{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "sol-max-0-153-4-retriever-r01-20260924T221413Z", "taskChecksum": "913bc6ffd7de5923ca86e4f567d9b29c25d7be3387bae3b1a17a181b0d314543", "source": { "format": "codex-rollout", "sha256": "86301d06eb1b8ebf38c392af5a4e8bb49bc125637faea6f175a243e9945fb7fe", "records": 1019, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 163, "item_completed:AgentMessage": 18, "token_count": 144, "task_complete": 1 } }, "sessionId": "01a0d5a6-369b-76d1-953a-8c850355d141", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-24T23:00:35.134Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:35.134Z", "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": "2026-09-24T23:00:36.666Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:36.666Z", "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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-24T23:00:36.666Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-24T23:00:36.666Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$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": "2026-09-24T23:00:36.666Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:36.666Z", "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": "2026-09-24T23:00:36.666Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:36.666Z", "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": "2026-09-24T23:00:36.666Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:36.666Z", "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": "2026-09-24T23:00:36.666Z", "text": "\n /app\n sh\n 2026-09-24\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-24T23:00:36.668Z", "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": "2026-09-24T23:00:36.669Z", "data": { "type": "turn_context", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "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": "2026-09-24T23:00:36.691Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-24T23:00:36.691Z", "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": "2026-09-24T23:00:45.741Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:00:45.741Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:00:47.062Z", "text": "I’ll inspect the dataset shapes, schemas, eligibility structure, and evaluator first, then build a scalable event-sampled two-tower baseline. I’ll keep a valid submission checkpointed early, tune only against the public window, and refit the chosen setup with validation events as history for the final artifact." }, { "kind": "tool", "timestamp": "2026-09-24T23:00:49.021Z", "callId": "call_Cn0kFKkhRtzUcfGq7OklbYW9", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"pwd && find /task/data -maxdepth 1 -type f -printf '%f\\\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:00:49.099Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:00:49.088Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c6522471-cd10-47b2-86be-99ded07904ec", "process_id": "16829", "command": [ "/bin/sh", "-lc", "pwd && find /task/data -maxdepth 1 -type f -printf '%f\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && find /task/data -maxdepth 1 -type f -printf '%f\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,260p' /task/evaluate_public.py && sed -n '1,220p' /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3850 }, "formatted_output": "/app\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } }, { "kind": "context", "timestamp": "2026-09-24T23:00:49.098Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ab94e34087d29e69f5d395c46793", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 307, "reasoning_output_tokens": 121, "total_tokens": 14883 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 307, "reasoning_output_tokens": 121, "total_tokens": 14883 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 307, "reasoning_output_tokens": 121, "total_tokens": 14883 } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-24T23:00:58.309Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:00:58.309Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:04.231Z", "callId": "call_C6C1g2vJGlCpVyUTE1COYKbd", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n d=pd.read_parquet(p)\\n print('\\\\n',p.name,d.shape,d.dtypes.to_dict())\\n print(d.head(3).to_dict('records'))\\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\\n if c in d:\\n s=d[c]\\n print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/name)\\n print('\\\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n counts=masks.sum(1)\\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'same users?',np.array_equal(z['uids'],pd.read_parquet(D/'target_users.parquet').uid))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:04.376Z", "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": "2026-09-24T23:01:04.270Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5aba17c3c87d2aeefa64a3812f8fd", "usage": { "input_tokens": 18817, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 426, "reasoning_output_tokens": 88, "total_tokens": 19243 }, "turn_token_usage": { "input_tokens": 33393, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 733, "reasoning_output_tokens": 209, "total_tokens": 34126 }, "thread_token_usage": { "input_tokens": 33393, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 733, "reasoning_output_tokens": 209, "total_tokens": 34126 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:04.371Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1f4c135e-58eb-4835-87ed-ea11e0c49a31", "process_id": "54897", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n if c in d:\n s=d[c]\n print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'same users?',np.array_equal(z['uids'],pd.read_parquet(D/'target_users.parquet').uid))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n if c in d:\n s=d[c]\n print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'same users?',np.array_equal(z['uids'],pd.read_parquet(D/'target_users.parquet').uid))\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": 5270 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-24T23:01:09.618Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:09.618Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:01:10.508Z", "text": "The default shell’s `python` is not the benchmark environment despite the supplied inventory. I’m locating the task’s pinned interpreter and will use that read-only environment for all training and evaluation." }, { "kind": "tool", "timestamp": "2026-09-24T23:01:12.765Z", "callId": "call_vV8BnJ0mJCcHkhWz2m0eioPC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"command -v python; command -v python3; python --version; python3 --version; find /task -maxdepth 3 -type f \\\\( -name python -o -name 'python3*' \\\\) -print 2>/dev/null | head -30; ls -la /task | sed -n '1,160p'; env | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:12.848Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPython 3.12.13\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=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\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": "2026-09-24T23:01:12.816Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-785f357a-2812-4bd1-a402-d4f80ea54264", "process_id": "63438", "command": [ "/bin/sh", "-lc", "command -v python; command -v python3; python --version; python3 --version; find /task -maxdepth 3 -type f \\( -name python -o -name 'python3*' \\) -print 2>/dev/null | head -30; ls -la /task | sed -n '1,160p'; env | sort | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v python; command -v python3; python --version; python3 --version; find /task -maxdepth 3 -type f \\( -name python -o -name 'python3*' \\) -print 2>/dev/null | head -30; ls -la /task | sed -n '1,160p'; env | sort | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPython 3.12.13\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=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\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\nPython 3.12.13\nPython 3.12.13\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=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\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": 4660 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPython 3.12.13\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=01a0d5a6-369b-76d1-953a-8c850355d141\nCODEX_THREAD_ID=01a0d5a6-369b-76d1-953a-8c850355d141\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e2af0984d070\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" } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:12.846Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abb099cc87d29bf53ffcbdf9f9e1", "usage": { "input_tokens": 19299, "cached_input_tokens": 18688, "cache_write_input_tokens": 0, "output_tokens": 206, "reasoning_output_tokens": 27, "total_tokens": 19505 }, "turn_token_usage": { "input_tokens": 52692, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 939, "reasoning_output_tokens": 236, "total_tokens": 53631 }, "thread_token_usage": { "input_tokens": 52692, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 939, "reasoning_output_tokens": 236, "total_tokens": 53631 } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-24T23:01:18.900Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:18.900Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:20.655Z", "callId": "call_N4aIAM2PKr0YKUSQbLpzshZm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python -m pip list --format=columns | sed -n '1,200p'; find / -type f -path '*/site-packages/pandas/__init__.py' -o -type f -path '*/bin/python' 2>/dev/null | head -100\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:21.887Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\nPackage Version\n------- -------\npip 25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:20.704Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abb9235487d29a4dbfd9f2c54526", "usage": { "input_tokens": 20175, "cached_input_tokens": 19072, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 15, "total_tokens": 20296 }, "turn_token_usage": { "input_tokens": 72867, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 1060, "reasoning_output_tokens": 251, "total_tokens": 73927 }, "thread_token_usage": { "input_tokens": 72867, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 1060, "reasoning_output_tokens": 251, "total_tokens": 73927 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:21.883Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-93b0cf2e-c982-4933-aacf-2dbfab43ef09", "process_id": "83793", "command": [ "/bin/sh", "-lc", "python -m pip list --format=columns | sed -n '1,200p'; find / -type f -path '*/site-packages/pandas/__init__.py' -o -type f -path '*/bin/python' 2>/dev/null | head -100" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -m pip list --format=columns | sed -n '1,200p'; find / -type f -path '*/site-packages/pandas/__init__.py' -o -type f -path '*/bin/python' 2>/dev/null | head -100" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 61696484 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\n" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-24T23:01:27.292Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:27.292Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:29.401Z", "callId": "call_3deZqHmkmWFpSnmVo1TUa22A", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python - <<'PY'\\nimport pandas, torch, pyarrow, sklearn\\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,sklearn.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:35.048Z", "content": "Script completed\nWall time 5.6 seconds\nOutput:\n\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 .\ndr-xr-xr-x 1 root root 4096 Sep 24 22:56 ..\n-r--r--r-- 1 root root 9033 Sep 24 22:56 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 24 22:56 activate\n-r--r--r-- 1 root root 920 Sep 24 22:56 activate.csh\n-r--r--r-- 1 root root 2195 Sep 24 22:56 activate.fish\n-r-xr-xr-x 1 root root 302 Sep 24 22:56 chardetect\n-r-xr-xr-x 1 root root 304 Sep 24 22:56 dotenv\n-r-xr-xr-x 1 root root 308 Sep 24 22:56 f2py\n-r-xr-xr-x 1 root root 299 Sep 24 22:56 idna\n-r-xr-xr-x 1 root root 297 Sep 24 22:56 isympy\n-r-xr-xr-x 1 root root 305 Sep 24 22:56 jsonschema\n-r-xr-xr-x 1 root root 311 Sep 24 22:56 jupyter\n-r-xr-xr-x 1 root root 311 Sep 24 22:56 jupyter-migrate\n-r-xr-xr-x 1 root root 316 Sep 24 22:56 jupyter-troubleshoot\n-r-xr-xr-x 1 root root 344 Sep 24 22:56 jupyter-trust\n-r-xr-xr-x 1 root root 311 Sep 24 22:56 jupytext\n-r-xr-xr-x 1 root root 315 Sep 24 22:56 jupytext-config\n-r-xr-xr-x 1 root root 301 Sep 24 22:56 kaggle\n-r-xr-xr-x 1 root root 312 Sep 24 22:56 markdown-it\n-r-xr-xr-x 1 root root 325 Sep 24 22:56 normalizer\n-r-xr-xr-x 1 root root 308 Sep 24 22:56 numpy-config\n-r-xr-xr-x 1 root root 313 Sep 24 22:56 pip\n-r-xr-xr-x 1 root root 313 Sep 24 22:56 pip3\n-r-xr-xr-x 1 root root 313 Sep 24 22:56 pip3.12\nlrwxrwxrwx 1 root root 21 Sep 24 22:56 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 24 22:56 python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 24 22:56 python3.12 -> python\n-r-xr-xr-x 1 root root 307 Sep 24 22:56 slugify\n-r-xr-xr-x 1 root root 321 Sep 24 22:56 torchfrtrace\n-r-xr-xr-x 1 root root 312 Sep 24 22:56 torchrun\n-r-xr-xr-x 1 root root 299 Sep 24 22:56 tqdm\nPython 3.12.13\n2.3.3 2.8.0+cpu 18.1.0 1.7.2\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:29.484Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abc2128c87d285b3b8bbaa322e70", "usage": { "input_tokens": 20354, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 139, "reasoning_output_tokens": 14, "total_tokens": 20493 }, "turn_token_usage": { "input_tokens": 93221, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 1199, "reasoning_output_tokens": 265, "total_tokens": 94420 }, "thread_token_usage": { "input_tokens": 93221, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 1199, "reasoning_output_tokens": 265, "total_tokens": 94420 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:35.043Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cb617059-3f65-42a0-8170-de63c297409e", "process_id": "85272", "command": [ "/bin/sh", "-lc", "ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python - <<'PY'\nimport pandas, torch, pyarrow, sklearn\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,sklearn.__version__)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python - <<'PY'\nimport pandas, torch, pyarrow, sklearn\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,sklearn.__version__)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 2.8.0+cpu 18.1.0 1.7.2\n", "stderr": "", "aggregated_output": "2.3.3 2.8.0+cpu 18.1.0 1.7.2\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 477455139 }, "formatted_output": "2.3.3 2.8.0+cpu 18.1.0 1.7.2\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-24T23:01:40.432Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:01:40.432Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:01:46.549Z", "callId": "call_YbPo0ZeqbLG8Bhz2YCCQ5F8Y", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n d=pd.read_parquet(p)\\n print('\\\\n',p.name,d.shape,d.dtypes.astype(str).to_dict())\\n print(d.head(3).to_dict('records'))\\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\\n if c in d:\\n s=d[c]\\n print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/name)\\n print('\\\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n counts=masks.sum(1)\\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'targets match set?',set(z['uids'])==set(pd.read_parquet(D/'target_users.parquet').uid))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:01:52.070Z", "content": "Script completed\nWall time 5.5 seconds\nOutput:\n\n\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\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}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] targets match set? True\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:01:46.611Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abcf3ff487d2b352501be9c4fc09", "usage": { "input_tokens": 21312, "cached_input_tokens": 20224, "cache_write_input_tokens": 0, "output_tokens": 376, "reasoning_output_tokens": 27, "total_tokens": 21688 }, "turn_token_usage": { "input_tokens": 114533, "cached_input_tokens": 103424, "cache_write_input_tokens": 0, "output_tokens": 1575, "reasoning_output_tokens": 292, "total_tokens": 116108 }, "thread_token_usage": { "input_tokens": 114533, "cached_input_tokens": 103424, "cache_write_input_tokens": 0, "output_tokens": 1575, "reasoning_output_tokens": 292, "total_tokens": 116108 } } }, { "kind": "context", "timestamp": "2026-09-24T23:01:52.065Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eaaa3677-52c2-4c98-b5e7-0f1ef980581b", "process_id": "59650", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.astype(str).to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n if c in d:\n s=d[c]\n print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'targets match set?',set(z['uids'])==set(pd.read_parquet(D/'target_users.parquet').uid))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n d=pd.read_parquet(p)\n print('\\n',p.name,d.shape,d.dtypes.astype(str).to_dict())\n print(d.head(3).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct','state']:\n if c in d:\n s=d[c]\n print(c,'nunique',s.nunique(),'min',s.min() if len(s) else None,'max',s.max() if len(s) else None)\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n counts=masks.sum(1)\n print('eligible counts',np.quantile(counts,[0,.01,.1,.5,.9,.99,1]),'targets match set?',set(z['uids'])==set(pd.read_parquet(D/'target_users.parquet').uid))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\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}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] targets match set? True\n", "stderr": "", "aggregated_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\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}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] targets match set? True\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 349052852 }, "formatted_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': 'uint32', 'positive_listens': 'int64', 'organic_positive_listens': 'uint64', 'popularity_rank': 'int32'}\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}, {'item_id': 5635052, 'positive_listens': 24691, 'organic_positive_listens': 17194, 'popularity_rank': 3}]\nitem_id nunique 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': 'uint32', 'recent_positive_listens': 'float64', 'history_positive_listens': 'int64'}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}, {'uid': 300, 'recent_positive_listens': 168.0, 'history_positive_listens': 359}]\nuid nunique 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7092 min 100 max 1000000\nitem_id nunique 28863 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n train_dislikes.parquet (101075, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727, 'is_organic': 1}]\nuid nunique 5732 min 100 max 1000000\nitem_id nunique 50876 min 105 max 9390541\ntimestamp nunique 67779 min 75 max 24786580\nis_organic nunique 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243, 'is_organic': 1}]\nuid nunique 8144 min 100 max 1000000\nitem_id nunique 172303 min 26 max 9390522\ntimestamp nunique 562774 min 80 max 24786790\nis_organic nunique 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 185}]\nuid nunique 9238 min 100 max 1000000\nitem_id nunique 845395 min 22 max 9390623\ntimestamp nunique 4806896 min 0 max 24786795\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319, 'is_organic': 1}]\nuid nunique 2769 min 100 max 999900\nitem_id nunique 14725 min 178 max 9390240\ntimestamp nunique 6956 min 15950 max 24785395\nis_organic nunique 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676, 'is_organic': 0}]\nuid nunique 6197 min 100 max 1000000\nitem_id nunique 113072 min 26 max 9390601\ntimestamp nunique 87635 min 250 max 24786570\nis_organic nunique 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}, {'uid': 4900, 'timestamp': 25081160, 'item_id': 3237642, 'is_organic': 0}]\nuid nunique 955 min 1600 max 998800\nitem_id nunique 3135 min 651 max 9388630\ntimestamp nunique 2386 min 24789920 max 25393280\nis_organic nunique 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046970, 'item_id': 2674136, 'is_organic': 1}]\nuid nunique 4114 min 100 max 999900\nitem_id nunique 22902 min 209 max 9390461\ntimestamp nunique 19790 min 24788815 max 25393390\nis_organic nunique 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8', 'played_ratio_pct': 'uint16', 'track_length_seconds': 'uint32'}\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}, {'uid': 100, 'timestamp': 24943765, 'item_id': 6448777, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 160}]\nuid nunique 8196 min 100 max 1000000\nitem_id nunique 191747 min 50 max 9390541\ntimestamp nunique 119851 min 24788600 max 25393395\nis_organic nunique 2 min 0 max 1\nplayed_ratio_pct nunique 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 3628099, 'is_organic': 1}]\nuid nunique 205 min 900 max 998800\nitem_id nunique 579 min 15149 max 9385279\ntimestamp nunique 242 min 24810715 max 25393035\nis_organic nunique 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': 'uint32', 'timestamp': 'uint32', 'item_id': 'uint32', 'is_organic': 'uint8'}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 4400472, 'is_organic': 1}]\nuid nunique 1313 min 1100 max 998800\nitem_id nunique 8080 min 1928 max 9390163\ntimestamp nunique 2941 min 24789005 max 25393385\nis_organic nunique 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': 'uint32', 'item_id': 'uint32', 'state': 'object'}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}, {'uid': 100, 'item_id': 982903, 'state': 'liked'}]\nuid nunique 7038 min 100 max 1000000\nitem_id nunique 28814 min 206 max 9390294\nstate nunique 2 min disliked max liked\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] targets match set? True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] targets match set? True\n" } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-24T23:02:04.703Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:02:04.703Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:02:05.886Z", "callId": "call_xxItoX1HlzKbckYV90gjlQ45", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; nproc; df -h /app; du -h /task/data/* | sort -h | tail -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:02:05.939Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/bin/sh: 1: free: not found\n8\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:05.933Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d36bcfa4-3020-4bca-8072-8f50dfa38a7a", "process_id": "75456", "command": [ "/bin/sh", "-lc", "free -h; nproc; df -h /app; du -h /task/data/* | sort -h | tail -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; nproc; df -h /app; du -h /task/data/* | sort -h | tail -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: free: not found\n8\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n", "stderr": "", "aggregated_output": "/bin/sh: 1: free: not found\n8\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3780 }, "formatted_output": "/bin/sh: 1: free: not found\n8\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-24T23:02:05.937Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abe0594887d2a35f785b7a0fef4b", "usage": { "input_tokens": 24619, "cached_input_tokens": 21120, "cache_write_input_tokens": 0, "output_tokens": 404, "reasoning_output_tokens": 329, "total_tokens": 25023 }, "turn_token_usage": { "input_tokens": 139152, "cached_input_tokens": 124544, "cache_write_input_tokens": 0, "output_tokens": 1979, "reasoning_output_tokens": 621, "total_tokens": 141131 }, "thread_token_usage": { "input_tokens": 139152, "cached_input_tokens": 124544, "cache_write_input_tokens": 0, "output_tokens": 1979, "reasoning_output_tokens": 621, "total_tokens": 141131 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-24T23:02:11.902Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:02:11.902Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:02:13.152Z", "callId": "call_5VCglurVXAovYcBhwFMqgtTX", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:02:13.206Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nMemTotal: 15988972 kB\nMemAvailable: 14990484 kB\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:13.185Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abee3c3087d28c945541d0c45f5d", "usage": { "input_tokens": 25296, "cached_input_tokens": 24448, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 9, "total_tokens": 25368 }, "turn_token_usage": { "input_tokens": 164448, "cached_input_tokens": 148992, "cache_write_input_tokens": 0, "output_tokens": 2051, "reasoning_output_tokens": 630, "total_tokens": 166499 }, "thread_token_usage": { "input_tokens": 164448, "cached_input_tokens": 148992, "cache_write_input_tokens": 0, "output_tokens": 2051, "reasoning_output_tokens": 630, "total_tokens": 166499 } } }, { "kind": "context", "timestamp": "2026-09-24T23:02:13.198Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bd307d74-b71d-4047-a00a-7a146b388a15", "process_id": "51595", "command": [ "/bin/sh", "-lc", "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo", "name": "meminfo", "path": "/proc/meminfo" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "MemTotal: 15988972 kB\nMemAvailable: 14990484 kB\n", "stderr": "", "aggregated_output": "MemTotal: 15988972 kB\nMemAvailable: 14990484 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3331 }, "formatted_output": "MemTotal: 15988972 kB\nMemAvailable: 14990484 kB\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-24T23:02:26.451Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:02:26.451Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:02:28.010Z", "text": "The data is tractable in memory (the large log is a compact 305 MB parquet), and eligibility removes only a modest user-specific slice of the 30k catalog. I’m establishing strong measurable baselines—global popularity, repeat affinity, recent history, and explicit-state variants—before spending CPU on neural training; this will reveal which signals the towers need to encode." }, { "kind": "tool", "timestamp": "2026-09-24T23:02:42.747Z", "callId": "call_grQP3qvXwnLqlIUYwFzF5rLK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, time\\nD=Path('/task/data'); O=Path('/app/solution')\\nt=time.time()\\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\ncatitems=cat.item_id.to_numpy(np.int64)\\nz=np.load(D/'validation_eligible_candidates.npz')\\nuidrow={int(u):i for i,u in enumerate(z['uids'])}\\n# catalog is already popularity order. Create globally popular eligible ranking.\\nrows=[]\\nfor u in target:\\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\\n top=catitems[mask][:100]\\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\\n# truth descriptive stats\\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\ntset=set(target.tolist()); cset=set(catitems.tolist())\\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)&vl.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(tset)&vk.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\n# check eligible\\npacked=z['packed_eligible']; col={int(x):i for i,x in enumerate(z['item_ids'])}\\nok=[]\\nfor u,i in rel[['uid','item_id']].itertuples(index=False,name=None):\\n rr=uidrow[int(u)]; cc=col[int(i)]; ok.append((int(packed[rr,cc//8])>>(cc%8))&1)\\nrel=rel[np.array(ok,dtype=bool)]\\nrankmap=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nrr=np.array([rankmap[int(x)] for x in rel.item_id])\\nprint('load/build seconds',time.time()-t)\\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\\nprint('relevant popularity rank quantiles',dict(zip([0,.1,.25,.5,.75,.9,.99,1],np.quantile(rr,[0,.1,.25,.5,.75,.9,.99,1]))))\\nprint('fraction rank<=100/1k/5k/10k',[(k,float((rr<=k).mean())) for k in [10,100,500,1000,5000,10000]])\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:03:12.917Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:02:42.795Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5abf587cc87d2bcf9f5efa1d24a4b", "usage": { "input_tokens": 25413, "cached_input_tokens": 25088, "cache_write_input_tokens": 0, "output_tokens": 1218, "reasoning_output_tokens": 285, "total_tokens": 26631 }, "turn_token_usage": { "input_tokens": 189861, "cached_input_tokens": 174080, "cache_write_input_tokens": 0, "output_tokens": 3269, "reasoning_output_tokens": 915, "total_tokens": 193130 }, "thread_token_usage": { "input_tokens": 189861, "cached_input_tokens": 174080, "cache_write_input_tokens": 0, "output_tokens": 3269, "reasoning_output_tokens": 915, "total_tokens": 193130 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-24T23:03:19.930Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:03:19.930Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:03:21.363Z", "callId": "call_l1tu7zhJtX4cNSNl3SvAq8HY", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ps -eo pid,etime,pcpu,pmem,args | grep -E 'task-venv|evaluate_public' | grep -v grep || true; ls -lh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:03:21.436Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/bin/sh: 1: ps: not found\ntotal 0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:03:21.405Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4e819db4-44c8-40d2-a10a-c5409d512353", "process_id": "23832", "command": [ "/bin/sh", "-lc", "ps -eo pid,etime,pcpu,pmem,args | grep -E 'task-venv|evaluate_public' | grep -v grep || true; ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -eo pid,etime,pcpu,pmem,args | grep -E 'task-venv|evaluate_public' | grep -v grep || true; ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\ntotal 0\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\ntotal 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4371 }, "formatted_output": "/bin/sh: 1: ps: not found\ntotal 0\n" } } }, { "kind": "context", "timestamp": "2026-09-24T23:03:21.434Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac31206c87d283be4aff31569660", "usage": { "input_tokens": 26654, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 47, "total_tokens": 26789 }, "turn_token_usage": { "input_tokens": 216515, "cached_input_tokens": 199296, "cache_write_input_tokens": 0, "output_tokens": 3404, "reasoning_output_tokens": 962, "total_tokens": 219919 }, "thread_token_usage": { "input_tokens": 216515, "cached_input_tokens": 199296, "cache_write_input_tokens": 0, "output_tokens": 3404, "reasoning_output_tokens": 962, "total_tokens": 219919 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-24T23:03:29.573Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:03:29.573Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:03:35.698Z", "callId": "call_hWWMTIdQM449odKDFh7Wq7k6", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, time\\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.to_numpy(np.int64)\\nz=np.load(D/'validation_eligible_candidates.npz'); uidrow={int(u):i for i,u in enumerate(z['uids'])}\\nrows=np.empty((len(target)*100,3),dtype=np.int64); p=0\\nfor u in target:\\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\\n top=catitems[mask][:100]; n=len(top)\\n rows[p:p+n,0]=u; rows[p:p+n,1]=top; rows[p:p+n,2]=np.arange(1,n+1); p+=n\\npd.DataFrame(rows[:p],columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\\nprint('built',p,'seconds',time.time()-t,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:03:36.866Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=83142", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:03:35.741Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac39c7ec87d28d21bc1be2df4dcf", "usage": { "input_tokens": 26827, "cached_input_tokens": 26496, "cache_write_input_tokens": 0, "output_tokens": 490, "reasoning_output_tokens": 142, "total_tokens": 27317 }, "turn_token_usage": { "input_tokens": 243342, "cached_input_tokens": 225792, "cache_write_input_tokens": 0, "output_tokens": 3894, "reasoning_output_tokens": 1104, "total_tokens": 247236 }, "thread_token_usage": { "input_tokens": 243342, "cached_input_tokens": 225792, "cache_write_input_tokens": 0, "output_tokens": 3894, "reasoning_output_tokens": 1104, "total_tokens": 247236 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-24T23:03:43.945Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:03:43.945Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:03:43.946Z", "callId": "call_cMl4oeNN86KFmCWf5TdmZ2Cl", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":83142,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:04:13.955Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"6dfbc0\",\"wall_time_seconds\":30.001186034,\"session_id\":83142,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:03:43.971Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac490c7c87d282af1ad4eca98f6a", "usage": { "input_tokens": 27345, "cached_input_tokens": 26624, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 27409 }, "turn_token_usage": { "input_tokens": 270687, "cached_input_tokens": 252416, "cache_write_input_tokens": 0, "output_tokens": 3958, "reasoning_output_tokens": 1121, "total_tokens": 274645 }, "thread_token_usage": { "input_tokens": 270687, "cached_input_tokens": 252416, "cache_write_input_tokens": 0, "output_tokens": 3958, "reasoning_output_tokens": 1121, "total_tokens": 274645 } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-24T23:04:18.357Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:18.357Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:21.004Z", "callId": "call_WYyDZhHgWaCTAlLe6kqsUpzu", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:04:21.077Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:21.043Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac6e3ef087d28b5dccfefa5c5ab3", "usage": { "input_tokens": 27463, "cached_input_tokens": 27136, "cache_write_input_tokens": 0, "output_tokens": 226, "reasoning_output_tokens": 101, "total_tokens": 27689 }, "turn_token_usage": { "input_tokens": 298150, "cached_input_tokens": 279552, "cache_write_input_tokens": 0, "output_tokens": 4184, "reasoning_output_tokens": 1222, "total_tokens": 302334 }, "thread_token_usage": { "input_tokens": 298150, "cached_input_tokens": 279552, "cache_write_input_tokens": 0, "output_tokens": 4184, "reasoning_output_tokens": 1222, "total_tokens": 302334 } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:21.072Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b206bb06-7873-40d5-af64-5b572c8b8e39", "process_id": "25596", "command": [ "/bin/sh", "-lc", "ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n", "stderr": "", "aggregated_output": "/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8740 }, "formatted_output": "/bin/sh: 1: cannot open /proc/9548/cmdline: No such file\n/bin/sh: 1: cannot open /proc/9549/cmdline: No such file\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=8778\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9004\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\npython /task/evaluate_public.py solution/validation.parquet\nPID=9012\n/bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet \nPID=9492\n/opt/task-venv/bin/python - \nPID=9494\n/bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nPID=9523\n/opt/task-venv/bin/python -u - \nPID=9524\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9547\n/bin/sh -c ls /proc | sed -n '/^[0-9][0-9]*$/p' | while read p; do tr '\\0' ' ' < /proc/$p/cmdline 2>/dev/null | grep -E '/opt/task-venv|evaluate_public' && echo PID=$p; done | head -40; ls -lh /app/solution \nPID=9550\ntotal 0\n" } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-24T23:04:24.733Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:24.733Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:25.957Z", "callId": "call_3XwjOcactIXILv4sJGgJULKV", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for p in 9492 9494 9523 9524; do echo PID=$p; awk '/State|VmRSS|Threads/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:04:26.010Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nPID=9492\nState:\tS (sleeping)\nVmRSS:\t 1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t 175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t 1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t 162300 kB\nThreads:\t15\n0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:25.998Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac756c2487d2a268c96add60f730", "usage": { "input_tokens": 28150, "cached_input_tokens": 27264, "cache_write_input_tokens": 0, "output_tokens": 196, "reasoning_output_tokens": 88, "total_tokens": 28346 }, "turn_token_usage": { "input_tokens": 326300, "cached_input_tokens": 306816, "cache_write_input_tokens": 0, "output_tokens": 4380, "reasoning_output_tokens": 1310, "total_tokens": 330680 }, "thread_token_usage": { "input_tokens": 326300, "cached_input_tokens": 306816, "cache_write_input_tokens": 0, "output_tokens": 4380, "reasoning_output_tokens": 1310, "total_tokens": 330680 } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:26.005Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4c13fcc9-a5d6-4bea-ae19-7621ac0fa656", "process_id": "78106", "command": [ "/bin/sh", "-lc", "for p in 9492 9494 9523 9524; do echo PID=$p; awk '/State|VmRSS|Threads/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in 9492 9494 9523 9524; do echo PID=$p; awk '/State|VmRSS|Threads/ {print}' /proc/$p/status 2>/dev/null; cat /proc/$p/wchan 2>/dev/null; echo; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PID=9492\nState:\tS (sleeping)\nVmRSS:\t 1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t 175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t 1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t 162300 kB\nThreads:\t15\n0\n", "stderr": "", "aggregated_output": "PID=9492\nState:\tS (sleeping)\nVmRSS:\t 1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t 175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t 1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t 162300 kB\nThreads:\t15\n0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4361 }, "formatted_output": "PID=9492\nState:\tS (sleeping)\nVmRSS:\t 1712 kB\nThreads:\t1\ndo_wait\nPID=9494\nState:\tR (running)\nVmRSS:\t 175244 kB\nThreads:\t15\n0\nPID=9523\nState:\tS (sleeping)\nVmRSS:\t 1732 kB\nThreads:\t1\ndo_wait\nPID=9524\nState:\tR (running)\nVmRSS:\t 162300 kB\nThreads:\t15\n0\n" } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-24T23:04:29.961Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:29.961Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:31.023Z", "callId": "call_ZXtm1gVSZMl9M5NVzkkNwD0s", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for p in 9494 9524; do awk '{print \\\"pid\\\",$1,\\\"utime\\\",$14,\\\"stime\\\",$15}' /proc/$p/stat; done; ls -lh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:04:31.070Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\npid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:31.064Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-abd54bf2-17c1-42eb-b21e-e5eb2f49e02c", "process_id": "39870", "command": [ "/bin/sh", "-lc", "for p in 9494 9524; do awk '{print \"pid\",$1,\"utime\",$14,\"stime\",$15}' /proc/$p/stat; done; ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for p in 9494 9524; do awk '{print \"pid\",$1,\"utime\",$14,\"stime\",$15}' /proc/$p/stat; done; ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "pid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n", "stderr": "", "aggregated_output": "pid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4100 }, "formatted_output": "pid 9494 utime 10769 stime 127\npid 9524 utime 5530 stime 63\ntotal 0\n" } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:31.068Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac7a3b9087d2961187c2e8ce1802", "usage": { "input_tokens": 28485, "cached_input_tokens": 28032, "cache_write_input_tokens": 0, "output_tokens": 151, "reasoning_output_tokens": 58, "total_tokens": 28636 }, "turn_token_usage": { "input_tokens": 354785, "cached_input_tokens": 334848, "cache_write_input_tokens": 0, "output_tokens": 4531, "reasoning_output_tokens": 1368, "total_tokens": 359316 }, "thread_token_usage": { "input_tokens": 354785, "cached_input_tokens": 334848, "cache_write_input_tokens": 0, "output_tokens": 4531, "reasoning_output_tokens": 1368, "total_tokens": 359316 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-24T23:04:35.921Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:35.921Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:41.496Z", "callId": "call_4G9eGwnjnjjPyR6uiTi0q0Ac", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"kill 9494 9524 9492 9523 2>/dev/null || true; /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, time\\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\ncatitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\nz=np.load(D/'validation_eligible_candidates.npz')\\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(catitems),bitorder='little').astype(bool)\\nprint('loaded mask',time.time()-t,flush=True)\\n# np.argpartition on neg bool does not preserve popularity; cumulative rank trick finds first 100 vectorized\\nsel=mask & (np.cumsum(mask,axis=1)<=100)\\nrr,cc=np.nonzero(sel)\\nranks=np.cumsum(sel,axis=1)[rr,cc]\\nrows=pd.DataFrame({'uid':z['uids'][rr].astype(np.int64),'item_id':z['item_ids'][cc].astype(np.int64),'rank':ranks.astype(np.int64)})\\nrows.to_parquet(O/'validation_popularity.parquet',index=False)\\nprint('built',len(rows),'seconds',time.time()-t,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:04:42.664Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=32338", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:41.523Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f999d642-d11c-4133-9458-5224266c1482", "process_id": "83142", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz'); uidrow={int(u):i for i,u in enumerate(z['uids'])}\nrows=np.empty((len(target)*100,3),dtype=np.int64); p=0\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]; n=len(top)\n rows[p:p+n,0]=u; rows[p:p+n,1]=top; rows[p:p+n,2]=np.arange(1,n+1); p+=n\npd.DataFrame(rows[:p],columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',p,'seconds',time.time()-t,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz'); uidrow={int(u):i for i,u in enumerate(z['uids'])}\nrows=np.empty((len(target)*100,3),dtype=np.int64); p=0\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]; n=len(top)\n rows[p:p+n,0]=u; rows[p:p+n,1]=top; rows[p:p+n,2]=np.arange(1,n+1); p+=n\npd.DataFrame(rows[:p],columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',p,'seconds',time.time()-t,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 65, "nanos": 659279147 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:41.525Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-69b778e1-c76d-4678-9f61-cf6ef7f6aa6b", "process_id": "7984", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution')\nt=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ncatitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}\n# catalog is already popularity order. Create globally popular eligible ranking.\nrows=[]\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\n# truth descriptive stats\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntset=set(target.tolist()); cset=set(catitems.tolist())\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)&vl.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(tset)&vk.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\n# check eligible\npacked=z['packed_eligible']; col={int(x):i for i,x in enumerate(z['item_ids'])}\nok=[]\nfor u,i in rel[['uid','item_id']].itertuples(index=False,name=None):\n rr=uidrow[int(u)]; cc=col[int(i)]; ok.append((int(packed[rr,cc//8])>>(cc%8))&1)\nrel=rel[np.array(ok,dtype=bool)]\nrankmap=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nrr=np.array([rankmap[int(x)] for x in rel.item_id])\nprint('load/build seconds',time.time()-t)\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nprint('relevant popularity rank quantiles',dict(zip([0,.1,.25,.5,.75,.9,.99,1],np.quantile(rr,[0,.1,.25,.5,.75,.9,.99,1]))))\nprint('fraction rank<=100/1k/5k/10k',[(k,float((rr<=k).mean())) for k in [10,100,500,1000,5000,10000]])\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution')\nt=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ncatitems=cat.item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}\n# catalog is already popularity order. Create globally popular eligible ranking.\nrows=[]\nfor u in target:\n r=uidrow[int(u)]; mask=np.unpackbits(z['packed_eligible'][r],count=len(catitems),bitorder='little').astype(bool)\n top=catitems[mask][:100]\n rows.extend((int(u),int(x),j+1) for j,x in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'validation_popularity.parquet',index=False)\n# truth descriptive stats\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntset=set(target.tolist()); cset=set(catitems.tolist())\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)&vl.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(tset)&vk.item_id.isin(cset)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\n# check eligible\npacked=z['packed_eligible']; col={int(x):i for i,x in enumerate(z['item_ids'])}\nok=[]\nfor u,i in rel[['uid','item_id']].itertuples(index=False,name=None):\n rr=uidrow[int(u)]; cc=col[int(i)]; ok.append((int(packed[rr,cc//8])>>(cc%8))&1)\nrel=rel[np.array(ok,dtype=bool)]\nrankmap=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nrr=np.array([rankmap[int(x)] for x in rel.item_id])\nprint('load/build seconds',time.time()-t)\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nprint('relevant popularity rank quantiles',dict(zip([0,.1,.25,.5,.75,.9,.99,1],np.quantile(rr,[0,.1,.25,.5,.75,.9,.99,1]))))\nprint('fraction rank<=100/1k/5k/10k',[(k,float((rr<=k).mean())) for k in [10,100,500,1000,5000,10000]])\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 118, "nanos": 610697699 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:41.545Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac7f8c5487d29109d18567763c30", "usage": { "input_tokens": 28691, "cached_input_tokens": 28288, "cache_write_input_tokens": 0, "output_tokens": 514, "reasoning_output_tokens": 140, "total_tokens": 29205 }, "turn_token_usage": { "input_tokens": 383476, "cached_input_tokens": 363136, "cache_write_input_tokens": 0, "output_tokens": 5045, "reasoning_output_tokens": 1508, "total_tokens": 388521 }, "thread_token_usage": { "input_tokens": 383476, "cached_input_tokens": 363136, "cache_write_input_tokens": 0, "output_tokens": 5045, "reasoning_output_tokens": 1508, "total_tokens": 388521 } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-24T23:04:43.952Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:43.952Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:44.624Z", "callId": "call_rkMxtAV06DRNBF1RS05UjGKG", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":32338,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:04:46.174Z", "content": "Script completed\nWall time 1.5 seconds\nOutput:\n\n{\"chunk_id\":\"c899a7\",\"wall_time_seconds\":1.427806102,\"exit_code\":0,\"original_token_count\":18}\nloaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:44.685Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac8ae26887d28c6e57bddc71e683", "usage": { "input_tokens": 29233, "cached_input_tokens": 28544, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 29288 }, "turn_token_usage": { "input_tokens": 412709, "cached_input_tokens": 391680, "cache_write_input_tokens": 0, "output_tokens": 5100, "reasoning_output_tokens": 1516, "total_tokens": 417809 }, "thread_token_usage": { "input_tokens": 412709, "cached_input_tokens": 391680, "cache_write_input_tokens": 0, "output_tokens": 5100, "reasoning_output_tokens": 1516, "total_tokens": 417809 } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:46.166Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c365d311-683c-4862-a6b2-be86fd4b2e0d", "process_id": "32338", "command": [ "/bin/sh", "-lc", "kill 9494 9524 9492 9523 2>/dev/null || true; /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncatitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(catitems),bitorder='little').astype(bool)\nprint('loaded mask',time.time()-t,flush=True)\n# np.argpartition on neg bool does not preserve popularity; cumulative rank trick finds first 100 vectorized\nsel=mask & (np.cumsum(mask,axis=1)<=100)\nrr,cc=np.nonzero(sel)\nranks=np.cumsum(sel,axis=1)[rr,cc]\nrows=pd.DataFrame({'uid':z['uids'][rr].astype(np.int64),'item_id':z['item_ids'][cc].astype(np.int64),'rank':ranks.astype(np.int64)})\nrows.to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',len(rows),'seconds',time.time()-t,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill 9494 9524 9492 9523 2>/dev/null || true; /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); O=Path('/app/solution'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\ncatitems=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(catitems),bitorder='little').astype(bool)\nprint('loaded mask',time.time()-t,flush=True)\n# np.argpartition on neg bool does not preserve popularity; cumulative rank trick finds first 100 vectorized\nsel=mask & (np.cumsum(mask,axis=1)<=100)\nrr,cc=np.nonzero(sel)\nranks=np.cumsum(sel,axis=1)[rr,cc]\nrows=pd.DataFrame({'uid':z['uids'][rr].astype(np.int64),'item_id':z['item_ids'][cc].astype(np.int64),'rank':ranks.astype(np.int64)})\nrows.to_parquet(O/'validation_popularity.parquet',index=False)\nprint('built',len(rows),'seconds',time.time()-t,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n", "stderr": "", "aggregated_output": "loaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 503136699 }, "formatted_output": "loaded mask 0.22538971900939941\nbuilt 845600 seconds 3.0828750133514404\n" } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-24T23:04:49.517Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:49.517Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:04:50.619Z", "callId": "call_0uRPRizK1phndShEebp4eMSM", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:04:54.081Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:04:50.677Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac8e58b087d292cba272408cd74d", "usage": { "input_tokens": 29368, "cached_input_tokens": 29056, "cache_write_input_tokens": 0, "output_tokens": 108, "reasoning_output_tokens": 22, "total_tokens": 29476 }, "turn_token_usage": { "input_tokens": 442077, "cached_input_tokens": 420736, "cache_write_input_tokens": 0, "output_tokens": 5208, "reasoning_output_tokens": 1538, "total_tokens": 447285 }, "thread_token_usage": { "input_tokens": 442077, "cached_input_tokens": 420736, "cache_write_input_tokens": 0, "output_tokens": 5208, "reasoning_output_tokens": 1538, "total_tokens": 447285 } } }, { "kind": "context", "timestamp": "2026-09-24T23:04:54.076Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5c4b914e-c0f0-44cd-bbc1-77110c8ad008", "process_id": "48982", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet" } ], "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}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 292329300 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-24T23:04:56.427Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:04:56.427Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:05:14.640Z", "callId": "call_Ac5PXVrXEba62ZJBLwIpVNNE", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, time\\nD=Path('/task/data'); t=time.time()\\ntarget=pd.read_parquet(D/'target_users.parquet').uid.astype('uint32')\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.astype('uint32')\\nz=np.load(D/'validation_eligible_candidates.npz')\\nuidrow={int(u):i for i,u in enumerate(z['uids'])}; col={int(x):i for i,x in enumerate(z['item_ids'])}\\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nprint('read',time.time()-t,flush=True)\\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(target)&vl.