\\n+ ]\\n+ ).astype(np.float32),\\n+ \\\"train_recent\\\": np.stack(train_recent),\\n+ \\\"train_organic\\\": np.stack(train_organic),\\n+ \\\"train_liked\\\": np.stack(train_liked),\\n+ \\\"train_disliked\\\": np.stack(train_disliked),\\n+ \\\"train_features\\\": np.stack(train_features),\\n+ \\\"context_age\\\": np.asarray(context_age, np.float32),\\n+ \\\"label_context\\\": np.asarray(label_context, np.int32),\\n+ \\\"label_item\\\": np.asarray(label_item, np.int32),\\n+ \\\"label_grade\\\": np.asarray(label_grade, np.uint8),\\n+ \\\"infer_recent\\\": infer_recent,\\n+ \\\"infer_organic\\\": infer_organic,\\n+ \\\"infer_liked\\\": infer_liked,\\n+ \\\"infer_disliked\\\": infer_disliked,\\n+ \\\"infer_features\\\": infer_features,\\n+ }\\n+ # Normalize fixed features using catalog-wide moments.\\n+ mean = arrays[\\\"item_features\\\"].mean(axis=0)\\n+ std = arrays[\\\"item_features\\\"].std(axis=0).clip(1e-6)\\n+ arrays[\\\"item_features\\\"] = (arrays[\\\"item_features\\\"] - mean) / std\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ np.savez(output, **arrays)\\n+ print(\\n+ json.dumps(\\n+ {\\n+ \\\"stage\\\": stage,\\n+ \\\"contexts\\\": len(train_recent),\\n+ \\\"positives\\\": len(label_item),\\n+ \\\"grade3\\\": int((arrays[\\\"label_grade\\\"] == 3).sum()),\\n+ \\\"seconds\\\": round(time.time() - started, 1),\\n+ \\\"output\\\": str(output),\\n+ },\\n+ indent=2,\\n+ )\\n+ )\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"Encode interaction history; deliberately has no user-ID embedding.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, token_dim: int, output_dim: int) -> None:\\n+ super().__init__()\\n+ self.padding = n_items\\n+ self.history_embedding = nn.Embedding(\\n+ n_items + 1, token_dim, padding_idx=n_items\\n+ )\\n+ self.channel_norms = nn.ModuleList([nn.LayerNorm(token_dim) for _ in range(4)])\\n+ self.network = nn.Sequential(\\n+ nn.Linear(4 * token_dim + 6, 2 * output_dim),\\n+ nn.GELU(),\\n+ nn.LayerNorm(2 * output_dim),\\n+ nn.Dropout(0.05),\\n+ nn.Linear(2 * output_dim, output_dim),\\n+ )\\n+ nn.init.normal_(self.history_embedding.weight, std=0.04)\\n+ with torch.no_grad():\\n+ self.history_embedding.weight[n_items].zero_()\\n+\\n+ def pool(self, ids: torch.Tensor, channel: int) -> torch.Tensor:\\n+ mask = ids.ne(self.padding)\\n+ values = self.history_embedding(ids)\\n+ pooled = (values * mask.unsqueeze(-1)).sum(dim=1)\\n+ pooled = pooled / mask.sum(dim=1, keepdim=True).clamp_min(1)\\n+ return self.channel_norms[channel](pooled)\\n+\\n+ def forward(\\n+ self,\\n+ recent: torch.Tensor,\\n+ organic: torch.Tensor,\\n+ liked: torch.Tensor,\\n+ disliked: torch.Tensor,\\n+ features: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ combined = torch.cat(\\n+ [\\n+ self.pool(recent, 0),\\n+ self.pool(organic, 1),\\n+ self.pool(liked, 2),\\n+ self.pool(disliked, 3),\\n+ features,\\n+ ],\\n+ dim=1,\\n+ )\\n+ latent = F.normalize(self.network(combined), dim=1)\\n+ # Constant coordinate represents item bias and remains a dot product.\\n+ return torch.cat((latent, torch.ones_like(latent[:, :1])), dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently encode one catalog ID and fixed catalog features.\\\"\\\"\\\"\\n+\\n+ def __init__(self, item_features: np.ndarray, output_dim: int) -> None:\\n+ super().__init__()\\n+ n_items, feature_dim = item_features.shape\\n+ self.register_buffer(\\\"features\\\", torch.from_numpy(item_features.copy()))\\n+ self.item_embedding = nn.Embedding(n_items, output_dim)\\n+ self.feature_network = nn.Sequential(\\n+ nn.Linear(feature_dim, output_dim), nn.GELU(), nn.Linear(output_dim, output_dim)\\n+ )\\n+ self.output_network = nn.Sequential(\\n+ nn.LayerNorm(output_dim), nn.Linear(output_dim, output_dim), nn.GELU(), nn.Linear(output_dim, output_dim)\\n+ )\\n+ self.item_bias = nn.Embedding(n_items, 1)\\n+ self.feature_bias = nn.Linear(feature_dim, 1, bias=False)\\n+ self.log_scale = nn.Parameter(torch.tensor(math.log(8.0)))\\n+ nn.init.normal_(self.item_embedding.weight, std=0.04)\\n+ nn.init.zeros_(self.item_bias.weight)\\n+\\n+ def forward(self, items: torch.Tensor) -> torch.Tensor:\\n+ features = self.features[items]\\n+ hidden = self.item_embedding(items) + self.feature_network(features)\\n+ latent = F.normalize(self.output_network(hidden) + hidden, dim=-1)\\n+ scale = self.log_scale.exp().clamp(1.0, 30.0)\\n+ bias = self.item_bias(items) + self.feature_bias(features)\\n+ return torch.cat((latent * scale, bias), dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, item_features: np.ndarray, token_dim: int = 48, dim: int = 64) -> None:\\n+ super().__init__()\\n+ self.user_tower = UserTower(len(item_features), token_dim, dim)\\n+ self.item_tower = ItemTower(item_features, dim)\\n+\\n+\\n+def context_batch(data: dict[str, torch.Tensor], indices: torch.Tensor) -> tuple[torch.Tensor, ...]:\\n+ return (\\n+ data[\\\"recent\\\"][indices].long(),\\n+ data[\\\"organic\\\"][indices].long(),\\n+ data[\\\"liked\\\"][indices].long(),\\n+ data[\\\"disliked\\\"][indices].long(),\\n+ data[\\\"features\\\"][indices],\\n+ )\\n+\\n+\\n+def train_model(\\n+ prepared: Path,\\n+ output: Path,\\n+ epochs: int,\\n+ batch_size: int,\\n+ negatives: int,\\n+ seed: int,\\n+) -> None:\\n+ seed_everything(seed)\\n+ torch.set_num_threads(min(8, max(1, torch.get_num_threads() * 2)))\\n+ archive = np.load(prepared, allow_pickle=False)\\n+ item_features = archive[\\\"item_features\\\"].astype(np.float32)\\n+ model = TwoTower(item_features)\\n+ model.train()\\n+ contexts = {\\n+ \\\"recent\\\": torch.from_numpy(archive[\\\"train_recent\\\"]),\\n+ \\\"organic\\\": torch.from_numpy(archive[\\\"train_organic\\\"]),\\n+ \\\"liked\\\": torch.from_numpy(archive[\\\"train_liked\\\"]),\\n+ \\\"disliked\\\": torch.from_numpy(archive[\\\"train_disliked\\\"]),\\n+ \\\"features\\\": torch.from_numpy(archive[\\\"train_features\\\"]),\\n+ }\\n+ label_context = torch.from_numpy(archive[\\\"label_context\\\"].astype(np.int64))\\n+ label_item = torch.from_numpy(archive[\\\"label_item\\\"].astype(np.int64))\\n+ grades = torch.from_numpy(archive[\\\"label_grade\\\"].astype(np.float32))\\n+ ages = torch.from_numpy(archive[\\\"context_age\\\"])[label_context]\\n+ n_labels = len(label_item)\\n+\\n+ # Half the negatives are uniform and half emphasize plausible popular items.\\n+ positive_counts = np.bincount(\\n+ archive[\\\"label_item\\\"], minlength=N_ITEMS\\n+ ).astype(np.float64)\\n+ sampling = np.sqrt(positive_counts + 2.0)\\n+ sampling /= sampling.sum()\\n+ sampling_tensor = torch.from_numpy(sampling.astype(np.float32))\\n+\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\\n+ generator = torch.Generator().manual_seed(seed + 17)\\n+ history: list[dict[str, float]] = []\\n+ for epoch in range(epochs):\\n+ epoch_start = time.time()\\n+ permutation = torch.randperm(n_labels, generator=generator)\\n+ total_loss = 0.0\\n+ total_examples = 0\\n+ for offset in range(0, n_labels, batch_size):\\n+ rows = permutation[offset : offset + batch_size]\\n+ ctx = label_context[rows]\\n+ positives = label_item[rows]\\n+ user_vector = model.user_tower(*context_batch(contexts, ctx))\\n+ positive_vector = model.item_tower(positives)\\n+ positive_score = (user_vector * positive_vector).sum(dim=1)\\n+\\n+ current = len(rows)\\n+ uniform_count = negatives // 2\\n+ uniform = torch.randint(\\n+ N_ITEMS, (current, uniform_count), generator=generator\\n+ )\\n+ hard = torch.multinomial(\\n+ sampling_tensor,\\n+ current * (negatives - uniform_count),\\n+ replacement=True,\\n+ generator=generator,\\n+ ).view(current, -1)\\n+ negative_items = torch.cat((uniform, hard), dim=1)\\n+ negative_vector = model.item_tower(negative_items)\\n+ negative_score = (user_vector[:, None, :] * negative_vector).sum(dim=2)\\n+ pair_loss = F.softplus(negative_score - positive_score[:, None]).mean(dim=1)\\n+ grade_weight = torch.where(grades[rows] >= 3, 4.0, 1.0)\\n+ recency_weight = 0.55 + 0.45 * torch.exp(-ages[rows] / 6.0)\\n+ loss = (pair_loss * grade_weight * recency_weight).sum() / (\\n+ grade_weight * recency_weight\\n+ ).sum()\\n+ optimizer.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step()\\n+ total_loss += float(loss) * current\\n+ total_examples += current\\n+ record = {\\n+ \\\"epoch\\\": epoch + 1,\\n+ \\\"loss\\\": total_loss / total_examples,\\n+ \\\"seconds\\\": time.time() - epoch_start,\\n+ \\\"scale\\\": float(model.item_tower.log_scale.exp()),\\n+ }\\n+ history.append(record)\\n+ print(json.dumps(record), flush=True)\\n+\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"token_dim\\\": 48,\\n+ \\\"dim\\\": 64,\\n+ \\\"item_features\\\": item_features,\\n+ \\\"history\\\": history,\\n+ \\\"seed\\\": seed,\\n+ },\\n+ output,\\n+ )\\n+ print(f\\\"saved {output}\\\")\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(\\n+ stage: str,\\n+ prepared: Path,\\n+ checkpoint: Path,\\n+ output: Path,\\n+ popularity_weight: float,\\n+) -> None:\\n+ torch.set_num_threads(8)\\n+ archive = np.load(prepared, allow_pickle=False)\\n+ saved = torch.load(checkpoint, map_location=\\\"cpu\\\", weights_only=False)\\n+ model = TwoTower(\\n+ saved[\\\"item_features\\\"], saved[\\\"token_dim\\\"], saved[\\\"dim\\\"]\\n+ )\\n+ model.load_state_dict(saved[\\\"state_dict\\\"])\\n+ model.eval()\\n+ contexts = {\\n+ \\\"recent\\\": torch.from_numpy(archive[\\\"infer_recent\\\"]),\\n+ \\\"organic\\\": torch.from_numpy(archive[\\\"infer_organic\\\"]),\\n+ \\\"liked\\\": torch.from_numpy(archive[\\\"infer_liked\\\"]),\\n+ \\\"disliked\\\": torch.from_numpy(archive[\\\"infer_disliked\\\"]),\\n+ \\\"features\\\": torch.from_numpy(archive[\\\"infer_features\\\"]),\\n+ }\\n+ item_vectors = model.item_tower(torch.arange(N_ITEMS)).numpy()\\n+ # A fixed popularity coordinate is an explicit part of both tower outputs.\\n+ popularity = archive[\\\"item_features\\\"][:, 1].astype(np.float32)\\n+ item_vectors = np.column_stack((item_vectors, popularity))\\n+\\n+ eligibility_path = DATA / f\\\"{stage}_eligible_candidates.npz\\\"\\n+ with np.load(eligibility_path, allow_pickle=False) as eligible:\\n+ eligible_uids = eligible[\\\"uids\\\"].astype(np.int64)\\n+ eligible_items = eligible[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = eligible[\\\"packed_eligible\\\"].copy()\\n+ if not np.array_equal(eligible_uids, archive[\\\"uids\\\"]):\\n+ raise ValueError(\\\"prepared and eligibility user orders differ\\\")\\n+ if not np.array_equal(eligible_items, archive[\\\"item_ids\\\"]):\\n+ # Keep vector rows aligned to the archive catalog if an artifact changes order.\\n+ position = {int(item): i for i, item in enumerate(archive[\\\"item_ids\\\"])}\\n+ item_vectors = item_vectors[[position[int(item)] for item in eligible_items]]\\n+\\n+ rows: list[pd.DataFrame] = []\\n+ chunk_size = 128\\n+ for start in range(0, len(eligible_uids), chunk_size):\\n+ stop = min(len(eligible_uids), start + chunk_size)\\n+ indices = torch.arange(start, stop)\\n+ user_vectors = model.user_tower(*context_batch(contexts, indices)).numpy()\\n+ user_vectors = np.column_stack(\\n+ (user_vectors, np.full(stop - start, popularity_weight, np.float32))\\n+ )\\n+ scores = user_vectors @ item_vectors.T\\n+ mask = np.unpackbits(\\n+ packed[start:stop], axis=1, count=N_ITEMS, bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ scores[~mask] = -np.inf\\n+ top = np.argpartition(scores, -100, axis=1)[:, -100:]\\n+ top_scores = np.take_along_axis(scores, top, axis=1)\\n+ order = np.argsort(-top_scores, axis=1, kind=\\\"stable\\\")\\n+ top = np.take_along_axis(top, order, axis=1)\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(eligible_uids[start:stop], 100),\\n+ \\\"item_id\\\": eligible_items[top.reshape(-1)],\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), stop - start),\\n+ }\\n+ )\\n+ )\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.concat(rows, ignore_index=True).to_parquet(output, index=False)\\n+ print(\\n+ json.dumps(\\n+ {\\n+ \\\"stage\\\": stage,\\n+ \\\"output\\\": str(output),\\n+ \\\"rows\\\": len(eligible_uids) * 100,\\n+ \\\"popularity_weight\\\": popularity_weight,\\n+ },\\n+ indent=2,\\n+ )\\n+ )\\n+\\n+\\n+def parse_args() -> argparse.Namespace:\\n+ parser = argparse.ArgumentParser()\\n+ subparsers = parser.add_subparsers(dest=\\\"command\\\", required=True)\\n+ prep = subparsers.add_parser(\\\"prepare\\\")\\n+ prep.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"test\\\"), required=True)\\n+ prep.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ prep.add_argument(\\\"--snapshots\\\", type=int, default=16)\\n+ fit = subparsers.add_parser(\\\"train\\\")\\n+ fit.add_argument(\\\"--prepared\\\", type=Path, required=True)\\n+ fit.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ fit.add_argument(\\\"--epochs\\\", type=int, default=4)\\n+ fit.add_argument(\\\"--batch-size\\\", type=int, default=512)\\n+ fit.add_argument(\\\"--negatives\\\", type=int, default=24)\\n+ fit.add_argument(\\\"--seed\\\", type=int, default=20260924)\\n+ infer = subparsers.add_parser(\\\"rank\\\")\\n+ infer.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"test\\\"), required=True)\\n+ infer.add_argument(\\\"--prepared\\\", type=Path, required=True)\\n+ infer.add_argument(\\\"--checkpoint\\\", type=Path, required=True)\\n+ infer.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ infer.add_argument(\\\"--popularity-weight\\\", type=float, default=0.0)\\n+ return parser.parse_args()\\n+\\n+\\n+def main() -> None:\\n+ args = parse_args()\\n+ if args.command == \\\"prepare\\\":\\n+ prepare(args.stage, args.output, args.snapshots)\\n+ elif args.command == \\\"train\\\":\\n+ train_model(\\n+ args.prepared,\\n+ args.output,\\n+ args.epochs,\\n+ args.batch_size,\\n+ args.negatives,\\n+ args.seed,\\n+ )\\n+ else:\\n+ rank(\\n+ args.stage,\\n+ args.prepared,\\n+ args.checkpoint,\\n+ args.output,\\n+ args.popularity_weight,\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:11:32.477Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:32.430Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-bd346fe0-1058-45eb-995a-a6740ae2f260", "changes": { "/app/solution/train_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a CPU PyTorch history-to-item two-tower ranker.\n\nThe preparation code streams event tables into event arrays and creates rolling\ntemporal examples. It never materializes or factorizes a user-item matrix.\nThe request-time path is ``UserTower(history)`` followed by one matrix product\nagainst independently precomputed ``ItemTower(item)`` vectors.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport gc\nimport json\nimport math\nimport random\nimport time\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterable\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nDATA = Path(\"/task/data\")\nSOLUTION = Path(\"/app/solution\")\nTRAIN_CUTOFF = 24_786_800\nVALIDATION_END = 25_393_400\nGAP = 1_800\nWINDOW = 7 * 86_400\nSTRIDE = GAP + WINDOW\nN_ITEMS = 30_000\nK_RECENT = 128\nK_ORGANIC = 128\nK_LIKED = 128\nK_DISLIKED = 32\nPAD = N_ITEMS\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\n@dataclass\nclass EventSource:\n \"\"\"One uid-sorted event source with O(1) per-user slicing.\"\"\"\n\n uid: np.ndarray\n timestamp: np.ndarray\n item: np.ndarray\n fields: dict[str, np.ndarray]\n starts: np.ndarray\n\n def user(self, user_index: int) -> tuple[np.ndarray, np.ndarray, dict[str, np.ndarray]]:\n lo, hi = int(self.starts[user_index]), int(self.starts[user_index + 1])\n return (\n self.timestamp[lo:hi],\n self.item[lo:hi],\n {key: value[lo:hi] for key, value in self.fields.items()},\n )\n\n\ndef dense_lookup(values: np.ndarray) -> np.ndarray:\n lookup = np.full(int(values.max()) + 1, -1, dtype=np.int32)\n lookup[values.astype(np.int64)] = np.arange(len(values), dtype=np.int32)\n return lookup\n\n\ndef mapped(values: np.ndarray, lookup: np.ndarray) -> np.ndarray:\n result = np.full(len(values), -1, dtype=np.int32)\n ok = values < len(lookup)\n result[ok] = lookup[values[ok].astype(np.int64)]\n return result\n\n\ndef load_source(\n path: Path,\n uid_lookup: np.ndarray,\n item_lookup: np.ndarray,\n n_users: int,\n extra_columns: Iterable[str],\n) -> EventSource:\n columns = [\"uid\", \"timestamp\", \"item_id\", *extra_columns]\n frame = pd.read_parquet(path, columns=columns)\n uid = mapped(frame.uid.to_numpy(), uid_lookup)\n item = mapped(frame.item_id.to_numpy(), item_lookup)\n keep = (uid >= 0) & (item >= 0)\n uid = uid[keep]\n timestamp = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep].astype(np.int32)\n item = item[keep]\n fields = {\n column: frame[column].to_numpy(copy=False)[keep].copy()\n for column in extra_columns\n }\n # Input parquet is sorted by uid then timestamp. Filtering preserves order.\n starts = np.searchsorted(uid, np.arange(n_users + 1), side=\"left\").astype(np.int64)\n del frame\n gc.collect()\n return EventSource(uid, timestamp, item, fields, starts)\n\n\ndef join_user_parts(\n sources: list[EventSource], user_index: int\n) -> tuple[np.ndarray, np.ndarray, dict[str, np.ndarray]]:\n parts = [source.user(user_index) for source in sources]\n if not parts:\n return np.empty(0, np.int32), np.empty(0, np.int32), {}\n times = np.concatenate([part[0] for part in parts])\n items = np.concatenate([part[1] for part in parts])\n keys = parts[0][2].keys()\n fields = {key: np.concatenate([part[2][key] for part in parts]) for key in keys}\n # Sources are chronological stage blocks (train followed by validation).\n return times, items, fields\n\n\ndef pad_recent(values: np.ndarray, width: int) -> np.ndarray:\n out = np.full(width, PAD, dtype=np.int32)\n if len(values):\n chosen = values[-width:]\n out[: len(chosen)] = chosen\n return out\n\n\ndef pad_spread(values: np.ndarray, width: int) -> np.ndarray:\n out = np.full(width, PAD, dtype=np.int32)\n if len(values):\n if len(values) > width:\n positions = np.linspace(0, len(values) - 1, width).astype(np.int64)\n values = values[positions]\n out[: len(values)] = values\n return out\n\n\ndef pad_state(values: Iterable[int], width: int) -> np.ndarray:\n # Lower catalog index means better-supported/more-popular source embedding.\n chosen = np.asarray(sorted(values)[:width], dtype=np.int32)\n out = np.full(width, PAD, dtype=np.int32)\n out[: len(chosen)] = chosen\n return out\n\n\ndef state_from_actions(\n action_time: np.ndarray,\n action_item: np.ndarray,\n action_kind: np.ndarray,\n cutoff: int,\n) -> tuple[set[int], set[int]]:\n liked: set[int] = set()\n disliked: set[int] = set()\n for item, kind in zip(\n action_item[action_time <= cutoff], action_kind[action_time <= cutoff]\n ):\n value = int(item)\n if kind == 1:\n liked.add(value)\n elif kind == 2:\n liked.discard(value)\n elif kind == 3:\n disliked.add(value)\n else:\n disliked.discard(value)\n return liked, disliked\n\n\ndef make_context(\n listen_time: np.ndarray,\n listen_item: np.ndarray,\n organic: np.ndarray,\n ratio: np.ndarray,\n liked: set[int],\n disliked: set[int],\n cutoff: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n end = int(np.searchsorted(listen_time, cutoff, side=\"right\"))\n times = listen_time[:end]\n items = listen_item[:end]\n org = organic[:end]\n completion = ratio[:end]\n\n positive = completion >= 50\n positive_items = items[positive]\n recent = pad_recent(positive_items, K_RECENT)\n\n long_mask = (org == 1) & (completion >= 80) & (times >= cutoff - 180 * 86_400)\n organic_items = items[long_mask]\n long_organic = pad_spread(organic_items, K_ORGANIC)\n\n recent_90 = positive & (times >= cutoff - 90 * 86_400)\n features = np.asarray(\n [\n math.log1p(int(positive.sum())) / 10.0,\n math.log1p(int(recent_90.sum())) / 8.0,\n math.log1p(int(long_mask.sum())) / 8.0,\n math.log1p(len(liked)) / 5.0,\n math.log1p(len(disliked)) / 4.0,\n float(org[positive].mean()) if positive.any() else 0.0,\n ],\n dtype=np.float32,\n )\n return (\n recent,\n long_organic,\n pad_state(liked, K_LIKED),\n pad_state(disliked, K_DISLIKED),\n features,\n )\n\n\ndef active_state_from_file(\n path: Path,\n uid_lookup: np.ndarray,\n item_lookup: np.ndarray,\n n_users: int,\n) -> tuple[list[set[int]], list[set[int]]]:\n frame = pd.read_parquet(path)\n uid = mapped(frame.uid.to_numpy(), uid_lookup)\n item = mapped(frame.item_id.to_numpy(), item_lookup)\n state = frame.state.to_numpy()\n liked = [set() for _ in range(n_users)]\n disliked = [set() for _ in range(n_users)]\n for user, track, value in zip(uid, item, state):\n if user < 0 or track < 0:\n continue\n (liked if value == \"liked\" else disliked)[int(user)].add(int(track))\n return liked, disliked\n\n\ndef prepare(stage: str, output: Path, snapshots: int) -> None:\n \"\"\"Create rolling training contexts and the requested inference context.\"\"\"\n started = time.time()\n targets = pd.read_parquet(DATA / \"target_users.parquet\")\n uids = targets.uid.to_numpy(dtype=np.int64)\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n item_ids = catalog.item_id.to_numpy(dtype=np.int64)\n if len(item_ids) != N_ITEMS:\n raise ValueError(f\"expected {N_ITEMS} catalog items\")\n uid_lookup = dense_lookup(uids)\n item_lookup = dense_lookup(item_ids)\n n_users = len(uids)\n\n include_validation = stage == \"test\"\n prefixes = [\"train\", \"val\"] if include_validation else [\"train\"]\n listens = [\n load_source(\n DATA / f\"{prefix}_listens.parquet\",\n uid_lookup,\n item_lookup,\n n_users,\n (\"is_organic\", \"played_ratio_pct\"),\n )\n for prefix in prefixes\n ]\n\n action_specs = ((\"likes\", 1), (\"unlikes\", 2), (\"dislikes\", 3), (\"undislikes\", 4))\n actions: list[tuple[EventSource, int]] = []\n likes_sources: list[EventSource] = []\n for prefix in prefixes:\n for family, kind in action_specs:\n source = load_source(\n DATA / f\"{prefix}_{family}.parquet\",\n uid_lookup,\n item_lookup,\n n_users,\n (\"is_organic\",),\n )\n actions.append((source, kind))\n if family == \"likes\":\n likes_sources.append(source)\n\n inference_cutoff = VALIDATION_END if include_validation else TRAIN_CUTOFF\n latest_training_cutoff = TRAIN_CUTOFF if include_validation else TRAIN_CUTOFF - STRIDE\n cutoffs = np.asarray(\n sorted(latest_training_cutoff - np.arange(snapshots) * STRIDE), dtype=np.int64\n )\n pref_file = DATA / (\"test_preference_state.parquet\" if include_validation else \"validation_preference_state.parquet\")\n inference_liked, inference_disliked = active_state_from_file(\n pref_file, uid_lookup, item_lookup, n_users\n )\n\n train_recent: list[np.ndarray] = []\n train_organic: list[np.ndarray] = []\n train_liked: list[np.ndarray] = []\n train_disliked: list[np.ndarray] = []\n train_features: list[np.ndarray] = []\n context_age: list[float] = []\n label_context: list[int] = []\n label_item: list[int] = []\n label_grade: list[int] = []\n infer_recent = np.empty((n_users, K_RECENT), np.int32)\n infer_organic = np.empty((n_users, K_ORGANIC), np.int32)\n infer_liked = np.empty((n_users, K_LIKED), np.int32)\n infer_disliked = np.empty((n_users, K_DISLIKED), np.int32)\n infer_features = np.empty((n_users, 6), np.float32)\n\n for user in range(n_users):\n lt, li, lf = join_user_parts(listens, user)\n lo = lf[\"is_organic\"].astype(np.uint8, copy=False)\n lr = lf[\"played_ratio_pct\"].astype(np.uint16, copy=False)\n\n action_parts: list[tuple[np.ndarray, np.ndarray, np.ndarray]] = []\n for source, kind in actions:\n at, ai, _ = source.user(user)\n if len(at):\n action_parts.append((at, ai, np.full(len(at), kind, np.uint8)))\n if action_parts:\n at = np.concatenate([part[0] for part in action_parts])\n ai = np.concatenate([part[1] for part in action_parts])\n ak = np.concatenate([part[2] for part in action_parts])\n order = np.argsort(at, kind=\"stable\")\n at, ai, ak = at[order], ai[order], ak[order]\n else:\n at = np.empty(0, np.int32)\n ai = np.empty(0, np.int32)\n ak = np.empty(0, np.uint8)\n\n kt, ki, kf = join_user_parts(likes_sources, user)\n ko = kf.get(\"is_organic\", np.empty(0, np.uint8))\n\n # Earliest event per item approximates the historical eligibility rule.\n all_time = np.concatenate((lt, at))\n all_item = np.concatenate((li, ai))\n if len(all_time):\n time_order = np.argsort(all_time, kind=\"stable\")\n chronological_items = all_item[time_order]\n chronological_times = all_time[time_order]\n seen_items, first_positions = np.unique(\n chronological_items, return_index=True\n )\n first_times = chronological_times[first_positions]\n else:\n seen_items = np.empty(0, np.int32)\n first_times = np.empty(0, np.int32)\n\n for cutoff_number, cutoff in enumerate(cutoffs):\n start, stop = int(cutoff + GAP), int(cutoff + GAP + WINDOW)\n left, right = np.searchsorted(lt, (start, stop), side=\"left\")\n positive_mask = (lo[left:right] == 1) & (lr[left:right] >= 80)\n future_items = li[left:right][positive_mask]\n grades: dict[int, int] = {int(item): 1 for item in future_items}\n kleft, kright = np.searchsorted(kt, (start, stop), side=\"left\")\n for item in ki[kleft:kright][ko[kleft:kright] == 1]:\n grades[int(item)] = 3\n if grades:\n candidate_items = np.fromiter(grades.keys(), dtype=np.int32)\n positions = np.searchsorted(seen_items, candidate_items)\n prior = np.zeros(len(candidate_items), dtype=bool)\n valid = positions < len(seen_items)\n valid_indices = np.flatnonzero(valid)\n exact = np.zeros(len(candidate_items), dtype=bool)\n exact[valid_indices] = seen_items[positions[valid_indices]] == candidate_items[valid_indices]\n exact_indices = np.flatnonzero(exact)\n prior[exact_indices] = first_times[positions[exact_indices]] <= cutoff\n candidate_items = candidate_items[~prior]\n else:\n candidate_items = np.empty(0, np.int32)\n if not len(candidate_items):\n continue\n liked, disliked = state_from_actions(at, ai, ak, int(cutoff))\n context = make_context(lt, li, lo, lr, liked, disliked, int(cutoff))\n context_index = len(train_recent)\n train_recent.append(context[0])\n train_organic.append(context[1])\n train_liked.append(context[2])\n train_disliked.append(context[3])\n train_features.append(context[4])\n context_age.append(float(len(cutoffs) - 1 - cutoff_number))\n for item in candidate_items:\n label_context.append(context_index)\n label_item.append(int(item))\n label_grade.append(grades[int(item)])\n\n inference_context = make_context(\n lt,\n li,\n lo,\n lr,\n inference_liked[user],\n inference_disliked[user],\n inference_cutoff,\n )\n infer_recent[user], infer_organic[user], infer_liked[user], infer_disliked[user], infer_features[user] = inference_context\n if (user + 1) % 1000 == 0:\n print(\n f\"prepared {user + 1}/{n_users} users, \"\n f\"{len(train_recent):,} contexts, {len(label_item):,} positives\",\n flush=True,\n )\n\n arrays = {\n \"uids\": uids,\n \"item_ids\": item_ids,\n \"item_features\": np.column_stack(\n [\n np.log1p(catalog.positive_listens.to_numpy()),\n np.log1p(catalog.organic_positive_listens.to_numpy()),\n np.log1p(catalog.organic_positive_listens.to_numpy())\n - np.log1p(catalog.positive_listens.to_numpy()),\n -np.log1p(catalog.popularity_rank.to_numpy()),\n ]\n ).astype(np.float32),\n \"train_recent\": np.stack(train_recent),\n \"train_organic\": np.stack(train_organic),\n \"train_liked\": np.stack(train_liked),\n \"train_disliked\": np.stack(train_disliked),\n \"train_features\": np.stack(train_features),\n \"context_age\": np.asarray(context_age, np.float32),\n \"label_context\": np.asarray(label_context, np.int32),\n \"label_item\": np.asarray(label_item, np.int32),\n \"label_grade\": np.asarray(label_grade, np.uint8),\n \"infer_recent\": infer_recent,\n \"infer_organic\": infer_organic,\n \"infer_liked\": infer_liked,\n \"infer_disliked\": infer_disliked,\n \"infer_features\": infer_features,\n }\n # Normalize fixed features using catalog-wide moments.\n mean = arrays[\"item_features\"].mean(axis=0)\n std = arrays[\"item_features\"].std(axis=0).clip(1e-6)\n arrays[\"item_features\"] = (arrays[\"item_features\"] - mean) / std\n output.parent.mkdir(parents=True, exist_ok=True)\n np.savez(output, **arrays)\n print(\n json.dumps(\n {\n \"stage\": stage,\n \"contexts\": len(train_recent),\n \"positives\": len(label_item),\n \"grade3\": int((arrays[\"label_grade\"] == 3).sum()),\n \"seconds\": round(time.time() - started, 1),\n \"output\": str(output),\n },\n indent=2,\n )\n )\n\n\nclass UserTower(nn.Module):\n \"\"\"Encode interaction history; deliberately has no user-ID embedding.\"\"\"\n\n def __init__(self, n_items: int, token_dim: int, output_dim: int) -> None:\n super().__init__()\n self.padding = n_items\n self.history_embedding = nn.Embedding(\n n_items + 1, token_dim, padding_idx=n_items\n )\n self.channel_norms = nn.ModuleList([nn.LayerNorm(token_dim) for _ in range(4)])\n self.network = nn.Sequential(\n nn.Linear(4 * token_dim + 6, 2 * output_dim),\n nn.GELU(),\n nn.LayerNorm(2 * output_dim),\n nn.Dropout(0.05),\n nn.Linear(2 * output_dim, output_dim),\n )\n nn.init.normal_(self.history_embedding.weight, std=0.04)\n with torch.no_grad():\n self.history_embedding.weight[n_items].zero_()\n\n def pool(self, ids: torch.Tensor, channel: int) -> torch.Tensor:\n mask = ids.ne(self.padding)\n values = self.history_embedding(ids)\n pooled = (values * mask.unsqueeze(-1)).sum(dim=1)\n pooled = pooled / mask.sum(dim=1, keepdim=True).clamp_min(1)\n return self.channel_norms[channel](pooled)\n\n def forward(\n self,\n recent: torch.Tensor,\n organic: torch.Tensor,\n liked: torch.Tensor,\n disliked: torch.Tensor,\n features: torch.Tensor,\n ) -> torch.Tensor:\n combined = torch.cat(\n [\n self.pool(recent, 0),\n self.pool(organic, 1),\n self.pool(liked, 2),\n self.pool(disliked, 3),\n features,\n ],\n dim=1,\n )\n latent = F.normalize(self.network(combined), dim=1)\n # Constant coordinate represents item bias and remains a dot product.\n return torch.cat((latent, torch.ones_like(latent[:, :1])), dim=1)\n\n\nclass ItemTower(nn.Module):\n \"\"\"Independently encode one catalog ID and fixed catalog features.\"\"\"\n\n def __init__(self, item_features: np.ndarray, output_dim: int) -> None:\n super().__init__()\n n_items, feature_dim = item_features.shape\n self.register_buffer(\"features\", torch.from_numpy(item_features.copy()))\n self.item_embedding = nn.Embedding(n_items, output_dim)\n self.feature_network = nn.Sequential(\n nn.Linear(feature_dim, output_dim), nn.GELU(), nn.Linear(output_dim, output_dim)\n )\n self.output_network = nn.Sequential(\n nn.LayerNorm(output_dim), nn.Linear(output_dim, output_dim), nn.GELU(), nn.Linear(output_dim, output_dim)\n )\n self.item_bias = nn.Embedding(n_items, 1)\n self.feature_bias = nn.Linear(feature_dim, 1, bias=False)\n self.log_scale = nn.Parameter(torch.tensor(math.log(8.0)))\n nn.init.normal_(self.item_embedding.weight, std=0.04)\n nn.init.zeros_(self.item_bias.weight)\n\n def forward(self, items: torch.Tensor) -> torch.Tensor:\n features = self.features[items]\n hidden = self.item_embedding(items) + self.feature_network(features)\n latent = F.normalize(self.output_network(hidden) + hidden, dim=-1)\n scale = self.log_scale.exp().clamp(1.0, 30.0)\n bias = self.item_bias(items) + self.feature_bias(features)\n return torch.cat((latent * scale, bias), dim=-1)\n\n\nclass TwoTower(nn.Module):\n def __init__(self, item_features: np.ndarray, token_dim: int = 48, dim: int = 64) -> None:\n super().__init__()\n self.user_tower = UserTower(len(item_features), token_dim, dim)\n self.item_tower = ItemTower(item_features, dim)\n\n\ndef context_batch(data: dict[str, torch.Tensor], indices: torch.Tensor) -> tuple[torch.Tensor, ...]:\n return (\n data[\"recent\"][indices].long(),\n data[\"organic\"][indices].long(),\n data[\"liked\"][indices].long(),\n data[\"disliked\"][indices].long(),\n data[\"features\"][indices],\n )\n\n\ndef train_model(\n prepared: Path,\n output: Path,\n epochs: int,\n batch_size: int,\n negatives: int,\n seed: int,\n) -> None:\n seed_everything(seed)\n torch.set_num_threads(min(8, max(1, torch.get_num_threads() * 2)))\n archive = np.load(prepared, allow_pickle=False)\n item_features = archive[\"item_features\"].astype(np.float32)\n model = TwoTower(item_features)\n model.train()\n contexts = {\n \"recent\": torch.from_numpy(archive[\"train_recent\"]),\n \"organic\": torch.from_numpy(archive[\"train_organic\"]),\n \"liked\": torch.from_numpy(archive[\"train_liked\"]),\n \"disliked\": torch.from_numpy(archive[\"train_disliked\"]),\n \"features\": torch.from_numpy(archive[\"train_features\"]),\n }\n label_context = torch.from_numpy(archive[\"label_context\"].astype(np.int64))\n label_item = torch.from_numpy(archive[\"label_item\"].astype(np.int64))\n grades = torch.from_numpy(archive[\"label_grade\"].astype(np.float32))\n ages = torch.from_numpy(archive[\"context_age\"])[label_context]\n n_labels = len(label_item)\n\n # Half the negatives are uniform and half emphasize plausible popular items.\n positive_counts = np.bincount(\n archive[\"label_item\"], minlength=N_ITEMS\n ).astype(np.float64)\n sampling = np.sqrt(positive_counts + 2.0)\n sampling /= sampling.sum()\n sampling_tensor = torch.from_numpy(sampling.astype(np.float32))\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\n generator = torch.Generator().manual_seed(seed + 17)\n history: list[dict[str, float]] = []\n for epoch in range(epochs):\n epoch_start = time.time()\n permutation = torch.randperm(n_labels, generator=generator)\n total_loss = 0.0\n total_examples = 0\n for offset in range(0, n_labels, batch_size):\n rows = permutation[offset : offset + batch_size]\n ctx = label_context[rows]\n positives = label_item[rows]\n user_vector = model.user_tower(*context_batch(contexts, ctx))\n positive_vector = model.item_tower(positives)\n positive_score = (user_vector * positive_vector).sum(dim=1)\n\n current = len(rows)\n uniform_count = negatives // 2\n uniform = torch.randint(\n N_ITEMS, (current, uniform_count), generator=generator\n )\n hard = torch.multinomial(\n sampling_tensor,\n current * (negatives - uniform_count),\n replacement=True,\n generator=generator,\n ).view(current, -1)\n negative_items = torch.cat((uniform, hard), dim=1)\n negative_vector = model.item_tower(negative_items)\n negative_score = (user_vector[:, None, :] * negative_vector).sum(dim=2)\n pair_loss = F.softplus(negative_score - positive_score[:, None]).mean(dim=1)\n grade_weight = torch.where(grades[rows] >= 3, 4.0, 1.0)\n recency_weight = 0.55 + 0.45 * torch.exp(-ages[rows] / 6.0)\n loss = (pair_loss * grade_weight * recency_weight).sum() / (\n grade_weight * recency_weight\n ).sum()\n optimizer.zero_grad(set_to_none=True)\n loss.backward()\n nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n optimizer.step()\n total_loss += float(loss) * current\n total_examples += current\n record = {\n \"epoch\": epoch + 1,\n \"loss\": total_loss / total_examples,\n \"seconds\": time.time() - epoch_start,\n \"scale\": float(model.item_tower.log_scale.exp()),\n }\n history.append(record)\n print(json.dumps(record), flush=True)\n\n output.parent.mkdir(parents=True, exist_ok=True)\n torch.save(\n {\n \"state_dict\": model.state_dict(),\n \"token_dim\": 48,\n \"dim\": 64,\n \"item_features\": item_features,\n \"history\": history,\n \"seed\": seed,\n },\n output,\n )\n print(f\"saved {output}\")\n\n\n@torch.inference_mode()\ndef rank(\n stage: str,\n prepared: Path,\n checkpoint: Path,\n output: Path,\n popularity_weight: float,\n) -> None:\n torch.set_num_threads(8)\n archive = np.load(prepared, allow_pickle=False)\n saved = torch.load(checkpoint, map_location=\"cpu\", weights_only=False)\n model = TwoTower(\n saved[\"item_features\"], saved[\"token_dim\"], saved[\"dim\"]\n )\n model.load_state_dict(saved[\"state_dict\"])\n model.eval()\n contexts = {\n \"recent\": torch.from_numpy(archive[\"infer_recent\"]),\n \"organic\": torch.from_numpy(archive[\"infer_organic\"]),\n \"liked\": torch.from_numpy(archive[\"infer_liked\"]),\n \"disliked\": torch.from_numpy(archive[\"infer_disliked\"]),\n \"features\": torch.from_numpy(archive[\"infer_features\"]),\n }\n item_vectors = model.item_tower(torch.arange(N_ITEMS)).numpy()\n # A fixed popularity coordinate is an explicit part of both tower outputs.