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(target)&vk.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nrr=np.fromiter((uidrow[int(x)] for x in rel.uid),np.int32,len(rel)); cc=np.fromiter((col[int(x)] for x in rel.item_id),np.int32,len(rel))\\nok=((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); rel=rel[ok]\\nrankmap=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id).to_dict(); ranks=np.fromiter((rankmap[int(x)] for x in rel.item_id),np.int32,len(rel))\\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\\nqs=[0,.1,.25,.5,.75,.9,.99,1]; print('pop rank qs',list(zip(qs,np.quantile(ranks,qs).astype(int))))\\nprint('fractions',[(k,round(float((ranks<=k).mean()),4)) for k in [10,100,500,1000,5000,10000,20000]])\\n# label repeats in prior interactions and preferences\\npref=pd.read_parquet(D/'validation_preference_state.parquet')\\nfor st in ['liked','disliked']:\\n pairs=set(map(tuple,pref[pref.state==st][['uid','item_id']].to_numpy()))\\n print('rel in pref',st, np.mean([tuple(x) in pairs for x in rel[['uid','item_id']].to_numpy()]), 'count',len(pairs))\\n# train positive history among target+candidate, using arrow/pandas all two cols\\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\\nprint('train read',len(tr),time.time()-t,flush=True)\\ntrp=tr[(tr.played_ratio_pct>=50)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\\ntr80=tr[(tr.played_ratio_pct>=80)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\\nfor label,x in [('p50',trp),('p80',tr80),('org80',tr80[tr80.is_organic==1]),('org50',trp[trp.is_organic==1])]:\\n pairs=set(map(tuple,x[['uid','item_id']].drop_duplicates().to_numpy()))\\n ov=np.mean([tuple(y) in pairs for y in rel[['uid','item_id']].to_numpy()])\\n print(label,'events',len(x),'pairs',len(pairs),'rel repeat frac',ov)\\nprint('done',time.time()-t)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:05:15.811Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=29527", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:05:14.709Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ac96412c87d28543685b19098f31", "usage": { "input_tokens": 29546, "cached_input_tokens": 29184, "cache_write_input_tokens": 0, "output_tokens": 1083, "reasoning_output_tokens": 51, "total_tokens": 30629 }, "turn_token_usage": { "input_tokens": 471623, "cached_input_tokens": 449920, "cache_write_input_tokens": 0, "output_tokens": 6291, "reasoning_output_tokens": 1589, "total_tokens": 477914 }, "thread_token_usage": { "input_tokens": 471623, "cached_input_tokens": 449920, "cache_write_input_tokens": 0, "output_tokens": 6291, "reasoning_output_tokens": 1589, "total_tokens": 477914 } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-24T23:05:17.049Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:05:17.049Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:05:17.745Z", "callId": "call_2OusOY9S2lXABf8kmcsf6MPr", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":29527,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:05:47.258Z", "content": "Script completed\nWall time 29.5 seconds\nOutput:\n\n{\"chunk_id\":\"9d458d\",\"wall_time_seconds\":29.495643714,\"exit_code\":0,\"original_token_count\":221}\nread 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:05:17.838Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5acac03a087d2828a036816fbed03", "usage": { "input_tokens": 30657, "cached_input_tokens": 29312, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 30710 }, "turn_token_usage": { "input_tokens": 502280, "cached_input_tokens": 479232, "cache_write_input_tokens": 0, "output_tokens": 6344, "reasoning_output_tokens": 1595, "total_tokens": 508624 }, "thread_token_usage": { "input_tokens": 502280, "cached_input_tokens": 479232, "cache_write_input_tokens": 0, "output_tokens": 6344, "reasoning_output_tokens": 1595, "total_tokens": 508624 } } }, { "kind": "context", "timestamp": "2026-09-24T23:05:47.253Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-282438a4-1108-4777-b2fb-752ebc3900c7", "process_id": "29527", "command": [ "/bin/sh", "-lc", "OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.astype('uint32')\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.astype('uint32')\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}; col={int(x):i for i,x in enumerate(z['item_ids'])}\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nprint('read',time.time()-t,flush=True)\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(target)&vl.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(target)&vk.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=np.fromiter((uidrow[int(x)] for x in rel.uid),np.int32,len(rel)); cc=np.fromiter((col[int(x)] for x in rel.item_id),np.int32,len(rel))\nok=((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); rel=rel[ok]\nrankmap=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id).to_dict(); ranks=np.fromiter((rankmap[int(x)] for x in rel.item_id),np.int32,len(rel))\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nqs=[0,.1,.25,.5,.75,.9,.99,1]; print('pop rank qs',list(zip(qs,np.quantile(ranks,qs).astype(int))))\nprint('fractions',[(k,round(float((ranks<=k).mean()),4)) for k in [10,100,500,1000,5000,10000,20000]])\n# label repeats in prior interactions and preferences\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor st in ['liked','disliked']:\n pairs=set(map(tuple,pref[pref.state==st][['uid','item_id']].to_numpy()))\n print('rel in pref',st, np.mean([tuple(x) in pairs for x in rel[['uid','item_id']].to_numpy()]), 'count',len(pairs))\n# train positive history among target+candidate, using arrow/pandas all two cols\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nprint('train read',len(tr),time.time()-t,flush=True)\ntrp=tr[(tr.played_ratio_pct>=50)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\ntr80=tr[(tr.played_ratio_pct>=80)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\nfor label,x in [('p50',trp),('p80',tr80),('org80',tr80[tr80.is_organic==1]),('org50',trp[trp.is_organic==1])]:\n pairs=set(map(tuple,x[['uid','item_id']].drop_duplicates().to_numpy()))\n ov=np.mean([tuple(y) in pairs for y in rel[['uid','item_id']].to_numpy()])\n print(label,'events',len(x),'pairs',len(pairs),'rel repeat frac',ov)\nprint('done',time.time()-t)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, time\nD=Path('/task/data'); t=time.time()\ntarget=pd.read_parquet(D/'target_users.parquet').uid.astype('uint32')\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); catitems=cat.item_id.astype('uint32')\nz=np.load(D/'validation_eligible_candidates.npz')\nuidrow={int(u):i for i,u in enumerate(z['uids'])}; col={int(x):i for i,x in enumerate(z['item_ids'])}\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nprint('read',time.time()-t,flush=True)\nstrong=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(target)&vl.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nlikes=vk[(vk.is_organic==1)&vk.uid.isin(target)&vk.item_id.isin(catitems)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrr=np.fromiter((uidrow[int(x)] for x in rel.uid),np.int32,len(rel)); cc=np.fromiter((col[int(x)] for x in rel.item_id),np.int32,len(rel))\nok=((z['packed_eligible'][rr,cc//8]>>(cc%8))&1).astype(bool); rel=rel[ok]\nrankmap=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id).to_dict(); ranks=np.fromiter((rankmap[int(x)] for x in rel.item_id),np.int32,len(rel))\nprint('strong',len(strong),'likes',len(likes),'eligible relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\nprint('relevant/user quantiles',rel.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nqs=[0,.1,.25,.5,.75,.9,.99,1]; print('pop rank qs',list(zip(qs,np.quantile(ranks,qs).astype(int))))\nprint('fractions',[(k,round(float((ranks<=k).mean()),4)) for k in [10,100,500,1000,5000,10000,20000]])\n# label repeats in prior interactions and preferences\npref=pd.read_parquet(D/'validation_preference_state.parquet')\nfor st in ['liked','disliked']:\n pairs=set(map(tuple,pref[pref.state==st][['uid','item_id']].to_numpy()))\n print('rel in pref',st, np.mean([tuple(x) in pairs for x in rel[['uid','item_id']].to_numpy()]), 'count',len(pairs))\n# train positive history among target+candidate, using arrow/pandas all two cols\ntr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nprint('train read',len(tr),time.time()-t,flush=True)\ntrp=tr[(tr.played_ratio_pct>=50)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\ntr80=tr[(tr.played_ratio_pct>=80)&tr.uid.isin(target)&tr.item_id.isin(catitems)]\nfor label,x in [('p50',trp),('p80',tr80),('org80',tr80[tr80.is_organic==1]),('org50',trp[trp.is_organic==1])]:\n pairs=set(map(tuple,x[['uid','item_id']].drop_duplicates().to_numpy()))\n ov=np.mean([tuple(y) in pairs for y in rel[['uid','item_id']].to_numpy()])\n print(label,'events',len(x),'pairs',len(pairs),'rel repeat frac',ov)\nprint('done',time.time()-t)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "read 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n", "stderr": "", "aggregated_output": "read 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n", "exit_code": 0, "duration": { "secs": 32, "nanos": 443527438 }, "formatted_output": "read 0.09413003921508789\nstrong 170791 likes 8528 eligible relevant 37769 users 4263 grades {1: 33352, 3: 4417}\nrelevant/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\npop rank qs [(0, np.int64(1)), (0.1, np.int64(428)), (0.25, np.int64(1733)), (0.5, np.int64(5622)), (0.75, np.int64(13374)), (0.9, np.int64(21680)), (0.99, np.int64(29101)), (1, np.int64(30000))]\nfractions [(10, 0.005), (100, 0.0319), (500, 0.1113), (1000, 0.1754), (5000, 0.4706), (10000, 0.6593), (20000, 0.8741)]\nrel in pref liked 0.0 count 418435\nrel in pref disliked 0.0 count 50689\ntrain read 43484159 1.6793272495269775\np50 events 19959853 pairs 5514505 rel repeat frac 0.0\np80 events 18711339 pairs 5290619 rel repeat frac 0.0\norg80 events 8661020 pairs 1935328 rel repeat frac 0.0\norg50 events 9376263 pairs 2043504 rel repeat frac 0.0\ndone 29.99735951423645\n" } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-24T23:06:03.970Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:03.970Z", "text": "" } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-24T23:06:16.330Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:16.330Z", "text": "" } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-24T23:06:29.640Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:29.640Z", "text": "" } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-24T23:06:43.591Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:43.591Z", "text": "" } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-24T23:06:56.874Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:06:56.874Z", "text": "" } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-24T23:07:08.994Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:08.994Z", "text": "" } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-24T23:07:23.161Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:23.161Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-24T23:07:35.044Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:35.044Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-24T23:07:47.665Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:47.665Z", "text": "" } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-24T23:07:59.397Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:07:59.397Z", "text": "" } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-24T23:08:10.542Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:08:10.542Z", "text": "" } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-24T23:08:22.491Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:08:22.491Z", "text": "" } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-24T23:08:33.449Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:08:33.449Z", "text": "" } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-24T23:08:44.358Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:08:44.358Z", "text": "" } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-24T23:08:55.743Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:08:55.743Z", "text": "" } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-24T23:09:07.077Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:09:07.077Z", "text": "" } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-24T23:09:17.437Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:09:17.437Z", "text": "" } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-24T23:09:18.099Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:09:18.099Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:09:20.260Z", "text": "The baseline is valid but weak at 0.010710 NDCG@10. Validation relevance is overwhelmingly novel (none of the relevant user–item pairs were previously consumed or explicitly rated), so repeat-item heuristics cannot solve it. I’m now encoding the actual production pattern: temporal “history → first discovery” examples, a trainable history encoder, an independently precomputable item tower, sampled-softmax training, and eligibility-masked dot-product retrieval." }, { "kind": "tool", "timestamp": "2026-09-24T23:11:11.757Z", "callId": "call_ux8f34yHpUTBLC6xHfoakfZN", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/model.py\\n+\\\"\\\"\\\"Neural towers used by the discovery ranker.\\n+\\n+The user tower consumes only a variable-length interaction history plus summary\\n+features. It deliberately has no user-ID parameter. The item tower can be\\n+run for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\\n+the final coordinate implements an item prior while preserving an exact dot\\n+product scoring function.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"Weighted history pooling followed by a small residual MLP.\\\"\\\"\\\"\\n+\\n+ def __init__(\\n+ self,\\n+ num_items: int,\\n+ embedding_dim: int = 64,\\n+ hidden_dim: int = 128,\\n+ num_context_features: int = 4,\\n+ ) -> None:\\n+ super().__init__()\\n+ self.embedding_dim = embedding_dim\\n+ self.history_embedding = nn.EmbeddingBag(\\n+ num_items, embedding_dim, mode=\\\"sum\\\", include_last_offset=True\\n+ )\\n+ nn.init.normal_(self.history_embedding.weight, std=0.04)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(embedding_dim + num_context_features, hidden_dim),\\n+ nn.GELU(),\\n+ nn.LayerNorm(hidden_dim),\\n+ nn.Linear(hidden_dim, embedding_dim),\\n+ )\\n+ self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\\n+ self.log_scale = nn.Parameter(torch.tensor(2.0))\\n+\\n+ def forward(\\n+ self,\\n+ item_ids: torch.Tensor,\\n+ offsets: torch.Tensor,\\n+ weights: torch.Tensor,\\n+ context_features: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ pooled = self.history_embedding(\\n+ item_ids, offsets, per_sample_weights=weights\\n+ )\\n+ # Signed preference weights are useful, but their absolute magnitude\\n+ # must not make heavy users arbitrarily larger than light users.\\n+ abs_sums = torch.zeros(\\n+ len(offsets) - 1, device=weights.device, dtype=weights.dtype\\n+ )\\n+ lengths = offsets[1:] - offsets[:-1]\\n+ bag_ids = torch.repeat_interleave(\\n+ torch.arange(len(lengths), device=weights.device), lengths\\n+ )\\n+ abs_sums.scatter_add_(0, bag_ids, weights.abs())\\n+ pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\\n+ latent = self.network(torch.cat((pooled, context_features), dim=1))\\n+ latent = F.normalize(latent + self.skip(pooled), dim=1)\\n+ # Constraining the scale prevents sampled-softmax from becoming\\n+ # numerically brittle. The constant last coordinate pairs with the\\n+ # item tower's learned popularity prior.\\n+ scale = self.log_scale.exp().clamp(1.0, 30.0)\\n+ return torch.cat(\\n+ (latent * scale, torch.ones_like(latent[:, :1])), dim=1\\n+ )\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"ID plus catalogue-statistic item encoder, suitable for offline export.\\\"\\\"\\\"\\n+\\n+ def __init__(\\n+ self,\\n+ num_items: int,\\n+ item_features: torch.Tensor,\\n+ embedding_dim: int = 64,\\n+ hidden_dim: int = 128,\\n+ ) -> None:\\n+ super().__init__()\\n+ self.embedding_dim = embedding_dim\\n+ self.id_embedding = nn.Embedding(num_items, embedding_dim)\\n+ nn.init.normal_(self.id_embedding.weight, std=0.04)\\n+ self.register_buffer(\\\"item_features\\\", item_features.float())\\n+ feature_dim = item_features.shape[1]\\n+ self.network = nn.Sequential(\\n+ nn.Linear(embedding_dim + feature_dim, hidden_dim),\\n+ nn.GELU(),\\n+ nn.LayerNorm(hidden_dim),\\n+ nn.Linear(hidden_dim, embedding_dim),\\n+ )\\n+ self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\\n+ self.prior = nn.Sequential(\\n+ nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\\n+ )\\n+ self.id_bias = nn.Embedding(num_items, 1)\\n+ nn.init.zeros_(self.id_bias.weight)\\n+\\n+ def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\\n+ ids = self.id_embedding(item_ids)\\n+ features = self.item_features[item_ids]\\n+ latent = self.network(torch.cat((ids, features), dim=1))\\n+ latent = F.normalize(latent + self.skip(ids), dim=1)\\n+ bias = self.prior(features) + self.id_bias(item_ids)\\n+ return torch.cat((latent, bias), dim=1)\\n+\\n+\\n+class TwoTowerRanker(nn.Module):\\n+ \\\"\\\"\\\"Container whose score is exactly ``user_vector @ item_vector.T``.\\\"\\\"\\\"\\n+\\n+ def __init__(\\n+ self,\\n+ num_items: int,\\n+ item_features: torch.Tensor,\\n+ embedding_dim: int = 64,\\n+ hidden_dim: int = 128,\\n+ ) -> None:\\n+ super().__init__()\\n+ self.user_tower = UserTower(\\n+ num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\\n+ )\\n+ self.item_tower = ItemTower(\\n+ num_items,\\n+ item_features=item_features,\\n+ embedding_dim=embedding_dim,\\n+ hidden_dim=hidden_dim,\\n+ )\\n+\\n+ def score(\\n+ self,\\n+ history_ids: torch.Tensor,\\n+ offsets: torch.Tensor,\\n+ history_weights: torch.Tensor,\\n+ context_features: torch.Tensor,\\n+ candidate_ids: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ users = self.user_tower(\\n+ history_ids, offsets, history_weights, context_features\\n+ )\\n+ items = self.item_tower(candidate_ids)\\n+ return users @ items.T\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply an event-scalable two-tower discovery ranker.\\n+\\n+No user-item matrix is created. Training examples are streamed conceptually\\n+as temporal user histories paired with novel organic discoveries, represented\\n+in compact CSR arrays. The only dense user-by-item object is a short-lived\\n+request batch of retrieval scores during ranking.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+import time\\n+from dataclasses import asdict, dataclass\\n+from pathlib import Path\\n+from typing import Iterable\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+from model import TwoTowerRanker\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+TRAIN_END = 24_786_800\\n+WEEK = 7 * 24 * 60 * 60\\n+GAP = 30 * 60\\n+VAL_END = TRAIN_END + GAP + WEEK\\n+\\n+\\n+@dataclass\\n+class Config:\\n+ embedding_dim: int = 64\\n+ hidden_dim: int = 128\\n+ max_history: int = 384\\n+ history_days: int = 180\\n+ training_weeks: int = 16\\n+ epochs: int = 16\\n+ batch_size: int = 256\\n+ negatives: int = 512\\n+ learning_rate: float = 0.002\\n+ weight_decay: float = 1e-5\\n+ like_sampling_weight: float = 4.0\\n+ seed: int = 20260924\\n+\\n+\\n+def id_lookup(values: np.ndarray, known: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Map arbitrary integer IDs to positions without a Python-sized hash map.\\\"\\\"\\\"\\n+ order = np.argsort(known)\\n+ sorted_known = known[order]\\n+ loc = np.searchsorted(sorted_known, values)\\n+ valid = loc < len(sorted_known)\\n+ valid_indices = np.flatnonzero(valid)\\n+ valid[valid_indices] &= sorted_known[loc[valid_indices]] == values[valid_indices]\\n+ mapped = np.full(len(values), -1, dtype=np.int32)\\n+ mapped[valid] = order[loc[valid]].astype(np.int32)\\n+ return mapped, valid\\n+\\n+\\n+def read_family(name: str, stage: str, columns: list[str]) -> pd.DataFrame:\\n+ frames = [pd.read_parquet(DATA / f\\\"train_{name}.parquet\\\", columns=columns)]\\n+ if stage == \\\"final\\\":\\n+ frames.append(pd.read_parquet(DATA / f\\\"val_{name}.parquet\\\", columns=columns))\\n+ return pd.concat(frames, ignore_index=True)\\n+\\n+\\n+def map_frame(\\n+ frame: pd.DataFrame, users: np.ndarray, items: np.ndarray\\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\\n+ u, uv = id_lookup(frame.uid.to_numpy(np.int64, copy=False), users)\\n+ i, iv = id_lookup(frame.item_id.to_numpy(np.int64, copy=False), items)\\n+ keep = uv & iv\\n+ return u[keep], i[keep], np.flatnonzero(keep)\\n+\\n+\\n+def sort_events(\\n+ uid: np.ndarray,\\n+ timestamp: np.ndarray,\\n+ item: np.ndarray,\\n+ *extra: np.ndarray,\\n+) -> tuple[np.ndarray, ...]:\\n+ order = np.lexsort((timestamp, uid))\\n+ return (uid[order], timestamp[order], item[order]) + tuple(x[order] for x in extra)\\n+\\n+\\n+def user_bounds(uid: np.ndarray, num_users: int) -> np.ndarray:\\n+ return np.searchsorted(uid, np.arange(num_users + 1), side=\\\"left\\\")\\n+\\n+\\n+def load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\\n+ \\\"\\\"\\\"Load compact candidate-catalog events; raw frames are promptly released.\\\"\\\"\\\"\\n+ started = time.time()\\n+ listens = read_family(\\n+ \\\"listens\\\",\\n+ stage,\\n+ [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ lu, li, keep = map_frame(listens, users, items)\\n+ lt = listens.timestamp.to_numpy(np.int64, copy=False)[keep]\\n+ lo = listens.is_organic.to_numpy(np.uint8, copy=False)[keep]\\n+ lr = listens.played_ratio_pct.to_numpy(np.int16, copy=False)[keep]\\n+\\n+ # All catalogue listens define novelty. Positive listens form histories.\\n+ all_listens = sort_events(lu, lt, li)\\n+ positive = lr >= 50\\n+ history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\\n+ 1.0 + 0.35 * lo[positive]\\n+ )\\n+ history = sort_events(\\n+ lu[positive],\\n+ lt[positive],\\n+ li[positive],\\n+ history_weight.astype(np.float32),\\n+ lo[positive].astype(np.float32),\\n+ )\\n+ strong = (lo == 1) & (lr >= 80)\\n+ relevant_parts = [\\n+ (lu[strong], lt[strong], li[strong], np.ones(strong.sum(), dtype=np.uint8))\\n+ ]\\n+ del listens, lr, lo, lu, li, lt\\n+\\n+ action_parts: list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []\\n+ action_spec = {\\n+ \\\"likes\\\": (0, 1, 3),\\n+ \\\"unlikes\\\": (0, 0, 0),\\n+ \\\"dislikes\\\": (1, 1, 0),\\n+ \\\"undislikes\\\": (1, 0, 0),\\n+ }\\n+ for name, (channel, active, grade) in action_spec.items():\\n+ frame = read_family(name, stage, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ au, ai, keep = map_frame(frame, users, items)\\n+ at = frame.timestamp.to_numpy(np.int64, copy=False)[keep]\\n+ organic = frame.is_organic.to_numpy(np.uint8, copy=False)[keep]\\n+ action_parts.append(\\n+ (\\n+ au,\\n+ at,\\n+ ai,\\n+ np.full(len(au), channel, dtype=np.uint8),\\n+ np.full(len(au), active, dtype=np.uint8),\\n+ )\\n+ )\\n+ if grade:\\n+ good = organic == 1\\n+ relevant_parts.append(\\n+ (\\n+ au[good],\\n+ at[good],\\n+ ai[good],\\n+ np.full(good.sum(), grade, dtype=np.uint8),\\n+ )\\n+ )\\n+ del frame\\n+\\n+ actions = sort_events(\\n+ np.concatenate([x[0] for x in action_parts]),\\n+ np.concatenate([x[1] for x in action_parts]),\\n+ np.concatenate([x[2] for x in action_parts]),\\n+ np.concatenate([x[3] for x in action_parts]),\\n+ np.concatenate([x[4] for x in action_parts]),\\n+ )\\n+ relevant = sort_events(\\n+ np.concatenate([x[0] for x in relevant_parts]),\\n+ np.concatenate([x[1] for x in relevant_parts]),\\n+ np.concatenate([x[2] for x in relevant_parts]),\\n+ np.concatenate([x[3] for x in relevant_parts]),\\n+ )\\n+ print(\\n+ f\\\"loaded {len(all_listens[0]):,} catalogue listens, \\\"\\n+ f\\\"{len(history[0]):,} positive history events and \\\"\\n+ f\\\"{len(relevant[0]):,} organic relevance events in \\\"\\n+ f\\\"{time.time() - started:.1f}s\\\",\\n+ flush=True,\\n+ )\\n+ return {\\n+ \\\"all\\\": all_listens,\\n+ \\\"history\\\": history,\\n+ \\\"actions\\\": actions,\\n+ \\\"relevant\\\": relevant,\\n+ }\\n+\\n+\\n+def latest_unique_history(\\n+ times: np.ndarray,\\n+ items: np.ndarray,\\n+ values: np.ndarray,\\n+ organic: np.ndarray,\\n+ cutoff: int,\\n+ cfg: Config,\\n+) -> tuple[np.ndarray, np.ndarray, float]:\\n+ hi = int(np.searchsorted(times, cutoff, side=\\\"left\\\"))\\n+ lo = int(np.searchsorted(times, cutoff - cfg.history_days * 86_400, side=\\\"left\\\"))\\n+ if hi <= lo:\\n+ return np.empty(0, np.int32), np.empty(0, np.float32), 0.0\\n+ reverse_items = items[lo:hi][::-1]\\n+ _, first_reverse = np.unique(reverse_items, return_index=True)\\n+ first_reverse.sort()\\n+ first_reverse = first_reverse[: cfg.max_history]\\n+ positions = hi - 1 - first_reverse\\n+ age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\\n+ decay = np.exp(-age_days / 120.0)\\n+ weights = values[positions] * decay\\n+ return (\\n+ items[positions].astype(np.int32, copy=False),\\n+ weights.astype(np.float32, copy=False),\\n+ float(organic[positions].mean()),\\n+ )\\n+\\n+\\n+def active_preferences(\\n+ times: np.ndarray,\\n+ items: np.ndarray,\\n+ channels: np.ndarray,\\n+ active: np.ndarray,\\n+ cutoff: int,\\n+) -> tuple[np.ndarray, np.ndarray, int, int]:\\n+ hi = int(np.searchsorted(times, cutoff, side=\\\"left\\\"))\\n+ if hi == 0:\\n+ return np.empty(0, np.int32), np.empty(0, np.float32), 0, 0\\n+ keys = items[:hi].astype(np.int64) * 2 + channels[:hi]\\n+ reverse_keys = keys[::-1]\\n+ _, first_reverse = np.unique(reverse_keys, return_index=True)\\n+ pos = hi - 1 - first_reverse\\n+ pos = pos[active[pos] == 1]\\n+ liked = pos[channels[pos] == 0]\\n+ disliked = pos[channels[pos] == 1]\\n+ out_items = np.concatenate((items[liked], items[disliked])).astype(np.int32)\\n+ out_weights = np.concatenate(\\n+ (\\n+ np.full(len(liked), 2.5, np.float32),\\n+ np.full(len(disliked), -1.25, np.float32),\\n+ )\\n+ )\\n+ return out_items, out_weights, len(liked), len(disliked)\\n+\\n+\\n+def make_history(\\n+ h: tuple[np.ndarray, ...],\\n+ a: tuple[np.ndarray, ...],\\n+ cutoff: int,\\n+ cfg: Config,\\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\\n+ li, lw, organic_rate = latest_unique_history(\\n+ h[0], h[1], h[2], h[3], cutoff, cfg\\n+ )\\n+ pi, pw, n_like, n_dislike = active_preferences(\\n+ a[0], a[1], a[2], a[3], cutoff\\n+ )\\n+ ids = np.concatenate((li, pi))\\n+ weights = np.concatenate((lw, pw))\\n+ if not len(ids):\\n+ # Every target user should have history, but EmbeddingBag requires a\\n+ # real entry. Item 0 at zero weight is a neutral fallback.\\n+ ids = np.array([0], dtype=np.int32)\\n+ weights = np.array([0.0], dtype=np.float32)\\n+ features = np.array(\\n+ [\\n+ math.log1p(len(li)) / math.log1p(cfg.max_history),\\n+ math.log1p(n_like) / 6.0,\\n+ math.log1p(n_dislike) / 4.0,\\n+ organic_rate,\\n+ ],\\n+ dtype=np.float32,\\n+ )\\n+ return ids, weights, features\\n+\\n+\\n+def relevant_targets(\\n+ r: tuple[np.ndarray, ...],\\n+ first_seen: np.ndarray,\\n+ cutoff: int,\\n+) -> tuple[np.ndarray, np.ndarray]:\\n+ lo = int(np.searchsorted(r[0], cutoff + GAP, side=\\\"left\\\"))\\n+ hi = int(np.searchsorted(r[0], cutoff + GAP + WEEK, side=\\\"left\\\"))\\n+ if hi <= lo:\\n+ return np.empty(0, np.int32), np.empty(0, np.uint8)\\n+ ri, rg = r[1][lo:hi], r[2][lo:hi]\\n+ novel = first_seen[ri] >= cutoff\\n+ ri, rg = ri[novel], rg[novel]\\n+ if not len(ri):\\n+ return np.empty(0, np.int32), np.empty(0, np.uint8)\\n+ unique, inverse = np.unique(ri, return_inverse=True)\\n+ grades = np.zeros(len(unique), dtype=np.uint8)\\n+ np.maximum.at(grades, inverse, rg)\\n+ return unique.astype(np.int32), grades\\n+\\n+\\n+def build_contexts(\\n+ arrays: dict,\\n+ users: np.ndarray,\\n+ stage_end: int,\\n+ cfg: Config,\\n+) -> dict[str, np.ndarray]:\\n+ num_users = len(users)\\n+ bounds = {k: user_bounds(v[0], num_users) for k, v in arrays.items()}\\n+ history_ids: list[np.ndarray] = []\\n+ history_weights: list[np.ndarray] = []\\n+ context_features: list[np.ndarray] = []\\n+ target_ids: list[np.ndarray] = []\\n+ target_grades: list[np.ndarray] = []\\n+ context_users: list[int] = []\\n+ context_cutoffs: list[int] = []\\n+\\n+ # End at stage_end-WEEK so every label window is fully observed. For the\\n+ # final refit, this includes the public validation window as the newest\\n+ # temporal training example.\\n+ cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\\n+ cutoffs.sort()\\n+ first_seen = np.empty(30_000, dtype=np.int64)\\n+ for u in range(num_users):\\n+ slices = {}\\n+ for key, values in arrays.items():\\n+ b = bounds[key]\\n+ slices[key] = tuple(x[b[u] : b[u + 1]] for x in values[1:])\\n+ first_seen.fill(np.iinfo(np.int64).max)\\n+ all_times, all_items = slices[\\\"all\\\"]\\n+ np.minimum.at(first_seen, all_items, all_times)\\n+ for cutoff in cutoffs:\\n+ ti, tg = relevant_targets(slices[\\\"relevant\\\"], first_seen, cutoff)\\n+ if not len(ti):\\n+ continue\\n+ hi, hw, cf = make_history(\\n+ slices[\\\"history\\\"], slices[\\\"actions\\\"], cutoff, cfg\\n+ )\\n+ history_ids.append(hi)\\n+ history_weights.append(hw)\\n+ context_features.append(cf)\\n+ target_ids.append(ti)\\n+ target_grades.append(tg)\\n+ context_users.append(u)\\n+ context_cutoffs.append(cutoff)\\n+\\n+ h_lengths = np.fromiter((len(x) for x in history_ids), np.int64)\\n+ t_lengths = np.fromiter((len(x) for x in target_ids), np.int64)\\n+ result = {\\n+ \\\"history_ids\\\": np.concatenate(history_ids).astype(np.int32),\\n+ \\\"history_weights\\\": np.concatenate(history_weights).astype(np.float32),\\n+ \\\"history_offsets\\\": np.r_[0, np.cumsum(h_lengths)].astype(np.int64),\\n+ \\\"features\\\": np.stack(context_features).astype(np.float32),\\n+ \\\"target_ids\\\": np.concatenate(target_ids).astype(np.int32),\\n+ \\\"target_grades\\\": np.concatenate(target_grades).astype(np.uint8),\\n+ \\\"target_offsets\\\": np.r_[0, np.cumsum(t_lengths)].astype(np.int64),\\n+ \\\"context_users\\\": np.asarray(context_users, dtype=np.int32),\\n+ \\\"context_cutoffs\\\": np.asarray(context_cutoffs, dtype=np.int64),\\n+ }\\n+ print(\\n+ f\\\"built {len(history_ids):,} temporal contexts with \\\"\\n+ f\\\"{len(result['target_ids']):,} novel targets and \\\"\\n+ f\\\"{len(result['history_ids']):,} weighted history entries\\\",\\n+ flush=True,\\n+ )\\n+ return result\\n+\\n+\\n+def inference_histories(\\n+ arrays: dict,\\n+ users: np.ndarray,\\n+ cutoff: int,\\n+ cfg: Config,\\n+ preference_path: Path,\\n+ candidate_items: np.ndarray,\\n+) -> dict[str, np.ndarray]:\\n+ \\\"\\\"\\\"Build one cutoff-correct history per request user.\\\"\\\"\\\"\\n+ num_users = len(users)\\n+ hb = user_bounds(arrays[\\\"history\\\"][0], num_users)\\n+ # The supplied state is authoritative and avoids reconstructing it from a\\n+ # potentially truncated event horizon at serving time.\\n+ pref = pd.read_parquet(preference_path)\\n+ pu, pi, keep = map_frame(pref, users, candidate_items)\\n+ ps = pref.state.to_numpy(copy=False)[keep]\\n+ order = np.argsort(pu, kind=\\\"stable\\\")\\n+ pu, pi, ps = pu[order], pi[order], ps[order]\\n+ pb = user_bounds(pu, num_users)\\n+\\n+ ids_list, weights_list, features = [], [], []\\n+ for u in range(num_users):\\n+ hs = tuple(x[hb[u] : hb[u + 1]] for x in arrays[\\\"history\\\"][1:])\\n+ li, lw, organic_rate = latest_unique_history(*hs, cutoff, cfg)\\n+ sl = slice(pb[u], pb[u + 1])\\n+ pitems = pi[sl].astype(np.int32, copy=False)\\n+ states = ps[sl]\\n+ pweights = np.where(states == \\\"liked\\\", 2.5, -1.25).astype(np.float32)\\n+ ids = np.concatenate((li, pitems))\\n+ weights = np.concatenate((lw, pweights))\\n+ if not len(ids):\\n+ ids, weights = np.array([0], np.int32), np.array([0.0], np.float32)\\n+ ids_list.append(ids)\\n+ weights_list.append(weights)\\n+ features.append(\\n+ np.array(\\n+ [\\n+ math.log1p(len(li)) / math.log1p(cfg.max_history),\\n+ math.log1p(int((states == \\\"liked\\\").sum())) / 6.0,\\n+ math.log1p(int((states == \\\"disliked\\\").sum())) / 4.0,\\n+ organic_rate,\\n+ ],\\n+ np.float32,\\n+ )\\n+ )\\n+ lengths = np.fromiter((len(x) for x in ids_list), np.int64)\\n+ return {\\n+ \\\"history_ids\\\": np.concatenate(ids_list).astype(np.int32),\\n+ \\\"history_weights\\\": np.concatenate(weights_list).astype(np.float32),\\n+ \\\"history_offsets\\\": np.r_[0, np.cumsum(lengths)].astype(np.int64),\\n+ \\\"features\\\": np.stack(features).astype(np.float32),\\n+ }\\n+\\n+\\n+def gather_histories(data: dict[str, np.ndarray], indices: np.ndarray):\\n+ offsets = data[\\\"history_offsets\\\"]\\n+ pieces_i = [data[\\\"history_ids\\\"][offsets[x] : offsets[x + 1]] for x in indices]\\n+ pieces_w = [\\n+ data[\\\"history_weights\\\"][offsets[x] : offsets[x + 1]] for x in indices\\n+ ]\\n+ lengths = np.fromiter((len(x) for x in pieces_i), np.int64)\\n+ return (\\n+ torch.from_numpy(np.concatenate(pieces_i).astype(np.int64)),\\n+ torch.from_numpy(np.r_[0, np.cumsum(lengths)].astype(np.int64)),\\n+ torch.from_numpy(np.concatenate(pieces_w)),\\n+ torch.from_numpy(data[\\\"features\\\"][indices]),\\n+ )\\n+\\n+\\n+def sample_targets(\\n+ data: dict[str, np.ndarray], indices: np.ndarray, rng: np.random.Generator, cfg: Config\\n+) -> np.ndarray:\\n+ offsets = data[\\\"target_offsets\\\"]\\n+ out = np.empty(len(indices), dtype=np.int64)\\n+ for j, x in enumerate(indices):\\n+ lo, hi = offsets[x], offsets[x + 1]\\n+ grades = data[\\\"target_grades\\\"][lo:hi]\\n+ weights = np.where(grades == 3, cfg.like_sampling_weight, 1.0)\\n+ weights = weights / weights.sum()\\n+ out[j] = data[\\\"target_ids\\\"][lo + rng.choice(hi - lo, p=weights)]\\n+ return out\\n+\\n+\\n+def train_model(\\n+ model: TwoTowerRanker, data: dict[str, np.ndarray], cfg: Config\\n+) -> list[float]:\\n+ rng = np.random.default_rng(cfg.seed)\\n+ torch.manual_seed(cfg.seed)\\n+ optimizer = torch.optim.AdamW(\\n+ model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay\\n+ )\\n+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n+ optimizer, T_max=cfg.epochs, eta_min=cfg.learning_rate * 0.12\\n+ )\\n+ n = len(data[\\\"features\\\"])\\n+ losses: list[float] = []\\n+ for epoch in range(cfg.epochs):\\n+ model.train()\\n+ order = rng.permutation(n)\\n+ total_loss = 0.0\\n+ seen = 0\\n+ for start in range(0, n, cfg.batch_size):\\n+ batch = order[start : start + cfg.batch_size]\\n+ hids, offsets, hweights, features = gather_histories(data, batch)\\n+ positives_np = sample_targets(data, batch, rng, cfg)\\n+ positives = torch.from_numpy(positives_np)\\n+ negatives = torch.from_numpy(\\n+ rng.integers(0, 30_000, size=cfg.negatives, dtype=np.int64)\\n+ )\\n+ users = model.user_tower(hids, offsets, hweights, features)\\n+ pos_vectors = model.item_tower(positives)\\n+ neg_vectors = model.item_tower(negatives)\\n+ positive_scores = (users * pos_vectors).sum(1, keepdim=True)\\n+ negative_scores = users @ neg_vectors.T\\n+ # A sampled candidate that equals the row's positive is not a\\n+ # negative. Mask it instead of teaching contradictory labels.\\n+ negative_scores.masked_fill_(\\n+ positives[:, None] == negatives[None, :], -1e4\\n+ )\\n+ logits = torch.cat((positive_scores, negative_scores), dim=1)\\n+ loss = F.cross_entropy(\\n+ logits, torch.zeros(len(batch), dtype=torch.long)\\n+ )\\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) * len(batch)\\n+ seen += len(batch)\\n+ scheduler.step()\\n+ mean_loss = total_loss / seen\\n+ losses.append(mean_loss)\\n+ print(\\n+ f\\\"epoch {epoch + 1:02d}/{cfg.epochs}: loss={mean_loss:.5f} \\\"\\n+ f\\\"lr={scheduler.get_last_lr()[0]:.6g}\\\",\\n+ flush=True,\\n+ )\\n+ return losses\\n+\\n+\\n+@torch.inference_mode()\\n+def rank_candidates(\\n+ model: TwoTowerRanker,\\n+ histories: dict[str, np.ndarray],\\n+ eligibility_path: Path,\\n+ output: Path,\\n+ batch_size: int = 192,\\n+) -> None:\\n+ model.eval()\\n+ archive = np.load(eligibility_path)\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ item_ids = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"packed_eligible\\\"]\\n+ all_item_vectors = model.item_tower(torch.arange(len(item_ids))).T.contiguous()\\n+ rows: list[np.ndarray] = []\\n+ for start in range(0, len(uids), batch_size):\\n+ stop = min(start + batch_size, len(uids))\\n+ idx = np.arange(start, stop)\\n+ hids, offsets, weights, features = gather_histories(histories, idx)\\n+ user_vectors = model.user_tower(hids, offsets, weights, features)\\n+ scores = user_vectors @ all_item_vectors\\n+ eligible = np.unpackbits(\\n+ packed[start:stop], axis=1, count=len(item_ids), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\\n+ # A tiny deterministic index term makes exact ties reproducible.\\n+ scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\\n+ top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\\n+ block = np.empty(((stop - start) * 100, 3), dtype=np.int64)\\n+ block[:, 0] = np.repeat(uids[start:stop], 100)\\n+ block[:, 1] = item_ids[top.reshape(-1)]\\n+ block[:, 2] = np.tile(np.arange(1, 101), stop - start)\\n+ rows.append(block)\\n+ frame = pd.DataFrame(np.concatenate(rows), columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"])\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ frame.to_parquet(output, index=False)\\n+ print(f\\\"wrote {len(frame):,} ranked rows to {output}\\\", flush=True)\\n+\\n+\\n+def item_features(catalog: pd.DataFrame) -> torch.Tensor:\\n+ raw = np.column_stack(\\n+ (\\n+ np.log1p(catalog.positive_listens.to_numpy(np.float32)),\\n+ np.log1p(catalog.organic_positive_listens.to_numpy(np.float32)),\\n+ np.log1p(catalog.popularity_rank.to_numpy(np.float32)),\\n+ catalog.organic_positive_listens.to_numpy(np.float32)\\n+ / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1),\\n+ )\\n+ )\\n+ raw = (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\\n+ return torch.from_numpy(raw.astype(np.float32))\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ parser.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--checkpoint\\\", type=Path)\\n+ parser.add_argument(\\\"--cache\\\", type=Path)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=Config.epochs)\\n+ parser.add_argument(\\\"--embedding-dim\\\", type=int, default=Config.embedding_dim)\\n+ parser.add_argument(\\\"--max-history\\\", type=int, default=Config.max_history)\\n+ parser.add_argument(\\\"--training-weeks\\\", type=int, default=Config.training_weeks)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)\\n+ parser.add_argument(\\\"--threads\\\", type=int, default=8)\\n+ args = parser.parse_args()\\n+ torch.set_num_threads(args.threads)\\n+ torch.set_num_interop_threads(1)\\n+ random.seed(args.seed)\\n+ np.random.seed(args.seed)\\n+ cfg = Config(\\n+ epochs=args.epochs,\\n+ embedding_dim=args.embedding_dim,\\n+ max_history=args.max_history,\\n+ training_weeks=args.training_weeks,\\n+ seed=args.seed,\\n+ )\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ candidate_items = catalog.item_id.to_numpy(np.int64)\\n+ target_users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ stage_end = TRAIN_END if args.stage == \\\"validation\\\" else VAL_END\\n+ cache = args.cache or Path(f\\\"/app/solution/cache_{args.stage}.pt\\\")\\n+ if cache.exists():\\n+ payload = torch.load(cache, weights_only=False)\\n+ contexts, histories = payload[\\\"contexts\\\"], payload[\\\"histories\\\"]\\n+ print(f\\\"loaded prepared arrays from {cache}\\\", flush=True)\\n+ else:\\n+ arrays = load_event_arrays(args.stage, target_users, candidate_items)\\n+ contexts = build_contexts(arrays, target_users, stage_end, cfg)\\n+ pref_path = DATA / (\\n+ \\\"validation_preference_state.parquet\\\"\\n+ if args.stage == \\\"validation\\\"\\n+ else \\\"test_preference_state.parquet\\\"\\n+ )\\n+ histories = inference_histories(\\n+ arrays, target_users, stage_end, cfg, pref_path, candidate_items\\n+ )\\n+ torch.save(\\n+ {\\\"contexts\\\": contexts, \\\"histories\\\": histories, \\\"config\\\": asdict(cfg)},\\n+ cache,\\n+ )\\n+ print(f\\\"saved prepared arrays to {cache}\\\", flush=True)\\n+ model = TwoTowerRanker(\\n+ len(candidate_items),\\n+ item_features(catalog),\\n+ embedding_dim=cfg.embedding_dim,\\n+ hidden_dim=cfg.hidden_dim,\\n+ )\\n+ losses = train_model(model, contexts, cfg)\\n+ checkpoint = args.checkpoint or Path(\\n+ f\\\"/app/solution/model_{args.stage}.pt\\\"\\n+ )\\n+ torch.save(\\n+ {\\n+ \\\"model_state\\\": model.state_dict(),\\n+ \\\"config\\\": asdict(cfg),\\n+ \\\"losses\\\": losses,\\n+ \\\"catalog_item_ids\\\": candidate_items,\\n+ },\\n+ checkpoint,\\n+ )\\n+ eligibility = DATA / f\\\"{args.stage}_eligible_candidates.npz\\\"\\n+ rank_candidates(model, histories, eligibility, args.output)\\n+ print(json.dumps({\\\"checkpoint\\\": str(checkpoint), \\\"config\\\": asdict(cfg)}))\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:11:11.815Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:11.776Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-96f71dd1-c863-4dc9-97ec-dd2d745a8f21", "changes": { "/app/solution/model.py": { "type": "add", "content": "\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures. It deliberately has no user-ID parameter. The item tower can be\nrun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n num_context_features: int = 4,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.history_embedding = nn.EmbeddingBag(\n num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n )\n nn.init.normal_(self.history_embedding.weight, std=0.04)\n self.network = nn.Sequential(\n nn.Linear(embedding_dim + num_context_features, hidden_dim),\n nn.GELU(),\n nn.LayerNorm(hidden_dim),\n nn.Linear(hidden_dim, embedding_dim),\n )\n self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n def forward(\n self,\n item_ids: torch.Tensor,\n offsets: torch.Tensor,\n weights: torch.Tensor,\n context_features: torch.Tensor,\n ) -> torch.Tensor:\n pooled = self.history_embedding(\n item_ids, offsets, per_sample_weights=weights\n )\n # Signed preference weights are useful, but their absolute magnitude\n # must not make heavy users arbitrarily larger than light users.\n abs_sums = torch.zeros(\n len(offsets) - 1, device=weights.device, dtype=weights.dtype\n )\n lengths = offsets[1:] - offsets[:-1]\n bag_ids = torch.repeat_interleave(\n torch.arange(len(lengths), device=weights.device), lengths\n )\n abs_sums.scatter_add_(0, bag_ids, weights.abs())\n pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n latent = self.network(torch.cat((pooled, context_features), dim=1))\n latent = F.normalize(latent + self.skip(pooled), dim=1)\n # Constraining the scale prevents sampled-softmax from becoming\n # numerically brittle. The constant last coordinate pairs with the\n # item tower's learned popularity prior.\n scale = self.log_scale.exp().clamp(1.0, 30.0)\n return torch.cat(\n (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n )\n\n\nclass ItemTower(nn.Module):\n \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n item_features: torch.Tensor,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.id_embedding = nn.Embedding(num_items, embedding_dim)\n nn.init.normal_(self.id_embedding.weight, std=0.04)\n self.register_buffer(\"item_features\", item_features.float())\n feature_dim = item_features.shape[1]\n self.network = nn.Sequential(\n nn.Linear(embedding_dim + feature_dim, hidden_dim),\n nn.GELU(),\n nn.LayerNorm(hidden_dim),\n nn.Linear(hidden_dim, embedding_dim),\n )\n self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n self.prior = nn.Sequential(\n nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n )\n self.id_bias = nn.Embedding(num_items, 1)\n nn.init.zeros_(self.id_bias.weight)\n\n def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n ids = self.id_embedding(item_ids)\n features = self.item_features[item_ids]\n latent = self.network(torch.cat((ids, features), dim=1))\n latent = F.normalize(latent + self.skip(ids), dim=1)\n bias = self.prior(features) + self.id_bias(item_ids)\n return torch.cat((latent, bias), dim=1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n item_features: torch.Tensor,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n ) -> None:\n super().__init__()\n self.user_tower = UserTower(\n num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n )\n self.item_tower = ItemTower(\n num_items,\n item_features=item_features,\n embedding_dim=embedding_dim,\n hidden_dim=hidden_dim,\n )\n\n def score(\n self,\n history_ids: torch.Tensor,\n offsets: torch.Tensor,\n history_weights: torch.Tensor,\n context_features: torch.Tensor,\n candidate_ids: torch.Tensor,\n ) -> torch.Tensor:\n users = self.user_tower(\n history_ids, offsets, history_weights, context_features\n )\n items = self.item_tower(candidate_ids)\n return users @ items.T\n" }, "/app/solution/train_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply an event-scalable two-tower discovery ranker.\n\nNo user-item matrix is created. Training examples are streamed conceptually\nas temporal user histories paired with novel organic discoveries, represented\nin compact CSR arrays. The only dense user-by-item object is a short-lived\nrequest batch of retrieval scores during ranking.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom dataclasses import asdict, 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\nfrom model import TwoTowerRanker\n\n\nDATA = Path(\"/task/data\")\nTRAIN_END = 24_786_800\nWEEK = 7 * 24 * 60 * 60\nGAP = 30 * 60\nVAL_END = TRAIN_END + GAP + WEEK\n\n\n@dataclass\nclass Config:\n embedding_dim: int = 64\n hidden_dim: int = 128\n max_history: int = 384\n history_days: int = 180\n training_weeks: int = 16\n epochs: int = 16\n batch_size: int = 256\n negatives: int = 512\n learning_rate: float = 0.002\n weight_decay: float = 1e-5\n like_sampling_weight: float = 4.0\n seed: int = 20260924\n\n\ndef id_lookup(values: np.ndarray, known: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"Map arbitrary integer IDs to positions without a Python-sized hash map.\"\"\"\n order = np.argsort(known)\n sorted_known = known[order]\n loc = np.searchsorted(sorted_known, values)\n valid = loc < len(sorted_known)\n valid_indices = np.flatnonzero(valid)\n valid[valid_indices] &= sorted_known[loc[valid_indices]] == values[valid_indices]\n mapped = np.full(len(values), -1, dtype=np.int32)\n mapped[valid] = order[loc[valid]].astype(np.int32)\n return mapped, valid\n\n\ndef read_family(name: str, stage: str, columns: list[str]) -> pd.DataFrame:\n frames = [pd.read_parquet(DATA / f\"train_{name}.parquet\", columns=columns)]\n if stage == \"final\":\n frames.append(pd.read_parquet(DATA / f\"val_{name}.parquet\", columns=columns))\n return pd.concat(frames, ignore_index=True)\n\n\ndef map_frame(\n frame: pd.DataFrame, users: np.ndarray, items: np.ndarray\n) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n u, uv = id_lookup(frame.uid.to_numpy(np.int64, copy=False), users)\n i, iv = id_lookup(frame.item_id.to_numpy(np.int64, copy=False), items)\n keep = uv & iv\n return u[keep], i[keep], np.flatnonzero(keep)\n\n\ndef sort_events(\n uid: np.ndarray,\n timestamp: np.ndarray,\n item: np.ndarray,\n *extra: np.ndarray,\n) -> tuple[np.ndarray, ...]:\n order = np.lexsort((timestamp, uid))\n return (uid[order], timestamp[order], item[order]) + tuple(x[order] for x in extra)\n\n\ndef user_bounds(uid: np.ndarray, num_users: int) -> np.ndarray:\n return np.searchsorted(uid, np.arange(num_users + 1), side=\"left\")\n\n\ndef load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\n \"\"\"Load compact candidate-catalog events; raw frames are promptly released.\"\"\"\n started = time.time()\n listens = read_family(\n \"listens\",\n stage,\n [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n lu, li, keep = map_frame(listens, users, items)\n lt = listens.timestamp.to_numpy(np.int64, copy=False)[keep]\n lo = listens.is_organic.to_numpy(np.uint8, copy=False)[keep]\n lr = listens.played_ratio_pct.to_numpy(np.int16, copy=False)[keep]\n\n # All catalogue listens define novelty. Positive listens form histories.\n all_listens = sort_events(lu, lt, li)\n positive = lr >= 50\n history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n 1.0 + 0.35 * lo[positive]\n )\n history = sort_events(\n lu[positive],\n lt[positive],\n li[positive],\n history_weight.astype(np.float32),\n lo[positive].astype(np.float32),\n )\n strong = (lo == 1) & (lr >= 80)\n relevant_parts = [\n (lu[strong], lt[strong], li[strong], np.ones(strong.sum(), dtype=np.uint8))\n ]\n del listens, lr, lo, lu, li, lt\n\n action_parts: list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = []\n action_spec = {\n \"likes\": (0, 1, 3),\n \"unlikes\": (0, 0, 0),\n \"dislikes\": (1, 1, 0),\n \"undislikes\": (1, 0, 0),\n }\n for name, (channel, active, grade) in action_spec.items():\n frame = read_family(name, stage, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n au, ai, keep = map_frame(frame, users, items)\n at = frame.timestamp.to_numpy(np.int64, copy=False)[keep]\n organic = frame.is_organic.to_numpy(np.uint8, copy=False)[keep]\n action_parts.append(\n (\n au,\n at,\n ai,\n np.full(len(au), channel, dtype=np.uint8),\n np.full(len(au), active, dtype=np.uint8),\n )\n )\n if grade:\n good = organic == 1\n relevant_parts.append(\n (\n au[good],\n at[good],\n ai[good],\n np.full(good.sum(), grade, dtype=np.uint8),\n )\n )\n del frame\n\n actions = sort_events(\n np.concatenate([x[0] for x in action_parts]),\n np.concatenate([x[1] for x in action_parts]),\n np.concatenate([x[2] for x in action_parts]),\n np.concatenate([x[3] for x in action_parts]),\n np.concatenate([x[4] for x in action_parts]),\n )\n relevant = sort_events(\n np.concatenate([x[0] for x in relevant_parts]),\n np.concatenate([x[1] for x in relevant_parts]),\n np.concatenate([x[2] for x in relevant_parts]),\n np.concatenate([x[3] for x in relevant_parts]),\n )\n print(\n f\"loaded {len(all_listens[0]):,} catalogue listens, \"\n f\"{len(history[0]):,} positive history events and \"\n f\"{len(relevant[0]):,} organic relevance events in \"\n f\"{time.time() - started:.1f}s\",\n flush=True,\n )\n return {\n \"all\": all_listens,\n \"history\": history,\n \"actions\": actions,\n \"relevant\": relevant,\n }\n\n\ndef latest_unique_history(\n times: np.ndarray,\n items: np.ndarray,\n values: np.ndarray,\n organic: np.ndarray,\n cutoff: int,\n cfg: Config,\n) -> tuple[np.ndarray, np.ndarray, float]:\n hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n lo = int(np.searchsorted(times, cutoff - cfg.history_days * 86_400, side=\"left\"))\n if hi <= lo:\n return np.empty(0, np.int32), np.empty(0, np.float32), 0.0\n reverse_items = items[lo:hi][::-1]\n _, first_reverse = np.unique(reverse_items, return_index=True)\n first_reverse.sort()\n first_reverse = first_reverse[: cfg.max_history]\n positions = hi - 1 - first_reverse\n age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\n decay = np.exp(-age_days / 120.0)\n weights = values[positions] * decay\n return (\n items[positions].astype(np.int32, copy=False),\n weights.astype(np.float32, copy=False),\n float(organic[positions].mean()),\n )\n\n\ndef active_preferences(\n times: np.ndarray,\n items: np.ndarray,\n channels: np.ndarray,\n active: np.ndarray,\n cutoff: int,\n) -> tuple[np.ndarray, np.ndarray, int, int]:\n hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n if hi == 0:\n return np.empty(0, np.int32), np.empty(0, np.float32), 0, 0\n keys = items[:hi].astype(np.int64) * 2 + channels[:hi]\n reverse_keys = keys[::-1]\n _, first_reverse = np.unique(reverse_keys, return_index=True)\n pos = hi - 1 - first_reverse\n pos = pos[active[pos] == 1]\n liked = pos[channels[pos] == 0]\n disliked = pos[channels[pos] == 1]\n out_items = np.concatenate((items[liked], items[disliked])).astype(np.int32)\n out_weights = np.concatenate(\n (\n np.full(len(liked), 2.5, np.float32),\n np.full(len(disliked), -1.25, np.float32),\n )\n )\n return out_items, out_weights, len(liked), len(disliked)\n\n\ndef make_history(\n h: tuple[np.ndarray, ...],\n a: tuple[np.ndarray, ...],\n cutoff: int,\n cfg: Config,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n li, lw, organic_rate = latest_unique_history(\n h[0], h[1], h[2], h[3], cutoff, cfg\n )\n pi, pw, n_like, n_dislike = active_preferences(\n a[0], a[1], a[2], a[3], cutoff\n )\n ids = np.concatenate((li, pi))\n weights = np.concatenate((lw, pw))\n if not len(ids):\n # Every target user should have history, but EmbeddingBag requires a\n # real entry. Item 0 at zero weight is a neutral fallback.\n ids = np.array([0], dtype=np.int32)\n weights = np.array([0.0], dtype=np.float32)\n features = np.array(\n [\n math.log1p(len(li)) / math.log1p(cfg.max_history),\n math.log1p(n_like) / 6.0,\n math.log1p(n_dislike) / 4.0,\n organic_rate,\n ],\n dtype=np.float32,\n )\n return ids, weights, features\n\n\ndef relevant_targets(\n r: tuple[np.ndarray, ...],\n first_seen: np.ndarray,\n cutoff: int,\n) -> tuple[np.ndarray, np.ndarray]:\n lo = int(np.searchsorted(r[0], cutoff + GAP, side=\"left\"))\n hi = int(np.searchsorted(r[0], cutoff + GAP + WEEK, side=\"left\"))\n if hi <= lo:\n return np.empty(0, np.int32), np.empty(0, np.uint8)\n ri, rg = r[1][lo:hi], r[2][lo:hi]\n novel = first_seen[ri] >= cutoff\n ri, rg = ri[novel], rg[novel]\n if not len(ri):\n return np.empty(0, np.int32), np.empty(0, np.uint8)\n unique, inverse = np.unique(ri, return_inverse=True)\n grades = np.zeros(len(unique), dtype=np.uint8)\n np.maximum.at(grades, inverse, rg)\n return unique.astype(np.int32), grades\n\n\ndef build_contexts(\n arrays: dict,\n users: np.ndarray,\n stage_end: int,\n cfg: Config,\n) -> dict[str, np.ndarray]:\n num_users = len(users)\n bounds = {k: user_bounds(v[0], num_users) for k, v in arrays.items()}\n history_ids: list[np.ndarray] = []\n history_weights: list[np.ndarray] = []\n context_features: list[np.ndarray] = []\n target_ids: list[np.ndarray] = []\n target_grades: list[np.ndarray] = []\n context_users: list[int] = []\n context_cutoffs: list[int] = []\n\n # End at stage_end-WEEK so every label window is fully observed. For the\n # final refit, this includes the public validation window as the newest\n # temporal training example.\n cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\n cutoffs.sort()\n first_seen = np.empty(30_000, dtype=np.int64)\n for u in range(num_users):\n slices = {}\n for key, values in arrays.items():\n b = bounds[key]\n slices[key] = tuple(x[b[u] : b[u + 1]] for x in values[1:])\n first_seen.fill(np.iinfo(np.int64).max)\n all_times, all_items = slices[\"all\"]\n np.minimum.at(first_seen, all_items, all_times)\n for cutoff in cutoffs:\n ti, tg = relevant_targets(slices[\"relevant\"], first_seen, cutoff)\n if not len(ti):\n continue\n hi, hw, cf = make_history(\n slices[\"history\"], slices[\"actions\"], cutoff, cfg\n )\n history_ids.append(hi)\n history_weights.append(hw)\n context_features.append(cf)\n target_ids.append(ti)\n target_grades.append(tg)\n context_users.append(u)\n context_cutoffs.append(cutoff)\n\n h_lengths = np.fromiter((len(x) for x in history_ids), np.int64)\n t_lengths = np.fromiter((len(x) for x in target_ids), np.int64)\n result = {\n \"history_ids\": np.concatenate(history_ids).astype(np.int32),\n \"history_weights\": np.concatenate(history_weights).astype(np.float32),\n \"history_offsets\": np.r_[0, np.cumsum(h_lengths)].astype(np.int64),\n \"features\": np.stack(context_features).astype(np.float32),\n \"target_ids\": np.concatenate(target_ids).astype(np.int32),\n \"target_grades\": np.concatenate(target_grades).astype(np.uint8),\n \"target_offsets\": np.r_[0, np.cumsum(t_lengths)].astype(np.int64),\n \"context_users\": np.asarray(context_users, dtype=np.int32),\n \"context_cutoffs\": np.asarray(context_cutoffs, dtype=np.int64),\n }\n print(\n f\"built {len(history_ids):,} temporal contexts with \"\n f\"{len(result['target_ids']):,} novel targets and \"\n f\"{len(result['history_ids']):,} weighted history entries\",\n flush=True,\n )\n return result\n\n\ndef inference_histories(\n arrays: dict,\n users: np.ndarray,\n cutoff: int,\n cfg: Config,\n preference_path: Path,\n candidate_items: np.ndarray,\n) -> dict[str, np.ndarray]:\n \"\"\"Build one cutoff-correct history per request user.\"\"\"\n num_users = len(users)\n hb = user_bounds(arrays[\"history\"][0], num_users)\n # The supplied state is authoritative and avoids reconstructing it from a\n # potentially truncated event horizon at serving time.\n pref = pd.read_parquet(preference_path)\n pu, pi, keep = map_frame(pref, users, candidate_items)\n ps = pref.state.to_numpy(copy=False)[keep]\n order = np.argsort(pu, kind=\"stable\")\n pu, pi, ps = pu[order], pi[order], ps[order]\n pb = user_bounds(pu, num_users)\n\n ids_list, weights_list, features = [], [], []\n for u in range(num_users):\n hs = tuple(x[hb[u] : hb[u + 1]] for x in arrays[\"history\"][1:])\n li, lw, organic_rate = latest_unique_history(*hs, cutoff, cfg)\n sl = slice(pb[u], pb[u + 1])\n pitems = pi[sl].astype(np.int32, copy=False)\n states = ps[sl]\n pweights = np.where(states == \"liked\", 2.5, -1.25).astype(np.float32)\n ids = np.concatenate((li, pitems))\n weights = np.concatenate((lw, pweights))\n if not len(ids):\n ids, weights = np.array([0], np.int32), np.array([0.0], np.float32)\n ids_list.append(ids)\n weights_list.append(weights)\n features.append(\n np.array(\n [\n math.log1p(len(li)) / math.log1p(cfg.max_history),\n math.log1p(int((states == \"liked\").sum())) / 6.0,\n math.log1p(int((states == \"disliked\").sum())) / 4.0,\n organic_rate,\n ],\n np.float32,\n )\n )\n lengths = np.fromiter((len(x) for x in ids_list), np.int64)\n return {\n \"history_ids\": np.concatenate(ids_list).astype(np.int32),\n \"history_weights\": np.concatenate(weights_list).astype(np.float32),\n \"history_offsets\": np.r_[0, np.cumsum(lengths)].astype(np.int64),\n \"features\": np.stack(features).astype(np.float32),\n }\n\n\ndef gather_histories(data: dict[str, np.ndarray], indices: np.ndarray):\n offsets = data[\"history_offsets\"]\n pieces_i = [data[\"history_ids\"][offsets[x] : offsets[x + 1]] for x in indices]\n pieces_w = [\n data[\"history_weights\"][offsets[x] : offsets[x + 1]] for x in indices\n ]\n lengths = np.fromiter((len(x) for x in pieces_i), np.int64)\n return (\n torch.from_numpy(np.concatenate(pieces_i).astype(np.int64)),\n torch.from_numpy(np.r_[0, np.cumsum(lengths)].astype(np.int64)),\n torch.from_numpy(np.concatenate(pieces_w)),\n torch.from_numpy(data[\"features\"][indices]),\n )\n\n\ndef sample_targets(\n data: dict[str, np.ndarray], indices: np.ndarray, rng: np.random.Generator, cfg: Config\n) -> np.ndarray:\n offsets = data[\"target_offsets\"]\n out = np.empty(len(indices), dtype=np.int64)\n for j, x in enumerate(indices):\n lo, hi = offsets[x], offsets[x + 1]\n grades = data[\"target_grades\"][lo:hi]\n weights = np.where(grades == 3, cfg.like_sampling_weight, 1.0)\n weights = weights / weights.sum()\n out[j] = data[\"target_ids\"][lo + rng.choice(hi - lo, p=weights)]\n return out\n\n\ndef train_model(\n model: TwoTowerRanker, data: dict[str, np.ndarray], cfg: Config\n) -> list[float]:\n rng = np.random.default_rng(cfg.seed)\n torch.manual_seed(cfg.seed)\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay\n )\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, T_max=cfg.epochs, eta_min=cfg.learning_rate * 0.12\n )\n n = len(data[\"features\"])\n losses: list[float] = []\n for epoch in range(cfg.epochs):\n model.train()\n order = rng.permutation(n)\n total_loss = 0.0\n seen = 0\n for start in range(0, n, cfg.batch_size):\n batch = order[start : start + cfg.batch_size]\n hids, offsets, hweights, features = gather_histories(data, batch)\n positives_np = sample_targets(data, batch, rng, cfg)\n positives = torch.from_numpy(positives_np)\n negatives = torch.from_numpy(\n rng.integers(0, 30_000, size=cfg.negatives, dtype=np.int64)\n )\n users = model.user_tower(hids, offsets, hweights, features)\n pos_vectors = model.item_tower(positives)\n neg_vectors = model.item_tower(negatives)\n positive_scores = (users * pos_vectors).sum(1, keepdim=True)\n negative_scores = users @ neg_vectors.T\n # A sampled candidate that equals the row's positive is not a\n # negative. Mask it instead of teaching contradictory labels.\n negative_scores.masked_fill_(\n positives[:, None] == negatives[None, :], -1e4\n )\n logits = torch.cat((positive_scores, negative_scores), dim=1)\n loss = F.cross_entropy(\n logits, torch.zeros(len(batch), dtype=torch.long)\n )\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) * len(batch)\n seen += len(batch)\n scheduler.step()\n mean_loss = total_loss / seen\n losses.append(mean_loss)\n print(\n f\"epoch {epoch + 1:02d}/{cfg.epochs}: loss={mean_loss:.5f} \"\n f\"lr={scheduler.get_last_lr()[0]:.6g}\",\n flush=True,\n )\n return losses\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: TwoTowerRanker,\n histories: dict[str, np.ndarray],\n eligibility_path: Path,\n output: Path,\n batch_size: int = 192,\n) -> None:\n model.eval()\n archive = np.load(eligibility_path)\n uids = archive[\"uids\"].astype(np.int64)\n item_ids = archive[\"item_ids\"].astype(np.int64)\n packed = archive[\"packed_eligible\"]\n all_item_vectors = model.item_tower(torch.arange(len(item_ids))).T.contiguous()\n rows: list[np.ndarray] = []\n for start in range(0, len(uids), batch_size):\n stop = min(start + batch_size, len(uids))\n idx = np.arange(start, stop)\n hids, offsets, weights, features = gather_histories(histories, idx)\n user_vectors = model.user_tower(hids, offsets, weights, features)\n scores = user_vectors @ all_item_vectors\n eligible = np.unpackbits(\n packed[start:stop], axis=1, count=len(item_ids), bitorder=\"little\"\n ).astype(bool)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n # A tiny deterministic index term makes exact ties reproducible.\n scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\n top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\n block = np.empty(((stop - start) * 100, 3), dtype=np.int64)\n block[:, 0] = np.repeat(uids[start:stop], 100)\n block[:, 1] = item_ids[top.reshape(-1)]\n block[:, 2] = np.tile(np.arange(1, 101), stop - start)\n rows.append(block)\n frame = pd.DataFrame(np.concatenate(rows), columns=[\"uid\", \"item_id\", \"rank\"])\n output.parent.mkdir(parents=True, exist_ok=True)\n frame.to_parquet(output, index=False)\n print(f\"wrote {len(frame):,} ranked rows to {output}\", flush=True)\n\n\ndef item_features(catalog: pd.DataFrame) -> torch.Tensor:\n raw = np.column_stack(\n (\n np.log1p(catalog.positive_listens.to_numpy(np.float32)),\n np.log1p(catalog.organic_positive_listens.to_numpy(np.float32)),\n np.log1p(catalog.popularity_rank.to_numpy(np.float32)),\n catalog.organic_positive_listens.to_numpy(np.float32)\n / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1),\n )\n )\n raw = (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\n return torch.from_numpy(raw.astype(np.float32))\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--checkpoint\", type=Path)\n parser.add_argument(\"--cache\", type=Path)\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--embedding-dim\", type=int, default=Config.embedding_dim)\n parser.add_argument(\"--max-history\", type=int, default=Config.max_history)\n parser.add_argument(\"--training-weeks\", type=int, default=Config.training_weeks)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--threads\", type=int, default=8)\n args = parser.parse_args()\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n random.seed(args.seed)\n np.random.seed(args.seed)\n cfg = Config(\n epochs=args.epochs,\n embedding_dim=args.embedding_dim,\n max_history=args.max_history,\n training_weeks=args.training_weeks,\n seed=args.seed,\n )\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n candidate_items = catalog.item_id.to_numpy(np.int64)\n target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n stage_end = TRAIN_END if args.stage == \"validation\" else VAL_END\n cache = args.cache or Path(f\"/app/solution/cache_{args.stage}.pt\")\n if cache.exists():\n payload = torch.load(cache, weights_only=False)\n contexts, histories = payload[\"contexts\"], payload[\"histories\"]\n print(f\"loaded prepared arrays from {cache}\", flush=True)\n else:\n arrays = load_event_arrays(args.stage, target_users, candidate_items)\n contexts = build_contexts(arrays, target_users, stage_end, cfg)\n pref_path = DATA / (\n \"validation_preference_state.parquet\"\n if args.stage == \"validation\"\n else \"test_preference_state.parquet\"\n )\n histories = inference_histories(\n arrays, target_users, stage_end, cfg, pref_path, candidate_items\n )\n torch.save(\n {\"contexts\": contexts, \"histories\": histories, \"config\": asdict(cfg)},\n cache,\n )\n print(f\"saved prepared arrays to {cache}\", flush=True)\n model = TwoTowerRanker(\n len(candidate_items),\n item_features(catalog),\n embedding_dim=cfg.embedding_dim,\n hidden_dim=cfg.hidden_dim,\n )\n losses = train_model(model, contexts, cfg)\n checkpoint = args.checkpoint or Path(\n f\"/app/solution/model_{args.stage}.pt\"\n )\n torch.save(\n {\n \"model_state\": model.state_dict(),\n \"config\": asdict(cfg),\n \"losses\": losses,\n \"catalog_item_ids\": candidate_items,\n },\n checkpoint,\n )\n eligibility = DATA / f\"{args.stage}_eligible_candidates.npz\"\n rank_candidates(model, histories, eligibility, args.output)\n print(json.dumps({\"checkpoint\": str(checkpoint), \"config\": asdict(cfg)}))\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/model.py\nA /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:11:11.814Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5accb89c887d293eed6ec4c587ede", "usage": { "input_tokens": 31174, "cached_input_tokens": 30464, "cache_write_input_tokens": 0, "output_tokens": 17945, "reasoning_output_tokens": 8840, "total_tokens": 49119 }, "turn_token_usage": { "input_tokens": 533454, "cached_input_tokens": 509696, "cache_write_input_tokens": 0, "output_tokens": 24289, "reasoning_output_tokens": 10435, "total_tokens": 557743 }, "thread_token_usage": { "input_tokens": 533454, "cached_input_tokens": 509696, "cache_write_input_tokens": 0, "output_tokens": 24289, "reasoning_output_tokens": 10435, "total_tokens": 557743 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-24T23:11:14.696Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:11:14.696Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:11:19.019Z", "callId": "call_2NB9gN7pElPxyJ80AIbKGBTq", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution && sed -n '1,40p' /app/solution/model.py 2>&1\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:11:19.135Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ntotal 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent 24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures. It deliberately has no user-ID parameter. The item tower can be\nrun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n num_context_features: int = 4,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.history_embedding = nn.EmbeddingBag(\n num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n )\n nn.init.normal_(self.history_embedding.weight, std=0.04)\n self.network = nn.Sequential(\n nn.Linear(embedding_dim + num_context_features, hidden_dim),\n nn.GELU(),\n nn.LayerNorm(hidden_dim),\n nn.Linear(hidden_dim, embedding_dim),\n )\n self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:19.069Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3a93add-3213-4fce-bf99-077a3c1b8471", "process_id": "4251", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution && sed -n '1,40p' /app/solution/model.py 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution && sed -n '1,40p' /app/solution/model.py 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent 24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures. It deliberately has no user-ID parameter. The item tower can be\nrun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n num_context_features: int = 4,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.history_embedding = nn.EmbeddingBag(\n num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n )\n nn.init.normal_(self.history_embedding.weight, std=0.04)\n self.network = nn.Sequential(\n nn.Linear(embedding_dim + num_context_features, hidden_dim),\n nn.GELU(),\n nn.LayerNorm(hidden_dim),\n nn.Linear(hidden_dim, embedding_dim),\n )\n self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n", "stderr": "", "aggregated_output": "total 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent 24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures. It deliberately has no user-ID parameter. The item tower can be\nrun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n num_context_features: int = 4,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.history_embedding = nn.EmbeddingBag(\n num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n )\n nn.init.normal_(self.history_embedding.weight, std=0.04)\n self.network = nn.Sequential(\n nn.Linear(embedding_dim + num_context_features, hidden_dim),\n nn.GELU(),\n nn.LayerNorm(hidden_dim),\n nn.Linear(hidden_dim, embedding_dim),\n )\n self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3720 }, "formatted_output": "total 744K\n-rw-r--r-- 1 agent agent 5.2K Sep 24 23:11 model.py\n-rw-r--r-- 1 agent agent 24K Sep 24 23:11 train_ranker.py\n-rw-r--r-- 1 agent agent 712K Sep 24 23:04 validation_popularity.parquet\n\"\"\"Neural towers used by the discovery ranker.\n\nThe user tower consumes only a variable-length interaction history plus summary\nfeatures. It deliberately has no user-ID parameter. The item tower can be\nrun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\nthe final coordinate implements an item prior while preserving an exact dot\nproduct scoring function.\n\"\"\"\nfrom __future__ import annotations\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass UserTower(nn.Module):\n \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n\n def __init__(\n self,\n num_items: int,\n embedding_dim: int = 64,\n hidden_dim: int = 128,\n num_context_features: int = 4,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.history_embedding = nn.EmbeddingBag(\n num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n )\n nn.init.normal_(self.history_embedding.weight, std=0.04)\n self.network = nn.Sequential(\n nn.Linear(embedding_dim + num_context_features, hidden_dim),\n nn.GELU(),\n nn.LayerNorm(hidden_dim),\n nn.Linear(hidden_dim, embedding_dim),\n )\n self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n self.log_scale = nn.Parameter(torch.tensor(2.0))\n\n" } } }, { "kind": "context", "timestamp": "2026-09-24T23:11:19.133Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae1043ec87d28a1f34ab4fc78e21", "usage": { "input_tokens": 49143, "cached_input_tokens": 30976, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 16, "total_tokens": 49233 }, "turn_token_usage": { "input_tokens": 582597, "cached_input_tokens": 540672, "cache_write_input_tokens": 0, "output_tokens": 24379, "reasoning_output_tokens": 10451, "total_tokens": 606976 }, "thread_token_usage": { "input_tokens": 582597, "cached_input_tokens": 540672, "cache_write_input_tokens": 0, "output_tokens": 24379, "reasoning_output_tokens": 10451, "total_tokens": 606976 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-24T23:11:32.185Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:11:32.185Z", "text": "" } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-24T23:11:41.145Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:11:41.145Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:11:44.218Z", "callId": "call_c3XWN5RhdHwFWKqgTmZx2S2i", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\\n+ cutoffs = [\\n+ stage_end - GAP - WEEK - j * WEEK for j in range(cfg.training_weeks)\\n+ ]\\n@@\\n- eligibility = DATA / f\\\"{args.stage}_eligible_candidates.npz\\\"\\n+ eligibility = DATA / (\\n+ \\\"validation_eligible_candidates.npz\\\"\\n+ if args.stage == \\\"validation\\\"\\n+ else \\\"test_eligible_candidates.npz\\\"\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:11:44.281Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:44.237Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c78adcf3-a593-4a3e-8e2a-683ad44a87e6", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -311,3 +311,5 @@\n # temporal training example.\n- cutoffs = [stage_end - WEEK - j * WEEK for j in range(cfg.training_weeks)]\n+ cutoffs = [\n+ stage_end - GAP - WEEK - j * WEEK for j in range(cfg.training_weeks)\n+ ]\n cutoffs.sort()\n@@ -620,3 +622,7 @@\n )\n- eligibility = DATA / f\"{args.stage}_eligible_candidates.npz\"\n+ eligibility = DATA / (\n+ \"validation_eligible_candidates.npz\"\n+ if args.stage == \"validation\"\n+ else \"test_eligible_candidates.npz\"\n+ )\n rank_candidates(model, histories, eligibility, args.output)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:11:44.280Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae177ec087d2bc8479f813a9a1b0", "usage": { "input_tokens": 49638, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 1106, "reasoning_output_tokens": 932, "total_tokens": 50744 }, "turn_token_usage": { "input_tokens": 632235, "cached_input_tokens": 589696, "cache_write_input_tokens": 0, "output_tokens": 25485, "reasoning_output_tokens": 11383, "total_tokens": 657720 }, "thread_token_usage": { "input_tokens": 632235, "cached_input_tokens": 589696, "cache_write_input_tokens": 0, "output_tokens": 25485, "reasoning_output_tokens": 11383, "total_tokens": 657720 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-24T23:11:45.840Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:11:45.840Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:11:52.484Z", "callId": "call_FVrilj1xRedcOk2bdz5X25ZG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport torch\\nfrom model import TwoTowerRanker\\nm=TwoTowerRanker(10,torch.randn(10,4),8,16)\\nh=torch.tensor([1,2,3,4]); o=torch.tensor([0,2,4]); w=torch.ones(4); f=torch.zeros(2,4); i=torch.arange(10)\\nu=m.user_tower(h,o,w,f); v=m.item_tower(i)\\nprint(u.shape,v.shape,(u@v.T).shape,m.score(h,o,w,f,i).shape)\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\\nfor p in ['validation','test']:\\n z=np.load(f'/task/data/{p}_eligible_candidates.npz')\\n print(p,'user order',np.array_equal(u,z['uids']))\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\\nz=np.load('/task/data/validation_eligible_candidates.npz')\\nprint('item order',np.array_equal(cat,z['item_ids']))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:11:57.340Z", "content": "Script completed\nWall time 4.8 seconds\nOutput:\n\ntorch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:11:52.586Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae309d8c87d281970029e8afa06a", "usage": { "input_tokens": 50768, "cached_input_tokens": 49408, "cache_write_input_tokens": 0, "output_tokens": 379, "reasoning_output_tokens": 9, "total_tokens": 51147 }, "turn_token_usage": { "input_tokens": 683003, "cached_input_tokens": 639104, "cache_write_input_tokens": 0, "output_tokens": 25864, "reasoning_output_tokens": 11392, "total_tokens": 708867 }, "thread_token_usage": { "input_tokens": 683003, "cached_input_tokens": 639104, "cache_write_input_tokens": 0, "output_tokens": 25864, "reasoning_output_tokens": 11392, "total_tokens": 708867 } } }, { "kind": "context", "timestamp": "2026-09-24T23:11:57.325Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b7bc631-21bc-47b8-b41a-cde842bf5870", "process_id": "34699", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch\nfrom model import TwoTowerRanker\nm=TwoTowerRanker(10,torch.randn(10,4),8,16)\nh=torch.tensor([1,2,3,4]); o=torch.tensor([0,2,4]); w=torch.ones(4); f=torch.zeros(2,4); i=torch.arange(10)\nu=m.user_tower(h,o,w,f); v=m.item_tower(i)\nprint(u.shape,v.shape,(u@v.T).shape,m.score(h,o,w,f,i).shape)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nfor p in ['validation','test']:\n z=np.load(f'/task/data/{p}_eligible_candidates.npz')\n print(p,'user order',np.array_equal(u,z['uids']))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint('item order',np.array_equal(cat,z['item_ids']))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch\nfrom model import TwoTowerRanker\nm=TwoTowerRanker(10,torch.randn(10,4),8,16)\nh=torch.tensor([1,2,3,4]); o=torch.tensor([0,2,4]); w=torch.ones(4); f=torch.zeros(2,4); i=torch.arange(10)\nu=m.user_tower(h,o,w,f); v=m.item_tower(i)\nprint(u.shape,v.shape,(u@v.T).shape,m.score(h,o,w,f,i).shape)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nfor p in ['validation','test']:\n z=np.load(f'/task/data/{p}_eligible_candidates.npz')\n print(p,'user order',np.array_equal(u,z['uids']))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint('item order',np.array_equal(cat,z['item_ids']))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n", "stderr": "", "aggregated_output": "torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 665873417 }, "formatted_output": "torch.Size([2, 9]) torch.Size([10, 9]) torch.Size([2, 10]) torch.Size([2, 10])\nvalidation user order True\ntest user order True\nitem order True\n" } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-24T23:11:59.383Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:11:59.383Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:12:01.862Z", "callId": "call_FOsYF4MyLv9vycFkQdKTgTMm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1.parquet --checkpoint /app/solution/model_validation_v1.pt --cache /app/solution/cache_validation.pt --epochs 12 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:12:03.039Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=79312", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:12:01.948Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae3d89cc87d2b5a667be88c48d15", "usage": { "input_tokens": 51216, "cached_input_tokens": 50560, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 59, "total_tokens": 51420 }, "turn_token_usage": { "input_tokens": 734219, "cached_input_tokens": 689664, "cache_write_input_tokens": 0, "output_tokens": 26068, "reasoning_output_tokens": 11451, "total_tokens": 760287 }, "thread_token_usage": { "input_tokens": 734219, "cached_input_tokens": 689664, "cache_write_input_tokens": 0, "output_tokens": 26068, "reasoning_output_tokens": 11451, "total_tokens": 760287 } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-24T23:12:04.867Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:12:04.867Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:12:05.558Z", "callId": "call_qDZitoWDJVT0CYojhmamkMuD", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":79312,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:12:35.567Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"88a455\",\"wall_time_seconds\":30.0010432,\"session_id\":79312,\"original_token_count\":55}\nloaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:12:05.654Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae433dac87d2ac824ddd008f2961", "usage": { "input_tokens": 51448, "cached_input_tokens": 51072, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 12, "total_tokens": 51507 }, "turn_token_usage": { "input_tokens": 785667, "cached_input_tokens": 740736, "cache_write_input_tokens": 0, "output_tokens": 26127, "reasoning_output_tokens": 11463, "total_tokens": 811794 }, "thread_token_usage": { "input_tokens": 785667, "cached_input_tokens": 740736, "cache_write_input_tokens": 0, "output_tokens": 26127, "reasoning_output_tokens": 11463, "total_tokens": 811794 } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-24T23:12:37.487Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:12:37.487Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:12:38.135Z", "callId": "call_FTfXb2Lb3H3Jk7C7tPOHHgVv", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":79312,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:13:08.154Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"ff0099\",\"wall_time_seconds\":30.001942815,\"session_id\":79312,\"original_token_count\":123}\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:12:38.244Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae63ce5087d29cb54a96265e1a5d", "usage": { "input_tokens": 51621, "cached_input_tokens": 51328, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 51685 }, "turn_token_usage": { "input_tokens": 837288, "cached_input_tokens": 792064, "cache_write_input_tokens": 0, "output_tokens": 26191, "reasoning_output_tokens": 11480, "total_tokens": 863479 }, "thread_token_usage": { "input_tokens": 837288, "cached_input_tokens": 792064, "cache_write_input_tokens": 0, "output_tokens": 26191, "reasoning_output_tokens": 11480, "total_tokens": 863479 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-24T23:13:09.714Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:13:09.714Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:13:11.414Z", "text": "The temporal sampler produced 58,790 training contexts and 548,638 novel organic targets without building a user–item matrix. Training is stable and the sampled-softmax loss is dropping (5.60 → 5.14 over the first three epochs); once retrieval finishes I’ll score it and use that result to decide whether the next iteration should emphasize representation quality, popularity calibration, or label weighting." }, { "kind": "tool", "timestamp": "2026-09-24T23:13:12.084Z", "callId": "call_AZl6pqVS8j83AQHZUWq5TTBR", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":79312,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:13:42.112Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"df0b1b\",\"wall_time_seconds\":30.001224676,\"session_id\":79312,\"original_token_count\":39}\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:13:12.173Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5ae846d3887d2b9ca7800f7ad5766", "usage": { "input_tokens": 51879, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 150, "reasoning_output_tokens": 15, "total_tokens": 52029 }, "turn_token_usage": { "input_tokens": 889167, "cached_input_tokens": 843520, "cache_write_input_tokens": 0, "output_tokens": 26341, "reasoning_output_tokens": 11495, "total_tokens": 915508 }, "thread_token_usage": { "input_tokens": 889167, "cached_input_tokens": 843520, "cache_write_input_tokens": 0, "output_tokens": 26341, "reasoning_output_tokens": 11495, "total_tokens": 915508 } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-24T23:13:43.424Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:13:43.424Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:13:44.105Z", "callId": "call_2gss6DEsdpjJFsG1dsbcF4F7", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":79312,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:14:14.142Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"39b3e3\",\"wall_time_seconds\":30.001165157,\"session_id\":79312,\"original_token_count\":40}\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:13:44.229Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5aea6613487d2ac6ea19d897ae9fd", "usage": { "input_tokens": 52162, "cached_input_tokens": 51712, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 52215 }, "turn_token_usage": { "input_tokens": 941329, "cached_input_tokens": 895232, "cache_write_input_tokens": 0, "output_tokens": 26394, "reasoning_output_tokens": 11501, "total_tokens": 967723 }, "thread_token_usage": { "input_tokens": 941329, "cached_input_tokens": 895232, "cache_write_input_tokens": 0, "output_tokens": 26394, "reasoning_output_tokens": 11501, "total_tokens": 967723 } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-24T23:14:15.712Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:15.712Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:14:16.457Z", "callId": "call_pxfqMzb9v5cDHSgCzefe1KHw", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":79312,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:14:21.063Z", "content": "Script completed\nWall time 4.6 seconds\nOutput:\n\n{\"chunk_id\":\"ea38cb\",\"wall_time_seconds\":4.579124073,\"exit_code\":0,\"original_token_count\":107}\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:16.560Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5aec66d4887d2969c7cd19abcfb6f", "usage": { "input_tokens": 52349, "cached_input_tokens": 51968, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 52402 }, "turn_token_usage": { "input_tokens": 993678, "cached_input_tokens": 947200, "cache_write_input_tokens": 0, "output_tokens": 26447, "reasoning_output_tokens": 11507, "total_tokens": 1020125 }, "thread_token_usage": { "input_tokens": 993678, "cached_input_tokens": 947200, "cache_write_input_tokens": 0, "output_tokens": 26447, "reasoning_output_tokens": 11507, "total_tokens": 1020125 } } }, { "kind": "context", "timestamp": "2026-09-24T23:14:21.051Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d849ed73-1c2f-4102-a96c-6541253600a4", "process_id": "79312", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1.parquet --checkpoint /app/solution/model_validation_v1.pt --cache /app/solution/cache_validation.pt --epochs 12 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1.parquet --checkpoint /app/solution/model_validation_v1.pt --cache /app/solution/cache_validation.pt --epochs 12 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 139, "nanos": 14165109 }, "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.5s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation.pt\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=5.60084 lr=0.00197001\nepoch 02/12: loss=5.35690 lr=0.0018821\nepoch 03/12: loss=5.13928 lr=0.00174225\nepoch 04/12: loss=4.94855 lr=0.00156\nepoch 05/12: loss=4.79465 lr=0.00134776\nepoch 06/12: loss=4.65739 lr=0.00112\nepoch 07/12: loss=4.55539 lr=0.000892239\nepoch 08/12: loss=4.44200 lr=0.00068\nepoch 09/12: loss=4.36064 lr=0.000497746\nepoch 10/12: loss=4.29660 lr=0.000357898\nepoch 11/12: loss=4.23379 lr=0.000269985\nepoch 12/12: loss=4.18560 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n" } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-24T23:14:22.454Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:22.454Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:14:23.812Z", "callId": "call_OFOkVp1wmTHqnzPl65fBNax8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:14:27.317Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020634,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:23.966Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5aecd4c2c87d2a35b31a0f2c72ca0", "usage": { "input_tokens": 52605, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 6, "total_tokens": 52685 }, "turn_token_usage": { "input_tokens": 1046283, "cached_input_tokens": 999424, "cache_write_input_tokens": 0, "output_tokens": 26527, "reasoning_output_tokens": 11513, "total_tokens": 1072810 }, "thread_token_usage": { "input_tokens": 1046283, "cached_input_tokens": 999424, "cache_write_input_tokens": 0, "output_tokens": 26527, "reasoning_output_tokens": 11513, "total_tokens": 1072810 } } }, { "kind": "context", "timestamp": "2026-09-24T23:14:27.304Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7fb8b300-828d-465e-88e1-b9134cc74c29", "process_id": "45340", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020634,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020634,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 318253357 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020634,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-24T23:14:40.704Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:40.704Z", "text": "" } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-24T23:14:46.463Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:14:46.463Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:14:48.164Z", "text": "The first neural model reaches 0.020634 NDCG@10—about 93% above popularity—so the history embedding is finding genuine taste structure. I’m separating its personalized similarity from its item-prior term now and calibrating their blend on validation; this is a cheap, high-leverage step because the chosen prior remains an extra coordinate in the exact two-tower dot product." }, { "kind": "tool", "timestamp": "2026-09-24T23:14:51.406Z", "callId": "call_IvXQ7pR0k3P7tPUFHlAPVBnn", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nimport torch,pandas as pd,numpy as np\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.eval()\\nwith torch.no_grad(): v=m.item_tower(torch.arange(30000));\\nprint('log_scale',m.user_tower.log_scale.item(),'scale',m.user_tower.log_scale.exp().item())\\nfor j,name in [(64,'bias')]:\\n x=v[:,j].numpy();print(name,'min/q/std/max',np.quantile(x,[0,.01,.1,.5,.9,.99,1]),x.std(),'corr -logrank',np.corrcoef(x,-np.log(cat.popularity_rank))[0,1])\\nprint('id_bias std',m.item_tower.id_bias.weight.detach().numpy().std())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:14:55.682Z", "content": "Script completed\nWall time 4.3 seconds\nOutput:\n\nlog_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361 0.87525663 1.91318414\n 2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:14:51.469Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5aed3a0ec87d2bdea6bf0a3d8d117", "usage": { "input_tokens": 52758, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 1151, "reasoning_output_tokens": 765, "total_tokens": 53909 }, "turn_token_usage": { "input_tokens": 1099041, "cached_input_tokens": 1051904, "cache_write_input_tokens": 0, "output_tokens": 27678, "reasoning_output_tokens": 12278, "total_tokens": 1126719 }, "thread_token_usage": { "input_tokens": 1099041, "cached_input_tokens": 1051904, "cache_write_input_tokens": 0, "output_tokens": 27678, "reasoning_output_tokens": 12278, "total_tokens": 1126719 } } }, { "kind": "context", "timestamp": "2026-09-24T23:14:55.668Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76e19889-02d1-41a5-9e90-a55d3f787c19", "process_id": "43940", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas as pd,numpy as np\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.eval()\nwith torch.no_grad(): v=m.item_tower(torch.arange(30000));\nprint('log_scale',m.user_tower.log_scale.item(),'scale',m.user_tower.log_scale.exp().item())\nfor j,name in [(64,'bias')]:\n x=v[:,j].numpy();print(name,'min/q/std/max',np.quantile(x,[0,.01,.1,.5,.9,.99,1]),x.std(),'corr -logrank',np.corrcoef(x,-np.log(cat.popularity_rank))[0,1])\nprint('id_bias std',m.item_tower.id_bias.weight.detach().numpy().std())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas as pd,numpy as np\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.eval()\nwith torch.no_grad(): v=m.item_tower(torch.arange(30000));\nprint('log_scale',m.user_tower.log_scale.item(),'scale',m.user_tower.log_scale.exp().item())\nfor j,name in [(64,'bias')]:\n x=v[:,j].numpy();print(name,'min/q/std/max',np.quantile(x,[0,.01,.1,.5,.9,.99,1]),x.std(),'corr -logrank',np.corrcoef(x,-np.log(cat.popularity_rank))[0,1])\nprint('id_bias std',m.item_tower.id_bias.weight.detach().numpy().std())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "log_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361 0.87525663 1.91318414\n 2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n", "stderr": "", "aggregated_output": "log_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361 0.87525663 1.91318414\n 2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 85449225 }, "formatted_output": "log_scale 2.2669990062713623 scale 9.650396347045898\nbias min/q/std/max [-1.70637202 -1.45669295 -0.98807303 -0.18618361 0.87525663 1.91318414\n 2.93312621] 0.7245352 corr -logrank 0.8124993059873334\nid_bias std 0.087633185\n" } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-24T23:15:04.394Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:04.394Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:15:23.835Z", "callId": "call_o9PJLaPzITOvlHaZbSVH65z8", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_blend.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Validation-only calibration of personalized similarity and item priors.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import sys\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+sys.path.insert(0, \\\"/app/solution\\\")\\n+sys.path.insert(0, \\\"/task\\\")\\n+from model import TwoTowerRanker\\n+from train_ranker import gather_histories, item_features\\n+from ranking_evaluation import CandidateSet, ndcg_at_k\\n+from evaluate_public import validation_truth\\n+\\n+\\n+torch.set_num_threads(8)\\n+catalog = pd.read_parquet(\\\"/task/data/candidate_catalog.parquet\\\")\\n+checkpoint = torch.load(\\\"/app/solution/model_validation_v1.pt\\\", weights_only=False)\\n+cache = torch.load(\\\"/app/solution/cache_validation.pt\\\", weights_only=False)\\n+histories = cache[\\\"histories\\\"]\\n+model = TwoTowerRanker(30_000, item_features(catalog), 64, 128)\\n+model.load_state_dict(checkpoint[\\\"model_state\\\"])\\n+model.eval()\\n+archive = np.load(\\\"/task/data/validation_eligible_candidates.npz\\\")\\n+candidate_set = CandidateSet(Path(\\\"/task/data/validation_eligible_candidates.npz\\\"))\\n+targets = set(pd.read_parquet(\\\"/task/data/target_users.parquet\\\").uid.astype(int))\\n+truth = validation_truth(candidate_set, targets)\\n+\\n+# learned-bias multiplier, standardized log-popularity addition\\n+grid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\\n+ for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\\n+ranked = {key: {} for key in grid}\\n+pop = -item_features(catalog)[:, 2]\\n+with torch.inference_mode():\\n+ item_vectors = model.item_tower(torch.arange(30_000))\\n+ latent = item_vectors[:, :-1].T.contiguous()\\n+ bias = item_vectors[:, -1]\\n+ for start in range(0, len(archive[\\\"uids\\\"]), 192):\\n+ stop = min(start + 192, len(archive[\\\"uids\\\"]))\\n+ batch = np.arange(start, stop)\\n+ hids, offsets, weights, features = gather_histories(histories, batch)\\n+ users = model.user_tower(hids, offsets, weights, features)\\n+ similarity = users[:, :-1] @ latent\\n+ if start == 0:\\n+ print(\\\"similarity std\\\", float(similarity.std()), \\\"bias std\\\", float(bias.std()))\\n+ eligible = np.unpackbits(\\n+ archive[\\\"packed_eligible\\\"][start:stop], axis=1,\\n+ count=30_000, bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ mask = torch.from_numpy(~eligible)\\n+ for key in grid:\\n+ b, p = key\\n+ scores = similarity + b * bias[None, :] + p * pop[None, :]\\n+ scores.masked_fill_(mask, -torch.inf)\\n+ top = torch.topk(scores, 10, dim=1).indices.numpy()\\n+ for row, uid in enumerate(archive[\\\"uids\\\"][start:stop]):\\n+ if int(uid) in truth:\\n+ ranked[key][int(uid)] = archive[\\\"item_ids\\\"][top[row]].astype(int).tolist()\\n+\\n+for key in grid:\\n+ values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\\n+ print(key, f\\\"{np.mean(values):.6f}\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:15:23.929Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:15:23.846Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e20552f0-6361-43e5-8d1b-d20cf2a40c31", "changes": { "/app/solution/tune_blend.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Validation-only calibration of personalized similarity and item priors.\"\"\"\nfrom pathlib import Path\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path.insert(0, \"/app/solution\")\nsys.path.insert(0, \"/task\")\nfrom model import TwoTowerRanker\nfrom train_ranker import gather_histories, item_features\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\n\n\ntorch.set_num_threads(8)\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncheckpoint = torch.load(\"/app/solution/model_validation_v1.pt\", weights_only=False)\ncache = torch.load(\"/app/solution/cache_validation.pt\", weights_only=False)\nhistories = cache[\"histories\"]\nmodel = TwoTowerRanker(30_000, item_features(catalog), 64, 128)\nmodel.load_state_dict(checkpoint[\"model_state\"])\nmodel.eval()\narchive = np.load(\"/task/data/validation_eligible_candidates.npz\")\ncandidate_set = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\ntruth = validation_truth(candidate_set, targets)\n\n# learned-bias multiplier, standardized log-popularity addition\ngrid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\n for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\nranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n item_vectors = model.item_tower(torch.arange(30_000))\n latent = item_vectors[:, :-1].T.contiguous()\n bias = item_vectors[:, -1]\n for start in range(0, len(archive[\"uids\"]), 192):\n stop = min(start + 192, len(archive[\"uids\"]))\n batch = np.arange(start, stop)\n hids, offsets, weights, features = gather_histories(histories, batch)\n users = model.user_tower(hids, offsets, weights, features)\n similarity = users[:, :-1] @ latent\n if start == 0:\n print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n eligible = np.unpackbits(\n archive[\"packed_eligible\"][start:stop], axis=1,\n count=30_000, bitorder=\"little\"\n ).astype(bool)\n mask = torch.from_numpy(~eligible)\n for key in grid:\n b, p = key\n scores = similarity + b * bias[None, :] + p * pop[None, :]\n scores.masked_fill_(mask, -torch.inf)\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n for row, uid in enumerate(archive[\"uids\"][start:stop]):\n if int(uid) in truth:\n ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n print(key, f\"{np.mean(values):.6f}\")\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_blend.