\n popularity = archive[\"item_features\"][:, 1].astype(np.float32)\n item_vectors = np.column_stack((item_vectors, popularity))\n\n eligibility_path = DATA / f\"{stage}_eligible_candidates.npz\"\n with np.load(eligibility_path, allow_pickle=False) as eligible:\n eligible_uids = eligible[\"uids\"].astype(np.int64)\n eligible_items = eligible[\"item_ids\"].astype(np.int64)\n packed = eligible[\"packed_eligible\"].copy()\n if not np.array_equal(eligible_uids, archive[\"uids\"]):\n raise ValueError(\"prepared and eligibility user orders differ\")\n if not np.array_equal(eligible_items, archive[\"item_ids\"]):\n # Keep vector rows aligned to the archive catalog if an artifact changes order.\n position = {int(item): i for i, item in enumerate(archive[\"item_ids\"])}\n item_vectors = item_vectors[[position[int(item)] for item in eligible_items]]\n\n rows: list[pd.DataFrame] = []\n chunk_size = 128\n for start in range(0, len(eligible_uids), chunk_size):\n stop = min(len(eligible_uids), start + chunk_size)\n indices = torch.arange(start, stop)\n user_vectors = model.user_tower(*context_batch(contexts, indices)).numpy()\n user_vectors = np.column_stack(\n (user_vectors, np.full(stop - start, popularity_weight, np.float32))\n )\n scores = user_vectors @ item_vectors.T\n mask = np.unpackbits(\n packed[start:stop], axis=1, count=N_ITEMS, bitorder=\"little\"\n ).astype(bool)\n scores[~mask] = -np.inf\n top = np.argpartition(scores, -100, axis=1)[:, -100:]\n top_scores = np.take_along_axis(scores, top, axis=1)\n order = np.argsort(-top_scores, axis=1, kind=\"stable\")\n top = np.take_along_axis(top, order, axis=1)\n rows.append(\n pd.DataFrame(\n {\n \"uid\": np.repeat(eligible_uids[start:stop], 100),\n \"item_id\": eligible_items[top.reshape(-1)],\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), stop - start),\n }\n )\n )\n output.parent.mkdir(parents=True, exist_ok=True)\n pd.concat(rows, ignore_index=True).to_parquet(output, index=False)\n print(\n json.dumps(\n {\n \"stage\": stage,\n \"output\": str(output),\n \"rows\": len(eligible_uids) * 100,\n \"popularity_weight\": popularity_weight,\n },\n indent=2,\n )\n )\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(dest=\"command\", required=True)\n prep = subparsers.add_parser(\"prepare\")\n prep.add_argument(\"--stage\", choices=(\"validation\", \"test\"), required=True)\n prep.add_argument(\"--output\", type=Path, required=True)\n prep.add_argument(\"--snapshots\", type=int, default=16)\n fit = subparsers.add_parser(\"train\")\n fit.add_argument(\"--prepared\", type=Path, required=True)\n fit.add_argument(\"--output\", type=Path, required=True)\n fit.add_argument(\"--epochs\", type=int, default=4)\n fit.add_argument(\"--batch-size\", type=int, default=512)\n fit.add_argument(\"--negatives\", type=int, default=24)\n fit.add_argument(\"--seed\", type=int, default=20260924)\n infer = subparsers.add_parser(\"rank\")\n infer.add_argument(\"--stage\", choices=(\"validation\", \"test\"), required=True)\n infer.add_argument(\"--prepared\", type=Path, required=True)\n infer.add_argument(\"--checkpoint\", type=Path, required=True)\n infer.add_argument(\"--output\", type=Path, required=True)\n infer.add_argument(\"--popularity-weight\", type=float, default=0.0)\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n if args.command == \"prepare\":\n prepare(args.stage, args.output, args.snapshots)\n elif args.command == \"train\":\n train_model(\n args.prepared,\n args.output,\n args.epochs,\n args.batch_size,\n args.negatives,\n args.seed,\n )\n else:\n rank(\n args.stage,\n args.prepared,\n args.checkpoint,\n args.output,\n args.popularity_weight,\n )\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:11:32.476Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ad4611ac87d2bd9318f6d5fe7f61", "usage": { "input_tokens": 38658, "cached_input_tokens": 37120, "cache_write_input_tokens": 0, "output_tokens": 12320, "reasoning_output_tokens": 3419, "total_tokens": 50978 }, "turn_token_usage": { "input_tokens": 384393, "cached_input_tokens": 354304, "cache_write_input_tokens": 0, "output_tokens": 27282, "reasoning_output_tokens": 12684, "total_tokens": 411675 }, "thread_token_usage": { "input_tokens": 384393, "cached_input_tokens": 354304, "cache_write_input_tokens": 0, "output_tokens": 27282, "reasoning_output_tokens": 12684, "total_tokens": 411675 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-24T23:11:34.118Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:11:34.118Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:11:36.084Z", "callId": "call_Fq0sS4iJhkqKyujqJzyfFLs2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py prepare --stage validation --output /app/solution/prepared_validation.npz --snapshots 16\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:12:01.376Z", "content": "Script completed\nWall time 25.3 seconds\nOutput:\n\nprepared 1000/8456 users, 6,927 contexts, 64,667 positives\nprepared 2000/8456 users, 14,020 contexts, 132,284 positives\nprepared 3000/8456 users, 20,949 contexts, 195,248 positives\nprepared 4000/8456 users, 27,933 contexts, 257,729 positives\nprepared 5000/8456 users, 34,679 contexts, 323,088 positives\nprepared 6000/8456 users, 41,515 contexts, 390,619 positives\nprepared 7000/8456 users, 48,247 contexts, 452,651 positives\nprepared 8000/8456 users, 55,373 contexts, 517,048 positives\n{\n \"stage\": \"validation\",\n \"contexts\": 58465,\n \"positives\": 544536,\n \"grade3\": 74642,\n \"seconds\": 20.6,\n \"output\": \"/app/solution/prepared_validation.npz\"\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:36.170Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ae24d1b487d2881d42e82e51eeb1", "usage": { "input_tokens": 51002, "cached_input_tokens": 38528, "cache_write_input_tokens": 0, "output_tokens": 128, "reasoning_output_tokens": 9, "total_tokens": 51130 }, "turn_token_usage": { "input_tokens": 435395, "cached_input_tokens": 392832, "cache_write_input_tokens": 0, "output_tokens": 27410, "reasoning_output_tokens": 12693, "total_tokens": 462805 }, "thread_token_usage": { "input_tokens": 435395, "cached_input_tokens": 392832, "cache_write_input_tokens": 0, "output_tokens": 27410, "reasoning_output_tokens": 12693, "total_tokens": 462805 } } }, { "kind": "context", "timestamp": "2026-09-24T23:12:01.372Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b5fe7546-5960-40b9-8af2-32dbf21271cd", "process_id": "92526", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py prepare --stage validation --output /app/solution/prepared_validation.npz --snapshots 16" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py prepare --stage validation --output /app/solution/prepared_validation.npz --snapshots 16" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared 1000/8456 users, 6,927 contexts, 64,667 positives\nprepared 2000/8456 users, 14,020 contexts, 132,284 positives\nprepared 3000/8456 users, 20,949 contexts, 195,248 positives\nprepared 4000/8456 users, 27,933 contexts, 257,729 positives\nprepared 5000/8456 users, 34,679 contexts, 323,088 positives\nprepared 6000/8456 users, 41,515 contexts, 390,619 positives\nprepared 7000/8456 users, 48,247 contexts, 452,651 positives\nprepared 8000/8456 users, 55,373 contexts, 517,048 positives\n{\n \"stage\": \"validation\",\n \"contexts\": 58465,\n \"positives\": 544536,\n \"grade3\": 74642,\n \"seconds\": 20.6,\n \"output\": \"/app/solution/prepared_validation.npz\"\n}\n", "stderr": "", "aggregated_output": "prepared 1000/8456 users, 6,927 contexts, 64,667 positives\nprepared 2000/8456 users, 14,020 contexts, 132,284 positives\nprepared 3000/8456 users, 20,949 contexts, 195,248 positives\nprepared 4000/8456 users, 27,933 contexts, 257,729 positives\nprepared 5000/8456 users, 34,679 contexts, 323,088 positives\nprepared 6000/8456 users, 41,515 contexts, 390,619 positives\nprepared 7000/8456 users, 48,247 contexts, 452,651 positives\nprepared 8000/8456 users, 55,373 contexts, 517,048 positives\n{\n \"stage\": \"validation\",\n \"contexts\": 58465,\n \"positives\": 544536,\n \"grade3\": 74642,\n \"seconds\": 20.6,\n \"output\": \"/app/solution/prepared_validation.npz\"\n}\n", "exit_code": 0, "duration": { "secs": 25, "nanos": 123532639 }, "formatted_output": "prepared 1000/8456 users, 6,927 contexts, 64,667 positives\nprepared 2000/8456 users, 14,020 contexts, 132,284 positives\nprepared 3000/8456 users, 20,949 contexts, 195,248 positives\nprepared 4000/8456 users, 27,933 contexts, 257,729 positives\nprepared 5000/8456 users, 34,679 contexts, 323,088 positives\nprepared 6000/8456 users, 41,515 contexts, 390,619 positives\nprepared 7000/8456 users, 48,247 contexts, 452,651 positives\nprepared 8000/8456 users, 55,373 contexts, 517,048 positives\n{\n \"stage\": \"validation\",\n \"contexts\": 58465,\n \"positives\": 544536,\n \"grade3\": 74642,\n \"seconds\": 20.6,\n \"output\": \"/app/solution/prepared_validation.npz\"\n}\n" } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-24T23:12:04.728Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:12:04.728Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:12:09.488Z", "callId": "call_58YHfd78VFu9uTrVjuY7TTXB", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution/prepared_validation.npz\\n/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\np=np.load('/app/solution/prepared_validation.npz')\\nfor k in p.files: print(k,p[k].shape,p[k].dtype)\\nprint('labels/item coverage',len(np.unique(p['label_item'])),'contexts',len(p['train_recent'])),'pad rates',{x:float((p[x]==30000).mean()) for x in ['train_recent','train_organic','train_liked','train_disliked']})\\nprint('inference pads',{x:float((p[x]==30000).mean()) for x in ['infer_recent','infer_organic','infer_liked','infer_disliked']})\\nprint('ages',np.unique(p['context_age'],return_counts=True))\\nPY\\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation.pt --epochs 4 --batch-size 512 --negatives 24 --seed 20260924\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:12:39.662Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n-rw-r--r-- 1 agent agent 114M Sep 24 23:12 /app/solution/prepared_validation.npz\n File \"\", line 4\n print('labels/item coverage',len(np.unique(p['label_item'])),'contexts',len(p['train_recent'])),'pad rates',{x:float((p[x]==30000).mean()) for x in ['train_recent','train_organic','train_liked','train_disliked']})\n ^\nSyntaxError: unmatched ')'\n/app/solution/train_ranker.py:614: 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_loss += float(loss) * current\n\nSESSION_ID=81769", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:12:09.526Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ae41920c87d29d0ea18caaaffa40", "usage": { "input_tokens": 51379, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 397, "reasoning_output_tokens": 93, "total_tokens": 51776 }, "turn_token_usage": { "input_tokens": 486774, "cached_input_tokens": 443648, "cache_write_input_tokens": 0, "output_tokens": 27807, "reasoning_output_tokens": 12786, "total_tokens": 514581 }, "thread_token_usage": { "input_tokens": 486774, "cached_input_tokens": 443648, "cache_write_input_tokens": 0, "output_tokens": 27807, "reasoning_output_tokens": 12786, "total_tokens": 514581 } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-24T23:12:41.689Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:12:41.689Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:12:42.872Z", "text": "Preparation produced 58,465 rolling user contexts and 544,536 unseen organic positives across 16 historical weeks; 13.7% are high-gain organic likes. Training is now running on those event-sampled examples. The popularity submission remains intact as the current fallback." }, { "kind": "tool", "timestamp": "2026-09-24T23:12:43.557Z", "callId": "call_4mCbX4Am0QPeGqTuFcpvypj4", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81769,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:13:13.594Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 1, \"loss\": 0.4485634167415527, \"seconds\": 54.384371519088745, \"scale\": 8.273462295532227}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:12:43.604Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ae67ef5487d28d0f83a530a3d9c0", "usage": { "input_tokens": 51981, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 24, "total_tokens": 52115 }, "turn_token_usage": { "input_tokens": 538755, "cached_input_tokens": 494848, "cache_write_input_tokens": 0, "output_tokens": 27941, "reasoning_output_tokens": 12810, "total_tokens": 566696 }, "thread_token_usage": { "input_tokens": 538755, "cached_input_tokens": 494848, "cache_write_input_tokens": 0, "output_tokens": 27941, "reasoning_output_tokens": 12810, "total_tokens": 566696 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-24T23:13:15.402Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:13:15.402Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:13:16.097Z", "callId": "call_mKLedwP9eDolryQ6bcDdXSSW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81769,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:13:46.139Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:13:16.170Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5ae89cca087d2a2e8fdea818274e4", "usage": { "input_tokens": 52181, "cached_input_tokens": 51840, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 31, "total_tokens": 52259 }, "turn_token_usage": { "input_tokens": 590936, "cached_input_tokens": 546688, "cache_write_input_tokens": 0, "output_tokens": 28019, "reasoning_output_tokens": 12841, "total_tokens": 618955 }, "thread_token_usage": { "input_tokens": 590936, "cached_input_tokens": 546688, "cache_write_input_tokens": 0, "output_tokens": 28019, "reasoning_output_tokens": 12841, "total_tokens": 618955 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-24T23:13:47.400Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:13:47.400Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:13:48.088Z", "callId": "call_ziubdhWiYE43yVMr65A0Tgup", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81769,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:14:18.141Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 2, \"loss\": 0.28520803439970405, \"seconds\": 52.08735775947571, \"scale\": 9.416604042053223}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:13:48.179Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5aeaa619c87d2b3434a57e85cbdce", "usage": { "input_tokens": 52282, "cached_input_tokens": 51968, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 52335 }, "turn_token_usage": { "input_tokens": 643218, "cached_input_tokens": 598656, "cache_write_input_tokens": 0, "output_tokens": 28072, "reasoning_output_tokens": 12847, "total_tokens": 671290 }, "thread_token_usage": { "input_tokens": 643218, "cached_input_tokens": 598656, "cache_write_input_tokens": 0, "output_tokens": 28072, "reasoning_output_tokens": 12847, "total_tokens": 671290 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-24T23:14:19.749Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:19.749Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:14:20.440Z", "callId": "call_W45ECW5u8eBit0wK6BPf26Mz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81769,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:14:50.486Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:20.493Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5aeca66a487d29a9fc63b0aebb20e", "usage": { "input_tokens": 52401, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 29, "total_tokens": 52477 }, "turn_token_usage": { "input_tokens": 695619, "cached_input_tokens": 650752, "cache_write_input_tokens": 0, "output_tokens": 28148, "reasoning_output_tokens": 12876, "total_tokens": 723767 }, "thread_token_usage": { "input_tokens": 695619, "cached_input_tokens": 650752, "cache_write_input_tokens": 0, "output_tokens": 28148, "reasoning_output_tokens": 12876, "total_tokens": 723767 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-24T23:14:52.536Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:52.536Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:14:53.870Z", "text": "Training loss has fallen from 0.449 to 0.285 after two epochs, with stable embedding scale. I’ll finish the four-epoch fit, score the validation ranking, then calibrate only the explicit popularity coordinate on the public window; that calibration remains part of the same exact dot-product representation." }, { "kind": "tool", "timestamp": "2026-09-24T23:14:54.586Z", "callId": "call_HC0E22L3rZ6TD2sg7ZtdgUuF", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81769,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:15:24.623Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 3, \"loss\": 0.20431842734181208, \"seconds\": 51.85065054893494, \"scale\": 11.44864559173584}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:54.700Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5aeeab80c87d2a0570aa923e39f90", "usage": { "input_tokens": 52500, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 136, "reasoning_output_tokens": 20, "total_tokens": 52636 }, "turn_token_usage": { "input_tokens": 748119, "cached_input_tokens": 702976, "cache_write_input_tokens": 0, "output_tokens": 28284, "reasoning_output_tokens": 12896, "total_tokens": 776403 }, "thread_token_usage": { "input_tokens": 748119, "cached_input_tokens": 702976, "cache_write_input_tokens": 0, "output_tokens": 28284, "reasoning_output_tokens": 12896, "total_tokens": 776403 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-24T23:15:25.969Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:25.969Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:15:26.679Z", "callId": "call_BvMNEpD1KSTFNlFNe2r73VLs", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81769,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:15:49.209Z", "content": "Script completed\nWall time 22.5 seconds\nOutput:\n\n{\"epoch\": 4, \"loss\": 0.1624372416901249, \"seconds\": 53.89589166641235, \"scale\": 13.867147445678711}\nsaved /app/solution/model_validation.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:15:26.771Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5af0cd9d887d2b820f471869c4cc1", "usage": { "input_tokens": 52702, "cached_input_tokens": 52352, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 52755 }, "turn_token_usage": { "input_tokens": 800821, "cached_input_tokens": 755328, "cache_write_input_tokens": 0, "output_tokens": 28337, "reasoning_output_tokens": 12902, "total_tokens": 829158 }, "thread_token_usage": { "input_tokens": 800821, "cached_input_tokens": 755328, "cache_write_input_tokens": 0, "output_tokens": 28337, "reasoning_output_tokens": 12902, "total_tokens": 829158 } } }, { "kind": "context", "timestamp": "2026-09-24T23:15:49.207Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-63748b43-c387-4011-bdd9-3e74af0277df", "process_id": "81769", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/prepared_validation.npz\n/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\np=np.load('/app/solution/prepared_validation.npz')\nfor k in p.files: print(k,p[k].shape,p[k].dtype)\nprint('labels/item coverage',len(np.unique(p['label_item'])),'contexts',len(p['train_recent'])),'pad rates',{x:float((p[x]==30000).mean()) for x in ['train_recent','train_organic','train_liked','train_disliked']})\nprint('inference pads',{x:float((p[x]==30000).mean()) for x in ['infer_recent','infer_organic','infer_liked','infer_disliked']})\nprint('ages',np.unique(p['context_age'],return_counts=True))\nPY\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation.pt --epochs 4 --batch-size 512 --negatives 24 --seed 20260924" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/prepared_validation.npz\n/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\np=np.load('/app/solution/prepared_validation.npz')\nfor k in p.files: print(k,p[k].shape,p[k].dtype)\nprint('labels/item coverage',len(np.unique(p['label_item'])),'contexts',len(p['train_recent'])),'pad rates',{x:float((p[x]==30000).mean()) for x in ['train_recent','train_organic','train_liked','train_disliked']})\nprint('inference pads',{x:float((p[x]==30000).mean()) for x in ['infer_recent','infer_organic','infer_liked','infer_disliked']})\nprint('ages',np.unique(p['context_age'],return_counts=True))\nPY\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation.pt --epochs 4 --batch-size 512 --negatives 24 --seed 20260924" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/train_ranker.py:614: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4485634167415527, \"seconds\": 54.384371519088745, \"scale\": 8.273462295532227}\n{\"epoch\": 2, \"loss\": 0.28520803439970405, \"seconds\": 52.08735775947571, \"scale\": 9.416604042053223}\n{\"epoch\": 3, \"loss\": 0.20431842734181208, \"seconds\": 51.85065054893494, \"scale\": 11.44864559173584}\n{\"epoch\": 4, \"loss\": 0.1624372416901249, \"seconds\": 53.89589166641235, \"scale\": 13.867147445678711}\nsaved /app/solution/model_validation.pt\n", "stderr": "", "aggregated_output": "/app/solution/train_ranker.py:614: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4485634167415527, \"seconds\": 54.384371519088745, \"scale\": 8.273462295532227}\n{\"epoch\": 2, \"loss\": 0.28520803439970405, \"seconds\": 52.08735775947571, \"scale\": 9.416604042053223}\n{\"epoch\": 3, \"loss\": 0.20431842734181208, \"seconds\": 51.85065054893494, \"scale\": 11.44864559173584}\n{\"epoch\": 4, \"loss\": 0.1624372416901249, \"seconds\": 53.89589166641235, \"scale\": 13.867147445678711}\nsaved /app/solution/model_validation.pt\n", "exit_code": 0, "duration": { "secs": 219, "nanos": 553901706 }, "formatted_output": "/app/solution/train_ranker.py:614: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4485634167415527, \"seconds\": 54.384371519088745, \"scale\": 8.273462295532227}\n{\"epoch\": 2, \"loss\": 0.28520803439970405, \"seconds\": 52.08735775947571, \"scale\": 9.416604042053223}\n{\"epoch\": 3, \"loss\": 0.20431842734181208, \"seconds\": 51.85065054893494, \"scale\": 11.44864559173584}\n{\"epoch\": 4, \"loss\": 0.1624372416901249, \"seconds\": 53.89589166641235, \"scale\": 13.867147445678711}\nsaved /app/solution/model_validation.pt\n" } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-24T23:15:51.114Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:51.114Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:15:53.721Z", "callId": "call_vPqQzi5WSAXs1mBrFem7XPNX", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --output /app/solution/validation_twotower_w0.parquet --popularity-weight 0.0 && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_w0.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:16:04.495Z", "content": "Script completed\nWall time 10.8 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015981,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:15:53.781Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5af257f9c87d2b769ffe11e0d58e1", "usage": { "input_tokens": 52830, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 177, "reasoning_output_tokens": 24, "total_tokens": 53007 }, "turn_token_usage": { "input_tokens": 853651, "cached_input_tokens": 807808, "cache_write_input_tokens": 0, "output_tokens": 28514, "reasoning_output_tokens": 12926, "total_tokens": 882165 }, "thread_token_usage": { "input_tokens": 853651, "cached_input_tokens": 807808, "cache_write_input_tokens": 0, "output_tokens": 28514, "reasoning_output_tokens": 12926, "total_tokens": 882165 } } }, { "kind": "context", "timestamp": "2026-09-24T23:16:04.491Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8120df0d-18f5-47c6-9031-380a5f7b8ba6", "process_id": "61876", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --output /app/solution/validation_twotower_w0.parquet --popularity-weight 0.0 && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_w0.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --output /app/solution/validation_twotower_w0.parquet --popularity-weight 0.0 && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_w0.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015981,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015981,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 604284840 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015981,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-24T23:16:08.870Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:08.870Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:16:11.209Z", "callId": "call_bFcBMe3qla1p9vnx49JLCFsm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for w in 0.5 1.0 2.0 3.0 -0.5; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --output /app/solution/validation_twotower_w${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_w${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:16:41.377Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020449,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022991,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 2.0\n}\n\nSESSION_ID=30289", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:11.282Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5af34ad8c87d2a1cdc99b7769f4e2", "usage": { "input_tokens": 53126, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 329, "reasoning_output_tokens": 147, "total_tokens": 53455 }, "turn_token_usage": { "input_tokens": 906777, "cached_input_tokens": 860416, "cache_write_input_tokens": 0, "output_tokens": 28843, "reasoning_output_tokens": 13073, "total_tokens": 935620 }, "thread_token_usage": { "input_tokens": 906777, "cached_input_tokens": 860416, "cache_write_input_tokens": 0, "output_tokens": 28843, "reasoning_output_tokens": 13073, "total_tokens": 935620 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-24T23:16:42.523Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:42.523Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:16:43.217Z", "callId": "call_29EHrNR5lv5Kqd19R7ojDvMc", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":30289,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:17:06.470Z", "content": "Script completed\nWall time 23.2 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020338,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 3.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016779,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": -0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01013,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:43.300Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5af599b1487d28a0a5e6c36edf921", "usage": { "input_tokens": 53731, "cached_input_tokens": 52992, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 53784 }, "turn_token_usage": { "input_tokens": 960508, "cached_input_tokens": 913408, "cache_write_input_tokens": 0, "output_tokens": 28896, "reasoning_output_tokens": 13079, "total_tokens": 989404 }, "thread_token_usage": { "input_tokens": 960508, "cached_input_tokens": 913408, "cache_write_input_tokens": 0, "output_tokens": 28896, "reasoning_output_tokens": 13079, "total_tokens": 989404 } } }, { "kind": "context", "timestamp": "2026-09-24T23:17:06.468Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7d417a19-4f15-4a5e-ba02-cabfb03b6036", "process_id": "30289", "command": [ "/bin/sh", "-lc", "for w in 0.5 1.0 2.0 3.0 -0.5; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --output /app/solution/validation_twotower_w${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_w${w}.parquet; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for w in 0.5 1.0 2.0 3.0 -0.5; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --output /app/solution/validation_twotower_w${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_w${w}.parquet; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020449,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022991,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020338,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 3.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016779,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": -0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01013,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020449,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022991,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020338,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 3.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016779,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": -0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01013,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 55, "nanos": 93172349 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020449,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022991,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020338,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 3.0\n}\n{\n \"model\": \"/app/solution/validation_twotower_w3.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016779,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": -0.5\n}\n{\n \"model\": \"/app/solution/validation_twotower_w-0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01013,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-24T23:17:18.700Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:18.700Z", "text": "" } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-24T23:17:28.502Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:28.502Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:17:31.147Z", "callId": "call_hcUjeiOQK55878XonEiEe5w8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\np=np.load('/app/solution/prepared_validation.npz')\\nprint('label coverage',len(np.unique(p['label_item'])), 'contexts',len(p['train_recent']), 'labels',len(p['label_item']))\\nprint('pad train',{x:round(float((p[x]==30000).mean()),3) for x in ['train_recent','train_organic','train_liked','train_disliked']})\\nprint('pad infer',{x:round(float((p[x]==30000).mean()),3) for x in ['infer_recent','infer_organic','infer_liked','infer_disliked']})\\nui,c=np.unique(p['context_age'],return_counts=True); print('contexts/age',dict(zip(ui.astype(int),c)))\\nli,c=np.unique(p['label_item'],return_counts=True); print('label count percentiles',np.quantile(c,[0,.25,.5,.75,.9,.99,1]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:17:31.663Z", "content": "Script completed\nWall time 0.5 seconds\nOutput:\n\nlabel coverage 28495 contexts 58465 labels 544536\npad train {'train_recent': 0.071, 'train_organic': 0.134, 'train_liked': 0.685, 'train_disliked': 0.893}\npad infer {'infer_recent': 0.048, 'infer_organic': 0.204, 'infer_liked': 0.717, 'infer_disliked': 0.896}\ncontexts/age {np.int64(0): np.int64(4284), np.int64(1): np.int64(4236), np.int64(2): np.int64(4089), np.int64(3): np.int64(4042), np.int64(4): np.int64(3991), np.int64(5): np.int64(3845), np.int64(6): np.int64(3788), np.int64(7): np.int64(3748), np.int64(8): np.int64(3598), np.int64(9): np.int64(3415), np.int64(10): np.int64(3374), np.int64(11): np.int64(3335), np.int64(12): np.int64(3261), np.int64(13): np.int64(3196), np.int64(14): np.int64(3132), np.int64(15): np.int64(3131)}\nlabel count percentiles [ 1. 4. 9. 20. 42. 164. 928.]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:17:31.193Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5af72bb4487d29dba2b8759f8390d", "usage": { "input_tokens": 54061, "cached_input_tokens": 53504, "cache_write_input_tokens": 0, "output_tokens": 1241, "reasoning_output_tokens": 971, "total_tokens": 55302 }, "turn_token_usage": { "input_tokens": 1014569, "cached_input_tokens": 966912, "cache_write_input_tokens": 0, "output_tokens": 30137, "reasoning_output_tokens": 14050, "total_tokens": 1044706 }, "thread_token_usage": { "input_tokens": 1014569, "cached_input_tokens": 966912, "cache_write_input_tokens": 0, "output_tokens": 30137, "reasoning_output_tokens": 14050, "total_tokens": 1044706 } } }, { "kind": "context", "timestamp": "2026-09-24T23:17:31.659Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2b2e699f-d7b3-4f32-81b3-c785158dba83", "process_id": "58327", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\np=np.load('/app/solution/prepared_validation.npz')\nprint('label coverage',len(np.unique(p['label_item'])), 'contexts',len(p['train_recent']), 'labels',len(p['label_item']))\nprint('pad train',{x:round(float((p[x]==30000).mean()),3) for x in ['train_recent','train_organic','train_liked','train_disliked']})\nprint('pad infer',{x:round(float((p[x]==30000).mean()),3) for x in ['infer_recent','infer_organic','infer_liked','infer_disliked']})\nui,c=np.unique(p['context_age'],return_counts=True); print('contexts/age',dict(zip(ui.astype(int),c)))\nli,c=np.unique(p['label_item'],return_counts=True); print('label count percentiles',np.quantile(c,[0,.25,.5,.75,.9,.99,1]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\np=np.load('/app/solution/prepared_validation.npz')\nprint('label coverage',len(np.unique(p['label_item'])), 'contexts',len(p['train_recent']), 'labels',len(p['label_item']))\nprint('pad train',{x:round(float((p[x]==30000).mean()),3) for x in ['train_recent','train_organic','train_liked','train_disliked']})\nprint('pad infer',{x:round(float((p[x]==30000).mean()),3) for x in ['infer_recent','infer_organic','infer_liked','infer_disliked']})\nui,c=np.unique(p['context_age'],return_counts=True); print('contexts/age',dict(zip(ui.astype(int),c)))\nli,c=np.unique(p['label_item'],return_counts=True); print('label count percentiles',np.quantile(c,[0,.25,.5,.75,.9,.99,1]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "label coverage 28495 contexts 58465 labels 544536\npad train {'train_recent': 0.071, 'train_organic': 0.134, 'train_liked': 0.685, 'train_disliked': 0.893}\npad infer {'infer_recent': 0.048, 'infer_organic': 0.204, 'infer_liked': 0.717, 'infer_disliked': 0.896}\ncontexts/age {np.int64(0): np.int64(4284), np.int64(1): np.int64(4236), np.int64(2): np.int64(4089), np.int64(3): np.int64(4042), np.int64(4): np.int64(3991), np.int64(5): np.int64(3845), np.int64(6): np.int64(3788), np.int64(7): np.int64(3748), np.int64(8): np.int64(3598), np.int64(9): np.int64(3415), np.int64(10): np.int64(3374), np.int64(11): np.int64(3335), np.int64(12): np.int64(3261), np.int64(13): np.int64(3196), np.int64(14): np.int64(3132), np.int64(15): np.int64(3131)}\nlabel count percentiles [ 1. 4. 9. 20. 42. 164. 928.]