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:15:23.928Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5aeefe8c887d2b3bcc280adc801fe", "usage": { "input_tokens": 54035, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 1285, "reasoning_output_tokens": 356, "total_tokens": 55320 }, "turn_token_usage": { "input_tokens": 1153076, "cached_input_tokens": 1104512, "cache_write_input_tokens": 0, "output_tokens": 28963, "reasoning_output_tokens": 12634, "total_tokens": 1182039 }, "thread_token_usage": { "input_tokens": 1153076, "cached_input_tokens": 1104512, "cache_write_input_tokens": 0, "output_tokens": 28963, "reasoning_output_tokens": 12634, "total_tokens": 1182039 } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-24T23:15:28.707Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:28.707Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:15:32.544Z", "callId": "call_573LNLG6jrwTAd99rPB3G1M1", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:15:33.735Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=50155", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:15:32.705Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5af0cdb6c87d2b0bf2bc4dadab6f0", "usage": { "input_tokens": 55344, "cached_input_tokens": 53888, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 6, "total_tokens": 55450 }, "turn_token_usage": { "input_tokens": 1208420, "cached_input_tokens": 1158400, "cache_write_input_tokens": 0, "output_tokens": 29069, "reasoning_output_tokens": 12640, "total_tokens": 1237489 }, "thread_token_usage": { "input_tokens": 1208420, "cached_input_tokens": 1158400, "cache_write_input_tokens": 0, "output_tokens": 29069, "reasoning_output_tokens": 12640, "total_tokens": 1237489 } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-24T23:15:35.580Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:15:35.580Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:15:39.723Z", "callId": "call_3j8wdnH12ww2QahosVZkw2AF", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:16:09.733Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"262d63\",\"wall_time_seconds\":30.001089076,\"session_id\":50155,\"original_token_count\":16}\nsimilarity std 2.5632805824279785 bias std 0.724547266960144\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:15:39.816Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5af160d6c87d2ace1c5912f61010d", "usage": { "input_tokens": 55478, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 55533 }, "turn_token_usage": { "input_tokens": 1263898, "cached_input_tokens": 1169536, "cache_write_input_tokens": 0, "output_tokens": 29124, "reasoning_output_tokens": 12648, "total_tokens": 1293022 }, "thread_token_usage": { "input_tokens": 1263898, "cached_input_tokens": 1169536, "cache_write_input_tokens": 0, "output_tokens": 29124, "reasoning_output_tokens": 12648, "total_tokens": 1293022 } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-24T23:16:11.085Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:11.085Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:16:11.790Z", "callId": "call_inBfGgIVLCH5a7PNdiMojU36", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:16:41.800Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"15270d\",\"wall_time_seconds\":30.001476476,\"session_id\":50155,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:11.880Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5af39ec4487d2a3fc8a729bc9f2da", "usage": { "input_tokens": 55609, "cached_input_tokens": 55296, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 23, "total_tokens": 55679 }, "turn_token_usage": { "input_tokens": 1319507, "cached_input_tokens": 1224832, "cache_write_input_tokens": 0, "output_tokens": 29194, "reasoning_output_tokens": 12671, "total_tokens": 1348701 }, "thread_token_usage": { "input_tokens": 1319507, "cached_input_tokens": 1224832, "cache_write_input_tokens": 0, "output_tokens": 29194, "reasoning_output_tokens": 12671, "total_tokens": 1348701 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-24T23:16:42.788Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:16:42.788Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:16:43.547Z", "callId": "call_S3lv0P80h9a2gPBRd55NH7Tv", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:17:13.567Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"9b4af4\",\"wall_time_seconds\":30.000820663,\"session_id\":50155,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:16:43.652Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5af5a013887d2990ec80c1f477882", "usage": { "input_tokens": 55732, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 55785 }, "turn_token_usage": { "input_tokens": 1375239, "cached_input_tokens": 1280256, "cache_write_input_tokens": 0, "output_tokens": 29247, "reasoning_output_tokens": 12677, "total_tokens": 1404486 }, "thread_token_usage": { "input_tokens": 1375239, "cached_input_tokens": 1280256, "cache_write_input_tokens": 0, "output_tokens": 29247, "reasoning_output_tokens": 12677, "total_tokens": 1404486 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-24T23:17:15.557Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:15.557Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:17:16.254Z", "callId": "call_Atha8JUzEZ1avhsYOAd1y9zT", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:17:22.104Z", "content": "Script completed\nWall time 5.8 seconds\nOutput:\n\n{\"chunk_id\":\"83a1de\",\"wall_time_seconds\":5.83452307,\"exit_code\":0,\"original_token_count\":153}\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:17:16.353Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5af79cb9087d28d158d57a91c5c72", "usage": { "input_tokens": 55840, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 99, "reasoning_output_tokens": 52, "total_tokens": 55939 }, "turn_token_usage": { "input_tokens": 1431079, "cached_input_tokens": 1335808, "cache_write_input_tokens": 0, "output_tokens": 29346, "reasoning_output_tokens": 12729, "total_tokens": 1460425 }, "thread_token_usage": { "input_tokens": 1431079, "cached_input_tokens": 1335808, "cache_write_input_tokens": 0, "output_tokens": 29346, "reasoning_output_tokens": 12729, "total_tokens": 1460425 } } }, { "kind": "context", "timestamp": "2026-09-24T23:17:22.102Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-81afd8b2-feb1-41c5-b30e-5e4dbaff0297", "process_id": "50155", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "similarity std 2.5632805824279785 bias std 0.724547266960144\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n", "stderr": "", "aggregated_output": "similarity std 2.5632805824279785 bias std 0.724547266960144\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n", "exit_code": 0, "duration": { "secs": 109, "nanos": 369087080 }, "formatted_output": "similarity std 2.5632805824279785 bias std 0.724547266960144\n(0.0, -0.4) 0.004865\n(0.0, 0.0) 0.007243\n(0.0, 0.25) 0.013214\n(0.0, 0.5) 0.018652\n(0.0, 0.8) 0.020974\n(0.4, -0.4) 0.006828\n(0.4, 0.0) 0.013569\n(0.4, 0.25) 0.018767\n(0.4, 0.5) 0.022700\n(0.4, 0.8) 0.021516\n(0.7, -0.4) 0.010281\n(0.7, 0.0) 0.018143\n(0.7, 0.25) 0.021729\n(0.7, 0.5) 0.023751\n(0.7, 0.8) 0.021224\n(1.0, -0.4) 0.014286\n(1.0, 0.0) 0.020634\n(1.0, 0.25) 0.023280\n(1.0, 0.5) 0.023213\n(1.0, 0.8) 0.021060\n(1.4, -0.4) 0.018775\n(1.4, 0.0) 0.022021\n(1.4, 0.25) 0.024001\n(1.4, 0.5) 0.022304\n(1.4, 0.8) 0.020244\n(2.0, -0.4) 0.022028\n(2.0, 0.0) 0.023459\n(2.0, 0.25) 0.022584\n(2.0, 0.5) 0.021282\n(2.0, 0.8) 0.018824\n" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-24T23:17:32.488Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:32.488Z", "text": "" } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-24T23:17:41.799Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:41.799Z", "text": "" } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-24T23:17:53.165Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:17:53.165Z", "text": "" } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-24T23:18:00.484Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:00.484Z", "text": "" } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-24T23:18:04.542Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:04.542Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:14.574Z", "callId": "call_KNyDTo6ew2HE2qane6Tuxv01", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/model.py\\n@@\\n self.id_bias = nn.Embedding(num_items, 1)\\n nn.init.zeros_(self.id_bias.weight)\\n+ # These two scalar buffers are selected on the public window. Keeping\\n+ # calibration inside the item tower means serving remains one exact\\n+ # maximum-inner-product search, with no post-retrieval reranker.\\n+ self.register_buffer(\\\"learned_prior_scale\\\", torch.tensor(1.0))\\n+ self.register_buffer(\\\"fixed_prior_scale\\\", torch.tensor(0.0))\\n@@\\n- bias = self.prior(features) + self.id_bias(item_ids)\\n+ bias = self.learned_prior_scale * (\\n+ self.prior(features) + self.id_bias(item_ids)\\n+ ) + self.fixed_prior_scale * (-features[:, 2:3])\\n return torch.cat((latent, bias), dim=1)\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n parser.add_argument(\\\"--threads\\\", type=int, default=8)\\n+ parser.add_argument(\\\"--negatives\\\", type=int, default=Config.negatives)\\n+ parser.add_argument(\\\"--learning-rate\\\", type=float, default=Config.learning_rate)\\n+ parser.add_argument(\\\"--like-weight\\\", type=float, default=Config.like_sampling_weight)\\n+ parser.add_argument(\\\"--resume\\\", type=Path)\\n+ parser.add_argument(\\\"--prior-scale\\\", type=float, default=1.4)\\n+ parser.add_argument(\\\"--pop-scale\\\", type=float, default=0.25)\\n@@\\n training_weeks=args.training_weeks,\\n seed=args.seed,\\n+ negatives=args.negatives,\\n+ learning_rate=args.learning_rate,\\n+ like_sampling_weight=args.like_weight,\\n@@\\n model = TwoTowerRanker(\\n@@\\n hidden_dim=cfg.hidden_dim,\\n )\\n+ if args.resume:\\n+ resumed = torch.load(args.resume, weights_only=False)\\n+ missing, unexpected = model.load_state_dict(\\n+ resumed[\\\"model_state\\\"], strict=False\\n+ )\\n+ # Calibration is only applied after optimization. A resumed fitted\\n+ # model therefore returns to its raw training parameterization.\\n+ model.item_tower.learned_prior_scale.fill_(1.0)\\n+ model.item_tower.fixed_prior_scale.fill_(0.0)\\n+ print(\\n+ f\\\"resumed parameters from {args.resume}; \\\"\\n+ f\\\"missing={list(missing)} unexpected={list(unexpected)}\\\",\\n+ flush=True,\\n+ )\\n losses = train_model(model, contexts, cfg)\\n+ model.item_tower.learned_prior_scale.fill_(args.prior_scale)\\n+ model.item_tower.fixed_prior_scale.fill_(args.pop_scale)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:18:14.648Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:14.585Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-9bf1eec6-6225-4b5c-a240-e3281eb54bdb", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -566,2 +566,8 @@\n parser.add_argument(\"--threads\", type=int, default=8)\n+ parser.add_argument(\"--negatives\", type=int, default=Config.negatives)\n+ parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n+ parser.add_argument(\"--like-weight\", type=float, default=Config.like_sampling_weight)\n+ parser.add_argument(\"--resume\", type=Path)\n+ parser.add_argument(\"--prior-scale\", type=float, default=1.4)\n+ parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n args = parser.parse_args()\n@@ -577,2 +583,5 @@\n seed=args.seed,\n+ negatives=args.negatives,\n+ learning_rate=args.learning_rate,\n+ like_sampling_weight=args.like_weight,\n )\n@@ -609,3 +618,19 @@\n )\n+ if args.resume:\n+ resumed = torch.load(args.resume, weights_only=False)\n+ missing, unexpected = model.load_state_dict(\n+ resumed[\"model_state\"], strict=False\n+ )\n+ # Calibration is only applied after optimization. A resumed fitted\n+ # model therefore returns to its raw training parameterization.\n+ model.item_tower.learned_prior_scale.fill_(1.0)\n+ model.item_tower.fixed_prior_scale.fill_(0.0)\n+ print(\n+ f\"resumed parameters from {args.resume}; \"\n+ f\"missing={list(missing)} unexpected={list(unexpected)}\",\n+ flush=True,\n+ )\n losses = train_model(model, contexts, cfg)\n+ model.item_tower.learned_prior_scale.fill_(args.prior_scale)\n+ model.item_tower.fixed_prior_scale.fill_(args.pop_scale)\n checkpoint = args.checkpoint or Path(\n", "move_path": null }, "/app/solution/model.py": { "type": "update", "unified_diff": "@@ -100,2 +100,7 @@\n nn.init.zeros_(self.id_bias.weight)\n+ # These two scalar buffers are selected on the public window. Keeping\n+ # calibration inside the item tower means serving remains one exact\n+ # maximum-inner-product search, with no post-retrieval reranker.\n+ self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n+ self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n \n@@ -106,3 +111,5 @@\n latent = F.normalize(latent + self.skip(ids), dim=1)\n- bias = self.prior(features) + self.id_bias(item_ids)\n+ bias = self.learned_prior_scale * (\n+ self.prior(features) + self.id_bias(item_ids)\n+ ) + self.fixed_prior_scale * (-features[:, 2:3])\n return torch.cat((latent, bias), dim=1)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/model.py\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:18:14.646Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5af825e1c87d2b1aaf45bec605248", "usage": { "input_tokens": 56471, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 2857, "reasoning_output_tokens": 2190, "total_tokens": 59328 }, "turn_token_usage": { "input_tokens": 1487550, "cached_input_tokens": 1391488, "cache_write_input_tokens": 0, "output_tokens": 32203, "reasoning_output_tokens": 14919, "total_tokens": 1519753 }, "thread_token_usage": { "input_tokens": 1487550, "cached_input_tokens": 1391488, "cache_write_input_tokens": 0, "output_tokens": 32203, "reasoning_output_tokens": 14919, "total_tokens": 1519753 } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-24T23:18:18.131Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:18.131Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:21.305Z", "callId": "call_1Nud40aRBkGeq4iZQvTMby7a", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1_cont.parquet --checkpoint /app/solution/model_validation_v1_cont.pt --cache /app/solution/cache_validation.pt --resume /app/solution/model_validation_v1.pt --epochs 12 --learning-rate 0.0006 --negatives 1024 --prior-scale 1.4 --pop-scale 0.25 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:18:22.473Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=7054", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:21.436Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5afb6ec8087d2805d771fe0691868", "usage": { "input_tokens": 59352, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 316, "reasoning_output_tokens": 130, "total_tokens": 59668 }, "turn_token_usage": { "input_tokens": 1546902, "cached_input_tokens": 1447808, "cache_write_input_tokens": 0, "output_tokens": 32519, "reasoning_output_tokens": 15049, "total_tokens": 1579421 }, "thread_token_usage": { "input_tokens": 1546902, "cached_input_tokens": 1447808, "cache_write_input_tokens": 0, "output_tokens": 32519, "reasoning_output_tokens": 15049, "total_tokens": 1579421 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-24T23:18:24.058Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:24.058Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:24.769Z", "callId": "call_SvM52yyX1kmaVQx4nkUzgZuq", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":7054,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:18:54.786Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"0f8893\",\"wall_time_seconds\":30.002057462,\"session_id\":7054,\"original_token_count\":153}\nloaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:24.854Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5afbea71087d2976b04b42bf97c30", "usage": { "input_tokens": 59696, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 59749 }, "turn_token_usage": { "input_tokens": 1606598, "cached_input_tokens": 1506944, "cache_write_input_tokens": 0, "output_tokens": 32572, "reasoning_output_tokens": 15055, "total_tokens": 1639170 }, "thread_token_usage": { "input_tokens": 1606598, "cached_input_tokens": 1506944, "cache_write_input_tokens": 0, "output_tokens": 32572, "reasoning_output_tokens": 15055, "total_tokens": 1639170 } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-24T23:18:56.537Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:18:56.537Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:18:57.213Z", "callId": "call_MyzJQCKWp8qVkIQFBDJt78qw", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":7054,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:19:27.242Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a37306\",\"wall_time_seconds\":30.000975379,\"session_id\":7054,\"original_token_count\":40}\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:18:57.324Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5afdf099087d2950f065457a57129", "usage": { "input_tokens": 59959, "cached_input_tokens": 59520, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 37, "total_tokens": 60043 }, "turn_token_usage": { "input_tokens": 1666557, "cached_input_tokens": 1566464, "cache_write_input_tokens": 0, "output_tokens": 32656, "reasoning_output_tokens": 15092, "total_tokens": 1699213 }, "thread_token_usage": { "input_tokens": 1666557, "cached_input_tokens": 1566464, "cache_write_input_tokens": 0, "output_tokens": 32656, "reasoning_output_tokens": 15092, "total_tokens": 1699213 } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-24T23:19:28.371Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:19:28.371Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:19:29.078Z", "callId": "call_DvqsNaLXDC4n7JqDdg5NU1cV", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":7054,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:19:59.118Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"2a9df1\",\"wall_time_seconds\":30.001260398,\"session_id\":7054,\"original_token_count\":41}\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:19:29.185Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5afff76b087d29d94fbea6faf1d04", "usage": { "input_tokens": 60174, "cached_input_tokens": 59776, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 60227 }, "turn_token_usage": { "input_tokens": 1726731, "cached_input_tokens": 1626240, "cache_write_input_tokens": 0, "output_tokens": 32709, "reasoning_output_tokens": 15098, "total_tokens": 1759440 }, "thread_token_usage": { "input_tokens": 1726731, "cached_input_tokens": 1626240, "cache_write_input_tokens": 0, "output_tokens": 32709, "reasoning_output_tokens": 15098, "total_tokens": 1759440 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-24T23:20:00.747Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:00.747Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:20:01.407Z", "callId": "call_TKCAl40AlUbRaLWzb7Qbk4yf", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":7054,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:20:15.176Z", "content": "Script completed\nWall time 13.8 seconds\nOutput:\n\n{\"chunk_id\":\"9c5ee5\",\"wall_time_seconds\":13.742299766,\"exit_code\":0,\"original_token_count\":120}\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:20:01.513Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b01f574487d2a270772d43f1b504", "usage": { "input_tokens": 60361, "cached_input_tokens": 60032, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 60414 }, "turn_token_usage": { "input_tokens": 1787092, "cached_input_tokens": 1686272, "cache_write_input_tokens": 0, "output_tokens": 32762, "reasoning_output_tokens": 15104, "total_tokens": 1819854 }, "thread_token_usage": { "input_tokens": 1787092, "cached_input_tokens": 1686272, "cache_write_input_tokens": 0, "output_tokens": 32762, "reasoning_output_tokens": 15104, "total_tokens": 1819854 } } }, { "kind": "context", "timestamp": "2026-09-24T23:20:15.171Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8d1ae989-32fb-4f6a-9b47-8d5106102866", "process_id": "7054", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1_cont.parquet --checkpoint /app/solution/model_validation_v1_cont.pt --cache /app/solution/cache_validation.pt --resume /app/solution/model_validation_v1.pt --epochs 12 --learning-rate 0.0006 --negatives 1024 --prior-scale 1.4 --pop-scale 0.25 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v1_cont.parquet --checkpoint /app/solution/model_validation_v1_cont.pt --cache /app/solution/cache_validation.pt --resume /app/solution/model_validation_v1.pt --epochs 12 --learning-rate 0.0006 --negatives 1024 --prior-scale 1.4 --pop-scale 0.25 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 113, "nanos": 699523051 }, "formatted_output": "loaded prepared arrays from /app/solution/cache_validation.pt\nresumed parameters from /app/solution/model_validation_v1.pt; missing=['item_tower.learned_prior_scale', 'item_tower.fixed_prior_scale'] unexpected=[]\n/app/solution/train_ranker.py:489: 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) * len(batch)\nepoch 01/12: loss=4.86950 lr=0.000591004\nepoch 02/12: loss=4.82408 lr=0.000564631\nepoch 03/12: loss=4.79085 lr=0.000522676\nepoch 04/12: loss=4.75469 lr=0.000468\nepoch 05/12: loss=4.70461 lr=0.000404328\nepoch 06/12: loss=4.67537 lr=0.000336\nepoch 07/12: loss=4.62046 lr=0.000267672\nepoch 08/12: loss=4.58827 lr=0.000204\nepoch 09/12: loss=4.54520 lr=0.000149324\nepoch 10/12: loss=4.51044 lr=0.000107369\nepoch 11/12: loss=4.49004 lr=8.09956e-05\nepoch 12/12: loss=4.48490 lr=7.2e-05\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v1_cont.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v1_cont.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 1024, \"learning_rate\": 0.0006, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"seed\": 20260924}}\n" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-24T23:20:16.523Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:16.523Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:20:17.726Z", "callId": "call_tTe3YKPe23isHCru6eFtPvCL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1_cont.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:20:21.260Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_v1_cont.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023379,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:20:17.840Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b02f7a9887d2b57dfc9e1945bdf0", "usage": { "input_tokens": 60647, "cached_input_tokens": 60160, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 6, "total_tokens": 60728 }, "turn_token_usage": { "input_tokens": 1847739, "cached_input_tokens": 1746432, "cache_write_input_tokens": 0, "output_tokens": 32843, "reasoning_output_tokens": 15110, "total_tokens": 1880582 }, "thread_token_usage": { "input_tokens": 1847739, "cached_input_tokens": 1746432, "cache_write_input_tokens": 0, "output_tokens": 32843, "reasoning_output_tokens": 15110, "total_tokens": 1880582 } } }, { "kind": "context", "timestamp": "2026-09-24T23:20:21.254Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d8588a3e-80f3-4293-adf7-2a61a5300cf2", "process_id": "42576", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1_cont.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v1_cont.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_v1_cont.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023379,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_v1_cont.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023379,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 364226674 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_v1_cont.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023379,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-24T23:20:23.719Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:23.719Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:20:28.852Z", "callId": "call_hFAsyobtsLFExYla4CieO6lr", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport torch,pandas as pd\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features,rank_candidates\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\\ncache=torch.load('/app/solution/cache_validation.pt',weights_only=False)\\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'],strict=False)\\nm.item_tower.learned_prior_scale.fill_(1.4);m.item_tower.fixed_prior_scale.fill_(0.25)\\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_best.parquet'))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:20:38.395Z", "content": "Script completed\nWall time 9.5 seconds\nOutput:\n\nwrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024001,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:20:28.963Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b03573d487d2b371f49dda092d9f", "usage": { "input_tokens": 60802, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 64, "total_tokens": 61160 }, "turn_token_usage": { "input_tokens": 1908541, "cached_input_tokens": 1806848, "cache_write_input_tokens": 0, "output_tokens": 33201, "reasoning_output_tokens": 15174, "total_tokens": 1941742 }, "thread_token_usage": { "input_tokens": 1908541, "cached_input_tokens": 1806848, "cache_write_input_tokens": 0, "output_tokens": 33201, "reasoning_output_tokens": 15174, "total_tokens": 1941742 } } }, { "kind": "context", "timestamp": "2026-09-24T23:20:38.382Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9c0637ff-2078-42fd-826b-725b50eb86a9", "process_id": "86543", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'],strict=False)\nm.item_tower.learned_prior_scale.fill_(1.4);m.item_tower.fixed_prior_scale.fill_(0.25)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_best.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_v1.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'],strict=False)\nm.item_tower.learned_prior_scale.fill_(1.4);m.item_tower.fixed_prior_scale.fill_(0.25)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_best.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024001,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024001,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 363686114 }, "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation_best.parquet\n{\n \"model\": \"/app/solution/validation_best.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024001,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-24T23:20:48.710Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:48.710Z", "text": "" } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-24T23:20:58.034Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:58.034Z", "text": "" } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-24T23:20:59.694Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:20:59.694Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:21:01.472Z", "text": "The calibrated 12-epoch checkpoint is currently best at 0.024001; extending it overfits slightly (0.023379), so I’m preserving the earlier model. The next experiment targets a modeling limitation visible in the current encoder: it treats each distinct track almost equally and underweights sought-out behavior. I’m testing a frequency-aware, recency-decayed pool with stronger organic weighting while keeping the same serving-time decomposition." }, { "kind": "tool", "timestamp": "2026-09-24T23:21:15.740Z", "callId": "call_mQS22MXuMoYvU8ZlGRixqDdy", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class Config:\\n@@\\n like_sampling_weight: float = 4.0\\n+ organic_boost: float = 1.5\\n@@\\n-def load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\\n+def load_event_arrays(\\n+ stage: str, users: np.ndarray, items: np.ndarray, cfg: Config\\n+) -> dict:\\n@@\\n- history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\\n- 1.0 + 0.35 * lo[positive]\\n+ history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\\n+ 1.0 + cfg.organic_boost * lo[positive]\\n@@\\n def latest_unique_history(\\n@@\\n- reverse_items = items[lo:hi][::-1]\\n- _, first_reverse = np.unique(reverse_items, return_index=True)\\n- first_reverse.sort()\\n- first_reverse = first_reverse[: cfg.max_history]\\n- positions = hi - 1 - first_reverse\\n- age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\\n- decay = np.exp(-age_days / 120.0)\\n- weights = values[positions] * decay\\n+ window_items = items[lo:hi]\\n+ age_days = (cutoff - times[lo:hi]).astype(np.float32) / 86_400.0\\n+ event_weights = values[lo:hi] * np.exp(-age_days / 120.0)\\n+ unique_items, inverse = np.unique(window_items, return_inverse=True)\\n+ summed = np.bincount(\\n+ inverse, weights=event_weights, minlength=len(unique_items)\\n+ ).astype(np.float32)\\n+ # Repeated positive plays are stronger taste evidence, with a square root\\n+ # preventing a handful of looped tracks from swallowing the whole profile.\\n+ aggregate_weights = np.sqrt(summed)\\n+ if len(unique_items) > cfg.max_history:\\n+ selected = np.argpartition(aggregate_weights, -cfg.max_history)[\\n+ -cfg.max_history:\\n+ ]\\n+ selected = selected[np.argsort(aggregate_weights[selected])[::-1]]\\n+ else:\\n+ selected = np.arange(len(unique_items))\\n return (\\n- items[positions].astype(np.int32, copy=False),\\n- weights.astype(np.float32, copy=False),\\n- float(organic[positions].mean()),\\n+ unique_items[selected].astype(np.int32, copy=False),\\n+ aggregate_weights[selected].astype(np.float32, copy=False),\\n+ float(organic[lo:hi].mean()),\\n@@\\n parser.add_argument(\\\"--pop-scale\\\", type=float, default=0.25)\\n+ parser.add_argument(\\\"--organic-boost\\\", type=float, default=Config.organic_boost)\\n@@\\n like_sampling_weight=args.like_weight,\\n+ organic_boost=args.organic_boost,\\n@@\\n- arrays = load_event_arrays(args.stage, target_users, candidate_items)\\n+ arrays = load_event_arrays(args.stage, target_users, candidate_items, cfg)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:21:15.856Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:15.749Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-28cba774-71e2-4058-8305-2ae8d078f566", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -48,2 +48,3 @@\n like_sampling_weight: float = 4.0\n+ organic_boost: float = 1.5\n seed: int = 20260924\n@@ -94,3 +95,5 @@\n \n-def load_event_arrays(stage: str, users: np.ndarray, items: np.ndarray) -> dict:\n+def load_event_arrays(\n+ stage: str, users: np.ndarray, items: np.ndarray, cfg: Config\n+) -> dict:\n \"\"\"Load compact candidate-catalog events; raw frames are promptly released.\"\"\"\n@@ -111,3 +114,3 @@\n history_weight = (0.5 + np.minimum(lr[positive], 100) / 100.0) * (\n- 1.0 + 0.35 * lo[positive]\n+ 1.0 + cfg.organic_boost * lo[positive]\n )\n@@ -199,14 +202,23 @@\n return np.empty(0, np.int32), np.empty(0, np.float32), 0.0\n- reverse_items = items[lo:hi][::-1]\n- _, first_reverse = np.unique(reverse_items, return_index=True)\n- first_reverse.sort()\n- first_reverse = first_reverse[: cfg.max_history]\n- positions = hi - 1 - first_reverse\n- age_days = (cutoff - times[positions]).astype(np.float32) / 86_400.0\n- decay = np.exp(-age_days / 120.0)\n- weights = values[positions] * decay\n+ window_items = items[lo:hi]\n+ age_days = (cutoff - times[lo:hi]).astype(np.float32) / 86_400.0\n+ event_weights = values[lo:hi] * np.exp(-age_days / 120.0)\n+ unique_items, inverse = np.unique(window_items, return_inverse=True)\n+ summed = np.bincount(\n+ inverse, weights=event_weights, minlength=len(unique_items)\n+ ).astype(np.float32)\n+ # Repeated positive plays are stronger taste evidence, with a square root\n+ # preventing a handful of looped tracks from swallowing the whole profile.\n+ aggregate_weights = np.sqrt(summed)\n+ if len(unique_items) > cfg.max_history:\n+ selected = np.argpartition(aggregate_weights, -cfg.max_history)[\n+ -cfg.max_history:\n+ ]\n+ selected = selected[np.argsort(aggregate_weights[selected])[::-1]]\n+ else:\n+ selected = np.arange(len(unique_items))\n return (\n- items[positions].astype(np.int32, copy=False),\n- weights.astype(np.float32, copy=False),\n- float(organic[positions].mean()),\n+ unique_items[selected].astype(np.int32, copy=False),\n+ aggregate_weights[selected].astype(np.float32, copy=False),\n+ float(organic[lo:hi].mean()),\n )\n@@ -572,2 +584,3 @@\n parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n+ parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n args = parser.parse_args()\n@@ -586,2 +599,3 @@\n like_sampling_weight=args.like_weight,\n+ organic_boost=args.organic_boost,\n )\n@@ -597,3 +611,3 @@\n else:\n- arrays = load_event_arrays(args.stage, target_users, candidate_items)\n+ arrays = load_event_arrays(args.stage, target_users, candidate_items, cfg)\n contexts = build_contexts(arrays, target_users, stage_end, cfg)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:21:15.854Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b04697c087d2b90f7ac360698c62", "usage": { "input_tokens": 61248, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 2017, "reasoning_output_tokens": 1125, "total_tokens": 63265 }, "turn_token_usage": { "input_tokens": 1969789, "cached_input_tokens": 1867520, "cache_write_input_tokens": 0, "output_tokens": 35218, "reasoning_output_tokens": 16299, "total_tokens": 2005007 }, "thread_token_usage": { "input_tokens": 1969789, "cached_input_tokens": 1867520, "cache_write_input_tokens": 0, "output_tokens": 35218, "reasoning_output_tokens": 16299, "total_tokens": 2005007 } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-24T23:21:18.621Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:21:18.621Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:21:22.218Z", "callId": "call_kBe7mtrTcMKCRmQNjhhd32kL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v2.parquet --checkpoint /app/solution/model_validation_v2.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --prior-scale 1.4 --pop-scale 0.25 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:21:23.384Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=43392", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:22.325Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b06c29b087d28efcdabcd349fea5", "usage": { "input_tokens": 63289, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 265, "reasoning_output_tokens": 56, "total_tokens": 63554 }, "turn_token_usage": { "input_tokens": 2033078, "cached_input_tokens": 1928576, "cache_write_input_tokens": 0, "output_tokens": 35483, "reasoning_output_tokens": 16355, "total_tokens": 2068561 }, "thread_token_usage": { "input_tokens": 2033078, "cached_input_tokens": 1928576, "cache_write_input_tokens": 0, "output_tokens": 35483, "reasoning_output_tokens": 16355, "total_tokens": 2068561 } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-24T23:21:24.655Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:21:24.655Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:21:25.372Z", "callId": "call_bXdrHGMc5hGcdzjWEs25yGpk", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43392,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:21:55.381Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"ab23f6\",\"wall_time_seconds\":30.001171841,\"session_id\":43392,\"original_token_count\":55}\nloaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:25.461Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b073ad4887d2a15d3aaf439ce8bd", "usage": { "input_tokens": 63582, "cached_input_tokens": 63104, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 63637 }, "turn_token_usage": { "input_tokens": 2096660, "cached_input_tokens": 1991680, "cache_write_input_tokens": 0, "output_tokens": 35538, "reasoning_output_tokens": 16363, "total_tokens": 2132198 }, "thread_token_usage": { "input_tokens": 2096660, "cached_input_tokens": 1991680, "cache_write_input_tokens": 0, "output_tokens": 35538, "reasoning_output_tokens": 16363, "total_tokens": 2132198 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-24T23:21:56.711Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:21:56.711Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:21:57.409Z", "callId": "call_5gO6hjbht4lKzLNHGb4G4sn9", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43392,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:22:27.426Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"f075a7\",\"wall_time_seconds\":30.001024145,\"session_id\":43392,\"original_token_count\":124}\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:21:57.530Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b09395e487d2b202b557ece8cd96", "usage": { "input_tokens": 63752, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 63816 }, "turn_token_usage": { "input_tokens": 2160412, "cached_input_tokens": 2055040, "cache_write_input_tokens": 0, "output_tokens": 35602, "reasoning_output_tokens": 16380, "total_tokens": 2196014 }, "thread_token_usage": { "input_tokens": 2160412, "cached_input_tokens": 2055040, "cache_write_input_tokens": 0, "output_tokens": 35602, "reasoning_output_tokens": 16380, "total_tokens": 2196014 } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-24T23:22:28.824Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:22:28.824Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:22:29.432Z", "callId": "call_bY2n6RqynWJpdpAjOp9aNYRJ", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43392,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:22:59.449Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"c085fb\",\"wall_time_seconds\":30.001364422,\"session_id\":43392,\"original_token_count\":48}\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:22:29.582Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b0b3a6d887d2981940a5606ea833", "usage": { "input_tokens": 64013, "cached_input_tokens": 63616, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 64068 }, "turn_token_usage": { "input_tokens": 2224425, "cached_input_tokens": 2118656, "cache_write_input_tokens": 0, "output_tokens": 35657, "reasoning_output_tokens": 16388, "total_tokens": 2260082 }, "thread_token_usage": { "input_tokens": 2224425, "cached_input_tokens": 2118656, "cache_write_input_tokens": 0, "output_tokens": 35657, "reasoning_output_tokens": 16388, "total_tokens": 2260082 } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-24T23:23:02.591Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:02.591Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:03.303Z", "callId": "call_OJqLvvSyQE5XikLGrzOWsO44", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43392,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:23:28.033Z", "content": "Script completed\nWall time 24.7 seconds\nOutput:\n\n{\"chunk_id\":\"c67df7\",\"wall_time_seconds\":24.702483828,\"exit_code\":0,\"original_token_count\":143}\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:03.408Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b0d4c2a087d2a06090779a5e6aa2", "usage": { "input_tokens": 64218, "cached_input_tokens": 63872, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 64271 }, "turn_token_usage": { "input_tokens": 2288643, "cached_input_tokens": 2182528, "cache_write_input_tokens": 0, "output_tokens": 35710, "reasoning_output_tokens": 16394, "total_tokens": 2324353 }, "thread_token_usage": { "input_tokens": 2288643, "cached_input_tokens": 2182528, "cache_write_input_tokens": 0, "output_tokens": 35710, "reasoning_output_tokens": 16394, "total_tokens": 2324353 } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:28.027Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d40c9b9-d7d7-43aa-ab97-c6adffd0b978", "process_id": "43392", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v2.parquet --checkpoint /app/solution/model_validation_v2.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --prior-scale 1.4 --pop-scale 0.25 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_v2.parquet --checkpoint /app/solution/model_validation_v2.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --prior-scale 1.4 --pop-scale 0.25 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 125, "nanos": 644338601 }, "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 13.8s\nbuilt 58,790 temporal contexts with 548,638 novel targets and 20,681,507 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60531 lr=0.00197001\nepoch 02/12: loss=5.35360 lr=0.0018821\nepoch 03/12: loss=5.14357 lr=0.00174225\nepoch 04/12: loss=4.96154 lr=0.00156\nepoch 05/12: loss=4.81365 lr=0.00134776\nepoch 06/12: loss=4.67550 lr=0.00112\nepoch 07/12: loss=4.57506 lr=0.000892239\nepoch 08/12: loss=4.46756 lr=0.00068\nepoch 09/12: loss=4.39426 lr=0.000497746\nepoch 10/12: loss=4.33325 lr=0.000357898\nepoch 11/12: loss=4.26866 lr=0.000269985\nepoch 12/12: loss=4.22436 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_v2.parquet\n{\"checkpoint\": \"/app/solution/model_validation_v2.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-24T23:23:29.870Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:29.870Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:31.040Z", "callId": "call_Fpqw18psVUehbfjcGXnRFcmR", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:23:34.427Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024272,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:31.240Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b0f0372c87d2a81da971078620b9", "usage": { "input_tokens": 64545, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 6, "total_tokens": 64625 }, "turn_token_usage": { "input_tokens": 2353188, "cached_input_tokens": 2246528, "cache_write_input_tokens": 0, "output_tokens": 35790, "reasoning_output_tokens": 16400, "total_tokens": 2388978 }, "thread_token_usage": { "input_tokens": 2353188, "cached_input_tokens": 2246528, "cache_write_input_tokens": 0, "output_tokens": 35790, "reasoning_output_tokens": 16400, "total_tokens": 2388978 } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:34.419Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e91748d1-2390-4298-99ff-c01f7618c849", "process_id": "9529", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v2.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_v2.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024272,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024272,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 214961999 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024272,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-24T23:23:38.614Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:38.614Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:46.928Z", "callId": "call_woHmzfacJJRHRCT8dXZrYGJ3", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blend.py\\n@@\\n-checkpoint = torch.load(\\\"/app/solution/model_validation_v1.pt\\\", weights_only=False)\\n-cache = torch.load(\\\"/app/solution/cache_validation.pt\\\", weights_only=False)\\n+checkpoint = torch.load(\\\"/app/solution/model_validation_v2.pt\\\", weights_only=False)\\n+cache = torch.load(\\\"/app/solution/cache_validation_v2.pt\\\", weights_only=False)\\n@@\\n-grid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\\n- for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\\n+grid = [(b, p) for b in (0.8, 1.1, 1.4, 1.7, 2.0, 2.4)\\n+ for p in (0.0, 0.15, 0.3, 0.45)]\\n@@\\n- for start in range(0, len(archive[\\\"uids\\\"]), 192):\\n- stop = min(start + 192, len(archive[\\\"uids\\\"]))\\n- batch = np.arange(start, stop)\\n+ relevant_rows = np.flatnonzero(np.isin(archive[\\\"uids\\\"], list(truth)))\\n+ for start in range(0, len(relevant_rows), 192):\\n+ batch = relevant_rows[start : start + 192]\\n@@\\n- archive[\\\"packed_eligible\\\"][start:stop], axis=1,\\n+ archive[\\\"packed_eligible\\\"][batch], axis=1,\\n@@\\n- for row, uid in enumerate(archive[\\\"uids\\\"][start:stop]):\\n+ for row, uid in enumerate(archive[\\\"uids\\\"][batch]):\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:23:47.040Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:46.938Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-769dcaff-49b8-4685-9fe7-6099039b7df0", "changes": { "/app/solution/tune_blend.py": { "type": "update", "unified_diff": "@@ -19,4 +19,4 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_v1.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_v2.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n histories = cache[\"histories\"]\n@@ -31,4 +31,4 @@\n # learned-bias multiplier, standardized log-popularity addition\n-grid = [(b, p) for b in (0.0, 0.4, 0.7, 1.0, 1.4, 2.0)\n- for p in (-0.4, 0.0, 0.25, 0.5, 0.8)]\n+grid = [(b, p) for b in (0.8, 1.1, 1.4, 1.7, 2.0, 2.4)\n+ for p in (0.0, 0.15, 0.3, 0.45)]\n ranked = {key: {} for key in grid}\n@@ -39,5 +39,5 @@\n bias = item_vectors[:, -1]\n- for start in range(0, len(archive[\"uids\"]), 192):\n- stop = min(start + 192, len(archive[\"uids\"]))\n- batch = np.arange(start, stop)\n+ relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n+ for start in range(0, len(relevant_rows), 192):\n+ batch = relevant_rows[start : start + 192]\n hids, offsets, weights, features = gather_histories(histories, batch)\n@@ -48,3 +48,3 @@\n eligible = np.unpackbits(\n- archive[\"packed_eligible\"][start:stop], axis=1,\n+ archive[\"packed_eligible\"][batch], axis=1,\n count=30_000, bitorder=\"little\"\n@@ -57,3 +57,3 @@\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n- for row, uid in enumerate(archive[\"uids\"][start:stop]):\n+ for row, uid in enumerate(archive[\"uids\"][batch]):\n if int(uid) in truth:\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:47.038Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b0f69a8c87d2859e6aaed51b61b3", "usage": { "input_tokens": 64698, "cached_input_tokens": 64384, "cache_write_input_tokens": 0, "output_tokens": 636, "reasoning_output_tokens": 166, "total_tokens": 65334 }, "turn_token_usage": { "input_tokens": 2417886, "cached_input_tokens": 2310912, "cache_write_input_tokens": 0, "output_tokens": 36426, "reasoning_output_tokens": 16566, "total_tokens": 2454312 }, "thread_token_usage": { "input_tokens": 2417886, "cached_input_tokens": 2310912, "cache_write_input_tokens": 0, "output_tokens": 36426, "reasoning_output_tokens": 16566, "total_tokens": 2454312 } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-24T23:23:48.463Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:48.463Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:49.519Z", "callId": "call_RfoQF8S1EyuVjCJ0iBYVNzMb", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '34,100p' /app/solution/tune_blend.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:23:49.631Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\nranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n item_vectors = model.item_tower(torch.arange(30_000))\n latent = item_vectors[:, :-1].T.contiguous()\n bias = item_vectors[:, -1]\n relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n for start in range(0, len(relevant_rows), 192):\n batch = relevant_rows[start : start + 192]\n hids, offsets, weights, features = gather_histories(histories, batch)\n users = model.user_tower(hids, offsets, weights, features)\n similarity = users[:, :-1] @ latent\n if start == 0:\n print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n eligible = np.unpackbits(\n archive[\"packed_eligible\"][batch], axis=1,\n count=30_000, bitorder=\"little\"\n ).astype(bool)\n mask = torch.from_numpy(~eligible)\n for key in grid:\n b, p = key\n scores = similarity + b * bias[None, :] + p * pop[None, :]\n scores.masked_fill_(mask, -torch.inf)\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n for row, uid in enumerate(archive[\"uids\"][batch]):\n if int(uid) in truth:\n ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n print(key, f\"{np.mean(values):.6f}\")\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:49.556Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a13b7610-8b43-48f8-9657-6408faeca4d0", "process_id": "77584", "command": [ "/bin/sh", "-lc", "sed -n '34,100p' /app/solution/tune_blend.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '34,100p' /app/solution/tune_blend.py", "name": "tune_blend.py", "path": "/app/solution/tune_blend.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n item_vectors = model.item_tower(torch.arange(30_000))\n latent = item_vectors[:, :-1].T.contiguous()\n bias = item_vectors[:, -1]\n relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n for start in range(0, len(relevant_rows), 192):\n batch = relevant_rows[start : start + 192]\n hids, offsets, weights, features = gather_histories(histories, batch)\n users = model.user_tower(hids, offsets, weights, features)\n similarity = users[:, :-1] @ latent\n if start == 0:\n print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n eligible = np.unpackbits(\n archive[\"packed_eligible\"][batch], axis=1,\n count=30_000, bitorder=\"little\"\n ).astype(bool)\n mask = torch.from_numpy(~eligible)\n for key in grid:\n b, p = key\n scores = similarity + b * bias[None, :] + p * pop[None, :]\n scores.masked_fill_(mask, -torch.inf)\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n for row, uid in enumerate(archive[\"uids\"][batch]):\n if int(uid) in truth:\n ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n print(key, f\"{np.mean(values):.6f}\")\n", "stderr": "", "aggregated_output": "ranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n item_vectors = model.item_tower(torch.arange(30_000))\n latent = item_vectors[:, :-1].T.contiguous()\n bias = item_vectors[:, -1]\n relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n for start in range(0, len(relevant_rows), 192):\n batch = relevant_rows[start : start + 192]\n hids, offsets, weights, features = gather_histories(histories, batch)\n users = model.user_tower(hids, offsets, weights, features)\n similarity = users[:, :-1] @ latent\n if start == 0:\n print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n eligible = np.unpackbits(\n archive[\"packed_eligible\"][batch], axis=1,\n count=30_000, bitorder=\"little\"\n ).astype(bool)\n mask = torch.from_numpy(~eligible)\n for key in grid:\n b, p = key\n scores = similarity + b * bias[None, :] + p * pop[None, :]\n scores.masked_fill_(mask, -torch.inf)\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n for row, uid in enumerate(archive[\"uids\"][batch]):\n if int(uid) in truth:\n ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n print(key, f\"{np.mean(values):.6f}\")\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5150 }, "formatted_output": "ranked = {key: {} for key in grid}\npop = -item_features(catalog)[:, 2]\nwith torch.inference_mode():\n item_vectors = model.item_tower(torch.arange(30_000))\n latent = item_vectors[:, :-1].T.contiguous()\n bias = item_vectors[:, -1]\n relevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\n for start in range(0, len(relevant_rows), 192):\n batch = relevant_rows[start : start + 192]\n hids, offsets, weights, features = gather_histories(histories, batch)\n users = model.user_tower(hids, offsets, weights, features)\n similarity = users[:, :-1] @ latent\n if start == 0:\n print(\"similarity std\", float(similarity.std()), \"bias std\", float(bias.std()))\n eligible = np.unpackbits(\n archive[\"packed_eligible\"][batch], axis=1,\n count=30_000, bitorder=\"little\"\n ).astype(bool)\n mask = torch.from_numpy(~eligible)\n for key in grid:\n b, p = key\n scores = similarity + b * bias[None, :] + p * pop[None, :]\n scores.masked_fill_(mask, -torch.inf)\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n for row, uid in enumerate(archive[\"uids\"][batch]):\n if int(uid) in truth:\n ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\n\nfor key in grid:\n values = [ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()]\n print(key, f\"{np.mean(values):.6f}\")\n" } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:49.630Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1034bb087d2b73db83ee2b04e26", "usage": { "input_tokens": 65358, "cached_input_tokens": 64512, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 13, "total_tokens": 65436 }, "turn_token_usage": { "input_tokens": 2483244, "cached_input_tokens": 2375424, "cache_write_input_tokens": 0, "output_tokens": 36504, "reasoning_output_tokens": 16579, "total_tokens": 2519748 }, "thread_token_usage": { "input_tokens": 2483244, "cached_input_tokens": 2375424, "cache_write_input_tokens": 0, "output_tokens": 36504, "reasoning_output_tokens": 16579, "total_tokens": 2519748 } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-24T23:23:52.291Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:52.291Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:54.319Z", "callId": "call_6yPIcTAwQ0ygEoWkm1xZZiM9", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blend.py\\n@@\\n model = TwoTowerRanker(30_000, item_features(catalog), 64, 128)\\n model.load_state_dict(checkpoint[\\\"model_state\\\"])\\n+model.item_tower.learned_prior_scale.fill_(1.0)\\n+model.item_tower.fixed_prior_scale.fill_(0.0)\\n model.eval()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:23:54.433Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:54.328Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-df9659fb-5855-474b-87c2-afb743746016", "changes": { "/app/solution/tune_blend.py": { "type": "update", "unified_diff": "@@ -24,2 +24,4 @@\n model.load_state_dict(checkpoint[\"model_state\"])\n+model.item_tower.learned_prior_scale.fill_(1.0)\n+model.item_tower.fixed_prior_scale.fill_(0.0)\n model.eval()\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:23:54.432Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b105f01087d29d3053be7492f85e", "usage": { "input_tokens": 65845, "cached_input_tokens": 65152, "cache_write_input_tokens": 0, "output_tokens": 193, "reasoning_output_tokens": 74, "total_tokens": 66038 }, "turn_token_usage": { "input_tokens": 2549089, "cached_input_tokens": 2440576, "cache_write_input_tokens": 0, "output_tokens": 36697, "reasoning_output_tokens": 16653, "total_tokens": 2585786 }, "thread_token_usage": { "input_tokens": 2549089, "cached_input_tokens": 2440576, "cache_write_input_tokens": 0, "output_tokens": 36697, "reasoning_output_tokens": 16653, "total_tokens": 2585786 } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-24T23:23:55.659Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:23:55.659Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:23:57.352Z", "callId": "call_n9S586zlM5QHW3YO9xpVJG7g", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:23:58.518Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=47970", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:23:57.480Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b10ab2ac87d28d1683e1e3d8daac", "usage": { "input_tokens": 66062, "cached_input_tokens": 65664, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 6, "total_tokens": 66168 }, "turn_token_usage": { "input_tokens": 2615151, "cached_input_tokens": 2506240, "cache_write_input_tokens": 0, "output_tokens": 36803, "reasoning_output_tokens": 16659, "total_tokens": 2651954 }, "thread_token_usage": { "input_tokens": 2615151, "cached_input_tokens": 2506240, "cache_write_input_tokens": 0, "output_tokens": 36803, "reasoning_output_tokens": 16659, "total_tokens": 2651954 } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-24T23:24:00.395Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:24:00.395Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:24:01.097Z", "callId": "call_Z13B442jtOdRaWxliO0DM31X", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":47970,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:24:31.107Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"2812d6\",\"wall_time_seconds\":30.00099088,\"session_id\":47970,\"original_token_count\":16}\nsimilarity std 2.4374141693115234 bias std 0.7092720866203308\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:24:01.228Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b10ebbf887d297a23d953498993a", "usage": { "input_tokens": 66196, "cached_input_tokens": 65920, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 66249 }, "turn_token_usage": { "input_tokens": 2681347, "cached_input_tokens": 2572160, "cache_write_input_tokens": 0, "output_tokens": 36856, "reasoning_output_tokens": 16665, "total_tokens": 2718203 }, "thread_token_usage": { "input_tokens": 2681347, "cached_input_tokens": 2572160, "cache_write_input_tokens": 0, "output_tokens": 36856, "reasoning_output_tokens": 16665, "total_tokens": 2718203 } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-24T23:24:32.441Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:24:32.441Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:24:33.170Z", "callId": "call_auQC4H42fdNsGGGC0kEM3uV6", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":47970,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:25:03.179Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"6b51d6\",\"wall_time_seconds\":30.001445428,\"session_id\":47970,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:24:33.272Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b12f6a2887d2a6c316306dbcc80a", "usage": { "input_tokens": 66327, "cached_input_tokens": 66048, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 66380 }, "turn_token_usage": { "input_tokens": 2747674, "cached_input_tokens": 2638208, "cache_write_input_tokens": 0, "output_tokens": 36909, "reasoning_output_tokens": 16671, "total_tokens": 2784583 }, "thread_token_usage": { "input_tokens": 2747674, "cached_input_tokens": 2638208, "cache_write_input_tokens": 0, "output_tokens": 36909, "reasoning_output_tokens": 16671, "total_tokens": 2784583 } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-24T23:25:04.549Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:25:04.549Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:25:05.390Z", "callId": "call_BQqn4SU1J4RGXolvSrHNMKJw", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":47970,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:25:16.051Z", "content": "Script completed\nWall time 10.6 seconds\nOutput:\n\n{\"chunk_id\":\"d7db81\",\"wall_time_seconds\":10.643284144999999,\"exit_code\":0,\"original_token_count\":123}\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:25:05.509Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b14f688887d29bdd225948a9af2d", "usage": { "input_tokens": 66435, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 66491 }, "turn_token_usage": { "input_tokens": 2814109, "cached_input_tokens": 2704384, "cache_write_input_tokens": 0, "output_tokens": 36965, "reasoning_output_tokens": 16680, "total_tokens": 2851074 }, "thread_token_usage": { "input_tokens": 2814109, "cached_input_tokens": 2704384, "cache_write_input_tokens": 0, "output_tokens": 36965, "reasoning_output_tokens": 16680, "total_tokens": 2851074 } } }, { "kind": "context", "timestamp": "2026-09-24T23:25:16.049Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3fa67eed-59c8-4e7c-a0ae-f7e8aae80c83", "process_id": "47970", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "similarity std 2.4374141693115234 bias std 0.7092720866203308\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n", "stderr": "", "aggregated_output": "similarity std 2.4374141693115234 bias std 0.7092720866203308\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n", "exit_code": 0, "duration": { "secs": 78, "nanos": 532714845 }, "formatted_output": "similarity std 2.4374141693115234 bias std 0.7092720866203308\n(0.8, 0.0) 0.020408\n(0.8, 0.15) 0.022309\n(0.8, 0.3) 0.024042\n(0.8, 0.45) 0.023693\n(1.1, 0.0) 0.022691\n(1.1, 0.15) 0.024435\n(1.1, 0.3) 0.024145\n(1.1, 0.45) 0.023237\n(1.4, 0.0) 0.023947\n(1.4, 0.15) 0.025061\n(1.4, 0.3) 0.024055\n(1.4, 0.45) 0.022497\n(1.7, 0.0) 0.024529\n(1.7, 0.15) 0.024414\n(1.7, 0.3) 0.023274\n(1.7, 0.45) 0.021976\n(2.0, 0.0) 0.024044\n(2.0, 0.15) 0.023444\n(2.0, 0.3) 0.022251\n(2.0, 0.45) 0.021551\n(2.4, 0.0) 0.023020\n(2.4, 0.15) 0.022360\n(2.4, 0.3) 0.021581\n(2.4, 0.45) 0.020636\n" } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-24T23:25:23.472Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:25:23.472Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:25:25.198Z", "text": "The frequency-aware profile improves the calibrated score to 0.025061. The best blend is a little less popularity-heavy than the first model (learned prior ×1.4 plus fixed popularity ×0.15). I’m doing one focused label-weight test next: grade-3 likes contribute gain 7 in NDCG, so I’m aligning positive sampling more closely with that gain before committing to the final refit." }, { "kind": "tool", "timestamp": "2026-09-24T23:25:28.310Z", "callId": "call_9mYJWSDhK2okqrCW559beOfI", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_like7.parquet --checkpoint /app/solution/model_validation_like7.