\n", "stderr": "", "aggregated_output": "label coverage 28495 contexts 58465 labels 544536\npad train {'train_recent': 0.071, 'train_organic': 0.134, 'train_liked': 0.685, 'train_disliked': 0.893}\npad infer {'infer_recent': 0.048, 'infer_organic': 0.204, 'infer_liked': 0.717, 'infer_disliked': 0.896}\ncontexts/age {np.int64(0): np.int64(4284), np.int64(1): np.int64(4236), np.int64(2): np.int64(4089), np.int64(3): np.int64(4042), np.int64(4): np.int64(3991), np.int64(5): np.int64(3845), np.int64(6): np.int64(3788), np.int64(7): np.int64(3748), np.int64(8): np.int64(3598), np.int64(9): np.int64(3415), np.int64(10): np.int64(3374), np.int64(11): np.int64(3335), np.int64(12): np.int64(3261), np.int64(13): np.int64(3196), np.int64(14): np.int64(3132), np.int64(15): np.int64(3131)}\nlabel count percentiles [ 1. 4. 9. 20. 42. 164. 928.]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 346832491 }, "formatted_output": "label coverage 28495 contexts 58465 labels 544536\npad train {'train_recent': 0.071, 'train_organic': 0.134, 'train_liked': 0.685, 'train_disliked': 0.893}\npad infer {'infer_recent': 0.048, 'infer_organic': 0.204, 'infer_liked': 0.717, 'infer_disliked': 0.896}\ncontexts/age {np.int64(0): np.int64(4284), np.int64(1): np.int64(4236), np.int64(2): np.int64(4089), np.int64(3): np.int64(4042), np.int64(4): np.int64(3991), np.int64(5): np.int64(3845), np.int64(6): np.int64(3788), np.int64(7): np.int64(3748), np.int64(8): np.int64(3598), np.int64(9): np.int64(3415), np.int64(10): np.int64(3374), np.int64(11): np.int64(3335), np.int64(12): np.int64(3261), np.int64(13): np.int64(3196), np.int64(14): np.int64(3132), np.int64(15): np.int64(3131)}\nlabel count percentiles [ 1. 4. 9. 20. 42. 164. 928.]\n" } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-24T23:17:45.424Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:45.424Z", "text": "" } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-24T23:17:46.850Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:46.850Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:17:48.626Z", "text": "The first neural ranker reaches 0.022991 NDCG@10 after calibration, versus 0.010710 for popularity alone. Diagnostics show the training set covers 28,495 of 30,000 items, but older snapshots still contribute heavily. I’m testing a more time-adaptive fit on the six most recent snapshots with stronger weighting for grade-3 likes; it is a bounded refinement and can be combined with the first tower by vector concatenation if complementary." }, { "kind": "tool", "timestamp": "2026-09-24T23:17:59.407Z", "callId": "call_47cl8cEE9T4pJDEGe6NgtTDP", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def train_model(\\n prepared: Path,\\n output: Path,\\n epochs: int,\\n batch_size: int,\\n negatives: int,\\n seed: int,\\n+ max_age: float,\\n+ grade3_weight: float,\\n+ age_floor: float,\\n+ age_decay: float,\\n+ learning_rate: float,\\n ) -> None:\\n@@\\n label_context = torch.from_numpy(archive[\\\"label_context\\\"].astype(np.int64))\\n label_item = torch.from_numpy(archive[\\\"label_item\\\"].astype(np.int64))\\n grades = torch.from_numpy(archive[\\\"label_grade\\\"].astype(np.float32))\\n ages = torch.from_numpy(archive[\\\"context_age\\\"])[label_context]\\n+ if math.isfinite(max_age):\\n+ keep = ages <= max_age\\n+ label_context = label_context[keep]\\n+ label_item = label_item[keep]\\n+ grades = grades[keep]\\n+ ages = ages[keep]\\n n_labels = len(label_item)\\n@@\\n- positive_counts = np.bincount(\\n- archive[\\\"label_item\\\"], minlength=N_ITEMS\\n- ).astype(np.float64)\\n+ positive_counts = np.bincount(label_item.numpy(), minlength=N_ITEMS).astype(np.float64)\\n@@\\n- optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\\n@@\\n- grade_weight = torch.where(grades[rows] >= 3, 4.0, 1.0)\\n- recency_weight = 0.55 + 0.45 * torch.exp(-ages[rows] / 6.0)\\n+ grade_weight = torch.where(grades[rows] >= 3, grade3_weight, 1.0)\\n+ recency_weight = age_floor + (1.0 - age_floor) * torch.exp(\\n+ -ages[rows] / age_decay\\n+ )\\n@@\\n \\\"history\\\": history,\\n \\\"seed\\\": seed,\\n+ \\\"training_config\\\": {\\n+ \\\"max_age\\\": max_age,\\n+ \\\"grade3_weight\\\": grade3_weight,\\n+ \\\"age_floor\\\": age_floor,\\n+ \\\"age_decay\\\": age_decay,\\n+ \\\"learning_rate\\\": learning_rate,\\n+ },\\n@@\\n fit.add_argument(\\\"--negatives\\\", type=int, default=24)\\n fit.add_argument(\\\"--seed\\\", type=int, default=20260924)\\n+ fit.add_argument(\\\"--max-age\\\", type=float, default=float(\\\"inf\\\"))\\n+ fit.add_argument(\\\"--grade3-weight\\\", type=float, default=4.0)\\n+ fit.add_argument(\\\"--age-floor\\\", type=float, default=0.55)\\n+ fit.add_argument(\\\"--age-decay\\\", type=float, default=6.0)\\n+ fit.add_argument(\\\"--learning-rate\\\", type=float, default=2e-3)\\n@@\\n args.negatives,\\n args.seed,\\n+ args.max_age,\\n+ args.grade3_weight,\\n+ args.age_floor,\\n+ args.age_decay,\\n+ args.learning_rate,\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:17:59.449Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:17:59.417Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7d0bd1ac-67f5-4198-ad97-4e49e37971ff", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -545,2 +545,7 @@\n seed: int,\n+ max_age: float,\n+ grade3_weight: float,\n+ age_floor: float,\n+ age_decay: float,\n+ learning_rate: float,\n ) -> None:\n@@ -563,2 +568,8 @@\n ages = torch.from_numpy(archive[\"context_age\"])[label_context]\n+ if math.isfinite(max_age):\n+ keep = ages <= max_age\n+ label_context = label_context[keep]\n+ label_item = label_item[keep]\n+ grades = grades[keep]\n+ ages = ages[keep]\n n_labels = len(label_item)\n@@ -566,5 +577,3 @@\n # Half the negatives are uniform and half emphasize plausible popular items.\n- positive_counts = np.bincount(\n- archive[\"label_item\"], minlength=N_ITEMS\n- ).astype(np.float64)\n+ positive_counts = np.bincount(label_item.numpy(), minlength=N_ITEMS).astype(np.float64)\n sampling = np.sqrt(positive_counts + 2.0)\n@@ -573,3 +582,3 @@\n \n- optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\n generator = torch.Generator().manual_seed(seed + 17)\n@@ -604,4 +613,6 @@\n pair_loss = F.softplus(negative_score - positive_score[:, None]).mean(dim=1)\n- grade_weight = torch.where(grades[rows] >= 3, 4.0, 1.0)\n- recency_weight = 0.55 + 0.45 * torch.exp(-ages[rows] / 6.0)\n+ grade_weight = torch.where(grades[rows] >= 3, grade3_weight, 1.0)\n+ recency_weight = age_floor + (1.0 - age_floor) * torch.exp(\n+ -ages[rows] / age_decay\n+ )\n loss = (pair_loss * grade_weight * recency_weight).sum() / (\n@@ -633,2 +644,9 @@\n \"seed\": seed,\n+ \"training_config\": {\n+ \"max_age\": max_age,\n+ \"grade3_weight\": grade3_weight,\n+ \"age_floor\": age_floor,\n+ \"age_decay\": age_decay,\n+ \"learning_rate\": learning_rate,\n+ },\n },\n@@ -735,2 +753,7 @@\n fit.add_argument(\"--seed\", type=int, default=20260924)\n+ fit.add_argument(\"--max-age\", type=float, default=float(\"inf\"))\n+ fit.add_argument(\"--grade3-weight\", type=float, default=4.0)\n+ fit.add_argument(\"--age-floor\", type=float, default=0.55)\n+ fit.add_argument(\"--age-decay\", type=float, default=6.0)\n+ fit.add_argument(\"--learning-rate\", type=float, default=2e-3)\n infer = subparsers.add_parser(\"rank\")\n@@ -756,2 +779,7 @@\n args.seed,\n+ args.max_age,\n+ args.grade3_weight,\n+ args.age_floor,\n+ args.age_decay,\n+ args.learning_rate,\n )\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:17:59.447Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5af8bd93c87d2a7ed9a9253aa122c", "usage": { "input_tokens": 55664, "cached_input_tokens": 53888, "cache_write_input_tokens": 0, "output_tokens": 1493, "reasoning_output_tokens": 583, "total_tokens": 57157 }, "turn_token_usage": { "input_tokens": 1070233, "cached_input_tokens": 1020800, "cache_write_input_tokens": 0, "output_tokens": 31630, "reasoning_output_tokens": 14633, "total_tokens": 1101863 }, "thread_token_usage": { "input_tokens": 1070233, "cached_input_tokens": 1020800, "cache_write_input_tokens": 0, "output_tokens": 31630, "reasoning_output_tokens": 14633, "total_tokens": 1101863 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-24T23:18:00.847Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:00.847Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:05.367Z", "callId": "call_IcteupE4Do7OZc7a9Ril2Hi4", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\\n/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\np=np.load('/app/solution/prepared_validation.npz'); ages=p['context_age'][p['label_context']]\\nfor n in [0,2,3,5,7,15]: print(n,int((ages<=n).sum()),len(np.unique(p['label_item'][ages<=n])))\\nPY\\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation_recent.pt --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:18:35.546Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n0 41567 14331\n2 121785 21794\n3 159693 23416\n5 232256 25301\n7 302299 26420\n15 544536 28495\n/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n\nSESSION_ID=61553", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:05.428Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5afa7c08087d2a3c33edb4adc4d19", "usage": { "input_tokens": 57181, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 279, "reasoning_output_tokens": 21, "total_tokens": 57460 }, "turn_token_usage": { "input_tokens": 1127414, "cached_input_tokens": 1076224, "cache_write_input_tokens": 0, "output_tokens": 31909, "reasoning_output_tokens": 14654, "total_tokens": 1159323 }, "thread_token_usage": { "input_tokens": 1127414, "cached_input_tokens": 1076224, "cache_write_input_tokens": 0, "output_tokens": 31909, "reasoning_output_tokens": 14654, "total_tokens": 1159323 } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-24T23:18:36.869Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:36.869Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:37.532Z", "callId": "call_SRpYGvN4PV5xu3pvHQkV7dyp", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61553,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:19:07.584Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 1, \"loss\": 0.4679334651063907, \"seconds\": 23.920013666152954, \"scale\": 7.921507358551025}\n{\"epoch\": 2, \"loss\": 0.29165031509840905, \"seconds\": 23.626760005950928, \"scale\": 8.948201179504395}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:37.574Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5afcbe78887d2a6f7b598689ce3b5", "usage": { "input_tokens": 57604, "cached_input_tokens": 56960, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 57657 }, "turn_token_usage": { "input_tokens": 1185018, "cached_input_tokens": 1133184, "cache_write_input_tokens": 0, "output_tokens": 31962, "reasoning_output_tokens": 14660, "total_tokens": 1216980 }, "thread_token_usage": { "input_tokens": 1185018, "cached_input_tokens": 1133184, "cache_write_input_tokens": 0, "output_tokens": 31962, "reasoning_output_tokens": 14660, "total_tokens": 1216980 } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-24T23:19:09.264Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:19:09.264Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:19:09.933Z", "callId": "call_0HVOEDZJu3by0ZKnwOfPjsGY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61553,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:19:39.987Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 3, \"loss\": 0.17250956395385875, \"seconds\": 23.885490894317627, \"scale\": 11.154593467712402}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:19:10.043Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5afebd85c87d2ab3c1a6014afb905", "usage": { "input_tokens": 57766, "cached_input_tokens": 57472, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 12, "total_tokens": 57825 }, "turn_token_usage": { "input_tokens": 1242784, "cached_input_tokens": 1190656, "cache_write_input_tokens": 0, "output_tokens": 32021, "reasoning_output_tokens": 14672, "total_tokens": 1274805 }, "thread_token_usage": { "input_tokens": 1242784, "cached_input_tokens": 1190656, "cache_write_input_tokens": 0, "output_tokens": 32021, "reasoning_output_tokens": 14672, "total_tokens": 1274805 } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-24T23:19:41.088Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:19:41.088Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:19:41.756Z", "callId": "call_LJam3QYdor0veuCX3qk3zFr6", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61553,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:20:11.809Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 4, \"loss\": 0.11417343927155713, \"seconds\": 23.789453744888306, \"scale\": 14.298315048217773}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:19:41.848Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b00c37bc87d29766cc8ad3bd3dc9", "usage": { "input_tokens": 57891, "cached_input_tokens": 57600, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 57944 }, "turn_token_usage": { "input_tokens": 1300675, "cached_input_tokens": 1248256, "cache_write_input_tokens": 0, "output_tokens": 32074, "reasoning_output_tokens": 14678, "total_tokens": 1332749 }, "thread_token_usage": { "input_tokens": 1300675, "cached_input_tokens": 1248256, "cache_write_input_tokens": 0, "output_tokens": 32074, "reasoning_output_tokens": 14678, "total_tokens": 1332749 } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-24T23:20:18.069Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:18.069Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:20:18.108Z", "text": "The recent-window variant uses 232,256 positives and still covers 25,301 catalog items. Its pairwise loss is down to 0.114 after four of six epochs; once complete I’ll compare both standalone and as an exact-dot-product concatenated ensemble, then retain whichever public ranking is strongest." }, { "kind": "tool", "timestamp": "2026-09-24T23:20:18.110Z", "callId": "call_GeOT8iIDuZLaLSqCyJBYIQhH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":61553,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:20:36.555Z", "content": "Script completed\nWall time 18.4 seconds\nOutput:\n\n{\"epoch\": 5, \"loss\": 0.08368248027870234, \"seconds\": 23.81665587425232, \"scale\": 17.6019344329834}\n{\"epoch\": 6, \"loss\": 0.0660213186092763, \"seconds\": 23.952566146850586, \"scale\": 20.474380493164062}\nsaved /app/solution/model_validation_recent.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:20:18.126Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b02c10a487d2b7d757c1691bbdc4", "usage": { "input_tokens": 58010, "cached_input_tokens": 57728, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 17, "total_tokens": 58143 }, "turn_token_usage": { "input_tokens": 1358685, "cached_input_tokens": 1305984, "cache_write_input_tokens": 0, "output_tokens": 32207, "reasoning_output_tokens": 14695, "total_tokens": 1390892 }, "thread_token_usage": { "input_tokens": 1358685, "cached_input_tokens": 1305984, "cache_write_input_tokens": 0, "output_tokens": 32207, "reasoning_output_tokens": 14695, "total_tokens": 1390892 } } }, { "kind": "context", "timestamp": "2026-09-24T23:20:36.554Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b3ef419-d3b2-402d-92b1-67f71ed1fa4f", "process_id": "61553", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\n/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\np=np.load('/app/solution/prepared_validation.npz'); ages=p['context_age'][p['label_context']]\nfor n in [0,2,3,5,7,15]: print(n,int((ages<=n).sum()),len(np.unique(p['label_item'][ages<=n])))\nPY\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation_recent.pt --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\n/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\np=np.load('/app/solution/prepared_validation.npz'); ages=p['context_age'][p['label_context']]\nfor n in [0,2,3,5,7,15]: print(n,int((ages<=n).sum()),len(np.unique(p['label_item'][ages<=n])))\nPY\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation_recent.pt --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 41567 14331\n2 121785 21794\n3 159693 23416\n5 232256 25301\n7 302299 26420\n15 544536 28495\n/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4679334651063907, \"seconds\": 23.920013666152954, \"scale\": 7.921507358551025}\n{\"epoch\": 2, \"loss\": 0.29165031509840905, \"seconds\": 23.626760005950928, \"scale\": 8.948201179504395}\n{\"epoch\": 3, \"loss\": 0.17250956395385875, \"seconds\": 23.885490894317627, \"scale\": 11.154593467712402}\n{\"epoch\": 4, \"loss\": 0.11417343927155713, \"seconds\": 23.789453744888306, \"scale\": 14.298315048217773}\n{\"epoch\": 5, \"loss\": 0.08368248027870234, \"seconds\": 23.81665587425232, \"scale\": 17.6019344329834}\n{\"epoch\": 6, \"loss\": 0.0660213186092763, \"seconds\": 23.952566146850586, \"scale\": 20.474380493164062}\nsaved /app/solution/model_validation_recent.pt\n", "stderr": "", "aggregated_output": "0 41567 14331\n2 121785 21794\n3 159693 23416\n5 232256 25301\n7 302299 26420\n15 544536 28495\n/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4679334651063907, \"seconds\": 23.920013666152954, \"scale\": 7.921507358551025}\n{\"epoch\": 2, \"loss\": 0.29165031509840905, \"seconds\": 23.626760005950928, \"scale\": 8.948201179504395}\n{\"epoch\": 3, \"loss\": 0.17250956395385875, \"seconds\": 23.885490894317627, \"scale\": 11.154593467712402}\n{\"epoch\": 4, \"loss\": 0.11417343927155713, \"seconds\": 23.789453744888306, \"scale\": 14.298315048217773}\n{\"epoch\": 5, \"loss\": 0.08368248027870234, \"seconds\": 23.81665587425232, \"scale\": 17.6019344329834}\n{\"epoch\": 6, \"loss\": 0.0660213186092763, \"seconds\": 23.952566146850586, \"scale\": 20.474380493164062}\nsaved /app/solution/model_validation_recent.pt\n", "exit_code": 0, "duration": { "secs": 151, "nanos": 19291004 }, "formatted_output": "0 41567 14331\n2 121785 21794\n3 159693 23416\n5 232256 25301\n7 302299 26420\n15 544536 28495\n/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4679334651063907, \"seconds\": 23.920013666152954, \"scale\": 7.921507358551025}\n{\"epoch\": 2, \"loss\": 0.29165031509840905, \"seconds\": 23.626760005950928, \"scale\": 8.948201179504395}\n{\"epoch\": 3, \"loss\": 0.17250956395385875, \"seconds\": 23.885490894317627, \"scale\": 11.154593467712402}\n{\"epoch\": 4, \"loss\": 0.11417343927155713, \"seconds\": 23.789453744888306, \"scale\": 14.298315048217773}\n{\"epoch\": 5, \"loss\": 0.08368248027870234, \"seconds\": 23.81665587425232, \"scale\": 17.6019344329834}\n{\"epoch\": 6, \"loss\": 0.0660213186092763, \"seconds\": 23.952566146850586, \"scale\": 20.474380493164062}\nsaved /app/solution/model_validation_recent.pt\n" } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-24T23:20:38.347Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:38.347Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:20:41.255Z", "callId": "call_P5QRvbrqyqK3AgVQClb7RAaB", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for w in 0.0 0.75 1.25; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_recent.pt --output /app/solution/validation_recent_w${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recent_w${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:21:11.422Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011799,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.75\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01563,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.25\n}\n\nSESSION_ID=71797", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:20:41.365Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b044c47487d29c455f5490c4e7e1", "usage": { "input_tokens": 58262, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 24, "total_tokens": 58457 }, "turn_token_usage": { "input_tokens": 1416947, "cached_input_tokens": 1363840, "cache_write_input_tokens": 0, "output_tokens": 32402, "reasoning_output_tokens": 14719, "total_tokens": 1449349 }, "thread_token_usage": { "input_tokens": 1416947, "cached_input_tokens": 1363840, "cache_write_input_tokens": 0, "output_tokens": 32402, "reasoning_output_tokens": 14719, "total_tokens": 1449349 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-24T23:21:12.720Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:21:12.720Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:21:13.437Z", "callId": "call_Z6THF8JcPa9gEaqEOmjUmXlW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71797,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:21:14.414Z", "content": "Script completed\nWall time 1.0 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01908,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:13.553Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b067d33087d2b44ff5682ff04f6d", "usage": { "input_tokens": 58723, "cached_input_tokens": 58112, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 58776 }, "turn_token_usage": { "input_tokens": 1475670, "cached_input_tokens": 1421952, "cache_write_input_tokens": 0, "output_tokens": 32455, "reasoning_output_tokens": 14725, "total_tokens": 1508125 }, "thread_token_usage": { "input_tokens": 1475670, "cached_input_tokens": 1421952, "cache_write_input_tokens": 0, "output_tokens": 32455, "reasoning_output_tokens": 14725, "total_tokens": 1508125 } } }, { "kind": "context", "timestamp": "2026-09-24T23:21:14.408Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-81362798-6644-45c0-8ef3-4b9fcc0ea427", "process_id": "71797", "command": [ "/bin/sh", "-lc", "for w in 0.0 0.75 1.25; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_recent.pt --output /app/solution/validation_recent_w${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recent_w${w}.parquet; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for w in 0.0 0.75 1.25; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_recent.pt --output /app/solution/validation_recent_w${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recent_w${w}.parquet; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011799,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.75\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01563,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.25\n}\n{\n \"model\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01908,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011799,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.75\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01563,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.25\n}\n{\n \"model\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01908,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 32, "nanos": 987802576 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011799,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.75\n}\n{\n \"model\": \"/app/solution/validation_recent_w0.75.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01563,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.25\n}\n{\n \"model\": \"/app/solution/validation_recent_w1.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01908,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-24T23:21:19.193Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:21:19.193Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:21:29.567Z", "callId": "call_8kuwKWhbLJq9GerpZB8QDWuA", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def rank(\\n stage: str,\\n prepared: Path,\\n checkpoint: Path,\\n output: Path,\\n popularity_weight: float,\\n+ checkpoint2: Path | None,\\n+ second_weight: float,\\n ) -> None:\\n@@\\n- item_vectors = model.item_tower(torch.arange(N_ITEMS)).numpy()\\n+ item_vectors = model.item_tower(torch.arange(N_ITEMS)).numpy()\\n+ second_model: TwoTower | None = None\\n+ if checkpoint2 is not None:\\n+ second_saved = torch.load(checkpoint2, map_location=\\\"cpu\\\", weights_only=False)\\n+ second_model = TwoTower(\\n+ second_saved[\\\"item_features\\\"],\\n+ second_saved[\\\"token_dim\\\"],\\n+ second_saved[\\\"dim\\\"],\\n+ )\\n+ second_model.load_state_dict(second_saved[\\\"state_dict\\\"])\\n+ second_model.eval()\\n+ second_items = second_model.item_tower(torch.arange(N_ITEMS)).numpy()\\n+ magnitude = math.sqrt(abs(second_weight))\\n+ # Scaling opposite sides handles a negative coefficient while retaining\\n+ # one exact dot product over concatenated tower outputs.\\n+ item_vectors = np.column_stack((item_vectors, second_items * magnitude))\\n@@\\n user_vectors = model.user_tower(*context_batch(contexts, indices)).numpy()\\n+ if second_model is not None:\\n+ second_users = second_model.user_tower(\\n+ *context_batch(contexts, indices)\\n+ ).numpy()\\n+ signed_magnitude = math.copysign(\\n+ math.sqrt(abs(second_weight)), second_weight\\n+ )\\n+ user_vectors = np.column_stack(\\n+ (user_vectors, second_users * signed_magnitude)\\n+ )\\n user_vectors = np.column_stack(\\n@@\\n \\\"popularity_weight\\\": popularity_weight,\\n+ \\\"second_weight\\\": second_weight if checkpoint2 is not None else None,\\n@@\\n infer.add_argument(\\\"--popularity-weight\\\", type=float, default=0.0)\\n+ infer.add_argument(\\\"--checkpoint2\\\", type=Path)\\n+ infer.add_argument(\\\"--second-weight\\\", type=float, default=0.0)\\n@@\\n args.output,\\n args.popularity_weight,\\n+ args.checkpoint2,\\n+ args.second_weight,\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:21:29.666Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:29.577Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ed9a2639-7b47-447e-8924-b46878d0a772", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -664,2 +664,4 @@\n popularity_weight: float,\n+ checkpoint2: Path | None,\n+ second_weight: float,\n ) -> None:\n@@ -681,2 +683,17 @@\n item_vectors = model.item_tower(torch.arange(N_ITEMS)).numpy()\n+ second_model: TwoTower | None = None\n+ if checkpoint2 is not None:\n+ second_saved = torch.load(checkpoint2, map_location=\"cpu\", weights_only=False)\n+ second_model = TwoTower(\n+ second_saved[\"item_features\"],\n+ second_saved[\"token_dim\"],\n+ second_saved[\"dim\"],\n+ )\n+ second_model.load_state_dict(second_saved[\"state_dict\"])\n+ second_model.eval()\n+ second_items = second_model.item_tower(torch.arange(N_ITEMS)).numpy()\n+ magnitude = math.sqrt(abs(second_weight))\n+ # Scaling opposite sides handles a negative coefficient while retaining\n+ # one exact dot product over concatenated tower outputs.\n+ item_vectors = np.column_stack((item_vectors, second_items * magnitude))\n # A fixed popularity coordinate is an explicit part of both tower outputs.\n@@ -703,2 +720,12 @@\n user_vectors = model.user_tower(*context_batch(contexts, indices)).numpy()\n+ if second_model is not None:\n+ second_users = second_model.user_tower(\n+ *context_batch(contexts, indices)\n+ ).numpy()\n+ signed_magnitude = math.copysign(\n+ math.sqrt(abs(second_weight)), second_weight\n+ )\n+ user_vectors = np.column_stack(\n+ (user_vectors, second_users * signed_magnitude)\n+ )\n user_vectors = np.column_stack(\n@@ -733,2 +760,3 @@\n \"popularity_weight\": popularity_weight,\n+ \"second_weight\": second_weight if checkpoint2 is not None else None,\n },\n@@ -764,2 +792,4 @@\n infer.add_argument(\"--popularity-weight\", type=float, default=0.0)\n+ infer.add_argument(\"--checkpoint2\", type=Path)\n+ infer.add_argument(\"--second-weight\", type=float, default=0.0)\n return parser.parse_args()\n@@ -792,2 +822,4 @@\n args.popularity_weight,\n+ args.checkpoint2,\n+ args.second_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": "2026-09-24T23:21:29.665Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b06aa4a487d2bb1f84d03b827084", "usage": { "input_tokens": 58849, "cached_input_tokens": 58496, "cache_write_input_tokens": 0, "output_tokens": 789, "reasoning_output_tokens": 205, "total_tokens": 59638 }, "turn_token_usage": { "input_tokens": 1534519, "cached_input_tokens": 1480448, "cache_write_input_tokens": 0, "output_tokens": 33244, "reasoning_output_tokens": 14930, "total_tokens": 1567763 }, "thread_token_usage": { "input_tokens": 1534519, "cached_input_tokens": 1480448, "cache_write_input_tokens": 0, "output_tokens": 33244, "reasoning_output_tokens": 14930, "total_tokens": 1567763 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-24T23:21:32.952Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:21:32.952Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:21:37.086Z", "callId": "call_dTZcGhDcD3ciwKCk5ZPx85cP", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\\nfor w in 0.1 0.25 0.5 1.0; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --checkpoint2 /app/solution/model_validation_recent.pt --second-weight $w --output /app/solution/validation_ensemble_s${w}_p1.parquet --popularity-weight 1.0; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_s${w}_p1.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:22:07.270Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.1\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024411,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.25\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025467,\n \"quality_users\": 4263\n }\n}\n\nSESSION_ID=1421", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:37.148Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b079e33087d28ebe02642f14f98a", "usage": { "input_tokens": 59662, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 115, "total_tokens": 59996 }, "turn_token_usage": { "input_tokens": 1594181, "cached_input_tokens": 1494784, "cache_write_input_tokens": 0, "output_tokens": 33578, "reasoning_output_tokens": 15045, "total_tokens": 1627759 }, "thread_token_usage": { "input_tokens": 1594181, "cached_input_tokens": 1494784, "cache_write_input_tokens": 0, "output_tokens": 33578, "reasoning_output_tokens": 15045, "total_tokens": 1627759 } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-24T23:22:08.547Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:22:08.547Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:22:09.213Z", "callId": "call_ZhYRB0BgrSUs1YqfWNfOIj72", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":1421,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:22:25.682Z", "content": "Script completed\nWall time 16.5 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026135,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024352,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:22:09.314Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b09f974087d288a875b129f78f47", "usage": { "input_tokens": 60248, "cached_input_tokens": 59520, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 60303 }, "turn_token_usage": { "input_tokens": 1654429, "cached_input_tokens": 1554304, "cache_write_input_tokens": 0, "output_tokens": 33633, "reasoning_output_tokens": 15053, "total_tokens": 1688062 }, "thread_token_usage": { "input_tokens": 1654429, "cached_input_tokens": 1554304, "cache_write_input_tokens": 0, "output_tokens": 33633, "reasoning_output_tokens": 15053, "total_tokens": 1688062 } } }, { "kind": "context", "timestamp": "2026-09-24T23:22:25.680Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16564873-046f-4d22-b242-b90f5e6ce6f1", "process_id": "1421", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\nfor w in 0.1 0.25 0.5 1.0; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --checkpoint2 /app/solution/model_validation_recent.pt --second-weight $w --output /app/solution/validation_ensemble_s${w}_p1.parquet --popularity-weight 1.0; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_s${w}_p1.parquet; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\nfor w in 0.1 0.25 0.5 1.0; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --checkpoint2 /app/solution/model_validation_recent.pt --second-weight $w --output /app/solution/validation_ensemble_s${w}_p1.parquet --popularity-weight 1.0; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_s${w}_p1.parquet; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.1\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024411,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.25\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025467,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026135,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024352,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.1\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024411,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.25\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025467,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026135,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024352,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 48, "nanos": 429369057 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.1\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.1_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024411,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.25\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.25_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025467,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s0.5_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026135,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": 1.0\n}\n{\n \"model\": \"/app/solution/validation_ensemble_s1.0_p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024352,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-24T23:22:32.541Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:22:32.541Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:22:34.171Z", "text": "The two variants are complementary: concatenating their outputs into one 131-dimensional user/item vector raises public NDCG@10 to 0.026135. This still performs one user-tower computation and one MIPS dot product. I’m checking grade-specific behavior now, then I’ll decide whether one final metric-aligned fit is worth adding before the mandatory train+validation refit." }, { "kind": "tool", "timestamp": "2026-09-24T23:22:40.657Z", "callId": "call_PbrkJr5Bnvk8cCrYY4t4JySE", "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, numpy as np\\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking,ranked_items,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); truth=validation_truth(e,targets)\\nfor name in ['validation_popularity.parquet','validation_twotower_w1.0.parquet','validation_recent_w1.25.parquet','validation_ensemble_s0.5_p1.parquet']:\\n r=ranked_items(validate_ranking(read_ranking(Path('/app/solution')/name),targets)); vals=[]; hit=[]; like_hit=[]; listen_hit=[]\\n for u,g in truth.items():\\n top=r[u][:10]; vals.append(ndcg_at_k(top,g,10)); hit.append(any(i in g for i in top)); like_hit.append(any(g.get(i)==3 for i in top)); listen_hit.append(any(g.get(i)==1 for i in top))\\n print(name,'ndcg',np.mean(vals),'users_hit',np.mean(hit),'like_hit',np.mean(like_hit),'listen_hit',np.mean(listen_hit),'positive_positions',sum(sum(i in g for i in r[u][:10]) for u,g in truth.items()))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:22:45.463Z", "content": "Script completed\nWall time 4.8 seconds\nOutput:\n\nvalidation_popularity.parquet ndcg 0.010709951701903768 users_hit 0.053718038939713816 like_hit 0.006568144499178982 listen_hit 0.04785362420830401 positive_positions 368\nvalidation_twotower_w1.0.parquet ndcg 0.022991195912338912 users_hit 0.1144733755571194 like_hit 0.02017358667604973 listen_hit 0.09781843771991555 positive_positions 713\nvalidation_recent_w1.25.parquet ndcg 0.01908035023954391 users_hit 0.09781843771991555 like_hit 0.0180623973727422 listen_hit 0.08233638282899367 positive_positions 598\nvalidation_ensemble_s0.5_p1.parquet ndcg 0.026134661535517537 users_hit 0.12643678160919541 like_hit 0.025803424818203143 listen_hit 0.10415200562983815 