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 7 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:25:29.476Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=98013", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:25:28.423Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b15c40c887d28d29fe73397a5e28", "usage": { "input_tokens": 66929, "cached_input_tokens": 66304, "cache_write_input_tokens": 0, "output_tokens": 623, "reasoning_output_tokens": 350, "total_tokens": 67552 }, "turn_token_usage": { "input_tokens": 2881038, "cached_input_tokens": 2770688, "cache_write_input_tokens": 0, "output_tokens": 37588, "reasoning_output_tokens": 17030, "total_tokens": 2918626 }, "thread_token_usage": { "input_tokens": 2881038, "cached_input_tokens": 2770688, "cache_write_input_tokens": 0, "output_tokens": 37588, "reasoning_output_tokens": 17030, "total_tokens": 2918626 } } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-24T23:25:30.811Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:25:30.811Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:25:31.505Z", "callId": "call_cLPYfUciN58kIVy4GklguwQV", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":98013,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:26:01.525Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b1bac6\",\"wall_time_seconds\":30.001248079,\"session_id\":98013,\"original_token_count\":125}\nloaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:25:31.621Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b169b0cc87d2a6c1737145154002", "usage": { "input_tokens": 67580, "cached_input_tokens": 66688, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 67633 }, "turn_token_usage": { "input_tokens": 2948618, "cached_input_tokens": 2837376, "cache_write_input_tokens": 0, "output_tokens": 37641, "reasoning_output_tokens": 17036, "total_tokens": 2986259 }, "thread_token_usage": { "input_tokens": 2948618, "cached_input_tokens": 2837376, "cache_write_input_tokens": 0, "output_tokens": 37641, "reasoning_output_tokens": 17036, "total_tokens": 2986259 } } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-24T23:26:02.797Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:26:02.797Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:26:03.638Z", "callId": "call_6pVSYHJWji4WyzSRoXQs3Am0", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":98013,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:26:33.667Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"d0baf2\",\"wall_time_seconds\":30.000983923,\"session_id\":98013,\"original_token_count\":39}\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:26:03.772Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b189c0c487d28142d4e0a0daeb6a", "usage": { "input_tokens": 67830, "cached_input_tokens": 67456, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 67885 }, "turn_token_usage": { "input_tokens": 3016448, "cached_input_tokens": 2904832, "cache_write_input_tokens": 0, "output_tokens": 37696, "reasoning_output_tokens": 17044, "total_tokens": 3054144 }, "thread_token_usage": { "input_tokens": 3016448, "cached_input_tokens": 2904832, "cache_write_input_tokens": 0, "output_tokens": 37696, "reasoning_output_tokens": 17044, "total_tokens": 3054144 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-24T23:26:35.950Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:26:35.950Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:26:36.609Z", "callId": "call_0BZS0OgLrzGwKt7t73G84ELi", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":98013,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:27:06.639Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"d981e2\",\"wall_time_seconds\":30.001097775,\"session_id\":98013,\"original_token_count\":50}\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:26:36.800Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1aa404487d28f1a152cb6686e61", "usage": { "input_tokens": 68018, "cached_input_tokens": 67712, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 68071 }, "turn_token_usage": { "input_tokens": 3084466, "cached_input_tokens": 2972544, "cache_write_input_tokens": 0, "output_tokens": 37749, "reasoning_output_tokens": 17050, "total_tokens": 3122215 }, "thread_token_usage": { "input_tokens": 3084466, "cached_input_tokens": 2972544, "cache_write_input_tokens": 0, "output_tokens": 37749, "reasoning_output_tokens": 17050, "total_tokens": 3122215 } } }, { "kind": "context", "timestamp": "2026-09-24T23:27:07.307Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-44243ea9-ccab-400f-b150-d3fd69afe2d7", "process_id": "98013", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_like7.parquet --checkpoint /app/solution/model_validation_like7.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 7 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_like7.parquet --checkpoint /app/solution/model_validation_like7.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 7 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 98, "nanos": 832896013 }, "formatted_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.59676 lr=0.00197001\nepoch 02/12: loss=5.34863 lr=0.0018821\nepoch 03/12: loss=5.13171 lr=0.00174225\nepoch 04/12: loss=4.93923 lr=0.00156\nepoch 05/12: loss=4.78946 lr=0.00134776\nepoch 06/12: loss=4.65686 lr=0.00112\nepoch 07/12: loss=4.55852 lr=0.000892239\nepoch 08/12: loss=4.45585 lr=0.00068\nepoch 09/12: loss=4.36784 lr=0.000497746\nepoch 10/12: loss=4.30615 lr=0.000357898\nepoch 11/12: loss=4.24130 lr=0.000269985\nepoch 12/12: loss=4.19851 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-24T23:27:08.244Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:08.244Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:27:08.926Z", "callId": "call_6KZBE5pYlQe1oWJ6FzcD7Ivx", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":98013,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:27:09.032Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"63eefd\",\"wall_time_seconds\":0.00000203,\"exit_code\":0,\"original_token_count\":105}\nwrote 845,600 ranked rows to /app/solution/validation_twotower_like7.parquet\n{\"checkpoint\": \"/app/solution/model_validation_like7.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 7.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:09.031Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1cae51887d2a683ef1b7f23d0a2", "usage": { "input_tokens": 68223, "cached_input_tokens": 67840, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 68277 }, "turn_token_usage": { "input_tokens": 3152689, "cached_input_tokens": 3040384, "cache_write_input_tokens": 0, "output_tokens": 37803, "reasoning_output_tokens": 17057, "total_tokens": 3190492 }, "thread_token_usage": { "input_tokens": 3152689, "cached_input_tokens": 3040384, "cache_write_input_tokens": 0, "output_tokens": 37803, "reasoning_output_tokens": 17057, "total_tokens": 3190492 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-24T23:27:10.798Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:10.798Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:27:11.987Z", "callId": "call_Xsk0G5xpOL42zjEcpYY6YhR8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_like7.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:27:15.387Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_like7.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023811,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:12.131Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1cd648c87d290cb3f12e23f4c38", "usage": { "input_tokens": 68471, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 6, "total_tokens": 68551 }, "turn_token_usage": { "input_tokens": 3221160, "cached_input_tokens": 3108480, "cache_write_input_tokens": 0, "output_tokens": 37883, "reasoning_output_tokens": 17063, "total_tokens": 3259043 }, "thread_token_usage": { "input_tokens": 3221160, "cached_input_tokens": 3108480, "cache_write_input_tokens": 0, "output_tokens": 37883, "reasoning_output_tokens": 17063, "total_tokens": 3259043 } } }, { "kind": "context", "timestamp": "2026-09-24T23:27:15.383Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e707479c-7d78-429e-86da-d648f54abb4c", "process_id": "96907", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_like7.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_like7.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_like7.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023811,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_like7.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023811,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 231775739 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_like7.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023811,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-24T23:27:25.388Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:25.388Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:27:26.917Z", "callId": "call_DDgqMMmhKOI8HbU8FShynOGH", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n torch.set_num_threads(args.threads)\\n torch.set_num_interop_threads(1)\\n+ torch.manual_seed(args.seed)\\n random.seed(args.seed)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:27:27.033Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:26.926Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a811a115-57ce-4f93-bfd4-b530669645db", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -588,2 +588,3 @@\n torch.set_num_interop_threads(1)\n+ torch.manual_seed(args.seed)\n random.seed(args.seed)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:27:27.031Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1d3ab2c87d2a18c483f3d6305c6", "usage": { "input_tokens": 68624, "cached_input_tokens": 68352, "cache_write_input_tokens": 0, "output_tokens": 574, "reasoning_output_tokens": 490, "total_tokens": 69198 }, "turn_token_usage": { "input_tokens": 3289784, "cached_input_tokens": 3176832, "cache_write_input_tokens": 0, "output_tokens": 38457, "reasoning_output_tokens": 17553, "total_tokens": 3328241 }, "thread_token_usage": { "input_tokens": 3289784, "cached_input_tokens": 3176832, "cache_write_input_tokens": 0, "output_tokens": 38457, "reasoning_output_tokens": 17553, "total_tokens": 3328241 } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-24T23:27:34.176Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:34.176Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:27:35.717Z", "text": "The NDCG-gain-matched like weighting was worse (0.023811), so the selected label weight remains 4. I also found and fixed a reproducibility issue: the sampling seed was recorded but model initialization happened before it was applied. I’m running one deterministic confirmation now; that makes the chosen validation recipe exactly reproducible for the train+validation refit." }, { "kind": "tool", "timestamp": "2026-09-24T23:27:38.810Z", "callId": "call_OA5Tv0QC1VkWLA9i5gLV2TEL", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_deterministic.parquet --checkpoint /app/solution/model_validation_deterministic.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:27:39.976Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=34699", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:38.943Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1df5a4087d29b1df7ab67df6c0b", "usage": { "input_tokens": 69222, "cached_input_tokens": 68480, "cache_write_input_tokens": 0, "output_tokens": 491, "reasoning_output_tokens": 226, "total_tokens": 69713 }, "turn_token_usage": { "input_tokens": 3359006, "cached_input_tokens": 3245312, "cache_write_input_tokens": 0, "output_tokens": 38948, "reasoning_output_tokens": 17779, "total_tokens": 3397954 }, "thread_token_usage": { "input_tokens": 3359006, "cached_input_tokens": 3245312, "cache_write_input_tokens": 0, "output_tokens": 38948, "reasoning_output_tokens": 17779, "total_tokens": 3397954 } } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-24T23:27:41.302Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:27:41.302Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:27:42.037Z", "callId": "call_fLqagrhV4UOYMRaqXagsN85a", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":34699,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:28:12.053Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"382581\",\"wall_time_seconds\":30.000819395,\"session_id\":34699,\"original_token_count\":125}\nloaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:27:42.130Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b1ec2e5c87d2a51463642cfe3e59", "usage": { "input_tokens": 69741, "cached_input_tokens": 68992, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 69794 }, "turn_token_usage": { "input_tokens": 3428747, "cached_input_tokens": 3314304, "cache_write_input_tokens": 0, "output_tokens": 39001, "reasoning_output_tokens": 17785, "total_tokens": 3467748 }, "thread_token_usage": { "input_tokens": 3428747, "cached_input_tokens": 3314304, "cache_write_input_tokens": 0, "output_tokens": 39001, "reasoning_output_tokens": 17785, "total_tokens": 3467748 } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-24T23:28:13.342Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:28:13.342Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:28:14.081Z", "callId": "call_VWakI8LUqLLrzIFQVKEP6U7A", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":34699,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:28:44.105Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"5746d2\",\"wall_time_seconds\":30.000984971,\"session_id\":34699,\"original_token_count\":39}\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:28:14.208Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b20c4c9087d297e760284abb49fd", "usage": { "input_tokens": 69989, "cached_input_tokens": 69504, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70042 }, "turn_token_usage": { "input_tokens": 3498736, "cached_input_tokens": 3383808, "cache_write_input_tokens": 0, "output_tokens": 39054, "reasoning_output_tokens": 17791, "total_tokens": 3537790 }, "thread_token_usage": { "input_tokens": 3498736, "cached_input_tokens": 3383808, "cache_write_input_tokens": 0, "output_tokens": 39054, "reasoning_output_tokens": 17791, "total_tokens": 3537790 } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-24T23:28:45.678Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:28:45.678Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:28:49.312Z", "callId": "call_sYlGn8akZaT1RmGxoHGr2m4V", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":34699,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:29:18.215Z", "content": "Script completed\nWall time 28.9 seconds\nOutput:\n\n{\"chunk_id\":\"4119b2\",\"wall_time_seconds\":28.871920956,\"exit_code\":0,\"original_token_count\":158}\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:28:50.059Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b22c59c887d2a5582eb4be75dadd", "usage": { "input_tokens": 70174, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70227 }, "turn_token_usage": { "input_tokens": 3568910, "cached_input_tokens": 3453568, "cache_write_input_tokens": 0, "output_tokens": 39107, "reasoning_output_tokens": 17797, "total_tokens": 3608017 }, "thread_token_usage": { "input_tokens": 3568910, "cached_input_tokens": 3453568, "cache_write_input_tokens": 0, "output_tokens": 39107, "reasoning_output_tokens": 17797, "total_tokens": 3608017 } } }, { "kind": "context", "timestamp": "2026-09-24T23:29:18.211Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-77daf3c9-a594-4d50-804d-74ee43cb3726", "process_id": "34699", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_deterministic.parquet --checkpoint /app/solution/model_validation_deterministic.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_deterministic.parquet --checkpoint /app/solution/model_validation_deterministic.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 99, "nanos": 235131244 }, "formatted_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60325 lr=0.00197001\nepoch 02/12: loss=5.34974 lr=0.0018821\nepoch 03/12: loss=5.13801 lr=0.00174225\nepoch 04/12: loss=4.94966 lr=0.00156\nepoch 05/12: loss=4.79977 lr=0.00134776\nepoch 06/12: loss=4.66945 lr=0.00112\nepoch 07/12: loss=4.57449 lr=0.000892239\nepoch 08/12: loss=4.46786 lr=0.00068\nepoch 09/12: loss=4.39488 lr=0.000497746\nepoch 10/12: loss=4.33856 lr=0.000357898\nepoch 11/12: loss=4.28199 lr=0.000269985\nepoch 12/12: loss=4.24280 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_deterministic.parquet\n{\"checkpoint\": \"/app/solution/model_validation_deterministic.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-24T23:29:19.618Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:29:19.618Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:29:20.791Z", "callId": "call_55Yjj0eAHeCc9RYCGOR4lgKw", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_deterministic.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:29:24.225Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_deterministic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024198,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:29:20.974Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b24e6d7087d2a6d0e071e959c06a", "usage": { "input_tokens": 70522, "cached_input_tokens": 70016, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 6, "total_tokens": 70603 }, "turn_token_usage": { "input_tokens": 3639432, "cached_input_tokens": 3523584, "cache_write_input_tokens": 0, "output_tokens": 39188, "reasoning_output_tokens": 17803, "total_tokens": 3678620 }, "thread_token_usage": { "input_tokens": 3639432, "cached_input_tokens": 3523584, "cache_write_input_tokens": 0, "output_tokens": 39188, "reasoning_output_tokens": 17803, "total_tokens": 3678620 } } }, { "kind": "context", "timestamp": "2026-09-24T23:29:24.221Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-635f863a-7ca3-457f-929a-172b3802f3b0", "process_id": "9212", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_deterministic.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_deterministic.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_deterministic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024198,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_deterministic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024198,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 264062803 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_deterministic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024198,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-24T23:29:28.653Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:29:28.653Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:29:29.714Z", "callId": "call_bYhR9x6F6bOkDhXifZfuepPE", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blend.py\\n@@\\n-checkpoint = torch.load(\\\"/app/solution/model_validation_v2.pt\\\", weights_only=False)\\n+checkpoint = torch.load(\\\"/app/solution/model_validation_deterministic.pt\\\", weights_only=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:29:29.849Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:29:29.723Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c56d4526-1366-44aa-b7b4-a07e91e8ef07", "changes": { "/app/solution/tune_blend.py": { "type": "update", "unified_diff": "@@ -19,3 +19,3 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_v2.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_deterministic.pt\", weights_only=False)\n cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:29:29.848Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b2548e3487d2acf891c6904339c5", "usage": { "input_tokens": 70677, "cached_input_tokens": 70400, "cache_write_input_tokens": 0, "output_tokens": 221, "reasoning_output_tokens": 130, "total_tokens": 70898 }, "turn_token_usage": { "input_tokens": 3710109, "cached_input_tokens": 3593984, "cache_write_input_tokens": 0, "output_tokens": 39409, "reasoning_output_tokens": 17933, "total_tokens": 3749518 }, "thread_token_usage": { "input_tokens": 3710109, "cached_input_tokens": 3593984, "cache_write_input_tokens": 0, "output_tokens": 39409, "reasoning_output_tokens": 17933, "total_tokens": 3749518 } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-24T23:29:31.860Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:29:31.860Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:29:33.777Z", "callId": "call_uwXYgTzJxALDCWzjJFw1Z3cU", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:29:34.945Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=78235", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:29:33.917Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b25a361087d2a12ab1af32d8bbf8", "usage": { "input_tokens": 70922, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 13, "total_tokens": 71035 }, "turn_token_usage": { "input_tokens": 3781031, "cached_input_tokens": 3664512, "cache_write_input_tokens": 0, "output_tokens": 39522, "reasoning_output_tokens": 17946, "total_tokens": 3820553 }, "thread_token_usage": { "input_tokens": 3781031, "cached_input_tokens": 3664512, "cache_write_input_tokens": 0, "output_tokens": 39522, "reasoning_output_tokens": 17946, "total_tokens": 3820553 } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-24T23:29:36.457Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:29:36.457Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:29:37.757Z", "callId": "call_sDLKjlqAJBGFCbtvd6ZC8KFn", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":78235,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:30:07.767Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"d7ae1b\",\"wall_time_seconds\":30.001236842,\"session_id\":78235,\"original_token_count\":16}\nsimilarity std 2.519658088684082 bias std 0.6085100173950195\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:29:37.912Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b25f35dc87d296effa4f4e126771", "usage": { "input_tokens": 71063, "cached_input_tokens": 70784, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 71119 }, "turn_token_usage": { "input_tokens": 3852094, "cached_input_tokens": 3735296, "cache_write_input_tokens": 0, "output_tokens": 39578, "reasoning_output_tokens": 17955, "total_tokens": 3891672 }, "thread_token_usage": { "input_tokens": 3852094, "cached_input_tokens": 3735296, "cache_write_input_tokens": 0, "output_tokens": 39578, "reasoning_output_tokens": 17955, "total_tokens": 3891672 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:30:11.947Z", "callId": "call_w612x0AB0dDYK4XwJ4uZ7CCm", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":78235,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:30:41.955Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b056a0\",\"wall_time_seconds\":30.001159139,\"session_id\":78235,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:30:12.070Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b27fff5487d29307835458b7fa61", "usage": { "input_tokens": 71197, "cached_input_tokens": 70912, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 71242 }, "turn_token_usage": { "input_tokens": 3923291, "cached_input_tokens": 3806208, "cache_write_input_tokens": 0, "output_tokens": 39623, "reasoning_output_tokens": 17955, "total_tokens": 3962914 }, "thread_token_usage": { "input_tokens": 3923291, "cached_input_tokens": 3806208, "cache_write_input_tokens": 0, "output_tokens": 39623, "reasoning_output_tokens": 17955, "total_tokens": 3962914 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:30:45.423Z", "callId": "call_yeBxESSIniCbplKLg3GZ7bSF", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":78235,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:30:54.673Z", "content": "Script completed\nWall time 9.2 seconds\nOutput:\n\n{\"chunk_id\":\"cd371f\",\"wall_time_seconds\":9.240704915,\"exit_code\":0,\"original_token_count\":123}\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:30:45.567Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b2a2384c87d2a809920521a2de24", "usage": { "input_tokens": 71296, "cached_input_tokens": 71040, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 71341 }, "turn_token_usage": { "input_tokens": 3994587, "cached_input_tokens": 3877248, "cache_write_input_tokens": 0, "output_tokens": 39668, "reasoning_output_tokens": 17955, "total_tokens": 4034255 }, "thread_token_usage": { "input_tokens": 3994587, "cached_input_tokens": 3877248, "cache_write_input_tokens": 0, "output_tokens": 39668, "reasoning_output_tokens": 17955, "total_tokens": 4034255 } } }, { "kind": "context", "timestamp": "2026-09-24T23:30:54.671Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e788dad4-a995-447f-ac42-8555ad34c8ad", "process_id": "78235", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "similarity std 2.519658088684082 bias std 0.6085100173950195\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n", "stderr": "", "aggregated_output": "similarity std 2.519658088684082 bias std 0.6085100173950195\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n", "exit_code": 0, "duration": { "secs": 80, "nanos": 726925202 }, "formatted_output": "similarity std 2.519658088684082 bias std 0.6085100173950195\n(0.8, 0.0) 0.020650\n(0.8, 0.15) 0.023018\n(0.8, 0.3) 0.024181\n(0.8, 0.45) 0.023781\n(1.1, 0.0) 0.022634\n(1.1, 0.15) 0.024173\n(1.1, 0.3) 0.023760\n(1.1, 0.45) 0.023340\n(1.4, 0.0) 0.023659\n(1.4, 0.15) 0.024198\n(1.4, 0.3) 0.023492\n(1.4, 0.45) 0.022787\n(1.7, 0.0) 0.023937\n(1.7, 0.15) 0.023743\n(1.7, 0.3) 0.022765\n(1.7, 0.45) 0.021945\n(2.0, 0.0) 0.023330\n(2.0, 0.15) 0.023145\n(2.0, 0.3) 0.022252\n(2.0, 0.45) 0.021189\n(2.4, 0.0) 0.022727\n(2.4, 0.15) 0.022221\n(2.4, 0.3) 0.021463\n(2.4, 0.45) 0.020523\n" } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-24T23:30:57.602Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:30:57.602Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:31:00.743Z", "callId": "call_mYkWcasgQKRzLAiCPnyv5Xaa", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_seed1337.parquet --checkpoint /app/solution/model_validation_seed1337.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 1337 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:31:01.910Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=21771", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:31:00.902Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b2aef99887d2b25a39edbc414fed", "usage": { "input_tokens": 71776, "cached_input_tokens": 71168, "cache_write_input_tokens": 0, "output_tokens": 258, "reasoning_output_tokens": 77, "total_tokens": 72034 }, "turn_token_usage": { "input_tokens": 4066363, "cached_input_tokens": 3948416, "cache_write_input_tokens": 0, "output_tokens": 39926, "reasoning_output_tokens": 18032, "total_tokens": 4106289 }, "thread_token_usage": { "input_tokens": 4066363, "cached_input_tokens": 3948416, "cache_write_input_tokens": 0, "output_tokens": 39926, "reasoning_output_tokens": 18032, "total_tokens": 4106289 } } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-24T23:31:04.564Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:31:04.564Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:31:05.256Z", "callId": "call_sqGTVTvoNOFek7XOUqVXN7s7", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":21771,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:31:35.270Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"ccb45b\",\"wall_time_seconds\":30.001344003,\"session_id\":21771,\"original_token_count\":125}\nloaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:31:05.371Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b2b62f8887d2bb71e980ef0c353a", "usage": { "input_tokens": 72062, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 72115 }, "turn_token_usage": { "input_tokens": 4138425, "cached_input_tokens": 4019968, "cache_write_input_tokens": 0, "output_tokens": 39979, "reasoning_output_tokens": 18038, "total_tokens": 4178404 }, "thread_token_usage": { "input_tokens": 4138425, "cached_input_tokens": 4019968, "cache_write_input_tokens": 0, "output_tokens": 39979, "reasoning_output_tokens": 18038, "total_tokens": 4178404 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:31:38.818Z", "callId": "call_dkLkiCUFS1zKifazBPX0BPrw", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":21771,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:32:08.838Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"82929b\",\"wall_time_seconds\":30.000558008,\"session_id\":21771,\"original_token_count\":48}\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:31:38.955Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b2d7819487d2ba8d76e75237109d", "usage": { "input_tokens": 72311, "cached_input_tokens": 71936, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 72356 }, "turn_token_usage": { "input_tokens": 4210736, "cached_input_tokens": 4091904, "cache_write_input_tokens": 0, "output_tokens": 40024, "reasoning_output_tokens": 18038, "total_tokens": 4250760 }, "thread_token_usage": { "input_tokens": 4210736, "cached_input_tokens": 4091904, "cache_write_input_tokens": 0, "output_tokens": 40024, "reasoning_output_tokens": 18038, "total_tokens": 4250760 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:32:10.817Z", "callId": "call_cz8EyytRfjLYkygBKnnvjVXh", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":21771,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:32:39.920Z", "content": "Script completed\nWall time 29.1 seconds\nOutput:\n\n{\"chunk_id\":\"78ae05\",\"wall_time_seconds\":29.08208224,\"exit_code\":0,\"original_token_count\":145}\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:32:10.949Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b2f91be487d280238903ff36d1a9", "usage": { "input_tokens": 72506, "cached_input_tokens": 72192, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 72551 }, "turn_token_usage": { "input_tokens": 4283242, "cached_input_tokens": 4164096, "cache_write_input_tokens": 0, "output_tokens": 40069, "reasoning_output_tokens": 18038, "total_tokens": 4323311 }, "thread_token_usage": { "input_tokens": 4283242, "cached_input_tokens": 4164096, "cache_write_input_tokens": 0, "output_tokens": 40069, "reasoning_output_tokens": 18038, "total_tokens": 4323311 } } }, { "kind": "context", "timestamp": "2026-09-24T23:32:39.918Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5aa4c1dc-7bb0-4915-be33-3b97af98f168", "process_id": "21771", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_seed1337.parquet --checkpoint /app/solution/model_validation_seed1337.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 1337 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_seed1337.parquet --checkpoint /app/solution/model_validation_seed1337.pt --cache /app/solution/cache_validation_v2.pt --epochs 12 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 1337 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n", "stderr": "", "aggregated_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n", "exit_code": 0, "duration": { "secs": 99, "nanos": 8955251 }, "formatted_output": "loaded prepared arrays from /app/solution/cache_validation_v2.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60734 lr=0.00197001\nepoch 02/12: loss=5.35055 lr=0.0018821\nepoch 03/12: loss=5.13109 lr=0.00174225\nepoch 04/12: loss=4.94432 lr=0.00156\nepoch 05/12: loss=4.79672 lr=0.00134776\nepoch 06/12: loss=4.67224 lr=0.00112\nepoch 07/12: loss=4.54466 lr=0.000892239\nepoch 08/12: loss=4.44736 lr=0.00068\nepoch 09/12: loss=4.36494 lr=0.000497746\nepoch 10/12: loss=4.29942 lr=0.000357898\nepoch 11/12: loss=4.23000 lr=0.000269985\nepoch 12/12: loss=4.19406 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_seed1337.parquet\n{\"checkpoint\": \"/app/solution/model_validation_seed1337.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 16, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 1337}}\n" } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-24T23:32:41.484Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:32:41.484Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:32:42.720Z", "callId": "call_VUopDcdWIVOLNHBDI1Qs1csm", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_seed1337.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:32:46.171Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023842,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:32:42.895Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b318405c87d2bdf1596190e55f00", "usage": { "input_tokens": 72825, "cached_input_tokens": 72320, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 6, "total_tokens": 72906 }, "turn_token_usage": { "input_tokens": 4356067, "cached_input_tokens": 4236416, "cache_write_input_tokens": 0, "output_tokens": 40150, "reasoning_output_tokens": 18044, "total_tokens": 4396217 }, "thread_token_usage": { "input_tokens": 4356067, "cached_input_tokens": 4236416, "cache_write_input_tokens": 0, "output_tokens": 40150, "reasoning_output_tokens": 18044, "total_tokens": 4396217 } } }, { "kind": "context", "timestamp": "2026-09-24T23:32:46.167Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ba34f893-cb1b-4d7c-b8e9-d09116fe80a2", "process_id": "13830", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_seed1337.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_seed1337.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023842,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023842,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 283815338 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_seed1337.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023842,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-24T23:33:01.602Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:33:01.602Z", "text": "" } ] }, { "id": 111, "role": "agent", "timestamp": "2026-09-24T23:33:04.389Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:33:04.389Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:33:06.039Z", "text": "The deterministic seed trials are close but do not beat the preserved 0.025061 checkpoint individually. I’m checking whether their errors are complementary. If a two-seed concatenation gives a meaningful gain, it still remains a valid two-tower MIPS model (concatenated user vectors against concatenated offline item vectors); otherwise I’ll keep the lower-latency 65-dimensional model." }, { "kind": "tool", "timestamp": "2026-09-24T23:33:21.511Z", "callId": "call_owlmwSzgwaPwVcz3PvMdUMKl", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/tune_ensemble.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Measure whether validation models have complementary retrieval errors.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import sys\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+sys.path[:0] = [\\\"/app/solution\\\", \\\"/task\\\"]\\n+from model import TwoTowerRanker\\n+from train_ranker import gather_histories, item_features\\n+from ranking_evaluation import CandidateSet, ndcg_at_k\\n+from evaluate_public import validation_truth\\n+\\n+torch.set_num_threads(8)\\n+cat = pd.read_parquet(\\\"/task/data/candidate_catalog.parquet\\\")\\n+features = item_features(cat)\\n+paths = [\\n+ \\\"/app/solution/model_validation_v2.pt\\\",\\n+ \\\"/app/solution/model_validation_deterministic.pt\\\",\\n+ \\\"/app/solution/model_validation_seed1337.pt\\\",\\n+]\\n+models = []\\n+for path in paths:\\n+ model = TwoTowerRanker(30_000, features, 64, 128)\\n+ model.load_state_dict(torch.load(path, weights_only=False)[\\\"model_state\\\"])\\n+ model.item_tower.learned_prior_scale.fill_(1.4)\\n+ model.item_tower.fixed_prior_scale.fill_(0.15)\\n+ model.eval()\\n+ models.append(model)\\n+histories = torch.load(\\n+ \\\"/app/solution/cache_validation_v2.pt\\\", weights_only=False\\n+)[\\\"histories\\\"]\\n+archive = np.load(\\\"/task/data/validation_eligible_candidates.npz\\\")\\n+candidates = CandidateSet(Path(\\\"/task/data/validation_eligible_candidates.npz\\\"))\\n+targets = set(pd.read_parquet(\\\"/task/data/target_users.parquet\\\").uid.astype(int))\\n+truth = validation_truth(candidates, targets)\\n+relevant_rows = np.flatnonzero(np.isin(archive[\\\"uids\\\"], list(truth)))\\n+weights_grid = {\\n+ \\\"a\\\": (1.0, 0.0, 0.0),\\n+ \\\"b\\\": (0.0, 1.0, 0.0),\\n+ \\\"c\\\": (0.0, 0.0, 1.0),\\n+ \\\"ab25\\\": (0.75, 0.25, 0.0),\\n+ \\\"ab50\\\": (0.5, 0.5, 0.0),\\n+ \\\"ab75\\\": (0.25, 0.75, 0.0),\\n+ \\\"ac50\\\": (0.5, 0.0, 0.5),\\n+ \\\"bc50\\\": (0.0, 0.5, 0.5),\\n+ \\\"abc\\\": (1 / 3, 1 / 3, 1 / 3),\\n+}\\n+ranked = {key: {} for key in weights_grid}\\n+with torch.inference_mode():\\n+ item_vectors = [m.item_tower(torch.arange(30_000)).T.contiguous() for m in models]\\n+ for start in range(0, len(relevant_rows), 160):\\n+ batch = relevant_rows[start : start + 160]\\n+ hids, offsets, hw, cf = gather_histories(histories, batch)\\n+ score_parts = [\\n+ m.user_tower(hids, offsets, hw, cf) @ iv\\n+ for m, iv in zip(models, item_vectors)\\n+ ]\\n+ eligible = np.unpackbits(\\n+ archive[\\\"packed_eligible\\\"][batch], axis=1,\\n+ count=30_000, bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ mask = torch.from_numpy(~eligible)\\n+ for key, combination in weights_grid.items():\\n+ scores = sum(w * score for w, score in zip(combination, score_parts))\\n+ scores.masked_fill_(mask, -torch.inf)\\n+ top = torch.topk(scores, 10, dim=1).indices.numpy()\\n+ for row, uid in enumerate(archive[\\\"uids\\\"][batch]):\\n+ ranked[key][int(uid)] = archive[\\\"item_ids\\\"][top[row]].astype(int).tolist()\\n+for key in weights_grid:\\n+ result = np.mean([ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()])\\n+ print(key, f\\\"{result:.6f}\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:33:21.604Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:33:21.519Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-9a90a11e-11e5-4cd4-9896-eac5b88a89e1", "changes": { "/app/solution/tune_ensemble.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Measure whether validation models have complementary retrieval errors.\"\"\"\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path[:0] = [\"/app/solution\", \"/task\"]\nfrom model import TwoTowerRanker\nfrom train_ranker import gather_histories, item_features\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\n\ntorch.set_num_threads(8)\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nfeatures = item_features(cat)\npaths = [\n \"/app/solution/model_validation_v2.pt\",\n \"/app/solution/model_validation_deterministic.pt\",\n \"/app/solution/model_validation_seed1337.pt\",\n]\nmodels = []\nfor path in paths:\n model = TwoTowerRanker(30_000, features, 64, 128)\n model.load_state_dict(torch.load(path, weights_only=False)[\"model_state\"])\n model.item_tower.learned_prior_scale.fill_(1.4)\n model.item_tower.fixed_prior_scale.fill_(0.15)\n model.eval()\n models.append(model)\nhistories = torch.load(\n \"/app/solution/cache_validation_v2.pt\", weights_only=False\n)[\"histories\"]\narchive = np.load(\"/task/data/validation_eligible_candidates.npz\")\ncandidates = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\ntruth = validation_truth(candidates, targets)\nrelevant_rows = np.flatnonzero(np.isin(archive[\"uids\"], list(truth)))\nweights_grid = {\n \"a\": (1.0, 0.0, 0.0),\n \"b\": (0.0, 1.0, 0.0),\n \"c\": (0.0, 0.0, 1.0),\n \"ab25\": (0.75, 0.25, 0.0),\n \"ab50\": (0.5, 0.5, 0.0),\n \"ab75\": (0.25, 0.75, 0.0),\n \"ac50\": (0.5, 0.0, 0.5),\n \"bc50\": (0.0, 0.5, 0.5),\n \"abc\": (1 / 3, 1 / 3, 1 / 3),\n}\nranked = {key: {} for key in weights_grid}\nwith torch.inference_mode():\n item_vectors = [m.item_tower(torch.arange(30_000)).T.contiguous() for m in models]\n for start in range(0, len(relevant_rows), 160):\n batch = relevant_rows[start : start + 160]\n hids, offsets, hw, cf = gather_histories(histories, batch)\n score_parts = [\n m.user_tower(hids, offsets, hw, cf) @ iv\n for m, iv in zip(models, item_vectors)\n ]\n eligible = np.unpackbits(\n archive[\"packed_eligible\"][batch], axis=1,\n count=30_000, bitorder=\"little\"\n ).astype(bool)\n mask = torch.from_numpy(~eligible)\n for key, combination in weights_grid.items():\n scores = sum(w * score for w, score in zip(combination, score_parts))\n scores.masked_fill_(mask, -torch.inf)\n top = torch.topk(scores, 10, dim=1).indices.numpy()\n for row, uid in enumerate(archive[\"uids\"][batch]):\n ranked[key][int(uid)] = archive[\"item_ids\"][top[row]].astype(int).tolist()\nfor key in weights_grid:\n result = np.mean([ndcg_at_k(ranked[key][u], gold, 10) for u, gold in truth.items()])\n print(key, f\"{result:.6f}\")\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/tune_ensemble.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:33:21.602Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b31e5f4887d28775801e2cd947ab", "usage": { "input_tokens": 72980, "cached_input_tokens": 72704, "cache_write_input_tokens": 0, "output_tokens": 1872, "reasoning_output_tokens": 707, "total_tokens": 74852 }, "turn_token_usage": { "input_tokens": 4429047, "cached_input_tokens": 4309120, "cache_write_input_tokens": 0, "output_tokens": 42022, "reasoning_output_tokens": 18751, "total_tokens": 4471069 }, "thread_token_usage": { "input_tokens": 4429047, "cached_input_tokens": 4309120, "cache_write_input_tokens": 0, "output_tokens": 42022, "reasoning_output_tokens": 18751, "total_tokens": 4471069 } } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-24T23:33:23.587Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:33:23.587Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:33:25.616Z", "callId": "call_UtJoZSfIOigSLTq0rYkC9sB3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_ensemble.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:33:26.783Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=34119", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:33:25.888Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b34204ec87d2be541dea9c6a5e5b", "usage": { "input_tokens": 74876, "cached_input_tokens": 72832, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 6, "total_tokens": 74982 }, "turn_token_usage": { "input_tokens": 4503923, "cached_input_tokens": 4381952, "cache_write_input_tokens": 0, "output_tokens": 42128, "reasoning_output_tokens": 18757, "total_tokens": 4546051 }, "thread_token_usage": { "input_tokens": 4503923, "cached_input_tokens": 4381952, "cache_write_input_tokens": 0, "output_tokens": 42128, "reasoning_output_tokens": 18757, "total_tokens": 4546051 } } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-24T23:33:28.334Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:33:28.334Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:33:29.138Z", "callId": "call_XqUWsOnIRi5KFu3VCG2Filwy", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":34119,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:33:59.148Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"68b4ae\",\"wall_time_seconds\":30.00156073,\"session_id\":34119,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:33:29.262Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b347050487d29eda848370a15ff5", "usage": { "input_tokens": 75010, "cached_input_tokens": 74752, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 75065 }, "turn_token_usage": { "input_tokens": 4578933, "cached_input_tokens": 4456704, "cache_write_input_tokens": 0, "output_tokens": 42183, "reasoning_output_tokens": 18765, "total_tokens": 4621116 }, "thread_token_usage": { "input_tokens": 4578933, "cached_input_tokens": 4456704, "cache_write_input_tokens": 0, "output_tokens": 42183, "reasoning_output_tokens": 18765, "total_tokens": 4621116 } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-24T23:34:01.144Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:34:01.144Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-24T23:34:03.066Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf5e19fb-1af4-415d-b49d-b6feb4d3f4b3", "process_id": "34119", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_ensemble.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_ensemble.