positive_positions 792\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:22:40.764Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b0b1f14487d2ab958a44e2f8848a", "usage": { "input_tokens": 60550, "cached_input_tokens": 60032, "cache_write_input_tokens": 0, "output_tokens": 779, "reasoning_output_tokens": 326, "total_tokens": 61329 }, "turn_token_usage": { "input_tokens": 1714979, "cached_input_tokens": 1614336, "cache_write_input_tokens": 0, "output_tokens": 34412, "reasoning_output_tokens": 15379, "total_tokens": 1749391 }, "thread_token_usage": { "input_tokens": 1714979, "cached_input_tokens": 1614336, "cache_write_input_tokens": 0, "output_tokens": 34412, "reasoning_output_tokens": 15379, "total_tokens": 1749391 } } }, { "kind": "context", "timestamp": "2026-09-24T23:22:45.459Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f720ff72-c8f8-4163-9ce8-2e85a1996129", "process_id": "79486", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking,ranked_items,ndcg_at_k\nfrom evaluate_public import validation_truth\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); truth=validation_truth(e,targets)\nfor name in ['validation_popularity.parquet','validation_twotower_w1.0.parquet','validation_recent_w1.25.parquet','validation_ensemble_s0.5_p1.parquet']:\n r=ranked_items(validate_ranking(read_ranking(Path('/app/solution')/name),targets)); vals=[]; hit=[]; like_hit=[]; listen_hit=[]\n for u,g in truth.items():\n top=r[u][:10]; vals.append(ndcg_at_k(top,g,10)); hit.append(any(i in g for i in top)); like_hit.append(any(g.get(i)==3 for i in top)); listen_hit.append(any(g.get(i)==1 for i in top))\n print(name,'ndcg',np.mean(vals),'users_hit',np.mean(hit),'like_hit',np.mean(like_hit),'listen_hit',np.mean(listen_hit),'positive_positions',sum(sum(i in g for i in r[u][:10]) for u,g in truth.items()))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking,ranked_items,ndcg_at_k\nfrom evaluate_public import validation_truth\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); truth=validation_truth(e,targets)\nfor name in ['validation_popularity.parquet','validation_twotower_w1.0.parquet','validation_recent_w1.25.parquet','validation_ensemble_s0.5_p1.parquet']:\n r=ranked_items(validate_ranking(read_ranking(Path('/app/solution')/name),targets)); vals=[]; hit=[]; like_hit=[]; listen_hit=[]\n for u,g in truth.items():\n top=r[u][:10]; vals.append(ndcg_at_k(top,g,10)); hit.append(any(i in g for i in top)); like_hit.append(any(g.get(i)==3 for i in top)); listen_hit.append(any(g.get(i)==1 for i in top))\n print(name,'ndcg',np.mean(vals),'users_hit',np.mean(hit),'like_hit',np.mean(like_hit),'listen_hit',np.mean(listen_hit),'positive_positions',sum(sum(i in g for i in r[u][:10]) for u,g in truth.items()))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation_popularity.parquet ndcg 0.010709951701903768 users_hit 0.053718038939713816 like_hit 0.006568144499178982 listen_hit 0.04785362420830401 positive_positions 368\nvalidation_twotower_w1.0.parquet ndcg 0.022991195912338912 users_hit 0.1144733755571194 like_hit 0.02017358667604973 listen_hit 0.09781843771991555 positive_positions 713\nvalidation_recent_w1.25.parquet ndcg 0.01908035023954391 users_hit 0.09781843771991555 like_hit 0.0180623973727422 listen_hit 0.08233638282899367 positive_positions 598\nvalidation_ensemble_s0.5_p1.parquet ndcg 0.026134661535517537 users_hit 0.12643678160919541 like_hit 0.025803424818203143 listen_hit 0.10415200562983815 positive_positions 792\n", "stderr": "", "aggregated_output": "validation_popularity.parquet ndcg 0.010709951701903768 users_hit 0.053718038939713816 like_hit 0.006568144499178982 listen_hit 0.04785362420830401 positive_positions 368\nvalidation_twotower_w1.0.parquet ndcg 0.022991195912338912 users_hit 0.1144733755571194 like_hit 0.02017358667604973 listen_hit 0.09781843771991555 positive_positions 713\nvalidation_recent_w1.25.parquet ndcg 0.01908035023954391 users_hit 0.09781843771991555 like_hit 0.0180623973727422 listen_hit 0.08233638282899367 positive_positions 598\nvalidation_ensemble_s0.5_p1.parquet ndcg 0.026134661535517537 users_hit 0.12643678160919541 like_hit 0.025803424818203143 listen_hit 0.10415200562983815 positive_positions 792\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 637668364 }, "formatted_output": "validation_popularity.parquet ndcg 0.010709951701903768 users_hit 0.053718038939713816 like_hit 0.006568144499178982 listen_hit 0.04785362420830401 positive_positions 368\nvalidation_twotower_w1.0.parquet ndcg 0.022991195912338912 users_hit 0.1144733755571194 like_hit 0.02017358667604973 listen_hit 0.09781843771991555 positive_positions 713\nvalidation_recent_w1.25.parquet ndcg 0.01908035023954391 users_hit 0.09781843771991555 like_hit 0.0180623973727422 listen_hit 0.08233638282899367 positive_positions 598\nvalidation_ensemble_s0.5_p1.parquet ndcg 0.026134661535517537 users_hit 0.12643678160919541 like_hit 0.025803424818203143 listen_hit 0.10415200562983815 positive_positions 792\n" } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-24T23:22:50.528Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:22:50.528Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:02.405Z", "callId": "call_mAbvrrMEq2KcoB9ooTEgBGHo", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np, pandas as pd, torch\\nfrom pathlib import Path\\nfrom train_ranker import TwoTower, context_batch, DATA, N_ITEMS\\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\\nfrom evaluate_public import validation_truth\\nP=np.load('/app/solution/prepared_validation.npz'); contexts={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\\ndef load(path):\\n s=torch.load(path,map_location='cpu',weights_only=False); m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\\nm1=load('/app/solution/model_validation.pt'); m2=load('/app/solution/model_validation_recent.pt')\\nwith torch.inference_mode(): i1=m1.item_tower(torch.arange(N_ITEMS)).numpy(); i2=m2.item_tower(torch.arange(N_ITEMS)).numpy()\\npop=P['item_features'][:,1]\\nE=CandidateSet(DATA/'validation_eligible_candidates.npz'); T=validation_truth(E,set(E.uids)); grids=[(s,p) for s in [.3,.4,.5,.6,.7] for p in [.6,.8,1.,1.2,1.4]]; sums={g:0. for g in grids}\\nfor st in range(0,len(E.uids),128):\\n en=min(st+128,len(E.uids)); idx=torch.arange(st,en)\\n with torch.inference_mode(): u1=m1.user_tower(*context_batch(contexts,idx)).numpy(); u2=m2.user_tower(*context_batch(contexts,idx)).numpy()\\n a=u1@i1.T; b=u2@i2.T; mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\\n for g in grids:\\n s,p=g; score=a+s*b+p*pop; score[~mask]=-np.inf; top=np.argpartition(score,-10,axis=1)[:,-10:]; ts=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\\n for j,u in enumerate(E.uids[st:en]):\\n gold=T.get(int(u));\\n if gold: sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\\nprint('users',len(T))\\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:15]:print(g,round(v/len(T),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:23:32.584Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=60412", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:02.512Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b0c5a54887d2b7d4ade378f6e06d", "usage": { "input_tokens": 61582, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 891, "reasoning_output_tokens": 225, "total_tokens": 62473 }, "turn_token_usage": { "input_tokens": 1776561, "cached_input_tokens": 1674752, "cache_write_input_tokens": 0, "output_tokens": 35303, "reasoning_output_tokens": 15604, "total_tokens": 1811864 }, "thread_token_usage": { "input_tokens": 1776561, "cached_input_tokens": 1674752, "cache_write_input_tokens": 0, "output_tokens": 35303, "reasoning_output_tokens": 15604, "total_tokens": 1811864 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-24T23:23:33.980Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:33.980Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:34.651Z", "callId": "call_ckyHwBdNj9SKFWvu3VTvmNk8", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":60412,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:23:38.059Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\nusers 4263\n(0.5, 1.2) 0.026712\n(0.6, 1.2) 0.026667\n(0.7, 1.4) 0.02659\n(0.6, 1.4) 0.026555\n(0.4, 1.0) 0.026496\n(0.4, 1.4) 0.026394\n(0.4, 1.2) 0.026384\n(0.5, 1.4) 0.026333\n(0.5, 1.0) 0.026135\n(0.7, 1.2) 0.026052\n(0.3, 1.0) 0.025974\n(0.6, 1.0) 0.025944\n(0.3, 1.2) 0.025941\n(0.4, 0.8) 0.025911\n(0.5, 0.8) 0.025762\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:34.775Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b0f4df6487d2a713dec0fb94e18b", "usage": { "input_tokens": 62501, "cached_input_tokens": 61440, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62554 }, "turn_token_usage": { "input_tokens": 1839062, "cached_input_tokens": 1736192, "cache_write_input_tokens": 0, "output_tokens": 35356, "reasoning_output_tokens": 15610, "total_tokens": 1874418 }, "thread_token_usage": { "input_tokens": 1839062, "cached_input_tokens": 1736192, "cache_write_input_tokens": 0, "output_tokens": 35356, "reasoning_output_tokens": 15610, "total_tokens": 1874418 } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:38.057Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a4f64dd-4b8d-411c-b195-1811c3181e5f", "process_id": "60412", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd, torch\nfrom pathlib import Path\nfrom train_ranker import TwoTower, context_batch, DATA, N_ITEMS\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz'); contexts={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False); m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nm1=load('/app/solution/model_validation.pt'); m2=load('/app/solution/model_validation_recent.pt')\nwith torch.inference_mode(): i1=m1.item_tower(torch.arange(N_ITEMS)).numpy(); i2=m2.item_tower(torch.arange(N_ITEMS)).numpy()\npop=P['item_features'][:,1]\nE=CandidateSet(DATA/'validation_eligible_candidates.npz'); T=validation_truth(E,set(E.uids)); grids=[(s,p) for s in [.3,.4,.5,.6,.7] for p in [.6,.8,1.,1.2,1.4]]; sums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids)); idx=torch.arange(st,en)\n with torch.inference_mode(): u1=m1.user_tower(*context_batch(contexts,idx)).numpy(); u2=m2.user_tower(*context_batch(contexts,idx)).numpy()\n a=u1@i1.T; b=u2@i2.T; mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n s,p=g; score=a+s*b+p*pop; score[~mask]=-np.inf; top=np.argpartition(score,-10,axis=1)[:,-10:]; ts=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold: sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nprint('users',len(T))\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:15]:print(g,round(v/len(T),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd, torch\nfrom pathlib import Path\nfrom train_ranker import TwoTower, context_batch, DATA, N_ITEMS\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz'); contexts={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False); m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nm1=load('/app/solution/model_validation.pt'); m2=load('/app/solution/model_validation_recent.pt')\nwith torch.inference_mode(): i1=m1.item_tower(torch.arange(N_ITEMS)).numpy(); i2=m2.item_tower(torch.arange(N_ITEMS)).numpy()\npop=P['item_features'][:,1]\nE=CandidateSet(DATA/'validation_eligible_candidates.npz'); T=validation_truth(E,set(E.uids)); grids=[(s,p) for s in [.3,.4,.5,.6,.7] for p in [.6,.8,1.,1.2,1.4]]; sums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids)); idx=torch.arange(st,en)\n with torch.inference_mode(): u1=m1.user_tower(*context_batch(contexts,idx)).numpy(); u2=m2.user_tower(*context_batch(contexts,idx)).numpy()\n a=u1@i1.T; b=u2@i2.T; mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n s,p=g; score=a+s*b+p*pop; score[~mask]=-np.inf; top=np.argpartition(score,-10,axis=1)[:,-10:]; ts=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold: sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nprint('users',len(T))\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:15]:print(g,round(v/len(T),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 4263\n(0.5, 1.2) 0.026712\n(0.6, 1.2) 0.026667\n(0.7, 1.4) 0.02659\n(0.6, 1.4) 0.026555\n(0.4, 1.0) 0.026496\n(0.4, 1.4) 0.026394\n(0.4, 1.2) 0.026384\n(0.5, 1.4) 0.026333\n(0.5, 1.0) 0.026135\n(0.7, 1.2) 0.026052\n(0.3, 1.0) 0.025974\n(0.6, 1.0) 0.025944\n(0.3, 1.2) 0.025941\n(0.4, 0.8) 0.025911\n(0.5, 0.8) 0.025762\n", "stderr": "", "aggregated_output": "users 4263\n(0.5, 1.2) 0.026712\n(0.6, 1.2) 0.026667\n(0.7, 1.4) 0.02659\n(0.6, 1.4) 0.026555\n(0.4, 1.0) 0.026496\n(0.4, 1.4) 0.026394\n(0.4, 1.2) 0.026384\n(0.5, 1.4) 0.026333\n(0.5, 1.0) 0.026135\n(0.7, 1.2) 0.026052\n(0.3, 1.0) 0.025974\n(0.6, 1.0) 0.025944\n(0.3, 1.2) 0.025941\n(0.4, 0.8) 0.025911\n(0.5, 0.8) 0.025762\n", "exit_code": 0, "duration": { "secs": 35, "nanos": 486883343 }, "formatted_output": "users 4263\n(0.5, 1.2) 0.026712\n(0.6, 1.2) 0.026667\n(0.7, 1.4) 0.02659\n(0.6, 1.4) 0.026555\n(0.4, 1.0) 0.026496\n(0.4, 1.4) 0.026394\n(0.4, 1.2) 0.026384\n(0.5, 1.4) 0.026333\n(0.5, 1.0) 0.026135\n(0.7, 1.2) 0.026052\n(0.3, 1.0) 0.025974\n(0.6, 1.0) 0.025944\n(0.3, 1.2) 0.025941\n(0.4, 0.8) 0.025911\n(0.5, 0.8) 0.025762\n" } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-24T23:23:39.785Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:39.785Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:42.312Z", "callId": "call_PKflt2PkYmxdukeHYwgrolKG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --checkpoint2 /app/solution/model_validation_recent.pt --second-weight 0.5 --popularity-weight 1.2 --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:23:56.701Z", "content": "Script completed\nWall time 14.4 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.2,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026712,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:42.374Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b0fa546887d280182dadef107292", "usage": { "input_tokens": 62822, "cached_input_tokens": 62336, "cache_write_input_tokens": 0, "output_tokens": 181, "reasoning_output_tokens": 35, "total_tokens": 63003 }, "turn_token_usage": { "input_tokens": 1901884, "cached_input_tokens": 1798528, "cache_write_input_tokens": 0, "output_tokens": 35537, "reasoning_output_tokens": 15645, "total_tokens": 1937421 }, "thread_token_usage": { "input_tokens": 1901884, "cached_input_tokens": 1798528, "cache_write_input_tokens": 0, "output_tokens": 35537, "reasoning_output_tokens": 15645, "total_tokens": 1937421 } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:56.696Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f28a13f0-2dc9-49e3-bb01-2b08bb43dcfb", "process_id": "17695", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --checkpoint2 /app/solution/model_validation_recent.pt --second-weight 0.5 --popularity-weight 1.2 --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation.pt --checkpoint2 /app/solution/model_validation_recent.pt --second-weight 0.5 --popularity-weight 1.2 --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.2,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026712,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.2,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026712,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 14, "nanos": 219255699 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.2,\n \"second_weight\": 0.5\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026712,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-24T23:24:01.421Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:24:01.421Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:24:03.921Z", "callId": "call_SNgGzuIzz3mJYCyBTwEhzYOW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation_metric.pt --epochs 4 --batch-size 512 --negatives 32 --seed 48611 --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:24:34.098Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n\nSESSION_ID=33478", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:24:03.999Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b10ce57c87d28baca551a64cc10a", "usage": { "input_tokens": 63122, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 350, "reasoning_output_tokens": 205, "total_tokens": 63472 }, "turn_token_usage": { "input_tokens": 1965006, "cached_input_tokens": 1861120, "cache_write_input_tokens": 0, "output_tokens": 35887, "reasoning_output_tokens": 15850, "total_tokens": 2000893 }, "thread_token_usage": { "input_tokens": 1965006, "cached_input_tokens": 1861120, "cache_write_input_tokens": 0, "output_tokens": 35887, "reasoning_output_tokens": 15850, "total_tokens": 2000893 } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-24T23:24:35.398Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:24:35.398Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:24:36.085Z", "callId": "call_ojexb5eZ01RlONnXA4kIUvpJ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:25:06.144Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:24:36.191Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b1326ca487d29ce20513bdea07a7", "usage": { "input_tokens": 63568, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 63621 }, "turn_token_usage": { "input_tokens": 2028574, "cached_input_tokens": 1924096, "cache_write_input_tokens": 0, "output_tokens": 35940, "reasoning_output_tokens": 15856, "total_tokens": 2064514 }, "thread_token_usage": { "input_tokens": 2028574, "cached_input_tokens": 1924096, "cache_write_input_tokens": 0, "output_tokens": 35940, "reasoning_output_tokens": 15856, "total_tokens": 2064514 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-24T23:25:12.082Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:25:12.082Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:25:12.087Z", "text": "The calibrated two-tower ensemble is now saved as `validation.parquet` with an official public score of 0.026712. I’m running one last all-window fit that more closely weights NDCG’s grade-3 gain; I’ll keep it only if validation confirms an improvement, then immediately refit the selected configuration with validation events included as observed history." }, { "kind": "tool", "timestamp": "2026-09-24T23:25:12.089Z", "callId": "call_YlStWCxMKbf2zkSc7CcHHAo5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:25:42.118Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 1, \"loss\": 0.4451584124886673, \"seconds\": 57.439289569854736, \"scale\": 7.719996929168701}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:25:12.116Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b152673487d2991c1e7f792655b8", "usage": { "input_tokens": 63644, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 15, "total_tokens": 63787 }, "turn_token_usage": { "input_tokens": 2092218, "cached_input_tokens": 1987456, "cache_write_input_tokens": 0, "output_tokens": 36083, "reasoning_output_tokens": 15871, "total_tokens": 2128301 }, "thread_token_usage": { "input_tokens": 2092218, "cached_input_tokens": 1987456, "cache_write_input_tokens": 0, "output_tokens": 36083, "reasoning_output_tokens": 15871, "total_tokens": 2128301 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-24T23:25:43.499Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:25:43.499Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:25:44.168Z", "callId": "call_aYUYEQaF7dLkDs7VAiwVTRui", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:26:14.217Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 2, \"loss\": 0.2735381604391605, \"seconds\": 56.75349473953247, \"scale\": 9.339584350585938}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:25:44.270Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b176696487d29f41e7b9ac9cf0cb", "usage": { "input_tokens": 63853, "cached_input_tokens": 63488, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 63911 }, "turn_token_usage": { "input_tokens": 2156071, "cached_input_tokens": 2050944, "cache_write_input_tokens": 0, "output_tokens": 36141, "reasoning_output_tokens": 15882, "total_tokens": 2192212 }, "thread_token_usage": { "input_tokens": 2156071, "cached_input_tokens": 2050944, "cache_write_input_tokens": 0, "output_tokens": 36141, "reasoning_output_tokens": 15882, "total_tokens": 2192212 } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-24T23:26:15.537Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:26:15.537Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:26:16.245Z", "callId": "call_q4ntf1q8yxZ5rfA9ZsX3M4bB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:26:46.276Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:26:16.338Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b1967b7487d289890cfe1fa9c848", "usage": { "input_tokens": 63977, "cached_input_tokens": 63616, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 64030 }, "turn_token_usage": { "input_tokens": 2220048, "cached_input_tokens": 2114560, "cache_write_input_tokens": 0, "output_tokens": 36194, "reasoning_output_tokens": 15888, "total_tokens": 2256242 }, "thread_token_usage": { "input_tokens": 2220048, "cached_input_tokens": 2114560, "cache_write_input_tokens": 0, "output_tokens": 36194, "reasoning_output_tokens": 15888, "total_tokens": 2256242 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-24T23:26:47.439Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:26:47.439Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:26:48.125Z", "callId": "call_ek5HrCY82rEXIhmoAWF451XD", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:27:18.145Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 3, \"loss\": 0.18352071033851705, \"seconds\": 54.79453730583191, \"scale\": 11.936638832092285}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:26:48.245Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b1b680a087d297b7f7124da7cf70", "usage": { "input_tokens": 64053, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 64106 }, "turn_token_usage": { "input_tokens": 2284101, "cached_input_tokens": 2178304, "cache_write_input_tokens": 0, "output_tokens": 36247, "reasoning_output_tokens": 15894, "total_tokens": 2320348 }, "thread_token_usage": { "input_tokens": 2284101, "cached_input_tokens": 2178304, "cache_write_input_tokens": 0, "output_tokens": 36247, "reasoning_output_tokens": 15894, "total_tokens": 2320348 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-24T23:27:19.360Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:19.360Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:27:21.723Z", "callId": "call_CYCu6tTghwhpP5bEEBeAlD6A", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:27:51.756Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:21.875Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b1d6576887d28a0b680e839392a9", "usage": { "input_tokens": 64172, "cached_input_tokens": 63872, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 64227 }, "turn_token_usage": { "input_tokens": 2348273, "cached_input_tokens": 2242176, "cache_write_input_tokens": 0, "output_tokens": 36302, "reasoning_output_tokens": 15902, "total_tokens": 2384575 }, "thread_token_usage": { "input_tokens": 2348273, "cached_input_tokens": 2242176, "cache_write_input_tokens": 0, "output_tokens": 36302, "reasoning_output_tokens": 15902, "total_tokens": 2384575 } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-24T23:27:53.375Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:53.375Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:27:54.296Z", "text": "The metric-weighted model is stable through three epochs (loss 0.184). The current best artifact remains untouched; this experiment is isolated until it proves better on the public evaluator." }, { "kind": "tool", "timestamp": "2026-09-24T23:27:54.923Z", "callId": "call_LmbOmtebX5LdKNniR8lLKLqz", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":33478,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:27:55.316Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\n{\"epoch\": 4, \"loss\": 0.1405361875008785, \"seconds\": 55.05872941017151, \"scale\": 14.866470336914062}\nsaved /app/solution/model_validation_metric.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:55.032Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b1f7fc8487d2bd7e88d6b65fe030", "usage": { "input_tokens": 64250, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 108, "reasoning_output_tokens": 17, "total_tokens": 64358 }, "turn_token_usage": { "input_tokens": 2412523, "cached_input_tokens": 2306176, "cache_write_input_tokens": 0, "output_tokens": 36410, "reasoning_output_tokens": 15919, "total_tokens": 2448933 }, "thread_token_usage": { "input_tokens": 2412523, "cached_input_tokens": 2306176, "cache_write_input_tokens": 0, "output_tokens": 36410, "reasoning_output_tokens": 15919, "total_tokens": 2448933 } } }, { "kind": "context", "timestamp": "2026-09-24T23:27:55.315Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-db9c9e21-bb55-4405-8036-8018b81527a5", "process_id": "33478", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation_metric.pt --epochs 4 --batch-size 512 --negatives 32 --seed 48611 --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_validation.npz --output /app/solution/model_validation_metric.pt --epochs 4 --batch-size 512 --negatives 32 --seed 48611 --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4451584124886673, \"seconds\": 57.439289569854736, \"scale\": 7.719996929168701}\n{\"epoch\": 2, \"loss\": 0.2735381604391605, \"seconds\": 56.75349473953247, \"scale\": 9.339584350585938}\n{\"epoch\": 3, \"loss\": 0.18352071033851705, \"seconds\": 54.79453730583191, \"scale\": 11.936638832092285}\n{\"epoch\": 4, \"loss\": 0.1405361875008785, \"seconds\": 55.05872941017151, \"scale\": 14.866470336914062}\nsaved /app/solution/model_validation_metric.pt\n", "stderr": "", "aggregated_output": "/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4451584124886673, \"seconds\": 57.439289569854736, \"scale\": 7.719996929168701}\n{\"epoch\": 2, \"loss\": 0.2735381604391605, \"seconds\": 56.75349473953247, \"scale\": 9.339584350585938}\n{\"epoch\": 3, \"loss\": 0.18352071033851705, \"seconds\": 54.79453730583191, \"scale\": 11.936638832092285}\n{\"epoch\": 4, \"loss\": 0.1405361875008785, \"seconds\": 55.05872941017151, \"scale\": 14.866470336914062}\nsaved /app/solution/model_validation_metric.pt\n", "exit_code": 0, "duration": { "secs": 231, "nanos": 224120779 }, "formatted_output": "/app/solution/train_ranker.py:625: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4451584124886673, \"seconds\": 57.439289569854736, \"scale\": 7.719996929168701}\n{\"epoch\": 2, \"loss\": 0.2735381604391605, \"seconds\": 56.75349473953247, \"scale\": 9.339584350585938}\n{\"epoch\": 3, \"loss\": 0.18352071033851705, \"seconds\": 54.79453730583191, \"scale\": 11.936638832092285}\n{\"epoch\": 4, \"loss\": 0.1405361875008785, \"seconds\": 55.05872941017151, \"scale\": 14.866470336914062}\nsaved /app/solution/model_validation_metric.pt\n" } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-24T23:27:56.918Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:56.918Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:27:59.834Z", "callId": "call_D3raBK66yNKVUeYJIdH7djMc", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for w in 0.0 1.0 1.5; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_metric.pt --output /app/solution/validation_metric_p${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_metric_p${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:28:30.000Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019138,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026896,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.5,\n \"second_weight\": null\n}\n\nSESSION_ID=32861", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:59.951Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b1fb806887d2a31cf5e48ecd465a", "usage": { "input_tokens": 64434, "cached_input_tokens": 64128, "cache_write_input_tokens": 0, "output_tokens": 199, "reasoning_output_tokens": 28, "total_tokens": 64633 }, "turn_token_usage": { "input_tokens": 2476957, "cached_input_tokens": 2370304, "cache_write_input_tokens": 0, "output_tokens": 36609, "reasoning_output_tokens": 15947, "total_tokens": 2513566 }, "thread_token_usage": { "input_tokens": 2476957, "cached_input_tokens": 2370304, "cache_write_input_tokens": 0, "output_tokens": 36609, "reasoning_output_tokens": 15947, "total_tokens": 2513566 } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-24T23:28:31.335Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:28:31.335Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:28:32.043Z", "callId": "call_FyipS8Vv3s25waNQKvlMq1Mk", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":32861,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:28:33.681Z", "content": "Script completed\nWall time 1.6 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026362,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:28:32.133Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b21e336487d2a2a7bb3fe9dfafce", "usage": { "input_tokens": 64920, "cached_input_tokens": 64256, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 16, "total_tokens": 64983 }, "turn_token_usage": { "input_tokens": 2541877, "cached_input_tokens": 2434560, "cache_write_input_tokens": 0, "output_tokens": 36672, "reasoning_output_tokens": 15963, "total_tokens": 2578549 }, "thread_token_usage": { "input_tokens": 2541877, "cached_input_tokens": 2434560, "cache_write_input_tokens": 0, "output_tokens": 36672, "reasoning_output_tokens": 15963, "total_tokens": 2578549 } } }, { "kind": "context", "timestamp": "2026-09-24T23:28:33.680Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7dee6194-ad40-4444-8600-f2b7616510f7", "process_id": "32861", "command": [ "/bin/sh", "-lc", "for w in 0.0 1.0 1.5; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_metric.pt --output /app/solution/validation_metric_p${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_metric_p${w}.parquet; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for w in 0.0 1.0 1.5; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_metric.pt --output /app/solution/validation_metric_p${w}.parquet --popularity-weight $w; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_metric_p${w}.parquet; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019138,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026896,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.5,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026362,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019138,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026896,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.5,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026362,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 33, "nanos": 681073687 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019138,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.0,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026896,\n \"quality_users\": 4263\n }\n}\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 1.5,\n \"second_weight\": null\n}\n{\n \"model\": \"/app/solution/validation_metric_p1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026362,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-24T23:28:41.948Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:28:41.948Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:28:55.498Z", "callId": "call_7l44rIvxOntHLh9Eabw5SFHA", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np, pandas as pd, torch\\nfrom train_ranker import TwoTower, context_batch, DATA, N_ITEMS\\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\\nfrom evaluate_public import validation_truth\\nP=np.load('/app/solution/prepared_validation.npz'); contexts={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\\ndef load(path):\\n s=torch.load(path,map_location='cpu',weights_only=False); m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\\nwith torch.inference_mode(): iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\\npop=P['item_features'][:,1]; E=CandidateSet(DATA/'validation_eligible_candidates.npz'); T=validation_truth(E,set(E.uids))\\ngrids=[]\\nfor a in [.2,.4,.6,.8,1.]:\\n for p in [.6,.8,1.,1.2,1.4]: grids.append((a,0.,p))\\nfor b in [.1,.25,.4,.6]:\\n for p in [.6,.8,1.,1.2,1.4]: grids.append((0.,b,p))\\nfor a in [.25,.5]:\\n for b in [.1,.25,.4]:\\n for p in [.8,1.,1.2,1.4]: grids.append((a,b,p))\\nsums={g:0. for g in grids}\\nfor st in range(0,len(E.uids),128):\\n en=min(st+128,len(E.uids)); idx=torch.arange(st,en)\\n with torch.inference_mode(): uv=[m.user_tower(*context_batch(contexts,idx)).numpy() for m in models]\\n scores=[u@i.T for u,i in zip(uv,iv)]; mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\\n for g in grids:\\n a,b,p=g; score=scores[0]+a*scores[1]+b*scores[2]+p*pop; score[~mask]=-np.inf; top=np.argpartition(score,-10,axis=1)[:,-10:]; ts=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\\n for j,u in enumerate(E.uids[st:en]):\\n gold=T.get(int(u));\\n if gold: sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\\nprint('users',len(T))\\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:25]:print(g,round(v/len(T),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:29:25.666Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=95921", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:28:55.624Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b221de1487d280672c42c9d125bc", "usage": { "input_tokens": 65056, "cached_input_tokens": 64768, "cache_write_input_tokens": 0, "output_tokens": 1150, "reasoning_output_tokens": 388, "total_tokens": 66206 }, "turn_token_usage": { "input_tokens": 2606933, "cached_input_tokens": 2499328, "cache_write_input_tokens": 0, "output_tokens": 37822, "reasoning_output_tokens": 16351, "total_tokens": 2644755 }, "thread_token_usage": { "input_tokens": 2606933, "cached_input_tokens": 2499328, "cache_write_input_tokens": 0, "output_tokens": 37822, "reasoning_output_tokens": 16351, "total_tokens": 2644755 } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-24T23:29:27.636Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:29:27.636Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:29:28.332Z", "callId": "call_dRTrzzcOA8cL6JBeRaGtOIRG", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":95921,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:29:58.355Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:29:28.446Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b255dbfc87d28642567db97f6410", "usage": { "input_tokens": 66234, "cached_input_tokens": 64896, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 66287 }, "turn_token_usage": { "input_tokens": 2673167, "cached_input_tokens": 2564224, "cache_write_input_tokens": 0, "output_tokens": 37875, "reasoning_output_tokens": 16357, "total_tokens": 2711042 }, "thread_token_usage": { "input_tokens": 2673167, "cached_input_tokens": 2564224, "cache_write_input_tokens": 0, "output_tokens": 37875, "reasoning_output_tokens": 16357, "total_tokens": 2711042 } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-24T23:30:03.701Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:30:03.701Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:30:03.708Z", "text": "The final validation experiment improved again: the