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "a 0.025061\nb 0.024198\nc 0.023842\nab25 0.024979\nab50 0.025044\nab75 0.024738\nac50 0.024700\nbc50 0.025103\nabc 0.025080\n", "stderr": "", "aggregated_output": "a 0.025061\nb 0.024198\nc 0.023842\nab25 0.024979\nab50 0.025044\nab75 0.024738\nac50 0.024700\nbc50 0.025103\nabc 0.025080\n", "exit_code": 0, "duration": { "secs": 37, "nanos": 286149564 }, "formatted_output": "a 0.025061\nb 0.024198\nc 0.023842\nab25 0.024979\nab50 0.025044\nab75 0.024738\nac50 0.024700\nbc50 0.025103\nabc 0.025080\n" } } }, { "kind": "tool", "timestamp": "2026-09-24T23:34:05.109Z", "callId": "call_DCLOhzGalicslyBYkPELpC2u", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":34119,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:34:05.240Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"c17bb7\",\"wall_time_seconds\":0.00000215,\"exit_code\":0,\"original_token_count\":29}\na 0.025061\nb 0.024198\nc 0.023842\nab25 0.024979\nab50 0.025044\nab75 0.024738\nac50 0.024700\nbc50 0.025103\nabc 0.025080\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:34:05.239Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b3675aa487d2815f455dc2607a0b", "usage": { "input_tokens": 75119, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 75172 }, "turn_token_usage": { "input_tokens": 4654052, "cached_input_tokens": 4531584, "cache_write_input_tokens": 0, "output_tokens": 42236, "reasoning_output_tokens": 18771, "total_tokens": 4696288 }, "thread_token_usage": { "input_tokens": 4654052, "cached_input_tokens": 4531584, "cache_write_input_tokens": 0, "output_tokens": 42236, "reasoning_output_tokens": 18771, "total_tokens": 4696288 } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-24T23:34:13.074Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:34:13.074Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:34:14.909Z", "text": "Seed concatenation does not provide a meaningful lift (best ensemble 0.025103 versus 0.025061), so I’m retaining the faster single tower. One remaining high-value test is temporal data volume: the current model uses 16 weekly cutoffs. I’m expanding to 28 cutoffs to improve tail-item and taste-space coverage while preserving the most recent 180-day history at each cutoff." }, { "kind": "tool", "timestamp": "2026-09-24T23:34:17.688Z", "callId": "call_AuxuUSN92saTC1Rh7kUYG2Me", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_28w.parquet --checkpoint /app/solution/model_validation_28w.pt --cache /app/solution/cache_validation_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:34:18.864Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=12344", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:34:17.827Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b36daf3487d2afc6ca87cf7d8b30", "usage": { "input_tokens": 75293, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 574, "reasoning_output_tokens": 296, "total_tokens": 75867 }, "turn_token_usage": { "input_tokens": 4729345, "cached_input_tokens": 4606464, "cache_write_input_tokens": 0, "output_tokens": 42810, "reasoning_output_tokens": 19067, "total_tokens": 4772155 }, "thread_token_usage": { "input_tokens": 4729345, "cached_input_tokens": 4606464, "cache_write_input_tokens": 0, "output_tokens": 42810, "reasoning_output_tokens": 19067, "total_tokens": 4772155 } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-24T23:34:20.184Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:34:20.184Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:34:20.877Z", "callId": "call_jksdyKLGKr14mYBz79IXap0P", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":12344,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:34:50.886Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"83a41e\",\"wall_time_seconds\":30.001746576,\"session_id\":12344,\"original_token_count\":30}\nloaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:34:21.032Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b37b169c87d2aaffeb14d2828f9c", "usage": { "input_tokens": 75895, "cached_input_tokens": 75136, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 75948 }, "turn_token_usage": { "input_tokens": 4805240, "cached_input_tokens": 4681600, "cache_write_input_tokens": 0, "output_tokens": 42863, "reasoning_output_tokens": 19073, "total_tokens": 4848103 }, "thread_token_usage": { "input_tokens": 4805240, "cached_input_tokens": 4681600, "cache_write_input_tokens": 0, "output_tokens": 42863, "reasoning_output_tokens": 19073, "total_tokens": 4848103 } } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-24T23:34:53.899Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:34:53.899Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:34:54.878Z", "callId": "call_PwycFIlY7njZySSCUWc85Pyy", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":12344,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:35:24.894Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b40905\",\"wall_time_seconds\":30.000674666,\"session_id\":12344,\"original_token_count\":129}\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:34:54.977Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b39b25e487d2a31e9afcca55d677", "usage": { "input_tokens": 76038, "cached_input_tokens": 75776, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 76093 }, "turn_token_usage": { "input_tokens": 4881278, "cached_input_tokens": 4757376, "cache_write_input_tokens": 0, "output_tokens": 42918, "reasoning_output_tokens": 19081, "total_tokens": 4924196 }, "thread_token_usage": { "input_tokens": 4881278, "cached_input_tokens": 4757376, "cache_write_input_tokens": 0, "output_tokens": 42918, "reasoning_output_tokens": 19081, "total_tokens": 4924196 } } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-24T23:35:27.003Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:35:27.003Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:35:28.005Z", "callId": "call_bI12ijxethRlhEXRbI5k4YYz", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":12344,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:35:58.048Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"c457b9\",\"wall_time_seconds\":30.000954706,\"session_id\":12344,\"original_token_count\":29}\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:35:28.153Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b3bd3bc887d2a50ac2e7487994b2", "usage": { "input_tokens": 76275, "cached_input_tokens": 75904, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 76339 }, "turn_token_usage": { "input_tokens": 4957553, "cached_input_tokens": 4833280, "cache_write_input_tokens": 0, "output_tokens": 42982, "reasoning_output_tokens": 19098, "total_tokens": 5000535 }, "thread_token_usage": { "input_tokens": 4957553, "cached_input_tokens": 4833280, "cache_write_input_tokens": 0, "output_tokens": 42982, "reasoning_output_tokens": 19098, "total_tokens": 5000535 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:36:01.642Z", "callId": "call_4AH0Pl4QCUsffbizKRze3hTW", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":12344,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:36:31.671Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"2ce0c8\",\"wall_time_seconds\":30.000726331,\"session_id\":12344,\"original_token_count\":30}\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:36:01.751Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b3de49ac87d28f1a379555a086d0", "usage": { "input_tokens": 76452, "cached_input_tokens": 76160, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 76497 }, "turn_token_usage": { "input_tokens": 5034005, "cached_input_tokens": 4909440, "cache_write_input_tokens": 0, "output_tokens": 43027, "reasoning_output_tokens": 19098, "total_tokens": 5077032 }, "thread_token_usage": { "input_tokens": 5034005, "cached_input_tokens": 4909440, "cache_write_input_tokens": 0, "output_tokens": 43027, "reasoning_output_tokens": 19098, "total_tokens": 5077032 } } } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-24T23:36:33.525Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:36:33.525Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:36:34.244Z", "callId": "call_QmcDmBNVr5Oh4tuoL8o1joNS", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":12344,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:37:04.261Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"7e4490\",\"wall_time_seconds\":30.00068313,\"session_id\":12344,\"original_token_count\":30}\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:36:34.398Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b3ffdf7087d292994fc51b84ca75", "usage": { "input_tokens": 76611, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 31, "total_tokens": 76689 }, "turn_token_usage": { "input_tokens": 5110616, "cached_input_tokens": 4985728, "cache_write_input_tokens": 0, "output_tokens": 43105, "reasoning_output_tokens": 19129, "total_tokens": 5153721 }, "thread_token_usage": { "input_tokens": 5110616, "cached_input_tokens": 4985728, "cache_write_input_tokens": 0, "output_tokens": 43105, "reasoning_output_tokens": 19129, "total_tokens": 5153721 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:37:07.167Z", "callId": "call_TcVylso7Mvu6lS79x2knNkSq", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":12344,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:37:26.181Z", "content": "Script completed\nWall time 19.0 seconds\nOutput:\n\n{\"chunk_id\":\"faaa82\",\"wall_time_seconds\":18.985649923,\"exit_code\":0,\"original_token_count\":123}\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:37:07.339Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4208a3c87d28292b4c5eba3e7bc", "usage": { "input_tokens": 76802, "cached_input_tokens": 76416, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 76847 }, "turn_token_usage": { "input_tokens": 5187418, "cached_input_tokens": 5062144, "cache_write_input_tokens": 0, "output_tokens": 43150, "reasoning_output_tokens": 19129, "total_tokens": 5230568 }, "thread_token_usage": { "input_tokens": 5187418, "cached_input_tokens": 5062144, "cache_write_input_tokens": 0, "output_tokens": 43150, "reasoning_output_tokens": 19129, "total_tokens": 5230568 } } }, { "kind": "context", "timestamp": "2026-09-24T23:37:26.179Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dcb6fd08-c0ec-4311-a75a-4654e27293a4", "process_id": "12344", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_28w.parquet --checkpoint /app/solution/model_validation_28w.pt --cache /app/solution/cache_validation_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_28w.parquet --checkpoint /app/solution/model_validation_28w.pt --cache /app/solution/cache_validation_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.4 --pop-scale 0.15 --seed 20260924 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 188, "nanos": 317079489 }, "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.1s\nbuilt 95,241 temporal contexts with 887,403 novel targets and 32,568,886 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_28w.pt\n/app/solution/train_ranker.py:501: 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) * len(batch)\nepoch 01/12: loss=5.60752 lr=0.00197001\nepoch 02/12: loss=5.34331 lr=0.0018821\nepoch 03/12: loss=5.11578 lr=0.00174225\nepoch 04/12: loss=4.96024 lr=0.00156\nepoch 05/12: loss=4.84123 lr=0.00134776\nepoch 06/12: loss=4.73274 lr=0.00112\nepoch 07/12: loss=4.64446 lr=0.000892239\nepoch 08/12: loss=4.54605 lr=0.00068\nepoch 09/12: loss=4.46279 lr=0.000497746\nepoch 10/12: loss=4.39505 lr=0.000357898\nepoch 11/12: loss=4.31178 lr=0.000269985\nepoch 12/12: loss=4.27743 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_28w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_28w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 120, "role": "agent", "timestamp": "2026-09-24T23:37:28.925Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:37:28.925Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:37:30.191Z", "callId": "call_uxTrlWqlsOv2mLyn8LfheSSc", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_28w.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:37:33.633Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_28w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028014,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:37:30.300Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b436832c87d2ba14ae934eb191e5", "usage": { "input_tokens": 77082, "cached_input_tokens": 76672, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 6, "total_tokens": 77163 }, "turn_token_usage": { "input_tokens": 5264500, "cached_input_tokens": 5138816, "cache_write_input_tokens": 0, "output_tokens": 43231, "reasoning_output_tokens": 19135, "total_tokens": 5307731 }, "thread_token_usage": { "input_tokens": 5264500, "cached_input_tokens": 5138816, "cache_write_input_tokens": 0, "output_tokens": 43231, "reasoning_output_tokens": 19135, "total_tokens": 5307731 } } }, { "kind": "context", "timestamp": "2026-09-24T23:37:33.630Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07ac797e-eb88-485d-bcc6-ed52cc02f310", "process_id": "92987", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_28w.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_28w.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_28w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028014,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_28w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028014,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 269916808 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_28w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028014,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 121, "role": "agent", "timestamp": "2026-09-24T23:37:39.876Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:37:39.876Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:37:43.536Z", "text": "The expanded temporal training set is a clear win: 0.028014 NDCG@10, about 17% above the previous deterministic model and 162% above popularity. I’m calibrating this checkpoint’s prior once, then I’ll lock the recipe and perform the required final refit with validation included as observed history and training signal." }, { "kind": "tool", "timestamp": "2026-09-24T23:37:49.114Z", "callId": "call_uBbOUWai702spGiCrn9Bzo8M", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blend.py\\n@@\\n-checkpoint = torch.load(\\\"/app/solution/model_validation_deterministic.pt\\\", weights_only=False)\\n-cache = torch.load(\\\"/app/solution/cache_validation_v2.pt\\\", weights_only=False)\\n+checkpoint = torch.load(\\\"/app/solution/model_validation_28w.pt\\\", weights_only=False)\\n+cache = torch.load(\\\"/app/solution/cache_validation_28w.pt\\\", weights_only=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:37:49.273Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:37:49.130Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-edcba17c-26ef-4a33-a85b-0c131734dd8f", "changes": { "/app/solution/tune_blend.py": { "type": "update", "unified_diff": "@@ -19,4 +19,4 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_deterministic.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation_v2.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_28w.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_28w.pt\", weights_only=False)\n histories = cache[\"histories\"]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:37:49.271Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b43de5a087d29bdc17ff9529e21f", "usage": { "input_tokens": 77237, "cached_input_tokens": 76928, "cache_write_input_tokens": 0, "output_tokens": 256, "reasoning_output_tokens": 45, "total_tokens": 77493 }, "turn_token_usage": { "input_tokens": 5341737, "cached_input_tokens": 5215744, "cache_write_input_tokens": 0, "output_tokens": 43487, "reasoning_output_tokens": 19180, "total_tokens": 5385224 }, "thread_token_usage": { "input_tokens": 5341737, "cached_input_tokens": 5215744, "cache_write_input_tokens": 0, "output_tokens": 43487, "reasoning_output_tokens": 19180, "total_tokens": 5385224 } } } ] }, { "id": 122, "role": "agent", "timestamp": "2026-09-24T23:37:51.892Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:37:51.892Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:37:54.582Z", "callId": "call_u36orlOFhG71W1Nl5smrwx0G", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:37:55.756Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=22382", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:37:54.715Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b44dbce887d29a5212f88b51cdb4", "usage": { "input_tokens": 77517, "cached_input_tokens": 77056, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 6, "total_tokens": 77623 }, "turn_token_usage": { "input_tokens": 5419254, "cached_input_tokens": 5292800, "cache_write_input_tokens": 0, "output_tokens": 43593, "reasoning_output_tokens": 19186, "total_tokens": 5462847 }, "thread_token_usage": { "input_tokens": 5419254, "cached_input_tokens": 5292800, "cache_write_input_tokens": 0, "output_tokens": 43593, "reasoning_output_tokens": 19186, "total_tokens": 5462847 } } } ] }, { "id": 123, "role": "agent", "timestamp": "2026-09-24T23:37:57.644Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:37:57.644Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:37:58.622Z", "callId": "call_N6zQSzjcIDiwV5E969gyfokK", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":22382,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:38:28.630Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"84da30\",\"wall_time_seconds\":30.000624545,\"session_id\":22382,\"original_token_count\":16}\nsimilarity std 2.5203545093536377 bias std 0.8141705393791199\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:37:58.801Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4544b1487d2a7bce4437b97400a", "usage": { "input_tokens": 77651, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 77704 }, "turn_token_usage": { "input_tokens": 5496905, "cached_input_tokens": 5370112, "cache_write_input_tokens": 0, "output_tokens": 43646, "reasoning_output_tokens": 19192, "total_tokens": 5540551 }, "thread_token_usage": { "input_tokens": 5496905, "cached_input_tokens": 5370112, "cache_write_input_tokens": 0, "output_tokens": 43646, "reasoning_output_tokens": 19192, "total_tokens": 5540551 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:38:30.652Z", "callId": "call_2EL3DNxp7zvcWvbokAtLvYCb", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":22382,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:39:00.661Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"7e5e61\",\"wall_time_seconds\":30.000990551,\"session_id\":22382,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:38:30.817Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b474d37487d2b18a9d69d9a594f9", "usage": { "input_tokens": 77781, "cached_input_tokens": 77440, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 77826 }, "turn_token_usage": { "input_tokens": 5574686, "cached_input_tokens": 5447552, "cache_write_input_tokens": 0, "output_tokens": 43691, "reasoning_output_tokens": 19192, "total_tokens": 5618377 }, "thread_token_usage": { "input_tokens": 5574686, "cached_input_tokens": 5447552, "cache_write_input_tokens": 0, "output_tokens": 43691, "reasoning_output_tokens": 19192, "total_tokens": 5618377 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:39:07.451Z", "callId": "call_gpEcEOxusetlnYAZscQ7umor", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":22382,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:39:15.359Z", "content": "Script completed\nWall time 7.9 seconds\nOutput:\n\n{\"chunk_id\":\"dcb8d9\",\"wall_time_seconds\":7.880737151,\"exit_code\":0,\"original_token_count\":123}\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:39:07.561Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4951f7c87d2a64af4c3d250e5ac", "usage": { "input_tokens": 77881, "cached_input_tokens": 77568, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 77926 }, "turn_token_usage": { "input_tokens": 5652567, "cached_input_tokens": 5525120, "cache_write_input_tokens": 0, "output_tokens": 43736, "reasoning_output_tokens": 19192, "total_tokens": 5696303 }, "thread_token_usage": { "input_tokens": 5652567, "cached_input_tokens": 5525120, "cache_write_input_tokens": 0, "output_tokens": 43736, "reasoning_output_tokens": 19192, "total_tokens": 5696303 } } }, { "kind": "context", "timestamp": "2026-09-24T23:39:15.351Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-642d5937-0907-4e02-a519-76809647cf56", "process_id": "22382", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "similarity std 2.5203545093536377 bias std 0.8141705393791199\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n", "stderr": "", "aggregated_output": "similarity std 2.5203545093536377 bias std 0.8141705393791199\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n", "exit_code": 0, "duration": { "secs": 80, "nanos": 596589990 }, "formatted_output": "similarity std 2.5203545093536377 bias std 0.8141705393791199\n(0.8, 0.0) 0.025516\n(0.8, 0.15) 0.027364\n(0.8, 0.3) 0.028302\n(0.8, 0.45) 0.027872\n(1.1, 0.0) 0.027427\n(1.1, 0.15) 0.028698\n(1.1, 0.3) 0.027746\n(1.1, 0.45) 0.026243\n(1.4, 0.0) 0.028118\n(1.4, 0.15) 0.028014\n(1.4, 0.3) 0.027370\n(1.4, 0.45) 0.025841\n(1.7, 0.0) 0.028058\n(1.7, 0.15) 0.027544\n(1.7, 0.3) 0.026112\n(1.7, 0.45) 0.024940\n(2.0, 0.0) 0.027640\n(2.0, 0.15) 0.026669\n(2.0, 0.3) 0.025101\n(2.0, 0.45) 0.023340\n(2.4, 0.0) 0.026593\n(2.4, 0.15) 0.025202\n(2.4, 0.3) 0.023914\n(2.4, 0.45) 0.022422\n" } } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-24T23:39:25.857Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:39:25.857Z", "text": "" } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-24T23:39:26.919Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:39:26.919Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:39:29.342Z", "callId": "call_h9CNFNf1G4uQH7cGuUKnE7Od", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- total_loss += float(loss) * len(batch)\\n+ total_loss += float(loss.detach()) * len(batch)\\n@@\\n- parser.add_argument(\\\"--prior-scale\\\", type=float, default=1.4)\\n- parser.add_argument(\\\"--pop-scale\\\", type=float, default=0.25)\\n+ parser.add_argument(\\\"--prior-scale\\\", type=float, default=1.1)\\n+ parser.add_argument(\\\"--pop-scale\\\", type=float, default=0.15)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:39:29.441Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:39:29.359Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-911cfd5e-2598-4332-ab75-abba8643f75e", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -500,3 +500,3 @@\n optimizer.step()\n- total_loss += float(loss) * len(batch)\n+ total_loss += float(loss.detach()) * len(batch)\n seen += len(batch)\n@@ -582,4 +582,4 @@\n parser.add_argument(\"--resume\", type=Path)\n- parser.add_argument(\"--prior-scale\", type=float, default=1.4)\n- parser.add_argument(\"--pop-scale\", type=float, default=0.25)\n+ parser.add_argument(\"--prior-scale\", type=float, default=1.1)\n+ parser.add_argument(\"--pop-scale\", type=float, default=0.15)\n parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:39:29.439Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4a38e4487d2a549d7fe27e21a17", "usage": { "input_tokens": 78363, "cached_input_tokens": 77696, "cache_write_input_tokens": 0, "output_tokens": 715, "reasoning_output_tokens": 558, "total_tokens": 79078 }, "turn_token_usage": { "input_tokens": 5730930, "cached_input_tokens": 5602816, "cache_write_input_tokens": 0, "output_tokens": 44451, "reasoning_output_tokens": 19750, "total_tokens": 5775381 }, "thread_token_usage": { "input_tokens": 5730930, "cached_input_tokens": 5602816, "cache_write_input_tokens": 0, "output_tokens": 44451, "reasoning_output_tokens": 19750, "total_tokens": 5775381 } } } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-24T23:39:32.956Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:39:32.956Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:39:38.454Z", "callId": "call_UknGypQhWyn1yLrOy0y60MoY", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport torch,pandas as pd\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features,rank_candidates\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\\nck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False)\\ncache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\\nm.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\nls -lh /app/solution | sort -k5h | tail -15\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:39:48.656Z", "content": "Script completed\nWall time 10.2 seconds\nOutput:\n\nwrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent 16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:39:38.596Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4b1ff6087d28212e93130a7f43c", "usage": { "input_tokens": 79102, "cached_input_tokens": 78208, "cache_write_input_tokens": 0, "output_tokens": 321, "reasoning_output_tokens": 9, "total_tokens": 79423 }, "turn_token_usage": { "input_tokens": 5810032, "cached_input_tokens": 5681024, "cache_write_input_tokens": 0, "output_tokens": 44772, "reasoning_output_tokens": 19759, "total_tokens": 5854804 }, "thread_token_usage": { "input_tokens": 5810032, "cached_input_tokens": 5681024, "cache_write_input_tokens": 0, "output_tokens": 44772, "reasoning_output_tokens": 19759, "total_tokens": 5854804 } } }, { "kind": "context", "timestamp": "2026-09-24T23:39:48.651Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e22b26b2-a32a-4277-b98f-bb5aa98df470", "process_id": "71430", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nm.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -15" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nm.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -15" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent 16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n", "stderr": "", "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent 16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 30610348 }, "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:27 validation_twotower_like7.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:29 validation_twotower_deterministic.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:32 validation_twotower_seed1337.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:37 validation_twotower_28w.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:39 validation.parquet\n-rw-r--r-- 1 agent agent 16M Sep 24 23:14 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:20 model_validation_v1_cont.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:23 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:27 model_validation_like7.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:29 model_validation_deterministic.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:32 model_validation_seed1337.pt\n-rw-r--r-- 1 agent agent 16M Sep 24 23:37 model_validation_28w.pt\n-rw-r--r-- 1 agent agent 226M Sep 24 23:21 cache_validation_v2.pt\n-rw-r--r-- 1 agent agent 230M Sep 24 23:12 cache_validation.pt\n-rw-r--r-- 1 agent agent 342M Sep 24 23:35 cache_validation_28w.pt\n" } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-24T23:39:52.528Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:39:52.528Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:39:54.553Z", "text": "The selected validation ranker is now saved at 0.028698 NDCG@10. I’ve locked the 28-cutoff, 64-latent-dimension recipe and am starting the final refit on `train_* + val_*`; its latest temporal target window is exactly the public validation period, while serving histories extend through that period and use the supplied test preference state." }, { "kind": "tool", "timestamp": "2026-09-24T23:39:57.231Z", "callId": "call_37RDhGkDQOYXl8ejtykODKGr", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --checkpoint /app/solution/model_final.pt --cache /app/solution/cache_final_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:39:58.399Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=50313", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:39:57.320Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4c5b3f887d2a211d3f0a46483c0", "usage": { "input_tokens": 79932, "cached_input_tokens": 78976, "cache_write_input_tokens": 0, "output_tokens": 301, "reasoning_output_tokens": 35, "total_tokens": 80233 }, "turn_token_usage": { "input_tokens": 5889964, "cached_input_tokens": 5760000, "cache_write_input_tokens": 0, "output_tokens": 45073, "reasoning_output_tokens": 19794, "total_tokens": 5935037 }, "thread_token_usage": { "input_tokens": 5889964, "cached_input_tokens": 5760000, "cache_write_input_tokens": 0, "output_tokens": 45073, "reasoning_output_tokens": 19794, "total_tokens": 5935037 } } } ] }, { "id": 128, "role": "agent", "timestamp": "2026-09-24T23:40:00.932Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:40:00.932Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:40:01.699Z", "callId": "call_mjDgKxf8l6dWhu365gHklYJ2", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50313,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:40:31.708Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"6e4b8f\",\"wall_time_seconds\":30.00132107,\"session_id\":50313,\"original_token_count\":30}\nloaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:40:01.821Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4cedc4c87d28a2e2274f44ee5ad", "usage": { "input_tokens": 80261, "cached_input_tokens": 79744, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80314 }, "turn_token_usage": { "input_tokens": 5970225, "cached_input_tokens": 5839744, "cache_write_input_tokens": 0, "output_tokens": 45126, "reasoning_output_tokens": 19800, "total_tokens": 6015351 }, "thread_token_usage": { "input_tokens": 5970225, "cached_input_tokens": 5839744, "cache_write_input_tokens": 0, "output_tokens": 45126, "reasoning_output_tokens": 19800, "total_tokens": 6015351 } } } ] }, { "id": 129, "role": "agent", "timestamp": "2026-09-24T23:40:37.854Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:40:37.854Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:40:38.519Z", "callId": "call_jBLRPA2fZM7oH5fyVFpMkiDI", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50313,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:41:08.534Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"662afe\",\"wall_time_seconds\":30.000440382,\"session_id\":50313,\"original_token_count\":49}\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:40:38.627Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b4f2a3a487d2b20b5ce09f5dd4f5", "usage": { "input_tokens": 80406, "cached_input_tokens": 80128, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80459 }, "turn_token_usage": { "input_tokens": 6050631, "cached_input_tokens": 5919872, "cache_write_input_tokens": 0, "output_tokens": 45179, "reasoning_output_tokens": 19806, "total_tokens": 6095810 }, "thread_token_usage": { "input_tokens": 6050631, "cached_input_tokens": 5919872, "cache_write_input_tokens": 0, "output_tokens": 45179, "reasoning_output_tokens": 19806, "total_tokens": 6095810 } } } ] }, { "id": 130, "role": "agent", "timestamp": "2026-09-24T23:41:09.925Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:41:09.925Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:41:10.616Z", "callId": "call_qqF2gdOAbNtek1ukIGOFwKhZ", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50313,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:41:40.631Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a2ab58\",\"wall_time_seconds\":30.001124663,\"session_id\":50313,\"original_token_count\":29}\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:41:10.747Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b514becc87d2af39226cd26e2134", "usage": { "input_tokens": 80571, "cached_input_tokens": 80256, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80624 }, "turn_token_usage": { "input_tokens": 6131202, "cached_input_tokens": 6000128, "cache_write_input_tokens": 0, "output_tokens": 45232, "reasoning_output_tokens": 19812, "total_tokens": 6176434 }, "thread_token_usage": { "input_tokens": 6131202, "cached_input_tokens": 6000128, "cache_write_input_tokens": 0, "output_tokens": 45232, "reasoning_output_tokens": 19812, "total_tokens": 6176434 } } } ] }, { "id": 131, "role": "agent", "timestamp": "2026-09-24T23:41:43.116Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:41:43.116Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:41:44.580Z", "callId": "call_bmYTfiesK7QRQGiUzA4l7dn2", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50313,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:42:14.614Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"faf174\",\"wall_time_seconds\":30.011950398,\"session_id\":50313,\"original_token_count\":30}\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:41:44.773Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b5356eb087d2b95be2cbcd05f992", "usage": { "input_tokens": 80737, "cached_input_tokens": 80384, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80790 }, "turn_token_usage": { "input_tokens": 6211939, "cached_input_tokens": 6080512, "cache_write_input_tokens": 0, "output_tokens": 45285, "reasoning_output_tokens": 19818, "total_tokens": 6257224 }, "thread_token_usage": { "input_tokens": 6211939, "cached_input_tokens": 6080512, "cache_write_input_tokens": 0, "output_tokens": 45285, "reasoning_output_tokens": 19818, "total_tokens": 6257224 } } } ] }, { "id": 132, "role": "agent", "timestamp": "2026-09-24T23:42:16.229Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:42:16.229Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:42:16.954Z", "callId": "call_DEm3mBomLIQtoKNe2qcHbyKb", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50313,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:42:46.998Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"fb1623\",\"wall_time_seconds\":30.001309684,\"session_id\":50313,\"original_token_count\":30}\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:42:17.094Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b556d89487d2a0359b21068fd3c6", "usage": { "input_tokens": 80901, "cached_input_tokens": 80512, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 80954 }, "turn_token_usage": { "input_tokens": 6292840, "cached_input_tokens": 6161024, "cache_write_input_tokens": 0, "output_tokens": 45338, "reasoning_output_tokens": 19824, "total_tokens": 6338178 }, "thread_token_usage": { "input_tokens": 6292840, "cached_input_tokens": 6161024, "cache_write_input_tokens": 0, "output_tokens": 45338, "reasoning_output_tokens": 19824, "total_tokens": 6338178 } } } ] }, { "id": 133, "role": "agent", "timestamp": "2026-09-24T23:42:49.198Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:42:49.198Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:42:49.950Z", "callId": "call_9QT6spD799KdqdFpBJJeQoqq", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":50313,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:43:07.260Z", "content": "Script completed\nWall time 17.3 seconds\nOutput:\n\n{\"chunk_id\":\"d09c7f\",\"wall_time_seconds\":17.288392837,\"exit_code\":0,\"original_token_count\":118}\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:42:50.060Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b5773f1487d2b8ae0de863dce26f", "usage": { "input_tokens": 81066, "cached_input_tokens": 80768, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 81119 }, "turn_token_usage": { "input_tokens": 6373906, "cached_input_tokens": 6241792, "cache_write_input_tokens": 0, "output_tokens": 45391, "reasoning_output_tokens": 19830, "total_tokens": 6419297 }, "thread_token_usage": { "input_tokens": 6373906, "cached_input_tokens": 6241792, "cache_write_input_tokens": 0, "output_tokens": 45391, "reasoning_output_tokens": 19830, "total_tokens": 6419297 } } }, { "kind": "context", "timestamp": "2026-09-24T23:43:07.258Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e5d123a4-467e-4b1a-af03-8ade8b62c585", "process_id": "50313", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --checkpoint /app/solution/model_final.pt --cache /app/solution/cache_final_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission.parquet --checkpoint /app/solution/model_final.pt --cache /app/solution/cache_final_28w.pt --epochs 12 --training-weeks 28 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 189, "nanos": 860602424 }, "formatted_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 15.4s\nbuilt 96,568 temporal contexts with 896,360 novel targets and 33,258,867 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_28w.pt\nepoch 01/12: loss=5.60964 lr=0.00197001\nepoch 02/12: loss=5.33694 lr=0.0018821\nepoch 03/12: loss=5.10709 lr=0.00174225\nepoch 04/12: loss=4.94666 lr=0.00156\nepoch 05/12: loss=4.82699 lr=0.00134776\nepoch 06/12: loss=4.72550 lr=0.00112\nepoch 07/12: loss=4.63698 lr=0.000892239\nepoch 08/12: loss=4.54422 lr=0.00068\nepoch 09/12: loss=4.45552 lr=0.000497746\nepoch 10/12: loss=4.39559 lr=0.000357898\nepoch 11/12: loss=4.33815 lr=0.000269985\nepoch 12/12: loss=4.29374 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission.parquet\n{\"checkpoint\": \"/app/solution/model_final.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 28, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 134, "role": "agent", "timestamp": "2026-09-24T23:43:14.636Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:43:14.636Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:43:22.574Z", "callId": "call_zfwAoIRAvlJ0P0pbNAg5D4gM", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,sys\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking,read_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=validate_ranking(read_ranking(p),targets)\\nz=np.load(D/'test_eligible_candidates.npz')\\nuidpos={int(x):i for i,x in enumerate(z['uids'])}; itempos={int(x):i for i,x in enumerate(z['item_ids'])}\\nr=np.fromiter((uidpos[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((itempos[int(x)] for x in f.item_id),np.int32,len(f))\\nelig=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\\nprint('shape',f.shape,'dtypes',f.dtypes.astype(str).to_dict())\\nprint('users',f.uid.nunique(),'items/user',f.groupby('uid').size().min(),f.groupby('uid').size().max())\\nprint('eligible',elig.all(),'eligible_count',int(elig.sum()),'duplicates',f.duplicated(['uid','item_id']).sum())\\nprint('rank bounds',f['rank'].min(),f['rank'].max(),'file_MB',p.stat().st_size/2**20)\\npref=pd.read_parquet(D/'test_preference_state.parquet')\\nq=pref.groupby('uid').size();print('preference count quantiles',q.quantile([0,.5,.9,.99,1]).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:43:25.035Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\nshape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:43:22.757Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b58ba60887d2a4b2ca20a37de898", "usage": { "input_tokens": 81347, "cached_input_tokens": 80896, "cache_write_input_tokens": 0, "output_tokens": 779, "reasoning_output_tokens": 329, "total_tokens": 82126 }, "turn_token_usage": { "input_tokens": 6455253, "cached_input_tokens": 6322688, "cache_write_input_tokens": 0, "output_tokens": 46170, "reasoning_output_tokens": 20159, "total_tokens": 6501423 }, "thread_token_usage": { "input_tokens": 6455253, "cached_input_tokens": 6322688, "cache_write_input_tokens": 0, "output_tokens": 46170, "reasoning_output_tokens": 20159, "total_tokens": 6501423 } } }, { "kind": "context", "timestamp": "2026-09-24T23:43:25.031Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3106fe39-14fb-4818-a73a-102574388b48", "process_id": "18249", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nuidpos={int(x):i for i,x in enumerate(z['uids'])}; itempos={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((uidpos[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((itempos[int(x)] for x in f.item_id),np.int32,len(f))\nelig=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nprint('shape',f.shape,'dtypes',f.dtypes.astype(str).to_dict())\nprint('users',f.uid.nunique(),'items/user',f.groupby('uid').size().min(),f.groupby('uid').size().max())\nprint('eligible',elig.all(),'eligible_count',int(elig.sum()),'duplicates',f.duplicated(['uid','item_id']).sum())\nprint('rank bounds',f['rank'].min(),f['rank'].max(),'file_MB',p.stat().st_size/2**20)\npref=pd.read_parquet(D/'test_preference_state.parquet')\nq=pref.groupby('uid').size();print('preference count quantiles',q.quantile([0,.5,.9,.99,1]).to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nuidpos={int(x):i for i,x in enumerate(z['uids'])}; itempos={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((uidpos[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((itempos[int(x)] for x in f.item_id),np.int32,len(f))\nelig=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nprint('shape',f.shape,'dtypes',f.dtypes.astype(str).to_dict())\nprint('users',f.uid.nunique(),'items/user',f.groupby('uid').size().min(),f.groupby('uid').size().max())\nprint('eligible',elig.all(),'eligible_count',int(elig.sum()),'duplicates',f.duplicated(['uid','item_id']).sum())\nprint('rank bounds',f['rank'].min(),f['rank'].max(),'file_MB',p.stat().st_size/2**20)\npref=pd.read_parquet(D/'test_preference_state.parquet')\nq=pref.groupby('uid').size();print('preference count quantiles',q.quantile([0,.5,.9,.99,1]).to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n", "stderr": "", "aggregated_output": "shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 293223355 }, "formatted_output": "shape (845600, 3) dtypes {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}\nusers 8456 items/user 100 100\neligible True eligible_count 845600 duplicates 0\nrank bounds 1 100 file_MB 1.5793027877807617\npreference count quantiles {0.0: 1.0, 0.5: 30.0, 0.9: 174.90000000000055, 0.99: 523.3600000000006, 1.0: 1861.0}\n" } } } ] }, { "id": 135, "role": "agent", "timestamp": "2026-09-24T23:43:41.458Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:43:41.458Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:43:42.783Z", "text": "The final artifact is built and passes all structural checks: 845,600 rows, exactly 100 unique items for each of 8,456 users, ranks 1–100, and every item is test-eligible. I’m finishing the reproducibility notes and one last code/artifact audit now." }, { "kind": "tool", "timestamp": "2026-09-24T23:43:55.984Z", "callId": "call_0qulPtEHRNxDa686deYQVOMu", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made-for-You two-tower ranker\\n+\\n+## Model\\n+\\n+`model.py` defines two separate CPU PyTorch towers. The user tower has **no\\n+user-ID embedding**. It pools a cutoff-correct 180-day interaction history\\n+(at most 384 distinct positive-listen tracks, plus active explicit state) with\\n+frequency, recency, completion, organic, like, and dislike weights, then uses a\\n+`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\\n+embedding with four catalog popularity statistics and a `68 -> 128 -> 64`\\n+residual MLP. Both latent outputs are L2-normalized.\\n+\\n+Each tower emits 65 values: 64 personalized dimensions plus one calibrated\\n+item-prior coordinate (the corresponding user coordinate is exactly one).\\n+Thus the complete relevance score is exactly\\n+\\n+```text\\n+score(user history, item) = user_vector @ item_vector\\n+```\\n+\\n+Candidate vectors are computed once offline; request-time work is one bounded\\n+history pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\\n+the platform-owned retrieval mask.\\n+\\n+## Training\\n+\\n+Training uses 28 rolling weekly cutoffs. A positive is a previously unseen\\n+catalog item with a subsequent organic 80%-complete listen or organic like.\\n+The loss is sampled softmax with 512 uniform catalog negatives; likes receive\\n+sampling weight 4. Histories are held in event/CSR form, so training never\\n+constructs or factorizes a global user-item interaction matrix. The final model\\n+is refit on `train_* + val_*`, with validation events treated as observed\\n+history and temporal training outcomes.\\n+\\n+Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\\n+with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n+```\\n+\\n+## Final submission\\n+\\n+The checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\\n+checkpoint. Rebuild it deterministically from `/app` with:\\n+\\n+```sh\\n+PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\\\\n+ /opt/task-venv/bin/python /app/solution/train_ranker.py \\\\\\n+ --stage final \\\\\\n+ --output /app/solution/submission.parquet \\\\\\n+ --checkpoint /app/solution/model_final.pt \\\\\\n+ --cache /app/solution/cache_final_28w.pt \\\\\\n+ --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\\\\n+ --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\\\\n+ --seed 20260924 --threads 8\\n+```\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:43:56.370Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:43:55.993Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-be068958-2953-4c43-99a9-c2b560df5ac5", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final \\\n --output /app/solution/submission.parquet \\\n --checkpoint /app/solution/model_final.pt \\\n --cache /app/solution/cache_final_28w.pt \\\n --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n --seed 20260924 --threads 8\n```\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:43:56.368Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b59d3de887d2a709a728c7b1edc1", "usage": { "input_tokens": 82285, "cached_input_tokens": 81152, "cache_write_input_tokens": 0, "output_tokens": 1241, "reasoning_output_tokens": 466, "total_tokens": 83526 }, "turn_token_usage": { "input_tokens": 6537538, "cached_input_tokens": 6403840, "cache_write_input_tokens": 0, "output_tokens": 47411, "reasoning_output_tokens": 20625, "total_tokens": 6584949 }, "thread_token_usage": { "input_tokens": 6537538, "cached_input_tokens": 6403840, "cache_write_input_tokens": 0, "output_tokens": 47411, "reasoning_output_tokens": 20625, "total_tokens": 6584949 } } } ] }, { "id": 136, "role": "agent", "timestamp": "2026-09-24T23:44:07.700Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:44:07.700Z", "text": "" } ] }, { "id": 137, "role": "agent", "timestamp": "2026-09-24T23:44:08.925Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:44:08.925Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:44:22.721Z", "callId": "call_LNhfoNEq2wiCtzZMDQkFSycx", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def relevant_targets(\\n r: tuple[np.ndarray, ...],\\n- first_seen: np.ndarray,\\n+ seen_items: np.ndarray,\\n+ first_seen_times: np.ndarray,\\n cutoff: int,\\n ) -> tuple[np.ndarray, np.ndarray]:\\n@@\\n if hi <= lo:\\n return np.empty(0, np.int32), np.empty(0, np.uint8)\\n ri, rg = r[1][lo:hi], r[2][lo:hi]\\n- novel = first_seen[ri] >= cutoff\\n+ positions = np.searchsorted(seen_items, ri)\\n+ found = positions < len(seen_items)\\n+ safe_positions = np.minimum(positions, max(len(seen_items) - 1, 0))\\n+ if len(seen_items):\\n+ found &= seen_items[safe_positions] == ri\\n+ seen_times = np.full(len(ri), np.iinfo(np.int64).max, dtype=np.int64)\\n+ seen_times[found] = first_seen_times[safe_positions[found]]\\n+ novel = seen_times >= cutoff\\n@@\\n- first_seen = np.empty(30_000, dtype=np.int64)\\n for u in range(num_users):\\n@@\\n- first_seen.fill(np.iinfo(np.int64).max)\\n all_times, all_items = slices[\\\"all\\\"]\\n- np.minimum.at(first_seen, all_items, all_times)\\n+ seen_items, inverse = np.unique(all_items, return_inverse=True)\\n+ first_seen_times = np.full(\\n+ len(seen_items), np.iinfo(np.int64).max, dtype=np.int64\\n+ )\\n+ np.minimum.at(first_seen_times, inverse, all_times)\\n for cutoff in cutoffs:\\n- ti, tg = relevant_targets(slices[\\\"relevant\\\"], first_seen, cutoff)\\n+ ti, tg = relevant_targets(\\n+ slices[\\\"relevant\\\"], seen_items, first_seen_times, cutoff\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:44:22.850Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:44:22.733Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-095ea205-2801-4faa-a488-032c4437f39d", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -286,3 +286,4 @@\n r: tuple[np.ndarray, ...],\n- first_seen: np.ndarray,\n+ seen_items: np.ndarray,\n+ first_seen_times: np.ndarray,\n cutoff: int,\n@@ -294,3 +295,10 @@\n ri, rg = r[1][lo:hi], r[2][lo:hi]\n- novel = first_seen[ri] >= cutoff\n+ positions = np.searchsorted(seen_items, ri)\n+ found = positions < len(seen_items)\n+ safe_positions = np.minimum(positions, max(len(seen_items) - 1, 0))\n+ if len(seen_items):\n+ found &= seen_items[safe_positions] == ri\n+ seen_times = np.full(len(ri), np.iinfo(np.int64).max, dtype=np.int64)\n+ seen_times[found] = first_seen_times[safe_positions[found]]\n+ novel = seen_times >= cutoff\n ri, rg = ri[novel], rg[novel]\n@@ -327,3 +335,2 @@\n cutoffs.sort()\n- first_seen = np.empty(30_000, dtype=np.int64)\n for u in range(num_users):\n@@ -333,7 +340,12 @@\n slices[key] = tuple(x[b[u] : b[u + 1]] for x in values[1:])\n- first_seen.fill(np.iinfo(np.int64).max)\n all_times, all_items = slices[\"all\"]\n- np.minimum.at(first_seen, all_items, all_times)\n+ seen_items, inverse = np.unique(all_items, return_inverse=True)\n+ first_seen_times = np.full(\n+ len(seen_items), np.iinfo(np.int64).max, dtype=np.int64\n+ )\n+ np.minimum.at(first_seen_times, inverse, all_times)\n for cutoff in cutoffs:\n- ti, tg = relevant_targets(slices[\"relevant\"], first_seen, cutoff)\n+ ti, tg = relevant_targets(\n+ slices[\"relevant\"], seen_items, first_seen_times, cutoff\n+ )\n if not len(ti):\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:44:22.848Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b5bcd4e887d29b6fef9dee892b2d", "usage": { "input_tokens": 83550, "cached_input_tokens": 82048, "cache_write_input_tokens": 0, "output_tokens": 1087, "reasoning_output_tokens": 577, "total_tokens": 84637 }, "turn_token_usage": { "input_tokens": 6621088, "cached_input_tokens": 6485888, "cache_write_input_tokens": 0, "output_tokens": 48498, "reasoning_output_tokens": 21202, "total_tokens": 6669586 }, "thread_token_usage": { "input_tokens": 6621088, "cached_input_tokens": 6485888, "cache_write_input_tokens": 0, "output_tokens": 48498, "reasoning_output_tokens": 21202, "total_tokens": 6669586 } } } ] }, { "id": 138, "role": "agent", "timestamp": "2026-09-24T23:44:35.242Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:44:35.242Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:44:37.531Z", "callId": "call_ffbhUCIVgHzo4PLIOroL3fxj", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- # A tiny deterministic index term makes exact ties reproducible.\\n- scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\\n top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:44:37.686Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:44:37.541Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-cf136e65-bc9a-466b-acea-eeae54eb8968", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -550,4 +550,2 @@\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n- # A tiny deterministic index term makes exact ties reproducible.\n- scores -= torch.arange(len(item_ids), dtype=scores.dtype)[None, :] * 1e-10\n top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:44:37.684Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b5d7478c87d2b427730e387aa263", "usage": { "input_tokens": 84661, "cached_input_tokens": 83328, "cache_write_input_tokens": 0, "output_tokens": 496, "reasoning_output_tokens": 386, "total_tokens": 85157 }, "turn_token_usage": { "input_tokens": 6705749, "cached_input_tokens": 6569216, "cache_write_input_tokens": 0, "output_tokens": 48994, "reasoning_output_tokens": 21588, "total_tokens": 6754743 }, "thread_token_usage": { "input_tokens": 6705749, "cached_input_tokens": 6569216, "cache_write_input_tokens": 0, "output_tokens": 48994, "reasoning_output_tokens": 21588, "total_tokens": 6754743 } } } ] }, { "id": 139, "role": "agent", "timestamp": "2026-09-24T23:44:53.708Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:44:53.708Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:45:06.284Z", "callId": "call_2RdIESLMATG1W7GnKDYCrzco", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport torch,pandas as pd\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features,rank_candidates\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\\ncache=torch.load('/app/solution/cache_final_28w.pt',weights_only=False)\\ncfg=ck['config']; m=TwoTowerRanker(30000,item_features(cat),cfg['embedding_dim'],cfg['hidden_dim'])\\nm.load_state_dict(ck['model_state'])\\nrank_candidates(m,cache['histories'],Path('/task/data/test_eligible_candidates.npz'),Path('/app/solution/submission_candidate.parquet'))\\nold=pd.read_parquet('/app/solution/submission.parquet').sort_values(['uid','rank']).reset_index(drop=True)\\nnew=pd.read_parquet('/app/solution/submission_candidate.parquet').sort_values(['uid','rank']).reset_index(drop=True)\\nprint('ranking_identical_after_exact-dot_audit',old.equals(new))\\nprint('calibration',float(m.item_tower.learned_prior_scale),float(m.item_tower.fixed_prior_scale))\\n# Directly verify that the public score API and explicit tower calls agree.\\nh=cache['histories']; idx=__import__('numpy').arange(2)\\nfrom train_ranker import gather_histories\\nids,offs,w,f=gather_histories(h,idx); cand=torch.arange(7)\\nwith torch.inference_mode():\\n direct=m.score(ids,offs,w,f,cand)\\n separate=m.user_tower(ids,offs,w,f)@m.item_tower(cand).T\\nprint('exact_tower_dot_check',torch.equal(direct,separate))\\nPY\\nmv /app/solution/submission_candidate.parquet /app/solution/submission.parquet\\nPYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\\n\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:45:13.208Z", "content": "Script completed\nWall time 6.9 seconds\nOutput:\n\nwrote 845,600 ranked rows to /app/solution/submission_candidate.parquet\nranking_identical_after_exact-dot_audit False\ncalibration 1.100000023841858 0.15000000596046448\nexact_tower_dot_check True\n8fce31901997fcb7699e8fd8e225116176f3cf87b638b2f4f76afbd9bc210e75 /app/solution/submission.parquet\n409a03cd66f18bfb806e38602d8949504a4b3c7c44470498a53d20aead119f03 /app/solution/model_final.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:45:06.489Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b5e62b5887d289cadde3582b880c", "usage": { "input_tokens": 85181, "cached_input_tokens": 84480, "cache_write_input_tokens": 0, "output_tokens": 737, "reasoning_output_tokens": 204, "total_tokens": 85918 }, "turn_token_usage": { "input_tokens": 6790930, "cached_input_tokens": 6653696, "cache_write_input_tokens": 0, "output_tokens": 49731, "reasoning_output_tokens": 21792, "total_tokens": 6840661 }, "thread_token_usage": { "input_tokens": 6790930, "cached_input_tokens": 6653696, "cache_write_input_tokens": 0, "output_tokens": 49731, "reasoning_output_tokens": 21792, "total_tokens": 6840661 } } }, { "kind": "context", "timestamp": "2026-09-24T23:45:13.199Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-78968dbf-5932-4041-9179-62663619a198", "process_id": "66810", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_final_28w.pt',weights_only=False)\ncfg=ck['config']; m=TwoTowerRanker(30000,item_features(cat),cfg['embedding_dim'],cfg['hidden_dim'])\nm.load_state_dict(ck['model_state'])\nrank_candidates(m,cache['histories'],Path('/task/data/test_eligible_candidates.npz'),Path('/app/solution/submission_candidate.parquet'))\nold=pd.read_parquet('/app/solution/submission.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nnew=pd.read_parquet('/app/solution/submission_candidate.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nprint('ranking_identical_after_exact-dot_audit',old.equals(new))\nprint('calibration',float(m.item_tower.learned_prior_scale),float(m.item_tower.fixed_prior_scale))\n# Directly verify that the public score API and explicit tower calls agree.\nh=cache['histories']; idx=__import__('numpy').arange(2)\nfrom train_ranker import gather_histories\nids,offs,w,f=gather_histories(h,idx); cand=torch.arange(7)\nwith torch.inference_mode():\n direct=m.score(ids,offs,w,f,cand)\n separate=m.user_tower(ids,offs,w,f)@m.item_tower(cand).T\nprint('exact_tower_dot_check',torch.equal(direct,separate))\nPY\nmv /app/solution/submission_candidate.parquet /app/solution/submission.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\n" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\ncache=torch.load('/app/solution/cache_final_28w.pt',weights_only=False)\ncfg=ck['config']; m=TwoTowerRanker(30000,item_features(cat),cfg['embedding_dim'],cfg['hidden_dim'])\nm.load_state_dict(ck['model_state'])\nrank_candidates(m,cache['histories'],Path('/task/data/test_eligible_candidates.npz'),Path('/app/solution/submission_candidate.parquet'))\nold=pd.read_parquet('/app/solution/submission.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nnew=pd.read_parquet('/app/solution/submission_candidate.parquet').sort_values(['uid','rank']).reset_index(drop=True)\nprint('ranking_identical_after_exact-dot_audit',old.equals(new))\nprint('calibration',float(m.item_tower.learned_prior_scale),float(m.item_tower.fixed_prior_scale))\n# Directly verify that the public score API and explicit tower calls agree.\nh=cache['histories']; idx=__import__('numpy').arange(2)\nfrom train_ranker import gather_histories\nids,offs,w,f=gather_histories(h,idx); cand=torch.arange(7)\nwith torch.inference_mode():\n direct=m.score(ids,offs,w,f,cand)\n separate=m.user_tower(ids,offs,w,f)@m.item_tower(cand).T\nprint('exact_tower_dot_check',torch.equal(direct,separate))\nPY\nmv /app/solution/submission_candidate.parquet /app/solution/submission.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\n" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 ranked rows to /app/solution/submission_candidate.parquet\nranking_identical_after_exact-dot_audit False\ncalibration 1.100000023841858 0.15000000596046448\nexact_tower_dot_check True\n8fce31901997fcb7699e8fd8e225116176f3cf87b638b2f4f76afbd9bc210e75 /app/solution/submission.parquet\n409a03cd66f18bfb806e38602d8949504a4b3c7c44470498a53d20aead119f03 /app/solution/model_final.pt\n", "stderr": "", "aggregated_output": "wrote 845,600 ranked rows to /app/solution/submission_candidate.parquet\nranking_identical_after_exact-dot_audit False\ncalibration 1.100000023841858 0.15000000596046448\nexact_tower_dot_check True\n8fce31901997fcb7699e8fd8e225116176f3cf87b638b2f4f76afbd9bc210e75 /app/solution/submission.parquet\n409a03cd66f18bfb806e38602d8949504a4b3c7c44470498a53d20aead119f03 /app/solution/model_final.pt\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 748061740 }, "formatted_output": "wrote 845,600 ranked rows to /app/solution/submission_candidate.parquet\nranking_identical_after_exact-dot_audit False\ncalibration 1.100000023841858 0.15000000596046448\nexact_tower_dot_check True\n8fce31901997fcb7699e8fd8e225116176f3cf87b638b2f4f76afbd9bc210e75 /app/solution/submission.parquet\n409a03cd66f18bfb806e38602d8949504a4b3c7c44470498a53d20aead119f03 /app/solution/model_final.pt\n" } } } ] }, { "id": 140, "role": "agent", "timestamp": "2026-09-24T23:45:27.353Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:45:27.353Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:45:33.431Z", "callId": "call_yxXjH07A5CiLmYbWmRY3zg7e", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport torch,pandas as pd\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features,rank_candidates\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_exact.parquet'))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_exact.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:45:43.439Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\nwrote 845,600 ranked rows to /app/solution/validation_exact.parquet\n{\n \"model\": \"/app/solution/validation_exact.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:45:33.610Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b6098af087d2b3f01596c502bf2b", "usage": { "input_tokens": 86089, "cached_input_tokens": 84992, "cache_write_input_tokens": 0, "output_tokens": 470, "reasoning_output_tokens": 193, "total_tokens": 86559 }, "turn_token_usage": { "input_tokens": 6877019, "cached_input_tokens": 6738688, "cache_write_input_tokens": 0, "output_tokens": 50201, "reasoning_output_tokens": 21985, "total_tokens": 6927220 }, "thread_token_usage": { "input_tokens": 6877019, "cached_input_tokens": 6738688, "cache_write_input_tokens": 0, "output_tokens": 50201, "reasoning_output_tokens": 21985, "total_tokens": 6927220 } } }, { "kind": "context", "timestamp": "2026-09-24T23:45:43.430Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-74183811-cb11-45be-81b7-be2227e5463b", "process_id": "65913", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_exact.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_exact.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_28w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_28w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.15)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation_exact.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_exact.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 ranked rows to /app/solution/validation_exact.parquet\n{\n \"model\": \"/app/solution/validation_exact.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation_exact.parquet\n{\n \"model\": \"/app/solution/validation_exact.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 832539556 }, "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation_exact.parquet\n{\n \"model\": \"/app/solution/validation_exact.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028698,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 141, "role": "agent", "timestamp": "2026-09-24T23:45:47.515Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:45:47.515Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:46:04.722Z", "callId": "call_ztS8fgOwi2Os33TbRsk9IpLo", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"mv /app/solution/validation_exact.parquet /app/solution/validation.parquet\\nPYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,sys\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking,read_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\\nassert ok.all(); assert len(f)==845600; assert not f.duplicated(['uid','item_id']).any()\\nprint('FINAL_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\\nPY\\nsed -n '1,240p' /app/solution/README.md\\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort | tail -20\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:46:07.232Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\nFINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final \\\n --output /app/solution/submission.parquet \\\n --checkpoint /app/solution/model_final.pt \\\n --cache /app/solution/cache_final_28w.pt \\\n --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:46:04.853Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b627b35487d2abcc60c11045d3a6", "usage": { "input_tokens": 86647, "cached_input_tokens": 85888, "cache_write_input_tokens": 0, "output_tokens": 439, "reasoning_output_tokens": 25, "total_tokens": 87086 }, "turn_token_usage": { "input_tokens": 6963666, "cached_input_tokens": 6824576, "cache_write_input_tokens": 0, "output_tokens": 50640, "reasoning_output_tokens": 22010, "total_tokens": 7014306 }, "thread_token_usage": { "input_tokens": 6963666, "cached_input_tokens": 6824576, "cache_write_input_tokens": 0, "output_tokens": 50640, "reasoning_output_tokens": 22010, "total_tokens": 7014306 } } }, { "kind": "context", "timestamp": "2026-09-24T23:46:07.227Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-feaaf903-aed0-44f7-9dc0-dfa9777bd615", "process_id": "86821", "command": [ "/bin/sh", "-lc", "mv /app/solution/validation_exact.parquet /app/solution/validation.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all(); assert len(f)==845600; assert not f.duplicated(['uid','item_id']).any()\nprint('FINAL_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY\nsed -n '1,240p' /app/solution/README.md\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort | tail -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mv /app/solution/validation_exact.parquet /app/solution/validation.parquet\nPYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking,read_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all(); assert len(f)==845600; assert not f.duplicated(['uid','item_id']).any()\nprint('FINAL_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY\nsed -n '1,240p' /app/solution/README.md\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort | tail -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final \\\n --output /app/solution/submission.parquet \\\n --checkpoint /app/solution/model_final.pt \\\n --cache /app/solution/cache_final_28w.pt \\\n --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n", "stderr": "", "aggregated_output": "FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final \\\n --output /app/solution/submission.parquet \\\n --checkpoint /app/solution/model_final.pt \\\n --cache /app/solution/cache_final_28w.pt \\\n --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 336682943 }, "formatted_output": "FINAL_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100}\n# Made-for-You two-tower ranker\n\n## Model\n\n`model.py` defines two separate CPU PyTorch towers. The user tower has **no\nuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n(at most 384 distinct positive-listen tracks, plus active explicit state) with\nfrequency, recency, completion, organic, like, and dislike weights, then uses a\n`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\nembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\nresidual MLP. Both latent outputs are L2-normalized.\n\nEach tower emits 65 values: 64 personalized dimensions plus one calibrated\nitem-prior coordinate (the corresponding user coordinate is exactly one).\nThus the complete relevance score is exactly\n\n```text\nscore(user history, item) = user_vector @ item_vector\n```\n\nCandidate vectors are computed once offline; request-time work is one bounded\nhistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\nthe platform-owned retrieval mask.\n\n## Training\n\nTraining uses 28 rolling weekly cutoffs. A positive is a previously unseen\ncatalog item with a subsequent organic 80%-complete listen or organic like.\nThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\nsampling weight 4. Histories are held in event/CSR form, so training never\nconstructs or factorizes a global user-item interaction matrix. The final model\nis refit on `train_* + val_*`, with validation events treated as observed\nhistory and temporal training outcomes.\n\nPublic validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\nwith:\n\n```sh\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n## Final submission\n\nThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\ncheckpoint. Rebuild it deterministically from `/app` with:\n\n```sh\nPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final \\\n --output /app/solution/submission.parquet \\\n --checkpoint /app/solution/model_final.pt \\\n --cache /app/solution/cache_final_28w.pt \\\n --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n --seed 20260924 --threads 8\n```\nmodel_validation_deterministic.pt 16415649 bytes\nmodel_validation_like7.pt 16415401 bytes\nmodel_validation_seed1337.pt 16415494 bytes\nmodel_validation_v1.pt 16414586 bytes\nmodel_validation_v1_cont.pt 16415399 bytes\nmodel_validation_v2.pt 16415244 bytes\nsubmission.parquet 1656019 bytes\ntrain_ranker.py 26252 bytes\ntune_blend.py 2883 bytes\ntune_ensemble.py 2953 bytes\nvalidation.parquet 1658820 bytes\nvalidation_best.parquet 1643459 bytes\nvalidation_popularity.parquet 728599 bytes\nvalidation_twotower_28w.parquet 1648500 bytes\nvalidation_twotower_deterministic.parquet 1648294 bytes\nvalidation_twotower_like7.parquet 1650119 bytes\nvalidation_twotower_seed1337.parquet 1647015 bytes\nvalidation_twotower_v1.parquet 1666588 bytes\nvalidation_twotower_v1_cont.parquet 1660531 bytes\nvalidation_twotower_v2.parquet 1644070 bytes\n" } } } ] }, { "id": 142, "role": "agent", "timestamp": "2026-09-24T23:46:18.622Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:46:18.622Z", "text": "" } ] }, { "id": 143, "role": "agent", "timestamp": "2026-09-24T23:46:19.477Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:46:19.477Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:46:24.720Z", "text": "The audited exact-dot submission is safely checkpointed and valid. There is still enough budget for one bounded improvement test: the jump from 16 to 28 temporal cutoffs was large, so I’m testing 36 cutoffs while leaving the current final artifact untouched. I’ll only refit/replace it if public validation improves." }, { "kind": "tool", "timestamp": "2026-09-24T23:46:27.001Z", "callId": "call_wKzKI3JaDuLoIlyWqz2tyfhC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_36w.parquet --checkpoint /app/solution/model_validation_36w.pt --cache /app/solution/cache_validation_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:46:28.170Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=80155", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:46:27.117Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b63fc5c087d2be11a7f9b425f0da", "usage": { "input_tokens": 87952, "cached_input_tokens": 86528, "cache_write_input_tokens": 0, "output_tokens": 824, "reasoning_output_tokens": 562, "total_tokens": 88776 }, "turn_token_usage": { "input_tokens": 7051618, "cached_input_tokens": 6911104, "cache_write_input_tokens": 0, "output_tokens": 51464, "reasoning_output_tokens": 22572, "total_tokens": 7103082 }, "thread_token_usage": { "input_tokens": 7051618, "cached_input_tokens": 6911104, "cache_write_input_tokens": 0, "output_tokens": 51464, "reasoning_output_tokens": 22572, "total_tokens": 7103082 } } } ] }, { "id": 144, "role": "agent", "timestamp": "2026-09-24T23:46:31.241Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:46:31.241Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:46:31.952Z", "callId": "call_vH8JZjc3OGFiL1mFWteSamze", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:47:01.961Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"15a34d\",\"wall_time_seconds\":30.000970509,\"session_id\":80155,\"original_token_count\":30}\nloaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:46:32.080Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b654929887d2b6d6c048d048f2c1", "usage": { "input_tokens": 88804, "cached_input_tokens": 87808, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 88857 }, "turn_token_usage": { "input_tokens": 7140422, "cached_input_tokens": 6998912, "cache_write_input_tokens": 0, "output_tokens": 51517, "reasoning_output_tokens": 22578, "total_tokens": 7191939 }, "thread_token_usage": { "input_tokens": 7140422, "cached_input_tokens": 6998912, "cache_write_input_tokens": 0, "output_tokens": 51517, "reasoning_output_tokens": 22578, "total_tokens": 7191939 } } } ] }, { "id": 145, "role": "agent", "timestamp": "2026-09-24T23:47:04.017Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:47:04.017Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:47:04.732Z", "callId": "call_oZoSvilKfO3yxe2H3Pd4gm58", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:47:34.751Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"90317f\",\"wall_time_seconds\":30.001377533,\"session_id\":80155,\"original_token_count\":41}\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:47:04.893Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b676309887d29751c5228c857308", "usage": { "input_tokens": 88947, "cached_input_tokens": 88576, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 89003 }, "turn_token_usage": { "input_tokens": 7229369, "cached_input_tokens": 7087488, "cache_write_input_tokens": 0, "output_tokens": 51573, "reasoning_output_tokens": 22587, "total_tokens": 7280942 }, "thread_token_usage": { "input_tokens": 7229369, "cached_input_tokens": 7087488, "cache_write_input_tokens": 0, "output_tokens": 51573, "reasoning_output_tokens": 22587, "total_tokens": 7280942 } } } ] }, { "id": 146, "role": "agent", "timestamp": "2026-09-24T23:47:43.388Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:47:43.388Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:47:45.108Z", "callId": "call_1yd46sSm2VQtaf1CKM4sqKel", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:48:15.148Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1e001a\",\"wall_time_seconds\":30.001402615,\"session_id\":80155,\"original_token_count\":30}\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:47:45.223Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b6972f3487d2acc7142da04a411b", "usage": { "input_tokens": 89098, "cached_input_tokens": 88832, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 14, "total_tokens": 89159 }, "turn_token_usage": { "input_tokens": 7318467, "cached_input_tokens": 7176320, "cache_write_input_tokens": 0, "output_tokens": 51634, "reasoning_output_tokens": 22601, "total_tokens": 7370101 }, "thread_token_usage": { "input_tokens": 7318467, "cached_input_tokens": 7176320, "cache_write_input_tokens": 0, "output_tokens": 51634, "reasoning_output_tokens": 22601, "total_tokens": 7370101 } } } ] }, { "id": 147, "role": "agent", "timestamp": "2026-09-24T23:48:17.897Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:48:17.897Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:48:18.545Z", "callId": "call_U1PiWpo2msxBYEfgiQbj2B0q", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:48:48.572Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b9b257\",\"wall_time_seconds\":30.000747859,\"session_id\":80155,\"original_token_count\":20}\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:48:18.737Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b6bf791087d2824f17bdb58b427b", "usage": { "input_tokens": 89273, "cached_input_tokens": 88960, "cache_write_input_tokens": 0, "output_tokens": 59, "reasoning_output_tokens": 12, "total_tokens": 89332 }, "turn_token_usage": { "input_tokens": 7407740, "cached_input_tokens": 7265280, "cache_write_input_tokens": 0, "output_tokens": 51693, "reasoning_output_tokens": 22613, "total_tokens": 7459433 }, "thread_token_usage": { "input_tokens": 7407740, "cached_input_tokens": 7265280, "cache_write_input_tokens": 0, "output_tokens": 51693, "reasoning_output_tokens": 22613, "total_tokens": 7459433 } } } ] }, { "id": 148, "role": "agent", "timestamp": "2026-09-24T23:48:50.209Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:48:50.209Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:48:50.977Z", "callId": "call_503GDTVY5Y6r92nGVnPI42C8", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:49:20.997Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"676842\",\"wall_time_seconds\":30.00101921,\"session_id\":80155,\"original_token_count\":20}\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:48:51.095Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b6e0cdc087d29592a631b11411da", "usage": { "input_tokens": 89425, "cached_input_tokens": 89088, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 89479 }, "turn_token_usage": { "input_tokens": 7497165, "cached_input_tokens": 7354368, "cache_write_input_tokens": 0, "output_tokens": 51747, "reasoning_output_tokens": 22620, "total_tokens": 7548912 }, "thread_token_usage": { "input_tokens": 7497165, "cached_input_tokens": 7354368, "cache_write_input_tokens": 0, "output_tokens": 51747, "reasoning_output_tokens": 22620, "total_tokens": 7548912 } } } ] }, { "id": 149, "role": "agent", "timestamp": "2026-09-24T23:49:23.745Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:49:23.745Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:49:24.460Z", "callId": "call_Ita1mlj9dSXGSOyEURMsTfAt", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:49:54.483Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"218d91\",\"wall_time_seconds\":30.00094173,\"session_id\":80155,\"original_token_count\":30}\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:49:24.622Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7013fe887d2a423c4c14fbde2eb", "usage": { "input_tokens": 89570, "cached_input_tokens": 89216, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 89623 }, "turn_token_usage": { "input_tokens": 7586735, "cached_input_tokens": 7443584, "cache_write_input_tokens": 0, "output_tokens": 51800, "reasoning_output_tokens": 22626, "total_tokens": 7638535 }, "thread_token_usage": { "input_tokens": 7586735, "cached_input_tokens": 7443584, "cache_write_input_tokens": 0, "output_tokens": 51800, "reasoning_output_tokens": 22626, "total_tokens": 7638535 } } } ] }, { "id": 150, "role": "agent", "timestamp": "2026-09-24T23:49:56.227Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:49:56.227Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:49:56.886Z", "callId": "call_e6uUdsTq0omwbbpDBmam2YLB", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":80155,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:50:26.907Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"a80182\",\"wall_time_seconds\":30.000953287,\"session_id\":80155,\"original_token_count\":123}\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:49:57.069Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b722d05887d2b82a83f8a7d657bc", "usage": { "input_tokens": 89735, "cached_input_tokens": 89344, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 89788 }, "turn_token_usage": { "input_tokens": 7676470, "cached_input_tokens": 7532928, "cache_write_input_tokens": 0, "output_tokens": 51853, "reasoning_output_tokens": 22632, "total_tokens": 7728323 }, "thread_token_usage": { "input_tokens": 7676470, "cached_input_tokens": 7532928, "cache_write_input_tokens": 0, "output_tokens": 51853, "reasoning_output_tokens": 22632, "total_tokens": 7728323 } } }, { "kind": "context", "timestamp": "2026-09-24T23:50:27.569Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae8b44d5-2d44-4faa-a64e-5b2895c20b92", "process_id": "80155", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_36w.parquet --checkpoint /app/solution/model_validation_36w.pt --cache /app/solution/cache_validation_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --output /app/solution/validation_twotower_36w.parquet --checkpoint /app/solution/model_validation_36w.pt --cache /app/solution/cache_validation_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 --seed 20260924 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 240, "nanos": 401880632 }, "formatted_output": "loaded 31,062,540 catalogue listens, 19,959,853 positive history events and 8,884,066 organic relevance events in 14.6s\nbuilt 118,402 temporal contexts with 1,135,836 novel targets and 38,336,139 weighted history entries\nsaved prepared arrays to /app/solution/cache_validation_36w.pt\nepoch 01/12: loss=5.61316 lr=0.00197001\nepoch 02/12: loss=5.33079 lr=0.0018821\nepoch 03/12: loss=5.10368 lr=0.00174225\nepoch 04/12: loss=4.95152 lr=0.00156\nepoch 05/12: loss=4.84053 lr=0.00134776\nepoch 06/12: loss=4.74248 lr=0.00112\nepoch 07/12: loss=4.64462 lr=0.000892239\nepoch 08/12: loss=4.55725 lr=0.00068\nepoch 09/12: loss=4.47579 lr=0.000497746\nepoch 10/12: loss=4.41709 lr=0.000357898\nepoch 11/12: loss=4.36232 lr=0.000269985\nepoch 12/12: loss=4.32831 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/validation_twotower_36w.parquet\n{\"checkpoint\": \"/app/solution/model_validation_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 151, "role": "agent", "timestamp": "2026-09-24T23:50:29.717Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:50:29.717Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:50:30.964Z", "callId": "call_pbmMnXKZmDQLUIdLDAn4XKW9", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_36w.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:50:34.426Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029112,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:50:31.065Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b743208487d2918b6d8d68eabf62", "usage": { "input_tokens": 90024, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 6, "total_tokens": 90105 }, "turn_token_usage": { "input_tokens": 7766494, "cached_input_tokens": 7622528, "cache_write_input_tokens": 0, "output_tokens": 51934, "reasoning_output_tokens": 22638, "total_tokens": 7818428 }, "thread_token_usage": { "input_tokens": 7766494, "cached_input_tokens": 7622528, "cache_write_input_tokens": 0, "output_tokens": 51934, "reasoning_output_tokens": 22638, "total_tokens": 7818428 } } }, { "kind": "context", "timestamp": "2026-09-24T23:50:34.411Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-366af9df-8be3-46de-b1d2-6b4ef9b183dd", "process_id": "71290", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_36w.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_36w.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029112,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029112,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 271115613 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_twotower_36w.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029112,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 152, "role": "agent", "timestamp": "2026-09-24T23:50:36.266Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:50:36.266Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:50:37.465Z", "text": "The 36-cutoff model improves again, to 0.029112. That justifies replacing the selected recipe. I’m recalibrating its scalar prior now; then I’ll do the final 36-cutoff refit and repeat the eligibility audit before handoff." }, { "kind": "tool", "timestamp": "2026-09-24T23:50:39.750Z", "callId": "call_SAnLjBwUWqeel1IrhFu0mrFm", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/tune_blend.py\\n@@\\n-checkpoint = torch.load(\\\"/app/solution/model_validation_28w.pt\\\", weights_only=False)\\n-cache = torch.load(\\\"/app/solution/cache_validation_28w.pt\\\", weights_only=False)\\n+checkpoint = torch.load(\\\"/app/solution/model_validation_36w.pt\\\", weights_only=False)\\n+cache = torch.load(\\\"/app/solution/cache_validation_36w.pt\\\", weights_only=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:50:39.928Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:50:39.759Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c6160df2-9e09-4253-852c-08977d73f28b", "changes": { "/app/solution/tune_blend.py": { "type": "update", "unified_diff": "@@ -19,4 +19,4 @@\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n-checkpoint = torch.load(\"/app/solution/model_validation_28w.pt\", weights_only=False)\n-cache = torch.load(\"/app/solution/cache_validation_28w.pt\", weights_only=False)\n+checkpoint = torch.load(\"/app/solution/model_validation_36w.pt\", weights_only=False)\n+cache = torch.load(\"/app/solution/cache_validation_36w.pt\", weights_only=False)\n histories = cache[\"histories\"]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/tune_blend.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:50:39.926Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b74a9ea087d2bc70b24b7511e305", "usage": { "input_tokens": 90179, "cached_input_tokens": 89856, "cache_write_input_tokens": 0, "output_tokens": 235, "reasoning_output_tokens": 38, "total_tokens": 90414 }, "turn_token_usage": { "input_tokens": 7856673, "cached_input_tokens": 7712384, "cache_write_input_tokens": 0, "output_tokens": 52169, "reasoning_output_tokens": 22676, "total_tokens": 7908842 }, "thread_token_usage": { "input_tokens": 7856673, "cached_input_tokens": 7712384, "cache_write_input_tokens": 0, "output_tokens": 52169, "reasoning_output_tokens": 22676, "total_tokens": 7908842 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:50:43.413Z", "callId": "call_8AiHStVT3sWMWGP37pkxbBK1", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:50:44.589Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=4091", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:50:43.620Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b75071dc87d2adac3bedcb1df8f9", "usage": { "input_tokens": 90438, "cached_input_tokens": 89984, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 0, "total_tokens": 90536 }, "turn_token_usage": { "input_tokens": 7947111, "cached_input_tokens": 7802368, "cache_write_input_tokens": 0, "output_tokens": 52267, "reasoning_output_tokens": 22676, "total_tokens": 7999378 }, "thread_token_usage": { "input_tokens": 7947111, "cached_input_tokens": 7802368, "cache_write_input_tokens": 0, "output_tokens": 52267, "reasoning_output_tokens": 22676, "total_tokens": 7999378 } } } ] }, { "id": 153, "role": "agent", "timestamp": "2026-09-24T23:50:46.073Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:50:46.073Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:50:49.115Z", "callId": "call_CPWlGk77zoLEjHfVunWt7ipS", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":4091,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:51:19.123Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"10b248\",\"wall_time_seconds\":30.001311258,\"session_id\":4091,\"original_token_count\":16}\nsimilarity std 2.585146903991699 bias std 0.8208831548690796\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:50:49.219Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b754cb5c87d2ae196be4949b6670", "usage": { "input_tokens": 90564, "cached_input_tokens": 90240, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 90617 }, "turn_token_usage": { "input_tokens": 8037675, "cached_input_tokens": 7892608, "cache_write_input_tokens": 0, "output_tokens": 52320, "reasoning_output_tokens": 22682, "total_tokens": 8089995 }, "thread_token_usage": { "input_tokens": 8037675, "cached_input_tokens": 7892608, "cache_write_input_tokens": 0, "output_tokens": 52320, "reasoning_output_tokens": 22682, "total_tokens": 8089995 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:51:22.099Z", "callId": "call_7UNVSuUeb7FbEo2gyjJqGaEO", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":4091,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:51:52.108Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"f6f692\",\"wall_time_seconds\":30.001708944,\"session_id\":4091,\"original_token_count\":0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:51:22.282Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7778b4487d2b4962270e5c6dbf2", "usage": { "input_tokens": 90693, "cached_input_tokens": 90368, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 90738 }, "turn_token_usage": { "input_tokens": 8128368, "cached_input_tokens": 7982976, "cache_write_input_tokens": 0, "output_tokens": 52365, "reasoning_output_tokens": 22682, "total_tokens": 8180733 }, "thread_token_usage": { "input_tokens": 8128368, "cached_input_tokens": 7982976, "cache_write_input_tokens": 0, "output_tokens": 52365, "reasoning_output_tokens": 22682, "total_tokens": 8180733 } } }, { "kind": "tool", "timestamp": "2026-09-24T23:51:55.875Z", "callId": "call_iOYjyoZmWihtlhk9XNlI8Wi3", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":4091,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:52:03.804Z", "content": "Script completed\nWall time 7.9 seconds\nOutput:\n\n{\"chunk_id\":\"2422bb\",\"wall_time_seconds\":7.917609848,\"exit_code\":0,\"original_token_count\":123}\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:51:56.020Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7984be087d2833daa33e0e2c0b4", "usage": { "input_tokens": 90792, "cached_input_tokens": 90496, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 90837 }, "turn_token_usage": { "input_tokens": 8219160, "cached_input_tokens": 8073472, "cache_write_input_tokens": 0, "output_tokens": 52410, "reasoning_output_tokens": 22682, "total_tokens": 8271570 }, "thread_token_usage": { "input_tokens": 8219160, "cached_input_tokens": 8073472, "cache_write_input_tokens": 0, "output_tokens": 52410, "reasoning_output_tokens": 22682, "total_tokens": 8271570 } } }, { "kind": "context", "timestamp": "2026-09-24T23:52:03.799Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ef54299-8404-4a3b-bde6-0784a398c505", "process_id": "4091", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/tune_blend.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "similarity std 2.585146903991699 bias std 0.8208831548690796\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n", "stderr": "", "aggregated_output": "similarity std 2.585146903991699 bias std 0.8208831548690796\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n", "exit_code": 0, "duration": { "secs": 80, "nanos": 211638829 }, "formatted_output": "similarity std 2.585146903991699 bias std 0.8208831548690796\n(0.8, 0.0) 0.026068\n(0.8, 0.15) 0.029367\n(0.8, 0.3) 0.029383\n(0.8, 0.45) 0.028493\n(1.1, 0.0) 0.029384\n(1.1, 0.15) 0.029112\n(1.1, 0.3) 0.029123\n(1.1, 0.45) 0.027488\n(1.4, 0.0) 0.028755\n(1.4, 0.15) 0.028972\n(1.4, 0.3) 0.027894\n(1.4, 0.45) 0.025550\n(1.7, 0.0) 0.028444\n(1.7, 0.15) 0.028355\n(1.7, 0.3) 0.026403\n(1.7, 0.45) 0.024721\n(2.0, 0.0) 0.028043\n(2.0, 0.15) 0.027118\n(2.0, 0.3) 0.025028\n(2.0, 0.45) 0.023692\n(2.4, 0.0) 0.026566\n(2.4, 0.15) 0.024969\n(2.4, 0.3) 0.023879\n(2.4, 0.45) 0.022421\n" } } } ] }, { "id": 154, "role": "agent", "timestamp": "2026-09-24T23:52:08.416Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:52:08.416Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:52:14.049Z", "callId": "call_Am39lTcBFmRo8owYALDqLLM3", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- parser.add_argument(\\\"--pop-scale\\\", type=float, default=0.15)\\n+ parser.add_argument(\\\"--pop-scale\\\", type=float, default=0.0)\\n*** Update File: /app/solution/README.md\\n@@\\n-Training uses 28 rolling weekly cutoffs. A positive is a previously unseen\\n+Training uses 36 rolling weekly cutoffs. A positive is a previously unseen\\n@@\\n-Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\\n+Public validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\\n@@\\n- --cache /app/solution/cache_final_28w.pt \\\\\\n- --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\\\\n- --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\\\\n+ --cache /app/solution/cache_final_36w.pt \\\\\\n+ --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\\\\n+ --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\\\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:52:14.217Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:52:14.060Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4d387b2f-9619-4dfb-8e56-dac7e6d4d759", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -26,3 +26,3 @@\n \n-Training uses 28 rolling weekly cutoffs. A positive is a previously unseen\n+Training uses 36 rolling weekly cutoffs. A positive is a previously unseen\n catalog item with a subsequent organic 80%-complete listen or organic like.\n@@ -34,3 +34,3 @@\n \n-Public validation: **0.028698 graded NDCG@10** over 4,263 scored users, measured\n+Public validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n with:\n@@ -52,5 +52,5 @@\n --checkpoint /app/solution/model_final.pt \\\n- --cache /app/solution/cache_final_28w.pt \\\n- --epochs 12 --training-weeks 28 --organic-boost 1.5 \\\n- --like-weight 4 --prior-scale 1.1 --pop-scale 0.15 \\\n+ --cache /app/solution/cache_final_36w.pt \\\n+ --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n+ --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n --seed 20260924 --threads 8\n", "move_path": null }, "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -593,3 +593,3 @@\n parser.add_argument(\"--prior-scale\", type=float, default=1.1)\n- parser.add_argument(\"--pop-scale\", type=float, default=0.15)\n+ parser.add_argument(\"--pop-scale\", type=float, default=0.0)\n parser.add_argument(\"--organic-boost\", type=float, default=Config.organic_boost)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\nM /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:52:14.215Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7a4083087d2ab4057be84dd42a9", "usage": { "input_tokens": 91272, "cached_input_tokens": 90624, "cache_write_input_tokens": 0, "output_tokens": 491, "reasoning_output_tokens": 169, "total_tokens": 91763 }, "turn_token_usage": { "input_tokens": 8310432, "cached_input_tokens": 8164096, "cache_write_input_tokens": 0, "output_tokens": 52901, "reasoning_output_tokens": 22851, "total_tokens": 8363333 }, "thread_token_usage": { "input_tokens": 8310432, "cached_input_tokens": 8164096, "cache_write_input_tokens": 0, "output_tokens": 52901, "reasoning_output_tokens": 22851, "total_tokens": 8363333 } } } ] }, { "id": 155, "role": "agent", "timestamp": "2026-09-24T23:52:17.878Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:52:17.878Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:52:22.720Z", "callId": "call_jXQ0ryeGLp2q0a95Y6i4G96c", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport torch,pandas as pd\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features,rank_candidates\\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_36w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_36w.pt',weights_only=False)\\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.0)\\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:52:32.688Z", "content": "Script completed\nWall time 10.0 seconds\nOutput:\n\nwrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:52:22.869Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7aea99487d2bc5f11b89073bddd", "usage": { "input_tokens": 91787, "cached_input_tokens": 91136, "cache_write_input_tokens": 0, "output_tokens": 282, "reasoning_output_tokens": 7, "total_tokens": 92069 }, "turn_token_usage": { "input_tokens": 8402219, "cached_input_tokens": 8255232, "cache_write_input_tokens": 0, "output_tokens": 53183, "reasoning_output_tokens": 22858, "total_tokens": 8455402 }, "thread_token_usage": { "input_tokens": 8402219, "cached_input_tokens": 8255232, "cache_write_input_tokens": 0, "output_tokens": 53183, "reasoning_output_tokens": 22858, "total_tokens": 8455402 } } }, { "kind": "context", "timestamp": "2026-09-24T23:52:32.674Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-179608b5-1b4a-4f27-a3e7-d070a6c955ce", "process_id": "5077", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_36w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_36w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.0)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport torch,pandas as pd\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features,rank_candidates\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');ck=torch.load('/app/solution/model_validation_36w.pt',weights_only=False);cache=torch.load('/app/solution/cache_validation_36w.pt',weights_only=False)\nm=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state']);m.item_tower.learned_prior_scale.fill_(1.1);m.item_tower.fixed_prior_scale.fill_(0.0)\nrank_candidates(m,cache['histories'],Path('/task/data/validation_eligible_candidates.npz'),Path('/app/solution/validation.parquet'))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 789370287 }, "formatted_output": "wrote 845,600 ranked rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 156, "role": "agent", "timestamp": "2026-09-24T23:52:34.713Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:52:34.713Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:52:37.971Z", "callId": "call_QGaiDpp0cDFYDadxBiLMGpIM", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission_36w_candidate.parquet --checkpoint /app/solution/model_final_36w.pt --cache /app/solution/cache_final_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 --seed 20260924 --threads 8\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" }, "result": { "timestamp": "2026-09-24T23:52:39.136Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n\nSESSION_ID=43610", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:52:38.127Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7c0e23487d2b9b831cb65629966", "usage": { "input_tokens": 92155, "cached_input_tokens": 91648, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 8, "total_tokens": 92350 }, "turn_token_usage": { "input_tokens": 8494374, "cached_input_tokens": 8346880, "cache_write_input_tokens": 0, "output_tokens": 53378, "reasoning_output_tokens": 22866, "total_tokens": 8547752 }, "thread_token_usage": { "input_tokens": 8494374, "cached_input_tokens": 8346880, "cache_write_input_tokens": 0, "output_tokens": 53378, "reasoning_output_tokens": 22866, "total_tokens": 8547752 } } } ] }, { "id": 157, "role": "agent", "timestamp": "2026-09-24T23:52:41.428Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:52:41.428Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:52:41.926Z", "callId": "call_YBWKN69UNKamrMbXC68jyknl", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:53:11.939Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"043a72\",\"wall_time_seconds\":30.001193037,\"session_id\":43610,\"original_token_count\":30}\nloaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:52:42.144Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7c75d5087d2b27cad7a83aeb1ab", "usage": { "input_tokens": 92378, "cached_input_tokens": 92032, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92431 }, "turn_token_usage": { "input_tokens": 8586752, "cached_input_tokens": 8438912, "cache_write_input_tokens": 0, "output_tokens": 53431, "reasoning_output_tokens": 22872, "total_tokens": 8640183 }, "thread_token_usage": { "input_tokens": 8586752, "cached_input_tokens": 8438912, "cache_write_input_tokens": 0, "output_tokens": 53431, "reasoning_output_tokens": 22872, "total_tokens": 8640183 } } } ] }, { "id": 158, "role": "agent", "timestamp": "2026-09-24T23:53:13.302Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:53:13.302Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:53:14.044Z", "callId": "call_AhvUXbvfOeQKQUKqTQ8pQQoB", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:53:44.056Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"2c1ad7\",\"wall_time_seconds\":30.000816363,\"session_id\":43610,\"original_token_count\":40}\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:53:14.247Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b7e8281887d2984e76d39ae75dcc", "usage": { "input_tokens": 92520, "cached_input_tokens": 92160, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92573 }, "turn_token_usage": { "input_tokens": 8679272, "cached_input_tokens": 8531072, "cache_write_input_tokens": 0, "output_tokens": 53484, "reasoning_output_tokens": 22878, "total_tokens": 8732756 }, "thread_token_usage": { "input_tokens": 8679272, "cached_input_tokens": 8531072, "cache_write_input_tokens": 0, "output_tokens": 53484, "reasoning_output_tokens": 22878, "total_tokens": 8732756 } } } ] }, { "id": 159, "role": "agent", "timestamp": "2026-09-24T23:53:45.673Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:53:45.673Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:53:46.507Z", "callId": "call_0dxpV3kj54H88BREjnOzcYk8", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:54:16.523Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"eca137\",\"wall_time_seconds\":30.000816965,\"session_id\":43610,\"original_token_count\":30}\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:53:46.644Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b8084c8c87d2ac7fb5941beba3f6", "usage": { "input_tokens": 92670, "cached_input_tokens": 92288, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 92726 }, "turn_token_usage": { "input_tokens": 8771942, "cached_input_tokens": 8623360, "cache_write_input_tokens": 0, "output_tokens": 53540, "reasoning_output_tokens": 22887, "total_tokens": 8825482 }, "thread_token_usage": { "input_tokens": 8771942, "cached_input_tokens": 8623360, "cache_write_input_tokens": 0, "output_tokens": 53540, "reasoning_output_tokens": 22887, "total_tokens": 8825482 } } } ] }, { "id": 160, "role": "agent", "timestamp": "2026-09-24T23:54:17.865Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:54:17.865Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:54:18.575Z", "callId": "call_4kPsyUNEThBXF6DUYoAU3zqn", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:54:48.608Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"00de8a\",\"wall_time_seconds\":30.000973768,\"session_id\":43610,\"original_token_count\":20}\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:54:18.782Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b828bd6c87d2a5d33933fd981a0c", "usage": { "input_tokens": 92838, "cached_input_tokens": 92544, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 92891 }, "turn_token_usage": { "input_tokens": 8864780, "cached_input_tokens": 8715904, "cache_write_input_tokens": 0, "output_tokens": 53593, "reasoning_output_tokens": 22893, "total_tokens": 8918373 }, "thread_token_usage": { "input_tokens": 8864780, "cached_input_tokens": 8715904, "cache_write_input_tokens": 0, "output_tokens": 53593, "reasoning_output_tokens": 22893, "total_tokens": 8918373 } } } ] }, { "id": 161, "role": "agent", "timestamp": "2026-09-24T23:54:50.279Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:54:50.279Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:54:50.961Z", "callId": "call_CaObioAPmtSZGIzf3ZEUX5TB", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:55:20.992Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b1f6f6\",\"wall_time_seconds\":30.001073908,\"session_id\":43610,\"original_token_count\":20}\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:54:51.092Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b848e25087d293360a4854eb5794", "usage": { "input_tokens": 92984, "cached_input_tokens": 92672, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93037 }, "turn_token_usage": { "input_tokens": 8957764, "cached_input_tokens": 8808576, "cache_write_input_tokens": 0, "output_tokens": 53646, "reasoning_output_tokens": 22899, "total_tokens": 9011410 }, "thread_token_usage": { "input_tokens": 8957764, "cached_input_tokens": 8808576, "cache_write_input_tokens": 0, "output_tokens": 53646, "reasoning_output_tokens": 22899, "total_tokens": 9011410 } } } ] }, { "id": 162, "role": "agent", "timestamp": "2026-09-24T23:55:23.133Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:55:23.133Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:55:23.711Z", "callId": "call_vwVTisONPSSHFeX04zdnTGBK", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:55:53.748Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"71ae82\",\"wall_time_seconds\":30.000892248,\"session_id\":43610,\"original_token_count\":20}\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:55:23.858Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b869531487d28a25bda0e3e7949c", "usage": { "input_tokens": 93132, "cached_input_tokens": 92800, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93185 }, "turn_token_usage": { "input_tokens": 9050896, "cached_input_tokens": 8901376, "cache_write_input_tokens": 0, "output_tokens": 53699, "reasoning_output_tokens": 22905, "total_tokens": 9104595 }, "thread_token_usage": { "input_tokens": 9050896, "cached_input_tokens": 8901376, "cache_write_input_tokens": 0, "output_tokens": 53699, "reasoning_output_tokens": 22905, "total_tokens": 9104595 } } } ] }, { "id": 163, "role": "agent", "timestamp": "2026-09-24T23:55:55.361Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:55:55.361Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:55:56.128Z", "callId": "call_V3h5r7ahRSfXcrwvM9nNREmM", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:56:26.158Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"515bac\",\"wall_time_seconds\":30.000916003,\"session_id\":43610,\"original_token_count\":21}\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:55:56.273Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b889f8ac87d29f44dc2f38d4b222", "usage": { "input_tokens": 93277, "cached_input_tokens": 92928, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93330 }, "turn_token_usage": { "input_tokens": 9144173, "cached_input_tokens": 8994304, "cache_write_input_tokens": 0, "output_tokens": 53752, "reasoning_output_tokens": 22911, "total_tokens": 9197925 }, "thread_token_usage": { "input_tokens": 9144173, "cached_input_tokens": 8994304, "cache_write_input_tokens": 0, "output_tokens": 53752, "reasoning_output_tokens": 22911, "total_tokens": 9197925 } } } ] }, { "id": 164, "role": "agent", "timestamp": "2026-09-24T23:56:27.745Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:56:27.745Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:56:28.778Z", "callId": "call_W7uCU0VpI7vGUKNCOhovAfFP", "name": "exec", "input": { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":43610,\"yield_time_ms\":30000,\"chars\":\"\"});\ntext(rest);\ntext(output);\n" }, "result": { "timestamp": "2026-09-24T23:56:36.523Z", "content": "Script completed\nWall time 7.7 seconds\nOutput:\n\n{\"chunk_id\":\"c57c42\",\"wall_time_seconds\":7.696088155,\"exit_code\":0,\"original_token_count\":112}\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:56:28.969Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b8aa64dc87d2b7a53fcd6b40b48e", "usage": { "input_tokens": 93422, "cached_input_tokens": 93056, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93475 }, "turn_token_usage": { "input_tokens": 9237595, "cached_input_tokens": 9087360, "cache_write_input_tokens": 0, "output_tokens": 53805, "reasoning_output_tokens": 22917, "total_tokens": 9291400 }, "thread_token_usage": { "input_tokens": 9237595, "cached_input_tokens": 9087360, "cache_write_input_tokens": 0, "output_tokens": 53805, "reasoning_output_tokens": 22917, "total_tokens": 9291400 } } }, { "kind": "context", "timestamp": "2026-09-24T23:56:36.502Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-64dbdd5b-0dc4-4af4-96e0-3f85ff01d2cd", "process_id": "43610", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission_36w_candidate.parquet --checkpoint /app/solution/model_final_36w.pt --cache /app/solution/cache_final_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 --seed 20260924 --threads 8" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage final --output /app/solution/submission_36w_candidate.parquet --checkpoint /app/solution/model_final_36w.pt --cache /app/solution/cache_final_36w.pt --epochs 12 --training-weeks 36 --organic-boost 1.5 --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 --seed 20260924 --threads 8" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "stderr": "", "aggregated_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n", "exit_code": 0, "duration": { "secs": 238, "nanos": 366224167 }, "formatted_output": "loaded 32,061,602 catalogue listens, 20,610,148 positive history events and 9,183,300 organic relevance events in 14.5s\nbuilt 119,890 temporal contexts with 1,139,892 novel targets and 39,337,217 weighted history entries\nsaved prepared arrays to /app/solution/cache_final_36w.pt\nepoch 01/12: loss=5.60787 lr=0.00197001\nepoch 02/12: loss=5.31915 lr=0.0018821\nepoch 03/12: loss=5.09718 lr=0.00174225\nepoch 04/12: loss=4.95216 lr=0.00156\nepoch 05/12: loss=4.84323 lr=0.00134776\nepoch 06/12: loss=4.74727 lr=0.00112\nepoch 07/12: loss=4.64734 lr=0.000892239\nepoch 08/12: loss=4.56733 lr=0.00068\nepoch 09/12: loss=4.47345 lr=0.000497746\nepoch 10/12: loss=4.41508 lr=0.000357898\nepoch 11/12: loss=4.35731 lr=0.000269985\nepoch 12/12: loss=4.31833 lr=0.00024\nwrote 845,600 ranked rows to /app/solution/submission_36w_candidate.parquet\n{\"checkpoint\": \"/app/solution/model_final_36w.pt\", \"config\": {\"embedding_dim\": 64, \"hidden_dim\": 128, \"max_history\": 384, \"history_days\": 180, \"training_weeks\": 36, \"epochs\": 12, \"batch_size\": 256, \"negatives\": 512, \"learning_rate\": 0.002, \"weight_decay\": 1e-05, \"like_sampling_weight\": 4.0, \"organic_boost\": 1.5, \"seed\": 20260924}}\n" } } } ] }, { "id": 165, "role": "agent", "timestamp": "2026-09-24T23:56:39.822Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:56:39.822Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:56:40.877Z", "text": "The 36-cutoff final refit completed cleanly. I’m validating the new candidate against every test eligibility bit before promoting it over the already-valid 28-cutoff submission; the older artifact remains in place until this check passes." }, { "kind": "tool", "timestamp": "2026-09-24T23:56:51.162Z", "callId": "call_bwS2v46Q64wyMZdcSFehHJoG", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,sys,torch\\nsys.path[:0]=['/task','/app/solution']\\nfrom ranking_evaluation import validate_ranking,read_ranking\\nfrom model import TwoTowerRanker\\nfrom train_ranker import item_features\\nD=Path('/task/data'); p=Path('/app/solution/submission_36w_candidate.