metric-aligned tower scores 0.026896 by itself. I’m testing whether the earlier towers add independent signal via concatenation; this is score calibration only, with no validation events fed into user features or training." }, { "kind": "tool", "timestamp": "2026-09-24T23:30:03.723Z", "callId": "call_JnMUJ4bNJBiUpa01YZRsK6Ff", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":95921,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:30:31.987Z", "content": "Script completed\nWall time 28.3 seconds\nOutput:\n\nusers 4263\n(0.5, 0.4, 1.4) 0.029952\n(0.25, 0.4, 1.4) 0.029805\n(0.25, 0.25, 1.4) 0.029742\n(0.0, 0.6, 1.4) 0.029733\n(0.0, 0.4, 1.2) 0.02951\n(0.5, 0.4, 1.2) 0.029481\n(0.25, 0.4, 1.2) 0.029473\n(0.0, 0.6, 1.2) 0.029463\n(0.5, 0.25, 1.4) 0.029423\n(0.25, 0.25, 1.2) 0.02938\n(0.0, 0.25, 1.2) 0.029364\n(0.5, 0.25, 1.2) 0.029353\n(0.0, 0.4, 1.4) 0.029257\n(0.25, 0.1, 1.4) 0.02913\n(0.25, 0.4, 1.0) 0.028996\n(0.0, 0.25, 1.0) 0.028944\n(0.5, 0.1, 1.4) 0.02894\n(0.0, 0.25, 1.4) 0.028931\n(0.5, 0.4, 1.0) 0.028818\n(0.25, 0.25, 1.0) 0.028764\n(0.0, 0.4, 1.0) 0.028695\n(0.0, 0.1, 1.2) 0.028671\n(0.5, 0.25, 1.0) 0.028623\n(0.0, 0.6, 1.0) 0.028593\n(0.5, 0.1, 1.2) 0.028555\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:30:03.756Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b27692d487d2b18d5c7e16e2e017", "usage": { "input_tokens": 66310, "cached_input_tokens": 66048, "cache_write_input_tokens": 0, "output_tokens": 112, "reasoning_output_tokens": 6, "total_tokens": 66422 }, "turn_token_usage": { "input_tokens": 2739477, "cached_input_tokens": 2630272, "cache_write_input_tokens": 0, "output_tokens": 37987, "reasoning_output_tokens": 16363, "total_tokens": 2777464 }, "thread_token_usage": { "input_tokens": 2739477, "cached_input_tokens": 2630272, "cache_write_input_tokens": 0, "output_tokens": 37987, "reasoning_output_tokens": 16363, "total_tokens": 2777464 } } }, { "kind": "context", "timestamp": "2026-09-24T23:30:31.985Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a580e1b2-4d5c-46cd-a1bc-0ef1e43910e2", "process_id": "95921", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd, torch\nfrom train_ranker import TwoTower, context_batch, DATA, N_ITEMS\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz'); contexts={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False); m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode(): iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\npop=P['item_features'][:,1]; E=CandidateSet(DATA/'validation_eligible_candidates.npz'); T=validation_truth(E,set(E.uids))\ngrids=[]\nfor a in [.2,.4,.6,.8,1.]:\n for p in [.6,.8,1.,1.2,1.4]: grids.append((a,0.,p))\nfor b in [.1,.25,.4,.6]:\n for p in [.6,.8,1.,1.2,1.4]: grids.append((0.,b,p))\nfor a in [.25,.5]:\n for b in [.1,.25,.4]:\n for p in [.8,1.,1.2,1.4]: grids.append((a,b,p))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids)); idx=torch.arange(st,en)\n with torch.inference_mode(): uv=[m.user_tower(*context_batch(contexts,idx)).numpy() for m in models]\n scores=[u@i.T for u,i in zip(uv,iv)]; mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,p=g; score=scores[0]+a*scores[1]+b*scores[2]+p*pop; score[~mask]=-np.inf; top=np.argpartition(score,-10,axis=1)[:,-10:]; ts=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold: sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nprint('users',len(T))\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:25]:print(g,round(v/len(T),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd, torch\nfrom train_ranker import TwoTower, context_batch, DATA, N_ITEMS\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz'); contexts={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False); m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode(): iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\npop=P['item_features'][:,1]; E=CandidateSet(DATA/'validation_eligible_candidates.npz'); T=validation_truth(E,set(E.uids))\ngrids=[]\nfor a in [.2,.4,.6,.8,1.]:\n for p in [.6,.8,1.,1.2,1.4]: grids.append((a,0.,p))\nfor b in [.1,.25,.4,.6]:\n for p in [.6,.8,1.,1.2,1.4]: grids.append((0.,b,p))\nfor a in [.25,.5]:\n for b in [.1,.25,.4]:\n for p in [.8,1.,1.2,1.4]: grids.append((a,b,p))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids)); idx=torch.arange(st,en)\n with torch.inference_mode(): uv=[m.user_tower(*context_batch(contexts,idx)).numpy() for m in models]\n scores=[u@i.T for u,i in zip(uv,iv)]; mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,p=g; score=scores[0]+a*scores[1]+b*scores[2]+p*pop; score[~mask]=-np.inf; top=np.argpartition(score,-10,axis=1)[:,-10:]; ts=np.take_along_axis(score,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold: sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nprint('users',len(T))\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:25]:print(g,round(v/len(T),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 4263\n(0.5, 0.4, 1.4) 0.029952\n(0.25, 0.4, 1.4) 0.029805\n(0.25, 0.25, 1.4) 0.029742\n(0.0, 0.6, 1.4) 0.029733\n(0.0, 0.4, 1.2) 0.02951\n(0.5, 0.4, 1.2) 0.029481\n(0.25, 0.4, 1.2) 0.029473\n(0.0, 0.6, 1.2) 0.029463\n(0.5, 0.25, 1.4) 0.029423\n(0.25, 0.25, 1.2) 0.02938\n(0.0, 0.25, 1.2) 0.029364\n(0.5, 0.25, 1.2) 0.029353\n(0.0, 0.4, 1.4) 0.029257\n(0.25, 0.1, 1.4) 0.02913\n(0.25, 0.4, 1.0) 0.028996\n(0.0, 0.25, 1.0) 0.028944\n(0.5, 0.1, 1.4) 0.02894\n(0.0, 0.25, 1.4) 0.028931\n(0.5, 0.4, 1.0) 0.028818\n(0.25, 0.25, 1.0) 0.028764\n(0.0, 0.4, 1.0) 0.028695\n(0.0, 0.1, 1.2) 0.028671\n(0.5, 0.25, 1.0) 0.028623\n(0.0, 0.6, 1.0) 0.028593\n(0.5, 0.1, 1.2) 0.028555\n", "stderr": "", "aggregated_output": "users 4263\n(0.5, 0.4, 1.4) 0.029952\n(0.25, 0.4, 1.4) 0.029805\n(0.25, 0.25, 1.4) 0.029742\n(0.0, 0.6, 1.4) 0.029733\n(0.0, 0.4, 1.2) 0.02951\n(0.5, 0.4, 1.2) 0.029481\n(0.25, 0.4, 1.2) 0.029473\n(0.0, 0.6, 1.2) 0.029463\n(0.5, 0.25, 1.4) 0.029423\n(0.25, 0.25, 1.2) 0.02938\n(0.0, 0.25, 1.2) 0.029364\n(0.5, 0.25, 1.2) 0.029353\n(0.0, 0.4, 1.4) 0.029257\n(0.25, 0.1, 1.4) 0.02913\n(0.25, 0.4, 1.0) 0.028996\n(0.0, 0.25, 1.0) 0.028944\n(0.5, 0.1, 1.4) 0.02894\n(0.0, 0.25, 1.4) 0.028931\n(0.5, 0.4, 1.0) 0.028818\n(0.25, 0.25, 1.0) 0.028764\n(0.0, 0.4, 1.0) 0.028695\n(0.0, 0.1, 1.2) 0.028671\n(0.5, 0.25, 1.0) 0.028623\n(0.0, 0.6, 1.0) 0.028593\n(0.5, 0.1, 1.2) 0.028555\n", "exit_code": 0, "duration": { "secs": 96, "nanos": 321163190 }, "formatted_output": "users 4263\n(0.5, 0.4, 1.4) 0.029952\n(0.25, 0.4, 1.4) 0.029805\n(0.25, 0.25, 1.4) 0.029742\n(0.0, 0.6, 1.4) 0.029733\n(0.0, 0.4, 1.2) 0.02951\n(0.5, 0.4, 1.2) 0.029481\n(0.25, 0.4, 1.2) 0.029473\n(0.0, 0.6, 1.2) 0.029463\n(0.5, 0.25, 1.4) 0.029423\n(0.25, 0.25, 1.2) 0.02938\n(0.0, 0.25, 1.2) 0.029364\n(0.5, 0.25, 1.2) 0.029353\n(0.0, 0.4, 1.4) 0.029257\n(0.25, 0.1, 1.4) 0.02913\n(0.25, 0.4, 1.0) 0.028996\n(0.0, 0.25, 1.0) 0.028944\n(0.5, 0.1, 1.4) 0.02894\n(0.0, 0.25, 1.4) 0.028931\n(0.5, 0.4, 1.0) 0.028818\n(0.25, 0.25, 1.0) 0.028764\n(0.0, 0.4, 1.0) 0.028695\n(0.0, 0.1, 1.2) 0.028671\n(0.5, 0.25, 1.0) 0.028623\n(0.0, 0.6, 1.0) 0.028593\n(0.5, 0.1, 1.2) 0.028555\n" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-24T23:30:43.809Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:30:43.809Z", "text": "" } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-24T23:30:52.992Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:30:52.992Z", "text": "" } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-24T23:30:59.810Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:30:59.810Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:31:15.502Z", "callId": "call_M2S5d6OBCmhOCajdfAG13pB3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nD=Path('/task/data'); cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(len(ids),dtype=np.int32)\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); raw=x.item_id.to_numpy(); ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80)\\nlikes=pd.read_parquet(D/'train_likes.parquet'); rawl=likes.item_id.to_numpy(); il=np.full(len(likes),-1,np.int32);ok=rawl=0)&(likes.is_organic.to_numpy()==1)\\nfeatures={}\\nfor days in [3,7,14,28,56,112]:\\n m=base&(x.timestamp.to_numpy()>=24786800-days*86400); features[f'e{days}']=np.bincount(ii[m],minlength=30000)\\n m=lbase&(likes.timestamp.to_numpy()>=24786800-days*86400); features[f'l{days}']=np.bincount(il[m],minlength=30000)\\n # distinct users with strong organic event\\n if days in [7,28,56]:\\n z=pd.DataFrame({'u':x.uid.to_numpy()[base&(x.timestamp.to_numpy()>=24786800-days*86400)],'i':ii[base&(x.timestamp.to_numpy()>=24786800-days*86400)]}).drop_duplicates();features[f'u{days}']=np.bincount(z.i,minlength=30000)\\nfeatures['cum']=cat.organic_positive_listens.to_numpy();\\nE=CandidateSet(D/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));\\ndef score(s):\\n vals=[]\\n for r,u in enumerate(E.uids):\\n g=T.get(int(u));\\n if not g:continue\\n mask=np.unpackbits(E.packed[r],count=30000,bitorder='little').astype(bool); q=s.copy();q[~mask]=-np.inf;top=np.argpartition(q,-10)[-10:];top=top[np.argsort(-q[top])];vals.append(ndcg_at_k(E.item_ids[top].tolist(),g,10))\\n return np.mean(vals)\\nfor k,v in features.items(): print(k,score(np.log1p(v)))\\n# standardized blends cumulative + recent event/user + likes\\nzc=(np.log1p(features['cum'])-np.log1p(features['cum']).mean())/np.log1p(features['cum']).std()\\nfor key in ['e7','e14','e28','e56','e112','u7','u28','u56','l7','l28','l56']:\\n z=np.log1p(features[key]);z=(z-z.mean())/(z.std()+1e-9)\\n best=(0,None)\\n for w in [.1,.25,.5,.75,1,1.5,2,3]:\\n s=score(zc+w*z)\\n if s>best[0]:best=(s,w)\\n print('blend',key,best)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:31:45.677Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=95416", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:31:15.635Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b298385087d283fda9ae61429cb5", "usage": { "input_tokens": 66975, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 2286, "reasoning_output_tokens": 1407, "total_tokens": 69261 }, "turn_token_usage": { "input_tokens": 2806452, "cached_input_tokens": 2696448, "cache_write_input_tokens": 0, "output_tokens": 40273, "reasoning_output_tokens": 17770, "total_tokens": 2846725 }, "thread_token_usage": { "input_tokens": 2806452, "cached_input_tokens": 2696448, "cache_write_input_tokens": 0, "output_tokens": 40273, "reasoning_output_tokens": 17770, "total_tokens": 2846725 } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-24T23:31:47.555Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:31:47.555Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:31:48.246Z", "callId": "call_8iBtKH4wF0JNWKCxg9fxVMDJ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":95416,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:32:18.255Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:31:48.359Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b2e1e6fc87d29fbe0f6875c0bc19", "usage": { "input_tokens": 69289, "cached_input_tokens": 66816, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 69342 }, "turn_token_usage": { "input_tokens": 2875741, "cached_input_tokens": 2763264, "cache_write_input_tokens": 0, "output_tokens": 40326, "reasoning_output_tokens": 17776, "total_tokens": 2916067 }, "thread_token_usage": { "input_tokens": 2875741, "cached_input_tokens": 2763264, "cache_write_input_tokens": 0, "output_tokens": 40326, "reasoning_output_tokens": 17776, "total_tokens": 2916067 } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-24T23:32:20.466Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:32:20.466Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:32:21.663Z", "text": "I’m also checking a cheap production-safe signal before locking the refit: global organic momentum over recent weeks. It is another independently precomputable item coordinate, so if it helps, latency impact is one scalar—not another network." }, { "kind": "context", "timestamp": "2026-09-24T23:32:21.820Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60c10967-6aae-40fa-954c-c2abf85979e1", "process_id": "95416", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nD=Path('/task/data'); cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(len(ids),dtype=np.int32)\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); raw=x.item_id.to_numpy(); ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80)\nlikes=pd.read_parquet(D/'train_likes.parquet'); rawl=likes.item_id.to_numpy(); il=np.full(len(likes),-1,np.int32);ok=rawl=0)&(likes.is_organic.to_numpy()==1)\nfeatures={}\nfor days in [3,7,14,28,56,112]:\n m=base&(x.timestamp.to_numpy()>=24786800-days*86400); features[f'e{days}']=np.bincount(ii[m],minlength=30000)\n m=lbase&(likes.timestamp.to_numpy()>=24786800-days*86400); features[f'l{days}']=np.bincount(il[m],minlength=30000)\n # distinct users with strong organic event\n if days in [7,28,56]:\n z=pd.DataFrame({'u':x.uid.to_numpy()[base&(x.timestamp.to_numpy()>=24786800-days*86400)],'i':ii[base&(x.timestamp.to_numpy()>=24786800-days*86400)]}).drop_duplicates();features[f'u{days}']=np.bincount(z.i,minlength=30000)\nfeatures['cum']=cat.organic_positive_listens.to_numpy();\nE=CandidateSet(D/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));\ndef score(s):\n vals=[]\n for r,u in enumerate(E.uids):\n g=T.get(int(u));\n if not g:continue\n mask=np.unpackbits(E.packed[r],count=30000,bitorder='little').astype(bool); q=s.copy();q[~mask]=-np.inf;top=np.argpartition(q,-10)[-10:];top=top[np.argsort(-q[top])];vals.append(ndcg_at_k(E.item_ids[top].tolist(),g,10))\n return np.mean(vals)\nfor k,v in features.items(): print(k,score(np.log1p(v)))\n# standardized blends cumulative + recent event/user + likes\nzc=(np.log1p(features['cum'])-np.log1p(features['cum']).mean())/np.log1p(features['cum']).std()\nfor key in ['e7','e14','e28','e56','e112','u7','u28','u56','l7','l28','l56']:\n z=np.log1p(features[key]);z=(z-z.mean())/(z.std()+1e-9)\n best=(0,None)\n for w in [.1,.25,.5,.75,1,1.5,2,3]:\n s=score(zc+w*z)\n if s>best[0]:best=(s,w)\n print('blend',key,best)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nD=Path('/task/data'); cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(len(ids),dtype=np.int32)\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); raw=x.item_id.to_numpy(); ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80)\nlikes=pd.read_parquet(D/'train_likes.parquet'); rawl=likes.item_id.to_numpy(); il=np.full(len(likes),-1,np.int32);ok=rawl=0)&(likes.is_organic.to_numpy()==1)\nfeatures={}\nfor days in [3,7,14,28,56,112]:\n m=base&(x.timestamp.to_numpy()>=24786800-days*86400); features[f'e{days}']=np.bincount(ii[m],minlength=30000)\n m=lbase&(likes.timestamp.to_numpy()>=24786800-days*86400); features[f'l{days}']=np.bincount(il[m],minlength=30000)\n # distinct users with strong organic event\n if days in [7,28,56]:\n z=pd.DataFrame({'u':x.uid.to_numpy()[base&(x.timestamp.to_numpy()>=24786800-days*86400)],'i':ii[base&(x.timestamp.to_numpy()>=24786800-days*86400)]}).drop_duplicates();features[f'u{days}']=np.bincount(z.i,minlength=30000)\nfeatures['cum']=cat.organic_positive_listens.to_numpy();\nE=CandidateSet(D/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));\ndef score(s):\n vals=[]\n for r,u in enumerate(E.uids):\n g=T.get(int(u));\n if not g:continue\n mask=np.unpackbits(E.packed[r],count=30000,bitorder='little').astype(bool); q=s.copy();q[~mask]=-np.inf;top=np.argpartition(q,-10)[-10:];top=top[np.argsort(-q[top])];vals.append(ndcg_at_k(E.item_ids[top].tolist(),g,10))\n return np.mean(vals)\nfor k,v in features.items(): print(k,score(np.log1p(v)))\n# standardized blends cumulative + recent event/user + likes\nzc=(np.log1p(features['cum'])-np.log1p(features['cum']).mean())/np.log1p(features['cum']).std()\nfor key in ['e7','e14','e28','e56','e112','u7','u28','u56','l7','l28','l56']:\n z=np.log1p(features[key]);z=(z-z.mean())/(z.std()+1e-9)\n best=(0,None)\n for w in [.1,.25,.5,.75,1,1.5,2,3]:\n s=score(zc+w*z)\n if s>best[0]:best=(s,w)\n print('blend',key,best)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "e3 0.025670220936889792\nl3 0.03042538516539513\ne7 0.02813072548782609\nl7 0.029262951387165986\nu7 0.030295460966813934\ne14 0.022878205389350977\nl14 0.02537721842906461\ne28 0.012966745357908326\nl28 0.01968252414272987\nu28 0.019027902396046327\ne56 0.01603795732705134\nl56 0.0167143162456852\nu56 0.01736968284847791\ne112 0.014621972497985728\nl112 0.01502730424092601\ncum 0.01057706484365688\nblend e7 (np.float64(0.022579487221234037), 3)\nblend e14 (np.float64(0.018874350810622848), 3)\nblend e28 (np.float64(0.01417758861917847), 3)\nblend e56 (np.float64(0.015831068458021683), 3)\nblend e112 (np.float64(0.013918872881090697), 3)\nblend u7 (np.float64(0.023873132400251074), 3)\nblend u28 (np.float64(0.017079989293050633), 3)\nblend u56 (np.float64(0.01600208298755792), 3)\nblend l7 (np.float64(0.03208283290815126), 1.5)\nblend l28 (np.float64(0.019592879471119103), 2)\nblend l56 (np.float64(0.017363948492258266), 2)\n", "stderr": "", "aggregated_output": "e3 0.025670220936889792\nl3 0.03042538516539513\ne7 0.02813072548782609\nl7 0.029262951387165986\nu7 0.030295460966813934\ne14 0.022878205389350977\nl14 0.02537721842906461\ne28 0.012966745357908326\nl28 0.01968252414272987\nu28 0.019027902396046327\ne56 0.01603795732705134\nl56 0.0167143162456852\nu56 0.01736968284847791\ne112 0.014621972497985728\nl112 0.01502730424092601\ncum 0.01057706484365688\nblend e7 (np.float64(0.022579487221234037), 3)\nblend e14 (np.float64(0.018874350810622848), 3)\nblend e28 (np.float64(0.01417758861917847), 3)\nblend e56 (np.float64(0.015831068458021683), 3)\nblend e112 (np.float64(0.013918872881090697), 3)\nblend u7 (np.float64(0.023873132400251074), 3)\nblend u28 (np.float64(0.017079989293050633), 3)\nblend u56 (np.float64(0.01600208298755792), 3)\nblend l7 (np.float64(0.03208283290815126), 1.5)\nblend l28 (np.float64(0.019592879471119103), 2)\nblend l56 (np.float64(0.017363948492258266), 2)\n", "exit_code": 0, "duration": { "secs": 66, "nanos": 144739421 }, "formatted_output": "e3 0.025670220936889792\nl3 0.03042538516539513\ne7 0.02813072548782609\nl7 0.029262951387165986\nu7 0.030295460966813934\ne14 0.022878205389350977\nl14 0.02537721842906461\ne28 0.012966745357908326\nl28 0.01968252414272987\nu28 0.019027902396046327\ne56 0.01603795732705134\nl56 0.0167143162456852\nu56 0.01736968284847791\ne112 0.014621972497985728\nl112 0.01502730424092601\ncum 0.01057706484365688\nblend e7 (np.float64(0.022579487221234037), 3)\nblend e14 (np.float64(0.018874350810622848), 3)\nblend e28 (np.float64(0.01417758861917847), 3)\nblend e56 (np.float64(0.015831068458021683), 3)\nblend e112 (np.float64(0.013918872881090697), 3)\nblend u7 (np.float64(0.023873132400251074), 3)\nblend u28 (np.float64(0.017079989293050633), 3)\nblend u56 (np.float64(0.01600208298755792), 3)\nblend l7 (np.float64(0.03208283290815126), 1.5)\nblend l28 (np.float64(0.019592879471119103), 2)\nblend l56 (np.float64(0.017363948492258266), 2)\n" } } }, { "kind": "tool", "timestamp": "2026-09-24T23:32:22.173Z", "callId": "call_1cFyiCTtc6jpkOwBNKKrHtb1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":95416,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:32:24.250Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ne3 0.025670220936889792\nl3 0.03042538516539513\ne7 0.02813072548782609\nl7 0.029262951387165986\nu7 0.030295460966813934\ne14 0.022878205389350977\nl14 0.02537721842906461\ne28 0.012966745357908326\nl28 0.01968252414272987\nu28 0.019027902396046327\ne56 0.01603795732705134\nl56 0.0167143162456852\nu56 0.01736968284847791\ne112 0.014621972497985728\nl112 0.01502730424092601\ncum 0.01057706484365688\nblend e7 (np.float64(0.022579487221234037), 3)\nblend e14 (np.float64(0.018874350810622848), 3)\nblend e28 (np.float64(0.01417758861917847), 3)\nblend e56 (np.float64(0.015831068458021683), 3)\nblend e112 (np.float64(0.013918872881090697), 3)\nblend u7 (np.float64(0.023873132400251074), 3)\nblend u28 (np.float64(0.017079989293050633), 3)\nblend u56 (np.float64(0.01600208298755792), 3)\nblend l7 (np.float64(0.03208283290815126), 1.5)\nblend l28 (np.float64(0.019592879471119103), 2)\nblend l56 (np.float64(0.017363948492258266), 2)\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:32:24.249Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b3026e5c87d2814bc10e7e23c673", "usage": { "input_tokens": 69365, "cached_input_tokens": 69120, "cache_write_input_tokens": 0, "output_tokens": 132, "reasoning_output_tokens": 32, "total_tokens": 69497 }, "turn_token_usage": { "input_tokens": 2945106, "cached_input_tokens": 2832384, "cache_write_input_tokens": 0, "output_tokens": 40458, "reasoning_output_tokens": 17808, "total_tokens": 2985564 }, "thread_token_usage": { "input_tokens": 2945106, "cached_input_tokens": 2832384, "cache_write_input_tokens": 0, "output_tokens": 40458, "reasoning_output_tokens": 17808, "total_tokens": 2985564 } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-24T23:32:35.721Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:32:35.721Z", "text": "" } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-24T23:32:39.064Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:32:39.064Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:32:57.644Z", "callId": "call_wSxITfVXRggUktR9gmqo1QsI", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\\ndef load(path):\\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\\nwith torch.inference_mode(): iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\\n# cutoff-correct global organic-like momentum\\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)\\ndef zdays(d):\\n v=np.bincount(ii[base&(x.timestamp.to_numpy()>=24786800-d*86400)],minlength=N_ITEMS);z=np.log1p(v).astype(np.float32);return (z-z.mean())/(z.std()+1e-9)\\nl3,l7=zdays(3),zdays(7);pop=P['item_features'][:,1]\\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids))\\ngrids=[]\\n# (long, recent, cumulative pop, l3, l7)\\nfor p in [0.,.5,1.,1.5]:\\n for q in [.5,1.,1.5,2.,3.]: grids.append((.5,.4,p,q,0.))\\n for q in [.5,1.,1.5,2.,3.]: grids.append((.5,.4,p,0.,q))\\nfor p in [0.,.5,1.]:\\n for q in [.5,1.,1.5,2.]:\\n for r in [.5,1.,1.5,2.]:grids.append((.5,.4,p,q,r))\\n# latency-friendlier metric+recent without long\\nfor p in [0.,.5,1.]:\\n for q in [.5,1.,1.5,2.]:grids.append((0.,.5,p,q,0.))\\nsums={g:0. for g in grids}\\nfor st in range(0,len(E.uids),128):\\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\\n with torch.inference_mode():uv=[m.user_tower(*context_batch(ctx,idx)).numpy() for m in models]\\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\\n for g in grids:\\n a,b,p,q,r=g;s=sc[0]+a*sc[1]+b*sc[2]+p*pop+q*l3+r*l7;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\\n for j,u in enumerate(E.uids[st:en]):\\n gold=T.get(int(u));\\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\\nprint('users',len(T))\\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:30]:print(g,round(v/len(T),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:33:27.812Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=80323", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:32:57.728Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b308803487d2a7341c34a0125baf", "usage": { "input_tokens": 69933, "cached_input_tokens": 69248, "cache_write_input_tokens": 0, "output_tokens": 1735, "reasoning_output_tokens": 695, "total_tokens": 71668 }, "turn_token_usage": { "input_tokens": 3015039, "cached_input_tokens": 2901632, "cache_write_input_tokens": 0, "output_tokens": 42193, "reasoning_output_tokens": 18503, "total_tokens": 3057232 }, "thread_token_usage": { "input_tokens": 3015039, "cached_input_tokens": 2901632, "cache_write_input_tokens": 0, "output_tokens": 42193, "reasoning_output_tokens": 18503, "total_tokens": 3057232 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-24T23:33:29.550Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:33:29.550Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:33:30.225Z", "callId": "call_toLjopkX41KH0Zetg5tOXxX3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":80323,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:34:00.233Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:33:30.352Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b34852d487d29e4614208b9b235f", "usage": { "input_tokens": 71696, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 71749 }, "turn_token_usage": { "input_tokens": 3086735, "cached_input_tokens": 2971392, "cache_write_input_tokens": 0, "output_tokens": 42246, "reasoning_output_tokens": 18509, "total_tokens": 3128981 }, "thread_token_usage": { "input_tokens": 3086735, "cached_input_tokens": 2971392, "cache_write_input_tokens": 0, "output_tokens": 42246, "reasoning_output_tokens": 18509, "total_tokens": 3128981 } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-24T23:34:01.771Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:34:01.771Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:34:02.484Z", "callId": "call_uA6h6I0KWKJIUZJ8wPuY09o3", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":80323,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:34:32.493Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:34:02.612Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b368691887d2b253fa162ac1423a", "usage": { "input_tokens": 71772, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 71827 }, "turn_token_usage": { "input_tokens": 3158507, "cached_input_tokens": 3042944, "cache_write_input_tokens": 0, "output_tokens": 42301, "reasoning_output_tokens": 18517, "total_tokens": 3200808 }, "thread_token_usage": { "input_tokens": 3158507, "cached_input_tokens": 3042944, "cache_write_input_tokens": 0, "output_tokens": 42301, "reasoning_output_tokens": 18517, "total_tokens": 3200808 } } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-24T23:34:34.558Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:34:34.558Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:34:35.828Z", "text": "Recent organic-like momentum is unusually predictive here: by itself it exceeds the original popularity baseline by roughly 3×. I’m calibrating it jointly with the learned personalization scores; because it is a single item-side coordinate, it preserves offline precomputation and adds essentially no request-time work." }, { "kind": "tool", "timestamp": "2026-09-24T23:34:36.507Z", "callId": "call_6ofIQIDueLlL61RYPVUP1NFc", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":80323,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:35:06.528Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:34:36.625Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b388b8a887d29fe4cbe3b71e118d", "usage": { "input_tokens": 71850, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 127, "reasoning_output_tokens": 15, "total_tokens": 71977 }, "turn_token_usage": { "input_tokens": 3230357, "cached_input_tokens": 3114496, "cache_write_input_tokens": 0, "output_tokens": 42428, "reasoning_output_tokens": 18532, "total_tokens": 3272785 }, "thread_token_usage": { "input_tokens": 3230357, "cached_input_tokens": 3114496, "cache_write_input_tokens": 0, "output_tokens": 42428, "reasoning_output_tokens": 18532, "total_tokens": 3272785 } } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-24T23:35:08.065Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:35:08.065Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:35:08.809Z", "callId": "call_EHacSgigBzUuEw9c19SZM3po", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":80323,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:35:29.013Z", "content": "Script completed\nWall time 20.2 seconds\nOutput:\n\nusers 4263\n(0.5, 0.4, 0.0, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 1.5) 0.043209\n(0.5, 0.4, 0.0, 0.0, 1.5) 0.043184\n(0.5, 0.4, 0.5, 0.5, 1.0) 0.042893\n(0.5, 0.4, 0.0, 0.5, 1.5) 0.042866\n(0.5, 0.4, 1.0, 0.0, 2.0) 0.04286\n(0.5, 0.4, 0.0, 0.5, 1.0) 0.042829\n(0.5, 0.4, 0.5, 0.5, 1.5) 0.042702\n(0.5, 0.4, 1.0, 0.5, 1.5) 0.042627\n(0.5, 0.4, 0.0, 0.0, 1.0) 0.042435\n(0.5, 0.4, 0.0, 0.5, 2.0) 0.042362\n(0.5, 0.4, 1.0, 0.0, 1.5) 0.042285\n(0.5, 0.4, 1.0, 0.5, 1.0) 0.042162\n(0.5, 0.4, 0.5, 0.5, 2.0) 0.042116\n(0.5, 0.4, 0.5, 0.0, 1.0) 0.042018\n(0.5, 0.4, 0.0, 1.0, 1.0) 0.041999\n(0.5, 0.4, 1.0, 0.0, 1.0) 0.041997\n(0.5, 0.4, 1.5, 0.0, 2.0) 0.041912\n(0.5, 0.4, 0.5, 0.5, 0.5) 0.041903\n(0.5, 0.4, 0.5, 1.0, 1.0) 0.041885\n(0.5, 0.4, 0.0, 0.5, 0.5) 0.041865\n(0.5, 0.4, 0.5, 1.0, 0.5) 0.041859\n(0.5, 0.4, 1.0, 0.5, 2.0) 0.041827\n(0.5, 0.4, 0.5, 0.0, 3.0) 0.041739\n(0.5, 0.4, 0.0, 1.0, 1.5) 0.041718\n(0.5, 0.4, 0.0, 1.0, 0.5) 0.041713\n(0.5, 0.4, 1.0, 0.5, 0.5) 0.041649\n(0.5, 0.4, 1.5, 0.0, 1.5) 0.04164\n(0.5, 0.4, 1.0, 1.0, 1.5) 0.041624\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:35:08.912Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b3aad8e087d2b71120fa85ae0524", "usage": { "input_tokens": 72000, "cached_input_tokens": 71680, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 18, "total_tokens": 72065 }, "turn_token_usage": { "input_tokens": 3302357, "cached_input_tokens": 3186176, "cache_write_input_tokens": 0, "output_tokens": 42493, "reasoning_output_tokens": 18550, "total_tokens": 3344850 }, "thread_token_usage": { "input_tokens": 3302357, "cached_input_tokens": 3186176, "cache_write_input_tokens": 0, "output_tokens": 42493, "reasoning_output_tokens": 18550, "total_tokens": 3344850 } } }, { "kind": "context", "timestamp": "2026-09-24T23:35:29.011Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3873508d-1439-4294-b6a8-b0843039d6a4", "process_id": "80323", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode(): iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\n# cutoff-correct global organic-like momentum\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)\ndef zdays(d):\n v=np.bincount(ii[base&(x.timestamp.to_numpy()>=24786800-d*86400)],minlength=N_ITEMS);z=np.log1p(v).astype(np.float32);return (z-z.mean())/(z.std()+1e-9)\nl3,l7=zdays(3),zdays(7);pop=P['item_features'][:,1]\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids))\ngrids=[]\n# (long, recent, cumulative pop, l3, l7)\nfor p in [0.,.5,1.,1.5]:\n for q in [.5,1.,1.5,2.,3.]: grids.append((.5,.4,p,q,0.))\n for q in [.5,1.,1.5,2.,3.]: grids.append((.5,.4,p,0.,q))\nfor p in [0.,.5,1.]:\n for q in [.5,1.,1.5,2.]:\n for r in [.5,1.,1.5,2.]:grids.append((.5,.4,p,q,r))\n# latency-friendlier metric+recent without long\nfor p in [0.,.5,1.]:\n for q in [.5,1.,1.5,2.]:grids.append((0.,.5,p,q,0.))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\n with torch.inference_mode():uv=[m.user_tower(*context_batch(ctx,idx)).numpy() for m in models]\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,p,q,r=g;s=sc[0]+a*sc[1]+b*sc[2]+p*pop+q*l3+r*l7;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nprint('users',len(T))\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:30]:print(g,round(v/len(T),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode(): iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\n# cutoff-correct global organic-like momentum\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)\ndef zdays(d):\n v=np.bincount(ii[base&(x.timestamp.to_numpy()>=24786800-d*86400)],minlength=N_ITEMS);z=np.log1p(v).astype(np.float32);return (z-z.mean())/(z.std()+1e-9)\nl3,l7=zdays(3),zdays(7);pop=P['item_features'][:,1]\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids))\ngrids=[]\n# (long, recent, cumulative pop, l3, l7)\nfor p in [0.,.5,1.,1.5]:\n for q in [.5,1.,1.5,2.,3.]: grids.append((.5,.4,p,q,0.))\n for q in [.5,1.,1.5,2.,3.]: grids.append((.5,.4,p,0.,q))\nfor p in [0.,.5,1.]:\n for q in [.5,1.,1.5,2.]:\n for r in [.5,1.,1.5,2.]:grids.append((.5,.4,p,q,r))\n# latency-friendlier metric+recent without long\nfor p in [0.,.5,1.]:\n for q in [.5,1.,1.5,2.]:grids.append((0.,.5,p,q,0.))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\n with torch.inference_mode():uv=[m.user_tower(*context_batch(ctx,idx)).numpy() for m in models]\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,p,q,r=g;s=sc[0]+a*sc[1]+b*sc[2]+p*pop+q*l3+r*l7;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nprint('users',len(T))\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:30]:print(g,round(v/len(T),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 4263\n(0.5, 0.4, 0.0, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 1.5) 0.043209\n(0.5, 0.4, 0.0, 0.0, 1.5) 0.043184\n(0.5, 0.4, 0.5, 0.5, 1.0) 0.042893\n(0.5, 0.4, 0.0, 0.5, 1.5) 0.042866\n(0.5, 0.4, 1.0, 0.0, 2.0) 0.04286\n(0.5, 0.4, 0.0, 0.5, 1.0) 0.042829\n(0.5, 0.4, 0.5, 0.5, 1.5) 0.042702\n(0.5, 0.4, 1.0, 0.5, 1.5) 0.042627\n(0.5, 0.4, 0.0, 0.0, 1.0) 0.042435\n(0.5, 0.4, 0.0, 0.5, 2.0) 0.042362\n(0.5, 0.4, 1.0, 0.0, 1.5) 0.042285\n(0.5, 0.4, 1.0, 0.5, 1.0) 0.042162\n(0.5, 0.4, 0.5, 0.5, 2.0) 0.042116\n(0.5, 0.4, 0.5, 0.0, 1.0) 0.042018\n(0.5, 0.4, 0.0, 1.0, 1.0) 0.041999\n(0.5, 0.4, 1.0, 0.0, 1.0) 0.041997\n(0.5, 0.4, 1.5, 0.0, 2.0) 0.041912\n(0.5, 0.4, 0.5, 0.5, 0.5) 0.041903\n(0.5, 0.4, 0.5, 1.0, 1.0) 0.041885\n(0.5, 0.4, 0.0, 0.5, 0.5) 0.041865\n(0.5, 0.4, 0.5, 1.0, 0.5) 0.041859\n(0.5, 0.4, 1.0, 0.5, 2.0) 0.041827\n(0.5, 0.4, 0.5, 0.0, 3.0) 0.041739\n(0.5, 0.4, 0.0, 1.0, 1.5) 0.041718\n(0.5, 0.4, 0.0, 1.0, 0.5) 0.041713\n(0.5, 0.4, 1.0, 0.5, 0.5) 0.041649\n(0.5, 0.4, 1.5, 0.0, 1.5) 0.04164\n(0.5, 0.4, 1.0, 1.0, 1.5) 0.041624\n", "stderr": "", "aggregated_output": "users 4263\n(0.5, 0.4, 0.0, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 1.5) 0.043209\n(0.5, 0.4, 0.0, 0.0, 1.5) 0.043184\n(0.5, 0.4, 0.5, 0.5, 1.0) 0.042893\n(0.5, 0.4, 0.0, 0.5, 1.5) 0.042866\n(0.5, 0.4, 1.0, 0.0, 2.0) 0.04286\n(0.5, 0.4, 0.0, 0.5, 1.0) 0.042829\n(0.5, 0.4, 0.5, 0.5, 1.5) 0.042702\n(0.5, 0.4, 1.0, 0.5, 1.5) 0.042627\n(0.5, 0.4, 0.0, 0.0, 1.0) 0.042435\n(0.5, 0.4, 0.0, 0.5, 2.0) 0.042362\n(0.5, 0.4, 1.0, 0.0, 1.5) 0.042285\n(0.5, 0.4, 1.0, 0.5, 1.0) 0.042162\n(0.5, 0.4, 0.5, 0.5, 2.0) 0.042116\n(0.5, 0.4, 0.5, 0.0, 1.0) 0.042018\n(0.5, 0.4, 0.0, 1.0, 1.0) 0.041999\n(0.5, 0.4, 1.0, 0.0, 1.0) 0.041997\n(0.5, 0.4, 1.5, 0.0, 2.0) 0.041912\n(0.5, 0.4, 0.5, 0.5, 0.5) 0.041903\n(0.5, 0.4, 0.5, 1.0, 1.0) 0.041885\n(0.5, 0.4, 0.0, 0.5, 0.5) 0.041865\n(0.5, 0.4, 0.5, 1.0, 0.5) 0.041859\n(0.5, 0.4, 1.0, 0.5, 2.0) 0.041827\n(0.5, 0.4, 0.5, 0.0, 3.0) 0.041739\n(0.5, 0.4, 0.0, 1.0, 1.5) 0.041718\n(0.5, 0.4, 0.0, 1.0, 0.5) 0.041713\n(0.5, 0.4, 1.0, 0.5, 0.5) 0.041649\n(0.5, 0.4, 1.5, 0.0, 1.5) 0.04164\n(0.5, 0.4, 1.0, 1.0, 1.5) 0.041624\n", "exit_code": 0, "duration": { "secs": 151, "nanos": 201046000 }, "formatted_output": "users 4263\n(0.5, 0.4, 0.0, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 2.0) 0.043229\n(0.5, 0.4, 0.5, 0.0, 1.5) 0.043209\n(0.5, 0.4, 0.0, 0.0, 1.5) 0.043184\n(0.5, 0.4, 0.5, 0.5, 1.0) 0.042893\n(0.5, 0.4, 0.0, 0.5, 1.5) 0.042866\n(0.5, 0.4, 1.0, 0.0, 2.0) 0.04286\n(0.5, 0.4, 0.0, 0.5, 1.0) 0.042829\n(0.5, 0.4, 0.5, 0.5, 1.5) 0.042702\n(0.5, 0.4, 1.0, 0.5, 1.5) 0.042627\n(0.5, 0.4, 0.0, 0.0, 1.0) 0.042435\n(0.5, 0.4, 0.0, 0.5, 2.0) 0.042362\n(0.5, 0.4, 1.0, 0.0, 1.5) 0.042285\n(0.5, 0.4, 1.0, 0.5, 1.0) 0.042162\n(0.5, 0.4, 0.5, 0.5, 2.0) 0.042116\n(0.5, 0.4, 0.5, 0.0, 1.0) 0.042018\n(0.5, 0.4, 0.0, 1.0, 1.0) 0.041999\n(0.5, 0.4, 1.0, 0.0, 1.0) 0.041997\n(0.5, 0.4, 1.5, 0.0, 2.0) 0.041912\n(0.5, 0.4, 0.5, 0.5, 0.5) 0.041903\n(0.5, 0.4, 0.5, 1.0, 1.0) 0.041885\n(0.5, 0.4, 0.0, 0.5, 0.5) 0.041865\n(0.5, 0.4, 0.5, 1.0, 0.5) 0.041859\n(0.5, 0.4, 1.0, 0.5, 2.0) 0.041827\n(0.5, 0.4, 0.5, 0.0, 3.0) 0.041739\n(0.5, 0.4, 0.0, 1.0, 1.5) 0.041718\n(0.5, 0.4, 0.0, 1.0, 0.5) 0.041713\n(0.5, 0.4, 1.0, 0.5, 0.5) 0.041649\n(0.5, 0.4, 1.5, 0.0, 1.5) 0.04164\n(0.5, 0.4, 1.0, 1.0, 1.5) 0.041624\n" } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-24T23:35:36.354Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:35:36.354Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:35:52.885Z", "callId": "call_i5uc9JRYXLcdulOLm3d8hJkJ", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\\ndef load(path):\\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\\nwith torch.inference_mode():iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.timestamp.to_numpy()>=24786800-7*86400);z=np.log1p(np.bincount(ii[m],minlength=N_ITEMS)).astype(np.float32);l7=(z-z.mean())/z.std();pop=P['item_features'][:,1]\\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));grids=[]\\nfor b in [.2,.3,.4,.5,.6,.8]:\\n for q in [1.,1.5,2.,2.5]: grids.append((0.,b,0.,q));grids.append((0.,b,.5,q))\\nfor a in [.25,.5,.75]:\\n for q in [1.5,2.,2.5]:grids.append((a,0.,0.,q))\\nfor a in [.25,.5,.75]:\\n for b in [.25,.4,.55]:\\n for q in [1.5,2.,2.5]:grids.append((a,b,0.,q))\\nsums={g:0. for g in grids}\\nfor st in range(0,len(E.uids),128):\\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\\n with torch.inference_mode():uv=[m.user_tower(*context_batch(ctx,idx)).numpy() for m in models]\\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\\n for g in grids:\\n a,b,p,q=g;s=sc[0]+a*sc[1]+b*sc[2]+p*pop+q*l7;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\\n for j,u in enumerate(E.uids[st:en]):\\n gold=T.get(int(u));\\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:25]:print(g,round(v/len(T),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:36:23.052Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=81934", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:35:53.015Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b3c134dc87d28d8b6d854398c6ee", "usage": { "input_tokens": 73023, "cached_input_tokens": 71808, "cache_write_input_tokens": 0, "output_tokens": 1167, "reasoning_output_tokens": 241, "total_tokens": 74190 }, "turn_token_usage": { "input_tokens": 3375380, "cached_input_tokens": 3257984, "cache_write_input_tokens": 0, "output_tokens": 43660, "reasoning_output_tokens": 18791, "total_tokens": 3419040 }, "thread_token_usage": { "input_tokens": 3375380, "cached_input_tokens": 3257984, "cache_write_input_tokens": 0, "output_tokens": 43660, "reasoning_output_tokens": 18791, "total_tokens": 3419040 } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-24T23:36:24.262Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:36:24.262Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:36:24.982Z", "callId": "call_i56n1EuotmSWrFX7T5vxatyq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81934,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:36:54.990Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:36:25.083Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b3f740ac87d2933f67e1548570c4", "usage": { "input_tokens": 74218, "cached_input_tokens": 72832, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 74271 }, "turn_token_usage": { "input_tokens": 3449598, "cached_input_tokens": 3330816, "cache_write_input_tokens": 0, "output_tokens": 43713, "reasoning_output_tokens": 18797, "total_tokens": 3493311 }, "thread_token_usage": { "input_tokens": 3449598, "cached_input_tokens": 3330816, "cache_write_input_tokens": 0, "output_tokens": 43713, "reasoning_output_tokens": 18797, "total_tokens": 3493311 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-24T23:36:56.956Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:36:56.956Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:36:57.662Z", "callId": "call_mnbyApBIGmODSHu6GOgF4nZM", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81934,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:37:27.671Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:36:57.790Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b41734bc87d2a56433dbf77b5e52", "usage": { "input_tokens": 74294, "cached_input_tokens": 73984, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 74347 }, "turn_token_usage": { "input_tokens": 3523892, "cached_input_tokens": 3404800, "cache_write_input_tokens": 0, "output_tokens": 43766, "reasoning_output_tokens": 18803, "total_tokens": 3567658 }, "thread_token_usage": { "input_tokens": 3523892, "cached_input_tokens": 3404800, "cache_write_input_tokens": 0, "output_tokens": 43766, "reasoning_output_tokens": 18803, "total_tokens": 3567658 } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-24T23:37:28.990Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:37:28.990Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:37:29.682Z", "callId": "call_pkhzl33jtg0TPyP5OszjflCb", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":81934,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:37:55.715Z", "content": "Script completed\nWall time 26.0 seconds\nOutput:\n\n(0.75, 0.55, 0.0, 2.0) 0.043827\n(0.75, 0.4, 0.0, 2.0) 0.043651\n(0.5, 0.55, 0.0, 1.5) 0.043482\n(0.5, 0.55, 0.0, 2.0) 0.043361\n(0.25, 0.55, 0.0, 2.0) 0.043344\n(0.75, 0.55, 0.0, 2.5) 0.043275\n(0.5, 0.4, 0.0, 2.0) 0.043229\n(0.0, 0.4, 0.0, 1.5) 0.043209\n(0.25, 0.4, 0.0, 1.5) 0.043205\n(0.5, 0.4, 0.0, 1.5) 0.043184\n(0.75, 0.4, 0.0, 1.5) 0.043173\n(0.0, 0.8, 0.0, 1.5) 0.043169\n(0.25, 0.55, 0.0, 1.5) 0.043151\n(0.75, 0.25, 0.0, 2.0) 0.043113\n(0.0, 0.8, 0.5, 2.0) 0.043095\n(0.75, 0.55, 0.0, 1.5) 0.043074\n(0.0, 0.5, 0.0, 1.5) 0.043036\n(0.0, 0.8, 0.0, 2.0) 0.042976\n(0.0, 0.8, 0.5, 1.5) 0.042953\n(0.5, 0.55, 0.0, 2.5) 0.042904\n(0.75, 0.25, 0.0, 1.5) 0.042902\n(0.0, 0.6, 0.5, 1.5) 0.042878\n(0.5, 0.25, 0.0, 2.0) 0.042849\n(0.0, 0.6, 0.0, 1.5) 0.04284\n(0.75, 0.4, 0.0, 2.5) 0.042821\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:37:29.801Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b437de0c87d2850af6cf672fe339", "usage": { "input_tokens": 74370, "cached_input_tokens": 74112, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 74425 }, "turn_token_usage": { "input_tokens": 3598262, "cached_input_tokens": 3478912, "cache_write_input_tokens": 0, "output_tokens": 43821, "reasoning_output_tokens": 18811, "total_tokens": 3642083 }, "thread_token_usage": { "input_tokens": 3598262, "cached_input_tokens": 3478912, "cache_write_input_tokens": 0, "output_tokens": 43821, "reasoning_output_tokens": 18811, "total_tokens": 3642083 } } }, { "kind": "context", "timestamp": "2026-09-24T23:37:55.713Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fd8cd281-02fd-4547-8e29-46a3c6d6421e", "process_id": "81934", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode():iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.timestamp.to_numpy()>=24786800-7*86400);z=np.log1p(np.bincount(ii[m],minlength=N_ITEMS)).astype(np.float32);l7=(z-z.mean())/z.std();pop=P['item_features'][:,1]\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));grids=[]\nfor b in [.2,.3,.4,.5,.6,.8]:\n for q in [1.,1.5,2.,2.5]: grids.append((0.,b,0.,q));grids.append((0.,b,.5,q))\nfor a in [.25,.5,.75]:\n for q in [1.5,2.,2.5]:grids.append((a,0.,0.,q))\nfor a in [.25,.5,.75]:\n for b in [.25,.4,.55]:\n for q in [1.5,2.,2.5]:grids.append((a,b,0.,q))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\n with torch.inference_mode():uv=[m.user_tower(*context_batch(ctx,idx)).numpy() for m in models]\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,p,q=g;s=sc[0]+a*sc[1]+b*sc[2]+p*pop+q*l7;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:25]:print(g,round(v/len(T),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode():iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.timestamp.to_numpy()>=24786800-7*86400);z=np.log1p(np.bincount(ii[m],minlength=N_ITEMS)).astype(np.float32);l7=(z-z.mean())/z.std();pop=P['item_features'][:,1]\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));grids=[]\nfor b in [.2,.3,.4,.5,.6,.8]:\n for q in [1.,1.5,2.,2.5]: grids.append((0.,b,0.,q));grids.append((0.,b,.5,q))\nfor a in [.25,.5,.75]:\n for q in [1.5,2.,2.5]:grids.append((a,0.,0.,q))\nfor a in [.25,.5,.75]:\n for b in [.25,.4,.55]:\n for q in [1.5,2.,2.5]:grids.append((a,b,0.,q))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\n with torch.inference_mode():uv=[m.user_tower(*context_batch(ctx,idx)).numpy() for m in models]\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,p,q=g;s=sc[0]+a*sc[1]+b*sc[2]+p*pop+q*l7;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nfor g,v in sorted(sums.items(),key=lambda x:-x[1])[:25]:print(g,round(v/len(T),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.75, 0.55, 0.0, 2.0) 0.043827\n(0.75, 0.4, 0.0, 2.0) 0.043651\n(0.5, 0.55, 0.0, 1.5) 0.043482\n(0.5, 0.55, 0.0, 2.0) 0.043361\n(0.25, 0.55, 0.0, 2.0) 0.043344\n(0.75, 0.55, 0.0, 2.5) 0.043275\n(0.5, 0.4, 0.0, 2.0) 0.043229\n(0.0, 0.4, 0.0, 1.5) 0.043209\n(0.25, 0.4, 0.0, 1.5) 0.043205\n(0.5, 0.4, 0.0, 1.5) 0.043184\n(0.75, 0.4, 0.0, 1.5) 0.043173\n(0.0, 0.8, 0.0, 1.5) 0.043169\n(0.25, 0.55, 0.0, 1.5) 0.043151\n(0.75, 0.25, 0.0, 2.0) 0.043113\n(0.0, 0.8, 0.5, 2.0) 0.043095\n(0.75, 0.55, 0.0, 1.5) 0.043074\n(0.0, 0.5, 0.0, 1.5) 0.043036\n(0.0, 0.8, 0.0, 2.0) 0.042976\n(0.0, 0.8, 0.5, 1.5) 0.042953\n(0.5, 0.55, 0.0, 2.5) 0.042904\n(0.75, 0.25, 0.0, 1.5) 0.042902\n(0.0, 0.6, 0.5, 1.5) 0.042878\n(0.5, 0.25, 0.0, 2.0) 0.042849\n(0.0, 0.6, 0.0, 1.5) 0.04284\n(0.75, 0.4, 0.0, 2.5) 0.042821\n", "stderr": "", "aggregated_output": "(0.75, 0.55, 0.0, 2.0) 0.043827\n(0.75, 0.4, 0.0, 2.0) 0.043651\n(0.5, 0.55, 0.0, 1.5) 0.043482\n(0.5, 0.55, 0.0, 2.0) 0.043361\n(0.25, 0.55, 0.0, 2.0) 0.043344\n(0.75, 0.55, 0.0, 2.5) 0.043275\n(0.5, 0.4, 0.0, 2.0) 0.043229\n(0.0, 0.4, 0.0, 1.5) 0.043209\n(0.25, 0.4, 0.0, 1.5) 0.043205\n(0.5, 0.4, 0.0, 1.5) 0.043184\n(0.75, 0.4, 0.0, 1.5) 0.043173\n(0.0, 0.8, 0.0, 1.5) 0.043169\n(0.25, 0.55, 0.0, 1.5) 0.043151\n(0.75, 0.25, 0.0, 2.0) 0.043113\n(0.0, 0.8, 0.5, 2.0) 0.043095\n(0.75, 0.55, 0.0, 1.5) 0.043074\n(0.0, 0.5, 0.0, 1.5) 0.043036\n(0.0, 0.8, 0.0, 2.0) 0.042976\n(0.0, 0.8, 0.5, 1.5) 0.042953\n(0.5, 0.55, 0.0, 2.5) 0.042904\n(0.75, 0.25, 0.0, 1.5) 0.042902\n(0.0, 0.6, 0.5, 1.5) 0.042878\n(0.5, 0.25, 0.0, 2.0) 0.042849\n(0.0, 0.6, 0.0, 1.5) 0.04284\n(0.75, 0.4, 0.0, 2.5) 0.042821\n", "exit_code": 0, "duration": { "secs": 122, "nanos": 662789049 }, "formatted_output": "(0.75, 0.55, 0.0, 2.0) 0.043827\n(0.75, 0.4, 0.0, 2.0) 0.043651\n(0.5, 0.55, 0.0, 1.5) 0.043482\n(0.5, 0.55, 0.0, 2.0) 0.043361\n(0.25, 0.55, 0.0, 2.0) 0.043344\n(0.75, 0.55, 0.0, 2.5) 0.043275\n(0.5, 0.4, 0.0, 2.0) 0.043229\n(0.0, 0.4, 0.0, 1.5) 0.043209\n(0.25, 0.4, 0.0, 1.5) 0.043205\n(0.5, 0.4, 0.0, 1.5) 0.043184\n(0.75, 0.4, 0.0, 1.5) 0.043173\n(0.0, 0.8, 0.0, 1.5) 0.043169\n(0.25, 0.55, 0.0, 1.5) 0.043151\n(0.75, 0.25, 0.0, 2.0) 0.043113\n(0.0, 0.8, 0.5, 2.0) 0.043095\n(0.75, 0.55, 0.0, 1.5) 0.043074\n(0.0, 0.5, 0.0, 1.5) 0.043036\n(0.0, 0.8, 0.0, 2.0) 0.042976\n(0.0, 0.8, 0.5, 1.5) 0.042953\n(0.5, 0.55, 0.0, 2.5) 0.042904\n(0.75, 0.25, 0.0, 1.5) 0.042902\n(0.0, 0.6, 0.5, 1.5) 0.042878\n(0.5, 0.25, 0.0, 2.0) 0.042849\n(0.0, 0.6, 0.0, 1.5) 0.04284\n(0.75, 0.4, 0.0, 2.5) 0.042821\n" } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-24T23:38:06.338Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:38:06.338Z", "text": "" } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-24T23:38:11.459Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:38:11.459Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:38:12.876Z", "text": "The strongest validation composition is now 0.043827 NDCG@10: three complementary 64-D learned components plus one seven-day organic-like momentum scalar, with cumulative popularity dropping out. I’m locking those weights, wiring the momentum coordinate into the reproducible inference path, and starting the final train+validation refit." }, { "kind": "tool", "timestamp": "2026-09-24T23:38:34.989Z", "callId": "call_mIHOjv9qYpwYfDuba5rT6cG7", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def active_state_from_file(\\n@@\\n return liked, disliked\\n+\\n+\\n+def organic_like_momentum(\\n+ prefixes: list[str], item_lookup: np.ndarray, cutoff: int\\n+) -> np.ndarray:\\n+ \\\"\\\"\\\"Seven-day global organic-like counts, standardized after log1p.\\\"\\\"\\\"\\n+ counts = np.zeros(N_ITEMS, dtype=np.int64)\\n+ for prefix in prefixes:\\n+ frame = pd.read_parquet(\\n+ DATA / f\\\"{prefix}_likes.parquet\\\",\\n+ columns=[\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"],\\n+ )\\n+ item = mapped(frame.item_id.to_numpy(), item_lookup)\\n+ mask = (\\n+ (item >= 0)\\n+ & (frame.is_organic.to_numpy() == 1)\\n+ & (frame.timestamp.to_numpy() >= cutoff - WINDOW)\\n+ & (frame.timestamp.to_numpy() <= cutoff)\\n+ )\\n+ counts += np.bincount(item[mask], minlength=N_ITEMS)\\n+ transformed = np.log1p(counts).astype(np.float32)\\n+ return (transformed - transformed.mean()) / transformed.std().clip(1e-6)\\n@@\\n arrays = {\\n@@\\n \\\"item_features\\\": np.column_stack(\\n@@\\n ).astype(np.float32),\\n+ \\\"item_like_momentum\\\": organic_like_momentum(\\n+ prefixes, item_lookup, inference_cutoff\\n+ ),\\n@@\\n def rank(\\n@@\\n checkpoint2: Path | None,\\n second_weight: float,\\n+ checkpoint3: Path | None,\\n+ third_weight: float,\\n+ momentum_weight: float,\\n ) -> None:\\n@@\\n second_model: TwoTower | None = None\\n@@\\n item_vectors = np.column_stack((item_vectors, second_items * magnitude))\\n+ third_model: TwoTower | None = None\\n+ if checkpoint3 is not None:\\n+ third_saved = torch.load(checkpoint3, map_location=\\\"cpu\\\", weights_only=False)\\n+ third_model = TwoTower(\\n+ third_saved[\\\"item_features\\\"],\\n+ third_saved[\\\"token_dim\\\"],\\n+ third_saved[\\\"dim\\\"],\\n+ )\\n+ third_model.load_state_dict(third_saved[\\\"state_dict\\\"])\\n+ third_model.eval()\\n+ third_items = third_model.item_tower(torch.arange(N_ITEMS)).numpy()\\n+ magnitude = math.sqrt(abs(third_weight))\\n+ item_vectors = np.column_stack((item_vectors, third_items * magnitude))\\n@@\\n popularity = archive[\\\"item_features\\\"][:, 1].astype(np.float32)\\n- item_vectors = np.column_stack((item_vectors, popularity))\\n+ if \\\"item_like_momentum\\\" in archive.files:\\n+ momentum = archive[\\\"item_like_momentum\\\"].astype(np.float32)\\n+ else:\\n+ # Backward-compatible path for an earlier prepared validation cache.\\n+ lookup = dense_lookup(archive[\\\"item_ids\\\"].astype(np.int64))\\n+ prefixes = [\\\"train\\\", \\\"val\\\"] if stage == \\\"test\\\" else [\\\"train\\\"]\\n+ cutoff = VALIDATION_END if stage == \\\"test\\\" else TRAIN_CUTOFF\\n+ momentum = organic_like_momentum(prefixes, lookup, cutoff)\\n+ item_vectors = np.column_stack((item_vectors, popularity, momentum))\\n@@\\n if second_model is not None:\\n@@\\n user_vectors = np.column_stack(\\n (user_vectors, second_users * signed_magnitude)\\n )\\n+ if third_model is not None:\\n+ third_users = third_model.user_tower(\\n+ *context_batch(contexts, indices)\\n+ ).numpy()\\n+ signed_magnitude = math.copysign(\\n+ math.sqrt(abs(third_weight)), third_weight\\n+ )\\n+ user_vectors = np.column_stack(\\n+ (user_vectors, third_users * signed_magnitude)\\n+ )\\n user_vectors = np.column_stack(\\n- (user_vectors, np.full(stop - start, popularity_weight, np.float32))\\n+ (\\n+ user_vectors,\\n+ np.full(stop - start, popularity_weight, np.float32),\\n+ np.full(stop - start, momentum_weight, np.float32),\\n+ )\\n@@\\n \\\"second_weight\\\": second_weight if checkpoint2 is not None else None,\\n+ \\\"third_weight\\\": third_weight if checkpoint3 is not None else None,\\n+ \\\"momentum_weight\\\": momentum_weight,\\n@@\\n infer.add_argument(\\\"--checkpoint2\\\", type=Path)\\n infer.add_argument(\\\"--second-weight\\\", type=float, default=0.0)\\n+ infer.add_argument(\\\"--checkpoint3\\\", type=Path)\\n+ infer.add_argument(\\\"--third-weight\\\", type=float, default=0.0)\\n+ infer.add_argument(\\\"--momentum-weight\\\", type=float, default=0.0)\\n@@\\n args.checkpoint2,\\n args.second_weight,\\n+ args.checkpoint3,\\n+ args.third_weight,\\n+ args.momentum_weight,\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:38:35.063Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:38:34.999Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-0139a9ce-9ec3-4c23-90b7-ea445968d5ae", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -232,2 +232,24 @@\n \n+def organic_like_momentum(\n+ prefixes: list[str], item_lookup: np.ndarray, cutoff: int\n+) -> np.ndarray:\n+ \"\"\"Seven-day global organic-like counts, standardized after log1p.\"\"\"\n+ counts = np.zeros(N_ITEMS, dtype=np.int64)\n+ for prefix in prefixes:\n+ frame = pd.read_parquet(\n+ DATA / f\"{prefix}_likes.parquet\",\n+ columns=[\"timestamp\", \"item_id\", \"is_organic\"],\n+ )\n+ item = mapped(frame.item_id.to_numpy(), item_lookup)\n+ mask = (\n+ (item >= 0)\n+ & (frame.is_organic.to_numpy() == 1)\n+ & (frame.timestamp.to_numpy() >= cutoff - WINDOW)\n+ & (frame.timestamp.to_numpy() <= cutoff)\n+ )\n+ counts += np.bincount(item[mask], minlength=N_ITEMS)\n+ transformed = np.log1p(counts).astype(np.float32)\n+ return (transformed - transformed.mean()) / transformed.std().clip(1e-6)\n+\n+\n def prepare(stage: str, output: Path, snapshots: int) -> None:\n@@ -405,2 +427,5 @@\n ).astype(np.float32),\n+ \"item_like_momentum\": organic_like_momentum(\n+ prefixes, item_lookup, inference_cutoff\n+ ),\n \"train_recent\": np.stack(train_recent),\n@@ -666,2 +691,5 @@\n second_weight: float,\n+ checkpoint3: Path | None,\n+ third_weight: float,\n+ momentum_weight: float,\n ) -> None:\n@@ -698,5 +726,26 @@\n item_vectors = np.column_stack((item_vectors, second_items * magnitude))\n+ third_model: TwoTower | None = None\n+ if checkpoint3 is not None:\n+ third_saved = torch.load(checkpoint3, map_location=\"cpu\", weights_only=False)\n+ third_model = TwoTower(\n+ third_saved[\"item_features\"],\n+ third_saved[\"token_dim\"],\n+ third_saved[\"dim\"],\n+ )\n+ third_model.load_state_dict(third_saved[\"state_dict\"])\n+ third_model.eval()\n+ third_items = third_model.item_tower(torch.arange(N_ITEMS)).numpy()\n+ magnitude = math.sqrt(abs(third_weight))\n+ item_vectors = np.column_stack((item_vectors, third_items * magnitude))\n # A fixed popularity coordinate is an explicit part of both tower outputs.\n popularity = archive[\"item_features\"][:, 1].astype(np.float32)\n- item_vectors = np.column_stack((item_vectors, popularity))\n+ if \"item_like_momentum\" in archive.files:\n+ momentum = archive[\"item_like_momentum\"].astype(np.float32)\n+ else:\n+ # Backward-compatible path for an earlier prepared validation cache.\n+ lookup = dense_lookup(archive[\"item_ids\"].astype(np.int64))\n+ prefixes = [\"train\", \"val\"] if stage == \"test\" else [\"train\"]\n+ cutoff = VALIDATION_END if stage == \"test\" else TRAIN_CUTOFF\n+ momentum = organic_like_momentum(prefixes, lookup, cutoff)\n+ item_vectors = np.column_stack((item_vectors, popularity, momentum))\n \n@@ -730,4 +779,18 @@\n )\n+ if third_model is not None:\n+ third_users = third_model.user_tower(\n+ *context_batch(contexts, indices)\n+ ).numpy()\n+ signed_magnitude = math.copysign(\n+ math.sqrt(abs(third_weight)), third_weight\n+ )\n+ user_vectors = np.column_stack(\n+ (user_vectors, third_users * signed_magnitude)\n+ )\n user_vectors = np.column_stack(\n- (user_vectors, np.full(stop - start, popularity_weight, np.float32))\n+ (\n+ user_vectors,\n+ np.full(stop - start, popularity_weight, np.float32),\n+ np.full(stop - start, momentum_weight, np.float32),\n+ )\n )\n@@ -761,2 +824,4 @@\n \"second_weight\": second_weight if checkpoint2 is not None else None,\n+ \"third_weight\": third_weight if checkpoint3 is not None else None,\n+ \"momentum_weight\": momentum_weight,\n },\n@@ -794,2 +859,5 @@\n infer.add_argument(\"--second-weight\", type=float, default=0.0)\n+ infer.add_argument(\"--checkpoint3\", type=Path)\n+ infer.add_argument(\"--third-weight\", type=float, default=0.0)\n+ infer.add_argument(\"--momentum-weight\", type=float, default=0.0)\n return parser.parse_args()\n@@ -824,2 +892,5 @@\n args.second_weight,\n+ args.checkpoint3,\n+ args.third_weight,\n+ args.momentum_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": "2026-09-24T23:38:35.062Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b453e46887d2bd391b459ad5d422", "usage": { "input_tokens": 75098, "cached_input_tokens": 74240, "cache_write_input_tokens": 0, "output_tokens": 2110, "reasoning_output_tokens": 800, "total_tokens": 77208 }, "turn_token_usage": { "input_tokens": 3673360, "cached_input_tokens": 3553152, "cache_write_input_tokens": 0, "output_tokens": 45931, "reasoning_output_tokens": 19611, "total_tokens": 3719291 }, "thread_token_usage": { "input_tokens": 3673360, "cached_input_tokens": 3553152, "cache_write_input_tokens": 0, "output_tokens": 45931, "reasoning_output_tokens": 19611, "total_tokens": 3719291 } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-24T23:38:37.253Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:38:37.253Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:38:40.785Z", "callId": "call_6jX96gE1VnQvNr5lba4D2IZx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_metric.pt --checkpoint2 /app/solution/model_validation.pt --second-weight 0.75 --checkpoint3 /app/solution/model_validation_recent.pt --third-weight 0.55 --popularity-weight 0 --momentum-weight 2.0 --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:38:54.261Z", "content": "Script completed\nWall time 13.5 seconds\nOutput:\n\n{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.043827,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:38:40.914Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b47b4ec087d2a505d8640015749b", "usage": { "input_tokens": 77232, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 256, "reasoning_output_tokens": 50, "total_tokens": 77488 }, "turn_token_usage": { "input_tokens": 3750592, "cached_input_tokens": 3628032, "cache_write_input_tokens": 0, "output_tokens": 46187, "reasoning_output_tokens": 19661, "total_tokens": 3796779 }, "thread_token_usage": { "input_tokens": 3750592, "cached_input_tokens": 3628032, "cache_write_input_tokens": 0, "output_tokens": 46187, "reasoning_output_tokens": 19661, "total_tokens": 3796779 } } }, { "kind": "context", "timestamp": "2026-09-24T23:38:54.258Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a5a01065-1113-4de5-aa39-ad5b6825915e", "process_id": "91749", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_metric.pt --checkpoint2 /app/solution/model_validation.pt --second-weight 0.75 --checkpoint3 /app/solution/model_validation_recent.pt --third-weight 0.55 --popularity-weight 0 --momentum-weight 2.0 --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && /opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage validation --prepared /app/solution/prepared_validation.npz --checkpoint /app/solution/model_validation_metric.pt --checkpoint2 /app/solution/model_validation.pt --second-weight 0.75 --checkpoint3 /app/solution/model_validation_recent.pt --third-weight 0.55 --popularity-weight 0 --momentum-weight 2.0 --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.043827,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.043827,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 13, "nanos": 308278142 }, "formatted_output": "{\n \"stage\": \"validation\",\n \"output\": \"/app/solution/validation.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.043827,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-24T23:38:56.283Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:38:56.283Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:38:57.917Z", "callId": "call_m0vFETLULaCEu1d2RX8Bx06Z", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py prepare --stage test --output /app/solution/prepared_test.npz --snapshots 16\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:39:23.121Z", "content": "Script completed\nWall time 25.2 seconds\nOutput:\n\nprepared 1000/8456 users, 7,068 contexts, 66,714 positives\nprepared 2000/8456 users, 14,280 contexts, 134,667 positives\nprepared 3000/8456 users, 21,315 contexts, 198,188 positives\nprepared 4000/8456 users, 28,443 contexts, 262,040 positives\nprepared 5000/8456 users, 35,337 contexts, 328,356 positives\nprepared 6000/8456 users, 42,282 contexts, 396,471 positives\nprepared 7000/8456 users, 49,163 contexts, 460,239 positives\nprepared 8000/8456 users, 56,446 contexts, 526,878 positives\n{\n \"stage\": \"test\",\n \"contexts\": 59597,\n \"positives\": 555022,\n \"grade3\": 75546,\n \"seconds\": 20.7,\n \"output\": \"/app/solution/prepared_test.npz\"\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:38:58.025Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b48eee9487d2bbd002fa9b2498a8", "usage": { "input_tokens": 77628, "cached_input_tokens": 77056, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 8, "total_tokens": 77734 }, "turn_token_usage": { "input_tokens": 3828220, "cached_input_tokens": 3705088, "cache_write_input_tokens": 0, "output_tokens": 46293, "reasoning_output_tokens": 19669, "total_tokens": 3874513 }, "thread_token_usage": { "input_tokens": 3828220, "cached_input_tokens": 3705088, "cache_write_input_tokens": 0, "output_tokens": 46293, "reasoning_output_tokens": 19669, "total_tokens": 3874513 } } }, { "kind": "context", "timestamp": "2026-09-24T23:39:23.112Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4027fefd-6c12-4ebe-99ea-aafe943e6ac8", "process_id": "4338", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py prepare --stage test --output /app/solution/prepared_test.npz --snapshots 16" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py prepare --stage test --output /app/solution/prepared_test.npz --snapshots 16" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared 1000/8456 users, 7,068 contexts, 66,714 positives\nprepared 2000/8456 users, 14,280 contexts, 134,667 positives\nprepared 3000/8456 users, 21,315 contexts, 198,188 positives\nprepared 4000/8456 users, 28,443 contexts, 262,040 positives\nprepared 5000/8456 users, 35,337 contexts, 328,356 positives\nprepared 6000/8456 users, 42,282 contexts, 396,471 positives\nprepared 7000/8456 users, 49,163 contexts, 460,239 positives\nprepared 8000/8456 users, 56,446 contexts, 526,878 positives\n{\n \"stage\": \"test\",\n \"contexts\": 59597,\n \"positives\": 555022,\n \"grade3\": 75546,\n \"seconds\": 20.7,\n \"output\": \"/app/solution/prepared_test.npz\"\n}\n", "stderr": "", "aggregated_output": "prepared 1000/8456 users, 7,068 contexts, 66,714 positives\nprepared 2000/8456 users, 14,280 contexts, 134,667 positives\nprepared 3000/8456 users, 21,315 contexts, 198,188 positives\nprepared 4000/8456 users, 28,443 contexts, 262,040 positives\nprepared 5000/8456 users, 35,337 contexts, 328,356 positives\nprepared 6000/8456 users, 42,282 contexts, 396,471 positives\nprepared 7000/8456 users, 49,163 contexts, 460,239 positives\nprepared 8000/8456 users, 56,446 contexts, 526,878 positives\n{\n \"stage\": \"test\",\n \"contexts\": 59597,\n \"positives\": 555022,\n \"grade3\": 75546,\n \"seconds\": 20.7,\n \"output\": \"/app/solution/prepared_test.npz\"\n}\n", "exit_code": 0, "duration": { "secs": 25, "nanos": 30158004 }, "formatted_output": "prepared 1000/8456 users, 7,068 contexts, 66,714 positives\nprepared 2000/8456 users, 14,280 contexts, 134,667 positives\nprepared 3000/8456 users, 21,315 contexts, 198,188 positives\nprepared 4000/8456 users, 28,443 contexts, 262,040 positives\nprepared 5000/8456 users, 35,337 contexts, 328,356 positives\nprepared 6000/8456 users, 42,282 contexts, 396,471 positives\nprepared 7000/8456 users, 49,163 contexts, 460,239 positives\nprepared 8000/8456 users, 56,446 contexts, 526,878 positives\n{\n \"stage\": \"test\",\n \"contexts\": 59597,\n \"positives\": 555022,\n \"grade3\": 75546,\n \"seconds\": 20.7,\n \"output\": \"/app/solution/prepared_test.npz\"\n}\n" } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-24T23:39:27.483Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:39:27.483Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:39:29.978Z", "callId": "call_kBCi6hRCoDbGx7ic5o1k3irY", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_metric.pt --epochs 4 --batch-size 512 --negatives 32 --seed 48611 --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:40:00.156Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n\nSESSION_ID=73354", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:39:30.060Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b4ab53e087d2a269fdc85227c1d0", "usage": { "input_tokens": 77983, "cached_input_tokens": 77440, "cache_write_input_tokens": 0, "output_tokens": 278, "reasoning_output_tokens": 133, "total_tokens": 78261 }, "turn_token_usage": { "input_tokens": 3906203, "cached_input_tokens": 3782528, "cache_write_input_tokens": 0, "output_tokens": 46571, "reasoning_output_tokens": 19802, "total_tokens": 3952774 }, "thread_token_usage": { "input_tokens": 3906203, "cached_input_tokens": 3782528, "cache_write_input_tokens": 0, "output_tokens": 46571, "reasoning_output_tokens": 19802, "total_tokens": 3952774 } } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-24T23:40:01.415Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:40:01.415Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:40:02.131Z", "callId": "call_lvabF28yD8BudwEPGNSVHXOe", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:40:32.189Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:40:02.252Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b4d0689487d2ad761fa14663835c", "usage": { "input_tokens": 78357, "cached_input_tokens": 77824, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 78413 }, "turn_token_usage": { "input_tokens": 3984560, "cached_input_tokens": 3860352, "cache_write_input_tokens": 0, "output_tokens": 46627, "reasoning_output_tokens": 19811, "total_tokens": 4031187 }, "thread_token_usage": { "input_tokens": 3984560, "cached_input_tokens": 3860352, "cache_write_input_tokens": 0, "output_tokens": 46627, "reasoning_output_tokens": 19811, "total_tokens": 4031187 } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-24T23:40:33.517Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:40:33.517Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:40:34.732Z", "text": "Final preparation is complete: it incorporates validation interactions in history and adds the train-cutoff validation window as the newest supervised snapshot, yielding 555,022 positives. The first of three selected refits is training now; the fallback submission remains valid until the final ranking replaces it." }, { "kind": "tool", "timestamp": "2026-09-24T23:40:35.434Z", "callId": "call_dyBM3yv8Wzaq7u2SH5aV6rQe", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:41:05.485Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 1, \"loss\": 0.44617621383144, \"seconds\": 58.745795011520386, \"scale\": 7.727242946624756}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:40:35.534Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b4f067c887d29bb507bc3f7d6a29", "usage": { "input_tokens": 78436, "cached_input_tokens": 78208, "cache_write_input_tokens": 0, "output_tokens": 120, "reasoning_output_tokens": 11, "total_tokens": 78556 }, "turn_token_usage": { "input_tokens": 4062996, "cached_input_tokens": 3938560, "cache_write_input_tokens": 0, "output_tokens": 46747, "reasoning_output_tokens": 19822, "total_tokens": 4109743 }, "thread_token_usage": { "input_tokens": 4062996, "cached_input_tokens": 3938560, "cache_write_input_tokens": 0, "output_tokens": 46747, "reasoning_output_tokens": 19822, "total_tokens": 4109743 } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-24T23:41:07.707Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:41:07.707Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:41:08.398Z", "callId": "call_eEXNKTE6Y60siIQICjAKlOmH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:41:38.431Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 2, \"loss\": 0.27590151123458506, \"seconds\": 58.34678411483765, \"scale\": 8.979970932006836}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:41:08.512Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b511c08c87d28aac6656f046e217", "usage": { "input_tokens": 78621, "cached_input_tokens": 78208, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 78674 }, "turn_token_usage": { "input_tokens": 4141617, "cached_input_tokens": 4016768, "cache_write_input_tokens": 0, "output_tokens": 46800, "reasoning_output_tokens": 19828, "total_tokens": 4188417 }, "thread_token_usage": { "input_tokens": 4141617, "cached_input_tokens": 4016768, "cache_write_input_tokens": 0, "output_tokens": 46800, "reasoning_output_tokens": 19828, "total_tokens": 4188417 } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-24T23:41:41.033Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:41:41.033Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:41:41.712Z", "callId": "call_x3cwSooiBw5dKteM7OoNLTJx", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:42:11.755Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:41:41.840Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b532afa087d2a2224f039facf408", "usage": { "input_tokens": 78740, "cached_input_tokens": 78464, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 78793 }, "turn_token_usage": { "input_tokens": 4220357, "cached_input_tokens": 4095232, "cache_write_input_tokens": 0, "output_tokens": 46853, "reasoning_output_tokens": 19834, "total_tokens": 4267210 }, "thread_token_usage": { "input_tokens": 4220357, "cached_input_tokens": 4095232, "cache_write_input_tokens": 0, "output_tokens": 46853, "reasoning_output_tokens": 19834, "total_tokens": 4267210 } } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-24T23:42:12.992Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:42:12.992Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:42:13.659Z", "callId": "call_m3vtMFvyYwkU1lY8BC8T8UAY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:42:43.710Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 3, \"loss\": 0.19055657686073452, \"seconds\": 56.61568021774292, \"scale\": 11.528583526611328}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:42:13.762Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b553f53c87d287c16da6efbd3381", "usage": { "input_tokens": 78816, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 78869 }, "turn_token_usage": { "input_tokens": 4299173, "cached_input_tokens": 4173824, "cache_write_input_tokens": 0, "output_tokens": 46906, "reasoning_output_tokens": 19840, "total_tokens": 4346079 }, "thread_token_usage": { "input_tokens": 4299173, "cached_input_tokens": 4173824, "cache_write_input_tokens": 0, "output_tokens": 46906, "reasoning_output_tokens": 19840, "total_tokens": 4346079 } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-24T23:42:45.293Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:42:45.293Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:42:46.397Z", "text": "The primary final refit is tracking the validation fit closely (loss 0.191 after three epochs), so the added week has not destabilized training. One epoch remains for this component, followed by the long-history and recent-history companion fits." }, { "kind": "tool", "timestamp": "2026-09-24T23:42:49.294Z", "callId": "call_5XAnFuuZFcQjHBtKMgiwdZ9P", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:43:19.356Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:42:49.445Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b57426d487d29cf2c609a62e7e6a", "usage": { "input_tokens": 78935, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 11, "total_tokens": 79049 }, "turn_token_usage": { "input_tokens": 4378108, "cached_input_tokens": 4252416, "cache_write_input_tokens": 0, "output_tokens": 47020, "reasoning_output_tokens": 19851, "total_tokens": 4425128 }, "thread_token_usage": { "input_tokens": 4378108, "cached_input_tokens": 4252416, "cache_write_input_tokens": 0, "output_tokens": 47020, "reasoning_output_tokens": 19851, "total_tokens": 4425128 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:43:21.320Z", "callId": "call_1TwE8PEFg28vP6xShTUzEKeq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":73354,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:43:29.754Z", "content": "Script completed\nWall time 8.4 seconds\nOutput:\n\n{\"epoch\": 4, \"loss\": 0.15054072930852433, \"seconds\": 58.5591037273407, \"scale\": 13.98021125793457}\nsaved /app/solution/model_test_metric.