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\\nassert ok.all() and len(f)==845600 and not f.duplicated(['uid','item_id']).any()\\nck=torch.load('/app/solution/model_final_36w.pt',weights_only=False);cat=pd.read_parquet(D/'candidate_catalog.parquet');m=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\\nassert float(m.item_tower.fixed_prior_scale)==0.0\\nprint('PROMOTION_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'prior_scale':float(m.item_tower.learned_prior_scale),'fixed_pop_scale':float(m.item_tower.fixed_prior_scale)})\\nPY\\nmv /app/solution/submission.parquet /app/solution/submission_28w_archived.parquet\\nmv /app/solution/submission_36w_candidate.parquet /app/solution/submission.parquet\\nmv /app/solution/model_final.pt /app/solution/model_final_28w_archived.pt\\nmv /app/solution/model_final_36w.pt /app/solution/model_final.pt\\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:56:56.130Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\nPROMOTION_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100, 'prior_scale': 1.100000023841858, 'fixed_pop_scale': 0.0}\nd904b65b482a956c89be2fbb9c846bd5c4ab12b1639f36f58249c8963a37ff60 /app/solution/submission.parquet\n271272ff3d99652199cef46008423156045f0e13ef0275e20d1ff6a0d10ca423 /app/solution/model_final.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:56:51.365Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b8b4ba3487d2ae31aca1130e291f", "usage": { "input_tokens": 93689, "cached_input_tokens": 93184, "cache_write_input_tokens": 0, "output_tokens": 738, "reasoning_output_tokens": 105, "total_tokens": 94427 }, "turn_token_usage": { "input_tokens": 9331284, "cached_input_tokens": 9180544, "cache_write_input_tokens": 0, "output_tokens": 54543, "reasoning_output_tokens": 23022, "total_tokens": 9385827 }, "thread_token_usage": { "input_tokens": 9331284, "cached_input_tokens": 9180544, "cache_write_input_tokens": 0, "output_tokens": 54543, "reasoning_output_tokens": 23022, "total_tokens": 9385827 } } }, { "kind": "context", "timestamp": "2026-09-24T23:56:56.126Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-81f0c15c-05b0-4f79-a699-7ab2e31a7bb6", "process_id": "93729", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys,torch\nsys.path[:0]=['/task','/app/solution']\nfrom ranking_evaluation import validate_ranking,read_ranking\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\nD=Path('/task/data'); p=Path('/app/solution/submission_36w_candidate.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all() and len(f)==845600 and not f.duplicated(['uid','item_id']).any()\nck=torch.load('/app/solution/model_final_36w.pt',weights_only=False);cat=pd.read_parquet(D/'candidate_catalog.parquet');m=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nassert float(m.item_tower.fixed_prior_scale)==0.0\nprint('PROMOTION_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'prior_scale':float(m.item_tower.learned_prior_scale),'fixed_pop_scale':float(m.item_tower.fixed_prior_scale)})\nPY\nmv /app/solution/submission.parquet /app/solution/submission_28w_archived.parquet\nmv /app/solution/submission_36w_candidate.parquet /app/solution/submission.parquet\nmv /app/solution/model_final.pt /app/solution/model_final_28w_archived.pt\nmv /app/solution/model_final_36w.pt /app/solution/model_final.pt\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,sys,torch\nsys.path[:0]=['/task','/app/solution']\nfrom ranking_evaluation import validate_ranking,read_ranking\nfrom model import TwoTowerRanker\nfrom train_ranker import item_features\nD=Path('/task/data'); p=Path('/app/solution/submission_36w_candidate.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(p),targets)\nz=np.load(D/'test_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\nr=np.fromiter((ur[int(x)] for x in f.uid),np.int32,len(f)); c=np.fromiter((ic[int(x)] for x in f.item_id),np.int32,len(f)); ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool)\nassert ok.all() and len(f)==845600 and not f.duplicated(['uid','item_id']).any()\nck=torch.load('/app/solution/model_final_36w.pt',weights_only=False);cat=pd.read_parquet(D/'candidate_catalog.parquet');m=TwoTowerRanker(30000,item_features(cat),64,128);m.load_state_dict(ck['model_state'])\nassert float(m.item_tower.fixed_prior_scale)==0.0\nprint('PROMOTION_VALID',{'rows':len(f),'users':f.uid.nunique(),'eligible':bool(ok.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'prior_scale':float(m.item_tower.learned_prior_scale),'fixed_pop_scale':float(m.item_tower.fixed_prior_scale)})\nPY\nmv /app/solution/submission.parquet /app/solution/submission_28w_archived.parquet\nmv /app/solution/submission_36w_candidate.parquet /app/solution/submission.parquet\nmv /app/solution/model_final.pt /app/solution/model_final_28w_archived.pt\nmv /app/solution/model_final_36w.pt /app/solution/model_final.pt\nsha256sum /app/solution/submission.parquet /app/solution/model_final.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "PROMOTION_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100, 'prior_scale': 1.100000023841858, 'fixed_pop_scale': 0.0}\nd904b65b482a956c89be2fbb9c846bd5c4ab12b1639f36f58249c8963a37ff60 /app/solution/submission.parquet\n271272ff3d99652199cef46008423156045f0e13ef0275e20d1ff6a0d10ca423 /app/solution/model_final.pt\n", "stderr": "", "aggregated_output": "PROMOTION_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100, 'prior_scale': 1.100000023841858, 'fixed_pop_scale': 0.0}\nd904b65b482a956c89be2fbb9c846bd5c4ab12b1639f36f58249c8963a37ff60 /app/solution/submission.parquet\n271272ff3d99652199cef46008423156045f0e13ef0275e20d1ff6a0d10ca423 /app/solution/model_final.pt\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 789002100 }, "formatted_output": "PROMOTION_VALID {'rows': 845600, 'users': 8456, 'eligible': True, 'rank_min': 1, 'rank_max': 100, 'prior_scale': 1.100000023841858, 'fixed_pop_scale': 0.0}\nd904b65b482a956c89be2fbb9c846bd5c4ab12b1639f36f58249c8963a37ff60 /app/solution/submission.parquet\n271272ff3d99652199cef46008423156045f0e13ef0275e20d1ff6a0d10ca423 /app/solution/model_final.pt\n" } } } ] }, { "id": 166, "role": "agent", "timestamp": "2026-09-24T23:57:10.551Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:57:10.551Z", "text": "" } ] }, { "id": 167, "role": "agent", "timestamp": "2026-09-24T23:57:19.849Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:57:19.849Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:57:22.072Z", "callId": "call_NsuzuOzCRVHDlznkPNZccJyS", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/README.md\\n@@\\n-frequency, recency, completion, organic, like, and dislike weights, then uses a\\n-`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\\n+frequency, recency, completion, organic, like, and dislike weights, then uses a\\n+`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-24T23:57:22.361Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:57:22.081Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-be9a1d03-0956-45a7-a701-0ec9b7373448", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -8,3 +8,3 @@\n frequency, recency, completion, organic, like, and dislike weights, then uses a\n-`64 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n+`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n embedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-24T23:57:22.359Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b8c852cc87d2b31b467a12e9e2ad", "usage": { "input_tokens": 94599, "cached_input_tokens": 93568, "cache_write_input_tokens": 0, "output_tokens": 1157, "reasoning_output_tokens": 1026, "total_tokens": 95756 }, "turn_token_usage": { "input_tokens": 9425883, "cached_input_tokens": 9274112, "cache_write_input_tokens": 0, "output_tokens": 55700, "reasoning_output_tokens": 24048, "total_tokens": 9481583 }, "thread_token_usage": { "input_tokens": 9425883, "cached_input_tokens": 9274112, "cache_write_input_tokens": 0, "output_tokens": 55700, "reasoning_output_tokens": 24048, "total_tokens": 9481583 } } } ] }, { "id": 168, "role": "agent", "timestamp": "2026-09-24T23:57:26.595Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:57:26.595Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-24T23:57:30.250Z", "callId": "call_LVUlW5cxJ3yCuR8YRojNJIKo", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python - <<'PY'\\nimport torch\\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\\nprint('final_config',ck['config'])\\nprint('final_epochs',len(ck['losses']),'final_loss',ck['losses'][-1])\\nPY\\nnl -ba /app/solution/model.py | sed -n '1,180p'\\nnl -ba /app/solution/README.md | sed -n '1,180p'\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-24T23:57:36.798Z", "content": "Script completed\nWall time 6.5 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n 1\t\"\"\"Neural towers used by the discovery ranker.\n 2\t\n 3\tThe user tower consumes only a variable-length interaction history plus summary\n 4\tfeatures. It deliberately has no user-ID parameter. The item tower can be\n 5\trun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\n 6\tthe final coordinate implements an item prior while preserving an exact dot\n 7\tproduct scoring function.\n 8\t\"\"\"\n 9\tfrom __future__ import annotations\n 10\t\n 11\timport torch\n 12\tfrom torch import nn\n 13\tfrom torch.nn import functional as F\n 14\t\n 15\t\n 16\tclass UserTower(nn.Module):\n 17\t \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n 18\t\n 19\t def __init__(\n 20\t self,\n 21\t num_items: int,\n 22\t embedding_dim: int = 64,\n 23\t hidden_dim: int = 128,\n 24\t num_context_features: int = 4,\n 25\t ) -> None:\n 26\t super().__init__()\n 27\t self.embedding_dim = embedding_dim\n 28\t self.history_embedding = nn.EmbeddingBag(\n 29\t num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n 30\t )\n 31\t nn.init.normal_(self.history_embedding.weight, std=0.04)\n 32\t self.network = nn.Sequential(\n 33\t nn.Linear(embedding_dim + num_context_features, hidden_dim),\n 34\t nn.GELU(),\n 35\t nn.LayerNorm(hidden_dim),\n 36\t nn.Linear(hidden_dim, embedding_dim),\n 37\t )\n 38\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 39\t self.log_scale = nn.Parameter(torch.tensor(2.0))\n 40\t\n 41\t def forward(\n 42\t self,\n 43\t item_ids: torch.Tensor,\n 44\t offsets: torch.Tensor,\n 45\t weights: torch.Tensor,\n 46\t context_features: torch.Tensor,\n 47\t ) -> torch.Tensor:\n 48\t pooled = self.history_embedding(\n 49\t item_ids, offsets, per_sample_weights=weights\n 50\t )\n 51\t # Signed preference weights are useful, but their absolute magnitude\n 52\t # must not make heavy users arbitrarily larger than light users.\n 53\t abs_sums = torch.zeros(\n 54\t len(offsets) - 1, device=weights.device, dtype=weights.dtype\n 55\t )\n 56\t lengths = offsets[1:] - offsets[:-1]\n 57\t bag_ids = torch.repeat_interleave(\n 58\t torch.arange(len(lengths), device=weights.device), lengths\n 59\t )\n 60\t abs_sums.scatter_add_(0, bag_ids, weights.abs())\n 61\t pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n 62\t latent = self.network(torch.cat((pooled, context_features), dim=1))\n 63\t latent = F.normalize(latent + self.skip(pooled), dim=1)\n 64\t # Constraining the scale prevents sampled-softmax from becoming\n 65\t # numerically brittle. The constant last coordinate pairs with the\n 66\t # item tower's learned popularity prior.\n 67\t scale = self.log_scale.exp().clamp(1.0, 30.0)\n 68\t return torch.cat(\n 69\t (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n 70\t )\n 71\t\n 72\t\n 73\tclass ItemTower(nn.Module):\n 74\t \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n 75\t\n 76\t def __init__(\n 77\t self,\n 78\t num_items: int,\n 79\t item_features: torch.Tensor,\n 80\t embedding_dim: int = 64,\n 81\t hidden_dim: int = 128,\n 82\t ) -> None:\n 83\t super().__init__()\n 84\t self.embedding_dim = embedding_dim\n 85\t self.id_embedding = nn.Embedding(num_items, embedding_dim)\n 86\t nn.init.normal_(self.id_embedding.weight, std=0.04)\n 87\t self.register_buffer(\"item_features\", item_features.float())\n 88\t feature_dim = item_features.shape[1]\n 89\t self.network = nn.Sequential(\n 90\t nn.Linear(embedding_dim + feature_dim, hidden_dim),\n 91\t nn.GELU(),\n 92\t nn.LayerNorm(hidden_dim),\n 93\t nn.Linear(hidden_dim, embedding_dim),\n 94\t )\n 95\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 96\t self.prior = nn.Sequential(\n 97\t nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n 98\t )\n 99\t self.id_bias = nn.Embedding(num_items, 1)\n 100\t nn.init.zeros_(self.id_bias.weight)\n 101\t # These two scalar buffers are selected on the public window. Keeping\n 102\t # calibration inside the item tower means serving remains one exact\n 103\t # maximum-inner-product search, with no post-retrieval reranker.\n 104\t self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n 105\t self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n 106\t\n 107\t def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n 108\t ids = self.id_embedding(item_ids)\n 109\t features = self.item_features[item_ids]\n 110\t latent = self.network(torch.cat((ids, features), dim=1))\n 111\t latent = F.normalize(latent + self.skip(ids), dim=1)\n 112\t bias = self.learned_prior_scale * (\n 113\t self.prior(features) + self.id_bias(item_ids)\n 114\t ) + self.fixed_prior_scale * (-features[:, 2:3])\n 115\t return torch.cat((latent, bias), dim=1)\n 116\t\n 117\t\n 118\tclass TwoTowerRanker(nn.Module):\n 119\t \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n 120\t\n 121\t def __init__(\n 122\t self,\n 123\t num_items: int,\n 124\t item_features: torch.Tensor,\n 125\t embedding_dim: int = 64,\n 126\t hidden_dim: int = 128,\n 127\t ) -> None:\n 128\t super().__init__()\n 129\t self.user_tower = UserTower(\n 130\t num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n 131\t )\n 132\t self.item_tower = ItemTower(\n 133\t num_items,\n 134\t item_features=item_features,\n 135\t embedding_dim=embedding_dim,\n 136\t hidden_dim=hidden_dim,\n 137\t )\n 138\t\n 139\t def score(\n 140\t self,\n 141\t history_ids: torch.Tensor,\n 142\t offsets: torch.Tensor,\n 143\t history_weights: torch.Tensor,\n 144\t context_features: torch.Tensor,\n 145\t candidate_ids: torch.Tensor,\n 146\t ) -> torch.Tensor:\n 147\t users = self.user_tower(\n 148\t history_ids, offsets, history_weights, context_features\n 149\t )\n 150\t items = self.item_tower(candidate_ids)\n 151\t return users @ items.T\n 1\t# Made-for-You two-tower ranker\n 2\t\n 3\t## Model\n 4\t\n 5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n 6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n 7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n 8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n 9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n 10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n 11\tresidual MLP. Both latent outputs are L2-normalized.\n 12\t\n 13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n 14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n 15\tThus the complete relevance score is exactly\n 16\t\n 17\t```text\n 18\tscore(user history, item) = user_vector @ item_vector\n 19\t```\n 20\t\n 21\tCandidate vectors are computed once offline; request-time work is one bounded\n 22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n 23\tthe platform-owned retrieval mask.\n 24\t\n 25\t## Training\n 26\t\n 27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n 28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n 29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n 30\tsampling weight 4. Histories are held in event/CSR form, so training never\n 31\tconstructs or factorizes a global user-item interaction matrix. The final model\n 32\tis refit on `train_* + val_*`, with validation events treated as observed\n 33\thistory and temporal training outcomes.\n 34\t\n 35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n 36\twith:\n 37\t\n 38\t```sh\n 39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n 40\t```\n 41\t\n 42\t## Final submission\n 43\t\n 44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n 45\tcheckpoint. Rebuild it deterministically from `/app` with:\n 46\t\n 47\t```sh\n 48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n 49\t /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n 50\t --stage final \\\n 51\t --output /app/solution/submission.parquet \\\n 52\t --checkpoint /app/solution/model_final.pt \\\n 53\t --cache /app/solution/cache_final_36w.pt \\\n 54\t --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n 55\t --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n 56\t --seed 20260924 --threads 8\n 57\t```\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-24T23:57:30.445Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b8e2d7cc87d2ad59ec3ea76479ab", "usage": { "input_tokens": 95780, "cached_input_tokens": 94464, "cache_write_input_tokens": 0, "output_tokens": 324, "reasoning_output_tokens": 108, "total_tokens": 96104 }, "turn_token_usage": { "input_tokens": 9521663, "cached_input_tokens": 9368576, "cache_write_input_tokens": 0, "output_tokens": 56024, "reasoning_output_tokens": 24156, "total_tokens": 9577687 }, "thread_token_usage": { "input_tokens": 9521663, "cached_input_tokens": 9368576, "cache_write_input_tokens": 0, "output_tokens": 56024, "reasoning_output_tokens": 24156, "total_tokens": 9577687 } } }, { "kind": "context", "timestamp": "2026-09-24T23:57:36.792Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e0519f03-87b3-4c5c-9dca-430c27d77669", "process_id": "17924", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python - <<'PY'\nimport torch\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\nprint('final_config',ck['config'])\nprint('final_epochs',len(ck['losses']),'final_loss',ck['losses'][-1])\nPY\nnl -ba /app/solution/model.py | sed -n '1,180p'\nnl -ba /app/solution/README.md | sed -n '1,180p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python - <<'PY'\nimport torch\nck=torch.load('/app/solution/model_final.pt',weights_only=False)\nprint('final_config',ck['config'])\nprint('final_epochs',len(ck['losses']),'final_loss',ck['losses'][-1])\nPY\nnl -ba /app/solution/model.py | sed -n '1,180p'\nnl -ba /app/solution/README.md | sed -n '1,180p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n 1\t\"\"\"Neural towers used by the discovery ranker.\n 2\t\n 3\tThe user tower consumes only a variable-length interaction history plus summary\n 4\tfeatures. It deliberately has no user-ID parameter. The item tower can be\n 5\trun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\n 6\tthe final coordinate implements an item prior while preserving an exact dot\n 7\tproduct scoring function.\n 8\t\"\"\"\n 9\tfrom __future__ import annotations\n 10\t\n 11\timport torch\n 12\tfrom torch import nn\n 13\tfrom torch.nn import functional as F\n 14\t\n 15\t\n 16\tclass UserTower(nn.Module):\n 17\t \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n 18\t\n 19\t def __init__(\n 20\t self,\n 21\t num_items: int,\n 22\t embedding_dim: int = 64,\n 23\t hidden_dim: int = 128,\n 24\t num_context_features: int = 4,\n 25\t ) -> None:\n 26\t super().__init__()\n 27\t self.embedding_dim = embedding_dim\n 28\t self.history_embedding = nn.EmbeddingBag(\n 29\t num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n 30\t )\n 31\t nn.init.normal_(self.history_embedding.weight, std=0.04)\n 32\t self.network = nn.Sequential(\n 33\t nn.Linear(embedding_dim + num_context_features, hidden_dim),\n 34\t nn.GELU(),\n 35\t nn.LayerNorm(hidden_dim),\n 36\t nn.Linear(hidden_dim, embedding_dim),\n 37\t )\n 38\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 39\t self.log_scale = nn.Parameter(torch.tensor(2.0))\n 40\t\n 41\t def forward(\n 42\t self,\n 43\t item_ids: torch.Tensor,\n 44\t offsets: torch.Tensor,\n 45\t weights: torch.Tensor,\n 46\t context_features: torch.Tensor,\n 47\t ) -> torch.Tensor:\n 48\t pooled = self.history_embedding(\n 49\t item_ids, offsets, per_sample_weights=weights\n 50\t )\n 51\t # Signed preference weights are useful, but their absolute magnitude\n 52\t # must not make heavy users arbitrarily larger than light users.\n 53\t abs_sums = torch.zeros(\n 54\t len(offsets) - 1, device=weights.device, dtype=weights.dtype\n 55\t )\n 56\t lengths = offsets[1:] - offsets[:-1]\n 57\t bag_ids = torch.repeat_interleave(\n 58\t torch.arange(len(lengths), device=weights.device), lengths\n 59\t )\n 60\t abs_sums.scatter_add_(0, bag_ids, weights.abs())\n 61\t pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n 62\t latent = self.network(torch.cat((pooled, context_features), dim=1))\n 63\t latent = F.normalize(latent + self.skip(pooled), dim=1)\n 64\t # Constraining the scale prevents sampled-softmax from becoming\n 65\t # numerically brittle. The constant last coordinate pairs with the\n 66\t # item tower's learned popularity prior.\n 67\t scale = self.log_scale.exp().clamp(1.0, 30.0)\n 68\t return torch.cat(\n 69\t (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n 70\t )\n 71\t\n 72\t\n 73\tclass ItemTower(nn.Module):\n 74\t \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n 75\t\n 76\t def __init__(\n 77\t self,\n 78\t num_items: int,\n 79\t item_features: torch.Tensor,\n 80\t embedding_dim: int = 64,\n 81\t hidden_dim: int = 128,\n 82\t ) -> None:\n 83\t super().__init__()\n 84\t self.embedding_dim = embedding_dim\n 85\t self.id_embedding = nn.Embedding(num_items, embedding_dim)\n 86\t nn.init.normal_(self.id_embedding.weight, std=0.04)\n 87\t self.register_buffer(\"item_features\", item_features.float())\n 88\t feature_dim = item_features.shape[1]\n 89\t self.network = nn.Sequential(\n 90\t nn.Linear(embedding_dim + feature_dim, hidden_dim),\n 91\t nn.GELU(),\n 92\t nn.LayerNorm(hidden_dim),\n 93\t nn.Linear(hidden_dim, embedding_dim),\n 94\t )\n 95\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 96\t self.prior = nn.Sequential(\n 97\t nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n 98\t )\n 99\t self.id_bias = nn.Embedding(num_items, 1)\n 100\t nn.init.zeros_(self.id_bias.weight)\n 101\t # These two scalar buffers are selected on the public window. Keeping\n 102\t # calibration inside the item tower means serving remains one exact\n 103\t # maximum-inner-product search, with no post-retrieval reranker.\n 104\t self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n 105\t self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n 106\t\n 107\t def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n 108\t ids = self.id_embedding(item_ids)\n 109\t features = self.item_features[item_ids]\n 110\t latent = self.network(torch.cat((ids, features), dim=1))\n 111\t latent = F.normalize(latent + self.skip(ids), dim=1)\n 112\t bias = self.learned_prior_scale * (\n 113\t self.prior(features) + self.id_bias(item_ids)\n 114\t ) + self.fixed_prior_scale * (-features[:, 2:3])\n 115\t return torch.cat((latent, bias), dim=1)\n 116\t\n 117\t\n 118\tclass TwoTowerRanker(nn.Module):\n 119\t \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n 120\t\n 121\t def __init__(\n 122\t self,\n 123\t num_items: int,\n 124\t item_features: torch.Tensor,\n 125\t embedding_dim: int = 64,\n 126\t hidden_dim: int = 128,\n 127\t ) -> None:\n 128\t super().__init__()\n 129\t self.user_tower = UserTower(\n 130\t num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n 131\t )\n 132\t self.item_tower = ItemTower(\n 133\t num_items,\n 134\t item_features=item_features,\n 135\t embedding_dim=embedding_dim,\n 136\t hidden_dim=hidden_dim,\n 137\t )\n 138\t\n 139\t def score(\n 140\t self,\n 141\t history_ids: torch.Tensor,\n 142\t offsets: torch.Tensor,\n 143\t history_weights: torch.Tensor,\n 144\t context_features: torch.Tensor,\n 145\t candidate_ids: torch.Tensor,\n 146\t ) -> torch.Tensor:\n 147\t users = self.user_tower(\n 148\t history_ids, offsets, history_weights, context_features\n 149\t )\n 150\t items = self.item_tower(candidate_ids)\n 151\t return users @ items.T\n 1\t# Made-for-You two-tower ranker\n 2\t\n 3\t## Model\n 4\t\n 5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n 6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n 7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n 8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n 9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n 10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n 11\tresidual MLP. Both latent outputs are L2-normalized.\n 12\t\n 13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n 14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n 15\tThus the complete relevance score is exactly\n 16\t\n 17\t```text\n 18\tscore(user history, item) = user_vector @ item_vector\n 19\t```\n 20\t\n 21\tCandidate vectors are computed once offline; request-time work is one bounded\n 22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n 23\tthe platform-owned retrieval mask.\n 24\t\n 25\t## Training\n 26\t\n 27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n 28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n 29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n 30\tsampling weight 4. Histories are held in event/CSR form, so training never\n 31\tconstructs or factorizes a global user-item interaction matrix. The final model\n 32\tis refit on `train_* + val_*`, with validation events treated as observed\n 33\thistory and temporal training outcomes.\n 34\t\n 35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n 36\twith:\n 37\t\n 38\t```sh\n 39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n 40\t```\n 41\t\n 42\t## Final submission\n 43\t\n 44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n 45\tcheckpoint. Rebuild it deterministically from `/app` with:\n 46\t\n 47\t```sh\n 48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n 49\t /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n 50\t --stage final \\\n 51\t --output /app/solution/submission.parquet \\\n 52\t --checkpoint /app/solution/model_final.pt \\\n 53\t --cache /app/solution/cache_final_36w.pt \\\n 54\t --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n 55\t --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n 56\t --seed 20260924 --threads 8\n 57\t```\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n 1\t\"\"\"Neural towers used by the discovery ranker.\n 2\t\n 3\tThe user tower consumes only a variable-length interaction history plus summary\n 4\tfeatures. It deliberately has no user-ID parameter. The item tower can be\n 5\trun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\n 6\tthe final coordinate implements an item prior while preserving an exact dot\n 7\tproduct scoring function.\n 8\t\"\"\"\n 9\tfrom __future__ import annotations\n 10\t\n 11\timport torch\n 12\tfrom torch import nn\n 13\tfrom torch.nn import functional as F\n 14\t\n 15\t\n 16\tclass UserTower(nn.Module):\n 17\t \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n 18\t\n 19\t def __init__(\n 20\t self,\n 21\t num_items: int,\n 22\t embedding_dim: int = 64,\n 23\t hidden_dim: int = 128,\n 24\t num_context_features: int = 4,\n 25\t ) -> None:\n 26\t super().__init__()\n 27\t self.embedding_dim = embedding_dim\n 28\t self.history_embedding = nn.EmbeddingBag(\n 29\t num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n 30\t )\n 31\t nn.init.normal_(self.history_embedding.weight, std=0.04)\n 32\t self.network = nn.Sequential(\n 33\t nn.Linear(embedding_dim + num_context_features, hidden_dim),\n 34\t nn.GELU(),\n 35\t nn.LayerNorm(hidden_dim),\n 36\t nn.Linear(hidden_dim, embedding_dim),\n 37\t )\n 38\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 39\t self.log_scale = nn.Parameter(torch.tensor(2.0))\n 40\t\n 41\t def forward(\n 42\t self,\n 43\t item_ids: torch.Tensor,\n 44\t offsets: torch.Tensor,\n 45\t weights: torch.Tensor,\n 46\t context_features: torch.Tensor,\n 47\t ) -> torch.Tensor:\n 48\t pooled = self.history_embedding(\n 49\t item_ids, offsets, per_sample_weights=weights\n 50\t )\n 51\t # Signed preference weights are useful, but their absolute magnitude\n 52\t # must not make heavy users arbitrarily larger than light users.\n 53\t abs_sums = torch.zeros(\n 54\t len(offsets) - 1, device=weights.device, dtype=weights.dtype\n 55\t )\n 56\t lengths = offsets[1:] - offsets[:-1]\n 57\t bag_ids = torch.repeat_interleave(\n 58\t torch.arange(len(lengths), device=weights.device), lengths\n 59\t )\n 60\t abs_sums.scatter_add_(0, bag_ids, weights.abs())\n 61\t pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n 62\t latent = self.network(torch.cat((pooled, context_features), dim=1))\n 63\t latent = F.normalize(latent + self.skip(pooled), dim=1)\n 64\t # Constraining the scale prevents sampled-softmax from becoming\n 65\t # numerically brittle. The constant last coordinate pairs with the\n 66\t # item tower's learned popularity prior.\n 67\t scale = self.log_scale.exp().clamp(1.0, 30.0)\n 68\t return torch.cat(\n 69\t (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n 70\t )\n 71\t\n 72\t\n 73\tclass ItemTower(nn.Module):\n 74\t \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n 75\t\n 76\t def __init__(\n 77\t self,\n 78\t num_items: int,\n 79\t item_features: torch.Tensor,\n 80\t embedding_dim: int = 64,\n 81\t hidden_dim: int = 128,\n 82\t ) -> None:\n 83\t super().__init__()\n 84\t self.embedding_dim = embedding_dim\n 85\t self.id_embedding = nn.Embedding(num_items, embedding_dim)\n 86\t nn.init.normal_(self.id_embedding.weight, std=0.04)\n 87\t self.register_buffer(\"item_features\", item_features.float())\n 88\t feature_dim = item_features.shape[1]\n 89\t self.network = nn.Sequential(\n 90\t nn.Linear(embedding_dim + feature_dim, hidden_dim),\n 91\t nn.GELU(),\n 92\t nn.LayerNorm(hidden_dim),\n 93\t nn.Linear(hidden_dim, embedding_dim),\n 94\t )\n 95\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 96\t self.prior = nn.Sequential(\n 97\t nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n 98\t )\n 99\t self.id_bias = nn.Embedding(num_items, 1)\n 100\t nn.init.zeros_(self.id_bias.weight)\n 101\t # These two scalar buffers are selected on the public window. Keeping\n 102\t # calibration inside the item tower means serving remains one exact\n 103\t # maximum-inner-product search, with no post-retrieval reranker.\n 104\t self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n 105\t self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n 106\t\n 107\t def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n 108\t ids = self.id_embedding(item_ids)\n 109\t features = self.item_features[item_ids]\n 110\t latent = self.network(torch.cat((ids, features), dim=1))\n 111\t latent = F.normalize(latent + self.skip(ids), dim=1)\n 112\t bias = self.learned_prior_scale * (\n 113\t self.prior(features) + self.id_bias(item_ids)\n 114\t ) + self.fixed_prior_scale * (-features[:, 2:3])\n 115\t return torch.cat((latent, bias), dim=1)\n 116\t\n 117\t\n 118\tclass TwoTowerRanker(nn.Module):\n 119\t \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n 120\t\n 121\t def __init__(\n 122\t self,\n 123\t num_items: int,\n 124\t item_features: torch.Tensor,\n 125\t embedding_dim: int = 64,\n 126\t hidden_dim: int = 128,\n 127\t ) -> None:\n 128\t super().__init__()\n 129\t self.user_tower = UserTower(\n 130\t num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n 131\t )\n 132\t self.item_tower = ItemTower(\n 133\t num_items,\n 134\t item_features=item_features,\n 135\t embedding_dim=embedding_dim,\n 136\t hidden_dim=hidden_dim,\n 137\t )\n 138\t\n 139\t def score(\n 140\t self,\n 141\t history_ids: torch.Tensor,\n 142\t offsets: torch.Tensor,\n 143\t history_weights: torch.Tensor,\n 144\t context_features: torch.Tensor,\n 145\t candidate_ids: torch.Tensor,\n 146\t ) -> torch.Tensor:\n 147\t users = self.user_tower(\n 148\t history_ids, offsets, history_weights, context_features\n 149\t )\n 150\t items = self.item_tower(candidate_ids)\n 151\t return users @ items.T\n 1\t# Made-for-You two-tower ranker\n 2\t\n 3\t## Model\n 4\t\n 5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n 6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n 7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n 8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n 9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n 10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n 11\tresidual MLP. Both latent outputs are L2-normalized.\n 12\t\n 13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n 14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n 15\tThus the complete relevance score is exactly\n 16\t\n 17\t```text\n 18\tscore(user history, item) = user_vector @ item_vector\n 19\t```\n 20\t\n 21\tCandidate vectors are computed once offline; request-time work is one bounded\n 22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n 23\tthe platform-owned retrieval mask.\n 24\t\n 25\t## Training\n 26\t\n 27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n 28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n 29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n 30\tsampling weight 4. Histories are held in event/CSR form, so training never\n 31\tconstructs or factorizes a global user-item interaction matrix. The final model\n 32\tis refit on `train_* + val_*`, with validation events treated as observed\n 33\thistory and temporal training outcomes.\n 34\t\n 35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n 36\twith:\n 37\t\n 38\t```sh\n 39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n 40\t```\n 41\t\n 42\t## Final submission\n 43\t\n 44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n 45\tcheckpoint. Rebuild it deterministically from `/app` with:\n 46\t\n 47\t```sh\n 48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n 49\t /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n 50\t --stage final \\\n 51\t --output /app/solution/submission.parquet \\\n 52\t --checkpoint /app/solution/model_final.pt \\\n 53\t --cache /app/solution/cache_final_36w.pt \\\n 54\t --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n 55\t --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n 56\t --seed 20260924 --threads 8\n 57\t```\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 377710457 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029384,\n \"quality_users\": 4263\n }\n}\nfinal_config {'embedding_dim': 64, 'hidden_dim': 128, 'max_history': 384, 'history_days': 180, 'training_weeks': 36, 'epochs': 12, 'batch_size': 256, 'negatives': 512, 'learning_rate': 0.002, 'weight_decay': 1e-05, 'like_sampling_weight': 4.0, 'organic_boost': 1.5, 'seed': 20260924}\nfinal_epochs 12 final_loss 4.318330001489008\n 1\t\"\"\"Neural towers used by the discovery ranker.\n 2\t\n 3\tThe user tower consumes only a variable-length interaction history plus summary\n 4\tfeatures. It deliberately has no user-ID parameter. The item tower can be\n 5\trun for the whole catalogue offline. Both return ``embedding_dim + 1`` values;\n 6\tthe final coordinate implements an item prior while preserving an exact dot\n 7\tproduct scoring function.\n 8\t\"\"\"\n 9\tfrom __future__ import annotations\n 10\t\n 11\timport torch\n 12\tfrom torch import nn\n 13\tfrom torch.nn import functional as F\n 14\t\n 15\t\n 16\tclass UserTower(nn.Module):\n 17\t \"\"\"Weighted history pooling followed by a small residual MLP.\"\"\"\n 18\t\n 19\t def __init__(\n 20\t self,\n 21\t num_items: int,\n 22\t embedding_dim: int = 64,\n 23\t hidden_dim: int = 128,\n 24\t num_context_features: int = 4,\n 25\t ) -> None:\n 26\t super().__init__()\n 27\t self.embedding_dim = embedding_dim\n 28\t self.history_embedding = nn.EmbeddingBag(\n 29\t num_items, embedding_dim, mode=\"sum\", include_last_offset=True\n 30\t )\n 31\t nn.init.normal_(self.history_embedding.weight, std=0.04)\n 32\t self.network = nn.Sequential(\n 33\t nn.Linear(embedding_dim + num_context_features, hidden_dim),\n 34\t nn.GELU(),\n 35\t nn.LayerNorm(hidden_dim),\n 36\t nn.Linear(hidden_dim, embedding_dim),\n 37\t )\n 38\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 39\t self.log_scale = nn.Parameter(torch.tensor(2.0))\n 40\t\n 41\t def forward(\n 42\t self,\n 43\t item_ids: torch.Tensor,\n 44\t offsets: torch.Tensor,\n 45\t weights: torch.Tensor,\n 46\t context_features: torch.Tensor,\n 47\t ) -> torch.Tensor:\n 48\t pooled = self.history_embedding(\n 49\t item_ids, offsets, per_sample_weights=weights\n 50\t )\n 51\t # Signed preference weights are useful, but their absolute magnitude\n 52\t # must not make heavy users arbitrarily larger than light users.\n 53\t abs_sums = torch.zeros(\n 54\t len(offsets) - 1, device=weights.device, dtype=weights.dtype\n 55\t )\n 56\t lengths = offsets[1:] - offsets[:-1]\n 57\t bag_ids = torch.repeat_interleave(\n 58\t torch.arange(len(lengths), device=weights.device), lengths\n 59\t )\n 60\t abs_sums.scatter_add_(0, bag_ids, weights.abs())\n 61\t pooled = pooled / abs_sums.clamp_min(1.0).unsqueeze(1)\n 62\t latent = self.network(torch.cat((pooled, context_features), dim=1))\n 63\t latent = F.normalize(latent + self.skip(pooled), dim=1)\n 64\t # Constraining the scale prevents sampled-softmax from becoming\n 65\t # numerically brittle. The constant last coordinate pairs with the\n 66\t # item tower's learned popularity prior.\n 67\t scale = self.log_scale.exp().clamp(1.0, 30.0)\n 68\t return torch.cat(\n 69\t (latent * scale, torch.ones_like(latent[:, :1])), dim=1\n 70\t )\n 71\t\n 72\t\n 73\tclass ItemTower(nn.Module):\n 74\t \"\"\"ID plus catalogue-statistic item encoder, suitable for offline export.\"\"\"\n 75\t\n 76\t def __init__(\n 77\t self,\n 78\t num_items: int,\n 79\t item_features: torch.Tensor,\n 80\t embedding_dim: int = 64,\n 81\t hidden_dim: int = 128,\n 82\t ) -> None:\n 83\t super().__init__()\n 84\t self.embedding_dim = embedding_dim\n 85\t self.id_embedding = nn.Embedding(num_items, embedding_dim)\n 86\t nn.init.normal_(self.id_embedding.weight, std=0.04)\n 87\t self.register_buffer(\"item_features\", item_features.float())\n 88\t feature_dim = item_features.shape[1]\n 89\t self.network = nn.Sequential(\n 90\t nn.Linear(embedding_dim + feature_dim, hidden_dim),\n 91\t nn.GELU(),\n 92\t nn.LayerNorm(hidden_dim),\n 93\t nn.Linear(hidden_dim, embedding_dim),\n 94\t )\n 95\t self.skip = nn.Linear(embedding_dim, embedding_dim, bias=False)\n 96\t self.prior = nn.Sequential(\n 97\t nn.Linear(feature_dim, 16), nn.Tanh(), nn.Linear(16, 1)\n 98\t )\n 99\t self.id_bias = nn.Embedding(num_items, 1)\n 100\t nn.init.zeros_(self.id_bias.weight)\n 101\t # These two scalar buffers are selected on the public window. Keeping\n 102\t # calibration inside the item tower means serving remains one exact\n 103\t # maximum-inner-product search, with no post-retrieval reranker.\n 104\t self.register_buffer(\"learned_prior_scale\", torch.tensor(1.0))\n 105\t self.register_buffer(\"fixed_prior_scale\", torch.tensor(0.0))\n 106\t\n 107\t def forward(self, item_ids: torch.Tensor) -> torch.Tensor:\n 108\t ids = self.id_embedding(item_ids)\n 109\t features = self.item_features[item_ids]\n 110\t latent = self.network(torch.cat((ids, features), dim=1))\n 111\t latent = F.normalize(latent + self.skip(ids), dim=1)\n 112\t bias = self.learned_prior_scale * (\n 113\t self.prior(features) + self.id_bias(item_ids)\n 114\t ) + self.fixed_prior_scale * (-features[:, 2:3])\n 115\t return torch.cat((latent, bias), dim=1)\n 116\t\n 117\t\n 118\tclass TwoTowerRanker(nn.Module):\n 119\t \"\"\"Container whose score is exactly ``user_vector @ item_vector.T``.\"\"\"\n 120\t\n 121\t def __init__(\n 122\t self,\n 123\t num_items: int,\n 124\t item_features: torch.Tensor,\n 125\t embedding_dim: int = 64,\n 126\t hidden_dim: int = 128,\n 127\t ) -> None:\n 128\t super().__init__()\n 129\t self.user_tower = UserTower(\n 130\t num_items, embedding_dim=embedding_dim, hidden_dim=hidden_dim\n 131\t )\n 132\t self.item_tower = ItemTower(\n 133\t num_items,\n 134\t item_features=item_features,\n 135\t embedding_dim=embedding_dim,\n 136\t hidden_dim=hidden_dim,\n 137\t )\n 138\t\n 139\t def score(\n 140\t self,\n 141\t history_ids: torch.Tensor,\n 142\t offsets: torch.Tensor,\n 143\t history_weights: torch.Tensor,\n 144\t context_features: torch.Tensor,\n 145\t candidate_ids: torch.Tensor,\n 146\t ) -> torch.Tensor:\n 147\t users = self.user_tower(\n 148\t history_ids, offsets, history_weights, context_features\n 149\t )\n 150\t items = self.item_tower(candidate_ids)\n 151\t return users @ items.T\n 1\t# Made-for-You two-tower ranker\n 2\t\n 3\t## Model\n 4\t\n 5\t`model.py` defines two separate CPU PyTorch towers. The user tower has **no\n 6\tuser-ID embedding**. It pools a cutoff-correct 180-day interaction history\n 7\t(at most 384 distinct positive-listen tracks, plus active explicit state) with\n 8\tfrequency, recency, completion, organic, like, and dislike weights, then uses a\n 9\t`68 -> 128 -> 64` residual MLP. The item tower combines a trainable track\n 10\tembedding with four catalog popularity statistics and a `68 -> 128 -> 64`\n 11\tresidual MLP. Both latent outputs are L2-normalized.\n 12\t\n 13\tEach tower emits 65 values: 64 personalized dimensions plus one calibrated\n 14\titem-prior coordinate (the corresponding user coordinate is exactly one).\n 15\tThus the complete relevance score is exactly\n 16\t\n 17\t```text\n 18\tscore(user history, item) = user_vector @ item_vector\n 19\t```\n 20\t\n 21\tCandidate vectors are computed once offline; request-time work is one bounded\n 22\thistory pool, one small user MLP, and MIPS retrieval. Eligibility is applied as\n 23\tthe platform-owned retrieval mask.\n 24\t\n 25\t## Training\n 26\t\n 27\tTraining uses 36 rolling weekly cutoffs. A positive is a previously unseen\n 28\tcatalog item with a subsequent organic 80%-complete listen or organic like.\n 29\tThe loss is sampled softmax with 512 uniform catalog negatives; likes receive\n 30\tsampling weight 4. Histories are held in event/CSR form, so training never\n 31\tconstructs or factorizes a global user-item interaction matrix. The final model\n 32\tis refit on `train_* + val_*`, with validation events treated as observed\n 33\thistory and temporal training outcomes.\n 34\t\n 35\tPublic validation: **0.029384 graded NDCG@10** over 4,263 scored users, measured\n 36\twith:\n 37\t\n 38\t```sh\n 39\t/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n 40\t```\n 41\t\n 42\t## Final submission\n 43\t\n 44\tThe checked-in artifact is `submission.parquet`; `model_final.pt` is its fitted\n 45\tcheckpoint. Rebuild it deterministically from `/app` with:\n 46\t\n 47\t```sh\n 48\tPYTHONPATH=/app/solution OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 \\\n 49\t /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n 50\t --stage final \\\n 51\t --output /app/solution/submission.parquet \\\n 52\t --checkpoint /app/solution/model_final.pt \\\n 53\t --cache /app/solution/cache_final_36w.pt \\\n 54\t --epochs 12 --training-weeks 36 --organic-boost 1.5 \\\n 55\t --like-weight 4 --prior-scale 1.1 --pop-scale 0.0 \\\n 56\t --seed 20260924 --threads 8\n 57\t```\n" } } } ] }, { "id": 169, "role": "agent", "timestamp": "2026-09-24T23:57:41.964Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-24T23:57:41.964Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-24T23:57:45.107Z", "text": "Completed the latency-efficient PyTorch two-tower ranker.\n\n- Final artifact: [submission.parquet](/app/solution/submission.parquet)\n- Model architecture: [model.py](/app/solution/model.py:16)\n- Training and retrieval pipeline: [train_ranker.py](/app/solution/train_ranker.py)\n- Documentation and rebuild command: [README.md](/app/solution/README.md)\n- Final checkpoint: [model_final.pt](/app/solution/model_final.pt)\n- Public validation NDCG@10: **0.029384**\n- Submission validated: 8,456 users × 100 unique items, ranks 1–100, all test-eligible.\n- Serving score is exactly a 65-dimensional user/item dot product; no global interaction matrix is constructed." }, { "kind": "context", "timestamp": "2026-09-24T23:57:45.180Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "session_id": "01a0d5a6-369b-76d1-953a-8c850355d141", "root_turn_id": "01a0d5a6-36b4-7ba0-b66a-b651ffbcac75", "response_id": "resp_009ddf540f98c275016ab5b8f1030487d2bcf6cfa2ca7d30c4", "usage": { "input_tokens": 98811, "cached_input_tokens": 95616, "cache_write_input_tokens": 0, "output_tokens": 347, "reasoning_output_tokens": 176, "total_tokens": 99158 }, "turn_token_usage": { "input_tokens": 9620474, "cached_input_tokens": 9464192, "cache_write_input_tokens": 0, "output_tokens": 56371, "reasoning_output_tokens": 24332, "total_tokens": 9676845 }, "thread_token_usage": { "input_tokens": 9620474, "cached_input_tokens": 9464192, "cache_write_input_tokens": 0, "output_tokens": 56371, "reasoning_output_tokens": 24332, "total_tokens": 9676845 } } } ] } ] }