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:43:21.473Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b59797d087d29d23d66b1fb5b94d", "usage": { "input_tokens": 79072, "cached_input_tokens": 78720, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 79117 }, "turn_token_usage": { "input_tokens": 4457180, "cached_input_tokens": 4331136, "cache_write_input_tokens": 0, "output_tokens": 47065, "reasoning_output_tokens": 19851, "total_tokens": 4504245 }, "thread_token_usage": { "input_tokens": 4457180, "cached_input_tokens": 4331136, "cache_write_input_tokens": 0, "output_tokens": 47065, "reasoning_output_tokens": 19851, "total_tokens": 4504245 } } }, { "kind": "context", "timestamp": "2026-09-24T23:43:29.752Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c287f2d4-b3c3-4d80-9812-3c75ee502346", "process_id": "73354", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_metric.pt --epochs 4 --batch-size 512 --negatives 32 --seed 48611 --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_metric.pt --epochs 4 --batch-size 512 --negatives 32 --seed 48611 --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.44617621383144, \"seconds\": 58.745795011520386, \"scale\": 7.727242946624756}\n{\"epoch\": 2, \"loss\": 0.27590151123458506, \"seconds\": 58.34678411483765, \"scale\": 8.979970932006836}\n{\"epoch\": 3, \"loss\": 0.19055657686073452, \"seconds\": 56.61568021774292, \"scale\": 11.528583526611328}\n{\"epoch\": 4, \"loss\": 0.15054072930852433, \"seconds\": 58.5591037273407, \"scale\": 13.98021125793457}\nsaved /app/solution/model_test_metric.pt\n", "stderr": "", "aggregated_output": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.44617621383144, \"seconds\": 58.745795011520386, \"scale\": 7.727242946624756}\n{\"epoch\": 2, \"loss\": 0.27590151123458506, \"seconds\": 58.34678411483765, \"scale\": 8.979970932006836}\n{\"epoch\": 3, \"loss\": 0.19055657686073452, \"seconds\": 56.61568021774292, \"scale\": 11.528583526611328}\n{\"epoch\": 4, \"loss\": 0.15054072930852433, \"seconds\": 58.5591037273407, \"scale\": 13.98021125793457}\nsaved /app/solution/model_test_metric.pt\n", "exit_code": 0, "duration": { "secs": 239, "nanos": 608360354 }, "formatted_output": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.44617621383144, \"seconds\": 58.745795011520386, \"scale\": 7.727242946624756}\n{\"epoch\": 2, \"loss\": 0.27590151123458506, \"seconds\": 58.34678411483765, \"scale\": 8.979970932006836}\n{\"epoch\": 3, \"loss\": 0.19055657686073452, \"seconds\": 56.61568021774292, \"scale\": 11.528583526611328}\n{\"epoch\": 4, \"loss\": 0.15054072930852433, \"seconds\": 58.5591037273407, \"scale\": 13.98021125793457}\nsaved /app/solution/model_test_metric.pt\n" } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-24T23:43:31.439Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:43:31.439Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:43:33.451Z", "callId": "call_AZgWLY27d6nriKoxe4sBmpVL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_long.pt --epochs 4 --batch-size 512 --negatives 24 --seed 20260924\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:44:03.626Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n/app/solution/train_ranker.py:650: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. 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"cached_input_tokens": 4647552, "cache_write_input_tokens": 0, "output_tokens": 47375, "reasoning_output_tokens": 19900, "total_tokens": 4822382 } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-24T23:45:42.940Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:45:42.940Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:45:43.805Z", "text": "The primary refit is saved. The long-history companion is halfway through and shows the same expected convergence as development; no artifact replacement will occur until all eligibility and schema checks pass." }, { "kind": "tool", "timestamp": "2026-09-24T23:45:44.502Z", "callId": "call_cfKwEblNAievvlZK8d5MMXXH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":62990,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:46:14.544Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:45:44.650Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": 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completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 3, \"loss\": 0.20039719280393614, \"seconds\": 53.6753191947937, \"scale\": 11.603804588317871}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:46:16.899Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b646d3f087d2b61e378deb0a14e4", "usage": { "input_tokens": 79879, "cached_input_tokens": 79616, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 79924 }, "turn_token_usage": { "input_tokens": 4934640, "cached_input_tokens": 4806656, "cache_write_input_tokens": 0, "output_tokens": 47522, "reasoning_output_tokens": 19912, "total_tokens": 4982162 }, "thread_token_usage": { "input_tokens": 4934640, "cached_input_tokens": 4806656, "cache_write_input_tokens": 0, "output_tokens": 47522, "reasoning_output_tokens": 19912, "total_tokens": 4982162 } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-24T23:46:48.332Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:46:48.332Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:46:49.044Z", "callId": "call_1f0i0QeAwXozDxDXYgwmS7r0", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":62990,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:47:11.497Z", "content": "Script completed\nWall time 22.4 seconds\nOutput:\n\n{\"epoch\": 4, \"loss\": 0.15927537728086477, \"seconds\": 51.92118525505066, \"scale\": 14.089540481567383}\nsaved /app/solution/model_test_long.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:46:49.144Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b66728c087d289cc32e50ccb7d2b", "usage": { "input_tokens": 79990, "cached_input_tokens": 79744, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80043 }, "turn_token_usage": { "input_tokens": 5014630, "cached_input_tokens": 4886400, "cache_write_input_tokens": 0, "output_tokens": 47575, "reasoning_output_tokens": 19918, "total_tokens": 5062205 }, "thread_token_usage": { "input_tokens": 5014630, "cached_input_tokens": 4886400, "cache_write_input_tokens": 0, "output_tokens": 47575, "reasoning_output_tokens": 19918, "total_tokens": 5062205 } } }, { "kind": "context", "timestamp": "2026-09-24T23:47:11.491Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aabe296f-1c48-424d-8300-f9271698756d", "process_id": "62990", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_long.pt --epochs 4 --batch-size 512 --negatives 24 --seed 20260924" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_long.pt --epochs 4 --batch-size 512 --negatives 24 --seed 20260924" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4442524180894403, \"seconds\": 53.615320920944214, \"scale\": 8.297266006469727}\n{\"epoch\": 2, \"loss\": 0.2786328710454575, \"seconds\": 51.357505321502686, \"scale\": 9.576618194580078}\n{\"epoch\": 3, \"loss\": 0.20039719280393614, \"seconds\": 53.6753191947937, \"scale\": 11.603804588317871}\n{\"epoch\": 4, \"loss\": 0.15927537728086477, \"seconds\": 51.92118525505066, \"scale\": 14.089540481567383}\nsaved /app/solution/model_test_long.pt\n", "stderr": "", "aggregated_output": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4442524180894403, \"seconds\": 53.615320920944214, \"scale\": 8.297266006469727}\n{\"epoch\": 2, \"loss\": 0.2786328710454575, \"seconds\": 51.357505321502686, \"scale\": 9.576618194580078}\n{\"epoch\": 3, \"loss\": 0.20039719280393614, \"seconds\": 53.6753191947937, \"scale\": 11.603804588317871}\n{\"epoch\": 4, \"loss\": 0.15927537728086477, \"seconds\": 51.92118525505066, \"scale\": 14.089540481567383}\nsaved /app/solution/model_test_long.pt\n", "exit_code": 0, "duration": { "secs": 217, "nanos": 875247867 }, "formatted_output": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.4442524180894403, \"seconds\": 53.615320920944214, \"scale\": 8.297266006469727}\n{\"epoch\": 2, \"loss\": 0.2786328710454575, \"seconds\": 51.357505321502686, \"scale\": 9.576618194580078}\n{\"epoch\": 3, \"loss\": 0.20039719280393614, \"seconds\": 53.6753191947937, \"scale\": 11.603804588317871}\n{\"epoch\": 4, \"loss\": 0.15927537728086477, \"seconds\": 51.92118525505066, \"scale\": 14.089540481567383}\nsaved /app/solution/model_test_long.pt\n" } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-24T23:47:13.078Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:47:13.078Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:47:15.623Z", "callId": "call_DkaedBIWudEGIeXQxe5olzAt", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_recent.pt --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:47:45.795Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n\nSESSION_ID=38209", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:47:15.743Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b67faf4887d28eeae3556b2b22d6", "usage": { "input_tokens": 80119, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 181, "reasoning_output_tokens": 31, "total_tokens": 80300 }, "turn_token_usage": { "input_tokens": 5094749, "cached_input_tokens": 4966272, "cache_write_input_tokens": 0, "output_tokens": 47756, "reasoning_output_tokens": 19949, "total_tokens": 5142505 }, "thread_token_usage": { "input_tokens": 5094749, "cached_input_tokens": 4966272, "cache_write_input_tokens": 0, "output_tokens": 47756, "reasoning_output_tokens": 19949, "total_tokens": 5142505 } } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-24T23:47:47.125Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:47:47.125Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:47:47.823Z", "callId": "call_yXKtNsBFWzfTKusvH1l0wWlB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38209,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:48:17.856Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 1, \"loss\": 0.47506028572210923, \"seconds\": 25.985625982284546, \"scale\": 7.888355731964111}\n{\"epoch\": 2, \"loss\": 0.2965369585248576, \"seconds\": 24.38400626182556, \"scale\": 8.910850524902344}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:47:47.964Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b6a20b9887d293eaeeb0405f5031", "usage": { "input_tokens": 80396, "cached_input_tokens": 80000, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80449 }, "turn_token_usage": { "input_tokens": 5175145, "cached_input_tokens": 5046272, "cache_write_input_tokens": 0, "output_tokens": 47809, "reasoning_output_tokens": 19955, "total_tokens": 5222954 }, "thread_token_usage": { "input_tokens": 5175145, "cached_input_tokens": 5046272, "cache_write_input_tokens": 0, "output_tokens": 47809, "reasoning_output_tokens": 19955, "total_tokens": 5222954 } } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-24T23:48:19.184Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:48:19.184Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:48:19.892Z", "callId": "call_91ZgOqENVBFTq07kXXz4Rc2V", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38209,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:48:49.936Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 3, \"loss\": 0.1725684222963548, \"seconds\": 24.412874460220337, \"scale\": 11.060006141662598}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:48:20.012Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b6c2223487d2be86ac70c08b858f", "usage": { "input_tokens": 80558, "cached_input_tokens": 80256, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80611 }, "turn_token_usage": { "input_tokens": 5255703, "cached_input_tokens": 5126528, "cache_write_input_tokens": 0, "output_tokens": 47862, "reasoning_output_tokens": 19961, "total_tokens": 5303565 }, "thread_token_usage": { "input_tokens": 5255703, "cached_input_tokens": 5126528, "cache_write_input_tokens": 0, "output_tokens": 47862, "reasoning_output_tokens": 19961, "total_tokens": 5303565 } } } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-24T23:48:51.586Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:48:51.586Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:48:52.150Z", "text": "Two final components are saved; the recent-history component is at epoch three of six and remains stable. After it finishes, ranking plus full structural/eligibility validation should take under a minute." }, { "kind": "tool", "timestamp": "2026-09-24T23:48:52.870Z", "callId": "call_gYUTaN5oP2ewPJTJamdjXbsW", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38209,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:49:22.902Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"epoch\": 4, \"loss\": 0.11451948164469457, \"seconds\": 25.954304456710815, \"scale\": 14.025274276733398}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:48:52.971Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b6e2435087d2b411b9b1e44025a3", "usage": { "input_tokens": 80677, "cached_input_tokens": 80384, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 6, "total_tokens": 80775 }, "turn_token_usage": { "input_tokens": 5336380, "cached_input_tokens": 5206912, "cache_write_input_tokens": 0, "output_tokens": 47960, "reasoning_output_tokens": 19967, "total_tokens": 5384340 }, "thread_token_usage": { "input_tokens": 5336380, "cached_input_tokens": 5206912, "cache_write_input_tokens": 0, "output_tokens": 47960, "reasoning_output_tokens": 19967, "total_tokens": 5384340 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:49:25.195Z", "callId": "call_KEJ4v5nZ1vWBnyaK4KricXc2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":38209,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:49:52.675Z", "content": "Script completed\nWall time 27.5 seconds\nOutput:\n\n{\"epoch\": 5, \"loss\": 0.08241906799952095, \"seconds\": 24.48066473007202, \"scale\": 17.304595947265625}\n{\"epoch\": 6, \"loss\": 0.06401535313041537, \"seconds\": 24.46773600578308, \"scale\": 20.263809204101562}\nsaved /app/solution/model_test_recent.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:49:25.353Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b7032b6087d2860290dd8db8e5ad", "usage": { "input_tokens": 80841, "cached_input_tokens": 80512, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 80886 }, "turn_token_usage": { "input_tokens": 5417221, "cached_input_tokens": 5287424, "cache_write_input_tokens": 0, "output_tokens": 48005, "reasoning_output_tokens": 19967, "total_tokens": 5465226 }, "thread_token_usage": { "input_tokens": 5417221, "cached_input_tokens": 5287424, "cache_write_input_tokens": 0, "output_tokens": 48005, "reasoning_output_tokens": 19967, "total_tokens": 5465226 } } }, { "kind": "context", "timestamp": "2026-09-24T23:49:52.673Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-71923592-3f24-4207-838a-6a3814874742", "process_id": "38209", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_recent.pt --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py train --prepared /app/solution/prepared_test.npz --output /app/solution/model_test_recent.pt --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.47506028572210923, \"seconds\": 25.985625982284546, \"scale\": 7.888355731964111}\n{\"epoch\": 2, \"loss\": 0.2965369585248576, \"seconds\": 24.38400626182556, \"scale\": 8.910850524902344}\n{\"epoch\": 3, \"loss\": 0.1725684222963548, \"seconds\": 24.412874460220337, \"scale\": 11.060006141662598}\n{\"epoch\": 4, \"loss\": 0.11451948164469457, \"seconds\": 25.954304456710815, \"scale\": 14.025274276733398}\n{\"epoch\": 5, \"loss\": 0.08241906799952095, \"seconds\": 24.48066473007202, \"scale\": 17.304595947265625}\n{\"epoch\": 6, \"loss\": 0.06401535313041537, \"seconds\": 24.46773600578308, \"scale\": 20.263809204101562}\nsaved /app/solution/model_test_recent.pt\n", "stderr": "", "aggregated_output": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.47506028572210923, \"seconds\": 25.985625982284546, \"scale\": 7.888355731964111}\n{\"epoch\": 2, \"loss\": 0.2965369585248576, \"seconds\": 24.38400626182556, \"scale\": 8.910850524902344}\n{\"epoch\": 3, \"loss\": 0.1725684222963548, \"seconds\": 24.412874460220337, \"scale\": 11.060006141662598}\n{\"epoch\": 4, \"loss\": 0.11451948164469457, \"seconds\": 25.954304456710815, \"scale\": 14.025274276733398}\n{\"epoch\": 5, \"loss\": 0.08241906799952095, \"seconds\": 24.48066473007202, \"scale\": 17.304595947265625}\n{\"epoch\": 6, \"loss\": 0.06401535313041537, \"seconds\": 24.46773600578308, \"scale\": 20.263809204101562}\nsaved /app/solution/model_test_recent.pt\n", "exit_code": 0, "duration": { "secs": 156, "nanos": 885126625 }, "formatted_output": "/app/solution/train_ranker.py:650: 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_loss += float(loss) * current\n{\"epoch\": 1, \"loss\": 0.47506028572210923, \"seconds\": 25.985625982284546, \"scale\": 7.888355731964111}\n{\"epoch\": 2, \"loss\": 0.2965369585248576, \"seconds\": 24.38400626182556, \"scale\": 8.910850524902344}\n{\"epoch\": 3, \"loss\": 0.1725684222963548, \"seconds\": 24.412874460220337, \"scale\": 11.060006141662598}\n{\"epoch\": 4, \"loss\": 0.11451948164469457, \"seconds\": 25.954304456710815, \"scale\": 14.025274276733398}\n{\"epoch\": 5, \"loss\": 0.08241906799952095, \"seconds\": 24.48066473007202, \"scale\": 17.304595947265625}\n{\"epoch\": 6, \"loss\": 0.06401535313041537, \"seconds\": 24.46773600578308, \"scale\": 20.263809204101562}\nsaved /app/solution/model_test_recent.pt\n" } } } ] }, { "id": 120, "role": "agent", "timestamp": "2026-09-24T23:49:54.954Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:49:54.954Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:49:57.449Z", "callId": "call_Xxe0z3R1tRePxbDMm2croGZt", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage test --prepared /app/solution/prepared_test.npz --checkpoint /app/solution/model_test_metric.pt --checkpoint2 /app/solution/model_test_long.pt --second-weight 0.75 --checkpoint3 /app/solution/model_test_recent.pt --third-weight 0.55 --popularity-weight 0 --momentum-weight 2.0 --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:50:07.148Z", "content": "Script completed\nWall time 9.7 seconds\nOutput:\n\n{\n \"stage\": \"test\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:49:57.544Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b720ff7487d2a61dab09f2760743", "usage": { "input_tokens": 81005, "cached_input_tokens": 80640, "cache_write_input_tokens": 0, "output_tokens": 168, "reasoning_output_tokens": 20, "total_tokens": 81173 }, "turn_token_usage": { "input_tokens": 5498226, "cached_input_tokens": 5368064, "cache_write_input_tokens": 0, "output_tokens": 48173, "reasoning_output_tokens": 19987, "total_tokens": 5546399 }, "thread_token_usage": { "input_tokens": 5498226, "cached_input_tokens": 5368064, "cache_write_input_tokens": 0, "output_tokens": 48173, "reasoning_output_tokens": 19987, "total_tokens": 5546399 } } }, { "kind": "context", "timestamp": "2026-09-24T23:50:07.144Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-802ab69f-0777-4e2c-b3b5-b2fb83464b2f", "process_id": "21367", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage test --prepared /app/solution/prepared_test.npz --checkpoint /app/solution/model_test_metric.pt --checkpoint2 /app/solution/model_test_long.pt --second-weight 0.75 --checkpoint3 /app/solution/model_test_recent.pt --third-weight 0.55 --popularity-weight 0 --momentum-weight 2.0 --output /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_ranker.py rank --stage test --prepared /app/solution/prepared_test.npz --checkpoint /app/solution/model_test_metric.pt --checkpoint2 /app/solution/model_test_long.pt --second-weight 0.75 --checkpoint3 /app/solution/model_test_recent.pt --third-weight 0.55 --popularity-weight 0 --momentum-weight 2.0 --output /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"stage\": \"test\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n", "stderr": "", "aggregated_output": "{\n \"stage\": \"test\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 531041403 }, "formatted_output": "{\n \"stage\": \"test\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"popularity_weight\": 0.0,\n \"second_weight\": 0.75,\n \"third_weight\": 0.55,\n \"momentum_weight\": 2.0\n}\n" } } } ] }, { "id": 121, "role": "agent", "timestamp": "2026-09-24T23:50:17.421Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:50:17.421Z", "text": "" } ] }, { "id": 122, "role": "agent", "timestamp": "2026-09-24T23:50:17.773Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:50:17.773Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:50:19.067Z", "text": "The refit finished and `submission.parquet` has been replaced with the final train+validation model ranking. I’m performing the independent contract checks now—exact users/rows/ranks, duplicates, and every test-cutoff eligibility bit—then I’ll add the reproducible one-command runner and README." }, { "kind": "tool", "timestamp": "2026-09-24T23:50:26.209Z", "callId": "call_GwIwpl44oL2zJETG8axcJNaV", "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,numpy as np,hashlib\\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\\nD=Path('/task/data');S=Path('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));f=validate_ranking(read_ranking(S),targets);e=CandidateSet(D/'test_eligible_candidates.npz')\\nrows=np.fromiter((e.uid_row.get(int(x),-1) for x in f.uid),np.int32,len(f));cols=np.fromiter((e.item_column.get(int(x),-1) for x in f.item_id),np.int32,len(f));ok=(rows>=0)&(cols>=0);idx=np.flatnonzero(ok);ok[idx]=((e.packed[rows[idx],cols[idx]//8]>>(cols[idx]%8))&1).astype(bool)\\nprint({'rows':len(f),'users':f.uid.nunique(),'items':f.item_id.nunique(),'all_eligible':bool(ok.all()),'ineligible':int((~ok).sum()),'duplicate_pairs':int(f.duplicated(['uid','item_id']).sum()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'size_bytes':S.stat().st_size,'sha256':hashlib.sha256(S.read_bytes()).hexdigest()})\\nprint(f.head(12).to_string(index=False))\\nPY\\nls -lh /app/solution | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:50:28.837Z", "content": "Script completed\nWall time 2.6 seconds\nOutput:\n\n{'rows': 845600, 'users': 8456, 'items': 6149, 'all_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'size_bytes': 1520998, 'sha256': '34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887'}\n uid item_id rank\n 100 8250570 1\n 100 2635696 2\n 100 7419525 3\n 100 5387987 4\n 100 7579671 5\n 100 5010168 6\n 100 7958357 7\n 100 3972057 8\n 100 2055777 9\n 100 5234367 10\n 100 1005397 11\n 100 1265696 12\ntotal 345M\ndrwxr-xr-x 2 agent agent 4.0K Sep 24 23:38 __pycache__\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 15M Sep 24 23:47 model_test_long.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:43 model_test_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:49 model_test_recent.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:15 model_validation.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:27 model_validation_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:20 model_validation_recent.pt\n-rw-r--r-- 1 agent agent 116M Sep 24 23:39 prepared_test.npz\n-rw-r--r-- 1 agent agent 114M Sep 24 23:12 prepared_validation.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:50 submission.parquet\n-rw-r--r-- 1 agent agent 35K Sep 24 23:38 train_ranker.py\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.1_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.25_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:22 validation_ensemble_s0.5_p1.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:22 validation_ensemble_s1.0_p1.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:28 validation_metric_p0.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.5.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.0.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.75.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:21 validation_recent_w1.25.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:17 validation_twotower_w-0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:16 validation_twotower_w1.0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:16 validation_twotower_w2.0.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:16 validation_twotower_w3.0.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:50:26.318Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b72f557887d2af11557a6c82318b", "usage": { "input_tokens": 81268, "cached_input_tokens": 80768, "cache_write_input_tokens": 0, "output_tokens": 1005, "reasoning_output_tokens": 533, "total_tokens": 82273 }, "turn_token_usage": { "input_tokens": 5579494, "cached_input_tokens": 5448832, "cache_write_input_tokens": 0, "output_tokens": 49178, "reasoning_output_tokens": 20520, "total_tokens": 5628672 }, "thread_token_usage": { "input_tokens": 5579494, "cached_input_tokens": 5448832, "cache_write_input_tokens": 0, "output_tokens": 49178, "reasoning_output_tokens": 20520, "total_tokens": 5628672 } } }, { "kind": "context", "timestamp": "2026-09-24T23:50:28.833Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dbc8febd-686a-4277-91ec-48295fb0eeda", "process_id": "23011", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd,numpy as np,hashlib\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));f=validate_ranking(read_ranking(S),targets);e=CandidateSet(D/'test_eligible_candidates.npz')\nrows=np.fromiter((e.uid_row.get(int(x),-1) for x in f.uid),np.int32,len(f));cols=np.fromiter((e.item_column.get(int(x),-1) for x in f.item_id),np.int32,len(f));ok=(rows>=0)&(cols>=0);idx=np.flatnonzero(ok);ok[idx]=((e.packed[rows[idx],cols[idx]//8]>>(cols[idx]%8))&1).astype(bool)\nprint({'rows':len(f),'users':f.uid.nunique(),'items':f.item_id.nunique(),'all_eligible':bool(ok.all()),'ineligible':int((~ok).sum()),'duplicate_pairs':int(f.duplicated(['uid','item_id']).sum()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'size_bytes':S.stat().st_size,'sha256':hashlib.sha256(S.read_bytes()).hexdigest()})\nprint(f.head(12).to_string(index=False))\nPY\nls -lh /app/solution | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd,numpy as np,hashlib\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));f=validate_ranking(read_ranking(S),targets);e=CandidateSet(D/'test_eligible_candidates.npz')\nrows=np.fromiter((e.uid_row.get(int(x),-1) for x in f.uid),np.int32,len(f));cols=np.fromiter((e.item_column.get(int(x),-1) for x in f.item_id),np.int32,len(f));ok=(rows>=0)&(cols>=0);idx=np.flatnonzero(ok);ok[idx]=((e.packed[rows[idx],cols[idx]//8]>>(cols[idx]%8))&1).astype(bool)\nprint({'rows':len(f),'users':f.uid.nunique(),'items':f.item_id.nunique(),'all_eligible':bool(ok.all()),'ineligible':int((~ok).sum()),'duplicate_pairs':int(f.duplicated(['uid','item_id']).sum()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'size_bytes':S.stat().st_size,'sha256':hashlib.sha256(S.read_bytes()).hexdigest()})\nprint(f.head(12).to_string(index=False))\nPY\nls -lh /app/solution | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'items': 6149, 'all_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'size_bytes': 1520998, 'sha256': '34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887'}\n uid item_id rank\n 100 8250570 1\n 100 2635696 2\n 100 7419525 3\n 100 5387987 4\n 100 7579671 5\n 100 5010168 6\n 100 7958357 7\n 100 3972057 8\n 100 2055777 9\n 100 5234367 10\n 100 1005397 11\n 100 1265696 12\ntotal 345M\ndrwxr-xr-x 2 agent agent 4.0K Sep 24 23:38 __pycache__\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 15M Sep 24 23:47 model_test_long.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:43 model_test_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:49 model_test_recent.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:15 model_validation.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:27 model_validation_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:20 model_validation_recent.pt\n-rw-r--r-- 1 agent agent 116M Sep 24 23:39 prepared_test.npz\n-rw-r--r-- 1 agent agent 114M Sep 24 23:12 prepared_validation.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:50 submission.parquet\n-rw-r--r-- 1 agent agent 35K Sep 24 23:38 train_ranker.py\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.1_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.25_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:22 validation_ensemble_s0.5_p1.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:22 validation_ensemble_s1.0_p1.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:28 validation_metric_p0.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.5.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.0.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.75.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:21 validation_recent_w1.25.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:17 validation_twotower_w-0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:16 validation_twotower_w1.0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:16 validation_twotower_w2.0.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:16 validation_twotower_w3.0.parquet\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'items': 6149, 'all_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'size_bytes': 1520998, 'sha256': '34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887'}\n uid item_id rank\n 100 8250570 1\n 100 2635696 2\n 100 7419525 3\n 100 5387987 4\n 100 7579671 5\n 100 5010168 6\n 100 7958357 7\n 100 3972057 8\n 100 2055777 9\n 100 5234367 10\n 100 1005397 11\n 100 1265696 12\ntotal 345M\ndrwxr-xr-x 2 agent agent 4.0K Sep 24 23:38 __pycache__\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 15M Sep 24 23:47 model_test_long.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:43 model_test_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:49 model_test_recent.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:15 model_validation.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:27 model_validation_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:20 model_validation_recent.pt\n-rw-r--r-- 1 agent agent 116M Sep 24 23:39 prepared_test.npz\n-rw-r--r-- 1 agent agent 114M Sep 24 23:12 prepared_validation.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:50 submission.parquet\n-rw-r--r-- 1 agent agent 35K Sep 24 23:38 train_ranker.py\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.1_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.25_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:22 validation_ensemble_s0.5_p1.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:22 validation_ensemble_s1.0_p1.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:28 validation_metric_p0.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.5.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.0.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.75.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:21 validation_recent_w1.25.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:17 validation_twotower_w-0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:16 validation_twotower_w1.0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:16 validation_twotower_w2.0.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:16 validation_twotower_w3.0.parquet\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 457934148 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'items': 6149, 'all_eligible': True, 'ineligible': 0, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'size_bytes': 1520998, 'sha256': '34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887'}\n uid item_id rank\n 100 8250570 1\n 100 2635696 2\n 100 7419525 3\n 100 5387987 4\n 100 7579671 5\n 100 5010168 6\n 100 7958357 7\n 100 3972057 8\n 100 2055777 9\n 100 5234367 10\n 100 1005397 11\n 100 1265696 12\ntotal 345M\ndrwxr-xr-x 2 agent agent 4.0K Sep 24 23:38 __pycache__\n-rw-r--r-- 1 agent agent 1.4K Sep 24 23:04 make_popularity_baseline.py\n-rw-r--r-- 1 agent agent 15M Sep 24 23:47 model_test_long.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:43 model_test_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:49 model_test_recent.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:15 model_validation.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:27 model_validation_metric.pt\n-rw-r--r-- 1 agent agent 15M Sep 24 23:20 model_validation_recent.pt\n-rw-r--r-- 1 agent agent 116M Sep 24 23:39 prepared_test.npz\n-rw-r--r-- 1 agent agent 114M Sep 24 23:12 prepared_validation.npz\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:50 submission.parquet\n-rw-r--r-- 1 agent agent 35K Sep 24 23:38 train_ranker.py\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.1_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:21 validation_ensemble_s0.25_p1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:22 validation_ensemble_s0.5_p1.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:22 validation_ensemble_s1.0_p1.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:28 validation_metric_p0.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 validation_metric_p1.5.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.0.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:20 validation_recent_w0.75.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:21 validation_recent_w1.25.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:17 validation_twotower_w-0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.5.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:16 validation_twotower_w0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:16 validation_twotower_w1.0.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:16 validation_twotower_w2.0.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:16 validation_twotower_w3.0.parquet\n" } } } ] }, { "id": 123, "role": "agent", "timestamp": "2026-09-24T23:50:38.523Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:50:38.523Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:50:58.471Z", "callId": "call_HUsRRt8yFHh04RDETvoP6nK8", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/run_final.sh\\n+#!/bin/sh\\n+set -eu\\n+\\n+PY=/opt/task-venv/bin/python\\n+CODE=/app/solution/train_ranker.py\\n+PREP=/app/solution/prepared_test.npz\\n+\\n+\\\"$PY\\\" \\\"$CODE\\\" prepare --stage test --output \\\"$PREP\\\" --snapshots 16\\n+\\n+\\\"$PY\\\" \\\"$CODE\\\" train --prepared \\\"$PREP\\\" \\\\\\n+ --output /app/solution/model_test_metric.pt \\\\\\n+ --epochs 4 --batch-size 512 --negatives 32 --seed 48611 \\\\\\n+ --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002\\n+\\n+\\\"$PY\\\" \\\"$CODE\\\" train --prepared \\\"$PREP\\\" \\\\\\n+ --output /app/solution/model_test_long.pt \\\\\\n+ --epochs 4 --batch-size 512 --negatives 24 --seed 20260924\\n+\\n+\\\"$PY\\\" \\\"$CODE\\\" train --prepared \\\"$PREP\\\" \\\\\\n+ --output /app/solution/model_test_recent.pt \\\\\\n+ --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 \\\\\\n+ --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002\\n+\\n+\\\"$PY\\\" \\\"$CODE\\\" rank --stage test --prepared \\\"$PREP\\\" \\\\\\n+ --checkpoint /app/solution/model_test_metric.pt \\\\\\n+ --checkpoint2 /app/solution/model_test_long.pt --second-weight 0.75 \\\\\\n+ --checkpoint3 /app/solution/model_test_recent.pt --third-weight 0.55 \\\\\\n+ --popularity-weight 0 --momentum-weight 2.0 \\\\\\n+ --output /app/solution/submission.parquet\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+## Result\\n+\\n+`submission.parquet` contains 845,600 rows: exactly 100 distinct, test-eligible\\n+items for each of 8,456 target users. The selected model scores **0.043827\\n+graded NDCG@10** on 4,263 scored public-validation users. For reference, the\\n+catalog-popularity fallback scored 0.010710.\\n+\\n+## Model\\n+\\n+`train_ranker.py` implements separate PyTorch `UserTower` and `ItemTower`\\n+networks. There is no user-ID embedding. The user tower mean-pools four\\n+history channels (recent positive plays, 180-day organic >=80% plays, active\\n+likes, and active dislikes), concatenates six activity features, and applies a\\n+two-layer GELU MLP. The item tower applies a residual MLP to a trainable\\n+catalog-ID embedding plus fixed catalog-popularity features. Latent outputs are\\n+64-dimensional and cosine-normalized; an extra coordinate represents item\\n+bias.\\n+\\n+The selected ranker concatenates three complementary fits of this architecture\\n+(metric-weighted, all-window, and recent-window) and one standardized seven-day\\n+organic-like-momentum coordinate. Scaling is split between user and item\\n+coordinates, so the complete 197-dimensional relevance score is **exactly one\\n+dot product**. Candidate vectors are independently precomputable offline. At\\n+request time only the history-based user vector is computed before MIPS; no\\n+candidate cross-features or reranker are used.\\n+\\n+## Training objective and protocol\\n+\\n+Training uses 16 rolling temporal snapshots. Each example predicts an organic\\n+>=80% listen (grade 1) or organic like (grade 3) in the next seven days after a\\n+30-minute gap. Targets seen before a snapshot cutoff are removed. A sampled\\n+pairwise logistic/BPR loss uses a mixture of uniform and popularity-aware\\n+negatives, with grade-3 and recent examples upweighted. Preparation operates on\\n+event arrays and sampled histories; it never constructs or decomposes a global\\n+user-item interaction matrix.\\n+\\n+Development uses only `train_*` history and labels. The final artifacts are\\n+refit on `train_* + val_*`; validation events are then history for the test\\n+cutoff. Supplied cutoff-specific preference state and eligibility masks are\\n+used at their corresponding stages.\\n+\\n+## Reproduce the final submission\\n+\\n+From any working directory, run:\\n+\\n+```sh\\n+sh /app/solution/run_final.sh\\n+```\\n+\\n+The script recreates `/app/solution/submission.parquet` using CPU PyTorch 2.8.\\n+Individual `prepare`, `train`, and `rank` commands are also exposed by\\n+`train_ranker.py --help`.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:50:58.582Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:50:58.480Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1cb6ce44-98f9-4098-980c-8bb994626693", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You two-tower ranker\n\n## Result\n\n`submission.parquet` contains 845,600 rows: exactly 100 distinct, test-eligible\nitems for each of 8,456 target users. The selected model scores **0.043827\ngraded NDCG@10** on 4,263 scored public-validation users. For reference, the\ncatalog-popularity fallback scored 0.010710.\n\n## Model\n\n`train_ranker.py` implements separate PyTorch `UserTower` and `ItemTower`\nnetworks. There is no user-ID embedding. The user tower mean-pools four\nhistory channels (recent positive plays, 180-day organic >=80% plays, active\nlikes, and active dislikes), concatenates six activity features, and applies a\ntwo-layer GELU MLP. The item tower applies a residual MLP to a trainable\ncatalog-ID embedding plus fixed catalog-popularity features. Latent outputs are\n64-dimensional and cosine-normalized; an extra coordinate represents item\nbias.\n\nThe selected ranker concatenates three complementary fits of this architecture\n(metric-weighted, all-window, and recent-window) and one standardized seven-day\norganic-like-momentum coordinate. Scaling is split between user and item\ncoordinates, so the complete 197-dimensional relevance score is **exactly one\ndot product**. Candidate vectors are independently precomputable offline. At\nrequest time only the history-based user vector is computed before MIPS; no\ncandidate cross-features or reranker are used.\n\n## Training objective and protocol\n\nTraining uses 16 rolling temporal snapshots. Each example predicts an organic\n>=80% listen (grade 1) or organic like (grade 3) in the next seven days after a\n30-minute gap. Targets seen before a snapshot cutoff are removed. A sampled\npairwise logistic/BPR loss uses a mixture of uniform and popularity-aware\nnegatives, with grade-3 and recent examples upweighted. Preparation operates on\nevent arrays and sampled histories; it never constructs or decomposes a global\nuser-item interaction matrix.\n\nDevelopment uses only `train_*` history and labels. The final artifacts are\nrefit on `train_* + val_*`; validation events are then history for the test\ncutoff. Supplied cutoff-specific preference state and eligibility masks are\nused at their corresponding stages.\n\n## Reproduce the final submission\n\nFrom any working directory, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThe script recreates `/app/solution/submission.parquet` using CPU PyTorch 2.8.\nIndividual `prepare`, `train`, and `rank` commands are also exposed by\n`train_ranker.py --help`.\n" }, "/app/solution/run_final.sh": { "type": "add", "content": "#!/bin/sh\nset -eu\n\nPY=/opt/task-venv/bin/python\nCODE=/app/solution/train_ranker.py\nPREP=/app/solution/prepared_test.npz\n\n\"$PY\" \"$CODE\" prepare --stage test --output \"$PREP\" --snapshots 16\n\n\"$PY\" \"$CODE\" train --prepared \"$PREP\" \\\n --output /app/solution/model_test_metric.pt \\\n --epochs 4 --batch-size 512 --negatives 32 --seed 48611 \\\n --grade3-weight 7 --age-floor 0.2 --age-decay 4 --learning-rate 0.002\n\n\"$PY\" \"$CODE\" train --prepared \"$PREP\" \\\n --output /app/solution/model_test_long.pt \\\n --epochs 4 --batch-size 512 --negatives 24 --seed 20260924\n\n\"$PY\" \"$CODE\" train --prepared \"$PREP\" \\\n --output /app/solution/model_test_recent.pt \\\n --epochs 6 --batch-size 512 --negatives 32 --seed 9173 --max-age 5 \\\n --grade3-weight 7 --age-floor 0.2 --age-decay 3 --learning-rate 0.002\n\n\"$PY\" \"$CODE\" rank --stage test --prepared \"$PREP\" \\\n --checkpoint /app/solution/model_test_metric.pt \\\n --checkpoint2 /app/solution/model_test_long.pt --second-weight 0.75 \\\n --checkpoint3 /app/solution/model_test_recent.pt --third-weight 0.55 \\\n --popularity-weight 0 --momentum-weight 2.0 \\\n --output /app/solution/submission.parquet\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/run_final.sh\nA /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:50:58.580Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b7450ba887d2a8ce35c3ac1c33c6", "usage": { "input_tokens": 83436, "cached_input_tokens": 81152, "cache_write_input_tokens": 0, "output_tokens": 1583, "reasoning_output_tokens": 468, "total_tokens": 85019 }, "turn_token_usage": { "input_tokens": 5662930, "cached_input_tokens": 5529984, "cache_write_input_tokens": 0, "output_tokens": 50761, "reasoning_output_tokens": 20988, "total_tokens": 5713691 }, "thread_token_usage": { "input_tokens": 5662930, "cached_input_tokens": 5529984, "cache_write_input_tokens": 0, "output_tokens": 50761, "reasoning_output_tokens": 20988, "total_tokens": 5713691 } } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-24T23:51:02.067Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:51:02.067Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:51:05.737Z", "callId": "call_60BHMOwR42iDe4RD2aCc09Pm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"chmod +x /app/solution/run_final.sh /app/solution/train_ranker.py /app/solution/make_popularity_baseline.py\\nsh -n /app/solution/run_final.sh\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/make_popularity_baseline.py\\n/opt/task-venv/bin/python /app/solution/train_ranker.py --help | sed -n '1,120p'\\nwc -l /app/solution/README.md /app/solution/train_ranker.py /app/solution/run_final.sh\\nsha256sum /app/solution/submission.parquet /app/solution/validation.parquet\\nsed -n '1,240p' /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:51:10.156Z", "content": "Script completed\nWall time 4.4 seconds\nOutput:\n\nusage: train_ranker.py [-h] {prepare,train,rank} ...\n\npositional arguments:\n {prepare,train,rank}\n\noptions:\n -h, --help show this help message and exit\n 54 /app/solution/README.md\n 900 /app/solution/train_ranker.py\n 29 /app/solution/run_final.sh\n 983 total\n34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887 /app/solution/submission.parquet\n488c74c2fb8d0614c3a6b3289c26737db25b0a0b0e2a393433501aa31af94e54 /app/solution/validation.parquet\n# Made for You two-tower ranker\n\n## Result\n\n`submission.parquet` contains 845,600 rows: exactly 100 distinct, test-eligible\nitems for each of 8,456 target users. The selected model scores **0.043827\ngraded NDCG@10** on 4,263 scored public-validation users. For reference, the\ncatalog-popularity fallback scored 0.010710.\n\n## Model\n\n`train_ranker.py` implements separate PyTorch `UserTower` and `ItemTower`\nnetworks. There is no user-ID embedding. The user tower mean-pools four\nhistory channels (recent positive plays, 180-day organic >=80% plays, active\nlikes, and active dislikes), concatenates six activity features, and applies a\ntwo-layer GELU MLP. The item tower applies a residual MLP to a trainable\ncatalog-ID embedding plus fixed catalog-popularity features. Latent outputs are\n64-dimensional and cosine-normalized; an extra coordinate represents item\nbias.\n\nThe selected ranker concatenates three complementary fits of this architecture\n(metric-weighted, all-window, and recent-window) and one standardized seven-day\norganic-like-momentum coordinate. Scaling is split between user and item\ncoordinates, so the complete 197-dimensional relevance score is **exactly one\ndot product**. Candidate vectors are independently precomputable offline. At\nrequest time only the history-based user vector is computed before MIPS; no\ncandidate cross-features or reranker are used.\n\n## Training objective and protocol\n\nTraining uses 16 rolling temporal snapshots. Each example predicts an organic\n>=80% listen (grade 1) or organic like (grade 3) in the next seven days after a\n30-minute gap. Targets seen before a snapshot cutoff are removed. A sampled\npairwise logistic/BPR loss uses a mixture of uniform and popularity-aware\nnegatives, with grade-3 and recent examples upweighted. Preparation operates on\nevent arrays and sampled histories; it never constructs or decomposes a global\nuser-item interaction matrix.\n\nDevelopment uses only `train_*` history and labels. The final artifacts are\nrefit on `train_* + val_*`; validation events are then history for the test\ncutoff. Supplied cutoff-specific preference state and eligibility masks are\nused at their corresponding stages.\n\n## Reproduce the final submission\n\nFrom any working directory, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThe script recreates `/app/solution/submission.parquet` using CPU PyTorch 2.8.\nIndividual `prepare`, `train`, and `rank` commands are also exposed by\n`train_ranker.py --help`.\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:51:05.843Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b762e3bc87d2b1e60abc534f5970", "usage": { "input_tokens": 85043, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 331, "reasoning_output_tokens": 120, "total_tokens": 85374 }, "turn_token_usage": { "input_tokens": 5747973, "cached_input_tokens": 5613184, "cache_write_input_tokens": 0, "output_tokens": 51092, "reasoning_output_tokens": 21108, "total_tokens": 5799065 }, "thread_token_usage": { "input_tokens": 5747973, "cached_input_tokens": 5613184, "cache_write_input_tokens": 0, "output_tokens": 51092, "reasoning_output_tokens": 21108, "total_tokens": 5799065 } } }, { "kind": "context", "timestamp": "2026-09-24T23:51:10.152Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9591119e-160c-467e-bc3b-f9fff0733de0", "process_id": "65186", "command": [ "/bin/sh", "-lc", "chmod +x /app/solution/run_final.sh /app/solution/train_ranker.py /app/solution/make_popularity_baseline.py\nsh -n /app/solution/run_final.sh\n/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/make_popularity_baseline.py\n/opt/task-venv/bin/python /app/solution/train_ranker.py --help | sed -n '1,120p'\nwc -l /app/solution/README.md /app/solution/train_ranker.py /app/solution/run_final.sh\nsha256sum /app/solution/submission.parquet /app/solution/validation.parquet\nsed -n '1,240p' /app/solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "chmod +x /app/solution/run_final.sh /app/solution/train_ranker.py /app/solution/make_popularity_baseline.py\nsh -n /app/solution/run_final.sh\n/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/make_popularity_baseline.py\n/opt/task-venv/bin/python /app/solution/train_ranker.py --help | sed -n '1,120p'\nwc -l /app/solution/README.md /app/solution/train_ranker.py /app/solution/run_final.sh\nsha256sum /app/solution/submission.parquet /app/solution/validation.parquet\nsed -n '1,240p' /app/solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "usage: train_ranker.py [-h] {prepare,train,rank} ...\n\npositional arguments:\n {prepare,train,rank}\n\noptions:\n -h, --help show this help message and exit\n 54 /app/solution/README.md\n 900 /app/solution/train_ranker.py\n 29 /app/solution/run_final.sh\n 983 total\n34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887 /app/solution/submission.parquet\n488c74c2fb8d0614c3a6b3289c26737db25b0a0b0e2a393433501aa31af94e54 /app/solution/validation.parquet\n# Made for You two-tower ranker\n\n## Result\n\n`submission.parquet` contains 845,600 rows: exactly 100 distinct, test-eligible\nitems for each of 8,456 target users. The selected model scores **0.043827\ngraded NDCG@10** on 4,263 scored public-validation users. For reference, the\ncatalog-popularity fallback scored 0.010710.\n\n## Model\n\n`train_ranker.py` implements separate PyTorch `UserTower` and `ItemTower`\nnetworks. There is no user-ID embedding. The user tower mean-pools four\nhistory channels (recent positive plays, 180-day organic >=80% plays, active\nlikes, and active dislikes), concatenates six activity features, and applies a\ntwo-layer GELU MLP. The item tower applies a residual MLP to a trainable\ncatalog-ID embedding plus fixed catalog-popularity features. Latent outputs are\n64-dimensional and cosine-normalized; an extra coordinate represents item\nbias.\n\nThe selected ranker concatenates three complementary fits of this architecture\n(metric-weighted, all-window, and recent-window) and one standardized seven-day\norganic-like-momentum coordinate. Scaling is split between user and item\ncoordinates, so the complete 197-dimensional relevance score is **exactly one\ndot product**. Candidate vectors are independently precomputable offline. At\nrequest time only the history-based user vector is computed before MIPS; no\ncandidate cross-features or reranker are used.\n\n## Training objective and protocol\n\nTraining uses 16 rolling temporal snapshots. Each example predicts an organic\n>=80% listen (grade 1) or organic like (grade 3) in the next seven days after a\n30-minute gap. Targets seen before a snapshot cutoff are removed. A sampled\npairwise logistic/BPR loss uses a mixture of uniform and popularity-aware\nnegatives, with grade-3 and recent examples upweighted. Preparation operates on\nevent arrays and sampled histories; it never constructs or decomposes a global\nuser-item interaction matrix.\n\nDevelopment uses only `train_*` history and labels. The final artifacts are\nrefit on `train_* + val_*`; validation events are then history for the test\ncutoff. Supplied cutoff-specific preference state and eligibility masks are\nused at their corresponding stages.\n\n## Reproduce the final submission\n\nFrom any working directory, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThe script recreates `/app/solution/submission.parquet` using CPU PyTorch 2.8.\nIndividual `prepare`, `train`, and `rank` commands are also exposed by\n`train_ranker.py --help`.\n", "stderr": "", "aggregated_output": "usage: train_ranker.py [-h] {prepare,train,rank} ...\n\npositional arguments:\n {prepare,train,rank}\n\noptions:\n -h, --help show this help message and exit\n 54 /app/solution/README.md\n 900 /app/solution/train_ranker.py\n 29 /app/solution/run_final.sh\n 983 total\n34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887 /app/solution/submission.parquet\n488c74c2fb8d0614c3a6b3289c26737db25b0a0b0e2a393433501aa31af94e54 /app/solution/validation.parquet\n# Made for You two-tower ranker\n\n## Result\n\n`submission.parquet` contains 845,600 rows: exactly 100 distinct, test-eligible\nitems for each of 8,456 target users. The selected model scores **0.043827\ngraded NDCG@10** on 4,263 scored public-validation users. For reference, the\ncatalog-popularity fallback scored 0.010710.\n\n## Model\n\n`train_ranker.py` implements separate PyTorch `UserTower` and `ItemTower`\nnetworks. There is no user-ID embedding. The user tower mean-pools four\nhistory channels (recent positive plays, 180-day organic >=80% plays, active\nlikes, and active dislikes), concatenates six activity features, and applies a\ntwo-layer GELU MLP. The item tower applies a residual MLP to a trainable\ncatalog-ID embedding plus fixed catalog-popularity features. Latent outputs are\n64-dimensional and cosine-normalized; an extra coordinate represents item\nbias.\n\nThe selected ranker concatenates three complementary fits of this architecture\n(metric-weighted, all-window, and recent-window) and one standardized seven-day\norganic-like-momentum coordinate. Scaling is split between user and item\ncoordinates, so the complete 197-dimensional relevance score is **exactly one\ndot product**. Candidate vectors are independently precomputable offline. At\nrequest time only the history-based user vector is computed before MIPS; no\ncandidate cross-features or reranker are used.\n\n## Training objective and protocol\n\nTraining uses 16 rolling temporal snapshots. Each example predicts an organic\n>=80% listen (grade 1) or organic like (grade 3) in the next seven days after a\n30-minute gap. Targets seen before a snapshot cutoff are removed. A sampled\npairwise logistic/BPR loss uses a mixture of uniform and popularity-aware\nnegatives, with grade-3 and recent examples upweighted. Preparation operates on\nevent arrays and sampled histories; it never constructs or decomposes a global\nuser-item interaction matrix.\n\nDevelopment uses only `train_*` history and labels. The final artifacts are\nrefit on `train_* + val_*`; validation events are then history for the test\ncutoff. Supplied cutoff-specific preference state and eligibility masks are\nused at their corresponding stages.\n\n## Reproduce the final submission\n\nFrom any working directory, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThe script recreates `/app/solution/submission.parquet` using CPU PyTorch 2.8.\nIndividual `prepare`, `train`, and `rank` commands are also exposed by\n`train_ranker.py --help`.\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 249972691 }, "formatted_output": "usage: train_ranker.py [-h] {prepare,train,rank} ...\n\npositional arguments:\n {prepare,train,rank}\n\noptions:\n -h, --help show this help message and exit\n 54 /app/solution/README.md\n 900 /app/solution/train_ranker.py\n 29 /app/solution/run_final.sh\n 983 total\n34701e9f5d7628493d7a2e6e2c51f7d6ae54441e3955f0df0ee2918f64c73887 /app/solution/submission.parquet\n488c74c2fb8d0614c3a6b3289c26737db25b0a0b0e2a393433501aa31af94e54 /app/solution/validation.parquet\n# Made for You two-tower ranker\n\n## Result\n\n`submission.parquet` contains 845,600 rows: exactly 100 distinct, test-eligible\nitems for each of 8,456 target users. The selected model scores **0.043827\ngraded NDCG@10** on 4,263 scored public-validation users. For reference, the\ncatalog-popularity fallback scored 0.010710.\n\n## Model\n\n`train_ranker.py` implements separate PyTorch `UserTower` and `ItemTower`\nnetworks. There is no user-ID embedding. The user tower mean-pools four\nhistory channels (recent positive plays, 180-day organic >=80% plays, active\nlikes, and active dislikes), concatenates six activity features, and applies a\ntwo-layer GELU MLP. The item tower applies a residual MLP to a trainable\ncatalog-ID embedding plus fixed catalog-popularity features. Latent outputs are\n64-dimensional and cosine-normalized; an extra coordinate represents item\nbias.\n\nThe selected ranker concatenates three complementary fits of this architecture\n(metric-weighted, all-window, and recent-window) and one standardized seven-day\norganic-like-momentum coordinate. Scaling is split between user and item\ncoordinates, so the complete 197-dimensional relevance score is **exactly one\ndot product**. Candidate vectors are independently precomputable offline. At\nrequest time only the history-based user vector is computed before MIPS; no\ncandidate cross-features or reranker are used.\n\n## Training objective and protocol\n\nTraining uses 16 rolling temporal snapshots. Each example predicts an organic\n>=80% listen (grade 1) or organic like (grade 3) in the next seven days after a\n30-minute gap. Targets seen before a snapshot cutoff are removed. A sampled\npairwise logistic/BPR loss uses a mixture of uniform and popularity-aware\nnegatives, with grade-3 and recent examples upweighted. Preparation operates on\nevent arrays and sampled histories; it never constructs or decomposes a global\nuser-item interaction matrix.\n\nDevelopment uses only `train_*` history and labels. The final artifacts are\nrefit on `train_* + val_*`; validation events are then history for the test\ncutoff. Supplied cutoff-specific preference state and eligibility masks are\nused at their corresponding stages.\n\n## Reproduce the final submission\n\nFrom any working directory, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThe script recreates `/app/solution/submission.parquet` using CPU PyTorch 2.8.\nIndividual `prepare`, `train`, and `rank` commands are also exposed by\n`train_ranker.py --help`.\n" } } } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-24T23:51:19.690Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:51:19.690Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:51:21.266Z", "text": "All final contract checks pass: 845,600 rows, 8,456 users, ranks 1–100, no duplicate pairs, and zero ineligible items. I have a small amount of time left, so I’m doing one narrow coefficient check around the validated optimum; it requires no retraining and I’ll only replace the final ranking if the change is clearly supported." }, { "kind": "tool", "timestamp": "2026-09-24T23:51:36.842Z", "callId": "call_wsEJPBO5dhbdTpF1qQyo79Z5", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\\ndef load(path):\\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\\nwith torch.inference_mode():iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.timestamp.to_numpy()>=24786800-7*86400);z=np.log1p(np.bincount(ii[m],minlength=N_ITEMS)).astype(np.float32);mom=(z-z.mean())/z.std()\\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));\\ngrids=set()\\nfor a in [.6,.65,.7,.75,.8,.85,.9]:grids.add((a,.55,2.))\\nfor b in [.4,.45,.5,.55,.6,.65,.7]:grids.add((.75,b,2.))\\nfor q in [1.6,1.7,1.8,1.9,2.,2.1,2.2,2.3,2.4]:grids.add((.75,.55,q))\\nsums={g:0. for g in grids}\\nfor st in range(0,len(E.uids),128):\\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\\n with torch.inference_mode():uv=[mod.user_tower(*context_batch(ctx,idx)).numpy() for mod in models]\\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\\n for g in grids:\\n a,b,q=g;s=sc[0]+a*sc[1]+b*sc[2]+q*mom;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\\n for j,u in enumerate(E.uids[st:en]):\\n gold=T.get(int(u));\\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\\nfor g,v in sorted(sums.items(),key=lambda x:-x[1]):print(g,round(v/len(T),6))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:52:07.009Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n\nSESSION_ID=8748", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:51:36.906Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b76e579487d2b0d23bd7cc2aa4af", "usage": { "input_tokens": 86139, "cached_input_tokens": 84864, "cache_write_input_tokens": 0, "output_tokens": 1360, "reasoning_output_tokens": 406, "total_tokens": 87499 }, "turn_token_usage": { "input_tokens": 5834112, "cached_input_tokens": 5698048, "cache_write_input_tokens": 0, "output_tokens": 52452, "reasoning_output_tokens": 21514, "total_tokens": 5886564 }, "thread_token_usage": { "input_tokens": 5834112, "cached_input_tokens": 5698048, "cache_write_input_tokens": 0, "output_tokens": 52452, "reasoning_output_tokens": 21514, "total_tokens": 5886564 } } } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-24T23:52:08.627Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:52:08.627Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:52:09.302Z", "callId": "call_8KtVNYbqETzrcefqCnxPpHRu", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":8748,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:52:11.505Z", "content": "Script completed\nWall time 2.2 seconds\nOutput:\n\n(0.75, 0.55, 2.0) 0.043827\n(0.75, 0.55, 2.1) 0.043719\n(0.75, 0.5, 2.0) 0.043715\n(0.9, 0.55, 2.0) 0.04371\n(0.7, 0.55, 2.0) 0.043701\n(0.75, 0.55, 2.2) 0.043662\n(0.75, 0.4, 2.0) 0.043651\n(0.65, 0.55, 2.0) 0.043641\n(0.85, 0.55, 2.0) 0.043639\n(0.75, 0.6, 2.0) 0.043629\n(0.75, 0.65, 2.0) 0.043626\n(0.75, 0.55, 2.3) 0.043566\n(0.75, 0.7, 2.0) 0.043557\n(0.75, 0.45, 2.0) 0.043532\n(0.8, 0.55, 2.0) 0.04351\n(0.6, 0.55, 2.0) 0.043421\n(0.75, 0.55, 1.9) 0.043419\n(0.75, 0.55, 2.4) 0.043393\n(0.75, 0.55, 1.8) 0.043322\n(0.75, 0.55, 1.6) 0.043316\n(0.75, 0.55, 1.7) 0.043079\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:52:09.441Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b7a75e5c87d2ac3de281f9c08b77", "usage": { "input_tokens": 87527, "cached_input_tokens": 86016, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 87580 }, "turn_token_usage": { "input_tokens": 5921639, "cached_input_tokens": 5784064, "cache_write_input_tokens": 0, "output_tokens": 52505, "reasoning_output_tokens": 21520, "total_tokens": 5974144 }, "thread_token_usage": { "input_tokens": 5921639, "cached_input_tokens": 5784064, "cache_write_input_tokens": 0, "output_tokens": 52505, "reasoning_output_tokens": 21520, "total_tokens": 5974144 } } }, { "kind": "context", "timestamp": "2026-09-24T23:52:11.503Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fdc0de3f-e921-417f-b59b-600f5065b22c", "process_id": "8748", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode():iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.timestamp.to_numpy()>=24786800-7*86400);z=np.log1p(np.bincount(ii[m],minlength=N_ITEMS)).astype(np.float32);mom=(z-z.mean())/z.std()\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));\ngrids=set()\nfor a in [.6,.65,.7,.75,.8,.85,.9]:grids.add((a,.55,2.))\nfor b in [.4,.45,.5,.55,.6,.65,.7]:grids.add((.75,b,2.))\nfor q in [1.6,1.7,1.8,1.9,2.,2.1,2.2,2.3,2.4]:grids.add((.75,.55,q))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\n with torch.inference_mode():uv=[mod.user_tower(*context_batch(ctx,idx)).numpy() for mod in models]\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,q=g;s=sc[0]+a*sc[1]+b*sc[2]+q*mom;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nfor g,v in sorted(sums.items(),key=lambda x:-x[1]):print(g,round(v/len(T),6))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task:/app/solution /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd,torch\nfrom train_ranker import TwoTower,context_batch,DATA,N_ITEMS\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom evaluate_public import validation_truth\nP=np.load('/app/solution/prepared_validation.npz');ctx={k:torch.from_numpy(P['infer_'+k]) for k in ['recent','organic','liked','disliked','features']}\ndef load(path):\n s=torch.load(path,map_location='cpu',weights_only=False);m=TwoTower(s['item_features'],s['token_dim'],s['dim']);m.load_state_dict(s['state_dict']);m.eval();return m\nmodels=[load('/app/solution/model_validation_metric.pt'),load('/app/solution/model_validation.pt'),load('/app/solution/model_validation_recent.pt')]\nwith torch.inference_mode():iv=[m.item_tower(torch.arange(N_ITEMS)).numpy() for m in models]\nids=P['item_ids'];lut=np.full(ids.max()+1,-1,np.int32);lut[ids]=np.arange(N_ITEMS,dtype=np.int32);x=pd.read_parquet(DATA/'train_likes.parquet',columns=['timestamp','item_id','is_organic']);raw=x.item_id.to_numpy();ii=np.full(len(x),-1,np.int32);ok=raw=0)&(x.is_organic.to_numpy()==1)&(x.timestamp.to_numpy()>=24786800-7*86400);z=np.log1p(np.bincount(ii[m],minlength=N_ITEMS)).astype(np.float32);mom=(z-z.mean())/z.std()\nE=CandidateSet(DATA/'validation_eligible_candidates.npz');T=validation_truth(E,set(E.uids));\ngrids=set()\nfor a in [.6,.65,.7,.75,.8,.85,.9]:grids.add((a,.55,2.))\nfor b in [.4,.45,.5,.55,.6,.65,.7]:grids.add((.75,b,2.))\nfor q in [1.6,1.7,1.8,1.9,2.,2.1,2.2,2.3,2.4]:grids.add((.75,.55,q))\nsums={g:0. for g in grids}\nfor st in range(0,len(E.uids),128):\n en=min(st+128,len(E.uids));idx=torch.arange(st,en)\n with torch.inference_mode():uv=[mod.user_tower(*context_batch(ctx,idx)).numpy() for mod in models]\n sc=[u@i.T for u,i in zip(uv,iv)];mask=np.unpackbits(E.packed[st:en],axis=1,count=N_ITEMS,bitorder='little').astype(bool)\n for g in grids:\n a,b,q=g;s=sc[0]+a*sc[1]+b*sc[2]+q*mom;s[~mask]=-np.inf;top=np.argpartition(s,-10,axis=1)[:,-10:];ts=np.take_along_axis(s,top,axis=1);top=np.take_along_axis(top,np.argsort(-ts,axis=1),axis=1)\n for j,u in enumerate(E.uids[st:en]):\n gold=T.get(int(u));\n if gold:sums[g]+=ndcg_at_k(E.item_ids[top[j]].tolist(),gold,10)\nfor g,v in sorted(sums.items(),key=lambda x:-x[1]):print(g,round(v/len(T),6))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.75, 0.55, 2.0) 0.043827\n(0.75, 0.55, 2.1) 0.043719\n(0.75, 0.5, 2.0) 0.043715\n(0.9, 0.55, 2.0) 0.04371\n(0.7, 0.55, 2.0) 0.043701\n(0.75, 0.55, 2.2) 0.043662\n(0.75, 0.4, 2.0) 0.043651\n(0.65, 0.55, 2.0) 0.043641\n(0.85, 0.55, 2.0) 0.043639\n(0.75, 0.6, 2.0) 0.043629\n(0.75, 0.65, 2.0) 0.043626\n(0.75, 0.55, 2.3) 0.043566\n(0.75, 0.7, 2.0) 0.043557\n(0.75, 0.45, 2.0) 0.043532\n(0.8, 0.55, 2.0) 0.04351\n(0.6, 0.55, 2.0) 0.043421\n(0.75, 0.55, 1.9) 0.043419\n(0.75, 0.55, 2.4) 0.043393\n(0.75, 0.55, 1.8) 0.043322\n(0.75, 0.55, 1.6) 0.043316\n(0.75, 0.55, 1.7) 0.043079\n", "stderr": "", "aggregated_output": "(0.75, 0.55, 2.0) 0.043827\n(0.75, 0.55, 2.1) 0.043719\n(0.75, 0.5, 2.0) 0.043715\n(0.9, 0.55, 2.0) 0.04371\n(0.7, 0.55, 2.0) 0.043701\n(0.75, 0.55, 2.2) 0.043662\n(0.75, 0.4, 2.0) 0.043651\n(0.65, 0.55, 2.0) 0.043641\n(0.85, 0.55, 2.0) 0.043639\n(0.75, 0.6, 2.0) 0.043629\n(0.75, 0.65, 2.0) 0.043626\n(0.75, 0.55, 2.3) 0.043566\n(0.75, 0.7, 2.0) 0.043557\n(0.75, 0.45, 2.0) 0.043532\n(0.8, 0.55, 2.0) 0.04351\n(0.6, 0.55, 2.0) 0.043421\n(0.75, 0.55, 1.9) 0.043419\n(0.75, 0.55, 2.4) 0.043393\n(0.75, 0.55, 1.8) 0.043322\n(0.75, 0.55, 1.6) 0.043316\n(0.75, 0.55, 1.7) 0.043079\n", "exit_code": 0, "duration": { "secs": 34, "nanos": 496208505 }, "formatted_output": "(0.75, 0.55, 2.0) 0.043827\n(0.75, 0.55, 2.1) 0.043719\n(0.75, 0.5, 2.0) 0.043715\n(0.9, 0.55, 2.0) 0.04371\n(0.7, 0.55, 2.0) 0.043701\n(0.75, 0.55, 2.2) 0.043662\n(0.75, 0.4, 2.0) 0.043651\n(0.65, 0.55, 2.0) 0.043641\n(0.85, 0.55, 2.0) 0.043639\n(0.75, 0.6, 2.0) 0.043629\n(0.75, 0.65, 2.0) 0.043626\n(0.75, 0.55, 2.3) 0.043566\n(0.75, 0.7, 2.0) 0.043557\n(0.75, 0.45, 2.0) 0.043532\n(0.8, 0.55, 2.0) 0.04351\n(0.6, 0.55, 2.0) 0.043421\n(0.75, 0.55, 1.9) 0.043419\n(0.75, 0.55, 2.4) 0.043393\n(0.75, 0.55, 1.8) 0.043322\n(0.75, 0.55, 1.6) 0.043316\n(0.75, 0.55, 1.7) 0.043079\n" } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-24T23:52:21.126Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:52:21.126Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:52:24.661Z", "text": "Implemented and validated the CPU PyTorch two-tower ranker.\n\n- Final submission: [submission.parquet](/app/solution/submission.parquet)\n- Training/inference code: [train_ranker.py](/app/solution/train_ranker.py)\n- Reproduction script: [run_final.sh](/app/solution/run_final.sh)\n- Documentation: [README.md](/app/solution/README.md)\n\nResults:\n\n- Public validation NDCG@10: **0.043827**\n- 845,600 rows covering all 8,456 users\n- Exactly 100 unique items and ranks 1–100 per user\n- Zero test-ineligible items\n- Final model refitted using `train_* + val_*`\n- Exact dot-product retrieval with independently precomputable item vectors and history-only user towers\n\nReproduce with:\n\n```sh\nsh /app/solution/run_final.sh\n```" }, { "kind": "context", "timestamp": "2026-09-24T23:52:24.734Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "session_id": "01a0d5a6-394b-7651-808b-02bcfc8d9573", "root_turn_id": "01a0d5a6-395a-7c10-9817-a57eb8f8ab86", "response_id": "resp_0c83e1ba2a9d57bc016ab5b7abdd1887d2af846e1475fe1313", "usage": { "input_tokens": 88044, "cached_input_tokens": 87296, "cache_write_input_tokens": 0, "output_tokens": 644, "reasoning_output_tokens": 452, "total_tokens": 88688 }, "turn_token_usage": { "input_tokens": 6009683, "cached_input_tokens": 5871360, "cache_write_input_tokens": 0, "output_tokens": 53149, "reasoning_output_tokens": 21972, "total_tokens": 6062832 }, "thread_token_usage": { "input_tokens": 6009683, "cached_input_tokens": 5871360, "cache_write_input_tokens": 0, "output_tokens": 53149, "reasoning_output_tokens": 21972, "total_tokens": 6062832 } } } ] } ] }